Luo Xiaorong, Wang Xuedong, Xia Cunyan, Peng Jing, Wang Ying, Tang Yujie, Gao Fan
College of Resource Environment and Tourism, Capital Normal University, Beijing, 100048, China.
School of Space and Environment, Beihang University, Beijing, 100191, China.
Environ Sci Pollut Res Int. 2023 Feb;30(9):24521-24532. doi: 10.1007/s11356-022-23914-9. Epub 2022 Nov 7.
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 μM). 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 (R = 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 R 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 (σ) was significantly correlated with toxicity and provided the best prediction (R = 0.844, p < 0.001). We selected QICAR equations based on soft-hard ion classification and σ 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.
现代工农业的快速发展引入的新污染元素可能对土壤生态系统构成严重威胁。为了探究这些元素的生态毒性和风险，我们通过水培实验系统研究了18种金属（类金属）对生菜的急性毒性，并使用离子分组和配体结合理论方法研究了元素毒性与离子特性之间的定量关系，从而建立了一个定量离子特征-活性关系（QICAR）模型，用于预测数据匮乏元素的植物毒性阈值。18种离子对生菜的毒性相差超过四个数量级（0.05 - 804.44 μM）。相关性和线性回归分析表明，与这种毒性显著相关的离子特性仅解释了23.8 - 50.3%的毒性变化（R = 0.238 - 0.503，p < 0.05）。将金属（类金属）离子分为软离子和硬离子后，毒性与离子性质之间的关系显著改善，R分别为0.793和0.784（p < 0.05）。三个配体结合参数对生菜金属（类金属）毒性表现出不同的预测效果。与生物配体模型的结合常数（log K）和硬配体标度（HLScale）相比（p > 0.05），软度共识标度（σ）与毒性显著相关且提供了最佳预测（R = 0.844，p < 0.001）。我们基于软硬离子分类和σ方法选择了QICAR方程来预测金属（类金属）的植物毒性，可用于推导数据匮乏的金属（类金属）的生态毒性，为其生态风险提供初步评估。
