A study on the analysis of dynamical transmission behavior and mining key monitoring stations in PM and O3 networks in the Beijing-Tianjin-Hebei region of China

Abstract

To investigate the dynamical transmission behavior of pollutants and explore the roles played by monitoring stations in regional air pollutants transportation, we constructed a new model for the dynamical transmission index by adopting a statistics model that employs complex network analysis along with terrain data, meteorological variables, and air quality data. The study is conducted in Beijing-Tianjin-Hebei region with 70 stations in 13 cities. The findings indicated that the regional dynamical transmission networks were characterized by the participation of 67 out of 70 stations, as determined by node number. Among the model characteristics, the average path length and the average clustering coefficient, within the ranges of 2.08–2.32 and 0.26–0.51, respectively, maintained reasonable small-world characteristic. For the seasonal transmission features, the networks for PM2.5, PM10 in winter, and O3 in summer shared similar modeling characteristics with those of yearly networks. This suggested that the networks for these two seasons could represent the yearly transmission features. By employing the entropy weight method, the key monitoring stations numbered 1011 A, 1026 A, and 1010 A, which are located in Tianjin, Shijiazhuang, and Beijing, exerted significant impacts on air pollution transmission path in cities. The novel model has demonstrated its soundness and effectiveness in terms of capturing the behavior of transmission as well as the distinguishing roles of these crucial monitoring stations. This methodology could be employed for the construction of additional monitoring stations, identification of possible pollution sources, and prioritization of key pollution areas, thus providing valuable insights for environmental protection and management.