Knowledge-inspired fusion strategies for the inference of PM2.5 values with a Neural Network

Abstract. Ground-level concentrations of Particulate Matter (more precisely PM2.5) are a strong indicator of air quality, which is now widely recognized 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 AOD and surface PM2.5 concentrations are used as input of several networks 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 Atmospheric Monitoring Service (CAMS) as well as 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 both from the performance and interpretability points of view. Specifically, novel architectures based on 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 baseline benchmark for other studies and be used to develop further optimised models for the inference of PM2.5 concentrations from AOD at either global or regional scale.