RESUMEN
Fecally contaminated waters can be a source for human infections. We investigated the occurrence of fecal indicator bacteria (E. coli) and antibiotic-resistant E. coli, namely, extended spectrum beta-lactamase (ESBL)-producing E. coli (ESBL-EC) and carbapenemase-producing E. coli (CP-EC) in the Dutch-German transboundary catchment of the Vecht River. Over the course of one year, bacterial concentrations were monitored in wastewater treatment plant (WWTP) influents and effluents and in surface waters with and without WWTP influence. Subsequently, the GREAT-ER model was adopted for the prediction of (antibiotic-resistant) E. coli concentrations. The model was parametrized and evaluated for two distinct scenarios (average flow scenario, dry summer scenario). Statistical analysis of WWTP monitoring data revealed a significantly higher (factor 2) proportion of ESBL-EC among E. coli in German compared to Dutch WWTPs. CP-EC were present in 43% of influent samples. The modeling approach yielded spatially accurate descriptions of microbial concentrations for the average flow scenario. Predicted E. coli concentrations exceed the threshold value of the Bathing Water Directive for a good bathing water quality at less than 10% of potential swimming sites in both scenarios. During a single swimming event up to 61 CFU of ESBL-EC and less than 1 CFU of CP-EC could be taken up by ingestion.
Asunto(s)
Infecciones por Escherichia coli , Escherichia coli , Humanos , Antibacterianos , Aguas Residuales/microbiología , beta-Lactamasas , Infecciones por Escherichia coli/epidemiología , Infecciones por Escherichia coli/microbiología , Contaminación del AguaRESUMEN
Millions of people rely on active pharmaceutical ingredients (APIs) to prevent and cure a wide variety of illnesses in humans and animals, which has led to a steadily increasing consumption of APIs across the globe and concurrent releases of APIs into the environment. In the environment, APIs can have a detrimental impact on wildlife, particularly aquatic wildlife. Therefore, it is essential to assess their potential adverse effects to aquatic ecosystems. The European Water Framework Directive sets out that risk assessment should be performed at the catchment level, crossing borders where needed. The present study defines ecological risk profiles for surface water concentrations of 8 APIs (carbamazepine, ciprofloxacin, cyclophosphamide, diclofenac, erythromycin, 17α-ethinylestradiol, metformin, and metoprolol) in the Vecht River, a transboundary river that crosses several German and Dutch regions. Ultimately, 3 main goals were achieved: 1) the geo-referenced estimation of API concentrations in surface water using the geography-referenced regional exposure assessment tool for European rivers; 2) the derivation of new predicted-no-effect concentrations for 7 of the studied APIs, of which 3 were lower than previously derived values; and 3) the creation of detailed spatially explicit ecological risk profiles of APIs under 2 distinct water flow scenarios. Under average flow conditions, carbamazepine, diclofenac, and 17α-ethinylestradiol were systematically estimated to surpass safe ecological concentration thresholds in at least 68% of the catchment's water volume. This increases to 98% under dry summer conditions. Environ Toxicol Chem 2022;41:648-662. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Asunto(s)
Ríos , Contaminantes Químicos del Agua , Animales , Carbamazepina/análisis , Carbamazepina/toxicidad , Diclofenaco , Ecosistema , Monitoreo del Ambiente , Humanos , Países Bajos , Preparaciones Farmacéuticas , Medición de Riesgo , Agua , Contaminantes Químicos del Agua/análisis , Contaminantes Químicos del Agua/toxicidadRESUMEN
The geo-referenced regional exposure assessment tool for European rivers (GREAT-ER) is designed to support river basin management or the implementation process within the EU Water Framework Directive by predicting spatially resolved exposure concentrations in whole watersheds. The usefulness of the complimentary application of targeted monitoring and GREAT-ER simulations is demonstrated with case studies for three pharmaceuticals in selected German watersheds. Comparison with monitoring data corroborates the capability of the probabilistic model approach to predict the expected range of spatial surface water concentrations. Explicit consideration of local pharmaceutical emissions from hospitals or private doctor's offices (e.g., for X-ray contrast agents) can improve predictions on the local scale without compromising regional exposure assessment. Pharmaceuticals exhibiting low concentrations hardly detectable with established analytical methods (e.g., EE2) can be evaluated with model simulations. Management scenarios allow for a priori assessment of risk reduction measures. In combination with targeted monitoring approaches, the GREAT-ER model can serve as valuable support tool for exposure and risk assessment of pharmaceuticals in whole watersheds.