Seasonal Prior-Guided Dynamic Forecasting of Nonstationary Gridded PM2.5 Concentrations in China

Accurate and timely short-term forecasting of gridded PM2.5 concentrations is essential for air-quality management and public health protection, especially for the growing elderly population who are particularly vulnerable to air pollution. Advances in multi-source environmental data and deep learning have substantially improved the spatiotemporal prediction of PM2.5 concentrations. However, existing studies have mainly focused on developing increasingly sophisticated model architectures and optimization strategies while insufficiently considering the temporal nonstationarity of PM2.5 pollution and the recurrent characteristics associated with similar seasonal environmental regimes. Changes in natural environmental conditions and anthropogenic emissions can continuously alter the relationships governing PM2.5 concentrations, challenging the stability of continuous short-term forecasting; meanwhile, historically recurring seasonal pollution characteristics provide potentially valuable prior information for model adaptation. To address this problem, we propose a Seasonal Prior-guided Elastic Weight Consolidation (Seasonal EWC) framework for dynamic forecasting of nonstationary gridded PM2.5 concentrations in China. The framework derives season-specific parameter anchors and Fisher-based parameter importance from historical seasonal conditions and uses this prior knowledge to guide parameter updates during continuous short-term forecasting. Experiments with four representative spatiotemporal forecasting models showed that Seasonal EWC produced architecture-dependent changes in forecasting performance, reducing the annual mean MAE and RMSE by 30.3% and 31.7% for U-Net and by 10.9% and 7.6% for Earthformer, with only small improvements for IAM4VP and mixed changes for ConvLSTM. Parameter analysis revealed that Seasonal EWC reduced parameter-update magnitudes by approximately 27–32% and substantially limited long-term parameter deviation, indicating more stable adaptation during continuous forecasting. These findings demonstrate that seasonal prior knowledge can effectively guide dynamic model adaptation under evolving PM2.5 conditions, providing a promising approach for continuous short-term air-quality forecasting.