Simulating wildfire smoke plumes within a time-reversed Lagrangian Particle Dispersion Model

Wildfires impact the air quality not only locally, but also areas downstream of the fire by serving as concentrated sources of ozone precursors, PM2.5, and CO. Under the Clean Air Act, the U.S. EPA regulates these chemical species as they can cause significant health problems among the general population. Our previous work has quantified the impact of wildfires on air quality at Salt Lake City using time-reversed Lagrangian Dispersion Models in an effort to separate the contributions of upstream wildfires from other sources. This research aims to build upon prior work in order to reduce the uncertainty within these models. The plume rise due to additional buoyancy within wildfires remains a source of uncertainty. This research modeled wildfire smoke plume rises using a series of smoke plume formulations and then tested these parameterizations against data collected during field campaigns —e.g., the RxCADRE-prescribed burn study. The Stochastic Time-Inverted Lagrangian Transport (STILT) model, driven by meteorological fields simulated by the Weather Analysis and Forecasting (WRF) model, was used to model CO2, CO, and PM2.5 concentrations at observational locations downwind of the fires in order to test the validity of the model. A final sensitivity study was performed to test the impacts of regional wildfires on Salt Lake City's air quality using a new plume rise parameterization within the WRF-STILT model. These results were compared with existing model simulations that excluded plume rise processes.