Using quantitative ion character–activity relationship (QICAR) method in evaluation of metal toxicity toward wheat

It is important to assess the toxic effects posed by soil pollutants toward plants. However, plant toxicology experiments normally involve a considerable amount of manpower, consumables and time. Therefore, the use of metal toxicity prediction models, independent of toxicity tests, is critical. In this study, we investigated the toxicity of different metal ions to wheat using hydroponic experiments. We employed the methods of soft–hard ion grouping, soft–hard ligand theory and K (conditional binding constant based on the biotic ligand model principle) in combination with hydroponic experiments to explore the application of quantitative ion character-activity relationships in predicting phytotoxicity. The results showed that the toxicity of the 19 metal ions tested varied significantly, with EC50 ranging from 0.27 μM to 4463.36 μM. The linear regression relationships between the toxicity of these metal ions and their physicochemical properties were poor (R2 = 0.237–0.331, p < 0.05). These relationships were improved after grouping the metals according to the soft–hard theory (R2 = 0.527–0.744 and p < 0.05 for soft ions; R2 = 0.445–0.743 and p < 0.05 for hard ions). The application of soft–hard ligand theory, based on the binding affinity of the metals to the ligands, showed poor prediction of the phytotoxicity of metals, with R2 = 0.413 (p = 0.024) for the softness consensus scale (σCon) and R2 = 0.348 (p = 0.218) for the normalized hard ligands scale (HLScale). However, the method of K provided the closest fit in predicting toxicity (R2 = 0.803, p < 0.001). Our results showed that the application of soft–hard ion grouping and log K can improve prediction of the phytotoxicity of metals relatively well, which can potentially be used for deriving the toxicity of elements with limited toxicity data.