Long-short-term decoupled residual learning for high-resolution spatio-temporal air quality inference

Accurate high-resolution spatio-temporal air quality inference based on sparse monitoring stations is essential for environmental governance and public health. However, prevailing deep learning approaches often treat inference as direct regression on observed air quality concentrations, neglecting the distinct effects of a long-term static baseline and short-term dynamic perturbations on the inference. This limits the model's generalization ability and may cause spatial bias in the inference. In this paper, we propose a novel Spatio-Temporal Air Quality Inference Network (SAQIN) model, which provides an explicit decomposition of the static and dynamic effects in the inference. The static branch integrates high-resolution semantic segmentation, global context from remote sensing, population density, and elevation to construct a high-fidelity environmental prior. The dynamic branch employs multi-head self-attention mechanism to jointly encode geographic relationships and inter-pollutant chemical coupling effects. We further develop a residual learning module to model the inference process as a residual correction anchored to multiple reference stations and aggregates predictions through stepwise multi-station fusion with distance-weighted averaging, which successfully eliminates the Voronoi-partition-induced discontinuities and yielding a globally smooth physically plausible air quality inference field. SAQIN has been shown to demonstrate superior accuracy and strong zero-shot cross-domain generalisation when evaluated on large-scale real-world datasets, thus outperforming state-of-the-art methods across a range of criteria pollutants.