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The fate and transport of metal ions in soils and sediments may be controlled by sorption to the metastable iron (hydr)oxide, ferrihydrite. The reversibility of metal partitioning to ferrihydrite can be significantly influenced by its transformation to more thermodynamically stable structures such as goethite or hematite. We studied changes in metal partitioning during aging of coprecipitates of ferrihydrite containing Cd(II), Mn(II), Ni(II), or Pb(II) at pH 6 and temperatures of 40 or 70 °C and as a function of metal surface loading. Aqueous metal concentrations as well as the fraction extracted by 0.2 M ammonium oxalate were continuously monitored. At the end of aging, solids were characterized by thermogravimetric analysis and X-ray diffraction. Prior to aging, the extent of metal sorption decreased in the order Pb(II) >> Ni(II) > Mn(II) = Cd(II). However, with ferrihydrite transformation, the extent of sorption increased and apparent sorption reversibility decreased significantly for Mn(II) and Ni(II). Both Pb(II) and Cd(II) demonstrated net desorption with aging, and sorption reversibility remained essentially unchanged. These differences in metal behavior are consistent with structural incorporation of Mn(II) and Ni(II) into the goethite or hematite structure and minimal incorporation of Cd(II) and Pb(II) within these crystalline products at pH 6.

Despite more than 5 decades of aquatic toxicity tests conducted with metal mixtures, there is still a need to understand how metals interact in mixtures and to predict their toxicity more accurately than what is currently done. The present study provides a background for understanding the terminology, regulatory framework, qualitative and quantitative concepts, experimental approaches, and visualization and data-analysis methods for chemical mixtures, with an emphasis on bioavailability and metal-metal interactions in mixtures of waterborne metals. In addition, a Monte Carlo-type randomization statistical approach to test for nonadditive toxicity is presented, and an example with a binary-metal toxicity data set demonstrates the challenge involved in inferring statistically significant nonadditive toxicity. This background sets the stage for the toxicity results, data analyses, and bioavailability models related to metal mixtures that are described in the remaining articles in this special section from the Metal Mixture Modeling Evaluation project and workshop. It is concluded that although qualitative terminology such as additive and nonadditive toxicity can be useful to convey general concepts, failure to expand beyond that limited perspective could impede progress in understanding and predicting metal mixture toxicity. Instead of focusing on whether a given metal mixture causes additive or nonadditive toxicity, effort should be directed to develop models that can accurately predict the toxicity of metal mixtures.