PM2.5 Retrieval With Sentinel-5P Data Over Europe Exploiting Deep Learning

Monitoring particulate matter (PM) is of critical importance due to its significant impact on human health. Ground stations provide highly accurate measurements of various pollutants on a local scale. However, the limited distribution of these stations makes achieving global coverage challenging. To address this limitation, satellite imagery serves as a valuable resource, offering wide-area PM estimates in near real-time through abundant data and frequent revisit intervals. In contrast to other studies, this work introduces deep learning (DL) models to estimate ground-level PM concentration maps over Europe. These models rely exclusively on radiance data from the Sentinel-5P satellite, forgoing auxiliary information, such as meteorological data, which are commonly incorporated in similar studies. The proposed approach has demonstrated both robust estimation accuracy and effective generalization capabilities. Furthermore, the estimated PM concentration maps have been validated against ground-based measurements, showing superior performance with respect to widely used models and datasets that consider meteorological inputs. The dataset and the code are available here: https://github.com/antoniomazza88/PMUnet.