Methodological comparison of models to estimate organic complexation of Cu in CO2 seeps of the Bay of Plenty - Draft 8 (1)

Running head: ORGANIC COMPLEXATION OF COPPER 1
1. Introduction
Stefan Kuzmanovski
22.05.2014
Supervisors:
Research Associate Prof. Dr. Sylvia Sander
Prof. Dr. Andrea Koschinsky
Earth and Space Sciences
Bachelor of Science Thesis
Methodological comparison of models to estimate organic
complexation of Cu in CO2 seeps of the Bay of Plenty, New Zealand

ORGANIC COMPLEXATION OF COPPER 2
Contents
Acknowledgements .........................................................................................................4
Abstract...........................................................................................................................5
1. Introduction.................................................................................................................7
2. Materials and Methods...............................................................................................12
2.1 Study Site ............................................................................................................12
2.2 Sampling and handling.........................................................................................13
2.3 Obtaining data......................................................................................................14
2.3.1 Total dissolved copper determination (ICP-MS) ............................................14
2.3.2 Speciation Analysis .......................................................................................15
2.4 Theory of Metal-Ligand complexes......................................................................16
2.4.1 Conditional equilibrium of a metal with one organic ligand ...........................16
2.4.2 Conditional equilibrium of a metal with more than one organic ligand ..........17
2.4.3 Competitive exchange with an added ligand - Salicylaldoxime......................17
2.4.4 From Theory to Experimental Data ...............................................................18
2.5 Methods of titration data analysis.........................................................................19
2.6 Quality Assurance................................................................................................21
3. Results.......................................................................................................................21
3.1 Total dissolved copper..........................................................................................21
3.2 Speciation results .................................................................................................22

3.2.1 Analysis of one to two ligand classes using a single analytical window .........22
3.2.2 Analysis of one to two ligand classes comparing the use of KMS for single
analytical window versus a multi-window approach ..........................................................27
4. Discussion .................................................................................................................28
4.1 The Importance of Accurate Speciation Parameters..............................................28
4.3 Initial Data Manipulation .....................................................................................31
4.4 United multi-window analysis..............................................................................32
4.5 The Importance of Back-calculation.....................................................................33
5. Conclusion.................................................................................................................34
Appendix.......................................................................................................................36
References.....................................................................................................................43

Acknowledgements
Many thanks to Associate Prof. Dr. Sylvia Sander for her continued support starting with
my Internship at her laboratory in New Zealand to the final draft of this Thesis. Her comments
and insights in the fields of voltammetry and data analysis made this work possible. Her prompt
responses and continued patience during the writing process greatly improved the quality of this
work.
A wholehearted and special thank you to my academic advisor of three years, course
instructors in countless courses, employer and finally, thesis supervisor Prof. Dr. Andrea
Koschinsky. Her moral support and critical advice has always been a guiding hand during my
academic career and I can only be eternally grateful for having the opportunity to have met and
worked with her. Many things during my undergraduate career would have been impossible
without her belief in my abilities and unconditional support. Thank You!
I would like to thank all the people close to my heart who cheered me on as I was
working long hours on this piece of work. My friend and brother Archishman Sarkar for being
always there for me and giving me the motivation to continue pushing forward. Franziska Pohle
for her unconditional love and motherly care and ability to keep me calm and sane. My dearest
friend Ahmed Cheema who came into my life during times of major uncertainty, he helped me
keep calm, pushing forward and on the right path in life with his rational yet idealistic advice.
Luisa Passlick, who is literally a real life representation of that little voice in my head, always
telling me what the right thing is, and not letting me falter in times of adversity.
Finally, cheering me on from home, my sister and my parents for always being there for
me and offering me unconditional love and support during my whole life. Thank You!

Abstract
Speciation affects trace metal bioavailability. However, determining the organic
speciation of copper in seawater analytically has proven to be a challenging and time-consuming
task. While the electrochemical methods for analyzing seawater for organic copper complexes
and obtaining the complexometric titration data are well-established, the methods used to
analyze and fit the raw titration data are being constantly improved and refined. This paper
compares the speciation parameters ([L1], log K1, [L2] and log K2) obtained using the
conventional single-analytical window methods and the united multiple-analytical window
Gerringa method (AMO method) for a set of real titration data obtained from samples in the Bay
of Plenty, New Zealand. Back-calculation of the titration data from the parameters obtained is
used as a simple but very useful method for quality assurance and determining which method fits
best the original titration data.
Nine samples from the Bay of Plenty in New Zealand were analyzed for copper organic
speciation by CLE-AdCSV (competitive ligand exchange-adsorptive cathodic stripping
voltammetry). We obtain titration data for samples in a region of a CO2 vent system for two
analytical windows (2μM and 10μM SA). First, the 2μM SA analytical window datasets are
resolved with the conventional modeling methods: van den Berg/Ruzic, Scatchard, and single-
window Gerringa methods using the MCC Software (Omanović D1
.). Second, the titration data is
resolved with the united multiple-window Gerringa method using the newly introduced CuSA‐
KMS Template2
(based on Hudson & Bruland 2005 and Sander, Wells et al., 2011).
1
Omanovic D., “MCC – Metal Complexing Calculation”, Available at:
http://gss.srce.hr/pithos/rest/omanovic@irb.hr/files/Software/
2
KMS (Kineteql.xls) by Bob Hudson. Estimation of i‐ligands from multi detection window
titrations – as a united data set. 2011. https://sites.google.com/site/kineteql/

Third, the obtained speciation parameters were used to back-calculate the titration data
and compare the calculated to the experimental titration data for each of the modeling methods in
the single window approach using the BackCalc Software (Omanović D3
.).
This study finds that the single-window (2μM SA) conventional methods agree well with
each other both in the MCC Software and the KMS Template and yield very similar values for the
speciation parameters. However, speciation parameters obtained from a single detection window may
underestimate the actual special of copper compared to the results obtained by employing the united
multiple-analytical window method (specifically, the Automated Multiwindow Optimization: AMO
Method). It is recommended that the AMO method is used to determine the organic complexation of
copper in seawater due to its higher accuracy and better ability to resolve real (non-ideal) data from
natural systems.
Keywords: copper complexation, ligand titrations, voltammetry, CLE-AdCSV, methods
3
Omanovic D., “BackCalc”, Available at: http://gss.srce.hr/pithos/rest/omanovic@irb.hr/files/Software/

1. Introduction
Earth’s Ocean remains one of the most mysterious and unexplored areas on Earth. In fact
according to the NOAA we have investigated only 5% of the Earth Ocean and the rest remain
unexplored and unseen by human eyes4
. Therefore, understanding the biogeochemical cycles of
many elements in the ocean remains a pressing challenge in marine geochemistry. The
importance of understanding biogeochemical cycles in the ocean is underlined by the fact that
many trace metals in the ocean, such as copper (Cu), iron (Fe), cobalt (Co), nickel (Ni) and zinc
(Zn), are essential micronutrients for phytoplankton. In the ocean these micronutrients are what
limits and controls primary productivity (Coale, 1991; Fitzwater et al., 2000; Martin & Fitzwater,
1988). However, many trace metals, depending on concentration and speciation, may also exhibit
toxic effects (Moffett et al., 2011). In some areas of the ocean, such as in hydrothermally active
sites with high or low temperature vents, trace metals can be highly enriched in the water column
and can have a very different speciation compared to trace metals in the open ocean (Sander &
Koschinsky, 2011). The main factor governing the bioavailability and toxicity of trace metals in
the ocean is the speciation of a trace metal, i.e. the chemical form it takes (Batley et al., 2004).
The speciation of a chemical element involves the compounds it forms with other
inorganic and organic molecules or ions. In the case of trace metals, the dissolved compounds
they form are called complexes and they can be inorganic or organic complexes. The inorganic
complexes of trace metals have been studied in detail and are mostly well known and determined
not only for the marine environment but also for many freshwater systems, even though some
4
National Oceanic and Atmospheric Administration (NOAA), United States Department of Commerce
(n.d). To date, we have explored less than five percent of the ocean. NOAA Website. Retrieved September 18, 2013,
from http://oceanservice.noaa.gov/facts/exploration.html

uncertainties are still present (Campos & van den Berg, 1994). However, metals can also
associate with organic compounds (termed ligands) forming organic complexes.
It is well known that complexes with organic compounds play a major role in
determining trace metal bioavailability with the majority of copper and iron bound by organic
ligands in the ocean (Batley et al., 2004; Gledhill & van den Berg, 1994; Luther III, Rozan,
Witter, & Lewis, 2001). Organic complexation influences the bioavailability and toxicity of trace
metals. These organic ligands can either make trace metals more bioavailable, such as
siderophores which assist iron uptake (Hopkinson & Morel, 2009), or make potentially toxic
trace metals less bioavailable, such as in the case of bacterial exopolysaccharides (Hassler et al.,
2011).
While the presence and importance of metal-ligand complexes is widely acknowledged,
the composition and source of these organic ligands is largely unknown. Microorganisms
produce a variety of low molecular weight organic compounds that have very high stability
constants. These compounds contain a number of functional groups such as phosphate,
carboxylic acids, amines, thiols and hydroxyl groups (Luther III et al., 2001). However, the exact
structure of the entire organic macromolecules containing these functional groups is difficult to
determine with currently available analytical methods. In the past some studies have identified
siderophores, phytochelatins, bioremineralization products like humic substances and
exopolymeric substances as some of the many organic ligands capable of binding trace metals in
the ocean (Hassler et al., 2011; Kawakami, Gledhill, & Achterberg, 2006; Velasquez, Nunn, &
Ibisanmi, 2011).
Speciation is characterized by the binding capacity of a ligand for a specific metal of
interest. The binding capacity is governed by the total ligand concentration and the conditional

stability constants of the metal-ligand complex. The conditional stability constants are easily
determined for inorganic complexes of trace metals but determining the conditional stability
constants and total ligand concentration for metal-ligand complexes presents a much more
complex analytical challenge. The most common method of analyzing speciation is by
voltammetry. Most voltammetric work is performed with the hanging mercury drop electrode
(HMDE) or the rotating disk electrode (RDE) with a thin mercury film (TMF) which allows for
(sub)nanomolar measurement of metal-organic complexation in a solution of interest.
Voltammetry can be used for both total dissolved metal concentration determination as well as
for ligand concentration determination. The determinations can be of two types: anodic stripping
voltammetric (ASV), which is useful for metals that react directly at the Hg-electrode (such as
Cu2+
) and competitive ligand exchange-adsorptive cathodic stripping voltammetry (CLE-
AdCSV) which is used for metals that do not react at the electrode directly but have a metal-
ligand complex that does (such as Fe3+
, Cu2+
, etc.) (Campos & van den Berg, 1994; Luther III et
al., 2001). Independent of the type of titration used, much controversy has been present in the
literature about the way parameters reflecting the binding capacity of complexes are calculated
and how many ligand classes are actually reported for a particular metal-ligand complex (Sander
et al, 2011).
Analyzing the data obtained from a voltammetric analysis of seawater samples is a
challenge in its own and different methods have been proposed for the analysis of titration data
obtained for a particular sample. The speciation calculations using the different methods are
outlined in detail in the Methods section and what follows is only a brief outline of the most
common methods used as summarized in Wells et al., (2013)

i. Van den Berg/Ruzic Plot. In the 1980s both van den Berg and Ruzic
independently introduced a linearized form of a Langmuir isotherm that plots an
approximate free metal concentration [Mf] versus a ratio of [Mf]/([Mf]/[ML]).
[ML] is an estimate of the natural metal-ligand complex while [Mf] is indirectly
determined by the experimental (internal) voltammetric sensitivity S. This plot
has become commonly known as the van den Berg/Ruzic plot. If one ligand is
present then a straight line is observed in this plot. When a linear regression is
applied to the plot the total ligand concentration [LT] and the conditional stability
constant 𝐾ML
cond
can be obtained.
ii. Scatchard plot. There is one more linear representation used in the literature
which is called the Scatchard plot, in which [ML] is plotted against [ML]/[Mf]
and [LT] and 𝐾ML
𝑐ond
correspond to the x-intercept and the slope, respectively, in a
linear regression (Scatchard, 1949).
iii. Gerringa plot. A non-linear form of the Langmuir isotherm was developed by
Gerringa where [Mf] is plotted against [ML] such that [LT] and the conditional
stability constant 𝐾ML
cond
can be obtained by non-linear curve fitting. Gerringa et al.
(1995) performed a comparison of their non-linear method to the van den
Berg/Ruzic method and found the non-linear method to be superior to the linear
one because the non-linear method better accounts for the error structure of
complexometric titrations.
Please refer to Figure 1 for an overview of the simulated experimental data of ligand
titration curves and the above mentioned common approaches to data analysis. One or more
classes of ligands can be identified depending on the “detection window” (DW) of the

Figure 1. Diagram showing representative simulated experimental data of ligand titration curves for analysis of one
(panel A) and two (panel E) discrete classes of natural organic ligands present. Remaining panels illustrate common
approaches to data analysis for one (B-D) and two (F-H) ligands using the van den Berg/ Ružić linearization (B and
F), Scatchard linearization (C and G) and the Gerringa non-linear method (D and H) and first approximation of
parameters from these transformations (Scatchard 1949; Ruzic 1982; van den Berg 1982; Gerringa et al. 1995). Red
curves in Panel A and D represent titration data in the absence of a ligand with S being the slope of this linear
relationship (adopted from Wells et. al., 2013)
electrochemical window. However, complexometric titration data analysis by the above methods
becomes challenging when more than one ligand class is present or accounted for in a sample
measurement. This is especially a problem if the conditional stability constants of the unique
ligand classes are too close to each other (Sander et al., 2011).
iv. Multi-window method. Sander et al. (2011) developed the Automated Multi-
window Optimization (AMO) method. This new numerical approach to
voltammetric speciation and parameter estimation employs multiple analytical
windows and a two-step optimization process which simultaneously generates
both speciation parameters and a complete suite of corresponding species

concentrations. Sander et. al (2011) claim that this approach is more powerful,
systematic, and flexible than those previously reported, which since has been
proven (Pizeta et. al., 2014; Wells et al., 2013).
In the present study nine samples from the Bay of Plenty in New Zealand are analyzed
for total ambient copper and for copper speciation by CLE-AdCSV and titration data is obtained
for two analytical detection windows for each sample. The single-window analysis employing
the van den Berg/Ruzic, Scatchard, and Gerringa methods was conducted with the MCC
Software developed by Dario Omanović (Ruđer Bošković Institute) and both the single window
and multiple-window analysis based on the AMO Method was conducted with the KMS
Template based on Hudson & Bruland (2005) and Sander et al., (2011).
2. Materials and Methods
2.1 Study Site
The study site is located in the Southern Ocean in the northwest of the North Island of New
Zealand, more specifically in the Bay of Plenty (Figure 2). The Bay of Plenty is part of the
offshore extension of the Taupo Volcanic Zone, which has three main areas of submarine
hydrothermal activity on the Bay of Plenty continental shelf. From the three sites, the most
intense and extensive area is associated with the late Quaternary faulting of the offshore
Whakatane Fault. Within the Fault area there are four major hydrothermal vent sites one of
which is the Calypso Vent site (Pantin & Wright, 1994). Nine stations were sampled during the
New Zealand autumn (March) during the Bay of Plenty Vents Cruise KAH 1303. The cruise
included sampling locations around the White Island, Calypso and Whale Island vents.

Figure 2. Map showing the location of the sampling sites in the Bay of Plenty, NE New Zealand
2.2 Sampling and handling
In March 2013, a sampling campaign with the RV Kaharoa of NIWA (National Institute
of Water and Atmospheric Research) was carried out in the Bay of Plenty, New Zealand. Depth
profiles were taken using Teflon lined Niskin Go-Flo bottles (General Oceanics, USA) with
external plastic coated springs. The Bottles had been pre-cleaned. The protocol includes using
first mild detergent followed by rinsing with distilled water and then 1% analytical Grade HCL
dilute with MilliQ. This acid was left in the bottles for several days while turning the bottles
several times a day. The bottles were rinsed with MilliQ and dried inside a laminar flow bench
until further used. Go-Flo bottles were attached to a Kevlar hydroline and deployed using a
plastic covered weight and triggered by a PCV coated messenger. Temperature and pH were
measured in-situ, except for some stations where CTD data had to be used. After recovery the

samples were filtered through a 0.2µm polycarbonate cartridge filter (Pall) in a nitrogen filled
clove box to avoid contamination and filled into pre-cleaned LDPE bottles. The bottles for ligand
analysis were frozen immediately at -20°C and remained frozen until used. Hydrothermal
activity was detected by the ship’s sonar, detecting vigorous gas bubbling. All samples other than
the two controls were taken directly above such degassing.
2.3 Obtaining data
2.3.1 Total dissolved copper determination (ICP-MS)
In order to accurately determine the total ligand concentration in a sample, the total
dissolved copper concentrations needs to be determined. The procedure for determining the total
dissolved copper with Cathodic Stripping Voltammetry is well established and has been
extensively used in the past (Campos & van den Berg, 1994). In addition to this well-established
method, recently the reliability and accuracy of methods for running a multi-element dissolved
trace metal analysis of sea water with Graphite Furnace Atomic Absorbance Spectrometer
(GFAAS) and Inductively Coupled Plasma Mass Spectrometer (ICP-MS) have increased and are
widely used for determining dissolved total trace metal concentrations. In both methods, the
major concern is the ability to generate high-quality results while trying to avoid sample
contamination and trace metal losses during sampling, storage, preparation and analysis (Bruland
et al., 1979). However, if one keeps to strict trace metal protocols for sampling, handling, sample
preparation, and analysis, contamination can be kept at a minimum levels(Wurl, 2009).
The major concern with using ICP-MS to determine the total dissolved copper concentration is
the salt matrix and its removal prior to analysis. Removing the salt matrix reduces the possibility
of interferences from the salts and provides the opportunity to pre-concentrate the analyte.

Because the concentration of trace metals is usually 5-6 orders of magnitude lower than the
concentration of the major ions in seawater, pre-concentration of the analyte is crucial for
obtaining reliable results (Bruland et al., 1979).
Several procedures are commonly used for removing the salt matrix and pre-concentrating the
analyte: liquid-liquid extraction (LLE) after complexation of the analyte, solid phase extraction
using a column with stationary sorbent, and co-precipitation of the analyte with a solid phase
produced in-situ (Wurl, 2009). The procedure described by Bruland et al. (1979) based on liquid-
liquid extraction has been most commonly used and has proven reliable and widely applicable
especially to first row transition metals as well as some heavier elements like Cd and Pb. Another
advantage of LLE is high accuracy and high recoveries down to the pM-level, something that is
difficult to be achieved with other methods. This makes LLE the method of choice for open
ocean and coastal seawater analysis, both of which have low concentrations of dissolved trace
metals (Wurl, 2009). However, there are disadvantages to this procedure. The procedure is more
time consuming, requires the use of a lot of chloroform solvent and cannot be automated.
Valuating accuracy and reliability over these disadvantages, as well as the need to perform a
multi-element trace metal analysis of the sea water samples, the procedure of Bruland et al.
(1979) was used in this study to analyze the sample for total dissolved copper concentration.
2.3.2 Speciation Analysis
The procedure used to determine the organic complexation of Cu in the seawater samples
is cathodic stripping voltammetry (CSV) with ligand competition using salicylaldoxime (SA) as
outlined by Campos & van den Berg (1992).

10 mL sub-samples from every site were aliquoted into a series of up to 12 clean 10 mL
Teflon bottles. 100 µL of 0.01M borate buffer and 1mL of 2µM or 10µM SA solution were added
(therefore, data for both 2uM and 10um titration windows was obtained) and equilibrated for one
hour. Copper solution was then added to the twelve of the bottles in a range between 5 and
200nM (with more closely spaced additions at lower concentrations and more widely spaced
additions at higher concentrations). The very high Cu additions were used in an attempt to ensure
saturation of any weak ligand class. This way of preparing the samples yielded in total up to 12
titration data points for each detection window per sample (Station). The voltammetric Teflon
bottles were sealed tight and kept overnight to equilibrate (between 16 and 20 hours). The labile
Cu concentration (the Cu which reacted with the added SA) in each bottle was determined by
CSV using a 30 second deposition time, at a deposition potential of -0.15V.
2.4 Theory of Metal-Ligand complexes
The theory behind the chemical equilibrium of a metal with one or more organic ligand classes in
a natural aqueous system has been described previously together with the different analytical
methods for analysis of titration data (Campos & van den Berg, 1994; Gerringa et. al., 1995;
Scatchard, 1949). However, many different ways of representing the theory can be found in the
literature over the last few decades. In an effort to establish a common nomenclature here we
will summarize the nomenclature used by Wells et. al. (2013), which provides a very coherent
resume of the basics needed to understand the chemistry of metal-ligand complexes.
2.4.1 Conditional equilibrium of a metal with one organic ligand
The inorganic complexation of a metal, M, with an inorganic ligand, YIN, in a natural aqueous
system can be described as follows:

Mf + YIN
𝐾IN
↔ M’ (1)
where M’ is the inorganic complex.
The organic complexation with a natural uncomplexed organic ligand L’
Mf + L’
𝐾ML
cond
↔ ML (2)
where ML is the organic ligand – metal complex
Thus, the total speciation for a metal in a sample containing one organic ligand can be described
as:
[MT] = [M’] + [ML] + [Mf ] (3)
A simple conditional equilibrium constant can be written for Eq. 2:
𝐾ML
cond
=
[ML]
[Mf][L′]
, (4)
and
[L’]= [LT] - [ML], (5)
where LT stands for total dissolved ligands.
Finally a side reaction coefficient can be established: (see Wells et al., (2013) for the derivation)
αML =
𝐾ML
cond
[LT]
(1+𝐾ML
cond[Mf]
(6)
2.4.2 Conditional equilibrium of a metal with more than one organic ligand
For a system with more than one discrete natural ligand class (i.e. L1, L2, L3, … Li) where
L1 denotes the stronger ligand class measured and the L2, L3, etc. the progressively weaker ligand
classes, the above expressions need to be adjusted. Specifically, Eq. 3 becomes
[MT] = [M’] + [ML1] + [ML2] + … + [MLi] + [Mf ] (7)
2.4.3 Competitive exchange with an added ligand - Salicylaldoxime
CLE-AdCSV (Competitive Ligand Exchange- Adsorptive Cathodic Stripping Voltammetry)
requires the addition of a well characterized competitive added ligand (AL) that forms an

adsorptive and electroactive complex of the stoichiometry 1:x with the metal under investigation
at the surface of a mercury drop electrode. From this follows that the concentration of the metal-
ligand complex can be related to the free metal concentration via the side reaction coefficient:
[M(AL)x] = 𝛼M(AL)x ∗ [Mf] (8)
In this paper the method of determining of copper complexation in sea water by cathodic
stripping voltammetry and ligand competition with salicylaldoxime (See section 2.5.2 for
method description) was used to analyze the sea water samples (Campos & van den Berg, 1994).
Therefore, equation 7 becomes:
[CuSA] = 𝛼CuSA ∗ [Cuf] (9)
2.4.4 From Theory to Experimental Data
The electroactive species in an AdCSV scan, [M(AL)x] is proportional to the measured peak
current, IP, according to the following formula:
[Mf]=
𝐼P
𝑆∗αM(AL)x
(10)
where S is the analytical sensitivity
The sensitivity S is usually obtained as the slope of the linear regression of IP versus [MT] at the
highest concentrations of metal additions. This is called the internal sensitivity SSIC
. However,
other ways of calculating S have been proposed and might be superior (Hudson, Rue, & Bruland,
2003). However, whichever form of S is used in the end, it can be used in Eq. 10 to estimate the
[Mf] and [MLi], the parameters needed for estimating the speciation parameters 𝐾ML
cond
and [Li]
via the established titration data analysis methods.

2.5 Methods of titration data analysis
In this section a brief overview of the main mathematical formulas used to analyze the titration
data with the different evaluation methods is presented. Practically, the analysis and evaluation
was done with the MCC-Metal Complexation Calculation software (Omanović D.,) for the single
window analysis and the KMS Template (Based on Hudson & Bruland (2005) and Sander, Wells
et al.,(2011)) for single and multiple window analysis of the dataset. Figure 1 illustrates the
common approaches to data analysis for one (B-D) and two (F-H) ligands using the van den
Berg/ Ružić linearization (B and F), Scatchard linearization (C and G) and the Gerringa non-
linear method (D and H) and first approximation of parameters from these transformations
(Scatchard 1949; Ruzic 1982; Campos & van den Berg, 1994; Gerringa et al. 1995).
2.5.1 Van den Berg/Ruzic linearization
For only one organic ligand, the van den Berg/Ruzic linearization (Campos & van den
Berg, 1994; Ruzic, 1982; Wells et al., 2013) can be estimated with the following formula:
[𝑀𝑓]
[𝑀𝐿]
=
[𝑀𝑓]
[𝐿𝑡]
+
1+𝛼𝑀(𝐴𝐿)𝑥
[𝐿𝑡]∗𝐾 𝑀𝐿
𝑐𝑜𝑛𝑑 (11)
Assuming that [ALf] ≈ [ALT] and that for high values of added metal [MT] ~ α’ *[Mf], which
enables estimation of [Mf] from experimental data through Eq. 11.
[ML]=[MT]- [MT] ~ α’ *[Mf] (12)
If [Mf] vs. [Mf]/[ML] is plotted then it follows that [LT] is the reciprocal of the slope of the linear
regression of the plot and 1/𝐾ML
𝑐ond
is the x-intercept of this plot.
2.5.2 Scatchard linearization
The Scatchard linearization method (Ruzic, 1982; Scatchard, 1949) uses the same
quantities [Mf] and [ML] as estimated by the van den Berg/Ruzic linearization but employs the
following equilibrium equation:

[ML]
[Mf]
= − 𝐾ML
cond[ 𝑀𝐿] + 𝐾ML
cond
[LT] (13)
In this method [ML] is plotted against [Mf]/[ML] and the negative slope of this plot yields the
𝐾ML
cond
and the x-axis intercept is [LT].
2.5.3 Non-linear Gerringa equation
The non-linear Gerringa equation (Gerringa et al., 1995) can be derived if equations 4
and 5 are combined and rearranged so that the following relationship can be obtained:
[ML] =
𝐾ML
cond
[Mf][LT]
1+ 𝐾 𝑀𝐿
𝑐𝑜𝑛𝑑[Mf]
(14)
The plot of [ML] vs. [Mf] gives us the 𝐾ML
cond
and [LT] by non-linear fitting of the data.
2.5.4 Multiple analytical windows
This approach employs the Morel Tablature (Morel & Hering, 1993) for speciation
calculation using a matrix that is based on input parameters such as [MT], [LiT] and 𝐾ML
cond
and
information on AL. Using an initial guess for the input parameters as a start speciation is
calculated and titration curves are constructed, which are compared to the original measured data
displayed or transformations thereof, e.g. the Gerringa plot (as described above). The program
then iteratively changes the input parameters to minimize the difference in simulated and
measured data until the termination conditions occur. Hudson (2003) first introduced a method
for looking at multiple analytical windows to estimate complexometric titration speciation
parameters. Sander et al. (2011) improved on this method by developing a unified numerical
approach to resolving the multiple analytical windows. Sander et al. (2011) argue that the AMO
approach is more flexible and powerful than other approaches to date. The AMO Method is
automated and, capable of handling any number of discrete ligands, via the use of a front-end
genetic algorithm capable of producing a randomized output that avoids user input bias, and

capable of generating species concentrations that correspond to estimated parameters. A more
detailed discussion on the merits of employing the multiple analytical window approach are
presented in the discussion section of this study.
2.6 Quality Assurance
Determination of the speciation parameters 𝐾ML 𝑖
cond
and [Li] using any of the above three
methods of data analysis requires approximation. However, for any given set of 𝐾ML 𝑖
cond
and [Li],
the speciation in an aqueous system can be calculated exactly, without approximation. This
enables the back-calculation of the original experimental data from the speciation parameter. In
this paper we only use a visual inspection of graphs plotting the experimental titration curves
versus the back-calculated titration curves. However, a quantitative and more numerical
approach can be used, as outlined by Sander et al. (2011) where they define an error function
based on the difference between the calculated (fitted) and observed peak currents. Due to time
and space constraints, calculating the error function is left for a future development of this work.
3. Results
3.1 Total dissolved copper
In Table 1 the relevant information for all the sites sampled is presented. Moreover, total
ambient dissolved copper concentration ([Cu]amb) are shown to range from 0.46 to 1.04 nM
through the measured samples from the Bay of Plenty, New Zealand. Temperatures ranging from
14.1 to 20.6 °C are observed for the different sites, but no significant temperature difference can
be observed between control samples and vent samples for a particular site.

Table 1. Total dissolved copper, location, bottom depth (m), temperature (°C), actual depth (m) and pH for
the Bay of Plenty sites sampled in March 2013.
St. Site ([Cu]amb) Lat S Long E Bottom depth (m) Temperature °C Depth actual (m) pH
10 Whale Island 1.04 37°51.87' 176°58.58' 49.00 20.60 46.40 8.07
11 Whale Island control 0.94 37°46.30' 176°50.06' 49.00 20.00 43.40 8.03
16 Calypso vent 0.57 37°41.23' 177°07.39' 187.00 14.50 147.00 7.93
17 Calypso vent 0.58 37°41.24' 177°07.39' 187.00 14.15 161.19 7.90
19 Calypso control 0.59 37°36.02' 177°00.17' 153.00 14.72 161.19 7.96
21 White Island 0.71 37°32.25' 177°10.01' 216.00 14.10 209.50 7.98
22 White Island 0.50 37°32.28' 177°10.04' 214.00 14.80 168.00 7.98
23 White Island 0.46 37°32.28' 177°10.03' 225.00 20.10 180.00 7.97
3.2 Speciation results
The results obtained for analyzing an analytical window set by an [SA] of 2µM (from
now on referred to as 2 µM SA window) are presented in Section 3.2.1, while analyzing an
analytical window set by an [SA] of 10µM (from now on referred to as 10 µM SA window)
yielded sensible values only in very few instances, therefore, these results are not reported or
discussed here. The 10 µM SA window data was used only for the multiple-analytical window
approach analysis which is presented in Section 3.2.2.
3.2.1 Analysis of one to two ligand classes using a single analytical window
In Table 2 the speciation parameters obtained with the different methods of data analysis
by employing the MCC Software and the KMS Template for the 2 µM SA window. [L1] values
range from 3.70 nM to 29.60 nM and the log K1 values vary from 11.8 to 14.0 across methods
and across stations. The [L2] values range from 4.84 nM to 57.12 nM and the log K2 values range
from 11.00 to 12.3 also across stations and across methods. The experimental data was modeled
for a two ligand system in the MCC software and although the KMS Template allows for up to
three ligands systems a maximum of two ligands were found for the present data sets.

Table 2. All concentrations in nM. Comparison of speciation parameters obtained for a 2µM SA window with the Scatchard, non-linear Gerringa and Van den
Berg methods using the MCC software (Omanović D.) and a non-linear Gerringa obtained with the KMS Template (Hudson & Bruland 2005 and Sander
et al., 2011). a
The calculated [L2] value was bellow 10-16
in the KMS Template. b
Fitting for a 2 ligand system failed in the MCC software indicating the titration
data was better suited for a one ligand system. c
The calculated [L2] value was bellow 10-16
in the KMS Template, therefore the associated stability constant is not
reported.

The following observations can be made:
 A second ligand class was detected for 3 stations with both the MCC Software
and the KMS Template (Stations 10, 11, 17). Figure 3 shows [LT], which is the
sum of both [L1] and [L2].
Figure 3. Comparison of [LT] obtained for a single analytical window ([SA]=2µM) with the MCC Software
and the KMS Template (Hudson & Bruland 2005 and Sander et al., 2011). Error bars for the MCC Single window
results reflect the standard deviation of the three methods used in the MCC Software. *
a second ligand class was
detected with the KMS Template. #
a second ligand class was detected with the MCC Software
We can conclude that the [LT] values obtained with the KMS Template are higher or
equal to the values obtained with the MCC Software for these stations. The reason for this could
be the higher Sensitivity, S, used by the KMS Template compared to the MCC Software. If we
take a look at the S values shown in Table 3 and compare to the [LT] values shown in Figure 3,
we can see that the higher the difference of S used by the MCC or the KMS, the higher the
difference in [LT]. This is clearly seen if we look back at Eq. 10 in Section 2.3.4 where we can
see that S is used to determine the free metal concentration in a sample which is then used to
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50.00
60.00
70.00
10 11 16 17 19 21 22 23
[LT]
Station
MCC Single window
KMS Single window
* * *# # # # #

determine the ligand concentrations in a sample. A possible reason for the difference in S
calculated with the MCC and the KMS is that outliers can be better identified and removed with
the MCC Software as there is an overview of all the fitting methods as soon as the titration data
is inputted. In the KMS Template the outliers cannot be identified as easily and removed from
the original data, even though, every effort was made to do so while fitting the data with the
KMS Template. Looking at the back-calculation results in Figure 1A in Panel A (Available in the
Appendix) for Station 10 where there is the largest difference in S (consequently a large
difference in [LT]) we can see that because the second to last data point was not removed from
fitting in the KMS Template, but was removed while fitting in the MCC Software, a different S is
calculated. Please refer to Figures 2A and 3A in the Appendix for a graphical presentation of L1
and L2 concentrations.
Table 3. Comparison of the Sensitivity (S) among the Scatchard, non-linear Gerringa and Van den Berg
methods (2µM SA window) using the MCC software (Omanović D.) and a multi-window (2µM SA and 10µM SA
windows) non-linear Gerringa obtained with the KMS Template (Hudson & Bruland 2005 and Sander et al., 2011).
Another very important reason, which probably has an even greater contribution to the
difference in Sensitivity, S, is that the MCC Software makes the unjustified assumption that [Alf]
~ [ALT] for high Cu additions at 2uM SA. The KMS Template takes the change in [Alf] into
SCATCH GER VDB
AVERAGE
MCC
KMS
St. 2µM SA 2µM SA
Multi-
window
10 0.710 0.616 0.710 0.679 0.840 0.700
11 0.520 0.550 0.550 0.540 0.587 0.677
16 0.531 0.526 0.531 0.529 0.556 0.760
17 0.747 0.747 0.745 0.746 0.852 0.926
19 0.682 0.682 0.682 0.682 0.676 0.863
21 0.580 0.580 0.580 0.580 0.646 0.827
22 0.586 0.586 0.586 0.586 0.626 0.866
23 0.581 0.581 0.581 0.581 0.521 0.867

account in its result as it is able to recalculate the [Alf] for each titration data point individually
(Hudson & Bruland 2005).
In terms of the obtained stability coefficients for the two ligand classes for these stations
we cannot observe a significant difference between the average stability coefficients obtained
with the MCC Software and the KMS Template.
 A second ligand class was detected with only the MCC Software and not with the
KMS Template for 2 stations (Stations 16, 22). The [LT] values obtained with the
KMS Template were much lower compared to the values obtained with the MCC
Software on average. In this case we cannot identify the S as the reason for the
difference in [LT]. As we can see in Table 3, the S calculated with the MCC and
the KMS is comparable. We can conclude that the lower [LT] obtained with the
KMS Template is due to its inability to detect a second ligand class, therefore,
completely neglecting the contribution of the second ligand class towards the
[LT].
 A second ligand class was not detected with either the MCC or the KMS for 3
stations (Station 19, 21, 23). Looking the Figure 3, we can conclude that the [LT]
values obtained with the MCC Software are higher or equal to the values obtained
with the KMS Template for these stations. In this case, we can also relate the
difference in S to the difference in [LT] values. If we take a look at the back-
calculation results for Stations 21 and 23 where the largest difference in [LT] is
present we can conclude that the back-calculated curve for the results obtained
with the MCC Software with the different methods is closer to and better fits the

original experimental data curve. Again the reason can be traced to the ability of
better identifying and removing outliers with the MCC Software.
 No significant difference can be observed between the stability constants obtained
for the different stations with the different methods for a single analytical window.
All values are comparable and within an order of magnitude from each other for
each Stations as it can be seen in Figures 4A and 5A in the Appendix.
3.2.2 Analysis of one to two ligand classes comparing the use of KMS for single
analytical window versus a multi-window approach
Table 4 shows the speciation parameters obtained by employing the KMS Template for
the individual 2 µM SA detection window compared with the speciation parameters obtained
with a multi-window approach (a simulations resolution of 2µM SA and 10µM SA windows) in
the KMS Template. In both, the single window approach and the multi-window approach, a
strongest [L1] ligand class was detected with values ranging from 4.48 nM to 17.14 nM and log
K1 values between 12.1 and 13.6. Table 4, illustrates that significant differences can be seen
between the [L1] and log K1 obtained with a single window approach and a multi-window
approach. A second ligand class L2 was detected with the multi-window approach, for 6 out of
the 8 stations, compared to 3 out of 8 for the single-window approach. The [L1] values range
between 20.28 153.85 nM and the log K1 between 10.2 and 11.5. Refer to Figure 6A in the
Appendix for a graph comparing [LT] between the single and multi-window approaches.

Table 4. Comparison of speciation parameters obtained for a 2µM SA window and a multi-window (2µM
SA and 10µM SA windows) non-linear Gerringa fit obtained with the KMS Template
(Hudson & Bruland 2005 and Sander et al., 2011).
The KMS Template employs a numerical approach to calculating speciation called the
Automated Multiwindow Optimization (AMO) method. The advantages and higher accuracy of
the AMO Method which is behind the KMS Template are discussed in the Methods section of
this paper and contribute significantly to the reason for the KMS Template-multi results being
different compared to the single-window results, and why this paper considers the KMS
Template-multi results as a more accurate representation of the actual speciation of Copper in the
seawater samples. Moreover, recent developments concerning the intercomparison of simulated
data analysis show that the accuracy of using multi-detection window analysis is on average
better than using just one window and this can be show with back-calculation of data (See
Section 4. 5 of this Paper.
4. Discussion
4.1 The Importance of Accurate Speciation Parameters
There are two main reasons why the speciation of Copper, among other trace metals,
should be studied: Copper toxicity and characterization of the biogeochemical cycles of Copper.
These reasons are elaborated in detail in the Introduction section of this paper. The purpose of a
Site St. ([Cu]amb) 2µM Multi 2µM Multi 2µM Multi 2µM Multi 2µM Multi
Whale Island 10 1.04 4.58 7.54 13.6 12.8 57.12 32.41 11.4 11.3 61.71 39.96
Whale Island control 11 0.94 4.48 7.19 13.2 12.8 21.04 58.50 11.5 11.0 25.52 65.69
Calypso vent 16 0.57 10.19 4.35 12.6 13.5 - 77.87 - 11.1 10.19 82.22
Calypso vent 17 0.58 5.17 17.14 13.1 12.1 20.28 - 11.2 - 25.45 17.14
Calypso control 19 0.59 8.12 8.98 12.3 12.3 - 155.93 - 10.2 8.12 164.91
White Island 21 0.71 8.38 5.44 12.7 13.1 - 35.33 - 11.2 8.38 40.77
White Island 22 0.50 9.25 12.51 12.5 12.3 - - - - 9.25 12.51
White Island 23 0.46 9.41 7.17 12.3 12.6 - 153.85 - 10.7 9.41 161.02
L1 logK1 L2 logK2 LT
a
a
a
a
a
a
a
a
a
a
a
a

study will ultimately determine to what extent time should be put into determining precise
speciation parameters. This paper finds that in cases where the total dissolved copper
concentration exceed the strongest ligand class’s concentration, characterization of the weaker
ligand classes might not be of great importance, however, in the opposite case and in cases where
a study looks at the biogeochemical cycle of Copper, an accurate and precise determination of
speciation parameters is of great importance.
Throughout the Bay of Plenty, the total dissolved copper is strongly complexed by natural
organic ligands in solution. These ligands complex most of the total dissolved copper at the
stations throughout the Bay of Plenty with the strong Cu-binding ligands concentrations
exceeding the dissolved copper concentrations at all sides. The concentration of the stronger L1
ligand pool (determined at an analytical window set by an [SA] of 2µM for the Scatchard,
Gerringa and van den Berg methods with the MCC Software and the KMS Template) versus the
total dissolved copper concentrations at the different sides is illustrated in Figure 4.The Figure
indicates that the L1 concentrations exceed the [Cu]amb for all stations by a very large margin.
This means that the L2 ligand class doesn’t play a major role in complexing the ambient copper,
as almost all the [Cu]amb is already completely bound by the stronger L1 ligand class. This has
implications for the importance of studying the complexation of Copper beyond the strongest
ligand class and the time and attention that should be spent on estimating the weaker ligand
classes. Considering that in all the stations in the Bay of Plenty that were the subject of this
study, all of the free copper, Cu2+
(which is always less than the total dissolved [Cu]amb, as can be
seen in Eq. 3, the free Copper is already bound in organic complexes which render the copper
species nontoxic. Results of previous studies also support the argument that in cases where the

Figure 4. Detection window set at [SA]=2µM. Ambient total dissolved copper concentrations throughout
the Bay of Plenty, New Zealand and corresponding strong [L1] ligand class concentrations. A 1:1 line is drawn on
the graph.
strongest ligand class is by far in excess compared to the total dissolved Copper, the parameters
of the second ligand class are not of significance for copper toxicity (Hudson et al., 2003; Sander
et al., 2011). This leads to a discussion about the importance of providing an exact determination
of speciation parameters if there is clear indication from first estimates that the strongest ligand
class is in excess of the total dissolved Copper. If one is determined to obtain the most accurate
speciation parameters the analysis of titration data can be very time consuming. However, in
some environments the second ligand class can be of significant importance for complexing the
dissolved copper and a very accurate determination of speciation parameters is needed.
Consequently, studies comparing the advantages and efficacy of the different methods for
titration data analysis are of critical importance. In spite of the existence of different data
analysis methods, in the literature we can observe certain trends, for example most studies seem
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[L1],nM
Ambient [Cu]T , nM
SCAT
GER
vdB
KMS

to prefer using the non-linear Gerringa method, and studies have shown the advantage of the
non-linear approach, especially with dealing with data errors in the low end of the titration curve
(Gerringa et al., 1995; Powell & Donat, 2001). However, some have also argued that the non-
linear Gerringa approach can mask the presence of a second ligand class, and a two ligand class
system can be mistaken for a one ligand class (Powell & Donat, 2001). Moreover, recent
developments and a tendency towards employing a united multi-window approach have all
stressed further the use of the Gerringa method (Hudson et al., 2003; Sander et al., 2011; Wells
et al., 2013). The united multi-window approach employing the non-linear Gerringa fit has been
proven to yield the most accurate and precise speciation parameters, however, it does require
substantially more experimental data, as the speciation needs to be measured at multiple
analytical detection windows (ibid).
4.3 Initial Data Manipulation
One of the main differences between employing the MCC Software and the KMS
Template for a single detection window was the user interface of the MCC Software and the
better ability of the analyst to spot titration data outliers and remove them. This is extremely
important especially at the upper end of the titration curve, where outliers can have a significant
effect on the calculated Sensitivity, S. The importance of S for estimating ligand concentrations
is noted and discussed for the Bay of Plenty samples in Section 3.2.1. In terms of comparing the
Gerringa, van den Berg, and Scatchard methods as used to calculate speciation parameters with
the MCC Software by looking at only one detection window (analytical window set by an [SA]
of 2µM) this study cannot make any conclusions about the advantage of one method over the
others. The reason is that during the analysis outliers were removed to ensure the best fit and a
reasonable S for each method individually, therefore, very similar S values were obtained with

the different methods for each station, as it can be clearly seen in Figure 7A. While it has been
shown that some methods are better suited for certain titration data distributions, in most cases
removing the outliers while fitting with a specific data analysis method improves the accuracy of
that method and can yield results very similar across all conventional methods (Gerringa, van
den Berg, and Scatchard methods) when employing them on a analytical window set by an [SA]
of 2µM.
4.4 United multi-window analysis
In theory, there is only one physically possible value for each of the speciation
parameters, Ki, Litotal, and S, however, using single-window approaches, each window will yield
a different value for the parameters and the variability is quite large (Sander et al., 2011). The
multi-window method yields a single value for each parameter that is optimized to the whole
data set across windows. Hudson et. al. (2003) first introduced a method for looking at different
analytical windows to estimate complexometric titration speciation parameters. They introduced
a method for calibration of analytical sensitivity, S, and estimation of concentrations and stability
constants for discrete ligand classes into a single step using nonlinear regression and a new
analytical solution to the one-metal/two-ligand equilibrium problem. Sander et al. (2011)
improved on this method by developing a unified numerical approach to resolving the multiple
analytical windows. The Automated Multi-window Optimization (AMO) approach is automated
and, capable of handling any number of discrete ligands, via the use of a front-end genetic
algorithm capable of producing a randomized output that avoids user input bias, and capable of
generating species concentrations that correspond to estimated parameters. The comparative
results obtained by Sander et. al., (2011) indicate that the performance for both the conventional
methods and the AMO Method approach is challenged by the experimental data structure.

However, overall there are distinct advantages in the performance for the AMO Method (Sander
et al., 2011). Sander et al. (2011) conclude that the AMO Method approach is more flexible and
powerful than other approaches to date and have proved their method using both simulated data
with real noise and experimental data from seawater samples (Sander et al., 2011; Wells et al.,
2013)
When looking at the comparison of the speciation parameters outlined in Section 3.2.2
we can note a significant difference in speciation paramters obtained. However, due to time and
space constraints a back-calculation for the multi-window KMS data could not be performed and
presented in this paper. This might have strengthened the argument that the AMO method is
better to the conventional methods for our particular data set. On the other hand, findings
presented by the Scientific Committee on Oceanic Research Working Group 139
“Intercomparison of estimating metal binding ligand parameters from simulated data using
different fitting approaches”(Pizeta et al., in preparation 2014) clearly indicate that back-
calculated data from united multiple-window approaches match titration data much better
compared to single window datasets analyzed with conventional methods. What can actually be
deduced from our data anlysis is that the AMO Method is better at detecting a second ligand in
more cases that a single-window analysis which is one of the major strengths of the AMO
Method, as it can be seen in Table 4.
4.5 The Importance of Back-calculation
Wells et al., (2013) establish the importance of employing back-calculation of the original
titration curves from the calculated speciation parameters in order to visually investigate the fit
of the calculated versus the original titration curves and the associate error function (Wells et al.,
2013). While this paper used only a visual inspection of the back-calculation curves for the

single analytical window analysis to determine if a particular data modeling matched the original
titration curve, employing the more quantitative approach of Wells et. al. (2013) is highly
recommended and can give a better estimate of how much the calculated speciation parameters
reflect actual speciation of Copper in a particular water sample. As can be seen in Figure 1A, the
back-calculated curves can match closely the experimental data titration curves, even though, the
speciation parameters estimated with the different methods varied considerably. Therefore, a
numerical approach to estimating the difference between the calculated and the original titration
curves could be of assistance in determining which fitting method better reflects the original
data.
5. Conclusion
We analyzed nine samples from the Bay of Plenty in New Zealand for copper organic
speciation by CLE-AdCSV (competitive ligand exchange-adsorptive cathodic stripping
voltammetry) and, therefore, we obtained real titration data for samples in a region of a CO2 vent
system for two analytical windows (2μM and 10μM SA). First, the 2μM SA analytical window
datasets was resolved with the conventional modeling methods: van den Berg/Ruzic, Scatchard,
and single-window Gerringa methods using the MCC Software (Omanović D.). Second, the
titration data was resolved with the united multiple-window Gerringa method (more specifically
the AMO method) using the newly introduced KMS Template.
The study compared and analyzed the speciation parameters obtained for a single
detection window with the conventional methods in the MCC Software and the AMO Method in
the KMS Template. Our results indicate that similar speciation parameters can be obtained with
all the conventional methods if the data structure is similar and the outliers are removed from the
titration curves. Taking into account the importance of removing outliers, this study recommends

the use of the MCC Software for single detection window analysis, while not being able to make
any conclusions on the advantage of one conventional method over the others. Visual inspection
of back-calculated titration curves proved a powerful tool for determining which speciation
parameters reflect the actual speciation.
This study also reports speciation parameters obtained for a single detection window and
a multiple-window approach (AMO Method) using the KMS Template. Due to time and space
constrains and readily available literature on the AMO Method being more powerful, systematic
and flexible compared to other methods, a back-calculation was not conducted for the multiple-
window analysis. Based on recent developments and significant proofs (via back-calculation) of
the advantages of the AMO Method compared to single-window analysis, the recommendaton of
this study is to employ the AMO Method, when appropriate, in order to yield the most accurate
and precise speciation parameters.

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[MT][MT]
[MT][MT]
[MT][MT]
[MT][MT]
[Ip] [Ip]
[Ip] [Ip]
[Ip] [Ip]
[Ip] [Ip]
Appendix
Figure 1A. Inter-comparison of back-calculated curves from the fitted speciation parameters shown in
Table 2.
Panels: A Station 10
B Station 11
C Station 16
D Station 17
E Station 19
F Station 21
G Station 22
H Station 23

Figure 2A. Detection window set at [SA]=2µM. Comparison of [L1] values obtained with the Scatchard,
non-linear Gerringa and Van den Berg methods using the MCC software (Omanović D.) and a non-linear Gerringa
obtained with the KMS Template (Hudson & Bruland 2005 and Sander et al., 2011)
Whale
Island
Whale
Island
control
Calypso
vent
Calypso
vent
Calypso
control
White
Island
White
Island
White
Island
[L1] 3.94 3.70 4.22 4.59 8.51 27.10 10.80 21.40
GER 3.88 3.89 5.81 2.62 8.98 23.90 6.28 19.30
vdB 4.04 4.94 5.60 2.82 8.51 29.60 6.28 19.30
KMS 4.58 4.48 10.19 5.17 8.12 8.38 9.25 9.41
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Island
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Calypso
vent
Calypso
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White
Island
White
Island
White
Island
SCAT 39.40 15.80 17.60 7.69 0 0 4.84 0
GER 27.00 21.50 15.40 7.21 0 0 7.28 0
vdB 40.60 23.80 18.10 6.19 0 0 7.28 0
KMS 57.12 21.04 0 20.28 0 0 0.00 0
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Figure 3A. Detection window set at [SA]=2µM. Comparison of [L2] values obtained with the Scatchard,

10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5
Whale Island
Whale Island control
Calypso vent
Calypso vent
Calypso control
White Island
White Island
White Island
SCAT GER vdB KMS
Figure 4A. Detection window set at [SA]=2µM. Comparison of log K1values obtained with the Scatchard,

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0
Whale Island
Whale Island control
Calypso vent
Calypso vent
Calypso control
White Island
White Island
White Island
SCAT GER vdB KMS
Figure 5A. Detection window set at [SA]=2µM. Comparison of log K2 values obtained with the Scatchard,
non-linear Gerringa and van den Berg methods using the MCC software (Omanović D.) and a non-linear Gerringa

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[LT]
Station
2uM SA Window
KMS Multi
Figure 6A. Comparison of LT obtained for a single analytical window (2uM SA) and for multi-window
(2µM SA and 10µM SA windows) non-linear Gerringa fit obtained with the KMS Template.

Figure 7A. Sensitivities calculated by the MCC Software for the different titration data analysis methods
compared to average of all three methods for a particular station. The analyst removed outliers to obtain the best fit
for each individual method and for every station.
0.000
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