AeroChem：用于长期化学反应性空气质量预测的闭环物理知情状态空间模型——扩展版
AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version
AI总结：
针对长期空气质量预测的漂移与物理一致性问题，本文提出闭环物理知情状态空间框架AeroChem，采用双分支协同校正设计并结合HanoiAir数据集，提升了长期预测稳定性与突变预测能力。
摘要：
准确的长期空气质量预测颇具挑战，原因在于存在长距离传输、气象多变性以及二次污染物形成背后的非线性化学反应。纯数据驱动模型常存在长期预测漂移问题且缺乏物理一致性，而现有物理知情方法通常采用开环设计，仅在输出层面融合神经组件与物理组件。本文提出AeroChem，一种用于化学反应性空气质量预测的闭环物理知情状态空间框架。AeroChem采用双分支协同校正设计：基于UDE的过程分支提供结构化但不完美的物理先验，基于Mamba的虚拟测量分支捕捉长距离时间上下文并生成概率虚拟观测值。神经卡尔曼融合通过在每个预测步骤校正潜在物理状态并将后验状态反馈至模拟器来耦合两个分支，从而减少长期预测漂移。本文还引入HanoiAir，一个整合了污染物浓度、气象数据和排放清单的真实世界数据集。在HanoiAir数据集及一个公开基准上开展的实验表明，AeroChem提升了长期预测稳定性与突变预测能力。
英文摘要：
Accurate long-term air quality forecasting is challenging due to long-range transport, meteorological variability, and nonlinear chemical reactions underlying secondary pollutant formation. Purely data-driven models often suffer from long-horizon drift and lack physical consistency, while existing physics-informed approaches commonly follow open-loop designs that fuse neural and physical components only at the output level. We propose AeroChem, a closed-loop physics-informed state space framework for chemically reactive air quality forecasting. AeroChem adopts a two-branch co-correction design: a UDE-based process branch provides a structured but imperfect physical prior, while a Mamba-based virtual measurement branch captures long-range temporal context and produces probabilistic virtual observations. Neural Kalman Fusion couples the two branches by correcting the latent physical state at each forecasting step and feeding the posterior state back into the simulator, reducing long-horizon drift. We further introduce HanoiAir, a real-world dataset integrating pollutant concentrations, meteorology, and emission inventories. Experiments on HanoiAir and a public benchmark show that AeroChem improves long-horizon stability and sudden-change forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
