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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.

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.

Earley, PJ, Swope BL, Barbeau K, Bundy R, McDonald JA, Rivera-Duarte I.  2014.  Life cycle contributions of copper from vessel painting and maintenance activities. Biofouling. 30:51-68.   10.1080/08927014.2013.841891   AbstractWebsite

Copper-based epoxy and ablative antifouling painted panels were exposed in natural seawater to evaluate environmental loading parameters. In situ loading factors including initial exposure, passive leaching, and surface refreshment were measured utilizing two protocols developed by the US Navy: the dome method and the in-water hull cleaning sampling method. Cleaning techniques investigated included a soft-pile carpet and a medium duty 3M((TM)) pad for fouling removal. Results show that the passive leach rates of copper peaked three days after both initial deployment and cleaning events (CEs), followed by a rapid decrease over about 15days and a slow approach to asymptotic levels on approximately day 30. Additionally, copper was more bioavailable during a CE in comparison to the passive leaching that immediately followed. A paint life cycle model quantifying annual copper loading estimates for each paint and cleaning method based on a three-year cycle of painting, episodic cleaning, and passive leaching is presented.

Buck, KN, Moffett J, Barbeau KA, Bundy RM, Kondo Y, Wu JF.  2012.  The organic complexation of iron and copper: an intercomparison of competitive ligand exchange-adsorptive cathodic stripping voltammetry (CLE-ACSV) techniques. Limnology and Oceanography-Methods. 10:496-515.   10.4319/lom.2012.10.496   AbstractWebsite

Characterization of the speciation of iron and copper is an important objective of the GEOTRACES Science Plan. To incorporate speciation measurements into such a multinational program, standard practices must be adopted that allow data from multiple labs to be synthesized. Competitive ligand exchange-adsorptive cathodic stripping voltammetry (CLE-ACSV) is the primary technique employed for measuring metal-binding ligands and determining metal speciation in seawater. The determination of concentrations and conditional stability constants of metal-binding ligands is particularly challenging, as results can be influenced both by experimental conditions and interpretation of titration data. Here, we report an investigation between four laboratories to study the speciation of iron and copper using CLE-ACSV. Samples were collected on the GEOTRACES II intercomparison cruise in the North Pacific Ocean in May 2009 at 30 degrees N, 140 degrees W. This intercomparison was carried out shipboard and included an assessment of the viability of sample preservation by freezing. Results showed that consensus values could be obtained between different labs, but that some existing practices were problematic and require further attention in future work. A series of recommendations emerged from this study that will be useful in implementing multi-investigator programs like GEOTRACES.