School of Telecommunications Engineering, Xidian University, Xi'an 710071, Shaanxi, China.
School of Systems Science, Beijing Normal University, Beijing, 100875, China.
Sci Total Environ. 2022 Jun 25;827:154298. doi: 10.1016/j.scitotenv.2022.154298. Epub 2022 Mar 7.
Accurate air quality prediction can help cope with air pollution and improve the life quality. With the development of the deployments of low-cost air quality sensors, increasing data related to air quality has provided chances to find out more accurate prediction methods. Air quality is affected by many external factors such as the position, wind, meteorological information, and so on. Meanwhile, these factors are spatio-temporal dynamic and there are many dynamic contextual relationships between them. Many methods for air quality prediction do not consider these complex spatio-temporal correlations and dynamic contextual relationships. In this paper, we propose a dual-path dynamic directed graph convolutional network (DP-DDGCN) for air quality prediction. We first create a dual-path transposed dynamic directed graph according to static distance relationships of stations and the dynamic relationships generated by wind speed and directions. Then based on the dual-path dynamic directed graph, we can capture the dynamic spatial dependencies more comprehensively. After that we apply gated recurrent units (GRUs) and add the future meteorological features, to extract the complex temporal dependencies of historical air quality data. Using dual-path dynamic directed graph blocks and the GRUs, we finally construct a dynamic spatio-temporal gated recurrent block to capture the dynamic spatio-temporal contextual correlations. Based on real-world datasets, which record a large amount of PM concentration data, we compare the proposed model with the benchmark models. The experimental results show that our proposed model has the best performance in predicting the PM concentrations.
准确的空气质量预测有助于应对空气污染，提高生活质量。随着低成本空气质量传感器的广泛部署，越来越多的空气质量相关数据为发现更准确的预测方法提供了机会。空气质量受到位置、风、气象信息等许多外部因素的影响。同时，这些因素具有时空动态性，它们之间存在许多动态的上下文关系。许多空气质量预测方法没有考虑这些复杂的时空相关性和动态上下文关系。本文提出了一种用于空气质量预测的双通道动态有向图卷积网络（DP-DDGCN）。我们首先根据站点的静态距离关系和风速风向生成的动态关系，创建一个双通道转置动态有向图。然后，基于双通道动态有向图，我们可以更全面地捕捉动态空间依赖关系。之后，我们应用门控循环单元（GRU）并添加未来气象特征，以提取历史空气质量数据的复杂时间依赖关系。最后，我们使用双通道动态有向图块和 GRU 构建一个动态时空门控循环块，以捕获动态时空上下文相关性。基于记录大量 PM 浓度数据的真实数据集，我们将所提出的模型与基准模型进行了比较。实验结果表明，所提出的模型在预测 PM 浓度方面具有最佳性能。
