Compound similarity network as a novel data mining strategy for high-throughput investigation of degradation pathways of organic pollutants in industrial wastewater treatment

The identification of degradation products and pathways is crucial for investigating emerging pollutants and evaluating wastewater treatment methods. Non-targeted analysis is a powerful tool to systematically investigate the degradation pathways of organic pollutants in real wastewater samples, but often generate large data sets making it difficult to effectively find information of interests. Here, we propose that that it is possible to establish the linkages among compounds in the same degradation pathways based on their structure similarity. Using this principle, we introduce compound similarity network (CSN) as a novel data mining strategy to identify degradation pathways using MS data collected in LC-MS based non-targeted analysis of complex wastewater samples. We demonstrated this strategy by non-targeted identifying potential degradation products of organic pollutants in leather industrial wastewater during laboratory-scale activated carbon adsorption (ACD) and ozonation processes. Using LC-MS based non-targeted analysis and the CSN strategy, we putatively identified 4324 compounds in the untreated leather industrial wastewater, 3246 after ACD, and 3777 after ACD/ozonation. We located 145 potential degradation pathways of organic pollutants in the ACD/ozonation process using CSN and validated 9 pathways with 21 commercial standard compounds. Our CSN strategy for LC-MS based non-targeted analysis provides a highly efficient way to investigate degradation pathways of complicated organic pollutants in authentic wastewater.