A SPATIAL CAUSAL ANALYSIS OF WILDLAND FIRE-CONTRIBUTED PM_2.5 USING NUMERICAL MODEL OUTPUT.

Area of Science:

• Environmental Science
• Public Health
• Atmospheric Chemistry

Background:

• Wildland fire smoke releases harmful fine particulate matter (PM2.5).
• Accurately estimating fire-attributable PM2.5 is vital for understanding air quality and health impacts.
• Distinguishing fire PM2.5 from other sources is challenging due to spatial and temporal correlations.

Purpose of the Study:

• To develop and validate a novel causal inference framework for estimating fire-attributable PM2.5.
• To quantify the contribution of wildland fires to PM2.5 concentrations across the contiguous U.S.
• To assess the health burden associated with wildfire smoke exposure.

Main Methods:

• Utilized a causal inference framework with bias-adjusted chemical model (CMAQ) outputs.
• Simulated PM2.5 concentrations with and without fire emissions for 2008-2012.
• Employed a Bayesian spatial model to estimate wildland fire effects on PM2.5.

Main Results:

• Provided estimates of wildfire smoke contributions to PM2.5 for the contiguous U.S.
• Quantified the health burden linked to PM2.5 from wildland fires.
• Demonstrated a method to disentangle fire-related PM2.5 from other sources.

Conclusions:

• The proposed causal framework effectively estimates fire-attributable PM2.5.
• Understanding wildfire PM2.5 contributions is essential for public health risk assessment.
• This methodology aids in managing air quality impacts from increasing wildfire activity.