HQ-SGMN: A qualitative structure-guided molecular networking approach for comprehensive assessment of wastewater...

With the increasing emphasis on refined environmental management, understanding the transformation processes of organic contaminants (OCs) in wastewater treatment plants (WWTPs) is critical for evaluating treatment efficiency. High-resolution mass spectrometry (HRMS) coupled with non-targeted screening (NTS) enables high-throughput detection of contaminants. However, extracting transformation relationships from massive datasets remains a key challenge. In this study, we propose a high-confidence qualitative structure-guided molecular networking (HQ-SGMN) analytical approach that integrates SGMN with NTS, enabling transformation relationship identification without the need for reference standards or predefined reaction rules. In a full-scale WWTP case study, the static distribution and removal efficiency of organic compounds were first analyzed. Based on this, 44 potential transformation relationships that met the chemical transformation rules (CTS) and process environment were identified from 1069,622 removal-generation compound pairs. The A²/O process was identified as the dominant unit for organic compound transformation, primarily involving redox reactions, amide hydrolysis, and chain reactions. A novel criterion embedded within HQ-SGMN framework is introduced to identify potential transformation relationships, demonstrating strong generalizability and scalability. The integrated HQ-SGMN-NTS approach offers an effective tool for in-depth mining of massive NTS data, facilitating mechanistic insights into pollutant transformation and supporting the optimization of wastewater treatment processes.