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The increasing size and severity of wildfires across the western UnitedStates have generated dangerous levels of PM$_{2.5}$ concentrations in recentyears. In a changing climate, expanding the use of prescribed fires is widelyconsidered to be the most robust fire mitigation strategy. However, reliablyforecasting the potential air quality impact from prescribed fires, which iscritical in planning the prescribed fires' location and time, at hourly todaily time scales remains a challenging problem. In this paper, we introduce aspatial-temporal graph neural network (GNN) based forecasting model for hourlyPM$_{2.5}$ predictions across California. Using a two-step approach, weleverage our forecasting model to estimate the PM$_{2.5}$ contribution ofwildfires. Integrating the GNN-based PM$_{2.5}$ forecasting model withprescribed fire simulations, we propose a novel framework to forecast thePM$_{2.5}$ pollution of prescribed fires. This framework helps determine Marchas the optimal month for implementing prescribed fires in California andquantifies the potential air quality trade-offs involved in conducting moreprescribed fires outside the fire season.

Simulating the Air Quality Impact of Prescribed Fires Using Graph Neural Network-Based PM$_{2.5}$ Forecasts