Abstract：
With the transformation and upgrading of industries, the environmental problems caused by industrial residual contaminated sites are becoming increasingly prominent. Based on actual investigation cases, this study analyzed the soil pollution status of a remaining sites of the copper and zinc rolling industry, and found that the pollutants exceeding the screening values included Cu, Ni, Zn, Pb, total petroleum hydrocarbons and 6 polycyclic aromatic hydrocarbon monomers. Based on traditional analysis methods such as the correlation coefficient and spatial distribution, combined with machine learning methods such as SOM + K-means, it is inferred that the heavy metal Zn/Pb may be mainly related to the production history of zinc rolling. Cu/Ni may be mainly originated from the production history of copper rolling. PAHs are mainly due to the incomplete combustion of fossil fuels in the melting equipment. TPH pollution is speculated to be related to oil leakage during the industrial use period and later period of vehicle parking. The results showed that traditional analysis methods can quickly identify the correlation between site pollutants, while SOM + K-means machine learning methods can further effectively extract complex hidden relationships in data and achieve in-depth mining of site monitoring data.
摘要：
随着产业的转型升级,工业残留污染场地引起的环境问题日益突出。根据实际调查案例，本研究分析了铜和锌轧制行业剩余站点的土壤污染状况，发现超过筛选值的污染物包括铜，Ni,Zn,Pb,总石油烃和6种多环芳烃单体。基于传统的相关系数和空间分布等分析方法,结合SOM+K-means等机器学习方法，推测重金属Zn/Pb可能主要与锌轧制的生产历史有关。Cu/Ni可能主要来自铜轧制的生产历史。PAHs主要是由于熔融设备中化石燃料的不完全燃烧。据推测，TPH污染与工业使用期间和车辆停放后期的漏油有关。结果表明,传统分析方法能够快速识别场地污染物之间的相关性,而SOM+K-means机器学习方法可以进一步有效提取数据中复杂的隐藏关系,实现对现场监测数据的深度挖掘。
