Insights into the transformation of natural organic matter during UV/peroxydisulfate treatment by FT-ICR MS and machine learning: Non-negligible formation of organosulfates.

Journal: Water research
PMID: 38615605

Abstract

Natural organic matter (NOM) is a major sink of radicals in advanced oxidation processes (AOPs) and understanding the transformation of NOM is important in water treatment. By using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) in conjunction with machine learning, we comprehensively investigated the reactivity and transformation of NOM, and the formation of organosulfates during the UV/peroxydisulfate (PDS) process. After 60 min UV/PDS treatment, the CHO formula number and dissolved organic carbon concentration significantly decreased by 83.4 % and 74.8 %, respectively. Concurrently, the CHOS formula number increased substantially from 0.7 % to 20.5 %. Machine learning identifies DBE and AI as the critical characteristics determining the reactivity of NOM during UV/PDS treatment. Furthermore, linkage analysis suggests that decarboxylation and dealkylation reactions are dominant transformation pathways, while the additions of SO and SO are also non-negligible. According to SHAP analysis, the m/z, number of oxygens, DBE and O/C of NOM were positively correlated with the formation of organosulfates in UV/PDS process. 92 organosulfates were screened out by precursor ion scan of HPLC-MS/MS and verified by UPLC-Q-TOF-MS, among which, 7 organosufates were quantified by authentic standards with the highest concentrations ranging from 2.1 to 203.0 ng L. In addition, the cytotoxicity of NOM to Chinese Hamster Ovary (CHO) cells increased by 13.8 % after 30 min UV/PDS treatment, likely responsible for the formation of organosulfates. This is the first study to employ FT-ICR MS combined with machine learning to identify the dominant NOM properties affecting its reactivity and confirmed the formation of organosulfates from sulfate radical oxidation of NOM.

Authors

  • Junfang Li
    Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China; College of Chemistry and Chemical Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
  • Wenlei Qin
    Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China.
  • Bao Zhu
    State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
  • Ting Ruan
    Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science, South China Agricultural University, Guangzhou, 510642, China.
  • Zhechao Hua
    Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China.
  • Hongyu Du
    Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China.
  • Shengkun Dong
    Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China.
  • Jingyun Fang
    State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; University of Chinese Academy of Sciences, Beijing 100049, China; Department of Ecology, Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China. Electronic address: jyfang@urban.pku.edu.cn.

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