Marine dissolved organic matter (DOM) in surface and deep waters of the eastern Atlantic Ocean and Sargasso Sea was analyzed by excitation emission matrix (EEM) fluorescence spectroscopy and parallel factor analysis (PARAFAC). Photo-degradation with semi-continuous monitoring of EEMs and absorbance spectra was used to measure the photo-degradation kinetics and changes of the PARAFAC components in a depth profile of DOM at the Bermuda Atlantic Time Series (BATS) station in the Sargasso Sea. A five component model was fit to the EEMs, which included traditional terrestrial-like, marine-like, and protein-like components. Terrestrial-like components showed the expected high photo-reactivity, but surprisingly, the traditional marine-like peak showed slight photo-production in surface waters, which may account for its prevalence in marine systems. Surface waters were depleted in photo-labile components while protein-like fluorescent components were enriched, consistent with previous studies. Ultra-high resolution mass spectrometry detected unique aliphatic compounds in the surface waters at the BATS site, which may be photo-produced or photo-stable. Principle component and canonical analysis showed strong correlations between relative contributions of unsaturated/aromatic molecular formulas and depth, with aliphatic compounds more prevalent in surface waters and aromatic compounds in deep waters. Strong correlations were seen between these aromatic compounds and humic-like fluorescent components. The rapid photo-degradation of the deep-sea fluorescent DOM in addition to the surface water relative depletion of aromatic compounds suggests that deep-sea fluorescent DOM may be too photochemically labile to survive during overturning circulation.
Investigating the biogeochemistry of dissolved organic matter (DOM) requires the synthesis of data from several complementary analytical techniques. The traditional approach to data synthesis is to search for correlations between measurements made on the same sample using different instruments. In contrast, data fusion simultaneously decomposes data from multiple instruments into the underlying shared and unshared components. Here, Advanced Coupled Matrix and Tensor Factorization (ACMTF) was used to identify the molecular fingerprint of DOM fluorescence fractions in Arctic fjords. ACMTF explained 99.84% of the variability with six fully shared components. Individual molecular formulas were linked to multiple fluorescence components and vice versa. Molecular fingerprints differed in diversity and oceanographic patterns, suggesting a link to the biogeochemical sources and diagenetic state of DOM. The fingerprints obtained through ACMTF were more specific compared to traditional correlation analysis and yielded greater compositional insight. Multivariate data fusion aligns extremely complex, heterogeneous DOM data sets and thus facilitates a more holistic understanding of DOM biogeochemistry.
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