A spatial causal analysis of wildland fire-contributed PM2.5 using numerical model output
Alexandra Larsen, Shu Yang, Brian J. Reich, Ana G. Rappold

TL;DR
This paper introduces a causal inference framework using chemical model simulations and Bayesian spatial modeling to estimate wildfire smoke contributions to PM2.5 and assess associated health impacts across the U.S.
Contribution
It presents a novel causal inference approach combining chemical modeling and Bayesian methods to quantify wildfire-related PM2.5 contributions and health effects.
Findings
Wildfire smoke contributes significantly to PM2.5 levels across the U.S.
The framework estimates absolute and relative wildfire PM2.5 contributions.
Health burden from wildfire PM2.5 is quantified.
Abstract
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…
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Taxonomy
TopicsAir Quality and Health Impacts · Climate Change and Health Impacts · Atmospheric and Environmental Gas Dynamics
