Network-Based Methods for Deciphering the Oxidizability of Complex Leachate DOM with •OH/O3 via Molecular Signatures

Abstract

In landfill leachates containing complex dissolved organic matter (DOM), the link between individual DOM constituents and their inherent oxidizability is unclear. Here, we resolved the molecular signatures of DOM oxidized by ^•OH/O₃ using FT-ICR MS, thereby elucidating their oxidizability and resistance in concentrated leachates. The comprehensive gradual fragmentation of complex leachate DOM was then revealed through a modified machine-learning framework based on 43 key pathways during ozonation. Specifically, humic substances like humic acid (HA) and fulvic acid (FA) were measured to be the dominant DOM fractions in concentrated leachates, accounting for 35.9–51.7% of the total organic carbon, which was consistent with the observation by three-dimensional fluorescence spectroscopy. According to FT-ICR MS, carboxyl-rich alicyclic molecules (CRAMs) or lignin-like substances were the most abundant components, comprising 40.2–54.5% of all substances. The machine learning modeling showed that molecular weight was the most important structural factor for DOM resistance to ^•OH and O₃ degradation (SHAP value 0.84), followed by (DBE-O)/C (0.32), S/C (0.31), and H/C (0.08). During ^•OH and O₃ attacking, unsaturated and reduced compounds were the dominant precursors. For the molecular transformation of CRAMs-DOM, oxygen addition reactions were found to be the predominant O₃-attacking process, along with the dealkyl and carboxylic acid reactions during ^•OH oxidation that often resulted in more complete degradation of DOM. This study proposed a new framework integrating molecular signatures and machine learning for unraveling DOM’s inherent reactivity in complexity, which informs strategies for managing concentrated leachates.