QSAR modeling for the ozonation of diverse organic compounds in water

The ozonation-based advanced oxidation process is a promising treatment technology for wastewater with micropollutants. The second-order reaction rate constant (k_O3) of ozone (O₃) with organic compounds is an important index for estimating removal efficiency of organic pollutants in engineered treatment; however, the experimental k_O3 values are currently only available for hundreds of chemicals. In this study, two quantitative-structure activity relationship (QSAR) models were developed to predict k_O3 of various organic chemicals with multiple linear regression (MLR) and support vector machine (SVM) methods. The built QSAR models cover a large dataset (136 chemicals) and more structurally diverse chemicals as compared to the existing models. The MLR model possesses satisfactory goodness-of-fit (R²_tr = 0.734), robustness (Q²_LOO = 0.700, Q²_BOOT = 0.772) and predictive ability (R²_ext = 0.797, Q²_ext = 0.794), and the SVM model also has good fitness (R²_tr = 0.862) and predictability (R²_ext = 0.782, Q²_ext = 0.775). The applicability domain of the models has been extended and includes chemicals (especially some emerging pollutants) that are rarely covered in many previous models. The underlying molecular structural factors influencing ozonation are revealed. The energy of the highest occupied molecular orbital (E_HOMO) and the phenol/enol/carboxyl OH group (O-057) are the two most important molecular structural factors governing the reactivity of organic compounds with ozone. The developed models can serve as a prescreening tool for the removal prediction of organic pollutants by ozone.