Journal of Soils and Sediments

, Volume 18, Issue 9, pp 2960–2969 | Cite as

The solid-solution distribution of copper added to soils: influencing factors and models

  • Xiaoqing Zhang
  • Jumei Li
  • Dongpu Wei
  • Bo Li
  • Yibing MaEmail author
Soils, Sec 4 • Ecotoxicology • Research Article



A series of empirical and mechanistic geochemical models were developed to describe the solid-solution partitioning of copper (Cu) in typical fresh spiked Chinese soils.

Materials and methods

The influence of soil properties on Cu partitioning was assessed in a wide range of soils using multiple regression analysis. Geochemical models (WHAM VI and Visual MINTEQ) and simulation analyses in combination with experimental data (i.e., the bulk of soil properties and Cu contents) were performed in order to provide additional insight into the mechanisms controlling the Cu partitioning. Calculation of soluble Cu contents based on the two models was then simplified and optimized by adjusting input variables, and the calibrated outputs were used to produce reasonable predictions of soluble metal concentrations.

Results and discussion

The results of the multiple regression analyses presented in this paper show strong correlations between soluble Cu concentrations and soil Cu concentrations and properties, with adjusted coefficients of determination (Radj2) ranging between 0.84 and 0.91. Soil organic carbon (OC) content was an insignificant factor in most cases, but the active fraction of dissolved organic matter was important in improving model estimates. The best fit of root mean square error (RMSE) varied between 0.42 and 0.77 for the WHAM VI model and between 0.28 and 0.57 for the Visual MINTEQ model across all pH categories.


The models presented in this paper are suitable for investigating and simulating Cu solid-solution partitioning in a wide range of Chinese soils.


Copper Prediction models Risk assessment Soil solution Speciation 



The authors thank the financial support by Special Fund for Environmental Protection Scientific Research in the Public Interest (Project no. 201509032), the Natural Science Foundation of China (Project no. 41501537), and The High-level Leading Talent Introduction Program of GDAS. The authors also thank the national long-term soil experimental stations in China for soil collection, and Gillian Cozens and Cathy Fiebiger for technical assistance.

Supplementary material

11368_2018_1962_MOESM1_ESM.docx (597 kb)
ESM 1 (DOCX 596 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xiaoqing Zhang
    • 1
  • Jumei Li
    • 2
  • Dongpu Wei
    • 2
  • Bo Li
    • 3
  • Yibing Ma
    • 4
    Email author
  1. 1.School of Resource and Environmental EngineeringWuhan University of Science and TechnologyWuhanChina
  2. 2.Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijingChina
  3. 3.Institute of Plant Nutrition and Environmental ResourcesLiaoning Academy of Agricultural SciencesShenyangChina
  4. 4.Guangdong Key Laboratory of Integrated Agro-Environmental Pollution Control and ManagementGuangdong Institute of Eco-Environmental Science and TechnologyGuangzhouChina

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