Nowadays, real-time air pollution monitoring has been an important approach for supporting pollution control and reduction. However, due to the high construction cost and limited detection range of monitoring stations, not all the air pollutant concentrations in every corner can be monitored, and a whole picture of the spatial distribution of air pollution is usually lacked for comprehensive spatial analysis and air quality control. To address this problem, satellite remote sensing and spatial interpolation/extrapolation technologies have been commonly used in past research. However, the spatial distribution calculated by remote sensing techniques could be less accurate due to the limited amount of recorded data for testing and adjustments. Performance of traditional spatial interpolation/extrapolation techniques, such as Kriging and IDW, was limited by several subjective assumptions and pre-set formulations that are less suitable for non-linear real-world situations. As an alternative, machine learning and neural network-based methods have been proposed recently. However, most of these methods failed to well consider the long short temporal trend and spatial associations of air pollution simultaneously. To overcome these limitations, this paper proposed a newly designed spatial interpolation/extrapolation methodology namely Geo-LSTM to generate the spatial distribution of air pollutant concentrations. The model was developed based on the Long Short-Term Memory (LSTM) neural network to capture the long-term dependencies of air quality. A geo-layer was designed to integrate the spatial-temporal correlation from other monitoring stations. To evaluate the effectiveness of the proposed methodology, a case study in Washington state was conducted. The experimental results show that Geo-LSTM has a RMSE of 0.0437, and is almost 60.13% better than traditional methods like IDW.

如今，实时空气污染监测已成为支持污染控制和减少的重要方法。但是，由于建设成本高昂，监测站的检测范围有限，无法对每个角落的所有空气污染物浓度进行监测，因此通常缺乏对空气污染空间分布的整体了解，无法进行全面的空间分析和空气质量。控制。为了解决这个问题，在过去的研究中已经普遍使用了卫星遥感和空间内插/外推技术。但是，由于用于测试和调整的记录数据量有限，通过遥感技术计算出的空间分布可能不太准确。传统空间插值/外推技术（例如Kriging和IDW）的性能，受到一些主观假设和预设公式的限制，这些主观假设和预设公式不太适合非线性现实世界的情况。作为替代方案，最近已经提出了基于机器学习和基于神经网络的方法。然而，大多数这些方法都没有很好地同时考虑到短时趋势和空气污染的空间关联。为了克服这些限制，本文提出了一种新设计的空间插值/外推方法，即Geo-LSTM，以生成空气污染物浓度的空间分布。该模型是基于长期短期记忆（LSTM）神经网络开发的，用于捕获空气质量的长期依存关系。设计了一个地层，以整合其他监测站的时空相关性。为了评估所提出方法的有效性，在华盛顿州进行了案例研究。实验结果表明，Geo-LSTM的均方根误差（RMSE）为0.0437，比IDW等传统方法高出近60.13％。