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

Abstract

Wildland fire smoke contains hazardous levels of fine particulate matter (${\mathrm{PM}}_{2.5}$), a pollutant shown to adversely effect health. Estimating fire attributable ${\mathrm{PM}}_{2.5}$ concentrations is key to quantifying the impact on air quality and subsequent health burden. This is a challenging problem since only total ${\mathrm{PM}}_{2.5}$ is measured at monitoring stations and both fire-attributable ${\mathrm{PM}}_{2.5}$ and ${\mathrm{PM}}_{2.5}$ from all other sources are correlated in space and time. We propose a framework for estimating fire-contributed ${\mathrm{PM}}_{2.5}$ and ${\mathrm{PM}}_{2.5}$ from all other sources using a novel causal inference framework and bias-adjusted chemical model representations of ${\mathrm{PM}}_{2.5}$ under counterfactual scenarios. The chemical model representation of ${\mathrm{PM}}_{2.5}$ for this analysis is simulated using Community Multiscale 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 ${\mathrm{PM}}_{2.5}$ and state assumptions under which the estimate has a valid causal interpretation. Our results include estimates of the contributions of wildfire smoke to ${\mathrm{PM}}_{2.5}$ for the contiguous U.S. Additionally, we compute the health burden associated with the ${\mathrm{PM}}_{2.5}$ attributable to wildfire smoke.