4.7 Article

Arsenate and phosphate adsorption on ferrihydrite nanoparticles. Synergetic interaction with calcium ions

期刊

CHEMICAL GEOLOGY
卷 410, 期 -, 页码 53-62

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemgeo.2015.06.011

关键词

Ferrihydrite; Phosphate; Arsenate; Calcium; Surface complexation modelling; CD model

资金

  1. Ministerio de Educacion y Ciencia [CTM2011-24985]
  2. Xunta Galicia [EM2013/040]

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The geochemical behaviour of phosphate and arsenate ions in soil and aquatic systems is determined by the presence of mineral surfaces and major ions. Information about the distribution of oxyanions over the solid and solution phases is essential for understanding the transport, bioavailability and toxicity of these compounds in the environment. Here, we studied the adsorption of both arsenate and phosphate on ferrihydrite nanoparticles in the presence of calciumions. The presence of calciumions enhanced the retention of these oxyanions on ferrihydrite and vice versa. The arsenate-calcium and phosphate-calcium multi-component systems were described using a mechanistic surface complexation model. Use of this type of model enables prediction of the solution and surface speciation, along with analysis of oxyanion mobility in relation to environmental conditions. We were able to calibrate the charge distribution model with the macroscopic data obtained for the single-component systems, thus obtaining surface complexation constants for later use to simulate multi-component systems. The mutual interactions between arsenate and calcium were successfully described with these parameters, indicating that changes in the electrostatic forces at the solid/solution interface caused the observed enhanced adsorption. However, adsorption in the phosphate-calcium system was underestimated with the parameters obtained for the single-component systems, indicating that additional mechanisms or processes should be considered. Formation of insoluble mineral phases was ruled out, but the inclusion of a phosphate-calcium ternary surface complex improved the modelling predictions. (C) 2015 Elsevier B.V. All rights reserved.

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