BOOK OF ABSTRACTS 17 IHSS&IWA26 / BRNO / CZECHIA / 23–28 August 2026 Monday, 24 August 2026 / Hall B+C Analysis and Characterization SL2 Visualizing Organic Matter Quality Differences with Ultra-High-Resolution Mass Spectrometry Peter Herzsprung1, Wolf von Tümpling2, Norbert Kamjunke2, Oliver Lechtenfeld3 1 UFZ – Helmholtz Centre for Environmental Research, Dep. Lake Research, Brückstraße 3a, Magdeburg 39114, Germany, peter.herzsprung@ufz.de 2 UFZ – Helmholtz Centre for Environ. Research, Dep. River Ecology, Brückstraße 3a, Magdeburg 39114, Germany 3 UFZ – Helmholtz Centre for Environ. Research, Dep. Environ. Anal. Chem., Permoserstr. 15, Leipzig-04318, Germany The composition of natural organic matter (NOM) remains a conundrum. The highest resolution for elemental compositions of NOM can be achieved by Fourier- transform ion cyclotron resonance mass spectroscopy (FTICR-MS). This tool generates elemental compositions (i.e., molecular formulas, MFs) of thousands of NOM components which can be extracted from aqueous samples (e.g., via solid phase extraction, SPE) and which are ionizable (e.g. via negative mode electrospray ionization). NOM is not an inert mixture of compounds and its composition may be altered by photochemical or microbial reactions, adsorptive fractionation, or mixing of different water sources. In order to comprehend such NOM quality changes, specific data evaluation methods are required. The simplest approach to compare NOM quality between two samples is the presence of components in one sample and the absence in the other sample and display in van Krevelen diagrams (H/C versus O/C) or other descriptor combinations like H/C versus mass [1,2,3]. If several samples have to be compared, this approach is limited because of increasing possibilities of unique and partially shared MF with increasing number of samples [2]. Such partially shared MF often show low abundance (small peak magnitudes near the S/N threshold) [3]. A second, more sophisticated approach is the direct comparison of normalized peak magnitudes formula by formula and sample by sample for the shared formulas (i.e., present in all samples which are compared). A statistically robust method is the peak magnitude ranking and the calculation of inter sample ranking. The visualization of inter sample ranks can be easily realized in van Krevelen diagrams as well [2,3]. In this way, the abundance trends of biogeochemically similar groups of molecules (having similar molecular descriptors and reactivity) can be followed. KEY-components represent MF with the biggest impact on DOM quality difference [4] and can be performed by plotting of normalized peak magnitudes
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