State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun 130117, China.
Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.
Sci Total Environ. 2020 May 1;715:136816. doi: 10.1016/j.scitotenv.2020.136816. Epub 2020 Jan 21.
The ozonation-based advanced oxidation process is a promising treatment technology for wastewater with micropollutants. The second-order reaction rate constant (k) of ozone (O) with organic compounds is an important index for estimating removal efficiency of organic pollutants in engineered treatment; however, the experimental k values are currently only available for hundreds of chemicals. In this study, two quantitative-structure activity relationship (QSAR) models were developed to predict k 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 (R = 0.734), robustness (Q = 0.700, Q = 0.772) and predictive ability (R = 0.797, Q = 0.794), and the SVM model also has good fitness (R = 0.862) and predictability (R = 0.782, Q = 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) 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.
基于臭氧的高级氧化工艺是一种有前途的处理技术，可用于处理含有微量污染物的废水。臭氧（O）与有机化合物的二级反应速率常数（k）是估计工程处理中有机污染物去除效率的重要指标；然而，目前只有数百种化学物质的实验 k 值可用。在这项研究中，使用多元线性回归（MLR）和支持向量机（SVM）方法开发了两个定量结构活性关系（QSAR）模型，以预测各种有机化学品的 k 值。与现有模型相比，所建立的 QSAR 模型涵盖了更大的数据集（136 种化学品）和更多结构多样化的化学品。MLR 模型具有令人满意的拟合度（R=0.734）、稳健性（Q=0.700，Q=0.772）和预测能力（R=0.797，Q=0.794），SVM 模型也具有良好的拟合度（R=0.862）和预测能力（R=0.782，Q=0.775）。模型的适用域已得到扩展，包括许多以前模型很少涵盖的化学物质（特别是一些新兴污染物）。揭示了影响臭氧氧化的分子结构因素。最高占据分子轨道（E）的能量和酚/烯醇/羧酸 OH 基团（O-057）是控制有机化合物与臭氧反应性的两个最重要的分子结构因素。所开发的模型可以作为臭氧去除有机污染物的预测的预筛选工具。
