Advancing PM2.5 and NO₂ Prediction Accuracy through the ConvTrans Model: A Novel Application of Hybrid Deep Learning Technologies

Accurate forecasting of critical pollutants such as NO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> and PM2.5 is essential in the ongoing battle against air pollution. This paper introduces ConvTrans, a novel hybrid architecture that integrates convolutional and Transformer technologies to enhance the precision of PM2.5 and NO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> predictions. Our evaluations demonstrate that ConvTrans improves PM2.5 prediction accuracy, increasing the R-squared value by 3.6%, reducing RMSE by 5.4%, MSE by 7.5%, and MAE by 17.3%. Similarly, for NO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> predictions, the model shows a 4.2% increase in R-squared, along with a 5.4% reduction in RMSE, a 13.7% decrease in MSE, and a 5.6% reduction in MAE. These results highlight the model’s effectiveness in short-term forecasting, addressing key challenges in environmental management.