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Modeling of cobalt and copper speciation in metalliferous soils from Katanga (Democratic Republic of Congo)

par Pourret, Olivier;Lange, Bastien ;Houben, David;Colinet, Gilles ;Shutcha, Mylor Ngoy;Faucon, Michel-Pierre
Référence Journal of geochemical exploration, 149, page (87-96)
Publication Publié, 2015-02

Article révisé par les pairs

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Résumé :

Cobalt and copper fractionation in soils from the Copperbelt of Upper Katanga (Democratic Republic of Congo) was investigated to understand their mobility. Indeed, the fractionation and mobility of Co, Cu and Zn was assessed using the application of both ammonium acetate-EDTA extraction and speciation modeling (WHAM 6) to organic-rich soils contaminated by these metals. The resulting data set covers wide ranges of conditions (pH, trace metal concentration, and natural and anthropogenic soils) enabling comparisons between extraction and speciation modeling. Good agreement between the observations and model predictions (especially for Co and Cu) supports the validity of both approaches. A positive correlation was found between organic-bound and inorganic Co concentrations obtained by speciation modeling and extractable Co concentrations. Speciation calculation results were further considered to evaluate more precisely metal speciation and gain insights into the variability between sites considering or not major competitors (i.e., Ca and Mg). When the model was adjusted to remove the binding of major competitors and their competition with other metals, the predicted binding with HM of the studied metals (i.e., Co, Cu and Zn) increased. The extent of major cation competition is thus of potential significance. Thus to model the system as close as possible to natural and field conditions, major cation competition with metals should be selected. Strong affinity of Mn-oxides for Co, may explain lower Co mobility of Mn-rich soils. The high Mn and Fe contents of Cu-Co soils from Katanga may actually exert a protective effect against the toxic effects of Co. Finally Cu-Co speciation modeling of contaminated sites may lead to a better understanding of metal fractionation and provide a guide to define different practices of phytoremediation.