  Wildland fire smoke contains hazardous levels of fine particulate matter
PM2.5, a pollutant shown to adversely effect health. Estimating fire
attributable PM2.5 concentrations is key to quantifying the impact on air
quality and subsequent health burden. This is a challenging problem since only
total PM2.5 is measured at monitoring stations and both fire-attributable PM2.5
and PM2.5 from all other sources are correlated in space and time. We propose a
framework for estimating fire-contributed PM2.5 and PM2.5 from all other
sources using a novel causal inference framework and bias-adjusted chemical
model representations of PM2.5 under counterfactual scenarios. The chemical
model representation of PM2.5 for this analysis is simulated using Community
Multi-Scale Air Quality Modeling System (CMAQ), run with and without fire
emissions across the contiguous U.S. for the 2008-2012 wildfire seasons. The
CMAQ output is calibrated with observations from monitoring sites for the same
spatial domain and time period. We use a Bayesian model that accounts for
spatial variation to estimate the effect of wildland fires on PM2.5 and state
assumptions under which the estimate has a valid causal interpretation. Our
results include estimates of absolute, relative and cumulative contributions of
wildfire smoke to PM2.5 for the contiguous U.S. Additionally, we compute the
health burden associated with the PM2.5 attributable to wildfire smoke.
