Fires and burning are the chief causes of particulate matter (PM2.5), a key measurement of air quality in communities and cities worldwide. This work develops a live fire tracking platform to show active reported fires from over twenty cities in the U.S., as well as predict their smoke paths and impacts on the air quality of regions within their range. Specifically, our close to real-time tracking and predictions culminates in a digital twin to protect public health and inform the public of fire and air quality risk. This tool tracks fire incidents in real-time, utilizes the 3D building footprints of Austin to simulate smoke outputs, and predicts fire incident smoke falloffs within the complex city environment. Results from this study include a complete fire and smoke digital twin model for Austin. We work in cooperation with the City of Austin Fire Department to ensure the accuracy of our forecast and also show that air quality sensor density within our cities cannot validate urban fire presence. We additionally release code and methodology to replicate these results for any city in the world. This work paves the path for similar digital twin models to be developed and deployed to better protect the health and safety of citizens.

翻译：火灾与燃烧是细颗粒物（PM2.5）的主要来源，而PM2.5是全球城市社区空气质量的关键衡量指标。本研究开发了一个实时火灾追踪平台，可显示美国二十多个城市正在报告的火灾，并预测其烟雾路径及对区域内空气质量的影响。具体而言，我们的近实时追踪与预测最终构建了一个数字孪生系统，用于保护公众健康并向公众通报火灾与空气质量风险。该工具实时追踪火灾事件，利用奥斯汀市的3D建筑足迹模拟烟雾排放，并预测复杂城市环境中火灾烟雾的沉降分布。研究结果包括完整的奥斯汀市火灾与烟雾数字孪生模型。我们与奥斯汀市消防局合作确保预测准确性，同时证明城市内空气质量传感器密度不足以验证城市火灾存在。此外，我们公开了代码与方法论，以便在全球任何城市复现本研究成果。本工作为开发部署类似的数字孪生模型、更有效保护公民健康与安全铺平了道路。