Modeling the pH and temperature dependence of aqueousphase hydroxyl radical reaction rate constants of organic micropollutants using QSPR approach.

Designing of advanced oxidation process (AOP) requires knowledge of the aqueous phase hydroxyl radical (●OH) reactions rate constants (k OH), which are strictly dependent upon the pH and temperature of the medium. In this study, pH- and temperature-dependent quantitative structure-property relationship (QSPR) models based on the decision tree boost (DTB) approach were developed for the prediction of k OH of diverse organic contaminants following the OECD guidelines. Experimental datasets (n = 958) pertaining to the k OH values of aqueous phase reactions at different pH (n = 470; 1.4 × 106 to 3.8 × 1010 M-1 s-1) and temperature (n = 171; 1.0 × 107 to 2.6 × 1010 M-1 s-1) were considered and molecular descriptors of the compounds were derived. The Sanderson scale electronegativity, topological polar surface area, number of double bonds, and halogen atoms in the molecule, in addition to the pH and temperature, were found to be the relevant predictors. The models were validated and their external predictivity was evaluated in terms of most stringent criteria parameters derived on the test data. High values of the coefficient of determination (R 2) and small root mean squared error (RMSE) in respective training (> 0.972, ≤ 0.12) and test (≥ 0.936, ≤ 0.16) sets indicated high generalization and predictivity of the developed QSPR model. Other statistical parameters derived from the training and test data also supported the robustness of the models and their suitability for screening new chemicals within the defined chemical space. The developed QSPR models provide a valuable tool for predicting the ●OH reaction rate constants of emerging new water contaminants for their susceptibility to AOPs.