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

Transactions of Nonferrous Metals Society of China 2024年第9期34卷 3054-3068页

作者： Sheng-guo XUE Jing-pei FENG Wen-shun KE Mu LI Kun-yan QIU Chu-xuan LI Chuan WU Lin GUO School of Metallurgy and Environment Central South UniversityChangsha 410083China Henan Key Laboratory of Monitoring and Remediation in Heavy Metal Polluted Soil Jiyuan 454650China Henan Academy of Geology Zhengzhou 450000China

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 *** 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 *** 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,*** presented the highest ecological risk,and there existed high potential ecological risk at some positions with different depths due to high mobility of ***,the application of machine learning significantly increased the accuracy of pXRF measurements,and identified key environmental *** adapted potential ecological risk assessment emphasized the need to focus on Pb,Cd,and As in future site remediation efforts.

关键词： smelting site potentially toxic elements X-ray fluorescence potential ecological risk machine learning

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The Comparison of Three Environmental Metrics for Cr, Pb, and Zn in the Agricultural Region of the Mid-Continent of USA

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Journal of Geoscience and Environment Protection 2021年第4期9卷 147-165页

作者： Almesleh A. A. Najwah Goodell Philip Program of Environmental Science & Engineering University of Texas El Paso USA Department of Geological Science University of Texas El Paso USA

The chemical and physical properties of soil are critical factors that affect human health. The current geochemical study is designed to evaluate the concentrations of heavy metals (Cr, Pb, and Zn) in the soil in Iowa (IA), Kansas (KS), and Nebraska (NE). The basic descriptive statistical results suggest that there are some limited levels of the heavy metals in the soils that come from anthropogenic inputs. The results of three environmental metrics, the enrichment factor (EF), geoaccumulation (Igeo), and potential ecological risk (PERI), have been calculated, evaluated, and compared. EF values show that soils contain minimal enrichment of Cr, Pb, and Zn in the study area. In addition, PERI values presented low risk with Cr, Pb, and Zn. However, Igeo values showed no contamination of Cr, Pb, and Zn in the study area. These results suggest that the elevated levels of these heavy metals are dominated by the historic agricultural inputs derived from long-term anthropogenic applications, especially in the regions with extensive human activities, which means that soil is the sink for heavy metals released into the environment.

关键词： Chemical Elements Enrichment Factor Geo Accumulation potential ecological risk Geochemical Maps

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