Simulating the air quality impact of prescribed fires using graph neural network-based PM2.5 forecasts - Kyleen Liao; Jatan Buch; Kara D. Lamb; Pierre Gentine (Environmental Data Science)
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 spatio-temporal graph neural network (GNN)-based forecasting model for hourly PM2.5 predictions across California. Utilizing a two-step approach, we use our forecasting model to predict the net and ambient PM2.5 concentrations, which are used to estimate wildfire contributions. Integrating the GNN-based PM2.5 forecasting model with simulations of historically prescribed fires, we propose a novel framework to forecast their air quality impact. This framework determines that March is 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 peak of the fire season.