The environmental risk of heterogeneous oxidation is unneglectable: Time-resolved assessments beyond typical intermediate investigation.

Journal: Water research
Published Date: Mar 29, 2025

Abstract

The safety of advanced oxidation processes is paramount, surpassing treatment efficiency concerns. However, current research is limited to the qualitative toxicity investigations of targeted contaminants by-products, while the detoxification effects of heterogeneous advanced oxidation processes are largely unknown. Here we propose an environmental risk assessment that distinguishes between preferred oxidation pathways of the detoxification effects, thereby selecting the most suitable treatment system for each contaminant. Through environmental risk analyses based on the by-product quantification, >40 % of previously overlooked toxicity has been rediscovered, significantly improving the accuracy of contaminant detoxification evaluation. The by-products contributed risk mostly reached the maximum after 30 min of reaction, evenly distributed on aquatic indicators but largely originated from on radical oxidation pathways. Density functional theory is applied to determine the generation probability of isomers, and deep neural network regression modelling accelerated derivation on structural transformation of toxic molecules. Furthermore, an evaluation system is established using risk quotients and cluster analysis classification modelling, enabling the quantitative cross-comparison in oxidation systems. This approach enhances the understanding of the safety and efficiency within oxidation processes, introducing various new methods supporting quantitative environmental risk assessment of emerging contaminant degradation in complicated heterogeneous oxidation processes. SYNOPSIS: The environmental risks in advanced oxidation processes are quantified by deep learning and theoretical chemistry-assisted assessments.

Authors

  • Zijie Xiao
    State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, PR China; Department of Chemical Engineering, KU Leuven, 3001 Leuven, Belgium.
  • Bowen Yang
    Song are with Center for Cyber-Physical Systems, University of Georgia, Athens, GA 30602, USA.
  • Xiaochi Feng
    State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, PR China. Electronic address: fengxiaochi@hit.edu.cn.
  • Kai Sheng
    School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
  • Hongtao Shi
    School of Science and Information Science, Qingdao Agricultural University, Qingdao, Shandong 266109, China.
  • Chenyi Jiang
    State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, PR China.
  • Pengrui Jin
    Department of Chemical Engineering, KU Leuven, Celestijnenlaan 200F, Leuven, B-3001 Belgium.
  • Yu Tao
    Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, People's Republic of China.
  • Wanqian Guo
    State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China. Electronic address: guowanqian@hit.edu.cn.
  • Bart Van der Bruggen
    Department of Chemical Engineering, KU Leuven, Celestijnenlaan 200F, Leuven B-3001, Belgium.
  • Qilin Li
    Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA.
  • Nanqi Ren
    State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150001, China.

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