  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.
