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 (SEP) 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 (R2), 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 R2 and low deviation has remarkable potential for predicting ILs ecotoxicity on Vibrio fischeri.

Department of Applied Ecology Faculty of Environmental Sciences Czech University of Life Sciences Prague 16521 Prague 6 Czech Republic

Department of Chemical Engineering University of California Santa Barbara CA 93106 5080 USA

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