Air quality prediction is key to mitigating health impacts and guiding decisions, yet existing models tend to focus on temporal trends while overlooking spatial generalization. We propose AQ-Net, a spatiotemporal reanalysis model for both observed and unobserved stations in the near future. AQ-Net utilizes the LSTM and multi-head attention for the temporal regression. We also propose a cyclic encoding technique to ensure continuous time representation. To learn fine-grained spatial air quality estimation, we incorporate AQ-Net with the neural kNN to explore feature-based interpolation, such that we can fill the spatial gaps given coarse observation stations. To demonstrate the efficiency of our model for spatiotemporal reanalysis, we use data from 2013-2017 collected in northern China for PM2.5 analysis. Extensive experiments show that AQ-Net excels in air quality reanalysis, highlighting the potential of hybrid spatio-temporal models to better capture environmental dynamics, especially in urban areas where both spatial and temporal variability are critical.

翻译：空气质量预测对于减轻健康影响和指导决策至关重要，然而现有模型往往侧重于时间趋势而忽视了空间泛化。我们提出了AQ-Net，一种针对近期已观测和未观测站点的时空再分析模型。AQ-Net利用LSTM和多头注意力机制进行时间回归。我们还提出了一种循环编码技术以确保连续的时间表示。为了学习细粒度的空间空气质量估计，我们将AQ-Net与神经kNN结合，探索基于特征的插值方法，从而能够在给定粗粒度观测站点的条件下填补空间空白。为验证本模型在时空再分析中的效能，我们使用2013-2017年在中国北方收集的PM2.5数据进行实验分析。大量实验表明，AQ-Net在空气质量再分析方面表现优异，凸显了混合时空模型在更好捕捉环境动态方面的潜力，尤其在时空变异性均至关重要的城市区域。