Table of Contents

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

Surface water samples were collected on five consecutive one-day trips aboard R/V Trident along a transect from the Port of Houston to the Galveston Bay entrance following Hurricane Harvey from September 4 to September 28, 2017. Samples were filtered on board through 0.2-micrometer (μm) Whatman-Nucleopore Q-TEC filters (Filtration Solutions) for dissolved organic carbon (DOC), optical, and chemical analysis.

Concentrations of DOC were measured by high-temperature catalytic oxidation using a Shimadzu TOC-V total organic carbon analyzer. Deep seawater reference standards (Consensus Reference Program, University of Miami) were used to assure the accuracy of DOC measurements. Absorbance was measured in a 1-centimeter (cm) quartz cuvette from 200 to 800 nanometers (nm) using a dual-beam spectrophotometer (UV-1800, Shimadzu) with Milli-Q water as the reference blank. Specific UV absorbance (SUVA254 ) was determined by dividing the UV absorbance at 254 nm by the DOC concentration. The spectral slope (S275−295) was calculated using the linear regression of natural log-transformed absorption spectra (Helms et al., 2008).

Samples for dissolved lignin (900 milliliters (mL)) were acidified to pH 2.5 using 6 moles per liter (mol L-1) sulfuric acid and extracted through Agilent PPL cartridges (1 gram (g)) at 10 milliliters per minute (mL min-1). After extraction, cartridges were rinsed with 10 mL of deionized water acidified to pH 2.5 and dried for 30 seconds to remove residual water. The cartridges were eluted with 20 mL of methanol at 2 mL min-1, and the eluate was stored in glass vials at -20 degrees Celsius until analysis. Concentrations of lignin phenols were determined using ultra-high performance liquid chromatography-electrospray ionization-tandem mass spectrometry after CuSO4 oxidation, following the methods described in Yan and Kaiser (2018a,b). Aliquots of methanol extracts (∼30 microgram (μg) sample OC content) were dried in reaction vials and re-suspended in 200 microliters (μL) of 1.1 mol L-1 argon-sparged NaOH, followed by addition of 10 μL of 10 millimoles per liter (mmol L-1) CuSO4 and 10 μL of 0.2 mol L-1 ascorbic acid. Reaction vials were vigorously mixed and placed into 60-mL pressure-tight Teflon vessels filled with 5 mL of 1 mol L-1 NaOH. The oxidation was conducted at 150 degrees Celsius for 120 minutes. Sample solutions were spiked with 13C-labeled surrogate standards and purified with Waters HLB cartridges (30 milligram (mg), 1 mL). Separation and detection of lignin phenols was performed on an Agilent Infinity 1260 series UHPLC system coupled to an Agilent 6420 QqQ detector operating in alternating positive and negative modes with dynamic multiple reaction monitoring. Eleven lignin phenols were determined in all samples, including vanillyl phenols (V; vanillin, acetovanillone, and vanillic acid), syringyl phenols (S; syringaldehyde, acetosyringone, and syringic acid), p-hydroxyl phenols (P; p-hydroxybenzaldehyde, p-hydroxyacetophenone, and p-hydroxybenzoic acid), and cinnamyl phenols (C; p-coumaric acid and ferulic acid).

Total hydrolyzable enantiomeric dissolved amino acids (free and combined), including L-and D- forms of aspartic acid, glutamic acid, serine, histidine, threonine, glycine, arginine, alanine, tyrosine, valine, isoleucine, phenylalanine, leucine, and lysine were analyzed using high-performance liquid chromatography and fluorescence detection. After microwave-assisted vapor phase hydrolysis (Kaiser and Benner, 2005), amino acid monomers were derivatized with a mixture of N-isobutyryl- L-cysteine and o-phthaldialdehyde and separated on an Agilent Poroshell 120 EC-C18 column (4.6 millimeters (mm) × 100 mm, 2.7 μm). A binary solvent system was employed: mobile phase A was 48 mmol L-1 KH2PO4 with pH adjusted to 6.25, and mobile phase B was methanol/acetonitrile (13/1, v/v). The linear gradient program was: 0% B at 0 minutes, 39% B at 13.3 minutes, 54% B at 19.2 minutes, 60% B at 21.3 minutes, 80% B at 22 minutes, and hold at 80% B for 1 minute. The flow rate was 1.5 mL min-1 and column temperature was maintained at 35 degrees Celsius. Excitation and emission wavelength of the detector was set to 330 nm and 450 nm, respectively. Racemization of amino acid enantiomers occurring during acidic hydrolysis was corrected using the average rates determined on free and protein amino acids (Kaiser and Benner, 2005). Total D-amino acids (D-AA) was defined as the sum of the four D-enantiomers of aspartic acid (D-Asx), glutamic acid (D-Glx), serine (D-Ser), and alanine (D-Ala), which were ubiquitously present in all samples.

Instrument data were organized in Excel spreadsheets. Mass spectrometry data were processed with Matlab code provided in Fu et al. (2020). Final data were prepared with Anaconda Python distribution 2023.09-0, Python version 3.11.5.

- Imported original file "Chemical_data_GB.csv" into the BCO-DMO system.
- Marked "NA" as a missing data value (missing data are empty/blank in the final CSV).
- Converted Date column to YYYY-MM-DD format.
- Renamed fields to comply with BCO-DMO naming conventions.
- Saved the final file as "982177_v1_coastal_dom_galveston_bay.csv".

NSF Award Abstract:
Collaborative Research: Distribution and Cycling of Carboxyl-Rich Alicyclic Molecules (CRAM) in the Ocean

Dissolved organic matter is the largest pool of organic carbon in the ocean. Thus, it plays an important role in carbon storage and climate change. The components of dissolved organic matter are the remains of biological products. As a result, the chemical make-up of dissolved organic matter (DOM) can provide important clues about the processes affecting this carbon pool. By learning more about the chemical structures and reactions that produce DOM, connections can be made to biological and biochemical processes and their importance. This project will study the distribution, production, and removal of one important class of molecules called carboxyl-rich alicyclic molecules (CRAM). These molecules are abundant in marine DOM and play an important role in the ocean carbon cycle. They also affect the supply of the micronutrient iron, and thus have an important influence on primary production and ocean food webs. The research includes field and laboratory experiments and takes advantage of ocean locations with long-term data. Together, the lead scientists will provide training and resources for the graduate students, undergraduate research opportunities for underrepresented groups in STEM, and direct outreach to the public through a website, articles in local newspapers, and talks at public venues.

CRAM make-up 8 to 25% of marine dissolved organic carbon (DOC) and represent a carbon reservoir of >65 Pg C that is equivalent to at least 8% of the atmospheric CO2 pool. CRAM play a central role in the functioning of the microbial carbon pump, a conceptual framework for producing refractory carbon by microbial processes in the ocean. Further, CRAM act as an important ligand of Fe(III) in the ocean, thereby playing an important role in regulating marine primary production. The main project objectives are: (1) measure the variable distribution of CRAM in the ocean, and (2) determine the dominant mechanisms of CRAM production and removal. The project will leverage an extensive set of already collected samples across the world’s oceans and new field work within the framework of the Bermuda Atlantic and Hawaii Ocean Time Series. Field measurements will be combined with experimental approaches to specifically address microbial and photochemical reactions leading to CRAM production and removal. This research will fill important gaps in resolving the key questions about organic carbon cycling in the ocean and explore the role of DOM in mediating the cycles of iron and other trace elements.

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.