An integrated modelling approach to derive the grey water footprint of veterinary antibiotics

Abstract

Water pollution by veterinary antibiotics (VAs) resulting from livestock production is associated with severe environmental and human health risks. While upward trends in global animal product consumption signal that these risks might exacerbate toward the future, VA related water pollution is currently insufficiently understood. To increase this understanding, the present research assesses processes influencing VA pollution from VA administration to their discharge into freshwater bodies, using an integrated modelling approach (IMA). For the VAs amoxicillin, doxycycline, oxytetracycline, sulfamethazine, and tetracycline we estimate loads administered to livestock, excretion, degradation during manure storage, fate in soil and transport to surface water. Fate and transport are modelled using the VA transport model (VANTOM), which is fed with estimates from the Pan-European Soil Erosion Risk Assessment (PESERA). The grey water footprint (GWF) is used to indicate the severity of water pollution in volumetric terms by combining VA loads and predicted no effect concentrations. We apply our approach to the German-Dutch Vecht river catchment, which is characterized by high livestock densities. Results show a VA mass load decrease larger than 99% for all substances under investigation, from their administration to surface water emission. Due to metabolization in the body, degradation during manure storage and degradation in soil, VA loads are reduced by 45%, 80% and 90% on average, respectively. While amoxicillin and sulfamethazine dissipate quickly after field application, significant fractions of doxycycline, oxytetracycline and tetracycline accumulate in the soil. The overall Vecht catchment's GWF is estimated at 250,000 m3 yr?1, resulting from doxycycline (81% and 19% contribution from the German and Dutch catchment part respectively). Uncertainty ranges of several orders of magnitude, as well as several remaining limitations to the presented IMA, underscore the importance to further develop and refine the approach. © 2021 The Authors