Abstract

The presence of pharmaceuticals and personal care products (PPCPs) in aquatic environments is of increasing concern due to the presence of residues in fish and aquatic organisms, and emerging antibiotic resistance. Wastewater release is an important contributor to the presence of these compounds in aquatic ecosystems, where they may accumulate in food webs. The traditional environmental surveillance approach relies on the targeted analysis of specific compounds, but more suspect screening methods have been developed recently to allow for the identification of a variety of contaminants. In this study, a method based on QuEChERS extraction – using acetonitrile/water mixture as solvent and PSA/C18 for sample clean-up – was applied to identify pharmaceuticals and their metabolites in fish livers. Both target and suspect screening workflows were used and fish were sampled upstream and downstream of wastewater treatment plants from the Scioto River, Ohio (USA). The method performed well in terms of extraction of some target PPCPs, with recoveries generally above 90%, good repeatability (<20%), and linearity. Based on target analysis, lincomycin and sulfamethoxazole were two antibiotics identified in fish livers at average concentrations of 30.3 and 25.6 ng g−1 fresh weight, respectively. Using suspect screening, another antibiotic, azithromycin and an antidepressant metabolite, erythrohydrobupropion were identified (average concentrations: 27.8 and 13.8 ng g−1, respectively). The latter, reported, to the best of our knowledge, for the first time in fish livers, was also found at higher concentrations in fish livers sampled downstream vs. upstream. The higher frequency of detection for azithromycin in benthic feeding fish species (63%) as well as clusters identified between different foraging groups suggest that foraging behavior may be an important mechanism in the bioaccumulation of PPCPs. This study shows how suspect screening is effective in identifying new contaminants in fish livers, notably using differential analysis among different spatially distributed samples.

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