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

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 AImod 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 SO3 and SO4 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‒1. 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.