A Novel Hybrid CNN-Transformer Framework for Spatiotemporal Prediction of O₃

Air pollution presents serious threats to public health and environmental sustainability, especially when it comes to concentrations of ozone (O<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>). Even though they are accurate, traditional monitoring methods have spatial limitations that cause gaps in the coverage of data. In order to tackle this issue, we provide a hybrid deep learning model that predicts O<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> levels in different parts of India by integrating Transformer and Convolutional Neural Networks (CNN) using satellite and meteorological data. While the Transformer model uses meteorological data to identify temporal trends, the CNN component analyzes satellite pictures to extract spatial information. Multiple assessment criteria, including R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, MSE, RMSE, and MAE, show that we enhance prediction accuracy by merging both models. Our model performs better in both short- and long-term predictions than other deep learning techniques and RNN, such as CNN, GRU, and LSTM models. The results suggest that the hybrid CNN-Transformer model is a promising solution for accurate air pollution forecasting, offering critical insights for timely interventions and policy development.