Geography-referenced modeling of pharmaceuticals and fecal bacteria for risk assessment in river catchments
Creators: name: Niebaum, Gunnar
Publisher: name: Universität Osnabrück
Year: 2024
Subjects: subject: Geography-referenced modeling
subject: Pharmaceuticals
subject: Fecal bacteria
subject: GREAT-ER
subject: River catchments
subject: 500 - Naturwissenschaften

description: Water is the essential resource of all life. Humans, wildlife and entire ecosystems depend on a good quality of water resources. Therefore, the preservation and restoration of a good water quality in the world’s environment is indispensable. However, humans are largely responsible for increasing contamination of this resource. It is generally accepted that this contamination needs to be reduced. Thus, the protection of surface waters and groundwater is regulated in various guidelines and directives like the EU Water Framework Directive (WFD). The required assessment of the status of the waterbodies includes knowledge about the various types of contamination. In this thesis, two different pollutants negatively affecting surface water quality are studied, namely residues of pharmaceutics and fecal contamination by bacteria. Worldwide, the well-being of millions of people depends on pharmaceuticals to prevent and treat a variety of diseases. With greater accessibility and increased application frequency, more and more pharmaceutical residues are entering the water cycle, where they may cause adverse effects on aquatic wildlife. Fecal contamination of surface waters poses a specific threat to humans, for example, through contamination of rivers used for wild swimming. This type of contamination is usually assessed by the concentration of fecal indicator bacteria. In this thesis, fecal indicator bacteria were investigated with a special focus on antibiotic-resistant bacteria, as they are particularly risky for humans. Infections caused by antibiotic-resistant bacteria might be difficult to treat or not curable at all. In this work, concentrations of pharmaceuticals and (antibiotic-resistant) fecal bacteria in surface waters of whole catchments were simulated to provide a basis for risk assessment. Concentrations were simulated using the geography-referenced regional exposure assessment tool for European rivers (GREAT-ER). Four research articles are included to (i) present the current publicly available version of the GREAT-ER model, (ii) conduct an extensive risk assessment of human-use pharmaceuticals in a cross-border catchment, (iii) apply the GREAT-ER model for the first time to simulate the fate of (antibiotic-resistant) Escherichia coli (E. coli) bacteria (as an indicator bacterium for fecal contamination) in surface waters in deterministic and (iv) stochastic simulations. In the cross-border study area, investigation of pharmaceutical residues shows that safe ecological concentration limits are likely to be exceeded at least temporarily for diclofenac, carbamazepine, and 17α-ethinylestradiol, which are not regulated by the WFD. Likewise, the study highlights the importance to investigate (sub-)catchments across national boundaries. The application of the GREAT-ER model to predict (antibiotic-resistant) E. coli concentrations in river catchments demonstrates opportunities and limitations of the model with respect to its originally not intended application to bacteria. Model results can serve as a basis to assess river catchments in terms of fecal contamination. These results suggest that swimming in waters influenced by wastewater treatment plant effluents is not advisable year-round and that the uptake of antibiotic-resistant bacteria cannot be ruled out when swimming in these waters. Under average conditions, measured concentrations are well represented by the model, while it reaches its limits under extreme conditions. Extending the model for a stochastic simulation routine using the Monte Carlo approach, allows for adequate predictions of the range of measured E. coli concentrations. With this approach, also key drivers of the spread of predicted concentrations could be identified. Overall, the presented research highlights the strengths of predictive models in general and of GREAT-ER in particular for exposure assessment of contaminants in river basins and the advantage of the complementary approach of modeling in combination with monitoring.