Technological watch

New pollution elements introduced by the rapid development of modern industry and agriculture may pose a serious threat to the soil ecosystem. To explore the ecotoxicity and risk of these elements, we systematically studied the acute toxicity of 18 metal(loid)s toward lettuce using hydroponic experiments and quantitative relationships between element toxicity and ionic characteristics using ion-grouping and ligand-binding theory methods, thereby establishing a quantitative ion character-activity relationship (QICAR) model for predicting the phytotoxicity threshold of data-poor elements. The toxicity of 18 ions to lettuce differed by more than four orders of magnitude (0.05–804.44 ?M). Correlation and linear regression analysis showed that the ionic characteristics significantly associated with this toxicity explained only 23.8–50.3% of the toxicity variation (R2Adj?=?0.238–0.503, p?<?0.05). Relationships between toxicity and ionic properties significantly improved after separating metal(loid) ions into soft and hard, with R2Adj of 0.793 and 0.784 (p?<?0.05), respectively. Three ligand-binding parameters showed different predictive effects on lettuce metal(loid) toxicity. Compared with the binding constant of the biotic ligand model (log K) and the hard ligand scale (HLScale) (p?>?0.05), the softness consensus scale (?Con) was significantly correlated with toxicity and provided the best prediction (R2Adj?=?0.844, p?<?0.001). We selected QICAR equations based on soft-hard ion classification and ?Con methods to predict phytotoxicity of metal(loid)s, which can be used to derive ecotoxicity for data-poor metal(loid)s, providing preliminary assessment of their ecological risks.