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The increasing size and severity of wildfires across western North Americahave generated dangerous levels of PM$_{2.5}$ pollution in recent years. In awarming climate, expanding the use of prescribed fires is widely considered tobe the most robust fire mitigation strategy. However, reliably forecasting thepotential air quality impact from these prescribed fires, a critical ingredientin determining the fires' location and time, at hourly to daily time scalesremains a challenging problem. This paper proposes a novel integration ofprescribed fire simulation with a spatio-temporal graph neural network-basedPM$_{2.5}$ forecasting model. The experiments in this work focus on determiningthe optimal time for implementing prescribed fires in California as well asquantifying the potential air quality trade-offs involved in conducting moreprescribed fires outside the fire season.

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