Bioaerosols released during wildland and prescribed fires may influence ecosystems, air quality, and microbial dispersal, yet their transport and deposition remain poorly understood. This study combined infrared uncrewed aircraft system (UAS) observations of a prescribed burn with the coupled fire–atmosphere model WRF-SFIRE and a Lagrangian particle model in order to evaluate how uncertainties in simulated fire behavior affect predicted bioaerosol (bacterial cell) transport and deposition. A reconstruction of the observed spatiotemporal evolution of the fire was derived from thermal UAS measurements acquired during the burn and incorporated into a WRF-SFIRE simulation, in which the modeled fire spread was constrained to follow this reconstructed progression. This benchmark run was compared with two unconstrained, fully coupled simulations that used a low and a high estimate of fuel moisture content (FMC) to represent typical uncertainty in fire rate of spread (ROS) prediction. Despite substantial differences in fire intensity and plume dynamics among the simulations, the resulting bioaerosol transport pathways and deposition patterns were broadly consistent across cases. The horizontal transport of the bioaerosols was dominated by the ambient Easterly wind and the bioaerosols were lofted by fire-affected updrafts—some exceeding 10 m/s—within the buoyant plume structure resolved in WRF-SFIRE. Deposition hot-spots appeared in consistent locations in the three simulations, especially regions where topography forced up-slope transport. Although the most intense fire produced slightly greater local deposition—likely due to a combination of stronger fire-induced downdrafts and overturning from penetration into strong vertical wind shear above the boundary layer—differences were small relative to the overall deposition footprint. These results suggested that, for burns of this scale, bioaerosol transport and deposition predictions are relatively robust to realistic uncertainties in fire-behavior modeling. This finding indicates that coupled fire–atmosphere and particle-transport modeling frameworks could be employed to quantitatively forecast microbial transport and deposition during future controlled burn experiments.
Forrest et al. (Wed,) studied this question.