Simulating the Air Quality Impact of Prescribed Fires Using Graph Neural Network-Based PM2.5 Forecasts

The increasing size and severity of wildfires across the western United States have generated dangerous levels of PM2.5 concentrations in recent years. In a changing climate, expanding the use of prescribed fires is widely considered to be the most robust fire mitigation strategy. However, reliably forecasting the potential air quality impact from prescribed fires, which is critical in planning the prescribed fires' location and time, at hourly to daily time scales remains a challenging problem. In this paper, we introduce a spatial-temporal graph neural network (GNN) based forecasting model for hourly PM2.5 predictions across California. Using a two-step approach, we leverage our forecasting model to estimate the PM2.5 contribution of wildfires. Integrating the GNN-based PM2.5 forecasting model with prescribed fire simulations, we propose a novel framework to forecast the PM2.5 pollution of prescribed fires. This framework helps determine March as the optimal month for implementing prescribed fires in California and quantifies the potential air quality trade-offs involved in conducting more prescribed fires outside the fire season.