| Contributors | Affiliation | Role |
|---|---|---|
| Galy, Valier | Woods Hole Oceanographic Institution (WHOI) | Co-Principal Investigator |
| Rosenheim, Brad | University of South Florida (USF) | Co-Principal Investigator |
| Colombo, Manuel | Virginia Institute of Marine Science (VIMS) | Scientist |
| Dunlea, Ann | Woods Hole Oceanographic Institution (WHOI) | Scientist |
| Eglinton, Timothy I. | ETH-Zurich (ETH) | Scientist |
| Haghipour, Negar | ETH-Zurich (ETH) | Scientist |
| Hein, Christopher | Virginia Institute of Marine Science (VIMS) | Scientist |
| Moura, José Mauro | Federal University of Western Pará (UFOPA) | Scientist |
| Rauch, Shannon | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
Multicores from 14 different stations along the Guianas mudbanks coast (NE S. America, Brazil, French Guiana, and Suriname) were extruded and frozen aboard the R/V F.G. Walton Smith in the summer of 2023. Samples were subsequently portioned, with some portions dried (freeze-drying and oven-drying), acid-treated, and/or ground. Samples were analyzed in duplicate (acid-treated and not acid-treated) on an elemental analyzer connected to an isotope ratio mass spectrometer (IRMS) for nitrogen quantity and stable nitrogen isotope values (not acid-treated) and for carbon quantity, stable carbon isotope values, and carbon:nitrogen ratios (acid-treated).
A subset (n=35) of acid-treated samples was analyzed for bulk radiocarbon content. A subset (n=25) of non-acid-treated, unground samples was run for surface area, and all non-acid-treated and unground samples were measured for inorganic elemental content and grain size distribution. Sample names in this dataset include the code for the seagoing expedition (WS23-139), the multicore number, and the extrusion depth (in centimeters) from the multicore. Columns are provided with station numbers, latitude, longitude, and station name. The 14 stations aligned in sets of 3 cross-shelf transects (inner, middle, and outer) at 6 different locations along this coast.
Sediment samples were extruded on board the ship after cores were recovered. Extruded intervals were measured using a rotating core table on a calibrated threaded rod (Schwing et al., 2016). Extruded samples were placed in pre-combusted glass jars with pre-combusted foil covers and threaded lids, and they were frozen on board the ship. Upon return to the laboratories at the University of South Florida, samples were thawed and separated into three aliquots: acid treatment, no acid treatment, and archive.
Archive aliquots were refrozen.
Acid treatment consisted of rinsing with 1N HCl in pre-combusted glass beakers and rinsing with distilled water until pH testing strips rose to a stable pH when contacted with siphoned rinse water from the sediments. The sediments were dried at 50 degrees Celsius and ground. This aliquot was used for carbon isotopes, %TOC, C:N ratios, and radiocarbon measurements.
The non-acid-treated samples were divided for grain size analysis, surface area analysis, and inorganic geochemistry (undried, unground) and for nitrogen isotope, %C, and %N analyses (dried and ground). The %CaCO3 of the samples was calculated using the difference between %C and %TOC measurements, the latter of which had carbonate minerals removed by acid treatment, and the %TOC values were corrected for the missing mass of CaCO3 by multiplying the measured values by the term (1-%CaCO3). Both corrected and measured %TOC values are provided.
Stable isotope data were transferred to the international stable isotope scales of PDB (carbon isotopes) and AIR (nitrogen isotopes) by measuring laboratory reference materials that were calibrated to those scales. Radiocarbon data were transferred to fraction modern values using standards offered by the International Atomic Energy Association. Nitrogen and carbon composition of the organic matter was calculated from...
Calculation of the amount of CaCO3 and correction of %TOC are explained under the methods section. Data from different sources were merged using the pandas package (v. 2.2.2) of Python (v. 3.14.0) and output as a single .csv file.
This section documents curation actions performed prior to publication review with the submitter, and additional information relevant to understanding and reusing this dataset. It distinguishes changes made to the submitted (meta)data from unresolved issues and/or enhancements that improve future reuse and interoperability.
CURATION ACTIONS PERFORMED ON DATA
- Imported original file "MUDBENCS_MC_data_repository.csv" into the BCO-DMO data processing system.
- Deleted the unnamed column containing row numbers.
- Renamed columns to comply with BCO-DMO naming conventions.
- Replaced a non-standard character in the station_name value "Uaça Outer" with "Uaca Outer".
- Converted format and renamed column "on_station_time" to "on_station_time_local" (no timezone conversion, format change of %Y-%m-%d %H:%M:%S to %Y-%m-%dT%H:%M:%S).
- Converted "off_station_time" to local datetime "off_station_time_local" similarly.
- Converted "on_station_time" with input timezone of UTC-3 to UTC and named the new column "on_station_time_UTC".
- Converted "off_station_time" with input timezone of UTC-3 to UTC and named the new column "off_station_time_UTC".
- Deleted original on_station_time and off_station_time columns after creating local/UTC variants.
- Saved the final file as "1009552_v1_guiana_mudbanks_multicore.csv".
CURATION ACTIONS PERFORMED ON METADATA
- BCO-DMO's standard metadata entry and text formatting steps were performed. See: https://www.bco-dmo.org/how-to/standard-curation-edits
ISSUES POTENTIALLY IMPACTING REUSE
- The "Data Processing" section is incomplete.
- C to N mass ratio appears twice in the dataset as columns "C_to_N_mass_ratio" and "C_to_N_mass_ratio2". It is not known if or how these differ.
| Parameter | Description | Units |
| sample_ID | sample identification including ship abbreviation, year of expedition, sequential number of expedition, multicore number, depth range of sample in centimeters, and suffix for duplicate analyses | unitless |
| mass_mg | mass of sample analyzed for organic geochemistry (stable carbon isotopes, carbon and nitrogen content, derived values) | milligrams |
| d15N | stable nitrogen isotope value from non-acidified sample normalized to AIR standard scale | permille |
| d13C | stable carbon isotope value from acidified sample normalized to PDB standard scale | permille |
| pct_N | weight percent of nitrogen in acidified sample | percent |
| pct_C | weight percent of carbon in acidified sample | percent |
| micromols_N | number of micromoles of nitrogen measured in mass of acidified sample | micromoles |
| micromols_C | number of micromoles of carbon measured in mass of acidified sample | micromoles |
| Fm | fraction modern radiocarbon value | proportion activity ratio of the measured sample relative to activity ratio of 1950 wood |
| Si_wt_pct | weight percent of silicon | percent |
| Al_wt_pct | weight percent of aluminum | percent |
| Ti_wt_pct | weight percent of titanium | percent |
| Fe_wt_pct | weight percent of iron | percent |
| Mn_wt_pct | weight percent of manganese | percent |
| Ca_wt_pct | weight percent of calcium | percent |
| Mg_wt_pct | weight percent of magnesium | percent |
| Na_wt_pct | weight percent of sodium | percent |
| K_wt_pct | weight percent of potassium | percent |
| Ba_microg_per_g | barium content, micrograms per gram | micrograms Ba per gram sediment |
| Cr_microg_per_g | chromium content, micrograms per gram | micrograms Cr per gram sediment |
| Sc_microg_per_g | scandium content, micrograms per gram | micrograms Sc per gram sediment |
| Sr_microg_per_g | strontium content, micrograms per gram | micrograms Sr per gram sediment |
| V_microg_per_g | vanadium content, micrograms per gram | micrograms V per gram sediment |
| Y_microg_per_g | yttrium content, micrograms per gram | micrograms Y per gram sediment |
| Zn_microg_per_g | zinc content, micrograms per gram | micrograms Zn per gram sediment |
| Zr_microg_per_g | zirconium content, micrograms per gram | micrograms Zr per gram sediment |
| pct_clay | percent clay | percent |
| pct_silt | percent silt | percent |
| pct_vfn | percent very fine | percent |
| pct_fine | percent fine | percent |
| pct_med | percent medium | percent |
| pct_crs | percent coarse | percent |
| pct_vcrs | percent very coarse | percent |
| d10 | grain diameter at the 10th percentile | micrometers |
| d50 | grain diameter at the 50th percentile | micrometers |
| d90 | grain diameter at the 90th percentile | micrometers |
| mean | mean grain diameter | micrometers |
| sorting | statistic for variability in grain size | dimensionless |
| skewness | moment coefficient of skewness of grain size distribution | dimensionless |
| kurtosis | moment coefficient of kurtosis of grain size distribution | dimensionless |
| mass_mg_N | mass of unacidified sample analyzed for organic geochemistry (stable nitrogen isotopes, derived values) | milligrams |
| d15N_N | stable nitrogen isotope value from unacidified samples relative to AIR standard scale | permille |
| pct_C_N | weight percent of carbon in unacidified sample | percent |
| C_to_N_mass_ratio | mass ratio of carbon to nitrogen in acidified sample | dimensionless |
| flags | Flags/comments | unitless |
| top_depth | depth at the top of the multicore | centimeters (cm) |
| bottom_depth | depth at the bottom of the multicore | centimeters (cm) |
| core | sequential core number | unitless |
| pct_CaCO3 | percent calcium carbon calculated from different in C_pct between acidified and unacidified samples | percent |
| C_pct_corrected | carbon content of acidified sample corrected for missing mass of CaCO3 removed by acidification | percent |
| C_to_N_mass_ratio2 | mass ratio of carbon to nitrogen in acidified sample | dimensionless |
| C_to_N_mass_corrected | mass ratio of carbon to nitrogen in acidified samples using the C_pct_corrected variable | dimensionless |
| latitude | latitude | decimal degrees |
| longitude | longitude | decimal degrees |
| station_number | sequential station number | unitless |
| station_name | station name | unitless |
| on_station_time_local | date and time that over-the-side work began on station, local time zone of UTC-3 | unitless |
| off_station_time_local | date and time that over-the-side work ended on station, local time zone of UTC-3 | unitless |
| on_station_time_UTC | date and time that over-the-side work began on station, UTC time zone | unitless |
| off_station_time_UTC | date and time that over-the-side work ended on station, UTC time zone | unitless |
| Dataset-specific Instrument Name | IonPlus Micadas Accelerator Mass Spectrometer |
| Generic Instrument Name | Accelerator Mass Spectrometer |
| Dataset-specific Description | Radiocarbon: IonPlus Micadas Accelerator Mass Spectrometer |
| Generic Instrument Description | An AMS measures "long-lived radionuclides that occur naturally in our environment. AMS uses a particle accelerator in conjunction with ion sources, large magnets, and detectors to separate out interferences and count single atoms in the presence of 1x1015 (a thousand million million) stable atoms, measuring the mass-to-charge ratio of the products of sample molecule disassociation, atom ionization and ion acceleration." AMS permits ultra low-level measurement of compound concentrations and isotope ratios that traditional alpha-spectrometry cannot provide. More from Purdue University: http://www.physics.purdue.edu/primelab/introduction/ams.html |
| Dataset-specific Instrument Name | NOVA 4000e SA analyzer |
| Generic Instrument Name | BET surface area analyzer |
| Dataset-specific Description | Surface Area: Quantachrome Surface Area Analyzer (NOVA 4000e SA analyzer; Quantachrome Instruments, USA) |
| Generic Instrument Description | Also called a "Gas Adsorption Surface Area Analyzer". A BET surface area analyzer is a laboratory instrument used to determine the specific surface area of solid materials using gas adsorption techniques, typically based on the Brunauer-Emmett-Teller (BET) method. The instrument measures the quantity of gas, commonly nitrogen, adsorbed onto the surface of a solid sample at different pressures. Depending on the model, it may also be used to determine pore volume and pore size distribution. |
| Dataset-specific Instrument Name | Carlo Erba CHN 1108 |
| Generic Instrument Name | Carlo-Erba EA-1108 elemental analyzer |
| Dataset-specific Description | Stable isotopes and elemental content, organic matter: ThermoFinnigan Delta+XL IRMS, Carlo Erba CHN 1108 |
| Generic Instrument Description | An elemental analyzer that simultaneously determines the total carbon, hydrogen, nitrogen, and sulfur in a small solid or liquid sample. A sample is put into a tin capsule that is placed in the combustion tube. Here, the sample is in an oxygen-rich gas stream, which causes flash combustion of the tin capsule, and in turn, the sample is rapidly combusted. The resulting gases are separated on a packed gas chromatography column and quantified using a thermal conductivity detector. The instrument was originally manufactured by Carlo-Erba, which has since been replaced by Thermo Scientific (part of Thermo Fisher Scientific). This model is no longer in production. It is also possible to determine oxygen with a modification to the systems configuration. Analysis times: CHN in 7 min, S in 5 min and O in 6 min. Range: 10 ppm to 100%. |
| Dataset-specific Instrument Name | ThermoFisher iCAP |
| Generic Instrument Name | Inductively Coupled Plasma Mass Spectrometer |
| Dataset-specific Description | Inorganic elemental content: ThermoFisher iCAP inductively coupled plasma mass spectrometer |
| Generic Instrument Description | An ICP Mass Spec is an instrument that passes nebulized samples into an inductively-coupled gas plasma (8-10000 K) where they are atomized and ionized. Ions of specific mass-to-charge ratios are quantified in a quadrupole mass spectrometer. |
| Dataset-specific Instrument Name | ThermoFinnigan Delta+XL |
| Generic Instrument Name | Isotope-ratio Mass Spectrometer |
| Dataset-specific Description | Stable isotopes and elemental content, organic matter: ThermoFinnigan Delta+XL IRMS, Carlo Erba CHN 1108 |
| Generic Instrument Description | The Isotope-ratio Mass Spectrometer is a particular type of mass spectrometer used to measure the relative abundance of isotopes in a given sample (e.g. VG Prism II Isotope Ratio Mass-Spectrometer). |
| Dataset-specific Instrument Name | Beckman Coulter Laser Diffraction Particle Size Analyzer |
| Generic Instrument Name | Laser Diffraction Particle Size Analyzer |
| Dataset-specific Description | Grain size: Beckman Coulter Laser Diffraction Particle Size Analyzer |
| Generic Instrument Description | Laser diffraction is particle sizing technique for materials ranging from hundreds of nanometers up to several millimeters in size. Laser diffraction measures particle size distributions by measuring the angular variation in intensity of light scattered as a laser beam passes through a dispersed particulate sample. One example is the Beckman Coulter LS200. |
| Dataset-specific Instrument Name | Sediment multicores |
| Generic Instrument Name | Multi Corer |
| Generic Instrument Description | The Multi Corer is a benthic coring device used to collect multiple, simultaneous, undisturbed sediment/water samples from the seafloor. Multiple coring tubes with varying sampling capacity depending on tube dimensions are mounted in a frame designed to sample the deep ocean seafloor. For more information, see Barnett et al. (1984) in Oceanologica Acta, 7, pp. 399-408. |
| Website | |
| Platform | R/V F.G. Walton Smith |
| Start Date | 2023-05-13 |
| End Date | 2023-07-18 |
| Description | See more information at R2R: https://www.rvdata.us/search/cruise/WS23139 |
NSF Award Abstract:
The Amazon River is one of the largest and most important rivers in the world. It not only carries water through the Amazon rain forests, it also carries mud - lots of mud. This mud settles when the river joins the ocean, forming the Amazon delta. Waves and wind re-suspend some of this mud, which forms mud banks that slowly move from the coast of Brazil, to French Guiana, Suriname, and Guyana. Not only do these mud banks shape the coastlines of these areas, but they also serve as a storage for carbon that had been removed from the atmosphere by the Amazon rainforest, dropped to the forest floor and washed into the river, ultimately transported on particles of mud into the Amazon delta. Normally, this represents a transfer of carbon from the atmosphere to the sediment and rock record, hiding it away for thousands to millions of years and participating in the natural regulation of atmospheric carbon dioxide concentration. However, the Amazon's special case of mud banks that travel hundreds of miles along the coast has long been believed to stir up this carbon and release most of it back to the atmosphere. This idea has held for decades, but if wrong, may have a large impact on our understanding of the ocean's carbon cycle. This research will use new science and technology to change the way we look at river carbon cycling in the ocean by sampling the mud banks, ocean waters, and Amazon River waters more thoroughly than has been previously done, with the aim to understand and quantify the storage of land-derived carbon in marine mudbank sediments offshore the Amazon River. This research will serve as the core of two Ph.D. dissertations and will interface with the Woods Hole Partnership Educational Program (PEP) to provide under-represented minority college students an opportunity to gain practical experience in marine and environmental sciences. The project will also be incorporated into the Saint Petersburg Science Fest, an event that brings several thousand school children to University of South Florida campus during one weekend every autumn to showcase different research projects and initiatives.
Twenty years ago, John Hedges posed a seminal question pertaining to the role of the ocean in the carbon cycle: "what happens to terrestrial organic matter in the oceans?" That question was set upon a conundrum - because riverine organic matter is highly degraded, it should be expected to suffer minimal respiration in the ocean, yet by most accounts continental margins seem to be incinerators of terrestrial organic matter. However, much of this paradigm was originally built upon biogeochemical studies of the Amazon sub-aqueous delta and the mobile fluidized mudbanks along the Guianas coast. However, preliminary state of the art characterizations of organic carbon in Amazon River and mudbank sediments suggest that a fraction of terrestrial organic carbon is actually not refractory and as such is rapidly and efficiently oxidized in the ocean, whereas another fraction is refractory and is in fact quantitatively preserved in marine sediments. Overall, the data suggest an underestimation of burial efficiency in the realm of 50-100%. This proposal focuses on gaining a transformative mechanistic and quantitative understanding of the fate of terrestrial organic carbon in the coastal ocean offshore of the Amazon River. This will be done through i) sampling the marine province offshore of the Guianas coast (the Guianas mudbanks) as well as the Amazon River delta, using a shallow-draft research vessel and ii) using novel state of the art geochemical analysis of organic carbon - in particular ramped oxidation and compound specific radiocarbon analysis - and elemental composition to demonstrate the selective nature of terrestrial organic carbon oxidation and provide new estimates of burial efficiency of this system - with implications for the global inventory of terrestrial organic carbon in the ocean.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
| Funding Source | Award |
|---|---|
| NSF Division of Ocean Sciences (NSF OCE) |