Degradability of organic micropollutants with sonolysis—Quantification of the structural influence through QSPR modelling

Local and climate-driven challenges combined with an increasing anthropogenic pollution of the water compartment all around the world make a sustainable handling of wastewater imperative. New additional treatment methods are under examination, including cavitation-based advanced oxidation processes. To quantify structural influences on chemical processes, quantitative structure-property relationship (QSPR) modelling can be used, which calculates a correlation between a defined endpoint and structural properties expressed by molecular descriptors. In this study, QSPR modelling has been applied to investigate the structural influence on the degradability of organic micropollutants with high-frequency sonolysis. The dataset of a previous study on 32 phenol derivates was expanded by 60 mostly aromatic compounds, whose kinetic degradation constants were obtained in a standardized experimental setup. QSPR modelling was conducted using the software PaDEL for descriptor calculation and QSARINS for the modelling process using a multiple linear regression approach and genetic algorithm. All five OECD-requirements for applicable QSPR models were respected. The obtained model included 12 model descriptors, was evaluated with numerous statistical quality parameters, and shows good regression abilities as well as robustness and predictability (R2 = 0.8651, CCCtr = 0.9277, Q2loo = 0.8010, R2ext = 0.7836, CCCext = 0.8838, Q2F1 = 0.7697). The interpretation of selected model descriptors showed interesting connections between the model results and the experimental background. A strong influence of the polarity of organic compounds on their degradability with high-frequency sonolysis could been quantified, as more nonpolar molecules are degraded faster. Additionally, the impact of specific fingerprints, including for example substituents with heteroatoms, the number of fused and non-fused aromatic rings as well as the numerical appearance of secondary carbon could be identified as relevant for this cavitation-based treatment method.