Accurate and interpretable air pollution forecasting is crucial for public health, but most models face a trade-off between performance and interpretability. This study proposes a physics-guided, interpretable-by-design spatiotemporal learning framework. The model decomposes the spatiotemporal behavior of air pollutant concentrations into two transparent, additive modules. The first is a physics-guided transport kernel with directed weights conditioned on wind and geography (advection). The second is an explainable attention mechanism that learns local responses and attributes future concentrations to specific historical lags and exogenous drivers. Evaluated on a comprehensive dataset from the Stockholm region, our model consistently outperforms state-of-the-art baselines across multiple forecasting horizons. Our model's integration of high predictive performance and spatiotemporal interpretability provides a more reliable foundation for operational air-quality management in real-world applications.

翻译：准确且可解释的空气污染预测对公共卫生至关重要，但多数模型在性能与可解释性之间存在权衡。本研究提出一种物理引导、设计可解释的时空学习框架。该模型将空气污染物浓度的时空行为分解为两个透明、可加的模块。第一个模块是基于物理引导的传输核，其定向权重由风场和地理条件（平流）决定。第二个模块是可解释的注意力机制，用于学习局部响应并将未来浓度归因于特定的历史滞后项及外生驱动因子。在斯德哥尔摩地区的综合数据集上进行评估，我们的模型在多个预测时间跨度上均持续优于现有先进基线方法。该模型将高预测性能与时空可解释性相结合，为实际应用中的空气质量业务化管理提供了更可靠的基础。