Identification of biotransformation products of veterinary drugs present in piggery wastewater during treatment with photobioreactors based on microalgae-bacteria and purple phototrophic bacteria consortia

Paper ID: 
cest2019_00375
Topic: 
Emerging pollutants
Published under CEST2019
Proceedings ISBN: 978-618-86292-0-2
Proceedings ISSN: 2944-9820
Authors: 
(Corresponding) López-Serna R., García D., Bolado S., Jiménez J., Lai F., Golovko O., Ahrens L., Wiberg K., Gago-Ferrero P., Muñoz R.
Abstract: 
This work identified transformation products (TPs) from 5 antibiotics and 1 analgesic, namely enrofloxacin, marbofloxacin, danofloxacin, amoxicillin, penicillin G and dexamethasone, respectively, present in spiked real piggery wastewater (PWW) before and after two different treatments in two open photobioreactors operated continuously with a consortium of microalgae-bacteria and purple photosynthetic bacteria. For this purpose, suspect and non-target strategies based on liquid chromatography quadrupole-time-of-flight mass spectrometry (LC-QTOF-MS) were used. The application of quantitative structure-retention relationship (QSRR) prediction models, in addition to a comprehensive evaluation of the obtained MS/MS spectra, provided valuable information to support the identifications. The confirmation of the TPs was carried out with the corresponding reference standards, when these were commercially available. Alternatively, probable structures of the TPs based on diagnostic evidence were proposed. To the best of our knowledge, some of the identified TPs have never been reported before. A transformation pathway for their biotransformation has been proposed. The presence of the identified TPs was assessed in real PWW samples through retrospective analysis. Ultimately, the potential ecotoxicological risk posed by these six veterinary drugs and their TPs was evaluated by means of risk quotients.
Keywords: 
veterinary drugs, pig slurry, algal-bacterial processes, PPB, photobioreactor, transformation products, metabolites, retrospective analysis, LC-QTOF-MS