PM2.5 concentration prediction algorithm integrating traffic congestion index

Abstract

In this study, a strategy is proposed to use the congestion index as a new input feature. This approach can reveal more deeply the complex effects of traffic conditions on variations in particulate matter (PM_2.5) concentrations. To assess the effectiveness of this strategy, we conducted an ablation experiment on the congestion index and implemented a multi-scale input model. Compared with conventional models, the strategy reduces the root mean square error (RMSE) of all benchmark models by > 6.07 % on average, and the best-performing model reduces it by 12.06 %, demonstrating excellent performance improvement. In addition, even with high traffic emissions, the RMSE during peak hours is still below 9.83 µg/m³, which proves the effectiveness of the strategy by effectively addressing pollution hotspots. This study provides new ideas for improving urban environmental quality and public health and anticipates inspiring further research in this domain.