Knowledge-inspired fusion strategies for the inference of PM_2.5 values with a neural network

Année de publication

2025

Ground-level concentrations of particulate matter (more precisely PM2.5) are a strong indicator of air quality, which is now widely recognised to impact human health. Accurately inferring or predicting PM2.5 concentrations is therefore an important step for health hazard monitoring and the implementation of air-quality-related policies. Various methods have been used to achieve this objective, and neural networks are one of the most recent and popular solutions. In this study, a limited set of quantities that are known to impact the relation between column aerosol optical depth (AOD) and surface PM2.5 concentrations are used as input of several network architectures to investigate how different fusion strategies can impact and help explain predicted PM2.5 concentrations. Different models are trained on two different sets of simulated data, namely, global-scale atmospheric composition reanalysis provided by the Copernicus Atmosphere Monitoring Service (CAMS) and higher-resolution data simulated over Europe with the Centre National de Recherches Météorologiques ALADIN model. Based on an extensive set of experiments, this work proposes several models of knowledge-inspired neural networks, achieving interesting results from both the performance and interpretability points of view. Specifically, novel architectures based on boundary condition generative adversarial networks (BC-GANs, which are able to leverage information from sparse ground observation networks) and on more traditional UNets, employing various information fusion methods, are designed and evaluated against each other. Our results can serve as a baseline benchmark for other studies and be used to develop further optimised models for the inference of PM2.5 concentrations from AOD at either the global or regional scale.</div>

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