QSAR Applications for Environmental Studies: Predicting the Degradation Rate Constants of Aromatics by OH Radicals in Water

摘要

A large amount of overall organic chemicals produced and used annually, polluting the water bodies, pertain to aromatic compounds. They can be either removed by physical or degraded by biological and chemical treatment methods. Among chemical processes, advanced oxidation processes producing OH radicals, highly reactive and unselective species, represent one of the promising water/wastewater methods. Hence, the rate constants of reactions between aromatic organic pollutants and OH?are one of the most important characteristics related to their degradation. Such data are valuable for the treatment of complex wastewaters containing several aromatic compounds. Due to the complexity of analytical methods and the high cost of experiments, the application of the theoretical predictive methods, particularly QSAR/QSPR (quantitative structure-activity/property relationship) modeling, is fast and convenient for preliminary assessment and the estimation of degradation rates, and moreover cost-effective. This powerful technique quantitatively relates variations in biological activity, e.g. degradation rate constant, to changes in molecular structure and properties. The goal of the study was to predict degradation rate constant of aromatic compounds structured by single benzene ring and including presence and absence of different substitute groups such as hydroxyl-, nitro-, amino-, methyl-, methoxy-, etc, by OH radical using QSAR/QSPR tool. A genetic algorithm and multiple regression analysis were applied to select the descriptors and to generate the correlation models. Additionally to DRAGON descriptors, the parameters from quantum-chemical calculations at density functional theory level (B3LYP/6-31 G (d, p)) were applied. Evaluation of models was performed by calculating and comparing their model performances (R2, s, F, Q2) after splitting set of organic compounds to training and test sets. As the most predictive model is shown the 3-variable model having also a good ratio of the number of descriptors and the predictive ability. The main contribution to the degradation rate constant was given by EHOMO descriptor representing the energy of the highest occupied molecular orbital, determining the nucleophilic reactivity property of each compound and hence the possibility of attack by such a strong electrophile as the OH radical. The GA-MLRA approach showed good results in this study, which allows to built simple, interpretable and transparent model that can be used for future studies of organic pollutants, their presence in the environment and particularly their degradation by hydroxyl radicals as a part water/wastewater treatment.