Modeling wildland fire-specific PM2.5 for uncertainty-aware health impact assessments

Wildland fire is a major emission source of fine particulate matter (PM_2.5) and has serious adverse health effects. Most fire-related health studies have estimated human exposures to PM_2.5 using ground observations, which have limited spatial/temporal coverage and could not separate PM_2.5 emanating from wildland fires from other sources. The Community Multiscale Air Quality (CMAQ) model has the potential to fill the gaps left by ground observations and estimate wildland fire-specific PM_2.5 concentrations, although the issues around systematic bias in CMAQ models remain to be resolved. To address these problems, we developed a two-step calibration strategy under the consideration of prediction uncertainties. In the first stage, we employed the Bayesian downscaler model to correct biases in CMAQ-based total PM_2.5 predictions, and in the second phase, we extracted wildland fire-specific PM_2.5 concentrations from calibrated total PM_2.5 values obtained from stage 1, while accounting for the uncertainty associated with the predictions. In a case study of the eastern US in 2014, we evaluated the calibration performance using three different cross validation (CV) methods, including 10-fold CV, leave-one-state-out CV, and leave-one-month-out CV, which consistently indicated that the prediction accuracy was improved with R² in the range of 0.47 to 0.64. In an air pollution health impact assessment study, we identified regions with excess respiratory hospital admissions due to wildland fire-specific PM_2.5 concentrations and quantified the uncertainties in fire-related health burdens propagated from both uncertain wildland fire-specific PM_2.5 predictions and risk coefficients. We concluded that the proposed calibration strategy could provide reliable wildland fire-specific PM_2.5 predictions and health burden estimates to support policy development for reducing fire-related risks.