Deep Learning for NO2 Forecasting in Italy: An LSTM-Based Approach

Accurate prediction of nitrogen dioxide (NO 2 ) concentrations is essential for air quality management and public health protection, particularly in urban environments. In this study, we developed and evaluated a deep learning framework based on Long Short-Term Memory (LSTM) networks to forecast daily NO 2 concentrations across Italy on a high-resolution (5 km) hexagonal grid. The model integrates a heterogeneous set of predictors, including satellite-derived tropospheric NO 2 (Sentinel-5P), meteorological reanalysis data (ERA5), land cover and elevation data (CORINE, Google Earth Engine), and demographic and infrastructure features (WorldPop, OpenStreetMap). Ground-truth data from ARPA monitoring stations between 2019 and 2022 were used for training and validation. We compared the performance of the LSTM with a linear regression baseline and XGBoost, employing both Random 5-Fold and Leave-One-Year-Out cross-validation strategies. Model performance was assessed using RMSE, MAE, and R 2 , showing that the LSTM consistently outperformed the other models in both temporal and spatial prediction accuracy. This framework demonstrates the effectiveness of AI-based approaches in air pollution modeling and highlights their potential for supporting One Health applications by providing high-resolution exposure estimates that can inform public health interventions and environmental policy.