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Geo-referenced simulation of pharmaceuticals in whole watersheds: application of GREAT-ER 4.1 in Germany

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...

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Autores principales: Lämmchen, Volker, Niebaum, Gunnar, Berlekamp, Jürgen, Klasmeier, Jörg
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8106600/
https://www.ncbi.nlm.nih.gov/pubmed/33411301
http://dx.doi.org/10.1007/s11356-020-12189-7
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author Lämmchen, Volker
Niebaum, Gunnar
Berlekamp, Jürgen
Klasmeier, Jörg
author_facet Lämmchen, Volker
Niebaum, Gunnar
Berlekamp, Jürgen
Klasmeier, Jörg
author_sort Lämmchen, Volker
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-020-12189-7.
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spelling pubmed-81066002021-05-24 Geo-referenced simulation of pharmaceuticals in whole watersheds: application of GREAT-ER 4.1 in Germany Lämmchen, Volker Niebaum, Gunnar Berlekamp, Jürgen Klasmeier, Jörg Environ Sci Pollut Res Int Research Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-020-12189-7. Springer Berlin Heidelberg 2021-01-07 2021 /pmc/articles/PMC8106600/ /pubmed/33411301 http://dx.doi.org/10.1007/s11356-020-12189-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Lämmchen, Volker
Niebaum, Gunnar
Berlekamp, Jürgen
Klasmeier, Jörg
Geo-referenced simulation of pharmaceuticals in whole watersheds: application of GREAT-ER 4.1 in Germany
title Geo-referenced simulation of pharmaceuticals in whole watersheds: application of GREAT-ER 4.1 in Germany
title_full Geo-referenced simulation of pharmaceuticals in whole watersheds: application of GREAT-ER 4.1 in Germany
title_fullStr Geo-referenced simulation of pharmaceuticals in whole watersheds: application of GREAT-ER 4.1 in Germany
title_full_unstemmed Geo-referenced simulation of pharmaceuticals in whole watersheds: application of GREAT-ER 4.1 in Germany
title_short Geo-referenced simulation of pharmaceuticals in whole watersheds: application of GREAT-ER 4.1 in Germany
title_sort geo-referenced simulation of pharmaceuticals in whole watersheds: application of great-er 4.1 in germany
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8106600/
https://www.ncbi.nlm.nih.gov/pubmed/33411301
http://dx.doi.org/10.1007/s11356-020-12189-7
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