Description: 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 ([Mf]), 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 [Mf]. 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 [Mf] and in estimated weak ligand (L2) 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 [Mf] (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/Ružić, and isotherm fitting by nonlinear regression, best known as the Langmuir or Gerringa methods - [Mf] is defined as independent and the dependent variable calculated from both [M]T and [Mf]. Close inspection of the biases and variability in the estimates of ligand parameters and in predictions of ambient [Mf] 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 (Omanović 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.

Description: We will present the combined results of the French GEOTRACES GEOVIDE cruise in the North Atlantic Ocean and the 2015 German GEOTRACES cruise TransArc II in the central Arctic Ocean. Research vessel "Pourquoi pas?" sailed on May 15th from Lisbon to Greenland to arrive in Newfoundland on June 30th 2014, and icebreaker "Polarstern" sailed on August 17th from Tromsoe to explore the Nansen, the Amundsen and the Makarov basins, to arrive in Bremerhaven on October 15th 2015. Total mercury was sampled using ultra-trace clean rosettes and determined on board. In the Atlantic Ocean, surface waters of the Gulf Stream are cooled down as they travel north, and mix at the same time with waters exiting the Arctic Ocean via Fram Strait. These cool and dense surface waters dive to depth in the Greenland and Labrador seas. The North Atlantic Ocean predominantly receives Hg via atmospheric deposition from Europe and North America where industrial Hg emissions peaked in the 1970s. The Hg inputs to the Arctic Ocean are less well-constrained if not unknown. The current debate opposes a primary atmospheric with a river-dominated scenario. We find consistent surface depleted profiles in the North Atlantic Ocean, while we exclusively observe surface enrichments in the Arctic Ocean, at all sampling stations. We will make use of the combined data sets of both cruises to investigate how climate may impact Hg marine biogeochemical cycle, how anthropogenic Hg makes its way into the deep ocean and whether the temporal evolution of emissions is traceable in water masses of different ages. We will also put our new observations in context with recent numerical model evaluations.

Description: In the summer of 2015 a coordinated pan-arctic GEOTRACES study was executed by the Canadian CCGS Amundsen, the US CGC Healy and the German RV Polarstern. For intercalibration purposes, three cross-over stations were visited, one of them at the North Pole. The Polarstern expedition visited the Nansen, Amundsen and Mendeleev basins. On sections across these basins and the Gakkel and Lomonosov Ridge we collected samples for the full set of GEOTRACES key parameters and many additional analyses. The team of natural radionuclides took samples for U-series nuclides. During earlier work in the central Arctic with Polarstern we have quantified export production with 234Th, studied the interaction between scavenging and deep water ventilation using 230Th and 231Pa, and investigated the shelf-basin exchange with radium isotopes. I will give an overview of these results obtained on earlier expeditions, mention first results of the 2015 expedition, and discuss how these tracers can help us to observe changes in deep water circulation and particle flux that may be related to Arctic Oscillation or caused by sea ice retreat.