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
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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。
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.
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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.
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