Title: Soil solution concentration of Cd and Zn can be predicted with a CaCl2 soil extract
Authors: Degryse, Fien ×
Broos, Kris
Smolders, Erik
Merckx, Roel #
Issue Date: 2003
Publisher: Blackwell publishing ltd
Series Title: European journal of soil science vol:54 issue:1 pages:149-157
Abstract: Risk assessment of heavy metals in soil requires an estimate of the concentrations in the soil solution. In spite of the numerous studies on the distribution of Cd and Zn in soil, few measurements of the distribution coefficient in situ , K (d) , have been reported. We determined the K (d) of soils contaminated with Cd and Zn by measuring metal concentrations in the soil and in the soil solution and attempted to predict them from other soil variables by regression. Soil pH explained most of the variation in logK (d) (R (2) = 0.55 for Cd and 0.70 for Zn). Introducing organic carbon content or cation exchange capacity (CEC) as second explanatory variable improved the prediction (R (2) = 0.67 for Cd and 0.72 for Zn), but these regression models, however, left more than a factor of 10 of uncertainty in the predicted K (d) . This large degree of uncertainty may partly be due to the variable degree of metal fixation in contaminated soils. The labile metal content was measured by isotopic dilution (E value). The E value ranged from 18 to 92% of the total metal content for Cd and from 5 to 68% for Zn. The prediction of K (d) improved when metals in solution were assumed to be in equilibrium with the labile metal pool instead of the total metal pool. It seems necessary therefore to discriminate between 'labile' and 'fixed' pools to predict K (d) for Cd and Zn in field contaminated soils accurately. Dilute salt extracts (e.g. 0.01 m CaCl2 ) can mimic soil solution and are unlikely to extract metals from the fixed pool. Concentrations of Cd and Zn in the soil solution were predicted from the concentrations of Cd and Zn in a 0.01 m CaCl2 extract. These predictions were better correlated with the observations for field contaminated soils than the predictions based on the regression equations relating logK (d) to soil properties (pH, CEC and organic C).
ISSN: 1351-0754
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Division Soil and Water Management
× corresponding author
# (joint) last author

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