摘要：

高精度的空气污染预测对环境治理与公共健康防护至关重要。本文针对预测任务中存在的时空异质性和多特征耦合问题，提出了一种多重解耦的时空动态图卷积网络（MD-STDGCN），旨在精细化建模本地污染物排放的特异性时序模式和跨区域污染物输送的动态交互过程。该模型首先采用双路径自监督掩码预训练进行特征增强，时间路径通过局部子序列重建强化时序特征提取能力，空间路径通过节点序列重建捕捉空间异质性，从而缓解分布偏移和异质性带来的表征退化问题。其次，模型引入多级残差分解与层级式预测框架，逐步提取时空序列的全局时序模式、局部时空模式和短期扰动成分：利用通道独立卷积与多尺度因果时间注意力提取宏观趋势；通过自适应权重估计门与动态图卷积，建模具有方向性和时滞性的空间输送特征；再由GRU补充性地建模短期扰动成分。最终，模型融合双路径增强表征与多分支预测结果，实现端到端的多步预测。实验结果表明，MD-STDGCN全面优于基线模型，其预测精度在所有数据集上均有显著提升：在KnowAir、长三角和KnowAir_V2数据集上，平均MAE分别降低了7.34%、1.88%和12.57%，RMSE分别降低了7.64%、2.44%和11.29%。MD-STDGCN通过双路径特征增强、多级解耦与动态图学习有效缓解了特征纠缠和异质性的影响，提升了预测精度与鲁棒性，可为空气质量监测与治理决策提供可靠支持。

Abstract:

High-accuracy air pollution prediction is crucial for environmental management and public health protection. To address the issues of spatiotemporal heterogeneity and multi-feature coupling in prediction tasks, this paper proposes a Multi- Decoupled Spatio-Temporal Dynamic Graph Convolutional Network (MD-STDGCN). The model aims to precisely capture the specific temporal patterns of local pollutant emissions and the dynamic interactions of cross-regional pollutant transport. The model first employs a dual-path self-supervised masked pretraining strategy for feature enhancement. The temporal path improves the ability to extract temporal features through local subsequence reconstruction, while the spatial path captures spatial heterogeneity via node sequence reconstruction. This mitigates the issue of representation degradation caused by distribution shift and heterogeneity. Second, the model introduces a multi-level residual decomposition and hierarchical prediction framework to progressively extract global temporal patterns, local spatiotemporal patterns, and short-term disturbances from the spatiotemporal series. The framework integrates channel-independent convolutions and multi-scale causal temporal attention for long-term trend modeling, an adaptive weight gating with dynamic graph convolution for directional and lagged transport, and GRUs for short-term fluctuations. Finally, multi-branch predictions are fused with dual-path enhanced representations to achieve end-to-end multi-step forecasting. Experimental results show that MD-STDGCN outperforms all baseline models with significant improvements in prediction accuracy across all datasets: on KnowAir, Yangtze River Delta, and KnowAir_V2, the average MAE is reduced by 7.34%、1.88% and 12.57%, and the RMSE is reduced by 7.64%、2.44% and 11.29%, respectively. By leveraging dual-path feature enhancement, multi-level decoupling, and dynamic graph learning, MD-STDGCN effectively alleviates the impact of feature entanglement and heterogeneity, improving both prediction accuracy and robustness. It can provide reliable support for air quality monitoring and governance decision-making.