Routes and clustering features of PM2.5 spillover within the Jing-Jin-Ji region at multiple timescales identified using complex network-based methods
Abstract：spillover networks at multiple timescales were constructed using discrete wavelet transform, the GARCH-BEKK model and complex networks. The results showed that the interaction of the PM2.5 spillover behavior within Jing-Jin-Ji is notably strong and that the spillover relationships are normally active for up to approximately one week.The Shijiazhuang and Baoding cities require more control due to their wide PM2.5 spillover range to other cities, such as Beijing and Chengde.
Moreover, the PM2.5 spillover routes differ at different timescales. In the short term, the initial cities of spillover routes, e.g., the “Cangzhou–Tianjin-Handan–Beijing” route, are the critical control cities for the government, and the cities along the routes should take advanced measures to prevent bad air conditions when the former cities have heavy haze days. In addition, the Jing-Jin-Ji cities are divided into two to three clusters, and cities in the same cluster are mostly adjacent to each other. Therefore, the most cost-effective method for achieving joint regional air pollution control in Jing-Jin-Ji cities is to treat the cities in the same clusters as a whole. This study proposes a novel perspective for the regional joint control of air pollution based on complex networks, an approach that can be used to holistically and cost-effectively select areas of collaborative governance.
簡介：
we mainly focus on identifying the routes and clustering features of air pollution spillover among Jing-Jin-Ji citiesto provide a decision-making basis for the joint regional control of air pollution in Jing-Jin-Ji cities.
First, we used grasping technology to achieve the daily air pollution value (PM 2.5) of 13 cities in the Jing-Jin-Ji area.
Second, using the DWT method, we transformed the air pollution time series of each city into multiple timescales.
Third, wed use the GARCH-BEKK model to calculate the air pollu-tion spillover relationships between any two cities at multiple time scales.
Fourth, using a complex network-based method, we constructed directed air pollution spillover networks at multiple time scales by taking the cities as nodes, the significant spillover relationships as edges, and the spillover coefficients as weights.Using methods for route and community detection, we then ob-tained the routes and clustering features of air pollution spilloveramong Jing-Jin-Ji cities at multiple timescales.