空气质量预测的新方法，AirFlow 用双流结构分别处理不同污染物的变化节奏，36项指标赢34项，参数还不到5万，轻量又准。
AirFlow 是一个面向空气质量预测的污染物感知双流框架，针对不同污染物通道在周期性和分布漂移上的差异，设计了统计引导的归一化路由机制，根据24小时自相关和分布漂移为每种污染物选择归一化路径。其分层双流状态模型结合多尺度状态空间传播与可学习响应系数，通过门控双向交叉注意力交换信息并自适应融合表示。在多个城市真实数据上的实验中，AirFlow 在36项指标对比中取得34项最优，相比最先进基线将均方根误差最多降低11.11%。该模型仅需0.0483M参数和0.0215G FLOPs，在保持高预测精度的同时计算开销极低。
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 shared latent representation for channel-specific distributions and changes at different rates. To address this limitation, we propose AirFlow, a pollutant-aware dual-stream framework that operates on station multivariate observations without additional graph propagation or predefined signal decomposition. Specifically, AirFlow designs two novel blocks: (1) a statistic-guided normalization routing mechanism that selects a normalization path for each pollutant according to its 24-hour autocorrelation and distribution drift; and (2) a hierarchical dual-stream state model that combines multi-scale state space propagation with learnable response coefficients, where gated bidirectional cross-attention exchanges information and adaptively fuses the resulting representations. Experiments on real-world data from multiple cities show that AirFlow achieves the best performance in 34 of 36 metrics comparisons, with reductions of up to 11.11% root mean square error over the state-of-the-art baseline. AirFlow also requires only 0.0483M parameters and 0.0215G FLOPs, achieving high forecasting accuracy with low computational overhead.