Estimation of Ground NO2 Measurements from Sentinel-5P Tropospheric Data through Categorical Boosting

Abstract

This study aims to analyse the Nitrogen Dioxide (NO2) pollution in the Emilia Romagna Region (Northern Italy) during 2019, with the help of satellite retrievals from the Sentinel-5P mission of the European Copernicus Programme and ground-based measurements, obtained from the ARPA site (Regional Agency for the Protection of the Environment). The final goal is the estimation of ground NO2 measurements when only satellite data are available. For this task, we used a Machine Learning (ML) model, Categorical Boosting, which was demonstrated to work quite well and allowed us to achieve a Root-Mean-Square Error (RM SE) of 0.0242 over the 43 stations utilised to get the Ground Truth values. This procedure represents the starting point to understand which other actions must be taken to improve the final performance of the model and extend its validity.