The potential ecological risk assessment of soil heavy metals using self-organizing map

分区 环境科学 星团(航天器) 污染 分水岭 地理 生态学 环境资源管理 环境保护 生物 工程类 计算机科学 土木工程 机器学习 程序设计语言
作者
Qing Xiang,Huan Yu,Hongliang Chu,Mengke Hu,Tao Xu,Xiaoyu Xu,Ziyi He
出处
期刊:Science of The Total Environment [Elsevier BV]
卷期号:843: 156978-156978 被引量:35
标识
DOI:10.1016/j.scitotenv.2022.156978
摘要

Heavy metal pollution control zoning aiming at the health maintenance of watershed soil ecosystem has become an important means of soil environmental protection and governance. Based on the self-organizing map (SOM), this study classifies the data sets of eight heavy metals such as Co, Cd, Zn, Cr, Cu, Pb, Ni, and Tl in 354 samples, calculates the potential ecological risk value of soil heavy metals in combination with the potential Hakansom Risk index (HRI), and uses the geographic information system (GIS) for visualization. In the research results, SOM has divided five soil ecological risk categories. The highest average ecological risk value of 85.95 is found in cluster IV, which is clustered and distributed in urban development areas in the upper reaches of the river. The average ecological risk values of cluster I and cluster V are relatively close at 79.64 and 79.19, respectively. Cluster I and cluster V are distributed in the north of the river in a linear and cluster manner, respectively, and are located on a concave bank with a relatively gentle slope. The average ecological risk of soil pollution in cluster II is 77.59, which is linearly distributed on both banks of the river. The ecological risk of soil pollution in cluster III is the lowest (74.39), mainly scattered in the south of rivers with less human activities. The study further identified the environmental factors that affect the soil ecological risk value in different cluster units and put forward the classified and differentiated management and control strategies for different cluster units. The research shows that SOM can cluster the data sets of heavy metals with high sensitivity and low threshold through competitive learning to effectively provide the distribution information of abnormal soil ecological risk areas. This information is helpful for urban environmental management departments and planning departments to take targeted management and recovery measures to avoid the health risks related to soil heavy metal pollution.
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