POSSMs: a parsimonious speciation model for metals in soils
Stephen Lofts
A *
+ Author Affiliations
- Author Affiliations
A UK Centre for Ecology and Hydrology, Lancaster Environment Centre, Lancaster, LA1 4AP, UK.
Environmental Chemistry 18(8) 335-351 https://doi.org/10.1071/EN21100
Submitted: 21 July 2021 Accepted: 18 November 2021 Published: 22 February 2022
© 2021 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution 4.0 International License (CC BY)
Environmental context. Predicting the chemistry of metals is important for understanding their movement and impacts in the environment. Metal chemistry models are generally complex and difficult to apply, but a simpler model, which does not need large amounts of input data, can also provide good results. A simpler model can be more easily included in large-scale models of metal transport and impacts in the environment.
Abstract. Mechanistic geochemical models are useful for detailed study of the speciation of metals in well-characterised soils, but can be challenging to apply when driving soil compositional data are sparse, for example, at large scales. Empirical models, using minimal driving data, have been developed either for prediction of solid–solution partitioning or for the computation of the free metal ion from the total or geochemically active metal. This work presents an empirical speciation model, POSSMs (ParsimOniouS Speciation of Metals in soils), which predicts the free, solution-bound and sorbed metal in a soil in a single calculation, using a minimal set of soil parameters. The model has been parameterised for Ni, Cu, Zn, Cd and Pb using datasets of geochemically active soil metal and solution phase composition. The parameterised model can also be used to compute the free metal ion from the solution metal. The model was tested by applying it to literature datasets on the speciation of metals in soil solutions and extracts, and on the metal solid–solution partitioning. The performance of the model was comparable to other empirical models of similar complexity. Some test datasets produced biased predictions, particularly in the underestimation of measured free ion at circumneutral and alkaline pH, where the model predicted low free ion concentrations. The model is not a replacement for mechanistic geochemical models, but is a useful tool for soil metal speciation where comprehensive driving data are not available.
Keywords: chemical speciation, dissolve dorganic matter, free metal ion, modelling, partition coefficient, pH, soil organic matter, soils, trace metals.
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