Accurate PM2.5 prediction is essential for effective urban air quality management. However, existing methods often struggle to capture the complex, nonlinear, and coupled spatiotemporal dynamics in long-term air pollution evolution. Most existing models rely on short-term observations and overlook long-range temporal trends and inter-station dependencies, which limit their ability to capture the spatiotemporal evolution of air pollution. To address these challenges, we propose a novel dynamic global–local spatiotemporal graph framework for PM2.5 long-term forecasting across multiple cities. Specifically, we introduce a Multi-Station iTransformer (MS-iTransformer) module to capture long-term temporal dependencies from station-specific historical sequences. To globally model evolving inter-city relationships, we design a bilinear spatiotemporal attention (BSTA) module to adaptively build dynamic spatiotemporal graphs using bilinear spatial and temporal attention. Furthermore, we propose a Graph-Enhanced Spatiotemporal Module (GESM) to capture localized spatiotemporal dependencies through graph convolution and recurrent modeling. The experimental results demonstrate that our model has significant improvements across PM2.5 forecasting tasks on three real-world air quality datasets, outperforming widely adopted baseline approaches. The MAE and RMSE are decreased by 1.7665 and 1.8578, respectively. The FAR is reduced by 0.0312. The CSI and R2 are improved by 0.0194 and 0.0260, respectively. Therefore, the proposed method achieves accurate air quality forecasting by effectively capturing long-term temporal trends, dynamic spatial dependencies, and localized spatiotemporal interactions.
Huang et al. (Fri,) studied this question.