Accurate air quality forecasting is crucial for protecting public health and guiding environmental policy, yet it remains challenging due to nonlinear spatiotemporal dynamics, wind-driven transport, and distribution shifts across regions. Physics-based models are interpretable but computationally expensive and often rely on restrictive assumptions, whereas purely data-driven models can be accurate but may lack robustness and calibrated uncertainty. To address these limitations, we propose Neural Dynamic Diffusion-Advection Fields (NeuroDDAF), a physics-informed forecasting framework that unifies neural representation learning with open-system transport modeling. NeuroDDAF integrates (i) a GRU-Graph Attention encoder to capture temporal dynamics and wind-aware spatial interactions, (ii) a Fourier-domain diffusion-advection module with learnable residuals, (iii) a wind-modulated latent Neural ODE to model continuous-time evolution under time-varying connectivity, and (iv) an evidential fusion mechanism that adaptively combines physics-guided and neural forecasts while quantifying uncertainty. Experiments on four urban datasets (Beijing, Shenzhen, Tianjin, and Ancona) across 1-3 day horizons show that NeuroDDAF consistently outperforms strong baselines, including AirPhyNet, achieving up to 9.7% reduction in RMSE and 9.4% reduction in MAE on long-term forecasts. On the Beijing dataset, NeuroDDAF attains an RMSE of 41.63 $μ$g/m$^3$ for 1-day prediction and 48.88 $μ$g/m$^3$ for 3-day prediction, representing the best performance among all compared methods. In addition, NeuroDDAF improves cross-city generalization and yields well-calibrated uncertainty estimates, as confirmed by ensemble variance analysis and case studies under varying wind conditions.

翻译：准确预测空气质量对保护公众健康和指导环境政策至关重要，但由于非线性时空动态、风驱动输运以及区域间分布偏移，该任务仍具挑战性。基于物理的模型可解释性强但计算成本高且常依赖严格假设，而纯数据驱动模型虽预测准确但可能缺乏鲁棒性和校准的不确定性。为克服这些局限，我们提出神经动态扩散-平流场（NeuroDDAF），这是一个融合神经表示学习与开放系统输运建模的物理信息预测框架。NeuroDDAF集成以下模块：(i) GRU-图注意力编码器，用于捕捉时间动态和风意识空间交互；(ii) 具有可学习残差的傅里叶域扩散-平流模块；(iii) 风调制潜在神经常微分方程，用于在时变连接性下建模连续时间演化；(iv) 证据融合机制，可自适应组合物理引导与神经预测并量化不确定性。在北京、深圳、天津和安科纳四个城市数据集上的1-3天预测实验表明，NeuroDDAF持续优于强基线模型（包括AirPhyNet），在长期预测中均方根误差降低达9.7%，平均绝对误差降低达9.4%。在北京数据集上，NeuroDDAF的1天预测均方根误差为41.63 μg/m³，3天预测为48.88 μg/m³，在所有对比方法中表现最优。此外，通过集成方差分析和不同风况案例研究验证，NeuroDDAF提升了跨城市泛化能力并生成校准良好的不确定性估计。