Although considerable success has been achieved in urban air quality prediction (AQP) with machine learning techniques, accurate and long-term prediction is still challenging. One of the key issues for existing AQP approaches is that air quality monitoring stations are sparsely distributed, typically with around ten monitoring stations per city. As air quality may change abruptly in a local area, it is difficult to perform AQP accurately in areas that are far away from observation points. In addition, due to the large distance between every two monitoring stations, we cannot effectively leverage spatial relations among them to improve the AQP accuracy. In this paper, thanks to the development of low-cost air quality sensors, we are now able to collect a large-scale air quality dataset with 393 deployed air quality monitoring stations in a 120 km × 70 km region, which is more than ten times denser than existing AQP datasets. Further, we present a novel method to handle the data effectively. Specifically, we first convert the observed data from irregularly distributed monitoring stations into a regular image-like pollution map, which can then be processed with advanced deep convolutional networks. The experimental results show that the proposed approach can simultaneously model the temporal and spatial relations in our large-scale densely-observed dataset, leading to significantly improved AQP results.

尽管使用机器学习技术在城市空气质量预测 (AQP) 中取得了相当大的成功，但准确和长期的预测仍然具有挑战性。现有 AQP 方法的关键问题之一是空气质量监测站分布稀疏，通常每个城市大约有 10 个监测站。由于局部地区的空气质量可能会发生突变，因此在远离观测点的地区很难准确地进行 AQP。此外，由于每两个监测站之间的距离较大，我们无法有效利用它们之间的空间关系来提高 AQP 精度。在本文中，由于低成本空气质量传感器的发展，我们现在能够在 120 公里 × 70 公里的区域收集 393 个部署的空气质量监测站的大规模空气质量数据集，其密度是现有 AQP 数据集的十倍以上。此外，我们提出了一种有效处理数据的新方法。具体来说，我们首先将来自不规则分布的监测站的观测数据转换为规则的类似图像的污染图，然后可以用先进的深度卷积网络进行处理。实验结果表明，所提出的方法可以同时对我们大规模密集观测数据集中的时间和空间关系进行建模，从而显着改善 AQP 结果。我们首先将来自不规则分布的监测站的观测数据转换为规则的类似图像的污染图，然后可以用先进的深度卷积网络进行处理。实验结果表明，所提出的方法可以同时对我们大规模密集观测数据集中的时间和空间关系进行建模，从而显着改善 AQP 结果。我们首先将来自不规则分布的监测站的观测数据转换为规则的类似图像的污染图，然后可以用先进的深度卷积网络进行处理。实验结果表明，所提出的方法可以同时对我们大规模密集观测数据集中的时间和空间关系进行建模，从而显着改善 AQP 结果。