Rapid detection and risk assessment of soil contamination at lead smelting site based on machine learning

A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model correction coefficients.The eXtreme Gradient Boosting(XGBoost)model was used to fit the relationship between the content of heavy metals and environment characteristics to evaluate the soil ecological risk of the smelting site.The results demonstrated that the generalized prediction model developed for Pb,Cd,and As was highly accurate with fitted coefficients(R~2)values of 0.911,0.950,and 0.835,respectively.Topsoil presented the highest ecological risk,and there existed high potential ecological risk at some positions with different depths due to high mobility of Cd.Generally,the application of machine learning significantly increased the accuracy of pXRF measurements,and identified key environmental factors.The adapted potential ecological risk assessment emphasized the need to focus on Pb,Cd,and As in future site remediation efforts.

基于随机模拟的冶炼场地重金属污染及生态风险评价

选择某铅冶炼场地,采用地统计学和铅同位素示踪法分析重金属空间分布格局和污染来源,并基于不确定性理论的风险模型,系统评估场地重金属生态风险。结果表明,土壤铅、锌、砷、镉、铬和汞污染严重,空间异质性强。采用MixSIAR模型解构3个潜在源的铅同位素贡献密度分布,土壤重金属主要来自冶炼活动(49.9%),燃煤(16.4%)和土壤母质(33.7%)。基于随机模拟的潜在生态风险指数(RI)表明,场地重金属处于中等生态风险;其中冶炼活动对RI累积贡献最大;镉、砷、铅和汞对RI的方差贡献最大,对场地的污染起主导作用。因此,应该优先关注场地土壤镉、砷、铅和汞污染的防控及潜在冶炼源的处置。