Leveraging the capabilities of ML for environmental monitoring, recent studies have harnessed SourceTracker, an ML tool based on a Bayesian classification algorithm , to identify the sources of ARGs in drinking water and water sources. A study used SourceTracker to identify the complex sources of ARGs and assessed their contributions to ARG pollution in a periurban river . The results show that the discharge from sewage plants was the largest contributor of ARGs (81.6% to 92.1%) in the river sediments. Another work used SourceTracker to monitor the presence of ARGs in household drinking water and tracked their origins back to anthropogenic sources, highlighting the significant impact of human activities on drinking water quality (Figure ) . The data generated by SourceTracker have a strong Pearson correlation (r = 0.98) with the corresponding expected proportion by artificial source inputs. Source tracking analysis from that study indicates that a significant proportion of ARGs (37.1%) was from anthropogenic sources, especially wastewater effluent.