The biotic ligand model for plants and metals: Technical challenges for field application

Abstract

To improve predictions of phytoavailable metal, the mechanistic bases of bioaccumulation and toxicity of metals to plants can be integrated into a biotic ligand model (BLM). There are a number of significant challenges to the application of the BLM to plants in soils, including reliable measurements of free ion concentrations for the metals of interest in rhizospheric soil solution, as well as other free ions, and concentrations of ligands to which the ions could bind; identification of the simplest model that can adequately predict root accumulation, and the potential for more complex models to add accuracy to the predictions; incorporating the dissociation of labile metal complexes (i.e., nonequilibrium processes) into a BLM, which is an equilibrium model; application of factors in a BLM that adequately describe translocation, in order to estimate metal concentration and speciation in plant shoots. The review concluded that the ability to estimate trace metal speciation in samples of soil solution are not likely to be better than within one order of magnitude of actual, thus this would be an additional source of uncertainty to the predictions of toxicity. Further, regulatory use of the BLM would require mechanistic bases; and, until root ligands associated with toxicity are well characterized, incorporating the ameliorative effects of competitive cations cannot be mechanistically based. As well, a functional BLM for soils with lower metal free ion activities will have to include kinetic data for metal-ligand complexes, as their association/disassociation may constitute a greater metal supply to roots than what would be predicted by the free ion concentration in soil solution. To apply the BLM to trophic transfer where metal concentration in plant shoots is the main focus, a probabilistic approach using experimentally determined root-shoot partitioning of metals might permit estimates of shoot accumulation from root data, to within one or two orders of magnitude.