Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Processing Description
• Related Publications
• Parameters
• Instruments
• Deployments
• Project Information
• Funding

Sample collection

Water column samples were collected in July 2023 aboard the R/V Endeavor using a trace metal clean rosette (SeaBird) outfitted with 12-L Niskin-X samplers (Ocean Test Equipment), a trace metal clean pump system, or a custom surface pump “towfish” system (Mellett and Buck, 2020). Seawater samples for dissolved (D) trace metal analysis were filtered through 0.2 µm Pall Acropak Super membrane filter capsules and collected in acid-cleaned and triple-rinsed 125-mL low density polyethylene (LDPE) bottles.

Samples from seawater incubation experiments were filtered through consecutive acid-cleaned 5 µm and 0.4 µm polycarbonate track-etched (PCTE, Whatman) filters in a dual stage Teflon filter rig (Savillex) on a custom acrylic tower. Filtrate from the 0.4 µm filters was collected in acid-cleaned and triple-rinsed 125-mL LDPE bottles for subsequent dissolved trace metal measurements. The 5 µm and 0.4 µm filters were folded in eighths, placed in acid-cleaned snap-cap polypropylene centrifuge tubes, and frozen at -20 ºC for labile particulate metals.

All dissolved trace metal samples were acidified shipboard with ultrapure hydrochloric acid (HCl, Optima) to 0.024 N HCl, double bagged in buckets and stored at room temperature until analyzed at Oregon State University (OSU).

Analysis of dissolved trace metal concentrations

Analysis of seawater samples for the concentrations of dissolved (<0.2 or <0.4 µm) aluminum (Al), scandium (Sc), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), cadmium (Cd), gadolinium (Gd), lutetium (Lu) and lead (Pb) were performed using an automated SeaFAST pico (Elemental Scientific, Inc.) in line with a Thermo Scientific iCAP-RQ Quadrupole CCT ICP-MS. This instrument is maintained at OSU in the Keck Collaboratory for Plasma Spectrometry.

Trace metals were eluted from the Nobias-1A resin in seaFAST using 10% nitric acid (Optima, Fisher) containing 1 ppb indium as an internal standard and quantified using nine-point calibration curves in filtered (<0.2 µm) surface seawater collected offshore in the Gulf of Mexico in 2018 (Hollister et al. 2020) and stored unacidified in an acid-cleaned and double-bagged polypropylene in the dark; prior to use for the seawater matrix, a 2-L aliquot was pulled from the carboy and acidified to 0.024 N hydrochloric acid (HCl, Optima) to match the sample matrix. Subsamples of two in-house seawater quality control samples (collected previously from offshore surface waters, and from the EPZ GEOTRACES GP16 transect) were measured in each run and were also analyzed in tandem with community-established reference materials (SAFe-D1, SAFe-D2, GSC and NASS-7). All reference material results are provided in the supplemental file. Analytical blanks for the seaFAST method were obtained using air blanks, and limits of detection were calculated as three times the standard deviation of the averaged air blanks in each analytical run (Hollister et al. 2020).

Samples were analyzed a second time following the same procedure outlined above but after 90 minutes of UV-oxidation to ensure complete recovery of any organic-bound Cu and Co (Biller and Bruland 2012). The UV-oxidation was carried out on aliquots of samples in 15-mL Teflon vials (Savillex) with custom quartz lids (Hollister et al. 2020). For all elements except Cu and Co, results from analyses after UV-oxidation were combined with no UV results to achieve an additional analytical replicate.

Sample analyses for trace metals were performed by senior researcher Dr. Salvatore Caprara in the College of Earth, Ocean and Atmospheric Sciences at Oregon State University.

Data were flagged using the SeaDataNet quality flag scheme recommended by GEOTRACES (https://www.geotraces.org/geotraces-quality-flag-policy/) and described below. Notes specific to the application of these flags to this dataset are noted in brackets […].

0: No Quality Control: No quality control procedures have been applied to the data value. This is the initial status for all data values entering the working archive. [Not used].

1: Good Value: Good quality data value that is not part of any identified malfunction and has been verified as consistent with real phenomena during the quality control process. [Used for parameters analyzed in replicate or accompanied by certified reference material analyses. See accompanying Table 1 in the supplemental file for certified reference material values obtained in this study].

2: Probably Good Value: Data value that is probably consistent with real phenomena, but this is unconfirmed or data value forming part of a malfunction that is considered too small to affect the overall quality of the data object of which it is a part. [Used when no replicate measurements and no certified reference were available to check the quality of the data].

3: Probably Bad Value: Data value recognized as unusual during quality control that forms part of a feature that is probably inconsistent with real phenomena. [Used when either precision of the results was poor or results were inconsistent with expected distributions].

4: Bad Value: An obviously erroneous data value. [Not used].

5: Changed Value: Data value adjusted during quality control. Best practice strongly recommends that the value before the change be preserved in the data or its accompanying metadata. [Not used].

6: Value Below Detection Limit: The level of the measured phenomenon was less than the limit of detection (LOD) for the method employed to measure it. The accompanying value is the detection limit for the technique or zero if that value is unknown. [alues below detection are reported as 0 and should be interpreted using the associated quality flag = 6. Average blanks and detection limits for each parameter are provided in accompanying Table 2, found in the supplemental file].

7: Value in Excess: The level of the measured phenomenon was too large to be quantified by the technique employed to measure it. The accompanying value is the measurement limit for the technique. [Not used].

8: Interpolated Value: This value has been derived by interpolation from other values in the data object. [Not used].

9: Missing Value: The data value is missing. Any accompanying value will be a magic number representing absent data [Used when no data is available for this parameter].

A: Value Phenomenon Uncertain: There is uncertainty in the description of the measured phenomenon associated with the value such as chemical species or biological entity. [Not used.]

- Loaded CSV file BCO-DMO_Buck_STING-II_Metals_260206.csv with header row 1, skipping row 2 (units row), treating "", "nd", "na", and "nda" as missing values; table named using filename
- Combined DATE_UTC (format %m/%d/%y) and TIME_UTC (format %H:%M) into new datetime column DATETIME_UTC formatted as %Y-%m-%dT%H:%MZ
- Combined DATE_EST (format %m/%d/%y) and TIME_EST (format %H:%M) into new datetime column DATETIME_EST formatted as %Y-%m-%dT%H:%M
- Reordered all 77 columns with STING_ID through Pb_D_FLAG in specified order, placing DATETIME_UTC and DATETIME_EST after identifier columns and before individual date/time columns
- Converted DATE_UTC from %m/%d/%y to %Y-%m-%d format (UTC timezone preserved)
- Converted DATE_EST from %m/%d/%y to %Y-%m-%d format (America/New_York timezone preserved)
- Renamed DATETIME_EST to DATETIME_EDT, DATE_EST to DATE_EDT, and TIME_EST to TIME_EDT to reflect the EDT
- Output written to 997847_v1_sting_ii_tm.csv

NSF Award Abstract:
This project will investigate how groundwater discharge delivers important nutrients to the coastal ecosystems of the West Florida Shelf. Preliminary studies indicate that groundwater may supply both dissolved organic nitrogen (DON) and iron in this region. In coastal ecosystems like the West Florida Shelf that have very low nitrate and ammonium concentrations, DON is the main form of nitrogen available to organisms. Nitrogen cycling is strongly affected by iron availability because iron is essential for both photosynthesis and for nitrogen fixation. This study will investigate the sources and composition of DON and iron, and their influence on the coastal ecosystem. The team will sample offshore groundwater wells, river and estuarine waters, and conduct two expeditions across the West Florida Shelf in winter and summer. Investigators will participate in K-12 and outreach activities to increase awareness of the project and related science. The project will fund the work of six graduate and eight undergraduate students across five institutions, furthering NSF’s goals of education and training.

Motivated by preliminary observations of unexplained, tightly-correlated DON and dissolved iron concentrations across the West Florida Shelf (WFS), the proposed work will quantify the flux and isotopic signatures of submarine groundwater discharge (SGD)-derived DON and iron to the WFS, and evaluate the bioavailability of this temporally-variable source using four seasonal near-shore campaigns sampling offshore groundwater wells, estuarine, and riverine endmembers and two cross-shelf cruises. The work will evaluate whether SGD stimulates nitrogen fixation on the WFS, and the potential for the stimulated nitrogen fixation to further modify the chemistry of DON and dissolved iron in the region. The cross-shelf cruises will investigate hypothesized periods of maximum SGD and Trichodesmium abundance (June), and reduced river discharge and SGD (February), thus comparing two distinct biogeochemical regimes. The concentrations and isotopic compositions of DON and dissolved iron, molecular composition of DON, and the concentration and composition of iron-binding ligands will be characterized. Nitrogen fixation rates and Trichodesmium spp. abundance and expression of iron stress genes will be measured. Fluxes of DON and iron from SGD and rivers will be quantified with radium isotope mass balances. The impacts of SGD on nitrogen fixation and DON/ligand production will be constrained with incubations of natural phytoplankton communities with submarine groundwater amendments. Two hypotheses will be tested: 1) SGD is the dominant source of bioavailable DON and dissolved iron on the WFS, and 2) SGD-alleviation of iron stress changes the dominant Trichodesmium species on the WFS, increases nitrogen fixation rates and modifies DON and iron composition. Overall, the work will establish connections between marine nitrogen and iron cycling and evaluate the potential for coastal inputs to modify water along the WFS before export to the Atlantic Ocean. This study will thus provide a framework to consider these boundary fluxes in oligotrophic coastal systems and the relative importance of rivers and SGD as sources of nitrogen and iron in other analogous locations, such as coastal systems in Australia, India, and Africa, where nitrogen fixation and SGD have also been documented.

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.