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.

翻译：近年来，北美西部野火的规模与严重程度不断加剧，导致PM$_{2.5}\)污染达到危险水平。在气候变暖背景下，扩大规定燃烧的应用被广泛认为是抵御野火最可靠的策略。然而，如何可靠地预测这些规定燃烧对空气质量在小时至日时间尺度上的潜在影响——这一决定燃烧地点与时间的关键因素——仍是极具挑战的问题。本文提出一种融合规定燃烧模拟与时空图神经网络PM$_{2.5}$预测模型的新方法。实验重点聚焦于确定加州实施规定燃烧的最佳时间，并量化在火灾季之外开展更多规定燃烧所涉及的潜在空气质量权衡。