Temporal–spatial variation and partitioning prediction of antibiotics in surface water and sediments from the intertidal zones of the Yellow River Delta, China

As special zones, the intertidal zones of the Yellow River Delta (YRD) are highly variable along with time and space. Fluvial–marine and land–ocean interactions which frequently occur in these areas have a great impact on the fate of pollutants. Antibiotics, which contribute to antibiotic-resistant genes (ARGs), are widely detected in wastewater, natural water, soil, sediments, and even drinking water. Therefore, it is meaningful to investigate the occurrence and fate of antibiotics in these special zones. In this study, eight antibiotics belonging to tetracyclines (TCs), fluoroquinolones (FQs), and macrolides (MLs) were detected in the surface water and sediments from the intertidal zones of YRD during two seasons. Two models were established to predict the partitioning coefficients of norfloxacin (NOR) and erythromycin (ETM) using physicochemical properties of sediments, respectively. The total concentrations of these antibiotics were 82.94–230.96ng·L−1 and 40.97–207.44ng·g−1, respectively, in the surface water and sediments. Seasonal variation was mainly influenced by the frequency of antibiotics use and environment factors. The regions with river supply exhibited the highest concentrations of antibiotics in surface water and sediments. Meanwhile, particle-size fractions, cation exchange capability (CEC), and metal ions content played dominant roles in the partitioning behaviors of NOR and ETM between the surface water and sediments. Both models established in this study featured accuracy and feasibility, which provided the methods for predicting the partitioning coefficients of emerging contaminants similar in structures to NOR and ETM in the intertidal zones. [Display omitted] •The intertidal zones of YRD were polluted by antibiotics to some extent.•The river supply was a major pathway for the antibiotic pollution of the intertidal zones of YRD.•The partitioning coefficients of NOR and ETM can be predicted using the physicochemical properties of sediments in studied areas.