eMONARCH: a Deep Learning emission sensitive Chemical-Transport Model (CTM) for Air Quality planning

Air pollution is now the second highest risk factor globally, highlighting the importance of air quality simulations, policies, and pollution peaks mitigation. Chemical Transport Models (CTMs) such as MONARCH are essential tools for designing air pollution mitigation plans but are limited by their high computational cost.In the frame of the AIRE Spanish national project, we are developing eMONARCH, an emission- and meteorology-sensitive deep-learning-based surrogate model of MONARCH. eMONARCH aims at providing a cost-effective tool for generating ensemble of atmospheric composition simulations, supporting needs of air quality planning and data assimilation. We chose to start using a U-Net type architecture as a baseline model for its simplicity. The training dataset is composed of an ensemble of multi-annual MONARCH simulations with pertirbued emissions. A high performance was obtained for one-hour predictions, and we are now engaged in investigating ways to reduce the error accumulation and instabilities in multi-days autoregressive predictions. The first implementation of the model focuses on surface NOₓ concentrations, while the following versions include PMs, and multiple pollutants across several layers of atmosphere. In parallel, we are also developing a new Graph Neural Network (GNN) architecture composed of an encoder-processor-decoder structure whose preliminary results will also be presented.