https://ogma.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:54785 Tue 12 Mar 2024 12:35:11 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:45963 Tue 08 Nov 2022 13:30:01 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:45317 Thu 27 Oct 2022 13:31:11 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:46492 2+, Zn²⁺, and Ni²⁺ concentrations, but also to inorganic metal complexes. In binary mixtures, the two metals in the Cu–Ni mixture showed a weakly antagonistic effect, whereas the two metals in the Zn–Ni mixture showed greater antagonism. Using data from single-metal Cu, Zn, and Ni BLMs, combined with the toxicity index and the overall amounts of metal ions bound to the biotic ligands, one simple model was developed. This model consisted of the toxic unit (TU_M, no competition included) and two extended BLMs, BLM-TU_f (f as a function of TU, including competition between Mg²⁺ and metal ions) and BLM-f_mix (including the competition between Mg²⁺ and metal ions, as well as between free metal ions). They were then used to predict the joint toxicity of Cu–Ni and Zn–Ni binary mixtures to wheat. Both of the extended BLMs could provide more accurate predictions of toxic effects of Cu–Ni and Zn–Ni than TU_M. BLM-f_mix performed best for the Zn–Ni binary mixture (r² = 0.93; root-mean-square error, RMSE = 9.87). On the other hand, for the Cu–Ni mixture, the predictive effect based on BLM-TUf (r² = 0.93; RMSE = 9.60) was similar to that of BLM-f_mix (r² = 0.93; RMSE = 9.56). The results provide a theoretical basis for the evaluation and remediation of soils contaminated with mixtures of heavy metals.]]> Thu 24 Nov 2022 12:06:43 AEDT ]]>