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    In:  Supplement to: Zhuang, Guang-Chao; Lin, Yu-Shih; Elvert, Marcus; Heuer, Verena B; Hinrichs, Kai-Uwe (2014): Gas chromatographic analysis of methanol and ethanol in marine sediment pore waters: Validation and implementation of three pretreatment techniques. Marine Chemistry, 160, 82-90, https://doi.org/10.1016/j.marchem.2014.01.011
    Publication Date: 2023-03-03
    Description: Low-molecular-weight (LMW) alcohols are produced during the microbial degradation of organic matter from precursors such as lignin, pectin, and carbohydrates. The biogeochemical behavior of these alcohols in marine sediment is poorly constrained but potentially central to carbon cycling. Little is known about LMW alcohols in sediment pore waters because of their low concentrations and high water miscibility, both of which pose substantial analytical challenges. In this study, three alternative methods were adapted for the analysis of trace amounts of methanol and ethanol in small volumes of saline pore waters: direct aqueous injection (DAI), solid-phase microextraction (SPME), and purge and trap (P&T) in combination with gas chromatography (GC) coupled to either a flame ionization detector (FID) or a mass spectrometer (MS). Key modifications included the desalination of samples prior to DAI, the use of a threaded midget bubbler to purge small-volume samples under heated conditions and the addition of salt during P&T. All three methods were validated for LMW alcohol analysis, and the lowest detection limit (60 nM and 40 nM for methanol and ethanol, respectively) was achieved with the P&T technique. With these methods, ambient concentrations of volatile alcohols were determined for the first time in marine sediment pore waters of the Black Sea and the Gulf of Mexico. A strong correlation between the two compounds was observed and tentatively interpreted as being controlled by similar sources and sinks at the examined stations.
    Keywords: Center for Marine Environmental Sciences; MARUM
    Type: Dataset
    Format: application/zip, 2 datasets
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