The use of assemblage models to describe trace element partitioning, speciation, and fate: A review. (29th August 2014)

• Record Type: Journal Article
• Authors: Groenenberg, Jan E.; Lofts, Stephen
• Is Part Of: Environmental toxicology and chemistry. Volume 33:Number 10(2014:Oct.)
• Page Start: 2181
• Page End: 2196
• Publication Date: 2014-08-29
• Subjects: Pollution -- Environmental aspects -- Periodicals; Environmental chemistry -- Periodicals; 615.902
• DOI: 10.1002/etc.2642
• Languages: English
• ISSNs: 0730-7268

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <sec id="etc2642-sec-0001" sec-type="section"> <p>The fate of trace elements in soils, sediments, and surface waters is largely determined by their binding to reactive components, of which organic matter, metal oxides, and clays are considered most important. Assemblage models, combining separate mechanistic complexation models for each of the reactive components, can be used to predict the solid‐solution partitioning and speciation of trace elements in natural environments. In the present review, the authors provide a short overview of advanced ion‐binding models for organic matter and oxides and of their application to artificial and natural assemblages. Modeling of artificial assemblages of mineral components and organic matter indicates that the interactions between organic and mineral components are important for trace element binding, particularly for oxyanions. The modeling of solid‐solution partitioning in natural systems is generally adequate for metal cations but less so for oxyanions, probably because of the neglect of organic matter–oxide interactions in most assemblage models. The characterization of natural assemblages in terms of their components (active organic matter, reactive oxide surface) is key to successful model applications. Improved methods for characterization of reactive components in situ will enhance the applicability of assemblage models. Collection of compositional data for soil and water archetypes, or the development of relationships to estimate compositions from geospatially available data, will further facilitate assemblage model use for predictive purposes. <italic>Environ Toxicol Chem 2014;33:2181–2196</italic>. © 2014 SETAC</p> </sec> </abstract>