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通过识别土壤、景观和河流沉积物的指纹来追踪流域中痕量金属的来源。

Tracking the origin of trace metals in a watershed by identifying fingerprints of soils, landscape and river sediments.

机构信息

Department of Physical Geography, Faculty of Earth Sciences, Shahid Beheshti University, 1983969411 Tehran, Iran.

Department of Watershed Management and Engineering, Faculty of Natural Resources, Tarbiat Modares University, 46414-356 Tehran, Iran.

出版信息

Sci Total Environ. 2022 Aug 20;835:155583. doi: 10.1016/j.scitotenv.2022.155583. Epub 2022 Apr 27.

Abstract

The identification of the spatial distribution of soil trace-elements and the contribution of different sources to the sediment yield is necessary for a better watershed and river water quality management. Until now, less attention has been paid to comprehensive assessments of sediment sources and soil trace-elements with respect to the suspended sediment production. The present study aimed at modelling the spatial distribution of soil trace-elements, quantifying the sediment sources apportionment and relating the landforms to polluted soils. Different techniques and approaches such as the Nemerow pollution index, machine learning algorithms (Random Forest (RF), generalised boosting methods (GBM), generalised linear models (GLM) and sediment fingerprinting were applied to the Kan watershed. A total of 79 soil samples having different Nemerow index values were considered for spatial modelling. Using statistical methods (Range test, Kruskal-Wallis and discrimination function analysis), an optimal set of tracers was selected. An unmixing model was applied to calculate the relative contribution of landforms for eight rainfall events. The results of the soil trace-element mapping showed that RF had the best performance with an accuracy of 83%. The evaluation of polluted soil areas showed that the landforms 'steep hills' and 'valley' contributed the most with 51% and 27% in the riparian zone, respectively. In addition, these landforms give a high contribution to sediment production in late-winter-spring events (29%) with a GOF (goodness of fit) of 0.65. The landform 'plain' had the highest contribution (28%) in sediment yield with a GOF of 0.72 in early-winter events. This means that the valley and steep hill landforms accelerate the transport of trace-elements across the watershed. Interestingly, the contribution of landforms varies during the year. Overall, the new proposed approach enables to better trace the origin of suspended sediments and trace-elements discharge into the river environment.

摘要

确定土壤微量元素的空间分布以及不同来源对泥沙输出的贡献,对于更好地进行流域和河流水质管理是必要的。到目前为止,对于泥沙来源和土壤微量元素与悬移质产生的综合评估,关注较少。本研究旨在模拟土壤微量元素的空间分布,量化泥沙来源的分配,并将地貌与污染土壤联系起来。本研究采用了不同的技术和方法,如内梅罗污染指数、机器学习算法(随机森林(RF)、广义增强方法(GBM)、广义线性模型(GLM)和泥沙示踪技术,对 Kan 流域进行了研究。共考虑了 79 个具有不同内梅罗指数值的土壤样本进行空间建模。使用统计方法(范围检验、Kruskal-Wallis 和判别函数分析),选择了一组最佳示踪剂。应用混合模型计算了 8 次降雨事件中地貌的相对贡献。土壤微量元素制图的结果表明,RF 的性能最好,准确率为 83%。污染土壤区域的评估表明,地貌“陡坡”和“山谷”在河岸带的贡献最大,分别为 51%和 27%。此外,这些地貌在冬末春初事件(29%)中对泥沙产生高贡献,拟合优度(GOF)为 0.65。在冬季初期事件中,地貌“平原”对泥沙产量的贡献最高(28%),拟合优度为 0.72。这意味着山谷和陡坡地貌加速了流域内微量元素的迁移。有趣的是,地貌的贡献在一年中是变化的。总的来说,新提出的方法可以更好地追踪悬浮泥沙和微量元素排放到河流环境的来源。

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