Mapping Modeled Exposure of Wildland Fire Smoke for Human Health Studies in California

Wildland fire smoke exposure affects a broad proportion of the U.S. population and is increasing due to climate change, settlement patterns and fire seclusion. 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 PM(2.5). The 24-h average concentration of PM(2.5) from all sources in 12-km gridded output from all sources in California (2007–2013) was 4.91 μg/m(3). The average concentration of fire-PM(2.5) in California by year was 1.22 μg/m(3) (~25% of total PM(2.5)). The fire-PM(2.5) daily mean was estimated at 4.40 μg/m(3) in a high fire year (2008). Based on the model-derived fire-PM(2.5) data, 97.4% of California’s population lived in a county that experienced at least one episode of high smoke exposure (“smokewave”) from 2007–2013. Photochemical model predictions of wildfire impacts on daily average PM(2.5) 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/m(3) bias) and 2013 (1.6 μg/m(3) bias) while underestimating for 2009 (−2.1 μg/m(3) bias). The modeling system isolated wildfire and PM(2.5) 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.