Vascular plant and microbial biomarkers of dissolved organic matter data from incubation experiments

Website: https://www.bco-dmo.org/dataset/754885
Data Type: Cruise Results
Version: 1
Version Date: 2019-04-05

Project
» Collaborative Research: Calibration and application of vascular plant and aqueous microbial biomarkers to examine transformations of dissolved organic matter (DOM biomarkers)
ContributorsAffiliationRole
Hernes, PeterUniversity of California-Davis (UC Davis)Principal Investigator
Kaiser, KarlTexas A&M University (TAMU)Co-Principal Investigator
Spencer, RobertFlorida State University (FSU)Contact
Rauch, ShannonWoods Hole Oceanographic Institution (WHOI BCO-DMO)BCO-DMO Data Manager

Abstract
Incubation experiments were conducted in the dark or using a dark/light cycle. Incubations conducted in the dark alone are classified as "microbial, and incubations using a dark/light cycle are classified as "coupled".


Dataset Description

Incubation experiments were conducted in the dark or using a dark/light cycle. Incubations conducted in the dark alone are classified as "microbial, and incubations using a dark/light cycle are classified as "coupled".


Methods & Sampling

Samples were collect on the USGS R/V Mary Landsteiner and pumped directly from the surface (1 m deep) with a pump and clean tycoon tubing connected to an inline 0.2 um Whatman Polycap filter.

Incubation experiments were conducted in the dark or using a dark/light cycle. Incubations conducted in the dark alone are classified as "microbial, and incubations using a dark/light cycle are classified as "coupled".

All filters were pumped and field filtered through 0.7 um Whatman glass fiber filters (GF/F, precombusted at 550 degrees C) using a peristaltic pump after purging the line.

Samples for DOC concentration were acidified to pH 2 and stored in a refrigerator (4 degrees C) until analysis by high-temperature combustion on a Shimadzu TOC-L CPH within two weeks following collection. DOC was calculated as the mean of between three and five injections using a six-point standard curve using established protocols (Mann et al., 2012) and the coefficient of variance was always <2%.

Samples for CDOM absorbance were analyzed in a 1 cm cuvette on a Horiba Aqualog-UV-800-C. Absorbance spectra were measured from 230-800 nm, and corrected for a small offset either due to long-term baseline drift or derived from glass fiber particles during filtration (Blough et al., 1993), by subtracting the mean absorbance measured between 750-800 nm. Two spectral slopes were calculated at 275-295 nm and 350-400 nm (S275-295 and S350-400, respectively), and the spectral slope ratio (SR) was then calculated by dividing the former by the latter (Helms et al., 2008). The CDOM absorption ratio at 250 nm to 365 nm was calculated (a250:a365) and specific ultraviolet absorbance (SUVA254) was calculated by dividing the decadic absorption coefficient at 254 nm by DOC concentration (Weishaar et al., 2003; Fellman et al., 2009).

Fluorescence properties of FDOM were determined using a Horiba Aqualog-UV-800-C. The excitation emission matrices (EEMs) were generated in a 1 cm cuvette at varying integration times (1-10 seconds) to maximize the signal-to-noise ratio based on absorbance values. The EEMs were obtained at excitation (ex) 250-600 nm and at emission (em) 250-600 nm with 5 nm and 2 nm intervals respectively, and the EEMs were corrected for lamp intensity (Cory et al., 2010), inner filter effects (Kothawala et al., 2013), and normalized to Raman units (R.U.) (Stedmon et al., 2003). All corrections were performed using the FDOMcorr toolbox version 1.6 (Murphy, 2011). EEMs were analyzed with parallel factor analysis (PARAFAC) using the procedure described in Murphy et al. (2013). Furthermore, the fluorescence index (FI) (Cory et al., 2010), humification index (HIX) (Ohno, 2002; Zsolnay et al., 1999), and autotrophic productivity index (BIX) (Huguet et al., 2009) were calculated. FI was calculated from the emission wavelengths at 470 nm and 520 nm, obtained at excitation 370 nm (Cory and McKnight, 2005). HIX was calculated using the area under the emission sepctra 435-480 nm divided by the peak area 300-345 + 435-480 nm, at excitation 254 nm (Ohno, 2002). BIX was calculated from the emission intensity of 380 nm and 430 nm, obtained at excitation 310 nm (Wang et al., 2014).

Samples for FT-ICR MS analysis were solid-phase extracted using the procedure described in Dittmar et al., 2008. Filtered samples were acidified to pH 2 before solid phase extraction on 500 mg Agilent Bond Elut PPL cartridges. Each 1 L sample was extracted by eluting 2 mL of of methanol and then diluted to a DOC target concentration of 50 ug C mL-1. Extracted samples were stored at -20 degrees C prior to analysis on a 21 T (Bruker Daltonics, Billerica, MA, USA) FT-ICR MS located at the National High Magnetic Field Laboratory (NHMFL) (Tallahassee, Florida). Direct infusion electrospray ionization (ESI) generates negative ions at a flow rate of 700 nL min-1, and 100 time domain acquisitions were coadded for each mass spectrum.

Molecular formulas were assigned to signals >6RMS baseline noise with EnviroOrg ©,TM software (Koch et al., 2007; Stubbins et al., 2010). Elemental combinations of C1–45H1–92N0–4O1–25S0–2 with a mass accuracy of ≤300 ppb were considered for assignment. Classification of formulas were based on their elemental ratios (Corilo, 2015). The modified aromaticity index (Almod) of each formula was calculated and Almod values of 0.5-0.67 and ≥0.67 were classified as aromatic and condensed aromatic structures (Koch and Dittmar, 2006; Koch and Dittmar, 2016). Other compound classes were unsaturated low oxygen=Almod<0.5, H/C<1.5, O/C<0.5; unsaturated high oxygen=Almod<0.5, H/ C<1.5, O/C>0.5; aliphatics=H/C 1.5-2.0, O/C<0.9, N=0; peptide-like=H/C 1.5-2.0, O/C<0.9, N>0, and sugar-like= O/C>0.9. Sugar-like compounds provide a very minor contribution to %RA (mean = 0.05, ± 0.06 %RA) and so were combined with peptide-like compounds throughout. Although FT-ICR MS allows for the precise assignment of molecular formulas to signals that may represent multiple isomers, they describe the underlying molecular compounds comprising DOM, thus the term compound may be used when describing the signals detected by FT-ICR MS.

Lignin derived phenols were isolated from the dried solid phase extracts followed by cupric oxide oxidation and liquid-liquid extraction modified from Spencer et al., (2010). Briefly, PPL extracts were redissolved in O2 free 2 M NaOH in a 6 mL Teflon vial (Savillex Corp) containing 500 mg CuO, and amended with 100 mg ferrous ammonium sulfate and 50 mg glucose and reacted in a 155 degree C oven for 3 hours. Following oxidation, the samples were centrifuged and supernatants were decanted into 40 mL vials. Oxidation products were acidified to pH 1 with H3PO4 and t-cinnamic acid was added as an internal standard. Liquid-liquid extractions of the oxidation products were undertaken by addition of 4 mL ethyl acetate, vortexing, and centrifugation prior to removal of the ethyl acetate. Extracts were pipetted through drying columns containing sodium sulfate into a 4 mL vial. Samples were dried under ultra-high purity argon between each extraction for a total of three extractions, following the last extraction the sodium sulfate was rinsed with 1 mL of ethyl acetate into the extract vial. Dried ethyl acetate extracts were dissolved in pyridine and derivatized with N/O bis-trimethylsilyltrifluoromethylacetamide (BSTFA) at 60 degrees C for ten minutes. Lignin phenol monomers were measured as trimethylsilane derivatives using an Agilent 6890N GC/5975 MS and were quantified as the relative response factors of each compound compared to the response of t- cinnamic acid and a five-point calibration curve bracketing the concentration range. Eight lignin phenols from three phenol groups were quantified; vanillyl (vanillin, acetovanillone, vanillic acid), syringyl (syringaldehyde, acetosyringone, syringic acid), and coumaryl (coumaric acid, ferulic acid).

Seven neutral sugars (fucose, rhamnose, arabinose, galactose, glucose, mannose, xylose) were analyzed according to Skoog and Benner (1997) with modifications. Briefly, samples were hydrolyzed in 1.2 mol L−1 sulfuric acid and neutralized with a self-absorbed ion retardation resin (Kaiser and Benner, 2000). After desalting with a mixture of cation and anion exchange resins, neutral sugars were isocratically separated with 25 mM NaOH on a PA 1 column in a Dionex 500 system with a pulsed amperiometric detector (PAD).

The following amino acids were analyzed using the method of Kaiser and Benner, 2005: histidine, serine, arginine, glycine, aspartic acid, glutamic acid, threonine, alanine, lysine, tyrosine, methionine, valine, norvaline, isoleucine, leucine, phenylalanine.


Data Processing Description

BCO-DMO Processing:
- modified parameter names to conform with BCO-DMO naming conventions (removed units, replaced spaces with underscores);
- replaced "NaN" with "nd" (no data).


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Data Files

File
incubation_data.csv
(Comma Separated Values (.csv), 39.00 KB)
MD5:6375517a8d1e70676b6cf0d20683a73c
Primary data file for dataset ID 754885

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Related Publications

Blough, N. V., Zafiriou, O. C., & Bonilla, J. (1993). Optical absorption spectra of waters from the Orinoco River outflow: Terrestrial input of colored organic matter to the Caribbean. Journal of Geophysical Research: Oceans, 98(C2), 2271–2278. doi:10.1029/92jc02763 https://doi.org/10.1029/92JC02763
Methods
Corilo, Y. (2015) PetroOrg Software; Florida State University: Tallahassee, FL, 2014.
Methods
Cory, R. M., & McKnight, D. M. (2005). Fluorescence Spectroscopy Reveals Ubiquitous Presence of Oxidized and Reduced Quinones in Dissolved Organic Matter. Environmental Science & Technology, 39(21), 8142–8149. doi:10.1021/es0506962
Methods
Cory, R. M., Miller, M. P., McKnight, D. M., Guerard, J. J., & Miller, P. L. (2010). Effect of instrument-specific response on the analysis of fulvic acid fluorescence spectra. Limnology and Oceanography: Methods, 8(2), 67–78. doi:10.4319/lom.2010.8.67
Methods
Dittmar, T., Koch, B., Hertkorn, N., & Kattner, G. (2008). A simple and efficient method for the solid-phase extraction of dissolved organic matter (SPE-DOM) from seawater. Limnology and Oceanography: Methods, 6(6), 230–235. doi:10.4319/lom.2008.6.230
Methods
Fellman, J. B., Hood, E., D’Amore, D. V., Edwards, R. T., & White, D. (2009). Seasonal changes in the chemical quality and biodegradability of dissolved organic matter exported from soils to streams in coastal temperate rainforest watersheds. Biogeochemistry, 95(2-3), 277–293. doi:10.1007/s10533-009-9336-6
Methods
Helms, J. R., Stubbins, A., Ritchie, J. D., Minor, E. C., Kieber, D. J., & Mopper, K. (2008). Absorption spectral slopes and slope ratios as indicators of molecular weight, source, and photobleaching of chromophoric dissolved organic matter. Limnology and Oceanography, 53(3), 955–969. doi:10.4319/lo.2008.53.3.0955
Methods
Huguet, A., Vacher, L., Relexans, S., Saubusse, S., Froidefond, J. M., & Parlanti, E. (2009). Properties of fluorescent dissolved organic matter in the Gironde Estuary. Organic Geochemistry, 40(6), 706–719. doi:10.1016/j.orggeochem.2009.03.002
Methods
Kaiser, K., & Benner, R. (2005). Hydrolysis-induced racemization of amino acids. Limnology and Oceanography: Methods, 3(8), 318–325. doi:10.4319/lom.2005.3.318
Methods
Koch, B. P., & Dittmar, T. (2006). From mass to structure: an aromaticity index for high-resolution mass data of natural organic matter. Rapid Communications in Mass Spectrometry, 20(5), 926–932. doi:10.1002/rcm.2386
Methods
Koch, B. P., & Dittmar, T. (2015). From mass to structure: an aromaticity index for high-resolution mass data of natural organic matter. Rapid Communications in Mass Spectrometry, 30(1), 250–250. doi:10.1002/rcm.7433
Methods
Koch, B. P., Dittmar, T., Witt, M., & Kattner, G. (2007). Fundamentals of Molecular Formula Assignment to Ultrahigh Resolution Mass Data of Natural Organic Matter. Analytical Chemistry, 79(4), 1758–1763. doi:10.1021/ac061949s
Methods
Kothawala, D. N., Murphy, K. R., Stedmon, C. A., Weyhenmeyer, G. A., & Tranvik, L. J. (2013). Inner filter correction of dissolved organic matter fluorescence. Limnology and Oceanography: Methods, 11(12), 616–630. doi:10.4319/lom.2013.11.616
Methods
Mann, P. J., Davydova, A., Zimov, N., Spencer, R. G. M., Davydov, S., Bulygina, E., … Holmes, R. M. (2012). Controls on the composition and lability of dissolved organic matter in Siberia’s Kolyma River basin. Journal of Geophysical Research: Biogeosciences, 117(G1). doi:10.1029/2011jg001798 https://doi.org/10.1029/2011JG001798
Methods
Murphy, K. R. (2011). A Note on Determining the Extent of the Water Raman Peak in Fluorescence Spectroscopy. Applied Spectroscopy, 65(2), 233–236. doi:10.1366/10-06136
Methods
Ohno, T. (2002). Fluorescence Inner-Filtering Correction for Determining the Humification Index of Dissolved Organic Matter. Environmental Science & Technology, 36(4), 742–746. doi:10.1021/es0155276
Methods
Skoog, A., & Benner, R. (1997). Aldoses in various size fractions of marine organic matter: Implications for carbon cycling. Limnology and Oceanography, 42(8), 1803–1813. doi:10.4319/lo.1997.42.8.1803
Methods
Spencer, R. G. M., Aiken, G. R., Dyda, R. Y., Butler, K. D., Bergamaschi, B. A., & Hernes, P. J. (2010). Comparison of XAD with other dissolved lignin isolation techniques and a compilation of analytical improvements for the analysis of lignin in aquatic settings. Organic Geochemistry, 41(5), 445–453. doi:10.1016/j.orggeochem.2010.02.004
Methods
Stedmon, C. A., Markager, S., & Bro, R. (2003). Tracing dissolved organic matter in aquatic environments using a new approach to fluorescence spectroscopy. Marine Chemistry, 82(3-4), 239–254. doi:10.1016/s0304-4203(03)00072-0 https://doi.org/10.1016/S0304-4203(03)00072-0
Methods
Stubbins, A., Spencer, R. G. M., Chen, H., Hatcher, P. G., Mopper, K., Hernes, P. J., … Six, J. (2010). Illuminated darkness: Molecular signatures of Congo River dissolved organic matter and its photochemical alteration as revealed by ultrahigh precision mass spectrometry. Limnology and Oceanography, 55(4), 1467–1477. doi:10.4319/lo.2010.55.4.1467
Methods
Wang, Y., Zhang, D., Shen, Z., Chen, J., & Feng, C. (2014). Characterization and spacial distribution variability of chromophoric dissolved organic matter (CDOM) in the Yangtze Estuary. Chemosphere, 95, 353–362. doi:10.1016/j.chemosphere.2013.09.044
Methods
Weishaar, J. L., Aiken, G. R., Bergamaschi, B. A., Fram, M. S., Fujii, R., & Mopper, K. (2003). Evaluation of Specific Ultraviolet Absorbance as an Indicator of the Chemical Composition and Reactivity of Dissolved Organic Carbon. Environmental Science & Technology, 37(20), 4702–4708. doi:10.1021/es030360x
Methods
Zsolnay, A., Baigar, E., Jimenez, M., Steinweg, B., & Saccomandi, F. (1999). Differentiating with fluorescence spectroscopy the sources of dissolved organic matter in soils subjected to drying. Chemosphere, 38(1), 45–50. doi:10.1016/s0045-6535(98)00166-0 https://doi.org/10.1016/S0045-6535(98)00166-0
Methods

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Parameters

ParameterDescriptionUnits
incubation_type

Incubations conducted in the dark alone are classified as "microbial, and incubations using a dark/light cycle are classified as "coupled".

unitless
site_type

Sample type?

unitless
time_point

Time point

unitless
days

Time elapsed (days)

days
hours

Time elapsed (hours)

hours
Exposure

Exposure time

hours
Dose

Dose

MJ/m2
Irradiance

Irradiance

W/m2
DOC_mg_L

Dissolved organic carbon in milligrams per liter

mg/L
DOC_mM

Dissolved organic carbon in millimolar

mM
Fuc

Fucose

nanomoles per liter (nmol/L)
Rha

Rhamnose

nanomoles per liter (nmol/L)
Ara

Arabinose

nanomoles per liter (nmol/L)
Gal

Galactose

nanomoles per liter (nmol/L)
Glu

Glucose

nanomoles per liter (nmol/L)
Man

Mannose

nanomoles per liter (nmol/L)
Xyl

Xylose

nanomoles per liter (nmol/L)
D_Asx

D-Aspartate or D-Asparagine

nanomoles per liter (nmol/L)
L_Asx

L-Aspartate or L-Asparagine

nanomoles per liter (nmol/L)
D_Glx

D-Glutamate or D-Glutamine

nanomoles per liter (nmol/L)
L_Glx

L-Glutamate or L-Glutamine

nanomoles per liter (nmol/L)
D_Ser

D-Serine

nanomoles per liter (nmol/L)
L_Ser

L-Serine

nanomoles per liter (nmol/L)
D_His

D-Histidine

nanomoles per liter (nmol/L)
L_His

L-Histidine

nanomoles per liter (nmol/L)
D_Thr

D-Threonine

nanomoles per liter (nmol/L)
L_Thr

L-Threonine

nanomoles per liter (nmol/L)
Gly

Glycine

nanomoles per liter (nmol/L)
D_Arg

D-Arginine

nanomoles per liter (nmol/L)
L_Arg

L-Arginine

nanomoles per liter (nmol/L)
D_Ala

D-Alanine

nanomoles per liter (nmol/L)
L_Ala

L-Alanine

nanomoles per liter (nmol/L)
D_Tyr

D-Tyrosine

nanomoles per liter (nmol/L)
L_Tyr

L-Tyrosine

nanomoles per liter (nmol/L)
D_Val

D-Valine

nanomoles per liter (nmol/L)
L_Val

L-Valine

nanomoles per liter (nmol/L)
D_Met

D-Methionine

nanomoles per liter (nmol/L)
L_Met

L-Methionine

nanomoles per liter (nmol/L)
D_Ileu

D-Isoleucine

nanomoles per liter (nmol/L)
L_Ileu

L-Isoleucine

nanomoles per liter (nmol/L)
D_Phe

D-Phenylalanine

nanomoles per liter (nmol/L)
L_Phe

L-Phenylalanine

nanomoles per liter (nmol/L)
D_Leu

D-Leucine

nanomoles per liter (nmol/L)
L_Leu

L-Leucine

nanomoles per liter (nmol/L)
D_Lys

D-Lysine

nanomoles per liter (nmol/L)
L_Lys

L-Lysine

nanomoles per liter (nmol/L)
FI

Fluorescence Index (DOM composition metric)

unitless
HIX

Humification Index (DOM composition metric)

unitless
HIX_Norm

Humification Index Norm (DOM composition metric)

unitless
BIX

Autotrophic productivity index (DOM composition metric)

unitless
abs_250

CDOM absorbance at 250 nm

reciprocal meters (m-1)
abs_254

CDOM absorbance at 254 nm

reciprocal meters (m-1)
abs_350

CDOM absorbance at 350 nm

reciprocal meters (m-1)
abs_365

CDOM absorbance at 365 nm

reciprocal meters (m-1)
abs_412

CDOM absorbance at 412 nm

reciprocal meters (m-1)
abs_440

CDOM absorbance at 440 nm

reciprocal meters (m-1)
abs_ratio_250_365

Absorbance ratio; absorbance at 250/365

reciprocal meters (m-1)
S275_295

Spectral slope range 275-295

unitless
r2_of_fit

r2 of fit of S275_295

unitless
S350_400

Spectral slope range 350-400

unitless
r2_of_fit2

r2 of fit of S350_400

unitless
Sr

Spectral slope ratio (275-295/350-400)

unitless
C1

Fluorescence intensity of component 1

Raman units
C2

Fluorescence intensity of component 2

Raman units
C3

Fluorescence intensity of component 3

Raman units
C4

Fluorescence intensity of component 4

Raman units
C5

Fluorescence intensity of component 5

Raman units
C6

Fluorescence intensity of component 6

Raman units
C7

Fluorescence intensity of component 7

Raman units
C8

Fluorescence intensity of component 8

Raman units
C9

Fluorescence intensity of component 9

Raman units
C10

Fluorescence intensity of component 10

Raman units
Ctotal

Fluorescence intensity total

Raman units
C1_pcnt

Fluorescence intensity of component 1 as a percent

unitless (percent)
C2_pcnt

Fluorescence intensity of component 2 as a percent

unitless (percent)
C3_pcnt

Fluorescence intensity of component 1 as a percent

unitless (percent)
C4_pcnt

Fluorescence intensity of component 2 as a percent

unitless (percent)
C5_pcnt

Fluorescence intensity of component 1 as a percent

unitless (percent)
C6_pcnt

Fluorescence intensity of component 2 as a percent

unitless (percent)
C7_pcnt

Fluorescence intensity of component 1 as a percent

unitless (percent)
C8_pcnt

Fluorescence intensity of component 2 as a percent

unitless (percent)
C9_pcnt

Fluorescence intensity of component 1 as a percent

unitless (percent)
C10_pcnt

Fluorescence intensity of component 2 as a percent

unitless (percent)
PAL

p-hydroxybenzaldehyde

nanograms per liter (ng/L)
PON

p-hydroxyacetophenone

nanograms per liter (ng/L)
VAL

vanillin

nanograms per liter (ng/L)
VON

acetovanillone

nanograms per liter (ng/L)
PAD

p-hydroxybenzoic acid

nanograms per liter (ng/L)
SAL

syringaldehyde

nanograms per liter (ng/L)
VAD

vanillic acid

nanograms per liter (ng/L)
SON

acetosyringone

nanograms per liter (ng/L)
SAD

syringic acid

nanograms per liter (ng/L)
CAD

p-coumaric acid

nanograms per liter (ng/L)
FAD

ferulic acid

nanograms per liter (ng/L)


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Instruments

Dataset-specific Instrument Name
Horiba Aqualog-UV-800-C
Generic Instrument Name
Fluorometer
Dataset-specific Description
Samples for CDOM absorbance were analyzed in a 1 cm cuvette on a Horiba Aqualog-UV-800-C (benchtop fluorometer). Fluorescence properties of FDOM were also determined using a Horiba Aqualog-UV-800-C. 
Generic Instrument Description
A fluorometer or fluorimeter is a device used to measure parameters of fluorescence: its intensity and wavelength distribution of emission spectrum after excitation by a certain spectrum of light. The instrument is designed to measure the amount of stimulated electromagnetic radiation produced by pulses of electromagnetic radiation emitted into a water sample or in situ.

Dataset-specific Instrument Name
FT-ICR MS
Generic Instrument Name
Fourier Transform Ion Cyclotron Resonance Mass Spectrometer
Dataset-specific Description
Samples were analyzed on a 21 T (Bruker Daltonics, Billerica, MA, USA) FT-ICR MS located at the National High Magnetic Field Laboratory (NHMFL) (Tallahassee, Florida).
Generic Instrument Description
In Fourier Transform Ion Cyclotron Resonance Mass Spectrometry, the mass-to-charge ratio (m/z) of an ion is experimentally determined by measuring the frequency at which the ion processes in a magnetic field. These frequencies, which are typically in the 100 KHz to MHz regime, can be measured with modern electronics making it possible to determine the mass of an ion to within +/- 0.000005 amu or 5 ppm.

Dataset-specific Instrument Name
Agilent 6890N GC/5975 MS
Generic Instrument Name
Gas Chromatograph
Dataset-specific Description
Lignin phenol monomers were measured as trimethylsilane derivatives using an Agilent 6890N GC/5975 MS.
Generic Instrument Description
Instrument separating gases, volatile substances, or substances dissolved in a volatile solvent by transporting an inert gas through a column packed with a sorbent to a detector for assay. (from SeaDataNet, BODC)

Dataset-specific Instrument Name
Dionex 500 system
Generic Instrument Name
Ion Chromatograph
Dataset-specific Description
Neutral sugars were isocratically separated in a Dionex 500 system with a pulsed amperiometric detector (PAD).
Generic Instrument Description
Ion chromatography is a form of liquid chromatography that measures concentrations of ionic species by separating them based on their interaction with a resin. Ionic species separate differently depending on species type and size. Ion chromatographs are able to measure concentrations of major anions, such as fluoride, chloride, nitrate, nitrite, and sulfate, as well as major cations such as lithium, sodium, ammonium, potassium, calcium, and magnesium in the parts-per-billion (ppb) range. (from http://serc.carleton.edu/microbelife/research_methods/biogeochemical/ic....)

Dataset-specific Instrument Name
Agilent 6890N GC/5975 MS
Generic Instrument Name
Mass Spectrometer
Dataset-specific Description
Lignin phenol monomers were measured as trimethylsilane derivatives using an Agilent 6890N GC/5975 MS.
Generic Instrument Description
General term for instruments used to measure the mass-to-charge ratio of ions; generally used to find the composition of a sample by generating a mass spectrum representing the masses of sample components.

Dataset-specific Instrument Name
Shimadzu TOC-L CPH
Generic Instrument Name
Shimadzu TOC-L Analyzer
Dataset-specific Description
DOC concentration was determined on a Shimadzu TOC-L CPH.
Generic Instrument Description
A Shimadzu TOC-L Analyzer measures DOC by high temperature combustion method. Developed by Shimadzu, the 680 degree C combustion catalytic oxidation method is now used worldwide. One of its most important features is the capacity to efficiently oxidize hard-to-decompose organic compounds, including insoluble and macromolecular organic compounds. The 680 degree C combustion catalytic oxidation method has been adopted for the TOC-L series. http://www.shimadzu.com/an/toc/lab/toc-l2.html


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Project Information

Collaborative Research: Calibration and application of vascular plant and aqueous microbial biomarkers to examine transformations of dissolved organic matter (DOM biomarkers)

Coverage: San Francisco Bay Delta


NSF abstract:
Organic matter (OM) fluxes between and within terrestrial and oceanic reservoirs play an important role in the global carbon cycle. A clearer understanding of OM dynamics is critical for understanding fundamental processes and effects on greenhouse gases and climate. At present, researchers have an abundance of analytical methods and tools for investigating dissolved organic matter (DOM) cycling, but the field struggles to move past a qualitative understanding of sources, processing, and fates toward a quantitative understanding. Researchers from University of California-Davis, Woods Hole Oceanographic Institute, and Texas A&M University will develop biomarker tools to advance quantitative understanding of DOM cycling in riverine and estuarine environments in California, specifically targeting vascular plant and microbial markers. Results from this study will allow for future biomarker studies to quantitatively address DOM source and processing in aquatic environments and improve the limited understanding of the fate of terrestrial DOM in the ocean.

Broader Impacts: This study will provide interdisciplinary scientific training and development for undergraduate and graduate students, including individuals from underrepresented groups. Results from the study will be disseminated to the public, California stakeholders, and college students to educate them about the carbon cycle.



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Funding

Funding SourceAward
NSF Division of Ocean Sciences (NSF OCE)
NSF Division of Ocean Sciences (NSF OCE)
NSF Division of Ocean Sciences (NSF OCE)

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