  The increasing size and severity of wildfires across the western United
States have generated dangerous levels of PM$_{2.5}$ 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
PM$_{2.5}$ predictions across California. Using a two-step approach, we
leverage our forecasting model to estimate the PM$_{2.5}$ contribution of
wildfires. Integrating the GNN-based PM$_{2.5}$ forecasting model with
prescribed fire simulations, we propose a novel framework to forecast the
PM$_{2.5}$ 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.
