A Causal Inference Analysis of the Effect of Wildland Fire Smoke on Ambient Air Pollution Levels and the Associated Health Burden from Wildfire-Contributed PM2.5

摘要

Wildfire smoke contains hazardous levels of fine particulate matter (PM2.5), a pollutant shown to adversely effect respiratory and cardiovascular health. Estimating PM2.5 concentrations attributable to wildfires is key to understanding the extent to which wildfires contribute to poor air quality and subsequent health burdens. This is a challenging problem since only total PM2.5 is measured at monitoring stations in the U.S., meaning we only ever observe PM2.5 from all sources (wildfire smoke, anthropogenic sources, natural non-fire sources, etc.). We propose a method for separating estimates of wildfire-contributed PM2.5 from ambient PM2.5 concentrations using a novel causal inference framework and bias-adjusted computer simulations of PM2.5 under counterfactual scenarios. The numerical PM2.5 data for this analysis is from the Community Multi-Scale Air Quality (CMAQ) Modeling System, run with and without fire emissions across the contiguous U.S. for the 2008-2012 fire seasons. To account for biases, the CMAQ output is calibrated with observed data from the U.S. Environmental Protection Agency's Federal Reference Method (FRM) PM2.5 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 wildfires on PM2.5 and state assumptions under which the estimate has a valid causal interpretation. Our results include estimates of absolute, relative and cumulative contributes of wildfires smoke to PM2.5 for the contiguous U.S. Additionally, we compute the health burden associated with the PM2.5 attributable to wildfire smoke. Our results provide insight into using causal inference with numerical and spatial data, as well as a method that we extend to investigate the causal effects of wildfire smoke on public health outcomes. Disclaimer: This work does not necessarily represent EPA views or policy.