Novel approaches to identify drivers of chemical stress in small rivers

The pollution of freshwater bodies with organic micropollutants poses a major threat to human health and the integrity of aquatic ecosystems. In the aquatic environment, organic micropollutants are detected in complex mixtures. Moreover, the mixture composition varies with time (e.g., by seasonal emissions of pesticides), space (e.g., downstream of wastewater treatment plants (WWTPs)) and weather conditions (e.g., due to surface runoff during rain events) leading to highly variable chemical stress in water bodies. This is a major challenge for water monitoring and management. Thus, novel approaches are needed which are able to capture mixture this complexity and variability and to identify drivers of chemical stress. These drivers include risk driving compounds, source-related fingerprints and indicator compounds which present specific exposure scenarios. The approaches developed in this dissertation were applied in the Holtemme River (Saxony-Anhalt, Germany).In Chapter 2, a multitarget screening approach by liquid chromatography coupled to mass spectrometry in combination with a cluster analysis was applied to identify pollution patterns for seasonal emissions of WWTPs and precipitation-related emissions of a rain sewer. The identified pollution patterns were translated into risk patterns for fish, crustaceans and algae using a toxic unit approach. Acute risk for algae and crustaceans was driven by one to three individual compounds and was mainly caused by seasonal and random emissions, e.g., spills. Sublethal risk by constantly emitted pharmaceuticals posed a potential risk for all studied organisms. In Chapter 3, spatial pollution patterns along the Holtemme River were unraveled using a nontarget screening (NTS) approach with a longitudinal cluster analysis. Three main pollution patterns were identified reflecting i) inputs from WWTPs, ii) the confluence with the Bode River and iii) diffuse and random input. The latter represented natural background and discharge of untreated wastewater via small point sources such as rain sewers. Main patterns were characterized by specific isotopologue signatures and the number of peaks in homologue series. Further subpatterns revealed source-related fingerprints of WWTP-related inputs. By structure elucidation, 25 representative compounds for these patterns were identified. In Chapter 4, temporal pollution patterns during heavy rain events were identified using the workflow developed in Chapter 3. In six rain events, two main pollution patterns were unraveled. The two patterns represented i) pre-event emissions which were partly diluted during rain events and ii) increases of rain-related emissions via surface runoff or discharge of untreated wastewater by combined sewer overflow. Besides a high inter-event variation in mixture composition, a common mixture representing typical rain-related pollutants was determined. From this common mixture, indicator compounds with a high intra-event intensity increase were identified by target screening and NTS. Indicator compounds, which were suitable for event-based water monitoring, included biocides and surfactants for urban surface runoff as well as piperine and chenodeoxycholic acid for emissions of untreated wastewater. In this dissertation, approaches were developed which allow for capturing and unravelling complex mixtures of organic micropollutants. The findings of this dissertation showed that drivers of chemical stress were related to emissions of untreated wastewater, urban surface runoff and improper discharge of pesticides. This highlights the urgency for management and mitigation efforts to reduce emissions from these sources. However, the identified patterns, risk driving compounds and indicator compounds need to be tested in large scale studies to derive generally-valid organic micropollutants and strategies for water monitoring and management