Reduced Order Atmospheric Pollution Modelling using Machine Learning with Proper Orthogonal Decomposition

Atmospheric pollution modelling plays a crucial role, not only in understanding the effects of air pollution on human health and the environment but also in the day-to-day monitoring of air quality. However, the computational cost of high-resolution simulations poses challenges for operational pollution forecasting. To address this problem, we propose an approach using Machine Learning (ML) supervised by numerical simulation results obtained from the WRF-CHEM model with an 800m resolution over a 102*102 spatial domain. The latter includes the city of Marseille and the basins of the port of Marseille-Fos. This enables us to capture the pollution generated by the significant maritime traffic in Marseille. The main pollutants are modeled, including PM10, PM2.5, NO2, SO2, and O3. The high degrees of freedom in the input data, resulting from fine spatial resolution, raise learning difficulties for traditional ML models. To overcome this, we apply Proper Orthogonal Decomposition (POD) to reduce data dimensions while preserving essential information. By capturing dominant variability modes, POD facilitates efficient ML training on the reduced-order data. This hybrid ML-POD model demonstrates promising results, achieving accurate pollution predictions while significantly reducing computational costs.