Qian Lou, Xinyu Zhao, Xunhai Zhang, Lijun Liang, Liu Han, Wenqing Tu, Miao Chen
Antibiotic residues in receiving water bodies of densely populated urban areas have attracted widespread concern. In this study, 35 antibiotics, including tetracyclines, macrolides, sulfonamides, and quinolones, were identified in the Beiyun River system of Beijing, a megacity in China. The spatial distribution patterns and ecological risks related to the target antibiotics in different surface water types were investigated, while their main sources were quantitatively identified using principal component analysis-multiple linear regression (PCA-MLR). The results indicated that all 35 antibiotics occurred at varying levels, with trimethoprim, chlortetracycline, and sulfamethoxazole showing 100% detection frequencies. The total concentrations of individual antibiotics varied between 2.8 ng/L (lomefloxacin) and 1235.8 ng/L (erythromycin). Spatially, antibiotic concentrations followed the order of midstream > downstream > upstream, and levels in tributaries were, in general, higher compared with the main flow, indicating that the mainstream was strongly influenced by tributary inflows. Multiple-level ecological risk assessment indicated that the overall ecological risks posed by antibiotics within the Beiyun River system remained at acceptable levels. However, tetracyclines showed relatively high risk quotients (RQ), and clarithromycin exhibited low risks to certain aquatic species. The PCA-MLR model identified 6 pollution sources and one unknown source. Among these, mixed pollution source inputs (explaining 35.4% of variance) and wastewater treatment plants' discharges (explaining 31.1% of variance) were the primary sources of antibiotics in the surface water of the Beiyun River system. The former contributed most significantly to difloxacin (72.1%) and ciprofloxacin (71.4%), while the latter contributed substantially to sulfacetamide (74.8%), sulfachloropyridazine (73.9%), sulfadimethoxine (73.3%), and sulfadiazine (70.9%). These findings provide valuable insights for antibiotic pollution prevention and management of the risks to ecosystems in urban river systems.