Abstract
Microscale sealed vessel pyrolysis (MSSVpy) with online gas chromatography mass spectrometry (GC–MS) was used with several other established and complementary analytical methods to robustly characterize the structure of aquatic natural organic matter (NOM) and to practically assess the analytical value of MSSVpy. The NOM used in the study was from North Pine (NP) reservoir, which is one of the major source waters supplying Brisbane, the capital city of the Australian state of Queensland. The reservoir has moderate dissolved organic carbon (DOC; 5 mg L −1) levels and is impacted by algae which periodically occur in bloom proportions. The hydrophobic (HPO; 65% initial DOC) and transphilic (TPI; 12%) fractions showed H/C values >1, low UV abs and low aryl-C measured by NMR which are all indicative of low aromaticity. MSSVpy produced distinctly higher product concentrations than traditional flash pyrolysis and the molecular profile of the HPO and TPI fractions revealed by MSSVpy was correlated with the other analytical data to help establish their structural relevance. Prolific distributions of alkyl substituted aromatic (e.g., benzenes, naphthalenes) and hydroaromatic (e.g., tetralins) products detected in the HPO fraction were attributed to the aromatisation of terpanes and other aliphatic compounds from algal, and possibly also plant sources. Alkyl phenols also detected in HPO in high abundance, are probably from algal biopolymers, but may also reflect a contribution from non-methoxylated lignin units of catchment grasses. There was no analytical evidence of the dihydroxy or methoxy aromatic structures typical of wood lignin or tannin. N-organic pyrolysates (e.g., alkyl pyrroles, pyridines, indoles) of diagenetically altered proteins were concentrated in the TPI fraction. The quantitative importance of the N-organic moiety of the TPI fraction was corroborated by a low C/N ratio and distinctive amide and amine signals detected by 13C NMR and FTIR. This integrated study demonstrates that the qualitative speciation provided by MSSVpy can make a significant contribution to the structural characterisation and source recognition of aquatic NOM.
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