Modeling wildland fire-specific PM2.5 concentrations for uncertainty-aware health impact assessments.
Wildland fire is a major emission source of fine particulate matter (PM2.5) which has serious adverse health effects. Most fire-related health studies have estimated human exposures to PM2.5 using ground observations, which have limited spatial/temporal coverage and could not separate PM2.5 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 PM2.5 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 a case study of the eastern US in 2014, we evaluated the calibration performance using three cross validation methods, which consistently indicated that the prediction accuracy was improved with R2 of 0.47 to 0.64. In a health impact study based on the wildland fire-specific PM2.5 predictions, we identified regions with excess respiratory hospital admissions due to wildland fire events and quantified the estimation uncertainty propagated from multiple components in health impact function. We concluded that the proposed calibration strategy could provide reliable wildland fire-specific PM2.5 predictions and health burden estimates to support policy development for reducing fire-related risks.