In order to support both the provision of clean drinking water and the preservation of biodiversity in aquatic ecosystems, a comprehensive scientific understanding of the identity, concentration, and behavior of anthropogenic pollutants in the aquatic environment is essential. Organic micropollutants constitute a large group of anthropogenic pollutants and originate from all areas of daily human life: pharmaceuticals are part of the daily routine for many people, pesticides are crucial for food production, and organic chemicals are used in the industrial production of paper, plastics, paints, and many other products. These micropollutants enter the water cycle, either in their parent form or as transformation products, where they can lead to potentially harmful effects. In addition to target methods, non-target approaches have been established as powerful tools for comprehensively investigating these compounds in the water cycle. Beyond the analytical challenges of instrumentally detecting these compounds, the prioritization and evaluation of the large datasets generated by non-target pose both chemical and data science challenges, forming the overarching theme of this work. The work comprises three studies demonstrating the development and application of non-target screening (NTS) methodologies. In an NTS using high-performance liquid chromatography (HPLC) coupled with high-resolution mass spectrometry (QTOF-MS/MS), 112 samples from the river Nidda and seven of its tributaries were analyzed. On average, approximately 2700 signals, or features, were detected per sample. To filter these extensive data and prioritize unknown compounds, a method was first developed to reliably assign adducts, isotopologues, source fragments and other ionization products to a common component based on retention time and peak shape. In the next step, the prioritization of unknown compounds was achieved by highlighting features that were detected specifically at individual sites under investigation, but which were not typically considered to originate from municipal wastewater. This was accomplished by comparing the data from the Nidda river system with NTS data from municipal wastewater treatment plant effluents. Only the highlighted, Nidda-specific features were considered further. As a result, an average data prioritization of 7% across all samples was achieved, leading to the identification of nine compounds. Among these were the industrial compound Nylostab S-EED™, which had not been previously observed in the environment and three algal toxins, whose occurrence resulted from the algal bloom of a nearby water body. In the second study, investigations focused on permanently cationic compounds in suspended particulate matter samples from the rivers Rhine and Saar. The data prioritization was based on the specific physicochemical properties of this substance group. Following extraction, a two-step procedure was applied, relying on interactions with strong ion exchangers and chromatography using deuterated solvents. This resulted in 5% of the detected NTS signals being labeled as potentially cationic compounds. Based on this, 22 compounds were identified, four of which were previously unknown. Trend analyses covering the period from 2005/2006 to 2018, along with an assessment of the ecotoxicological risks based on semi-quantitatively determined concentrations, suggest that identified compounds such as Basic Yellow 28 and Fluorescent Brightener 363 may have a high ecotoxicological relevance. The third study focused on the analysis of samples from disconnected, inter-regional river systems. In collaboration with the local environmental authorities, a three-year study was conducted, analyzing 524 samples from 79 sites along rivers across Saxony. For this purpose, a method was developed that allowed for the characterization of sites based on five categories, considering both known and unknown compounds to assess their chemical contamination. The results were classified by calculating the modified z-scores within each category. The method was validated based on the results of target analysis in the same samples. As a result of the study, 13 sites were classified as anomalous due to high z-scores, as elevated levels of pharmaceuticals, industrial chemicals but also unclassified unknown compounds were detected. At two potentially industrially contaminated sites, the Münzbach and Dorfbach Oberschindmaas, nine compounds were identified and their concentrations were estimated using a 1-point calibration. For the compound hexa(methoxymethyl)melamine, a concentration in the range of 300 µg/L was determined, suggesting a considerable risk for the environment (risk quotient: 5.6).
Toni Köppe (Thu,) studied this question.