The emission of anthropogenic trace substances into the aquatic environment continuously poses challenges to water suppliers. The contamination of raw waters with organic trace substances requires complex water treatment processes to secure drinking water quality. The routine monitoring of these raw waters as well as the behavior and fate of organic trace substances during different treatment processes is of great interest to recognize and counter potential dangers at an early stage. Chromatographic separation techniques coupled to triple quadrupole mass spectrometers are conventionally used for the reliable monitoring of traces of known polar substances. However, such analytical techniques usually fail to recognize unknown compounds. This weakness presents a serious restriction with regard to the monitoring of treatment processes, since transformation products are often not - or not sufficiently - characterized and are thus only detected sporadically. Non-target screening using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) allows the detection of thousands of compounds within a single run and covers known as well as unknown substances. Compared to the established analytical techniques, this is a decisive advantage for the monitoring of raw and process waters during water treatment. While the analytical technique LC-HRMS has undergone significant developments in recent years, the algorithms for data processing reveal clear weaknesses. This dissertation therefore deals with reliable processing strategies for LC-HRMS data. The first part of this work seeks to highlight the problematics of false positive and false negative findings. Based on repeated measurements, various strategies of data processing were assessed with regard to the repeatability of the results. To ensure that real peaks were barely or not removed by the filtering procedure, samples were spiked with isotope-labeled standards. The results emphasize that the processing of sample triplicates results in sufficient repeatability and that the signal fluctuation across the triplicates emerged as a powerful filtering criteria. The number of false positives and false negatives could be significantly reduced by the developed strategies which consequently improve the validity of the data. The second part of this thesis addresses the development of processing strategies particularly aimed at assessing water treatment processes. The detected signals were tracked across the treatment process and classified based on their fold changes. A more reliable signal classification was achieved by implementing a recursive integration approach. Special integration algorithms allow a reliable signal classification even though the signal to be compared was below the intensity threshold. Different combinations of replicates of process influents and effluents were processed for evaluating the repeatability. The good repeatability was indicated by the results of both the plausibility checks and the ozonation process (ozonation of pretreated river water) and thus points to high reliability. The developed procedure enables the assessment of water treatment processes based on the changes in the pattern of all detected signals and offers a more comprehensive picture of the treatment efficiency. Particularly with regard to transformation products, existing knowledge gaps can be reduced by this approach, albeit the entire variety of chemicals cannot be covered completely. The applicability of the developed strategies to real world applications is demonstrated in the last part of this work. Besides the prioritization of the generated results, the main focus was the identification of recognized compounds. The developed strategies clearly improve the validity of the underlying data. The combination of LC-HRMS analysis with reliable processing strategies opens up multiple possibilities for a more comprehensive monitoring of water resources and for the assessment of water treatment processes. The processing strategies and validation concepts may be easily transferred to other research fields.
One of the consequences of the manifold uses of various organic substances in households, agriculture, and industry is that these can eventually end up in the aquatic environment. Each of these anthropogenic substances represents a potential harmful contamination for the different water resources. Therefore, a constant and comprehensive monitoring of organic trace substances in the resources used for drinking water treatment is essential to protect of the quality of drinking water. In addition to the commonly used target screening, the non-target screening with high performance liquid chromatography-mass spectrometry (HPLC-MS) has gained in importance in recent years and became a useful tool for the monitoring of organic trace substances in water This work describes new strategies for the screening and identification of unexpected or unknown organic trace substances in water. The approaches consider all compounds detectable by the used analytical method for further data evaluation. That is the fundamental meaning of non-target screening. However, focusing on relevant contaminants during the screening process is required as large numbers of substances are being detected for each sample. Consequently, a sample is not regarded as an isolated specimen, but rather evaluated in relation to a set of other samples based on considerations of their temporal, spatial, or process-related connections. The efficiency of the different developed strategies were demonstrated successfully for the identification of unknown contaminants in monitoring samples of different water resources as well as for the screening and structural elucidation of ozonation by-products of known contaminants (4- and 5-methyl-1H-benzotriazole) of the Danube water. In addition, to effectively support the identification of compounds, the concept of the DAIOS (Database-Assisted Identification of Organic Substances) was introduced. The new described strategies for the screening and identification of organic trace substances in water opens the possibilities of various other applications in environmental analysis, for example in the environmental remediation of contaminated sites, as well as for the evaluation of the process steps during drinking and waste water treatment.