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

Abstract

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.

Daily PM2.5 concentration estimates by county, ZIP code, and census tract in 11 western states 2008–2018

“…PM 2.5 concentration estimates from chemical transport models have been shown to be an important input to machine learning models for PM 2.5 15 , 17 . We obtained daily estimates of PM 2.5 at 12 km spatial resolution from runs of the CMAQ (Community Multi-scale Air Quality) model from the U.S. EPA for the years 2008–2016, the years for which the CMAQ estimates are available 37 .…”

Past Variance and Future Projections of the Environmental Conditions Driving Western U.S. Summertime Wildfire Burn Area

“…A growing body of research has demonstrated that ozone, a gas phase criteria pollutant in the U.S., can also be enhanced in the presence of wildfire smoke (Brey & Fischer, 2016;Jaffe et al, 2013;Lu et al, 2016;McClure & Jaffe, 2018a). The deteriorated air quality due to wildfire smoke can impact public health on large scales (Delfino et al, 2009;Fisk & Chan, 2017;Gan et al, 2017;Koman et al, 2019;Lipner et al, 2019;Rappold et al, 2011). Given this suite of impacts on air quality, alongside other impacts on people, property and economics, there is a need to understand 1) what drives yearto-year wildfire activity, 2) how those drivers may change in the future, and 3) how future burn area and total emissions may respond.…”

Estimating Wildfire Smoke Concentrations during the October 2017 California Fires through BME Space/Time Data Fusion of Observed, Modeled, and Satellite-Derived PM2.5

“…Population-level exposure to wildfire emissions is typically estimated using one or more primary datasets: monitoring station observations, chemical transport models (CTMs), and satellite-based measurements 15,16 . Each dataset has strengths and weaknesses for estimating wildfire PM2.5 concentrations.…”