AirFlow: Context Preserving and Multi-Rate State Modeling for Air Quality Forecasting

Accurate air quality forecasting is essential for public health and urban environmental management, but remains challenging because pollutant channels differ in periodicity and distribution drift, while their concentration trajectories contain both multi-scale dependencies and rapid changes. Recent methods have improved spatial dependency learning and meteorological covariate modeling. However, pollutant channels are still passed through the same normalization rule and temporal backbone, using a

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ethics.ai (10 August 2026), “AirFlow: Context Preserving and Multi-Rate State Modeling for Air Quality Forecasting,” evidence record 18259, https://ethics.ai/record/18259 (originally published by arXiv cs.AI).

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