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Wang P, Bu L, Zhou S, Wu Y, Deng L, Shi Z. Predictive models for the aqueous phase reactivity of inorganic radicals with organic micropollutants. Chemosphere 2023; 332:138793. [PMID: 37119929 DOI: 10.1016/j.chemosphere.2023.138793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/12/2023] [Accepted: 04/25/2023] [Indexed: 05/10/2023]
Abstract
Single-electron transfer (SET) is one of the most common reaction mechanisms for degrading organic micropollutants (OMPs) in advanced oxidation processes. We collected 300 SET reactions (CO3•-, SO4•-, Cl2•-, and Br2•--mediated) and calculated three key parameters for understanding the SET mechanism: aqueous phase free energies of activation (ΔG‡), free energies of reactions (ΔG), and orbital energy gaps of reactants (EOMPsHOMO-ERadiLUMO). Then, we classified the OMPs according to their structure, developed and evaluated linear energy relationships of the second-order rate constants (k) with ΔG‡, ΔG, or EOMPsHOMO-ERadiLUMO in each class. Considering that a single descriptor cannot capture all the chemical diversity, we combined ΔG‡, ΔG, and EOMPsHOMO-ERadiLUMO as inputs to develop multiple linear regression (MLR) models. Chemical classification is critical to the linear model described above. However, OMPs usually have multiple functional groups, making the classification challenging and uncertain. Therefore, we tried machine learning algorithms to predict k values without chemical classification. We found that decision trees (R2 = 0.88-0.95) and random forest (R2 = 0.90-0.94) algorithms show better performance on the prediction of the k values, whereas boosted tree algorithm cannot make an accurate prediction (R2 = 0.19-0.36). Overall, our study provides a powerful tool to predict the aqueous phase reactivity of OMP to certain radicals without the need for chemical classification.
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Affiliation(s)
- Pin Wang
- Hunan Engineering Research Center of Water Security Technology and Application, College of Civil Engineering, Hunan University, Changsha 410082, PR China; Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Hunan University, Changsha, 410082, PR China
| | - Lingjun Bu
- Hunan Engineering Research Center of Water Security Technology and Application, College of Civil Engineering, Hunan University, Changsha 410082, PR China; Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Hunan University, Changsha, 410082, PR China.
| | - Shiqing Zhou
- Hunan Engineering Research Center of Water Security Technology and Application, College of Civil Engineering, Hunan University, Changsha 410082, PR China; Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Hunan University, Changsha, 410082, PR China
| | - Yangtao Wu
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China
| | - Lin Deng
- Hunan Engineering Research Center of Water Security Technology and Application, College of Civil Engineering, Hunan University, Changsha 410082, PR China; Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Hunan University, Changsha, 410082, PR China
| | - Zhou Shi
- Hunan Engineering Research Center of Water Security Technology and Application, College of Civil Engineering, Hunan University, Changsha 410082, PR China; Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Hunan University, Changsha, 410082, PR China
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