State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun 130117, China.
State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun 130117, China.
Sci Total Environ. 2022 Aug 20;835:155501. doi: 10.1016/j.scitotenv.2022.155501. Epub 2022 Apr 26.
Ozonation is a significant technology for the mitigation of pollutants in water. The second-order reaction rate constant (k) of ozone (O) with compounds is essential for measuring their reactivity toward O and understanding their fate during ozonation. However, there is a huge gap between the number of existing chemicals and the available experimental k values. Moreover, the reactivity of ionizable compounds with different ionization forms toward O may differ greatly. In this study, two quantitative structure activity relationship (QSAR) models for non-ionic and ionic species, are respectively established with partial least squares (PLS) and support vector machine (SVM) methods based on the large datasets (324 non-ionic states and 188 ionic states). These models exhibit good fitting ability (non-ionic model: R > 0.760; ionic model: R > 0.780), robustness (Q > 0.700), predictive performance (non-ionic model: R > 0.760; ionic model: R > 0.810) and wide applicability domain. The molecular parameters in two models are revealed to be significantly different, which may be attributed to the significant difference in molecular structures in two datasets and different reactivities of uncharged and charged states toward O. Additionally, the overall k for compounds at certain pH can be estimated by combining the two single QSAR models. These models and methods can become the effective tools for predicting the conversion rate of pollutants by O in the urban sewage and drinking water treatment.
臭氧氧化是一种用于减少水中污染物的重要技术。臭氧（O）与化合物的二级反应速率常数（k）对于衡量它们对 O 的反应性以及了解它们在臭氧氧化过程中的命运至关重要。然而，现有的化学物质数量与可用的实验 k 值之间存在巨大差距。此外，具有不同电离形式的可电离化合物与 O 的反应性可能有很大差异。在这项研究中，分别基于大量数据集（324 个非离子态和 188 个离子态），使用偏最小二乘法（PLS）和支持向量机（SVM）方法，建立了两个用于非离子和离子物种的定量结构活性关系（QSAR）模型。这些模型表现出良好的拟合能力（非离子模型：R > 0.760；离子模型：R > 0.780）、稳健性（Q > 0.700）、预测性能（非离子模型：R > 0.760；离子模型：R > 0.810）和广泛的适用性域。两个模型中的分子参数被揭示出显著不同，这可能归因于两个数据集的分子结构存在显著差异以及未带电状态和带电状态对 O 的反应性不同。此外，还可以通过结合这两个单一 QSAR 模型来估计化合物在特定 pH 值下的总 k 值。这些模型和方法可以成为预测城市污水和饮用水处理中污染物被 O 转化的有效工具。
