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\begin{document}

\title{Mapping Modeled Exposure of Wildland Fire Smoke for Human Health Studies
in California}



\author[1]{Patricia Koman}%
\author[2]{Michael Billmire}%
\author[3]{Kirk Baker}%
\author[1]{Ricardo De Majo}%
\author[4]{Frank Anderson}%
\author[5]{Sumi Hoshiko}%
\author[2]{Brian Thelen}%
\author[2]{Nancy French}%
\affil[1]{University of Michigan School of Public Health}%
\affil[2]{Michigan Technology Research Institute}%
\affil[3]{U.S. Environmental Protection Agency}%
\affil[4]{University of Michigan School of Medicine}%
\affil[5]{California Department of Public Health}%


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  \date{June 14, 2024}


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\begin{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 PM2.5.
The 24-hour average concentration of PM2.5 from all sources in 12-km
gridded output from all sources in California (2007--2013) was 4.91
\selectlanguage{greek}μ\selectlanguage{english}g/m3. The average concentration of fire-PM2.5 in California by year was
1.22 \selectlanguage{greek}μ\selectlanguage{english}g/m3 (\textasciitilde{}25\% of total PM2.5). The fire-PM2.5 daily
mean was estimated at 4.40 \selectlanguage{greek}μ\selectlanguage{english}g/m3 in a high fire year (2008). Based on
the model-derived fire-PM2.5 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 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 \selectlanguage{greek}µ\selectlanguage{english}g/m3 bias) and 2013 (1.6 \selectlanguage{greek}µ\selectlanguage{english}g/m3 bias) while underestimating for
2009 (-2.1 \selectlanguage{greek}µ\selectlanguage{english}g/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.%
\end{abstract}%



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