Predicting the reaction rate constants of micropollutants with hydroxyl radicals in water using QSPR modeling. (November 2015)

• Record Type: Journal Article
• Authors: Jin, Xiaohui; Peldszus, Sigrid; Huck, Peter M.
• Is Part Of: Chemosphere. Volume 138(2015)
• Page Start: 1
• Page End: 9
• Publication Date: 2015-11
• Subjects: Molecular descriptors -- Hydroxyl radical -- Reaction rate constant -- Micropollutants -- Applicability domain; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2015.05.034
• Languages: English
• ISSNs: 0045-6535

Abstract

Highlights: QSPR model predicts hydroxyl radical rate constants of micropollutants. Multiple linear regression with forward selection method was used. Model was externally validated and the applicability domain was defined. Model determines susceptibility of contaminants to AOPs without experimentation. Abstract: Quantitative structure–property relationship (QSPR) models which predict hydroxyl radical rate constants ( k OH ) for a wide range of emerging micropollutants are a cost effective approach to assess the susceptibility of these contaminants to advanced oxidation processes (AOPs). A QSPR model for the prediction of k OH of emerging micropollutants from their physico-chemical properties was developed with special attention to model validation, applicability domain and mechanistic interpretation. In this study, 118 emerging micropollutants including those experimentally determined by the author and data collected from the literature, were randomly divided into the training set ( n = 89) and validation set ( n = 29). 951 DRAGON molecular descriptors were calculated for model development. The QSPR model was calibrated by applying forward multiple linear regression to the training set. As a result, 7 DRAGON descriptors were found to be important in predicting the k OH values which related to the electronegativity, polarizability, and double bonds, etc. of the compounds. With outliers identified and removed, the final model fits the training set very well and shows good robustness and internal predictivity. The model was then externally validated with the validation set showing good predictive power. The applicability domain of the model was also assessed using the Williams plot approach. Overall, the developed QSPR model provides a valuable tool for an initial assessment of the susceptibility of micropollutants to AOPs.