基于机器学习的太湖沉积柱中痕量金属化学组分和磁性模拟

IF 3 3区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Water Pub Date : 2024-09-14 DOI:10.3390/w16182604
Hui Xiao, Tong Ke, Liming Chen, Dehu Li, Wanru Yang, Xin Qian, Long Chen, Ligang Deng, Huiming Li
{"title":"基于机器学习的太湖沉积柱中痕量金属化学组分和磁性模拟","authors":"Hui Xiao, Tong Ke, Liming Chen, Dehu Li, Wanru Yang, Xin Qian, Long Chen, Ligang Deng, Huiming Li","doi":"10.3390/w16182604","DOIUrl":null,"url":null,"abstract":"In this study, the chemical fractions (CFs) of trace metal (TMs) and multiple magnetic parameters were analysed in the sedimentary column from the centre of Lake Taihu. The sedimentary column, measuring 53 cm in length, was dated using 210Pb and 137Cs to be 124 years old. Surface layers of the column were found to contain significantly higher concentrations of Cd, Co, Cu, Pb, Sb, Ti, and Zn than the middle and bottom layers. The sedimentary core contained a substantial amount of ferrimagnetic minerals. Most of the TMs were present in the residual state, except for Mn and Pb. The chemical fractions of Cd exhibited the most significant variation with depth. The pollution load index (PLI) indicated moderate TMs pollution levels in the region, whereas the risk assessment code (RAC) classified Mn as being heavily polluted. Multiple linear regression (MLR) and random forest (RF), support vector machine (SVM), and XGBoost (1.7.7.1) machine learning models were used to simulate the RAC and total concentration of TMs, using physical and chemical indicators and magnetic parameters of the sediments as input variables. The MLR model outperformed RF, SVM, and XGBoost in simulating the CFs and total concentrations of most TMs in the sedimentary column, with R2 up to 0.668 and 0.87. The SHapley Additive exPlanations (SHAP) method reveals that χarm/χ is the dominant factor influencing the RAC of As in the XGBoost models. For the RAC of Co and Cu in RF models, C% and N% exhibit greater contributions.","PeriodicalId":23788,"journal":{"name":"Water","volume":"15 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Chemical Fractions and Magnetic Simulation Based on Machine Learning for Trace Metals in a Sedimentary Column of Lake Taihu\",\"authors\":\"Hui Xiao, Tong Ke, Liming Chen, Dehu Li, Wanru Yang, Xin Qian, Long Chen, Ligang Deng, Huiming Li\",\"doi\":\"10.3390/w16182604\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, the chemical fractions (CFs) of trace metal (TMs) and multiple magnetic parameters were analysed in the sedimentary column from the centre of Lake Taihu. The sedimentary column, measuring 53 cm in length, was dated using 210Pb and 137Cs to be 124 years old. Surface layers of the column were found to contain significantly higher concentrations of Cd, Co, Cu, Pb, Sb, Ti, and Zn than the middle and bottom layers. The sedimentary core contained a substantial amount of ferrimagnetic minerals. Most of the TMs were present in the residual state, except for Mn and Pb. The chemical fractions of Cd exhibited the most significant variation with depth. The pollution load index (PLI) indicated moderate TMs pollution levels in the region, whereas the risk assessment code (RAC) classified Mn as being heavily polluted. Multiple linear regression (MLR) and random forest (RF), support vector machine (SVM), and XGBoost (1.7.7.1) machine learning models were used to simulate the RAC and total concentration of TMs, using physical and chemical indicators and magnetic parameters of the sediments as input variables. The MLR model outperformed RF, SVM, and XGBoost in simulating the CFs and total concentrations of most TMs in the sedimentary column, with R2 up to 0.668 and 0.87. The SHapley Additive exPlanations (SHAP) method reveals that χarm/χ is the dominant factor influencing the RAC of As in the XGBoost models. For the RAC of Co and Cu in RF models, C% and N% exhibit greater contributions.\",\"PeriodicalId\":23788,\"journal\":{\"name\":\"Water\",\"volume\":\"15 1\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Water\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.3390/w16182604\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.3390/w16182604","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

本研究分析了太湖中心沉积柱中痕量金属(TMs)的化学组分(CFs)和多个磁性参数。根据 210Pb 和 137Cs 测定,沉积柱长度为 53 厘米,距今 124 年。研究发现,沉积柱表层的镉、钴、铜、铅、锑、钛和锌含量明显高于中层和底层。沉积岩芯含有大量铁磁性矿物。除了锰和铅之外,大多数铁磁性矿物都以残余状态存在。镉的化学组分随深度的变化最为显著。污染负荷指数(PLI)表明该地区的 TMs 污染程度为中度,而风险评估代码(RAC)则将锰列为重度污染。采用多元线性回归(MLR)、随机森林(RF)、支持向量机(SVM)和 XGBoost(1.7.7.1)机器学习模型,以沉积物的理化指标和磁性参数为输入变量,模拟 RAC 和锰的总浓度。在模拟沉积柱中大多数 TMs 的 CFs 和总浓度方面,MLR 模型优于 RF、SVM 和 XGBoost,R2 分别高达 0.668 和 0.87。SHapley Additive exPlanations(SHAP)方法表明,在 XGBoost 模型中,χarm/χ 是影响 As RAC 的主要因素。对于射频模型中 Co 和 Cu 的 RAC,C% 和 N% 的贡献更大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Chemical Fractions and Magnetic Simulation Based on Machine Learning for Trace Metals in a Sedimentary Column of Lake Taihu
In this study, the chemical fractions (CFs) of trace metal (TMs) and multiple magnetic parameters were analysed in the sedimentary column from the centre of Lake Taihu. The sedimentary column, measuring 53 cm in length, was dated using 210Pb and 137Cs to be 124 years old. Surface layers of the column were found to contain significantly higher concentrations of Cd, Co, Cu, Pb, Sb, Ti, and Zn than the middle and bottom layers. The sedimentary core contained a substantial amount of ferrimagnetic minerals. Most of the TMs were present in the residual state, except for Mn and Pb. The chemical fractions of Cd exhibited the most significant variation with depth. The pollution load index (PLI) indicated moderate TMs pollution levels in the region, whereas the risk assessment code (RAC) classified Mn as being heavily polluted. Multiple linear regression (MLR) and random forest (RF), support vector machine (SVM), and XGBoost (1.7.7.1) machine learning models were used to simulate the RAC and total concentration of TMs, using physical and chemical indicators and magnetic parameters of the sediments as input variables. The MLR model outperformed RF, SVM, and XGBoost in simulating the CFs and total concentrations of most TMs in the sedimentary column, with R2 up to 0.668 and 0.87. The SHapley Additive exPlanations (SHAP) method reveals that χarm/χ is the dominant factor influencing the RAC of As in the XGBoost models. For the RAC of Co and Cu in RF models, C% and N% exhibit greater contributions.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Water
Water WATER RESOURCES-
CiteScore
5.80
自引率
14.70%
发文量
3491
审稿时长
19.85 days
期刊介绍: Water (ISSN 2073-4441) is an international and cross-disciplinary scholarly journal covering all aspects of water including water science and technology, and the hydrology, ecology and management of water resources. It publishes regular research papers, critical reviews and short communications, and there is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodical details must be provided for research articles. Computed data or files regarding the full details of the experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material.
期刊最新文献
EstuarySAT Database Development of Harmonized Remote Sensing and Water Quality Data for Tidal and Estuarine Systems. Study on Large-Scale Urban Water Distribution Network Computation Method Based on a GPU Framework Land-Use Pattern-Based Spatial Variation of Physicochemical Parameters and Efficacy of Safe Drinking Water Supply along the Mahaweli River, Sri Lanka Ensuring the Safety of an Extraction Well from an Upgradient Point Source of Pollution in a Computationally Constrained Setting The Impact of Catastrophic Floods on Macroinvertebrate Communities in Low-Order Streams: A Study from the Apennines (Northwest Italy)
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1