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2015
Fitzsimmons, JN, Bundy RM, Al-Subiai SN, Barbeau KA, Boyle EA.  2015.  The composition of dissolved iron in the dusty surface ocean: An exploration using size-fractionated iron-binding ligands. Marine Chemistry. 173:125-135.   10.1016/j.marchem.2014.09.002   AbstractWebsite

The size partitioning of dissolved iron and organic iron-binding ligands into soluble and colloidal phases was investigated in the upper 150 m of two stations along the GA03 U.S. GEOTRACES North Atlantic transect. The size fractionation was completed using cross-flow filtration methods, followed by analysis by isotope dilution inductively-coupled plasma mass spectrometry (ID-ICP-MS) for iron and competitive ligand exchange-adsorptive cathodic stripping voltammetry (CLE-ACSV) for iron-binding ligands. On average, 80% of the 0.1-0.65 nM dissolved iron (<0.2 mu m) was partitioned into the colloidal iron (cFe) size fraction (10 kDa < cFe <0.2 gm), as expected for areas of the ocean underlying a dust plume. The 1.3-2.0 nM strong organic iron-binding ligands, however, overwhelmingly (75-77%) fell into the soluble size fraction (<10 kDa). As a result, modeling the dissolved iron size fractionation at equilibrium using the observed ligand partitioning did not accurately predict the iron partitioning into colloidal and soluble pools. This suggests that either a portion of colloidal ligands is missed by current electrochemical methods because they react with iron more slowly than the equilibration time of our CLE-ACSV method, or part of the observed colloidal iron is actually inorganic in composition and thus cannot be predicted by our model of unbound iron-binding ligands. This potentially contradicts the prevailing view that greater than >99% of dissolved iron in the ocean is organically complexed. Disentangling the chemical form of iron in the upper ocean has important implications for surface ocean biogeochemistry and may affect iron uptake by phytoplankton. (C) 2014 Elsevier B.V. All rights reserved.

Pizeta, I, Sander SG, Hudson RJM, Omanovic D, Baars O, Barbeau KA, Buck KN, Bundy RM, Carrasco G, Croot PL, Garnier C, Gerringa LJA, Gledhill M, Hirose K, Kondo Y, Laglera LM, Nuester J, Rijkenberg MJA, Takeda S, Twining BS, Wells M.  2015.  Interpretation of complexometric titration data: An intercomparison of methods for estimating models of trace metal complexation by natural organic ligands. Marine Chemistry. 173:3-24.   10.1016/j.marchem.2015.03.006   AbstractWebsite

With the common goal of more accurately and consistently quantifying ambient concentrations of free metal ions and natural organic ligands in aquatic ecosystems, researchers from 15 laboratories that routinely analyze trace metal speciation participated in an intercomparison of statistical methods used to model their most common type of experimental dataset, the complexometric titration. All were asked to apply statistical techniques that they were familiar with to model synthetic titration data that are typical of those obtained by applying state-of-the-art electrochemical methods - anodic stripping voltammetry (ASV) and competitive ligand equilibration-adsorptive cathodic stripping voltammetry (CLE-ACSV) - to the analysis of natural waters. Herein, we compare their estimates for parameters describing the natural ligands, examine the accuracy of inferred ambient free metal ion concentrations (]M-f]), and evaluate the influence of the various methods and assumptions used on these results. The ASV-type titrations were designed to test each participant's ability to correctly describe the natural ligands present in a sample when provided with data free of measurement error, i.e., random noise. For the three virtual samples containing just one natural ligand, all participants were able to correctly identify the number of ligand classes present and accurately estimate their parameters. For the four samples containing two or three ligand classes, a few participants detected too few or too many classes and consequently reported inaccurate 'measurements' of ambient [M-f]. Since the problematic results arose from human error rather than any specific method of analyzing the data, we recommend that analysts should make a practice of using one's parameter estimates to generate simulated (back-calculated) titration curves for comparison to the original data. The root-mean-squared relative error between the fitted observations and the simulated curves should be comparable to the expected precision of the analytical method and upon visual inspection the distribution of residuals should not be skewed. Modeling the synthetic, CLE-ACSV-type titration dataset, which comprises 5 titration curves generated at different analytical-windows or levels of competing ligand added to the virtual sample, proved to be more challenging due to the random measurement error that was incorporated. Comparison of the submitted results was complicated by the participants' differing interpretations of their task. Most adopted the provided 'true' instrumental sensitivity in modeling the CLE-ACSV curves, but several estimated sensitivities using internal calibration, exactly as is required for actual samples. Since most fitted sensitivities were biased low, systematic error in inferred ambient [M-f] and in estimated weak ligand (L-2) concentrations resulted. The main distinction between the mathematical approaches taken by participants lies in the functional form of the speciation model equations, with their implicit definition of independent and dependent or manipulated variables. In 'direct modeling', the dependent variable is the measured [M-f] (or I-p) and the total metal concentration ([M](T)) is considered independent In other, much more widely used methods of analyzing titration data - classical linearization, best known as van den Berg/Ruzic and isotherm fitting by nonlinear regression, best known as the langmuir or Gerringa methods - [M-f] is defined as independent and the dependent variable calculated from both [M](T) and [M-f]. Close inspection of the biases and variability in the estimates of ligand parameters and in predictions of ambient [M-f] revealed that the best results were obtained by the direct approach. Linear regression of transformed data yielded the largest bias and greatest variability, while non-linear isotherm fitting generated results with mean bias comparable to direct modeling, but also with greater variability. Participants that performed a unified analysis of ACSV titration curves at multiple detection windows for a sample improved their results regardless of the basic mathematical approach taken. Overall, the three most accurate sets of results were obtained using direct modeling of the unified multiwindow dataset, while the single most accurate set of results also included simultaneous calibration. We therefore recommend that where sample volume and time permit, titration experiments for all natural water samples be designed to include two or more detection windows, especially for coastal and estuarine waters. It is vital that more practical experimental designs for multi-window titrations be developed. Finally, while all mathematical approaches proved to be adequate for some datasets, matrix-based equilibrium models proved to be most naturally suited for the most challenging cases encountered in this work, i.e., experiments where the added ligand in ACSV became titrated. The ProMCC program (Omanovic et al., this issue) as well as the Excel Add-in based KINETEQL Multiwindow Solver spreadsheet (Hudson, 2014) have this capability and have been made available for public use as a result of this intercomparison exercise. (C) 2015 The Authors. Published by Elsevier B.V.

Bundy, RM, Abdulla HAN, Hatcher PG, Biller DV, Buck KN, Barbeau KA.  2015.  Iron-binding ligands and humic substances in the San Francisco Bay estuary and estuarine-influenced shelf regions of coastal California. Marine Chemistry. 173:183-194.   10.1016/j.marchem.2014.11.005   AbstractWebsite

Dissolved iron (dFe) and organic dFe-binding ligands were determined in San Francisco Bay, California by competitive ligand exchange adsorptive cathodic stripping voltammetry (CLE-ACSV) along a salinity gradient from the freshwater endmember of the Sacramento River (salinity <2) to the mouth of the estuary (salinity >26). A range of dFe-binding ligand classes was simultaneously determined using multiple analytical window analysis, involving titrations with multiple concentrations of the added ligand,salicylaldoxime. The highest dFe and ligand concentrations were determined in the low salinity end of the estuary, with dFe equal to 131.5 nmol L-1 and strong ligand (log K-Fel, Fe'(cond) >= 12.0) concentrations equal to 139.5 nmol L-1. The weakest ligands (log K-Fel, Fe'(cond) < 10.0) were always in excess of dFe in low salinity waters, but were rapidly flocculated within the estuary and were not detected at salinities greater than 7. The strongest ligands (log K-Fel, Fe'(cond) > 11.0) were tightly coupled to dFe throughout the estuary, with average excess ligand concentrations ([L]-[dFe]) equal to 0.5 nmol L-1. Humic-like substances analyzed via both CLE-ACSV and proton nuclear magnetic resonance in several samples were found to be a significant portion of the dFe-binding ligand pool in San Francisco Bay, with concentrations ranging from 559.5 mu g L-1 to 67.5 mu g L-1 in the lowest and highest salinity samples, respectively. DFe-binding ligands and humic-like substances were also found in benthic boundary layer samples taken from the shelf near the mouths of San Francisco Bay and Eel River, suggesting estuaries are an important source of dFe-binding ligands to California coastal shelf waters. (C) 2014 Elsevier B.V. All rights reserved.

2014
Bundy, RM, Biller DV, Buck KN, Bruland KW, Barbeau KA.  2014.  Distinct pools of dissolved iron-binding ligands in the surface and benthic boundary layer of the California Current. Limnology and Oceanography. 59:769-787.   10.4319/lo.2014.59.3.0769   AbstractWebsite

Organic dissolved iron (dFe)-binding ligands were measured by competitive ligand exchange-adsorptive cathodic stripping voltammetry (CLE-ACSV) at multiple analytical windows (side reaction coefficient of salicylaldoxime, alpha(Fe(SA)2) = 30, 60, and 100) in surface and benthic boundary layer (BBL) samples along the central California coast during spring and summer. The weakest ligands were detected in the BBL at the lowest analytical window with average log K-FeL,Fe'(cond) = 10.2 +/- 0.4 in the summer and 10.8 +/- 0.2 in the spring. Between 3% and 18% of the dFe complexation in the BBL was accounted for by HS, which were measured separately in samples by ACSV and may indicate a source of dFe-binding ligands from San Francisco Bay. The strongest ligands were found in nearshore spring surface waters at the highest analytical window with average log K-FeL,Fe'(cond) = 11.9 +/- 0.3, and the concentrations of these ligands declined rapidly offshore. The ligand pools in the surface and BBL waters were distinct from each other based on principal components analysis, with variances in the BBL ligand pool explained by sample location, and variance in surface waters explained by water mass. The use of multiple analytical window analysis elucidated several distinct iron-binding ligand pools, each with unique distributions in the central California Current system.

2013
Bundy, RM, Barbeau KA, Buck KN.  2013.  Sources of strong copper-binding ligands in Antarctic Peninsula surface waters. Deep-Sea Research Part Ii-Topical Studies in Oceanography. 90:134-146.   10.1016/j.dsr2.2012.07.023   AbstractWebsite

Copper-binding organic ligands were measured during austral winter in surface waters around the Antarctic Peninsula using competitive ligand exchange-adsorptive cathodic stripping voltammetry with multiple analytical windows. Samples were collected from four distinct water masses including the Antarctic Circumpolar Current, Southern Antarctic Circumpolar Current Front, Bransfield Strait, and the shelf region of the Antarctic Peninsula. Strong copper-binding organic ligands were detected in each water mass. The strongest copper-binding ligands were detected at the highest competition strength in the Antarctic Circumpolar Current, with an average conditional stability constant of logK(CuL,Cu2+)(cond) = 16.00 +/- 0.82. The weakest ligands were found at the lowest competition strength in the shelf region with logK(CuL,Cu2+)(cond) = 12.68 +/- 0.48. No ligands with stability constants less than logK(CuL,Cu2+)(cond) = 13.5 were detected in the Antarctic Circumpolar Current at any competition strength, suggesting a shelf source of weaker copper-binding ligands. Free, hydrated copper ion concentrations, the biologically available form of dissolved copper, were less than 10(-14) M in all samples, approaching levels that may be limiting for some types of inducible iron acquisition. (C) 2012 Elsevier Ltd. All rights reserved.

2012
King, AL, Buck KN, Barbeau KA.  2012.  Quasi-Lagrangian drifter studies of iron speciation and cycling off Point Conception, California. Marine Chemistry. 128:1-12.   10.1016/j.marchem.2011.11.001   AbstractWebsite

The distribution and speciation of dissolved Fe (dFe) were measured during four quasi-Lagrangian drogued drifter studies (similar to 4 d duration each) that were conducted in the southern California Current System in May 2006 and April 2007. Three of the four drifter studies were within the coastal upwelling regime and one drifter study was in a warm-core anticyclonic eddy. Incubation bottle experiments were also conducted to determine the degree of phytoplankton Fe limitation and to assess changes in the concentration of Fe-binding ligands. In the coastal upwelling drifter studies, in situ dFe (1.4-1.8 nM) and macronutrients were initially high and declined over time. Fe addition incubation experiments indicated that the phytoplankton community was not Fe limited at the beginning of the coastal upwelling drifter experiments (when mu M nitrate:nM dFe ratios were similar to 7-8). By the end of two of the three drifter studies (when mu M nitrate:nM dFe ratios were similar to 12-19), Fe addition resulted in larger nitrate and silicic acid drawdown, and larger accumulations in chlorophyll a, particulate organic carbon and nitrogen, and diatom and dinoflagellate-specific carotenoid pigments. Fe speciation was measured in situ in three of the four drifter studies with stronger L-1-type ligands found to be present in excess of dFe in all samples. In Fe speciation incubation experiments. L-1-type ligand production was observed in conjunction with phytoplankton growth under Fe-limiting conditions. The results presented here support and add a quasi-Lagrangian perspective to previous observations of dFe and macronutrient cycling over space and time within the California coastal upwelling regime, including Fe limitation within the phytoplankton community in this region and the biological production of Fe-binding ligands concomitant with Fe limitation. (C) 2011 Elsevier B.V. All rights reserved.

2001
Barbeau, K, Rue EL, Bruland KW, Butler A.  2001.  Photochemical cycling of iron in the surface ocean mediated by microbial iron(III)-binding ligands. Nature. 413:409-413.   10.1038/35096545   AbstractWebsite

Iron is a limiting nutrient for primary production in large areas of the oceans(1-4). Dissolved iron(III) in the upper oceans occurs almost entirely in the form of complexes with strong organic ligands(5-7) presumed to be of biological origin(8,9). Although the importance of organic ligands to aquatic iron cycling is becoming clear, the mechanism by which they are involved in this process remains uncertain. Here we report observations of photochemical reactions involving Fe(III) bound to siderophores-high-affinity iron(III) ligands produced by bacteria to facilitate iron acquisition(10-12). We show that photolysis of Fe(III)-siderophore complexes leads to the formation of lower-affinity Fe(III) ligands and the reduction of Fe(III), increasing the availability of siderophore-bound iron for uptake by planktonic assemblages. These photochemical reactions are mediated by the alpha -hydroxy acid moiety, a group which has generally been found to be present in the marine siderophores that have been characterized(13-15). We suggest that Fe(III)-binding ligands can enhance the photolytic production of reactive iron species in the euphotic zone and so influence iron availability in aquatic systems.