Simulating the Air Quality Impact of Prescribed Fires Using a Graph Neural Network-Based PM2.5 Emissions Forecasting System

The increasing size and severity of wildfires across western North America have generated dangerous concentrations of PM2.5 pollution in recent years. In a warming 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 these prescribed fires, a critical ingredient in determining the fires' location and time, at hourly to daily time scales remains a challenging problem. This paper proposes a novel integration of prescribed fire simulation with spatial-temporal graph neural network-based PM2.5 forecasting. The experiments in this work focus on determining the optimal time for implementing prescribed fires in California as well as on quantifying the potential trade-offs involved in conducting more prescribed fires outside the fire season.