  Human health is negatively impacted by poor air quality including increased
risk for respiratory and cardiovascular disease. Due to a recent increase in
extreme air quality events, both globally and locally in the United States,
finer resolution air quality forecasting guidance is needed to effectively
adapt to these events. The National Oceanic and Atmospheric Administration
provides air quality forecasting guidance for the Continental United States.
Their air quality forecasting model is based on a 15 km spatial resolution;
however, the goal is to reach a three km spatial resolution. This is currently
not feasible due in part to prohibitive computational requirements for modeling
the transport of chemical species. In this work, we describe a deep learning
transport emulator that is able to reduce computations while maintaining skill
comparable with the existing numerical model. We show how this method maintains
skill in the presence of extreme air quality events, making it a potential
candidate for operational use. We also explore evaluating how well this model
maintains the physical properties of the modeled transport for a given set of
species.
