Enhanced iodinated disinfection byproducts formation in iodide/iodate-containing water undergoing UV-chloramine sequential disinfection: Machine learning-aided identification of reaction mechanisms.

Journal: Water research
PMID: 39708378

Abstract

Restricted to the complex nature of dissolved organic matter (DOM) in various aquatic environments, the mechanisms of enhanced iodinated disinfection byproducts (I-DBPs) formation in water containing both I and IO (designated as I/IO in this study) during the ultraviolet (UV)-chloramine sequential disinfection process remains unclear. In this study, four machine learning (ML) models were established to predict I-DBP formation by using DOM and disinfection features as input variables. Extreme gradient boosting (XGB) algorithm outperformed the others in model development using synthetic waters and in cross-dataset generalization of surface waters. Shapley additive explanation (SHAP) analysis, partial dependence plots (PDPs), and individual conditional expectation (ICE) analysis were then employed to explain the models' workings and feature interactions, aiding in identification and quantification of underlying mechanisms. A type of DOM component (namely DC_b) was found as the greatest contributor and identified as reduced quinones associated with broken-down lignin within higher plant-derived fulvic substance, serving as precursors and electron shuttles for I-DBP formation. Based on the interactional effects acquired from explanation results, the ejection of e from excited DOM and pre-existing I in the I/IO system were identified responsible for the enhanced generation of I-DBPs compared to that in the I or IO alone systems; extra DOM scavenged reactive iodine species (RIS), contributing to a limited enhancement. These findings and the methodology developed here together enhance our understanding of the mechanisms how DOM limitedly promotes I-DBP formation during UV-chloramine sequential disinfection of I/IO-containing water and facilitate effective online monitoring in the future.

Authors

  • Zhen-Ning Luo
    State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China.
  • Huan He
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
  • Tian-Yang Zhang
    State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China.
  • Xiu-Li Wei
    State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China.
  • Zheng-Yu Dong
    Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China; College of Environmental and Chemical Engineering, Shanghai University of Electric Power, Shanghai, 200090, PR China.
  • Meng-Yuan Xu
    State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China.
  • Heng-Xuan Zhao
    State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China.
  • Zheng-Xiong Zheng
    State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China.
  • Ren-Jie Pan
    State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China.
  • Chen-Yan Hu
    Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China; College of Environmental and Chemical Engineering, Shanghai University of Electric Power, Shanghai, 200090, PR China.
  • Chao Zeng
    China Communications Construction Company Second Highway Consultants Co., Ltd., Wuhan 430056, China.
  • Mohamed Gamal El-Din
    Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, T6G 1H9, Canada.
  • Bin Xu
    Department of Orthopaedics, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.

Keywords

External Resources

Popular Topics
Recent Journals