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Wildland fire smoke contains hazardous levels of fine particulate matterPM2.5, a pollutant shown to adversely effect health. Estimating fireattributable PM2.5 concentrations is key to quantifying the impact on airquality and subsequent health burden. This is a challenging problem since onlytotal PM2.5 is measured at monitoring stations and both fire-attributable PM2.5and PM2.5 from all other sources are correlated in space and time. We propose aframework for estimating fire-contributed PM2.5 and PM2.5 from all othersources using a novel causal inference framework and bias-adjusted chemicalmodel representations of PM2.5 under counterfactual scenarios. The chemicalmodel representation of PM2.5 for this analysis is simulated using CommunityMulti-Scale Air Quality Modeling System (CMAQ), run with and without fireemissions across the contiguous U.S. for the 2008-2012 wildfire seasons. TheCMAQ output is calibrated with observations from monitoring sites for the samespatial domain and time period. We use a Bayesian model that accounts forspatial variation to estimate the effect of wildland fires on PM2.5 and stateassumptions under which the estimate has a valid causal interpretation. Ourresults include estimates of absolute, relative and cumulative contributions ofwildfire smoke to PM2.5 for the contiguous U.S. Additionally, we compute thehealth burden associated with the PM2.5 attributable to wildfire smoke.

A spatial causal analysis of wildland fire-contributed PM2.5 using numerical model output