Interpretation of complexometric titration data: an intercomparison of methods for estimating models of trace metal complexation by natural organic ligands

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 Ip) 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/Ruži?, 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 (Omanovi? 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

Interpretation of complexometric titration data: An intercomparison of methods for estimating models of trace metal complexation by natural organic ligands

With the common goal ofmore accurately and consistently quantifying ambient concentrations of freemetal 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 stateof- 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., randomnoise. 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 fewparticipants detected too few or toomany classes and consequently reported inaccurate 'measurements' of ambient [Mf]. Since the problematic results arose fromhuman error rather than any specificmethod 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 rootmean 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

The protection and management of the Sargasso Sea: The golden floating rainforest of the Atlantic Ocean: Summary Science and Supporting Evidence Case

The Sargasso Sea is a fundamentally important part of the world’s ocean, located within the North Atlantic sub-tropical gyre with its boundaries defined by the surrounding currents. It is the only sea without land boundaries with water depths ranging from the surface coral reefs of Bermuda to abyssal plains at 4500 m. The Sargasso Sea’s importance derives from the interdependent mix of its physical structure and properties, its ecosystems, its role in global scale ocean and earth system processes, its socio-economic and cultural values, and its role in global scientific research. Despite this, the Sargasso Sea is threatened by a range of human activities that either directly adversely impact it or have the potential to do so. Being open ocean, the Sargasso Sea is part of the High Seas, the area of ocean that covers nearly 50% of the earth’s surface but which is beyond the jurisdiction and responsibility of any national government, and as such it enjoys little protection. To promote the importance of the Sargasso Sea, the Sargasso Sea Alliance was created under the leadership of the Government of Bermuda in 2010. This report provides a summary of the scientific and other supporting evidence for the importance of the Sargasso Sea and is intended to develop international recognition of this; to start the process of establishing appropriate management and precautionary regimes within existing agreements; and to stimulate a wider debate on appropriate management and protection for the High Seas. Nine reasons why the Sargasso Sea is important are described and discussed. It is a place of legend with a rich history of great importance to Bermuda; it has an iconic ecosystem based upon floating Sargassum, the world’s only holopelagic seaweed, hosting a rich and diverse community including ten endemic species; it provides essential habitat for nurturing a wide diversity of species many of which are endangered or threatened; it is the only breeding location for the threatened European and American eels; it lies within a large ocean gyre which concentrates pollutants and which has a variety of oceanographic processes that impact its productivity and species diversity; it plays a disproportionately large role in global ocean processes of carbon sequestration; it is of major importance for global scientific research and monitoring and is home to the world’s longest ocean time series of measurements; it has significant values to local and world-wide economies; and it is threatened by activities including over-fishing, pollution, shipping, and Sargassum harvesting. Apart from over-fishing many of the threats are potential, with few direct causal relationships between specific activities and adverse impacts. But there is accumulative evidence that the Sargasso Sea is being adversely impacted by human activities, and with the possibility of new uses for Sargassum in the future, the lack of direct scientific evidence does not preclude international action through the established precautionary approach. The opportunity to recognise the importance of the Sargasso Sea and to develop and implement procedures to protect this iconic region and the wider High Seas should be taken before it is too late