Wildland fire smoke contains hazardous levels of fine particulate matter (PM2.5), a pollutant shown to adversely affect 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 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.
Dr. Brian Reich, North Carolina State University Dept. of Statistics
Geospatial Data Science Distinguished Speaker Series