Molecular properties of ultrafiltered dissolved organic matter and dissolved black carbon in headwater streams as determined by pyrolysis-GC-MS

This study aimed to assess the molecular properties of dissolved organic matter (DOM) and dissolved black carbon (DBC) using analytical pyrolysis (Py-GC-MS). The sample set was comprised of ultrafiltered DOM (UDOM) from North American headwater streams associated with Long Term Ecological Research n...

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Bibliographic Details
Published in:Journal of Analytical and Applied Pyrolysis
Main Authors: Kaal, Joeri, Wagner, Sasha, Jaffé, Rudolf
Other Authors: National Science Foundation (US), George Barley Endowment
Format: Article in Journal/Newspaper
Language:unknown
Published: Elsevier 2016
Subjects:
Online Access:http://hdl.handle.net/10261/136740
https://doi.org/10.1016/j.jaap.2016.02.003
https://doi.org/10.13039/100000001
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Summary:This study aimed to assess the molecular properties of dissolved organic matter (DOM) and dissolved black carbon (DBC) using analytical pyrolysis (Py-GC-MS). The sample set was comprised of ultrafiltered DOM (UDOM) from North American headwater streams associated with Long Term Ecological Research network sites. Pyrolysis products for each UDOM sample were categorized as being sourced from non-pyrogenic sources and DBC. Major non-pyrogenic components of the headwater stream UDOM were comprised of phenolic compounds derived from lignin and chitin markers from microbial biomass, and their relative contributions indicated differences in organic matter dynamics of these ecosystems. The DBC pyrolyzates included benzene, PAHs and benzonitriles, which accounted for 12.5 ± 4.5% of total quantified peak area (TPQA), and decreased in the order Alaskan boreal forest (19%), Alaskan tundra (17%), Appalachian deciduous forest (11%), Colorado alpine tundra (9%), Puerto Rican mountainous tropical rainforest (9%) and Kansas tallgrass prairie (7%). Pyrolysis products were compared to DBC content as determined by the benzenepolycarboxylic acid (BPCA) method. Although Py-GC-MS has quantitative limitations, this technique can detect weakly condensed and other DBC structures which fall outside of the BPCA analytical window. This study was in part funded by NSF through the Florida Coastal Everglades long Term Ecological Research program (DEB-1237517). R.J. acknowledges additional funding through the George Barley Endowment. Peer Reviewed