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Extended biotic ligand model for predicting combined Cu–Zn toxicity to wheat (Triticum aestivum L.): Incorporating the effects of concentration ratio, major cations and pH

Wang, Xuedong, Ji, Dongxue, Chen, Xiaolin, Ma, Yibing, Yang, Junxing, Ma, Jingxing, Li, Xiaoxiu
Environmental pollution 2017
Triticum aestivum, bioavailability, calcium, cations, copper, ligands, magnesium, models, pH, prediction, risk assessment, toxicity, toxicity testing, wheat, zinc
Current risk assessment models for metals such as the biotic ligand model (BLM) are usually applied to individual metals, yet toxic metals are rarely found singly in the environment. In the present research, the toxicity of Cu and Zn alone and together were studied in wheat (Triticum aestivum L.) using different Ca2+ and Mg2+ concentrations, pH levels and Zn:Cu concentration ratios. The aim of the study was to better understand the toxicity effects of these two metals using BLMs and toxic units (TUs) from single and combined metal toxicity data. The results of single-metal toxicity tests showed that toxicity of Cu and Zn tended to decrease with increasing Ca2+ or Mg2+ concentrations, and that the effects of pH on Cu and Zn toxicity were related not only to free Cu2+ and Zn2+ activity, respectively, but also to other inorganic metal complex species. For the metal mixture, Cu–Zn interactions based on free ion activities were primarily additive for the different Ca2+ and Mg2+ concentrations and levels of pH. The toxicity data of individual metals derived by the BLM, which incorporated Ca2+ and Mg2+ competition and toxicity of inorganic metal complexes in a single-metal toxicity assessment, could predict the combined toxicity as a function of TU. There was good performance between the predicted and observed effects (root mean square error [RMSE] = 7.15, R2 = 0.97) compared to that using a TU method with a model based on free ion activity (RMSE = 14.29, R2 = 0.86). The overall findings indicated that bioavailability models that include those biochemistry processes may accurately predict the toxicity of metal mixtures.