Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID â–² Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-1291 PEER REVIEWED ARTICLE 2015 Emma L. Schymanski , Heinz P. Singer , Jaroslav Slobodnik , Ildiko M. Ipolyi , Peter Oswald , Martin Krauss , Tobias Schulze , Peter Haglund , Thomas Non-target screening with high-resolution mass spectrometry: critical review using a collaborative trial on water analysis 10.1007/s00216-015-8681-7 Uncategorized Uncategorized No abstract available 603437
publications-1292 PEER REVIEWED ARTICLE 2015 Christine Hug , Moritz Sievers , Richard Ottermanns , Henner Hollert , Werner Brack , Martin Krauss Linking mutagenic activity to micropollutant concentrations in wastewater samples by partial least square regression and subsequent identification of variables 10.1016/j.chemosphere.2015.05.072 Uncategorized River Basins No abstract available 603437
publications-1293 PEER REVIEWED ARTICLE 2015 Lieke J.C. Coppens , Jos A.G. van Gils , Thomas L. ter Laak , Bernard W. Raterman , Annemarie P. van Wezel Towards spatially smart abatement of human pharmaceuticals in surface waters: Defining impact of sewage treatment plants on susceptible functions 10.1016/j.watres.2015.05.061 Uncategorized Natural Water Bodies No abstract available 603437
publications-1294 PEER REVIEWED ARTICLE 2015 Soňa Smetanová , Janet Riedl , Dimitar Zitzkat , Rolf Altenburger , Wibke Busch High-throughput concentration-response analysis for omics datasets 10.1002/etc.3025 Uncategorized Uncategorized Abstract   Omics-based methods are increasingly used in current ecotoxicology. Therefore, a large number of observations for various toxic substances and organisms are available and may be used for identifying modes of action, adverse outcome pathways, or novel biomarkers. For these purposes, good statistical analysis of toxicogenomic data is vital. In contrast to established ecotoxicological techniques, concentration–response modeling is rarely used for large datasets. Instead, statistical hypothesis testing is prevalent, which provides only a limited scope for inference. The present study therefore applied automated concentration–response modeling for 3 different ecotoxicotranscriptomic and ecotoxicometabolomic datasets. The modeling process was performed by simultaneously applying 9 different regression models, representing distinct mechanistic, toxicological, and statistical ideas that result in different curve shapes. The best-fitting models were selected by using Akaike's information criterion. The linear and exponential models represented the best data description for more than 50% of responses. Models generating U-shaped curves were frequently selected for transcriptomic signals (30%), and sigmoid models were identified as best fit for many metabolomic signals (21%). Thus, selecting the models from an array of different types seems appropriate, because concentration–response functions may vary because of the observed response type, and they also depend on the compound, the organism, and the investigated concentration and exposure duration range. The application of concentration–response models can help to further tap the potential of omics data and is a necessary step for quantitative mixture effect assessment at the molecular response level. Environ Toxicol Chem 2015;34:2167–2180. © 2015 SETAC 603437
publications-1295 PEER REVIEWED ARTICLE 2015 C. Lindim , I.T. Cousins , J. vanGils Estimating emissions of PFOS and PFOA to the Danube River catchment and evaluating them using a catchment-scale chemical transport and fate model 10.1016/j.envpol.2015.08.050 Uncategorized Uncategorized No abstract available 603437
publications-1296 PEER REVIEWED ARTICLE 2016 C. Lindim , J. van Gils , I.T. Cousins A large-scale model for simulating the fate & transport of organic contaminants in river basins 10.1016/j.chemosphere.2015.09.051 Uncategorized Natural Water Bodies No abstract available 603437
publications-1297 PEER REVIEWED ARTICLE 2015 Werner Brack The Challenge : Prioritization of emerging pollutants 10.1002/etc.3046 Predictive Analytics Uncategorized No abstract available 603437
publications-1298 PEER REVIEWED ARTICLE 2015 Christiane Heiss , Anette Küster In Response : A regulatory perspective on prioritization of emerging pollutants in the context of the Water Framework Directive 10.1002/etc.3047 Uncategorized Natural Water Bodies No abstract available 603437
publications-1299 PEER REVIEWED ARTICLE 2015 Valeria Dulio , Jaroslav Slobodnik In Response : The NORMAN perspectives on prioritization of emerging pollutants 10.1002/etc.3048 Simulation & Modeling Precipitation & Ecological Systems No abstract available 603437
publications-1300 PEER REVIEWED ARTICLE 2015 Michael Faust , Thomas Backhaus In Response : Prioritization and standard setting for pollutant mixtures in the aquatic environment: A business consultants perspective 10.1002/etc.3049 Simulation & Modeling Precipitation & Ecological Systems No abstract available 603437