Kang Xuejing, Chen Zhongbing, Zhao Yongsheng
Department of Applied Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences, Prague 16521, Prague 6, Czech Republic.
Department of Applied Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences, Prague 16521, Prague 6, Czech Republic.
J Hazard Mater. 2020 Oct 5;397:122761. doi: 10.1016/j.jhazmat.2020.122761. Epub 2020 May 6.
Ionic liquids (ILs) have attracted increasing attention both in the scientific community and the industry in the past two decades. Their risk of being inevitable released to ecosystem lights up the urgent research on their toxicity to the environment. To reduce the time and capital consumption on testing tremendous ILs ecotoxicity experimentally, it is essential to construct predictive models for estimating their toxicity. The objective of this study is to provide a new approach for evaluating the ecotoxicity of ILs. A comprehensive ecotoxicity dataset for Vibrio fischeri involving 142 ILs, was collected and investigated. The electrostatic potential surface areas (S) of separate cations and anions of ILs were firstly applied to develop predictive models for ecotoxicity on Vibrio fischeri. In addition, an intelligent algorithm named extreme learning machine (ELM) was employed to establish the predictive model. The squared correlation coefficients (R), the average absolute error (AAE%) and the root-mean-square error (RMSE) of the developed model are 0.9272, 0.2101 and 0.3262 for the entire set, respectively. The proposed approach based on the high R and low deviation has remarkable potential for predicting ILs ecotoxicity on Vibrio fischeri.
在过去二十年中，离子液体（ILs）在科学界和工业界都受到了越来越多的关注。它们不可避免地释放到生态系统中的风险引发了对其环境毒性的迫切研究。为了减少通过实验测试大量离子液体生态毒性所消耗的时间和资金，构建用于估计其毒性的预测模型至关重要。本研究的目的是提供一种评估离子液体生态毒性的新方法。收集并研究了一个包含142种离子液体的费氏弧菌综合生态毒性数据集。首先将离子液体单独的阳离子和阴离子的静电势表面积（S）应用于构建费氏弧菌生态毒性的预测模型。此外，采用一种名为极限学习机（ELM）的智能算法来建立预测模型。所开发模型的决定系数（R²）、平均绝对误差（AAE%）和均方根误差（RMSE）对于整个数据集分别为0.9272、0.2101和0.3262。基于高R²和低偏差提出的方法在预测离子液体对费氏弧菌的生态毒性方面具有显著潜力。
