  Industrial accidents, chemical spills, and structural fires can release large
amounts of harmful materials that disperse into urban atmospheres and impact
populated areas. Computer models are typically used to predict the transport of
toxic plumes by solving fluid dynamical equations. However, these models can be
computationally expensive due to the need for many grid cells to simulate
turbulent flow and resolve individual buildings and streets. In emergency
response situations, alternative methods are needed that can run quickly and
adequately capture important spatiotemporal features. Here, we present a novel
deep learning model called ST-GasNet that was inspired by the mathematical
equations that govern the behavior of plumes as they disperse through the
atmosphere. ST-GasNet learns the spatiotemporal dependencies from a limited set
of temporal sequences of ground-level toxic urban plumes generated by a
high-resolution large eddy simulation model. On independent sequences,
ST-GasNet accurately predicts the late-time spatiotemporal evolution, given the
early-time behavior as an input, even for cases when a building splits a large
plume into smaller plumes. By incorporating large-scale wind boundary condition
information, ST-GasNet achieves a prediction accuracy of at least 90% on test
data for the entire prediction period.
