Deep learning techniques applied to super-resolution chemistry transport modeling for operational uses

Air quality modeling tools are largely used to assess air pollution mitigation and monitoring strategies. While neural networks (NN) were mostly developed based on observations to derive statistical models at stations, the use of Eulerian chemistry transport models (CTMs) was mainly devoted to air quality predictions over large areas and the evaluation of emission reduction strategies. In this study, we investigate deep learning architectures to create a metamodel of the process oriented CTM CHIMERE and significantly reduce the computing times required for super-resolution simulations. The key point is the selection of input variables and the way to implement them in the NN. We perform a quantitative evaluation of the proposed approaches on a real case-study. The best NN architecture displays very good performances in terms of prediction of pollutant concentrations observed at stations with respect to the raw super-resolution CHIMERE simulation, with a correlation coefficient above 0.95. The best NN is also able to display better performances when compared to observations than the raw high resolution simulation. Currently the model is designed to be used for air quality forecasting and requires improvement for the definition of air quality management strategies.