Identification and Risk Assessment of Antibiotics and Their Transformation Products in a Large-Scale River Using Suspect and Nontarget Screening and Machine Learning

The occurrence of antibiotics in aquatic environments
has been
well-known, and more research is needed about their transformation
products (TPs) and associated environmental risks, especially antimicrobial
resistance, in large-scale rivers. Here, we developed a highly comprehensive
target, suspect and nontarget screening machine-learning workflow
based on high-resolution mass spectrometry to identify unknown antibiotic
TPs in a large-scale river. We identified 46 antibiotics and 144 TPs
with a confidence level of 1 to 3. Parent antibiotics were dominated
by sulfonamides (26.1%), while TPs were dominated by macrolides (34.0%),
with main transformation pathways of oxidation and hydrolysis. More
TPs were found than the number of parent antibiotics. Fourteen, 18,
97, and 36 TPs had greater persistence, bioaccumulation, mobility
and toxicity, respectively. One PBT compound was identified, and no
PMT compounds were observed. Fifteen (10 antibiotics and 5 TPs) and
12 (3 antibiotics and 9 TPs) compounds were assessed as high ecological
risk and high antimicrobial resistance risk, respectively. The presence
of TPs leads to higher total risk. Thus, TPs should be included in
future monitoring and risk assessment.