首页|基于机器学习的铅冶炼场地污染快速识别与风险评估

基于机器学习的铅冶炼场地污染快速识别与风险评估

扫码查看
原文链接
  • 万方数据
以便携式X射线荧光光谱仪(pXRF)测量的207个土壤样品的重金属含量和6种环境因素作为模型修正 系数,建立了 7种重金属的通用预测模型.为了评估冶炼场地存在的潜在生态风险,使用XGBoost算法拟合重金属含量和环境特征之间的关系,建立了基于环境因素的潜在生态风险指数.结果表明,通用预测模型对铅(拟合 系数R2=0.911)、镉(R2=0.950)和砷(R2=0.835)均具有极高的预测精度;表层土壤重金属的潜在生态风险较高,部分点位因Cd的高迁移性在不同深度均有较高的潜在生态风险;机器学习显著提高了pXRF重金属测量结果的准确性,识别了影响测量过程的关键环境因素.基于改进的潜在生态风险评价,该铅冶炼场地铅、镉和砷的生态风险较高,应重点考虑对其进行修复.
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 207 soil 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(R2)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.

smelting sitepotentially toxic elementsX-ray fluorescencepotential ecological riskmachine learning

薛生国、冯静培、可文舜、李幕、邱坤艳、李楚璇、吴川、郭林

展开 >

中南大学冶金与环境学院,长沙4100083

河南省土壤重金属污染监测与修复重点实验室,济源 454650

河南省地质研究院,郑州 450000

冶炼场地 潜在有害元素 X射线荧光光谱 潜在生态风险 机器学习

National Key Research and Development Program of ChinaFundamental Research Funds for the Central Universities of Central South University,ChinaPostgraduate Innovative Project of Central South University,ChinaPostgraduate Scientific Research Innovation Project of Hunan Province,China

2019YFC18036012023ZZTS08012023XQLH068QL20230054

2024

10.1016/S1003-6326(24)66595-7

中国有色金属学报(英文版)
中国有色金属学会

中国有色金属学报(英文版)

CSTPCD
影响因子：1.183
ISSN：1003-6326
年,卷(期)：2024.34(9)
  • 4