CSIR- National Botanical Research Institute, Rana Pratap Marg, Lucknow, 226001, India.
Environmental and Technical Research Centre, Gomtinagar, Lucknow, 226010, India.
Chemosphere. 2017 Oct;185:1164-1172. doi: 10.1016/j.chemosphere.2017.07.057. Epub 2017 Jul 14.
The rate constants of the hydroxyl radical reactions (k) with organic micropollutants (OMPs) in aqueous medium are important in designing the advanced oxidation processes (AOPs) for their removal. In this study, a quantitative structure-property relationship (QSPR) model for the prediction of k of diverse and emerging OMPs was developed in accordance with the OECD guidelines. A large experimental data set (n = 995) comprised of compounds with k values ranging from 7.9 × 10 to 6.8 × 10 M s was considered and several molecular descriptors were calculated. As a result, five descriptors were found to be important in predicting the k values which related to the electronegativity, topological polar surface area, double bonds, average molecular weight, and halogen atoms in the molecule. The optimal model was validated internally and externally and several statistical stringent parameters were derived. High values of the coefficient of determination (R) and small root mean squared error (RMSE) in the training (0.954; 0.17) and validation (0.925; 0.14) sets indicated high generalization and predictivity of the developed model. Other statistical parameters derived from the training and validation data also supported the robustness of the model. The proposed model outperformed the earlier QSARs reported for k prediction. Overall, the developed QSPR model provides a valuable tool for an initial assessment of the susceptibility of organic micropollutants to AOPs.
在设计用于去除有机微污染物 (OMP) 的高级氧化工艺 (AOP) 时,羟基自由基与水中有机微污染物 (OMP) 的反应速率常数 (k) 非常重要。本研究根据 OECD 指南,开发了一种用于预测各种新兴 OMPs 的 k 值的定量构效关系 (QSPR) 模型。考虑了一个包含 k 值范围为 7.9×10 到 6.8×10 M s 的化合物的大型实验数据集 (n=995),并计算了几个分子描述符。结果发现,五个描述符对于预测 k 值很重要,这些描述符与分子中的电负性、拓扑极性表面积、双键、平均分子量和卤素原子有关。最优模型在内部和外部进行了验证,并得出了几个统计严格参数。训练集 (0.954; 0.17) 和验证集 (0.925; 0.14) 中高的决定系数 (R) 和小的均方根误差 (RMSE) 值表明了开发模型的高泛化和预测能力。从训练和验证数据中得出的其他统计参数也支持了模型的稳健性。所提出的模型优于先前报道的用于 k 值预测的 QSAR。总体而言,所开发的 QSPR 模型为评估有机微污染物对 AOP 的敏感性提供了一种有价值的工具。