Cu and Zn adsorption to a terrestrial sediment: Influence of solid-to-solution ratio

- Ca, Mg, and DOC leaching depended on SSR; Al and Fe leaching were less dependent.
- KD of Cu was not very sensitive to SSR at pH 3-6 but depended on SSR at pH 6-8.
- KD of Zn was not very sensitive to SSR at pH 3-8.
- Cautions are needed when extrapolating batch adsorption data to natural aquifers.

Laboratory batch adsorption experiments are commonly used to quantify contaminant adsorption to natural sediments. The distribution coefficients (KD) determined via these experiments are often incorporated into reactive transport models to predict contaminant movement in groundwater. The solid-to-solution ratio (SSR) in most laboratory experiments is much lower in comparison to that in aquifers, therefore it is questionable if distribution coefficients thus obtained can accurately quantify contaminant adsorption in the natural environment. SSR may also influence the leaching of multivalent cations and organic matters from natural sediments, which in turn could alter contaminant adsorption. The objective of this study is to determine how SSR influences heavy metal adsorption to a heterogeneous natural sediment. Cu and Zn adsorption was examined at SSRs of 250 and 25 g/L in the pH range of 3-8 using both batch experiments and surface complexation modelling. Results indicated that Ca, Mg, and DOC leaching depended on SSR, with higher SSR generally resulting in greater leaching. Conversely, Al and Fe leaching was less dependent on SSR. Cu distribution coefficients in the low pH range (3-6) and Zn distribution coefficients across the pH range (3-8) were not very sensitive to the SSR, despite higher leached concentrations of competing cations at a higher SSR. In contrast, Cu distribution coefficients at high pH (6-8) were more SSR-dependent, likely due to formation of non-adsorbing aqueous Cu-DOC complexes. This study demonstrates that cautions must be taken when distribution coefficients measured by laboratory batch experiments are used to predict contaminant transport in aquifers.

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