Air pollution is a serious environmental problem that has attracted much attention. Air quality prediction can provide useful information for urban environmental governance decision-making and residents’ daily health control. However, existing research methods have suffered from a weak ability to capture the spatial correlations and fail to model the long-term temporal dependencies of air quality. To overcome these limitations, we propose a multi-scale spatiotemporal graph convolution network (MST-GCN), which consists of a multi-scale block, several spatial-temporal blocks and a fusion block. We first divide the extracted features into several groups based on their domain categories, and represent the spatial correlations across stations as two graphs. Then we combine the grouped features and the constructed graphs in pairs to form a multi-scale block that feeds into spatial-temporal blocks. Each spatial-temporal block contains a graph convolution layer and a temporal convolution layer, which can model the spatial correlations and long-term temporal dependencies. To capture the group interactions, we use a fusion block to fuse multiple groups. Extensive experiments on a real-world dataset demonstrate that our model achieves the highest performance compared with state-of-the-art and baseline models for air quality prediction.

空气污染是一个严重的环境问题，引起了人们的广泛关注。空气质量预测可以为城市环境治理决策和居民的日常健康控制提供有用的信息。然而，现有的研究方法由于缺乏捕捉空间相关性的能力而无法模拟空气质量的长期时间依赖性。为了克服这些限制，我们提出了一种多尺度的时空图卷积网络（MST-GCN），该网络由一个多尺度的块，几个时空的块和一个融合块组成。我们首先根据提取的要素的领域类别将其分为几组，然后将站点之间的空间相关性表示为两个图形。然后，我们将成对的特征和构造的图成对组合，以形成一个多尺度块，并馈入时空块。每个时空块包含一个图形卷积层和一个时间卷积层，它们可以对空间相关性和长期时间依赖性进行建模。为了捕获组之间的相互作用，我们使用融合块融合多个组。在实际数据集上进行的大量实验表明，与最新的空气质量预测模型和基准模型相比，我们的模型可实现最高的性能。我们使用融合块融合多个组。在实际数据集上进行的大量实验表明，与最新的空气质量预测模型和基准模型相比，我们的模型可实现最高的性能。我们使用融合块融合多个组。在实际数据集上进行的大量实验表明，与最新的空气质量预测模型和基准模型相比，我们的模型可实现最高的性能。