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Chemical structure–based predictive model for the oxidation of trace organic contaminants by sulfate radical

Author:
Ye, Tiantian, Wei, Zongsu, Spinney, Richard, Tang, Chong-Jian, Luo, Shuang, Xiao, Ruiyang, Dionysiou, Dionysios D.
Source:
Water research 2017
ISSN:
0043-1354
Subject:
diagnostic techniques, environmental fate, neural networks, oxidation, prediction, reaction mechanisms, regression analysis, screening, sulfates, water treatment
Abstract:
Second-order rate constants (kSO4•−) for the reaction of sulfate radical anion (SO4•−) with trace organic contaminants (TrOCs) are of scientific and practical importance for assessing their environmental fate and removal efficiency in water treatment systems. Here, we developed a chemical structure-based model for predicting kSO4•− using 32 molecular fragment descriptors, as this type of model provides a quick estimate at low computational cost. The model was constructed using the multiple linear regression (MLR) and artificial neural network (ANN) methods. The MLR method yielded adequate fit for the training set (Rtraining2=0.88,n=75) and reasonable predictability for the validation set (Rvalidation2=0.62,n=38). In contrast, the ANN method produced a more statistical robustness but rather poor predictability (Rtraining2=0.99andRvalidation2=0.42). The reaction mechanisms of SO4•− reactivity with TrOCs were elucidated. Our result shows that the coefficients of functional groups reflect their electron donating/withdrawing characters. For example, electron donating groups typically exhibit positive coefficients, indicating enhanced SO4•− reactivity. Electron withdrawing groups exhibit negative values, indicating reduced reactivity. With its quick and accurate features, we applied this structure-based model to 55 discrete TrOCs culled from the Contaminant Candidate List 4, and quantitatively compared their removal efficiency with SO4•− and OH in the presence of environmental matrices. This high–throughput model helps prioritize TrOCs that are persistent to SO4•− based oxidation technologies at the screening level, and provide diagnostics of SO4•− reaction mechanism.
Agid:
5648664