Structural Molecular Network for Nontargeted Screening and Prioritization of New Pollutants in Urban Wastewater

High-throughput screening of new pollutants is critical for effective wastewater treatment. However, processing nontargeted high-resolution mass spectrometry (HRMS) data presents significant challenges. Molecular networks offer a promising strategy for discovering new pollutants by constructing networks that link structurally similar compounds. This study introduces a screening approach that integrates novel structural molecular networks with HRMS to effectively annotate new pollutants. This method uses limited known tandem mass spectrometry (MS/MS) compounds as seeds, enabling the large-scale screening of suspected pollutants without extensive MS/MS databases. The feasibility of the proposed method was validated using 319 and 172 pesticides in positive and negative electrospray ion modes, respectively, achieving annotation rates exceeding 92.81% and accuracy rates surpassing 83.56%. Notably, varying the number of seed compounds maintained an annotation accuracy above 80.16%. Applying this method to the influent and effluent samples from an urban wastewater treatment plant, 1583 compounds were annotated, with 232 transformation products being uniquely found in the effluent. Furthermore, the hazard prioritization of the effluent pollutants of 74 compounds poses a higher priority. Among these, 8 compounds are identified as transformation products, and their potential parent compounds were characterized. This study provides new insights into the comprehensive monitoring of new pollutants and transformation products.