AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version

Key points

• Accurate long-term air quality forecasting is challenging due to long-range transport, meteorological variability, and nonlinear chemical reactions underlying secondary pollutant formation.1
• 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.1
• We further introduce HanoiAir, a real-world dataset integrating pollutant concentrations, meteorology, and emission inventories.1
• Experiments on HanoiAir and a public benchmark show that AeroChem improves long-horizon stability and sudden-change forecasting.1

Sources (1)

• [1]AeroChem: Closed-loop Physics-Informed State Space Modeling for Long-term Chemically-Reactive Air Quality Forecasting--Extended Version
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 11:52 AM

  We propose AeroChem, a closed-loop physics-informed state space framework for chemically reactive air quality forecasting.

  Accurate long-term air quality forecasting is challenging due to long-range transport, meteorological variability, and nonlinear chemical reactions underlying secondary pollutant formation.

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