The University of Southampton
University of Southampton Institutional Repository

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

Interpretation of complexometric titration data: An intercomparison of methods for estimating models of trace metal complexation by natural organic ligands
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.
Metal ions, Organic ligands, Speciation, Complexation, Equilibrium constant, Titration, Voltammetry, Multi-window titration, Data analysis
0304-4203
3-24
Pižeta, I.
d6c78efb-8af5-4ca1-a000-39d507f6a454
Sander, S.G.
282be66d-741b-4a63-bb9b-a168bce1e8e6
Hudson, R.J.M.
a531949e-ee0b-4672-99e1-4886a1f0f1e2
Omanović, D.
38de8b2f-451b-475b-8d2c-d96e0f1c6a26
Baars, O.
2b28da1c-ab39-4fc6-9566-cd67dedf5786
Barbeau, K.A.
12a4a0ed-44c0-4eff-b563-add19320e81d
Buck, K.N.
f753f4db-f7c6-4967-93a7-553769aa96a7
Bundy, R.M.
068f74dd-3cb1-42b3-a84a-8cb83446963c
Carrasco, G.
ecd5520e-2903-4f3d-9341-de1d11abdf3d
Croot, P.L.
25daf592-4186-462b-b0fd-24c70d3fd605
Garnier, C.
8280c489-4355-465b-9c70-2b6b35ad0c0d
Gerringa, L.J.A.
740d8c6b-114c-4432-9f71-380fd9965179
Gledhill, M.
da795c1e-1489-4d40-9df1-fc6bde54382d
Hirose, K.
cd997f11-a62e-4f4a-9b28-a366e6525e96
Kondo, Y.
6f45bef3-8713-479f-978f-0a78908c92f5
Laglera, L.M.
b05e9f26-d867-4ecc-a85f-fd0b42dfe1b9
Nuester, J.
631b959e-e832-4e06-9a84-a963c0e93622
Rijkenberg, M.J.A.
b801d0be-bd6a-4dbf-beb0-48a777fe5b64
Takeda, S.
76b8ee0b-a770-4545-9d76-2d7207268c63
Twining, B.S.
b7863029-75cc-48ea-ba98-7997e166af2a
Wells, M.
e36f98e6-36ef-4fec-8fd5-9402faf02d6c
Pižeta, I.
d6c78efb-8af5-4ca1-a000-39d507f6a454
Sander, S.G.
282be66d-741b-4a63-bb9b-a168bce1e8e6
Hudson, R.J.M.
a531949e-ee0b-4672-99e1-4886a1f0f1e2
Omanović, D.
38de8b2f-451b-475b-8d2c-d96e0f1c6a26
Baars, O.
2b28da1c-ab39-4fc6-9566-cd67dedf5786
Barbeau, K.A.
12a4a0ed-44c0-4eff-b563-add19320e81d
Buck, K.N.
f753f4db-f7c6-4967-93a7-553769aa96a7
Bundy, R.M.
068f74dd-3cb1-42b3-a84a-8cb83446963c
Carrasco, G.
ecd5520e-2903-4f3d-9341-de1d11abdf3d
Croot, P.L.
25daf592-4186-462b-b0fd-24c70d3fd605
Garnier, C.
8280c489-4355-465b-9c70-2b6b35ad0c0d
Gerringa, L.J.A.
740d8c6b-114c-4432-9f71-380fd9965179
Gledhill, M.
da795c1e-1489-4d40-9df1-fc6bde54382d
Hirose, K.
cd997f11-a62e-4f4a-9b28-a366e6525e96
Kondo, Y.
6f45bef3-8713-479f-978f-0a78908c92f5
Laglera, L.M.
b05e9f26-d867-4ecc-a85f-fd0b42dfe1b9
Nuester, J.
631b959e-e832-4e06-9a84-a963c0e93622
Rijkenberg, M.J.A.
b801d0be-bd6a-4dbf-beb0-48a777fe5b64
Takeda, S.
76b8ee0b-a770-4545-9d76-2d7207268c63
Twining, B.S.
b7863029-75cc-48ea-ba98-7997e166af2a
Wells, M.
e36f98e6-36ef-4fec-8fd5-9402faf02d6c

Pižeta, I., Sander, S.G., Hudson, R.J.M., Omanović, D., Baars, O., Barbeau, K.A., Buck, K.N., Bundy, R.M., Carrasco, G., Croot, P.L., Garnier, C., Gerringa, L.J.A., Gledhill, M., Hirose, K., Kondo, Y., Laglera, L.M., Nuester, J., Rijkenberg, M.J.A., Takeda, S., Twining, B.S. and 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. (doi:10.1016/j.marchem.2015.03.006).

Record type: Article

Abstract

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.

Text
1-s2.0-S0304420315000596-main.pdf - Version of Record
Download (2MB)

More information

Published date: 20 July 2015
Keywords: Metal ions, Organic ligands, Speciation, Complexation, Equilibrium constant, Titration, Voltammetry, Multi-window titration, Data analysis
Organisations: Ocean and Earth Science

Identifiers

Local EPrints ID: 377383
URI: http://eprints.soton.ac.uk/id/eprint/377383
ISSN: 0304-4203
PURE UUID: 2f21d9c3-65f4-4650-ab8b-f545e767e5d8

Catalogue record

Date deposited: 22 May 2015 15:26
Last modified: 08 Jan 2022 09:24

Export record

Altmetrics

Contributors

Author: I. Pižeta
Author: S.G. Sander
Author: R.J.M. Hudson
Author: D. Omanović
Author: O. Baars
Author: K.A. Barbeau
Author: K.N. Buck
Author: R.M. Bundy
Author: G. Carrasco
Author: P.L. Croot
Author: C. Garnier
Author: L.J.A. Gerringa
Author: M. Gledhill
Author: K. Hirose
Author: Y. Kondo
Author: L.M. Laglera
Author: J. Nuester
Author: M.J.A. Rijkenberg
Author: S. Takeda
Author: B.S. Twining
Author: M. Wells

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×