Development of a quantitative structure-activity relationship model for predicting quantum yield of hydroxyl radical generation from organic compounds

Formation and toxicity contribution of chlorinated and dechlorinated halobenzoquinones from dichlorophenols after ozonation

Halobenzoquinones (HBQs) are a class of disinfection byproducts with high cytotoxicity and potential carcinogenicity, which have been widely detected in chlorination of drinking water and swimming pool water. However, to date, the formation of HBQs upon ozonation and the HBQ precursors have been overlooked. This study investigated the formation of chlorinated and dechlorinated HBQs from six dichlorophenol (DCP) isomers. The monomeric and dimeric HBQs were identified in all the ozonation effluents, exhibiting 1-100 times higher toxicity levels than their precursors. The sum of detected HBQs intensity had a satisfactory linear relation with the maximum toxic unit (R2 = 0.9657), indicating the primary toxicity contribution to the increased overall toxicity of effluents. Based on density functional theory calculations, when ozone attacks the para carbon to the hydroxyl group of 2,3-DCP, the probability of producing chlorinated HBQs is 80.41 %, indicating that the para carbon attack mainly resulted in the formation of monomeric HBQs. 2,3-dichlorophenoxy radicals were successfully detected in ozonated 2,3-DCP effluent through electron paramagnetic resonance and further validated using theoretical calculation, revealing the formation pathway of dimeric HBQs. The results indicate that chlorinated phenols, regardless of the positions of chlorine substitution, can potentially serve as precursors for both chlorinated and dechlorinated HBQs formation during ozonation.

Predicting rejection of emerging contaminants through RO membrane filtration based on ANN-QSAR modeling approach: trends in molecular descriptors and structures towards rejections

In this work, a quantitative structure-activity relationship (QSAR) study was performed on a set of emerging contaminants (ECs) to predict their rejections by reverse osmosis membrane (RO). A wide range of molecular descriptors was calculated by Dragon software for 72 ECs. The QSAR data was analyzed by an artificial neural network method (ANN), in which four out of 3000 theoretical molecular descriptors were chosen and their significance was computed based on the Garson method. The significance trends of descriptors were as follows in descending order: ESpm14u > R2e > SIC1 > EEig03d. The selected descriptors were ranked based on their importance and then an explorative study was conducted on the QSAR data to show the trends in molecular descriptors and structures toward the rejections values of ECs. The MLR algorithm was used to make a linear model and the results were compared with those of the nonlinear ANN algorithm. The comparison results revealed it is necessary to apply the ANN model to this data with non-linear properties. For the whole dataset, the correlation coefficient (R2) and residual mean squared error (RMSE) of the ANN and MLR methods were 0.9528, 6.4224; and 0.8753, 11.3400, respectively. The comparison results showed the superiority of ANN modeling in the analysis of ECs' QSAR data.