Interest in soil pollution by multiple heavy metals has been growing over the last decades. However, few experiments combining numerical analyses with solute transport in layered soil can be found in the literature. Here, the retention and fate of three coexisting metal ions, Cu, Cd, and Zn, in layered soils were investigated to evaluate soil co-contamination through batch and column experiments. Results showed high amounts of Cu adsorption and retention by soils, followed by Cd and Zn. The partial concentration of Zn in effluent was greater than the input from competition adsorption and the 'snow plow effect'. These findings indicate the high potential risk of Zn and Cd groundwater pollution when Cu, Cd, and Zn co-exist in the soil. Adsorption isotherms obtained from batch experiments were well described by Freundlich equation. Breakthrough curves (BTCs) obtained from column experiments were well described by standard convection-dispersion equation (CDE) for Br, and Tow-site (TSM) and One-site models (OSM) for metals except for Zn, using the Levenberg-Marquardt nonlinear optimization algorithm. However, the parameters were poorly constrained by the available observational data due to high correlation between parameters, rather than insensitivity to model outputs. The Generalized Likelihood Uncertainty Estimation (GLUE) method did not only qualify the uncertainty of parameters for solute transport in layered medium, but estimate prediction uncertainty. Prediction bounds basically captured the observed Br, Zn and Cd BTCs, while systematically overestimated the effluent Cu concentration. Comparing with the optimization, GLUE method can improve prediction reliability of heavy metal transport in layered soils.

中文翻译：

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层状饱和土壤中重金属的共运：特性和模拟。

在过去的几十年中，人们越来越关注多种重金属对土壤的污染。但是，在文献中很少能找到将数值分析与溶质运移相结合的实验。在这里，研究了三种土壤中铜，镉和锌共存的金属离子的保留和去向，以通过分批和柱实验评估土壤的共污染。结果表明，土壤大量吸附和保留了铜，其次是镉和锌。废水中锌的部分浓度大于竞争吸附和“除雪效应”的输入。这些发现表明当土壤中同时存在铜，镉和锌时，锌和镉地下水污染的潜在风险很高。批次实验获得的吸附等温线通过Freundlich方程很好地描述了。使用Levenberg-Marquardt用Br的标准对流弥散方程（CDE）以及除Zn以外的金属的Tow-site（TSM）和One-site model（OSM）很好地描述了从柱实验获得的突破曲线（BTC）。非线性优化算法。但是，由于参数之间的高度相关性，而不是对模型输出不敏感，因此可用的观测数据对参数的约束很弱。广义似然不确定度估计（GLUE）方法不仅可以验证层状介质中溶质运移参数的不确定性，还可以估计预测不确定性。预测范围基本上捕获了观察到的Br，Zn和Cd BTC，而系统地高估了废水中Cu的浓度。与优化相比，