Sediment core porewater and particulate measurements from three sites on the Louisiana Shelf sampled during R/V Pelican cruises from December 2021 through August 2022

Website: https://www.bco-dmo.org/dataset/915912
Data Type: Cruise Results
Version: 1
Version Date: 2023-12-11

Project
» Collaborative Research: RAPID: Extreme disturbances/perturbations to coastal deposition systems (Extreme Si)
ContributorsAffiliationRole
Rahman, ShailyUniversity of Colorado at BoulderPrincipal Investigator
Krause, Jeffrey W.Dauphin Island Sea Lab (DISL)Co-Principal Investigator
Lemke, Lindsey R.Dauphin Island Sea Lab (DISL)Scientist
Roseburrough, RyanDauphin Island Sea Lab (DISL)Student
Gerlach, Dana StuartWoods Hole Oceanographic Institution (WHOI BCO-DMO)BCO-DMO Data Manager

Abstract
In the northern Gulf of Mexico seasonal water column stratification may impact how sedimentary biogenic silica (bSi) is processed and preserved by limiting oxygen injections into the benthos, seasonally driving bSi burial efficiency. Hurricane Ida moved through the Mississippi Delta and adjacent Louisiana shelf at the end of August 2021. Thirteen days prior to Hurricane Ida’s landfall in Port Fourchon, LA we collected sediment cores at 3 sites in the topset delta sediment between the Southwest Pass (major Mississippi River distributary) and Cocodrie, LA aboard the R/V Pelican; all sites experienced Category 4 hurricane conditions from Ida with maximum sustained winds of 130 knots (67 m/s). Ida, a much stronger storm than either Hurricanes Harvey or Nate in 2017, likely introduced oxygen into the sediment of the proximal coastal zone, perhaps enough to turn most of the top ~1 m of the sediment column oxic. Whether or not these types of O2-injection events alter the modality of bSi sequestration (e.g., enhancement or reduction of bSi remineralization) in the sediments is unknown; understanding this effect is relevant to accurately determine burial (or recycling) efficiencies and the degree of coupling among bSi and major/trace elements (e.g. reverse weathering) in these coastal systems. Using a multi-proxy approach, this project analyzed how this major storm event altered this modality of bSi burial in the following 12 months. These data include operational particulate silica pools, particulate organic carbon/nitrogen and their stable isotopes, porewater constituents and general characteristics (e.g. porosity).


Coverage

Spatial Extent: N:29.218 E:-89.012 S:25.501 W:-90.501
Temporal Extent: 2021-12-15 - 2022-08-27

Methods & Sampling

I. Field sampling
Sample collection took place during R/V Pelican cruises on the Louisiana Shelf in the northern part of the Gulf of Mexico. At each site, an 8-spot Ocean Instruments Multi-corer collected sediment that was sectioned in 2 cm intervals and placed into 50mL metal clean, acid washed tubes, pre-weighed and labeled microcentrifuge tubes for porosity, and freezer-safe Ziploc bags. Tubes were centrifuged at 3500 rpm for 10 minutes to extract and filter supernatant. The filtered supernatant was placed into a metal clean/acid washed tube and frozen for later metal and dissolved silica analysis. The 50mL tube of sediment was frozen at -20°C for further analysis.

II. Porewater Dissolved Constituents
Where there was sufficient supernatant (porewater) from the centrifuged sediment sample, it was divided for three different analyses: dissolved silica analysis, nutrient analysis, and metal concentrations.  

1. DSi analysis: 50uL of porewater was analyzed for dissolved Si(OH)4 concentration using a spectrophotometric molybdate-blue method (Brzezinski & Nelson, 1986).

2. Skalar Analysis (N+N, NH4, PO4): Porewater was diluted with Milli‐Q (18.2 MΩ * cm) water and run through a Skalar Analyzer for Nitrate and Nitrite, Phosphorus, and Ammonia. For detection limits, please refer to the descriptions in the Parameters section below.

3. ICPMS (Metal Concentrations): Porewater was reconstituted in dilute HNO3 and diluted to minimize salt interferences before analysis on a Thermo Scientific Element XR High Resolution-ICP-MS housed at the University of Southern Mississippi at the Stennis Space Center. The following elements are reported in the data from the ICPMS analysis:  magnesium (Mg), sulfur (S), calcium (Ca), manganese (Mn), iron (Fe), potassium (K), cesium (Cs), uranium (U), lithium (Li), vanadium (V), cobalt (Co), nickel (Ni), copper (Cu), strontium (Sr), molybdenum (Mo), barium (Ba), phosphorus (P), and aluminum (Al), along with phosphorus (P) and silica (Si).  For each element's detection limit, please see the description in the Parameters section below.   

Sediments
Sediments were analyzed for physical properties, chemical properties, phases of silica, and isotopic composition. Each section below describes the specific methods in greater detail.  

III. Sediment properties
Porosity
For porosity measurements, microcentrifuge tubes were placed in a drying oven at 60°C until the sediment was dry. Once dried, tubes were weighed. Porosity was calculated as in Comeaux et al. (2012).

Loss on ignition (LOI)
For loss on ignition (LOI) analysis, dried sediment was ground into a fine consistency using a mortar and pestle. Then 1g of the ground sediment was weighed into pre-muffled and pre-weighed porcelain crucibles. To remove any carbon from the presence of calcium carbonate, samples were fumed by adding 1mL of mili-Q to each crucible and placing samples into a desiccator with 10 mL of 12M HCl for 6 hours (Harris et al. 2001, Ramnarine et al. 2011, Walthert et al. 2010). After 6 hours, samples were placed in a vacuum oven for roughly 16 hours, until dry (Ramnarine et al. 2011, Walthert et al. 2010). Once dry, samples were kept in the oven and weighed one at a time to keep moisture out of samples. If sample was still acidic (yellow in color), 1 mL of mili-Q was added and the sample was dried again. Sediment was ground again into a fine powder.

After the above preparation, fumed and ground sediment was weighed (100 mg) in triplicate into pre-weighed and muffled liquid scintillation (LSC) vials. Samples were combusted at 550°C for 6 hours (Kemp et al. 2021). After combustion, weights were recorded, and loss of organic matter was calculated (Kemp et al. 2021).

IV. Sediment Silica Pools  

Sequential Extractions  
Frozen samples were thawed, homogenized, and 50 mg were weighed in triplicates (per depth) into pre-labeled 50 mL centrifuge tubes (Krause et al., 2017; Pickering et al., 2020). Samples with procedural blanks (in triplicate) then went through the sequential extraction process. The sequential extraction methodology separates silica into operationally defined pools based on kinetics, reaction conditions and reaction sequence (DeMaster, 1981; Michalopoulos and Aller, 2004; Rahman et al., 2016; Pickering et al., 2020).

Operational Definitions
Based on prior studies, we use the following nomenclature:

1. Si-HCl: Mild acid-leachable pre-treatment; Highly reactive silica associated with authigenic clays and metal oxide coatings (Michalopoulos and Aller, 2004).

2. Si-Alk: Mild alkaline-leachable digestion completed after acid pretreatment; Frees reactive silica associated with the biogenic silica pool (Michalopoulos and Aller, 2004).

3. Si-NaOH (Alk): Harsh NaOH digestion done after Si-HCl and Si-Alk (Rahman et al., 2016; Rahman et al., 2017); Associated with the reactive lithogenic Si (LSi) pool and the comparatively refractory “dark bSiO2” (e.g. sponge spicules and Rhizaria).

4. tbSi: Following the traditional definition of biogenic silica (DeMaster, 1981), with no acid pre-treatment.

5. Si-NaOH (TbSi): Harsh NaOH digestion done after T-bSi.

V. Sediment Organic matter

Preparation
Same preparation as that listed above for Loss on Ignition

Particulate Organic Carbon and Nitrogen content and isotopes (δ13C and δ15N)
After fumigation (explained above), ~60 to 70 mg of sediment were packed in 5x9mm silver capsules, which were then packed in tin 5x9mm capsules in triplicate (UC Davis protocol recommendation). Samples were placed in a 96 well plate and kept in a desiccator until shipped to UC Davis for isotopic (δ13C and δ15N) analysis (Krause et al. 2017).

Results from UC Davis had an absolute accuracy for calibrated reference materials of ±0.04 ‰ (±0.05 ‰ SD) for δ13C and ±0.05 ‰ (±0.05 ‰ SD) for δ15N. Core depth 24-26cm was the only sample below detection (9.7ug) for δ15N, which is represented as 0 on the data sheet.


Data Processing Description

Raw data were input into Microsoft Excel (Version 2302) to calculate final reported values.

  • Porosity was calculated as in Comeaux et al. (2012).
  • Loss of organic matter was calculated as in Kemp et al. (2021).

 


BCO-DMO Processing Description

- Imported data from source file "Extreme_Si_MASTER_BCODMO.xlsx" into the BCO-DMO data system.
- Added R/V Pelican official cruise IDs corresponding to the submitted cruise name.
- Combined separate date and time columns into a single datetime column
- Kept local datetime but added a column for ISO8601 formatted UTC datetime
- Removed percent (%) signs from the column values
- Modified parameter (column) names to conform with BCO-DMO naming conventions. The only allowed characters are A-Z,a-z,0-9, and underscores. Replaced spaces with underscores.


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Related Publications

Brzezinski, M. A., & Nelson, D. M. (1986). A solvent extraction method for the colorimetric determination of nanomolar concentrations of silicic acid in seawater. Marine Chemistry, 19(2), 139–151. doi:10.1016/0304-4203(86)90045-9
Methods
Comeaux, R. S., Allison, M. A., & Bianchi, T. S. (2012). Mangrove expansion in the Gulf of Mexico with climate change: Implications for wetland health and resistance to rising sea levels. Estuarine, Coastal and Shelf Science, 96, 81–95. https://doi.org/10.1016/j.ecss.2011.10.003
Methods
DeMaster, D. J. (1981). The supply and accumulation of silica in the marine environment. Geochimica et Cosmochimica Acta, 45(10), 1715–1732. doi:10.1016/0016-7037(81)90006-5
Methods
Harris, D., Horwáth, W. R., & van Kessel, C. (2001). Acid fumigation of soils to remove carbonates prior to total organic carbon or CARBON-13 isotopic analysis. Soil Science Society of America Journal, 65(6), 1853–1856. doi:10.2136/sssaj2001.1853
Methods
Kemp, E., Roseburrough, R., Elliott, E., & Krause, J. (2021). Spatial Variability of Sediment Amorphous Silica and its Reactivity in a Northern Gulf of Mexico Estuary and Coastal Zone. Gulf and Caribbean Research, 32, SC6–SC11. https://doi.org/10.18785/gcr.3201.14
Methods
Krause, J. W., Darrow, E. S., Pickering, R. A., Carmichael, R. H., Larson, A. M., & Basaldua, J. L. (2017). Reactive silica fractions in coastal lagoon sediments from the northern Gulf of Mexico. Continental Shelf Research, 151, 8–14. doi:10.1016/j.csr.2017.09.014
Methods
Michalopoulos, P., & Aller, R. C. (2004). Early diagenesis of biogenic silica in the Amazon delta: alteration, authigenic clay formation, and storage. Geochimica et Cosmochimica Acta, 68(5), 1061–1085. doi:10.1016/j.gca.2003.07.018
Methods
Pickering, R. A., Cassarino, L., Hendry, K. R., Wang, X. L., Maiti, K., & Krause, J. W. (2020). Using Stable Isotopes to Disentangle Marine Sedimentary Signals in Reactive Silicon Pools. Geophysical Research Letters, 47(15). doi:10.1029/2020gl087877
Methods
Rahman, S., Aller, R. C., & Cochran, J. K. (2016). Cosmogenic 32Si as a tracer of biogenic silica burial and diagenesis: Major deltaic sinks in the silica cycle. Geophysical Research Letters, 43(13), 7124–7132. doi:10.1002/2016gl069929 https://doi.org/10.1002/2016GL069929
Methods
Rahman, S., Aller, R. C., & Cochran, J. K. (2017). The Missing Silica Sink: Revisiting the Marine Sedimentary Si Cycle Using Cosmogenic 32 Si. Global Biogeochemical Cycles, 31(10), 1559–1578. doi:10.1002/2017gb005746 https://doi.org/10.1002/2017GB005746
Methods
Ramnarine, R., Voroney, R. P., Wagner-Riddle, C., & Dunfield, K. E. (2011). Carbonate removal by acid fumigation for measuring the δ13C of soil organic carbon. Canadian Journal of Soil Science, 91(2), 247–250. https://doi.org/10.4141/cjss10066
Methods
Walthert, L., Graf, U., Kammer, A., Luster, J., Pezzotta, D., Zimmermann, S., & Hagedorn, F. (2010). Determination of organic and inorganic carbon, δ13C, and nitrogen in soils containing carbonates after acid fumigation with HCl. Journal of Plant Nutrition and Soil Science, 173(2), 207–216. Portico. https://doi.org/10.1002/jpln.200900158
Methods

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Parameters

ParameterDescriptionUnits
CruiseCruise name unitless
Cruise_IDCruise ID unitless
DateTime_LocalLocal datetime of event unitless
Event_numberEvent number described by GMT date and time unitless
ActivitySediment collection activity for event unitless
LatitudeLatitude for event decimal degrees
LongitudeLongitude for event decimal degrees
Station_NumberStation number of event unitless
Bottom_DepthStation bottom depth meters (m)
Core_Section_DepthCore depth shallow and deep range for each sample collected centimeter (cm)
Core_Section_Mid_DepthCore depth median for each sample collected centimeter (cm)
PW_orthosilicic_acid_avg_specAverage particulate dissolved silica in porewater using spectrophotometric method micromoles per liter (umol/L)
PW_orthosilicic_acid_stdev_specStandard deviation of particulate dissolved silica in porewater using spectrophotometric method micromoles per liter (umol/L)
PW_Phosphate_AAPorewater nutrients: Phosphate measured using an autoanalyzer. Detection limit = 0.062 umol/L. micromoles per liter (umol/L)
PW_Nitrate_AAPorewater nutrients: Nitrate measured using an autoanalyzer. Below detection (0.857 umol/L) represented as 0. micromoles per liter (umol/L)
PW_Nitrite_AAPorewater nutrients: Nitrite measured using an autoanalyzer. Below detection (0.041 umol/L) represented as 0. micromoles per liter (umol/L)
PW_Ammonium_AAPorewater nutrients: Ammonium measured using an autoanalyzer. Below detection = 1.704 umol/L. micromoles per liter (umol/L)
PW_Mg_ICPMSICPMS derived Porewater concentration: Magnesium. Estimated detection limit = 625 ppb. parts per billion (ppb)
PW_Si_ICPMSICPMS derived Porewater concentration: Silicon. Estimated detection limit = 2018 ppb. parts per billion (ppb)
PW_S_ICPMSICPMS derived Porewater concentration: Sulfur. Estimated detection limit = 580 ppb. parts per billion (ppb)
PW_Ca_ICPMSICPMS derived Porewater concentration: Calcium. Estimated detection limit = 297 ppb. parts per billion (ppb)
PW_Mn_ICPMSICPMS derived Porewater concentration: Manganese. Estimated detection limit = 6 ppb. parts per billion (ppb)
PW_Fe_ICPMSICPMS derived Porewater concentration: Iron. Estimated detection limit = 20 ppb. parts per billion (ppb)
PW_K_ICPMSICPMS derived Porewater concentration: Potassium. Estimated detection limit = 207 ppb. parts per billion (ppb)
PW_Cs_ICPMSICPMS derived Porewater concentration: Cesium. Estimated detection limit = 0.01 ppb. parts per billion (ppb)
PW_U_ICPMSICPMS derived Porewater concentration: Uranium. Estimated detection limit = parts per billion (ppb)
PW_Li_ICPMSICPMS derived Porewater concentration: Lithium. Estimated detection limit = 1.1 ppb. parts per billion (ppb)
PW_V_ICPMSICPMS derived Porewater concentration: Vanadium. Estimated detection limit = 0.2 ppb. parts per billion (ppb)
PW_Co_ICPMSICPMS derived Porewater concentration: Cobalt. Estimated detection limit = 0.2 ppb. parts per billion (ppb)
PW_Ni_ICPMSICPMS derived Porewater concentration: Nickel. Estimated detection limit = 0.3 ppb. parts per billion (ppb)
PW_Cu_ICPMSICPMS derived Porewater concentration: Copper. Estimated detection limit = 0.2 ppb. parts per billion (ppb)
PW_Sr_ICPMSICPMS derived Porewater concentration: Strontium. Estimated detection limit = 14 ppb. parts per billion (ppb)
PW_Mo_ICPMSICPMS derived Porewater concentration: Molybdenum. Estimated detection limit = 0.1 ppb. parts per billion (ppb)
PW_Ba_ICPMSICPMS derived Porewater concentration: Barium. Estimated detection limit = 1.9 ppb. parts per billion (ppb)
PW_P_ICPMSICPMS derived Porewater concentration: Phosphorus. Estimated detection limit = 8.1 ppb. parts per billion (ppb)
PW_Al_ICPMSICPMS derived Porewater concentration: Aluminum. Estimated detection limit = 27 ppb. parts per billion (ppb)
Sediment_Porosity_pctPorosity of each core sediment sample dimensionless
Partic_Si_HCl_avgAverage particulate silica after HCl leach micromoles per gram (umol/g)
Partic_Si_HCl_stdevStandard deviation of dissolved particulate silica after HCl leach micromoles per gram (umol/g)
Partic_Si_Alk_avgParticulate average of dissolved silica after Si-Alk leach micromoles per gram (umol/g)
Partic_Si_Alk_stdevParticulate standard deviation of dissolved silica after Si-Alk leach micromoles per gram (umol/g)
Partic_tbSi_avgParticulate average of dissolved silica after tbSi leach micromoles per gram (umol/g)
Partic_tbSi_stdevParticulate standard deviation of dissolved silica after tbSi leach micromoles per gram (umol/g)
Partic_Si_NaOH_from_Si_Alk_avgParticulate average of dissolved silica after Si-NaOH from Si-Alk leach material micromoles per gram (umol/g)
Partic_Si_NaOH_from_Si_Alk_stdevParticulate standard deviation of dissolved silica after Si-NaOH from Si-Alk leach material micromoles per gram (umol/g)
Partic_Si_NaOH_from_tbSi_avgParticulate average of dissolved silica after Si-NaOH from tbSi leach material micromoles per gram (umol/g)
Partic_Si_NaOH_from_tbSi_stdevParticulate standard deviation of dissolved silica after Si-NaOH from tbSi leach material micromoles per gram (umol/g)
POC_d13C_avgAverage particulate organic carbon isotopic composition parts per mil (‰)
POC_d13C_stdevStandard deviation of particulate organic carbon isotopic composition parts per mil (‰)
POC_Total_C_pct_avgAverage total of particulate organic carbon percent (%)
POC_Total_C_pct_stdevStandard deviation of averaged total particulate organic carbon percent (%)
POC_d15N_Air_avgAverage particulate organic nitrogen isotopic composition. Below detection represented as 0 (Below detection = 9.7ug) parts per mil (‰)
POC_d15N_Air_stdevStandard deviation of particulate organic nitrogen isotopic composition parts per mil (‰)
POC_Total_N_pct_avgAverage total of particulate organic nitrogen percent (%)
POC_Total_N_pct_stdevStandard deviation of averaged total particulate organic nitrogen percent (%)
Particulate_Sediment_LOI_pctAmount of organic matter lost per sediment sample percent (%)
ISO_DateTime_UTCDatetime of event in UTC and ISO8601 format unitless


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Instruments

Dataset-specific Instrument Name
CF-IRMS at UC Davis (continuous flow isotope ratio mass spectrometer)
Generic Instrument Name
Isotope-ratio Mass Spectrometer
Dataset-specific Description
Samples measured at University of California Davis were run on an elemental analyzer interfaced to a continuous flow isotope ratio mass spectrometer.  See Stable Isotope Facility instrument descriptions here: https://stableisotopefacility.ucdavis.edu
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
Box core
Generic Instrument Name
Box Corer
Dataset-specific Description
A box core was used on the December 2021 cruise to sample sediments
Generic Instrument Description
General description of a box corer: A box corer is a marine geological tool that recovers undisturbed soft surface sediments. It is designed for minimum disturbance of the sediment surface by bow wave effects. Traditionally, it consists of a weighted stem fitted to a square sampling box. The corer is lowered vertically until it impacts with the seabed. At this point the instrument is triggered by a trip as the main coring stem passes through its frame. While pulling the corer out of the sediment a spade swings underneath the sample to prevent loss. When hauled back on board, the spade is under the box. (definition from the SeaVox Device Catalog) Box corers are one of the simplest and most commonly used types of sediment corers. The stainless steel sampling box can contain a surface sediment block as large as 50cm x 50cm x 75cm with negligible disturbance. Once the sediment is recovered onboard, the sediment box can be detached from the frame and taken to a laboratory for subsampling and further analysis. The core sample size is controlled by the speed at which the corer is lowered into the ocean bottom. When the bottom is firm, a higher speed is required to obtain a complete sample. A depth pinger or other depth indicator is generally used to determine when the box is completely filled with sediment. Once the core box is filled with sediment, the sample is secured by moving the spade-closing lever arm to lower the cutting edge of the spade into the sediment, until the spade completely covers the bottom of the sediment box. (definition from Woods Hole Oceanographic Institution).

Dataset-specific Instrument Name
Thermo Scientific Element XR high resolution inductively coupled plasma-mass spectrometer (HR-ICP-MS)
Generic Instrument Name
Inductively Coupled Plasma Mass Spectrometer
Dataset-specific Description
Porewater was reconstituted in dilute HNO3 and diluted to minimize salt interferences before analysis on a Thermo Scientific Element XR High Resolution-ICP-MS housed at the University of Southern Mississippi at the Stennis Space Center.
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
Ocean Instruments Multicorer
Generic Instrument Name
Multi Corer
Dataset-specific Description
At each site, an 8-spot Ocean Instruments Multi-corer collected sediment that was sectioned in 2 centimeter intervals
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.

Dataset-specific Instrument Name
Thermo Scientific Genesys 10S UV-Vis Spectrophotometer
Generic Instrument Name
Spectrophotometer
Dataset-specific Description
Porewater was analyzed for dissolved si(OH)4 concentration using a spectrophotometric molybdate-blue method and a Thermo Scientific Genesys 10S UV-Vis Spectrophotometer.
Generic Instrument Description
An instrument used to measure the relative absorption of electromagnetic radiation of different wavelengths in the near infra-red, visible and ultraviolet wavebands by samples.

Dataset-specific Instrument Name
Elemental analyzer at UC Davis
Generic Instrument Name
Elemental Analyzer
Dataset-specific Description
Samples measured at University of California Davis were run on an elemental analyzer interfaced to a continuous flow isotope ratio mass spectrometer.  See Stable Isotope Facility instrument descriptions here: https://stableisotopefacility.ucdavis.edu
Generic Instrument Description
Instruments that quantify carbon, nitrogen and sometimes other elements by combusting the sample at very high temperature and assaying the resulting gaseous oxides. Usually used for samples including organic material.

Dataset-specific Instrument Name
centrifuge
Generic Instrument Name
Centrifuge
Dataset-specific Description
Mulitcorer samples were placed into tubes and centrifuged at 3500 rpm for 10 minutes to extract and filter supernatant, which was later used for dissolved silica analysis. 
Generic Instrument Description
A machine with a rapidly rotating container that applies centrifugal force to its contents, typically to separate fluids of different densities (e.g., cream from milk) or liquids from solids.

Dataset-specific Instrument Name
Skalar San++ Automated Wet Chemistry Analyzer
Generic Instrument Name
Continuous Flow Analyzer
Dataset-specific Description
The Skalar San++ Automated Wet Chemistry Analyzer, also known as continuous flow analyzer, was used to analyze porewater for nitrate, nitrite, phosphorus, and ammonia.  
Generic Instrument Description
A sample is injected into a flowing carrier solution passing rapidly through small-bore tubing. 

Dataset-specific Instrument Name
continuous flow interface at UC Davis
Generic Instrument Name
Continuous Flow Interface for Mass Spectrometers
Dataset-specific Description
Samples measured at University of California Davis were run on an elemental analyzer interfaced to a continuous flow isotope ratio mass spectrometer.  See Stable Isotope Facility instrument descriptions here: https://stableisotopefacility.ucdavis.edu
Generic Instrument Description
A Continuous Flow Interface connects solid and liquid sample preparation devices to instruments that measure isotopic composition. It allows the introduction of the sample and also reference and carrier gases. Examples: Finnigan MATConFlo II, ThermoScientific ConFlo IV, and Picarro Caddy. Note: This is NOT an analyzer


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Deployments

PE23-05

Website
Platform
R/V Pelican
Start Date
2022-08-26
End Date
2022-08-27

PE22-27

Website
Platform
R/V Pelican
Start Date
2022-06-01
End Date
2022-06-02

PE22-20

Website
Platform
R/V Pelican
Start Date
2022-03-17
End Date
2022-03-19

PE22-15

Website
Platform
R/V Pelican
Start Date
2021-12-15
End Date
2021-12-15


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Project Information

Collaborative Research: RAPID: Extreme disturbances/perturbations to coastal deposition systems (Extreme Si)

Coverage: Northern Gulf of Mexico, specifically the Louisiana Shelf region dominated by the discharge of the Mississippi River on the western side of the delta (28.2 to 29.1°N, -89.4 to -91.7°E)


NSF Award Abstract:
This project will take advantage of the passage of Hurricane Ida across the northern Gulf of Mexico shelf in August, 2021 to study important aspects of the cycling of silica in coastal sediments. In coastal systems, water column primary productivity is dominated by diatoms, a group of phytoplankton which produce a shell of amorphous biogenic silica. This biogenic silica can either be buried in its original unaltered form or undergo chemical reactions that convert it to aluminosilicate minerals (e.g. marine clays). This latter process is important in global chemical budgets for many elements, including carbon. One of the factors that influences whether silicon is buried as biogenic silica or converted to aluminosilicates may be the amount of oxygen in the sediments. Storms mix the ocean waters and can add oxygen to sediments in shallow water, potentially changing the silica balance. The investigators collected sediment samples in early August, 2021, two weeks before Hurricane Ida. Sampling through the year after the storm will allow them to test whether storms affect silica cycling. This project will support an early-career investigator and undergraduate student researchers.

Tropical and subtropical coastal deposition systems sequester 25-40% of the global silica sink, a disproportionately large impact relative to their area. In the northern Gulf of Mexico seasonal water column stratification may impact how sedimentary biogenic silica is processed and preserved by limiting oxygen injections into the benthos, seasonally driving biogenic silica burial efficiency. Hurricane Ida moved through the Mississippi Delta and adjacent Louisiana shelf at the end of August 2021. Thirteen days prior to Hurricane Ida’s landfall in Port Fourchon, LA, in August 2021, the team collected sediment cores at 3 sites in the topset delta sediment between the Southwest Pass (major Mississippi River distributary) and Cocodrie, LA; all sites experienced Category 4 hurricane conditions from Ida with maximum sustained winds of 130 knots (67 m/s). Ida, a much stronger storm than either Hurricanes Harvey or Nate in 2017, likely introduced oxygen into the sediment of the proximal coastal zone, perhaps enough to turn most of the top meter or so of the sediment column oxic. The investigators hypothesize that the storm-induced change in redox and diagenetic conditions initially favored burial of biogenic silica rather than an authigenic aluminosilicate. Using a multi-proxy approach, this project will analyze how this major storm event altered biogenic silica burial over the course of ten months.

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.



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Funding

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

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