2023/04/08 by F. Mauro, Mauro, Francesco, L. Russo +7
Earth and Planetary Sciences · Engineering · Environmental Science · Mathematics · #Air Quality Monitoring and Forecasting #Air Quality and Health Impacts #Atmospheric chemistry and aerosols #Boosting (machine learning) #Categorical variable #Computer science #Engineering #Environmental science #FOS: Electrical engineering #Geography #Geology #Gradient boosting #Ground level #Ground truth #Machine learning #Mathematics #Mean squared error #Meteorology #Remote sensing #Satellite #Signal Processing (eess.SP) #Statistics #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2304.04069
openalex publication_date 2023/04/08 · openalex created_date 2023/04/12 · openalex updated_date 2026/07/28
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.