Nowcasting and forecasting urban air pollution with meteorological and wind profile data: A framework for Smart City Digital Twins

Source abstract

This study presents a framework for nowcasting and forecasting urban air pollution using meteorological and wind profile data. It is designed to support urban air-quality management and to monitor the durability of the built environment within smart city digital twins. The proposed framework employs the Series-cOre Fused Time Series forecaster (SOFTS) as the primary predictive model. Its performance is evaluated using data from Lamezia Terme, Italy, under both fully informed and missing/reconstructed data scenarios. For benchmarking purposes, two widely used machine-learning approaches — extreme gradient boosting (XGBoost) and long short-term memory (LSTM) — are also considered. Numerical results demonstrate the accuracy and robustness of the framework. In real-time nowcasting scenarios at Lamezia, the SOFTS model achieved coefficients of determination ( R 2 ) exceeding 0.91 and weighted mean absolute percentage errors (WMAPE) below 21% for all target pollutants, outperforming the XGBoost benchmark. For short-term forecasting up to 6 h, the model maintained strong predictive skill, particularly for particulate matter, with R 2 values above 0.70 at the sixth hour. Results from the independent Cabauw site in the Netherlands further support the generalizability of the framework, with R 2 > 0 . 97 for nowcasting and R 2 > 0 . 62 at the 6-hour forecasting horizon. Model interpretability is also examined, and the results are discussed in relation to the existing literature. Overall, the framework advances short-term urban air-pollution prediction for smart city digital twin applications.