The air quality is directly related to people’s lives. This paper selects air quality data of Sichuan Province as the research object, and explores the inherent characteristics of air quality from the perspective of complex network theory. First, based on the complexity of network topology and nodes, a community detection algorithm which combines the clustering idea with principal component analysis (PCA) algorithm and self-organization competitive neural network (SOM) is designed (CSP). Compared with the classic community detection algorithm, the result proves that the CSP algorithm can accurately dig out a better community structure. Second, based on the strong correlation distance and strong correlation coefficient of the air quality network, the Sichuan Air Quality Complex Network (SCCN) was constructed. The SCCN is divided into five communities using the CSP algorithm. Combining the characteristics of each community and the Hurst coefficient, it is found that the air quality inside the community has long-term memory. Finally, based on the idea of time-dependent cross-correlation, this paper analyzes the cross-correlation of AQI time series of different stations in each community, constructs a directed air quality cross-correlation network combined with complex network theory, and locates the important pollution sources in each region of Sichuan Province according to the topological structure of the network. The work of this paper can provide the corresponding theoretical support and guidance for the current environmental pollution control.

中文翻译：

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基于复杂网络和CSP算法的四川省空气质量分析

空气质量直接关系到人们的生活。本文选取四川省空气质量数据作为研究对象，从复杂网络理论的角度探讨空气质量的内在特征。首先，基于网络拓扑结构和节点的复杂性，设计了一种将聚类思想与主成分分析（PCA）算法和自组织竞争神经网络（SOM）相结合的社区检测算法（CSP）。与经典社区检测算法相比，结果证明CSP算法能够准确挖掘出更好的社区结构。其次，基于空气质量网络的强相关距离和强相关系数，构建了四川空气质量综合网络（SCCN）。使用 CSP 算法将 SCCN 划分为五个社区。结合各个社区的特点和赫斯特系数，发现社区内的空气质量具有长时记忆。最后，基于时变互相关的思想，分析了各社区不同站点AQI时间序列的互相关，结合复杂网络理论构建了有向空气质量互相关网络，定位按网络拓扑结构划分四川省各区域重要污染源。本文的工作可为当前的环境污染控制提供相应的理论支持和指导。发现小区内的空气质量具有长时记忆。最后，基于时变互相关的思想，分析了各社区不同站点AQI时间序列的互相关，结合复杂网络理论构建了有向空气质量互相关网络，定位按网络拓扑结构划分四川省各区域重要污染源。本文的工作可为当前的环境污染控制提供相应的理论支持和指导。发现小区内的空气质量具有长时记忆。最后，基于时变互相关的思想，分析了各社区不同站点AQI时间序列的互相关，结合复杂网络理论构建了有向空气质量互相关网络，定位按网络拓扑结构划分四川省各区域重要污染源。本文的工作可为当前的环境污染控制提供相应的理论支持和指导。结合复杂网络理论构建有向空气质量互相关网络，根据网络拓扑结构定位四川省各区域的重要污染源。本文的工作可为当前的环境污染控制提供相应的理论支持和指导。结合复杂网络理论构建有向空气质量互相关网络，根据网络拓扑结构定位四川省各区域的重要污染源。本文的工作可为当前的环境污染控制提供相应的理论支持和指导。