Bayesian-Based Approaches to Exploring the Long-Term Alteration in Trace Metals of Surface Water and Its Driving Forces.
Bayesian-Based Approaches to Exploring the Long-Term Alteration in Trace Metals of Surface Water and Its Driving Forces.
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DOI：
10.1021/acs.est.2c07210
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发表时间：
2023-01
影响因子：
11.4
通讯作者：
Zhenyu Wang;P. Hua;Jin Zhang;Peter Krebs
中科院分区：
文献类型：
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作者：
Zhenyu Wang;P. Hua;Jin Zhang;Peter Krebs
微量金属污染对水生态系统构成严重威胁。因此，表征痕量金属的长期环境行为及其驱动力对于指导水质管理至关重要。基于1990 - 2019年的长期数据集，本研究系统地进行了德国易北河流域河流中微量元素的时空趋势评估、影响因子分析和源解析。结果表明，近30年来，各元素的平均浓度依次为Fe、Mn、（1179.5 ± 1221 μg·L-1）锰（209.6 ± 181.7 μg·L-1）锌（52.5 ± 166.2 μg·L-1）铜（5.3 ± 5.5 μg·L-1）> Ni（4.4 ± 8.3 μg·L-1）>铅（3.3 ± 4.4 μg·L-1）>砷（2.9 ± 2.3 μg·L-1）>铬（1.8 ± 2.4 μg·L-1）镉（0.3 ± 1.1 μg·L-1）>汞（0.05 ± 0.12 μg·L-1）。小波分析表明，河流的流量和洪水占主导地位的金属污染的周期性变化。贝叶斯网络表明，水化学因素（即，TOC、TP、TN、pH和EC）化学上影响水和沉积物之间的金属流动性。此外，贝叶斯多变量受体模型计算的源分配表明，给定的元素污染是典型的归因于地质来源（17.5，95%置信区间：13.1-17.6%），城市和工业污染源（22.1，18.0-27.2%）、耕地土壤侵蚀（24.2，16.4-31.5%）和历史人类活动（35.2，32.8-43.3%）。本文提供的结果表明，水化学对金属流动性的影响和人类活动的慢性干扰造成的微量金属污染的长期变化。
Trace metal pollution poses a serious threat to the aquatic ecosystem. Therefore, characterizing the long-term environmental behavior of trace metals and their driving forces is essential for guiding water quality management. Based on a long-term data set from 1990 to 2019, this study systematically conducted the spatiotemporal trend assessment, influential factor analysis, and source apportionment of trace elements in the rivers of the German Elbe River basin. Results show that the mean concentrations of the given elements in the last 30 years were found in the order of Fe (1179.5 ± 1221 μg·L-1) ≫ Mn (209.6 ± 181.7 μg·L-1) ≫ Zn (52.5 ± 166.2 μg·L-1) ≫ Cu (5.3 ± 5.5 μg·L-1) > Ni (4.4 ± 8.3 μg·L-1) > Pb (3.3 ± 4.4 μg·L-1) > As (2.9 ± 2.3 μg·L-1) > Cr (1.8 ± 2.4 μg·L-1) ≫ Cd (0.3 ± 1.1 μg·L-1) > Hg (0.05 ± 0.12 μg·L-1). Wavelet analyses show that river flow regimes and flooding dominated the periodic variations in metal pollution. Bayesian network suggests that the hydrochemical factors (i.e., TOC, TP, TN, pH, and EC) chemically influenced the metal mobility between water and sediments. Furthermore, the source apportionment computed by the Bayesian multivariate receptor model shows that the given element contamination was typically attributed to the geogenic sources (17.5, 95% confidence interval: 13.1-17.6%), urban and industrial sources (22.1, 18.0-27.2%), arable soil erosion (24.2, 16.4-31.5%), and historical anthropogenic activities (35.2, 32.8-43.3%). The results provided herein reveal that both the hydrochemical influence on metal mobility and the chronic disturbance from anthropogenic activities caused the long-term variation in trace metal pollution.