Abstract
With the development of heavy industry, urban soil suffers serious pollution, which threatens the sustainable development of cities. Understanding the spatial distribution characteristics of surface soil pollution aids in pollution prevention and control and promotes sustainable development. We use China’s Baotou as an example. Based on the data of 2820 sampling points in main urban areas and some suburban areas of Baotou, we constructed a relationship network model for sampling points in surface soil by using the complex network method. We combined the network method with spatial geographic information to analyze the spatial agglomeration characteristics of the surface soil pollution in Baotou China. Sampling points at Dalahai Village (including 506D, 538B, and 538D) and Hayenaobao Village (including 509C, 541A, and 541C), Puerhantu Town within the Kundulun District have the most serious pollution problems, and they are all concentrated in the tailings dam. Sampling points 328D and 544A are scattered in the Leng Community, Kunhe Town, Kundulun District and Changhan Village, Haringer Township, Jiuyuan District, but they have a close co-anomaly relationship with the tailings dam. We suggest that these areas should be unified to give priority to pollution control. There is an obvious difference for Al2O3, B, Hg, and U, which are abnormal in the power plant ash storage pools, but normal at the tailings dam. Consequently, pollution control for power plant ash storage pools needs to be different from pollution control at the tailings dam. Sampling points at the Fengying Community (including 580A and 580B), Kunhe Town, Kundulun District, and Gaoyoufang Village (579D and 643B), Rare Earth Road, Qingshan District as well as other sampling points upstream of the Kundulun River have a close co-anomaly relationship with the tailings dam. It is necessary to strengthen the purification treatment of sewage upstream of the Kundulun River to reduce the spread of pollutants. These results provide a theoretical basis for the government to formulate specific cross-regional collaborative governance measures.
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