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The effect of adsorbed soil fulvic (JGFA) and humic acid (JGHA) on Pb binding to goethite was studied with the ligand charge distribution (LCD) model and X-ray absorption fine structure (XAFS) spectroscopy analysis. In the LCD model, the adsorbed small JGFA particles were evenly located in the Stern layer, but the large JGHA particles were distributed over the Stern layer and the diffuse layer, which mainly depended on the JGHA diameter and concentrations. Specific interactions of humic substances (HS) with goethite were modeled by inner-sphere complexes between the -FeOH 2 0.5+ of goethite and the -COO - of HS and by Pb bridges between surface sites and COO - groups of HS. At low Pb levels, nearly 100% of Pb was bound as Pb bridges for both JGFA and JGHA. At high Pb levels and low HS loading, Pb-goethite almost dominated over the entire studied pH range, but at high HS loading, the primary species was goethite-HS-Pb at acidic pH and goethite-Pb at alkaline pH. Compared with JGFA, there was a constant contribution of Pb bridges of about 10% for JGHA. The linear combination fit of EXAFS, using Pb-HS and Pb-goethite as references, indicated that with increased HS loading more Pb was bound to adsorbed HS and less to goethite, which supported the LCD calculations.

Multi-surface models are widely used to assess the potential ecotoxicological risk in metal-contaminated soils. Their accuracy in predicting metal speciation in soils with low metal levels was not yet tested. Now highly sensitive analytical techniques are available to experimentally validate such models at low concentration levels. The objective of this study was to test the accuracy of a multi-surface model to predict the Zn(2+) concentration and to improve our understanding of Zn bioavailability in low-Zn soils. High-Zn soils were included as controls. Model parameters were determined independently on the basis of earlier peer-reviewed publications. Model output was validated against free Zn(2+) concentrations determined with the soil column Donnan membrane technique in a range of soils varying in potentially available Zn, organic matter, clay silicate, and iron (hydr)oxide contents and pH. Deviations between predicted Zn(2+) concentrations and experimentally determined values over the whole Zn concentration range were less or equal to the experimental standard error, except for one low-Zn soil. The Zn(2+) concentration was mainly controlled by adsorption, where organic matter was predicted to be the dominant soil sorbent. The predicted Zn(2+) concentration depends more sensitively upon changes of the reactive Zn pool (application of 0.6, 1.2, 2.4, and 3.6 mg of Zn kg(-1) of soil) and organic matter content (± 0.2 and 0.4%) than pH changes (± 0.5 and 1 pH unit).

The pH dependent solid-solution distribution of arsenate and phosphate in five Dutch agricultural soil samples was measured in the pH range 4–8, and the results were interpreted using the LCD (ligand and charge distribution) adsorption modeling. The pH dependency is similar for both oxyanions, with a minimum soluble concentration observed around pH 6–8. This pH dependency can be successfully described with the LCD model and it is attributed mainly to the synergistic effects from Ca adsorption. The solubility of phosphate is much lower than that of arsenate. This big difference cannot be sufficiently explained by the reduction of small amount of As(V) into As(III), neither by slow desorption/adsorption. The difference between phosphate and arsenate in their solid-solution distribution becomes larger with the increase of aluminum (hydr)oxides (Al-oxides) contribution to the total amount of metal (Al and Fe) (hydr)oxides. The influence of Al-oxides is much larger than its relative amount extracted from the soils. When Al-oxides account for >40% of the soil oxides, the whole adsorbents behave apparently similarly to that of pure Al-oxides. These results indicated that surface coating and substitution may have modified significantly oxyanion adsorption to Fe-oxides in soils, and how to account for this complexity is a challenge for geochemical modeling.

International audience; Model VI and the NICA-Donnan model are two successful models presently available for describing metal ion binding by humic materials. Both models deal with the complexity of the underlying processes (intrinsic heterogeneity, partial correlation between affinity distributions, variable stoichiometry, electrostatics) in a pragmatic way. The parameters of the NICA-Donnan model and their determination are discussed. The current interpretation of the ion-specific "non-ideality" parameters, n(i), found in the NICA-Donnan model, emphasizes their role in determining the stoichiometry for the competitive binding of ions. The ratio n(M)/n(H) is reflected in the corresponding exchange ratios and the pH dependence of the metal ion binding. Experimental complications occur in testing models. Although Model VI and the NICA-Donnan models have much in common, there are differences that may become more apparent as the models are more widely tested. Recently ion binding to complexes of humics and oxides has been described by combining the NICA-Donnan model (ion binding to humics) with the CD-MUSIC model (ion binding to oxides). The impact of humic to oxide binding on both cation and anion binding is briefly discussed.

This paper provides an improved understanding of the leaching behavior of waste incineration air-pollution-control (APC) residues in a long-term perspective. Leaching was investigated by a series of batch experiments reflecting leaching conditions after initial washout of highly soluble salts from residues. Leaching experiments were performed at a range of pH-values using carbonated and noncarbonated versions of two APC residues. The leaching data were evaluated by geochemical speciation modeling and discussed with respect to possible solubility control. The leaching of major elements as well as trace elements was generally found to be strongly dependent on pH. As leaching characterization was performed in the absence of high salt levels, the presented results represent long-term leaching after initial washout from a disposal site, that is, liquid-to-solid ratios above 1-2 L/kg. The leaching of Al, Ba, Ca, Cr, Pb, S, Si, V, and Zn was found influenced by solubility control from A12O3, Al(OH)3, Ba(S,Cr)04 solid solutions, BaSO4, Ca6Al2(SO4)3(OH)12 x 26H2O, CaAl2Si4O12 x 2H2O, Ca(OH)2, CaSiO3, CaSO4 x 2H2O, CaZn2(OH)6 x 2H2O, KAlSi2O6, PbCO3, PbCrO4, Pb2O3, Pb2V2O7, Pb3(VO4)2, ZnO, Zn2SiO4, and ZnSiO3. The presented dataset and modeling results form a thorough contribution to the assessment of long-term leaching behavior of APC residues under a wide range of conditions.

Carbonation of industrial alkaline residues can be used as a CO2 sequestration technology to reduce carbon dioxide emissions. In this study, steel slag samples were carbonated to a varying extent. Leaching experiments and geochemical modeling were used to identify solubility-controlling processes of major and trace elements, both with regard to carbonation mechanisms and the environmental properties of the (carbonated) steel slag. Carbonation was shown to reduce the leaching of alkaline earth metals (except Mg) by conversion of Ca-phases, such as portlandite, ettringite, and Ca-(Fe)-silicates into calcite, possibly containing traces of Ba and Sr. The leaching of vanadium increased substantially upon carbonation, probably due to the dissolution of a Ca-vanadate. The reactive surface area of Al- and Fe-(hydr)oxides increased with the carbonation degree, which tends to reduce the leaching of sorption-controlled trace elements. Sorption on Mn- (hydr)oxides was found to be required to adequately model the leaching of divalent cations, but was not influenced by carbonation. Consideration of these three distinct reactive surfaces and possible (surface) precipitation reactions resulted in adequate modeling predictions of oxyanion and trace metal leaching from (carbonated) steel slag. Hence, these surfaces exert a major influence on the environmental properties of both fresh and carbonated steel slag.