methodological comparison of models to estimate organic complexation of cu in co2 seeps of the bay...

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/[email protected]/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/

http://gss.srce.hr/pithos/rest/[email protected]/files/Software/

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/[email protected]/files/Software/

http://gss.srce.hr/pithos/rest/[email protected]/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

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 𝐾MLcond 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 𝐾ML𝑐ond

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 𝐾MLcondcan 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’ 𝐾MLcond

↔ 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:

𝐾MLcond =

[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 =𝐾MLcond[LT]

(1+𝐾MLcond[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 𝐾MLcond 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]∗𝐾𝑀𝐿𝑐𝑜𝑛𝑑 (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/𝐾ML𝑐ond 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] + 𝐾MLcond[LT] (13)

In this method [ML] is plotted against [Mf]/[ML] and the negative slope of this plot yields the

𝐾MLcond 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] = 𝐾MLcond[Mf][LT]

1+ 𝐾𝑀𝐿𝑐𝑜𝑛𝑑[Mf]

(14)

The plot of [ML] vs. [Mf] gives us the 𝐾MLcond 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 𝐾MLcond 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 𝐾ML𝑖cond and [Li] using any of the above three

methods of data analysis requires approximation. However, for any given set of 𝐾ML𝑖cond 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). aThe calculated [L2] value was bellow 10-16 in the KMS Template. bFitting for a 2 ligand system failed in the MCC software indicating the titration

data was better suited for a one ligand system. cThe 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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* * *# # # # #


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 contro l 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 contro l 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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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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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)

WhaleIsland

WhaleIsland

control

Calypsovent

Calypsovent

Calypsocontrol

WhiteIsland

WhiteIsland

WhiteIsland

[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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SCAT 39.40 15.80 17.60 7.69 0 0 4.84 0

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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

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Figure 4A. Detection window set at [SA]=2µM. Comparison of log K1values obtained with the Scatchard,




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Whale Island control

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Calypso vent

Calypso control

White Island

White Island

White Island

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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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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.

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