Prediction of degradability of micropollutants by sonolysis in water with QSPR - a case study on phenol derivates

The increasing quantity and variety of organic contaminants discharged into surface and groundwater increase the necessity of additional and suitable water treatment methods, which can be incorporated into existing wastewater treatment plants. The huge variety of micropollutants and local variability of the composition of the organic load or matrix effects paired with multiple possible degradation processes lead to the requirement of a recommendation tool for the best possible water treatment method under given local conditions. Due to the diversity of physicochemical properties of micropollutants, such predictions are challenging. In this study, a quantitative correlation between the structural properties of certain micropollutants and their degradability using high-frequency sonolysis has been investigated. Therefore, Quantitative Structure-Property Relationship (QSPR) has been applied on a set of phenol derivates. To obtain the kinetic data, all experiments have been conducted in standardized, constant conditions for all 32 investigated phenol derivates. QSPR modelling was then executed using the software PaDEL for descriptor calculation and the software QSARINS for the overall modelling process including genetic algorithm (GA) and multiple linear regression (MLR). The final model consisting of 5 molecular descriptors was selected using a multi-criteria decision-making method based on extensive statistical parameters. The predictive power and robustness of the model was evaluated by means of internal cross validation and external validation using an independent validation set. The final selected model showed very good values for regression abilities, predictive power as well as stability (R(2)(adj) = 0.9455, CCC(tr) = 0.9777, Q(2)(loo) = 0.9285, CCC(ext) = 0.9797 and Q(2)(ext-F1) = 0.9711). The applicability domain of the QSPR model was defined based on the Williams plot and Insubria plot. The five OECD principles for the application of QSPR/QSAR modelling in industry and regulation were fulfilled in the whole process to the best of our knowledge, including the collection of the underlying experimental data as well as the entire modelling process. The final QSPR model included the molecular polarity and occurrence of hydrogen bonds as major influences on the reaction rate constants in accordance with previous studies. Nevertheless, potential biases in the selection of these descriptors due to the small size of the dataset were highlighted.