Analysis of pollutants transport in heavy air pollution processes using a new complex-network-based model

A transport analysis model of air pollutants based on complex networks is proposed to study the spatiotemporal variation characteristics of air pollution during heavy air pollution processes and further analyze the contribution and interrelationship of air pollution of different cities. To construct the transmission network for each heavy air pollution process, a link is established between two cities according to the correlation coefficient of pollutants, the angle between the direction of cities and the wind direction, and its weight is assigned by integrating the factors of pollution, meteorology and geography. Then the PageRank algorithm is improved to identify the main contribution areas, and the label propagation algorithm is improved to find the associated air pollution areas. Finally, the period from January 1, 2014 to February 28, 2021 in Beijing–Tianjin–Hebei (BTH) region are used to demonstrate the performance of this model. The results show that a total of 58 heavy air pollution processes occur during the study period, the shortest lasting 3 days and the longest 13 days. These pollution processes occur mainly in winter, especially in January, and there are sufficient data to show that pollution levels decline year on year. Long-term correlation analysis for single-pollutant between different cities shows that geography is one of the most important factors affecting the regional transport of air pollutants. Moreover, according to the statistical analysis of each heavy pollution process, Tangshan, Tianjin and Langfang are the main contribution areas in the pollutant regional transport. In addition, apart from that in Zhangjiakou, Chengde and Qinhuangdao, the pollutants in the other cities in the BTH region have an effect on one another so that these cities are formed as an associated contaminated area. This study can provide better insights into local control and joint prevention of atmospheric pollution.