Simulating the Air Quality Impact of Prescribed Fires Using a Graph
  Neural Network-Based PM$_{2.5}$ Emissions Forecasting System
Kyleen Liao Jatan Buch Kara Lamb Pierre Gentine
The increasing size and severity of wildfires across western North America
have generated dangerous levels of PM$_{2.5}$ 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 a spatio-temporal graph neural network-based
PM$_{2.5}$ forecasting model. The experiments in this work focus on determining
the optimal time for implementing prescribed fires in California as well as
quantifying the potential air quality trade-offs involved in conducting more
prescribed fires outside the fire season.