Accurate forecasting of air pollution is critical for public health and environmental management. This paper proposes a hybrid spatiotemporal framework that integrates Neural Controlled Differential Equations (Neural CDE) with Graph Attention Networks (GAT), which is a type of Graph Neural Network (GNN) for 72-hour multi-pollutant forecasting in complex urban settings. The Neural CDE component models continuous-time temporal dynamics through cubic-spline path interpolation, effectively handling irregular and missing sensor data. Meanwhile, the GAT module captures spatial interactions among monitoring stations via an adaptive edge mechanism (edge-MLP), where each edge feature combines the Neural CDE-predicted pollutant states at the source and target stations with physical factors such as inter-station distance, geographic direction, and local wind vectors. Experiments using hourly observations from five stations in Beijing demonstrate that the proposed model achieves an average Mean Absolute Error (MAE) of 13.14, outperforming both CDE-only and GNN-only baselines. The hybrid architecture remains stable and accurate even under incomplete data scenarios, confirming that combining continuous-time neural dynamics with edge-aware graph reasoning yields physically consistent and robust air-quality forecasts.
Mirzaei et al. (2026) studied this question.