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COPPER BINDING ABILITY OF SUWANNEE RIVER HUMIC ACID
IN SEAWATER
by
ENO
Megan Brook Kogut
B.S. Chemistry
University of Washington
(1995)
Submitted to the Department of Civil
and Environmental Engineering in Partial
Fulfillment of the Requirements for the Degree of
MASTER OF SCIENCE
In Civil and Environmental Engineering
at the
Massachusetts Institute of Technology
June 2000
@Massachusetts Institute of Technology
All rights reserved.
MASSACHUSETTS INSTITUTE
J
I OF TECHNOLOGY
O
LI BRAR ES
Signature of Author_____________
Department of Civil and Environmental Engineering
5 May 2000
Certified by
Bettina Voelker
Professor, Civil and Environmental Engineering
Thesis Supervisor
Accepted by_
Daniele Veneziano
Chairman, Departmental Committee on Graduate Students
2
COPPER BINDING ABILITY OF SUWANNEE RIVER HUMIC ACID IN SEAWATER
by
Megan Brook Kogut
Submitted to the Department of Civil and Environmental Engineering
on 5 May 2000 in partial fulfillment of the requirements for the
Degree of Master of Science in Civil and Environmental Engineering
ABSTRACT
Elevated total copper concentrations ([CulT) due to industrial and municipal pollution are toxic to
microorganisms in coastal areas. Copper is up to 99.99% complexed by strong ligands in the
water column and is not immediately bioavailable, so that the toxicity of copper is frequently
correlated to the free copper concentration ([Cu 2+]) and not [Cu]T. However, [Cu 2 +] is too small
to measure directly with current methods. The copper binding ability, or the total effect of
copper ligands, of these coastal areas is therefore as important an indicator of copper toxicity of
the waters as [Cu]r. However, binding ability is more difficult to measure and predict than [Cu]T
and therefore is the limiting factor in our ability to monitor and regulate copper as a toxin.
Several sources of copper ligands have been proposed as major sources of strong copper ligands
to coastal areas. Strong copper ligands (KcuL = 101-10214) are produced by phytoplankton,
perhaps as a defense mechanism against copper toxicity. However, planktonic ligands are created
at some metabolic cost, possibly affecting phytoplankton viability. Recent field research has
shown that rivers, sewage outfalls, and sediment porewaters also contribute strong copper
ligands to coastal areas. The strong ligands in these samples are difficult to characterize, so
suspected ligands must be investigated separately.
Terrestrial humic substances are well recognized as weak ligands in sediments, rivers, and sewage.
They are ubiquitous because they have plentiful sources (plants, soils, sediments) and are
recalcitrant to degradation. However, they are usually not considered to be strong copper ligands
because there are no published studies of low concentration, high strength binding sites in humic
substances. For the first time, we have taken methods developed by oceanographers to study
copper speciation at ambient copper levels in seawater and applied them to a humic acid extract,
Suwannee River Humic Acid (SRHA). We show that SRHA contains strong copper ligands and
propose that other sources of humic acids may play a previously unrecognized role in buffering
copper toxicity in coastal areas.
Thesis Supervisor: Dr. Bettina Voelker
Title: Professor of Civil and Environmental Engineering
4
Acknowledgements
Deepest thanks to Tina Voelker for providing tireless, instructive feedback on thesis drafts and a
healthy balance of academic freedom and guidance. The "we" in this paper is far from the royal
"we"! This research was supported by a Parsons Fellowship and by her Doherty Professorship
for Young Investigators.
Endless thanks to my parents for freedom and guidance and support in all aspects of life.
I am very grateful to Glen T. Shen for letting me loose in his lab and then pointing eastward.
5
TABLE OF CONTENTS
ABSTRACT
3
ACKNOWLEDGEMENTS
5
LIST OF TABLES
8
LIST OF FIGURES
9
CHAPTER 1. INTRODUCTION
10
1.1.. Importance of copper speciation.
10
1.2. Copper ligand strength and influence on speciation.
13
1.3. Investigated sources of strong copper ligands.
14
1.4. Terrestrial humic acids and Suwannee River Humic Acid.
17
CHAPTER 2. INTRODUCTION TO METHODS
20
2.1. Competitive Ligand Exchange and Copper Titrations.
20
2.2. Interpretating and Modeling Copper Titration Data.
24
2.3. Adsorptive Cathodic Stripping Voltammetry.
29
2.4. Calibration and surfactant effect.
30
CHAPTER 3. PROCEDURE AND METHODS DEVELOPMENT
34
3.1. Sample preparation.
34
3.2. Titration set up and sample analysis.
37
3.3. Kinetics of humic acid and SA competition.
397
3.4. True sensitivity of ACSV with 1 mg/L SRHA.
41
3.5. Change in Cu(SA)x electrode sensitivity with SA speciation.
44
3.6. Surfactant effects of 1 mg/L SRHA on standard curves.
46
3.7. Determination of total copper concentrations.
54
6
CHAPTER 4. RESULTS AND DISCUSSION.
57
4.1. Titrations of 1 mg/L SRHA.
57
4.2. Modeling titrations of 1 mg/L SRHA.
61
4.3 Graphical presentation of the binding ability of 1 mg/L SRHA.
67
4.4. Comparison of SRHA titrations to estuary field sample titrations.
70
4.5. Suwannee River Humic Acid as Source of Copper Ligands
72
CHAPTER 5. SUMMARY AND FUTURE RESEARCH
73
5.1. Results of methods development.
73
5.2. Implications of binding ability of SRHA on coastal copper speciation.
74
5.3. Future Research and Directions.
75
APPENDIX. ERROR ANALYSIS.
78
REFERENCES
81
7
LIST OF FIGURES
Figure 2.1. Modeled copper titrations, both with SRC(AL)= 103 ([AL] = 1 gM and log
KCu(AL)2 = 15, assuming a bis complex).
23
Figure 2.2. Diagram of E[CuLi] versus [Cu2+] modeled with MINEQL for several ligand
mixtures titrated with total copper from 2 to 100 nM in a seawater sample.
25
Figure 3.1. Molecular structures of (a) salicylaldoxime (SA) and (b) benzoylacetone (bzac).
36
Figure 3.2. Sample potential scans: solid lines are samples with 1 mg/L SRHA, and
dashed lines are for the samples run the same day without SRHA.
39
Figure 3.3. Signal, or peak height, versus equilibration time for 1 gM SA and 10 nM
copper without (E) and with (0) 1 mg/L SRHA.
41
Figure 3.4. MINEQL models of 1 mg/L SRHA and several concentrations of a) SA
and b) bzac.
43
Figure. 3.5. Relative sensitivity of Cu(SA) 2 reduction with increasing [SA]. Inset is
the absolute sensitivity of the same samples.
47
Figure 3.6. Peak height versus deposition time for 5 uM SA and 10 nM copper.
50
Figure 3.7. Standard curves of UV-SW and overload titrations of 1 mg/L SRHA
with 25 [tM SA.
51
Figure 3.8. Standard curves corresponding to titrations presented.
55
Figure 4.1 (A-E). Sample titrations of Suwannee River Humic Acid.
58
Figure 4.2. Langmuir linearizations of all titrations presented.
63
Figure 4.3. Langmuir linearizations of all SA titrations presented.
64
Figure 4.4. Plot of I[CuLi] versus [Cu 2+] for all eight copper titrations of 1 mg/L SRHA,
plus ASV results of SRHA binding at high copper calculated from reported stability
constants and concentrations per mg/L SRHA.
68
Figure 4.5. Comparison of three copper titrations of 1 mg/L SRHA with multiple copper
titrations of two coastal samples (Vineyard Sound, MA, and Waquoit Bay, MA.)
71
8
LIST OF TABLES
Table 1.1. Copper toxicity thresholds of selected species of microorganisms.
12
Table 3.1. Side reaction coefficients used in modeling and titration calculations.
45
Table 3.2. Slopes of UW-SW standard curves and overload titrations of 1 mg/L SRHA
and 25 gM SA conducted over three days.
53
Table 4.1. Average conditional binding strengths and ligand concentrations of ligand
classes found in titrations A-H.
66
9
CHAPTER 1. INTRODUCTION
1.1. Importance of copper speciation.
Many highly populated coastal waters have dangerously elevated total copper concentrations
due to industrial and municipal pollution. For example, copper is released from metals processing
to coastal waters via wastewater effluents and runoff, and in recreational and shipping areas,
copper leaches from antifouling paints on boat hulls. Total dissolved copper concentrations
range from 2 nM in well -flushed coastal areas (Moffett 1997) to more than 100 nM in the most
heavily impacted areas such as San Francisco Bay (Donat 1994). The US EPA national ambient
marine water quality criterion for total dissolved copper is 2.9 gg/L, or 46 nM. This criterion,
chosen to be below copper concentrations that cause acute toxicity to a species of crab, is a
starting point for assessment of the effects of copper on aquatic ecological health. However, the
fact that total copper concentrations are often greater than current EPA mandated levels suggests
that copper pollution is a problem and that conflicts between regulators and polluters are likely.
Assessing the effects of copper on aquatic organisms is complicated by the fact that not all
copper is immediately bioavailable. Complexation of copper by strong ligands reduces the
concentration of free copper, [Cu 2 +]. Lower [Cu 2+] in the water column lead to decreased rates of
copper uptake by phytoplankton (Sunda 1976; Anderson 1978). Copper complexation also
decreases copper toxicity to fish, in which copper transport seems to occur primarily through
binding sites on gill tissue (Meyer 1999). Because the percentage of strongly complexed copper
varies widely from 80 to over 99.99% in different coastal systems, [Cu 2 +] is in the range of low
picomolar to low nanomolar concentrations and is not a simple function of [Cu]T. For example,
10
September 1995 copper speciation data from Waquoit Bay, Massachusetts, show that [Cu]T is
about 20 nM, but strong copper ligands decrease [Cu 2+] to roughly 1 pM (Moffett 1997). In the
San Francisco Bay, California, [Cu]T is about 50 nM, while [Cu2+] is 5 to 10 nM (Donat 1994).
San Francisco Bay has much higher [Cu 2+] and theoretically higher copper toxicity than Waquoit
Bay although the two samples have comparable [Cu]T. Because [Cu 2+] is a better indicator of
copper toxicity than [Cu]T, many biologists and toxicologists now report the toxic effects of
copper in relationship to measured [Cu2+].
Several species of microorganisms have copper
toxicity thresholds within the range of [Cu2+] found in coastal areas (Table 1.1); therefore copper
toxicity may affect ecosystems with dissolved copper levels currently in EPA compliance for
ambient water quality.
Instead of measuring [Cu2+] of a sample, usually below detection limits in natural waters, many
scientists measure the copper binding ability of that sample (the sum of the binding abilities of
the copper ligands in the sample). They then calculate [Cu2+] of the sample as a function of the
sample binding ability, (e.g. Moffett 1997; Sedlak 1997; van den Berg 1987). The speciation of
copper between free ionic and complexed forms is determined by the relationship between [CuT]
and the binding ability of the sample. The copper binding ability is much more difficult to
measure and predict than [Cu]T and therefore is the limiting factor in our ability to monitor
copper speciation and regulate copper as a toxin.
11
Table 1.1. Copper toxicity thresholds of selected species of microorganisms. Values of
[Cu 2 +] reported are the threshhold concentrations at which the indicated effects occur
significantly. *Free copper concentrations measured by electrochemical methods;
otherwise, [Cu2+] held constant during experiment by complexation with excess
concentrations of synthetic metal chelators (NTA or tris).
Organism
Eurytemora affinis
Toxicity Indicator
90% of control survival
(copepod)
(8 days)
Acartia tonsa
Decreased survival rate
(copepod)
(48 hr)
Acartia tonsa
Supressed grazing activity
[Cu 2 +] (nM)
2.0
Reference
(Hall 1997)*
0.5 to 0.05
(Sharp 1997)
0.1
(Sharp 1997)
0.01
(Sharp 1997)
(24 hr)
Acartia hudsonica
Supressed grazing activity
(copepod)
(24 hr)
Nannochloris atomus
Inhibited growth rate
0.04
(Sunda 1976)
Inhibited growth rate
0.003
(Sunda 1976)
0.005
(Moffett 1997)*
(alga)
Thalassiosira
pseudonana (diatom)
synechococcus
50% of maximum
(phytoplankton)
growth rate
I
12
I
1.2. Copper ligand strength and influence on speciation.
The binding strength of a copper ligand is represented by its conditional binding constant,
KcuLi= [CuLi]/([Cu 2+][Li]),
(1.1)
for which [CuLi] is the concentration of copper bound to ligands of class Li, [Cu2+] is the
concentration of free copper, and [Li] is the concentration of ligands in that class not bound to
copper. Copper ligands are polar or charged oxygen-, nitrogen-, and sulfur-containing sites on
molecules that bind copper. The binding site strength depends partly on electron cloud
"softness" of the binding site, (both "hard" 0 binding sites and "soft" S and N groups complex
"borderline soft" Cu2+), and number of chelators, or "claws", available to attach to the copper
ion. Because other cations (e.g. H*, Ca 2+, Mg 2+) compete with copper for binding sites, the
conditional constant KCuL depends on the concentrations of these cations and their abilities to
compete with copper. KCuL is also conditional because ionic strength effects may shield the
electrostatic attraction of Cu2 + to negatively charged binding sites. This decrease in ligand
strength due to competition and ionic strength effects is potentially important in estuaries, where
cation concentrations and ionic strength increase by several orders of magnitude from freshwater
to seawater.
The speciation of copper in natural waters is described by the equation
(1.2)
[Cu]T = [Cu 2 +] + I[CuLi],
where the sum of concentrations of all copper species is the total copper concentration.
Substituting Eq. 1.1 into Eq. 1.2 gives a speciation relationship that includes ligand binding
strengths and concentrations,
13
[CU]T=
[Cu 2 +] + X(KCuLi[Li][Cu2+]).
(1.3)
Dividing all terms by [Cu2+] gives the relationship
[Cu]T/[Cu 2 +]
=
(1.4)
1 + l(KCuLi[Li]),
which relates the extent of copper complexation directly to ligand binding strengths and
concentrations. The quantity (KCuLi[Li]), also called the side reaction coefficient (SRC) of ligand
class Li, is a useful value for comparing copper binding abilities of different ligand classes based
on their average strengths and concentrations.
Oceanographers have classified copper binding ligands based on their conditional copper binding
constants KCuLi. The strongest ligands found in coastal areas approaching seawater salinity have
a conditional copper binding constant of 1012 and greater. This ligand class is dubbed "LI" and
controls [Cu 2 +] when [Cu]T is less than [L1]1T. At higher copper concentrations, the ligand class
"L2", with conditional binding constants in the range of 108 to 1011, controls copper speciation
when the stronger L1 ligand class is copper saturated. The distinction between LI and L2 ligand
classes is likely artificial in coastal waters, where relative contributions of different sources of
different types of stronger and weaker ligands to the binding abilities of water samples are
unknown. However, ligand class models are useful for quick numerical comparisons of copper
binding in different waters.
1.3. Investigated sources of strong copper ligands.
Several sources of strong copper ligands to coastal areas have been proposed to be important,
each with its own implications for aquatic ecology and ligand fate. Competitive ligand exchange
14
experiments have repeatedly proven that strong organic copper ligands (KcL = 1012-1014)
dominate copper speciation in the open ocean. The ligand concentration versus depth profiles
suggest a plankton source of these ligands in the upper mixed layer of the water column (van den
Berg 1984; Coale 1988; Moffett 1990). One ubiquitous species of marine blue-green algae,
Synechococcus sp., excretes copper-complexing ligands (Kce
=
1013) when copper stressed in
culture (Moffett 1996). Another culture study shows that Emilianiahuxleyi, a marine
microalgae, excretes a compound with a copper binding constant of about 1012 (Leal 1999).
Excretion of copper-binding compounds is suspected to be a strategy to protect against copper
toxicity, since more copper ligands are produced in cultures with elevated copper concentrations
in both studies. Planktonic ligands are created at some metabolic cost to the organisms that
produce them, possibly compromising their viability as they compete for resources in the
ecosystem.
Therefore, although planktonic ligands are likely to be major contributors to strong copper ligand
pools in coastal areas, we are also interested in nonplanktonic sources of copper ligands from an
ecological standpoint. Nonplanktonic ligands could decrease the toxic effects of copper on coastal
ecosystems without metabolic cost to its organisms. Recent field research in riverine and coastal
waters has shown that terrestrial, anthropogenic, and porewater ligand sources may contribute
significant amounts of ligands with binding strengths comparable to those of planktonic ligands
produced in the water column. Stable copper sulfide complexes, which seem to resist oxidation
by oxygen for several days and have large conditional stability constants (e.g., KCu(HS)
ICu(HS)2 = 1020,
=
1013,
Al-Farawati 1999), can account for 10 to 60% of the total copper complexes in
15
several rivers in Connecticut (Rozan 1999). The sources of sulfide seem to be both anoxic
wetland areas and waters that are anoxic due to nutrient overloading. Determination of copper
speciation in several treated sewage effluents and creeks in South San Francisco Bay (Sedlak
1997) shows that effluent contains about 10 to 25 nM strong copper ligands (K > 10-'.) which
are subsequently released to the bay. Assuming conservative mixing and no UV or biological
destruction of these ligands, Sedlak et al. proposed that these sources could contribute enough
strong ligands so that their average concentrations in the South Bay in winter and summer are as
high as 7 nM and 3 nM, respectively. Finally, estuarine sediment porewaters in Chesapeake Bay
contain high concentrations of ligands (around 10,000 nM) with binding strengths as high as K =
1015. If these ligands are stable in the oxic water column, they could contribute to the pool of
strong Cu ligands in the whole estuary based on ligand flux measurements and a circulation model
of Chesapeake Bay (Skrabal 1997).
All of the above authors suggest humic substances as a possible identity of weak (L2) copper
binding ligands from these sources. Because humic acids have plentiful terrestrial sources (plants,
soils, sediments), are ubiquitous in aquatic systems, and are relatively recalcitrant to microbial
destruction, they are an interesting addition to the suite of ligand sources. Rozan et al. suggest
based on comparisons of the binding abilities of whole river water samples and humic and fulvic
acid standards that humic and fulvic acids contribute to the copper binding abilities of rivers.
Sedlak et al. propose that humic acids contribute to the weaker ligand class associated with
wastewater copper (average KCuL = 107, [L] = 200 to 500 nM), citing a study of fulvic acid
collected from a copper contaminated stream
(KCuFA =107-108,
16
[L] = 1 to 70 gM) (Breault
1996). Humic acids are well recognized as contributors to the weaker ligand class L2, e.g.
(Cabaniss 1988), but are usually not included in discussions of strong copper binding because
there are no published studies of very low concentration, high strength binding sites in humic
substances.
The one study that has probed binding sites with conditional binding strengths greater than 108 in
seawater is a study of humic acid isolated from swampwater. The binding ability of a 1 mg/L
humic acid solution could be modeled with a three ligand model as follows: KCuLa > 1011, [La]=
50 nM;
KCuLb =
9.2,
[Lb]
=
200 nM; KCuLc =
6.6;
[L] =1,800 nM (Hering 1988), where the
subscripts a, b, and c denote the strongest, weaker, and weakest ligand classes titrated with
copper. This study proves that at low copper concentrations, humic acid ligands are detected
that were copper saturated in other studies (and therefore not probed), and have a stronger
binding ability than humic ligands at higher concentrations. The detection limits of anodic
stripping voltammetry in this study prevented probing of binding sites at concentrations lower
than 50 nM, leaving open the possibility of stronger binding sites at lower concentrations which
could contribute to L1 in coastal waters. For the first time, we have taken the methods developed
by oceanographers to study planktonic ligands and applied them to humic acid extracts in order
to probe these stronger sites in saline waters.
1.4. Terrestrial humic acids and Suwannee River Humic Acid.
Terrestrial humic substances have long been suspected to bind metals because they contain a high
concentration of polar and acidic functional groups. Humic substances are operationally defined
17
as the fraction of dissolved organic matter that can be collected on a hydrophobic resin (XAD-8)
at pH = 2. Lowering the pH of the solution protonates acidic functional groups, neutralizing
their negative charge and causing the humic acids to become more hydrophobic and sorb to the
resin. The column is then rinsed with a strongly basic solution, and the dissolved organic matter
(DOM) released off the resin in this basic solution is defined as humic substances. In this way,
humic substances are separated from hydrophobic DOM with less acidic character, which
remains sorbed to the resin at higher pH. Subsequent reacidification of the collected humic
substance solution causes a fraction of the humic substances to precipitate; centrifugation of the
solution separates the precipitate, defined as humic acid, from the dissolved material, defined as
fulvic acid.
In and near terrestrial systems, major components of humic substances collected from water
samples are highly oxidized, aromatic, and stable degradation products, such as lignin, of trees,
grasses, and other plant material (Aiken 1985). For example, DOM derived from the degradation
of the salt marsh grass Spartina alterniflorais 34% humic substances collected with an XAD-8
column (Moran 1994). About 24% of these humic substances are degraded within 7 weeks, but
the remainder of the humic substances are resistant to further degradation on the timescale of
weeks to months. Sunlight increases the rate of degradation of humic substances by a factor of
three (Moran 1999), and there is a wide range of degradation rates of different humic acids
(Moran 1994). However, humic acids found in aquatic systems on average have a much greater
halflife than the rest of the DOM in the water column.
18
Plentiful terrestrial sources and chemical recalcitrance of humic substances (e.g. Moran 1994) are
consistent with findings of significant concentrations of lignin found in rivers and estuaries
(Mantoura 1983). The terrestrial signal in DOM (assumed to be proportional to total lignin,
phenolic components in the cell walls of vascular plants) can be traced from rivers and marshes
along the coast of Georgia to the edge of the continental shelf (Moran 1991). Concentrations of
humic acid in rivers and estuaries are consistently greater than 1 mg/L and can reach 5 and 10
mg/L in some systems, (e.g. Rozan 1999).
Of the previously isolated humic acids available from the International Humic Substance Society
(IHSS), we use Suwannee River aquatic humic substance standards from the base of the
Okefenokee Swamp in Georgia, USA. Humic substances were collected in one large batch,
separated into standard humic and fulvic acids, and freeze-dried. Initial studies on Suwannee
River Humic Acid (SRHA) character and structure were published together in a U.S.G.S. report
(Averett 1989). The aquatic humic acid is a mixture of grass, tree, and other degradation products
has an average lifetime of three years and an average molecular weight of 1000 Daltons (Averett
1989). We chose these humic acid reference materials because they are well studied and because
we can compare our results to those of the study of binding ability of SRHA at higher copper
concentrations (Hering 1988).
19
CHAPTER 2. INTRODUCTION TO METHODS
Competitive Ligand Exchange Adsorptive Cathodic Stripping Voltammetry (CLE-ACSV) is the
method used to measure the copper binding ability of water samples containing natural ligands.
This method is a two step process: first, known concentrations of copper and a wellcharacterized and purified synthetic ligand, AL, are added to the sample with the natural ligands,
and the natural ligands and AL compete with each other for copper. When the sample has
reached equilibrium, the amount of copper complexed with AL is measured with ACSV. Because
the concentrations of copper complexed with AL (nanomolar range) are orders of magnitude
greater than those of free copper (usually in the picomolar to femtomolar range in ocean waters),
competitive ligand exchange and measurement of copper complexed by AL greatly increases the
sensitivity of ACSV. CLE-ACSV and copper titrations of water samples have been used to
probe planktonic ligands (Moffett 1996) and natural ligands in marine systems (Donat 1994;
Campos 1994). In this chapter, CLE theory and data modeling practices are discussed first.
ACSV is introduced separately because related analytical issues are important to the presentation
of methods development discussed in Chapter 3.
2.1. Competitive Ligand Exchange and Copper Titrations.
Competitive Ligand Exchange between added and natural ligands allows us to determine very low
values of [Cu 2+], but [Cu 2+] and the concentration of copper complexes must be determined
indirectly When equilibrium exists among copper, natural ligands and the added ligand,
[Cu]T
=
[Cu 2 +] + I[CuLi] + ([CuAL] + [Cu(AL) 2]),
20
(2.1)
where CuAL is the mono complex and CuAL 2 is the bis complex of copper with the added ligand.
Concentrations of AL are often high enough so that the concentrations of mono and bis copper
complexes are comparable. The concentration of copper complexed with added ligand is
therefore
I[Cu(AL),]
=
(2.2)
[CuAL] + [Cu(AL) 2].
The value of I[CuLi] is obtained by rearranging Eq. 2.1 and omitting the negligible variable
[Cu 2+],
I[CuLi] = [Cu]T -
(2.3)
I[Cu(AL)x].
The value of [Cu 2 +] is calculated from I[Cu(AL)], [AL], and the conditional complex formation
constants of the mono and bis complexes,
KCuAL
and
ICu(AL)2-
Because the value determined with
ACSV is X[Cu(AL)] (Section 2.3), SRC(AL) is used, where
SRC(AL)
= KCuAL[AL] + PCu(AL)2[AL]
2
(2.4)
,
and [AL] is equal to [ALIT as long as [Cu]T is much less than [ALIT. Therefore,
[Cu 2 +]
=
(2.5)
Y[Cu(AL)x]/SRC(AL).
The fraction of total copper complexed by inorganic hydroxide and carbonate species in seawater
is usually a negligible fraction of X[CuLi] in samples that contain strong ligands. Even at the high
concentrations of hydroxide and carbonate in seawater with a pH of 8.3, the sum of
concentrations of major inorganic species CuCO 3* and ternary carbonate/hydroxide species is
about 24 times greater than [Cu2+] (Byrne 1985) and usually at least two orders of magnitude
lower than I[CuLi].
21
In order to determine the binding ability of a sample over a range of [Cu]T, CLE-ACSV is used to
analyze a copper titration of a natural ligand sample. Analysis after each addition of copper
determines the portion of total copper complexed by the added ligand. As [CulT is increased,
stronger natural ligands are saturated with copper and weaker natural ligands cannot compete as
well as the stronger ligands for additional copper. The fraction of each copper addition complexed
by AL increases as [Cu]T increases, as shown with a sample modeled with MINEQL (Westall
1994) in Figure 2.1. Eventually, if all natural ligands are completely saturated with copper, an
additional copper added to the sample ([CU]added) will be complexed by the added ligand, so that
the increase in I[Cu(AL)x] (1[Cu(AL)x]formed) is equal to [Cu]added (0, Fig. 2.1).
Finally, one copper titration with one SRC(AL) cannot probe the whole range of natural ligands
found in coastal waters. The titration range of one sample is constrained by detection limits and
error propogation during calculation of
X[CuLi]formed
=
[Cu]added - X[Cu(AL)x]formed
(2.6)
for each copper addition. At the beginning of the titration, if X[Cu(AL)x]formed is much less than
[Cu]added
because AL cannot effectively compete with natural ligands for copper, I[CuLi]formd is
equal to [Cu]added. The only information that has been gained from competitive ligand exchange is
that natural ligands with an SRC much greater than that of SRC(AL) are present. If
X[Cu(AL)x]fomed is very close to the value of [Cu]added then E[CuLi]ormed is the very small
difference between two large numbers. The range of values of X[CuLi] defined by these two
limiting conditions is the analytical window of the titration (Miller 1997).
22
5E-8
_
LI and L2
LI only
*
-
4E-8
-no
a
an3
natural ligands
3E-8
6
C
2E-8
a
a"
1E-8
OE+O
OE+O
IE-8
2E-8
3E-8
4E-8
5E-8
[Cu]T (M)
Figure 2.1. Modeled copper titrations, both with SRC(AL) =103 ([AL] =1 M
and log KCu(AL)2 = 15, assuming a bis complex). 0 Sample with one strong
ligand class (5 nM, log K = 13, SRC(L1)= 104.
Sample with strong ligand
class and weaker ligand class (50 nM, log K = 11, SRC(L2) = 103).
*
23
2.2. Interpretating and Modeling Copper Titration Data.
From Equations 2.3 and 2.5 and a copper titration where I[Cu(AL),] is obtained as a function of
[Cu]T, a plot of [Cu2 +] versus I[CuLi] can be calculated, showing the binding ability of the
ligands over a range of free copper values (Fig. 2.2). For any sample where [Cu 2 +]/I[CuLi] <
0.01, which is usually true in the presence of natural ligands, I[CuLi] is approximately equal to
[Cu]T*, the total copper that would be present in the water sample in the absence of AL but with
identical [Cu 2+]. From equation 2.3, [Cu]r* is therefore approximated by
(2.7)
[Cu]T*= [CulT - X[Cu(AL)x].
The values of [Cu]T* are lower than the corresponding values of [Cu]T for the titration with AL,
so that, depending on the analytical window of the titration, ligands with concentration of less
than [Cu]T of the original sample can be analyzed.
The concentration of [Cu 2 +] corresponding to the concentration of copper originaly present in the
sample ([CuIT,o) can be read directly off this kind of plot ([Cu]T* = [Cu]T,o= I[CuLi]). This
graphical representation of the sample's binding ability is also useful for predicting [Cu 2+] of the
natural ligands in the sample if it became more copper polluted, for example, by finding the value
of [Cu 2 +] which corresponds to a greater value of [Cu]T*. The visual representation of binding
ability also clearly shows the analytical window of the titration, so that a value of [Cu]T* can be
chosen within the analytical window for an accurate prediction of [Cu2+]. Corresponding
analytical windows are often not explicitly reported with tabulated values of [Li] and KCuLi,, SO
24
1E-8
1
E-9
'd'ILJ
""'
IIL"
u
IIYUI
^'
AIU''
JYIII
IIU IVIIII
'*
1E-10
IE-11
1E-12
IE-13
IE-14
1E-9
1E-8
1E-7
I[CuL ] (M)
Figure 2.2. Diagram of I[CuLJ] versus [Cu 2 + modeled with MINEQL for several
ligand mixtures titrated with total copper from 2 to 100 nM in a seawater sample.
LI has a conditional copper binding constant of KcuL1 = 10, while L2 has a
conditional copper binding constant of KcuL2 = 1011. When LI and L2 are saturated
with copper, and if no other weaker ligands are present (true for these examples),
the copper binding ability of the sample approaches that of carbonate and
hydroxide at salinity equal to 35%o. See text for additional discussion of the
relative binding abilities of mixtures.
25
in these cases it is difficult to calculate accurately the binding ability of ligands at very low or
high [Cu]T*.
Comparisons of the binding ability of different waters is theoretically easy with this graphical
method; overlaying the curves shows relative binding ability, which is a function of both ligand
strength and concentration, both at lower copper concentrations (stronger ligands) and at higher
copper concentrations (weaker ligands). This is demonstrated by modeling combinations of
ligands titrated from 2 to 100 nM total copper with MINEQL (Fig. 2.2). On a log-log scale, 20
nM strong ligands is offset at lower [Cu2+] than 10 nM strong ligands (* and 0), and the same
magnitude of offset occurs for a factor of two difference in concentration of weaker ligands
(*
and ZS). In addition, weak ligands lower [Cu 2+]slightly before the stronger ligand has been
titrated completely at low [Cu]T*
(*
and 8), and the strong ligand still influences [Cu 2 +] at high
[Cu]T* (* and 0). This overlap is evident for modeling two ligands with conditional binding
constants separated by two orders of magnitude. The overlap is more important for modeling
copper titrations of actual samples, which may contain ligands with a spectrum of copper
binding strengths, as combinations of ligand classes with average binding constants (Cernik 1995).
However, in general, as the copper titration proceeds, less abundant stronger ligands become less
important and the more abundant weaker ligands more important in the role of controlling [Cu 2 +].
When L 1 and L2 are saturated copper, and if no weaker ligands exist besides carbonate and
hydroxide, the slope of the binding ability plot increases sharply until the line approaches and
eventually overlaps the carbonate/hydroxide line (-).
26
It is common practice to model the ligands titrated in a sample as several classes, or bins, of
ligands with average copper binding strengths. The main purpose of dividing ligands pools into
bins with average binding strength is to compare concentrations of stronger and weaker ligands
among samples. There are several ways to model copper titrations as one or several ligand classes
with average conditional binding constants. The number of bins needed to model the natural
ligands in a sample depends on the analytical window of the titration and the ligand pool of the
sample. Two common techniques for determining average ligand binding strengths and
concentrations are the Langmuir linearization and the Scatchard plot, as discussed in Miller
(1997). We use the Langmuir linearization here.
To obtain the strength and concentration of the natural ligands with the Langmuir linearization,
titration data is plotted as [Cu 2+]/1[CuLi] versus [Cu 2 +] so that
[Cu 2 +]/X[CuLi] = [Cu 2 +]/[Li]T + 1/(KcuLi*[Li]T).
(2.8)
Therefore, the inverse of the intercept of this plot, i, is KcuLi*[Li]T and the inverse of the slope, s,
is
[Li]T.
If the linearization shows a curved line that is best fit by more than one straight line, the
titration is best fit more than one ligand bin. The straight line corresponding to the stronger
ligand class La is fit as in the one ligand case, with [La] = 1/si and KCuLa = 1*/(i*[La]). For the line
at higher [Cu 2 +] that represents the weaker ligand class Lb, the intercept, i 2 , is
(2.9)
i2= [Lb]/KCuLa*([La] +[Lb]) 2 }.
The slope s2 is
(2.10)
S2 = 1/([La] +[Lb]),
27
where subscripts 1 and 2 refer to the lines at lower and higher [Cu 2 1], and the subscripts a and b
refer to the stronger and weaker ligand classes in the analytical window of that titration. Finally,
iteration should be used to optimize the accuracy of a two ligand fit (Miller 1997). But, if the fit
of the two lines of a Langmuir linearization produce two ligand classes with conditional binding
constants no more than an order of magnitude apart, then iteration could fail to separate the
contributions of La to the Lb portion of the linearization and vice versa. Therefore modeling Lb is
possible only when there is enough curvature in a Langmuir linearization to produce two discreet
ligand classes.
The distinction between La and LI (and Lb and L2) is subtle because with CLE-ACSV analytical
windows, SRC(La) is in the range of SRC(L1) found in coastal samples. SRC(La) is often
centered near SRC(AL) of any titration, suggesting that SRC(La) is not a value corresponding to a
"real" concentration of LI and a single fixed conditional binding constant (van den Berg 1990). In
addition, linearization methods underestimate the concentration and overestimate the binding
strength of the weaker ligand class if the weaker ligand class is not fully titrated (Miller 1997). It
is impossible to fully titrate the spectrum of binding sites in humic acid, so for any titration of
humic acid, Lb will depend on the range of [Cu]T* obtained. However, La and Lb classes
reasonably represent titration data, and these ligand classes could be used to roughly compare
strong and weak ligand concentrations per mg/L SRHA to LI and L2 classes reported in coastal
copper speciation literature.
28
2.3. Adsorptive Cathodic Stripping Voltammetry.
Adsorptive Cathodic Stripping Voltammetry is used to measure I[Cu(AL).] in each equilibrated
sample. After the competitive ligand exchange step, the sample is transferred to a Teflon@ cup
and the cup attached to a hanging mercury drop electrode (HMDE). The sample is purged of
oxygen (because oxygen causes signal interferences when it is reduced), a fresh mercury drop is
produced, and added ligand copper complexes in the sample are adsorbed to the mercury drop for
a fixed amount of time called the deposition time.
The deposition step concentrates species on the drop as a function of their charge and
hydrophobicity. For example, for the added ligand salicylaldoxime (SA), with a net ligand charge
of 0 at pH = 8.3, both the Cu(SA) and the Cu(SA), complexes have a net positive charge of +2.
Experiments of deposition potential versus peak height show that more negative deposition
potential (and therefore more negative charge on the mercury drop) is correlated to increased peak
height, consistent with the expected increase in electrostatic attraction of positively charged SA
species [Campos, 1994 #64].
During the deposition step, a certain amount of Cu(AL)2 is collected on the drop, [Cu(AL) 2 ]drop,After the deposition step, scanning the electrical potential on the mercury drop in the negative
direction creates a more reducing environment on the drop. A potential negative enough to cause
the reduction of Cu(II) creates a flow of electrons measured as current. When current is plotted
as a function of potential, a peak centered on the reducing potential of the Cu(II) species appears.
All Cu(II) sorbed to the drop is reduced, and the potentials at which different Cu(II) species are
29
reduced (Cu2+, Cu(AL), Cu(AL) 2) are more negative for more stable copper complexes, so that
different peaks corresponding to the reduction of each copper species are obtained. The peak
caused by the reduction of Cu(AL) 2 on the drop dominates the potential scan for the added
ligands that we used. The height of the peak is proportional to [Cu(AL) 2]1rop, which in turn is
proportional to the deposition time, to the deposition potential, and to [Cu(AL)x] in the solution.
The sensitivity, or the relationship between peak height and [Cu(AL)x] is determined by
calibration.
2.4. Calibration and surfactant effect.
The sensitivity of ACSV is determined by calibration by standard additions of copper in a
sample to generate a linear plot of peak height (corresponding to the reduction of Cu(AL) )
2
versus [Cu]T where no strong ligands are present and the SRC of carbonate/hydroxide copper
complexation is negligible compared to AL), AL complexes almost all the copper, and [Cu 2 +] is
negligible compared to the concentrations of the complexes. In this case,
[CU]T
=
I[Cu(AL)x] = [CuAL] + [Cu(AL) 2],
(2.12)
where [AL] is equal to [AL]T if [AL]T is much greater than [Cu]T. Because [AL]T is usually in the
micromolar range and [Cu]T is usually in the nanomolar range, the assumption that [AL]T
is much greater than [Cu]T is usually correct.
The sensitivity, S,
S = I/[Cu(AL)x]
(2.13)
where I is the peak height, can be combined with Eq. 2.12, so that
30
S = I/[Cur.
(2.14)
Therefore, although we are measuring the height of the peak corresponding to the reduction of
Cu(AL) 2 , for example, the peak height is proportional to X[Cu(AL)]. Although in principle we
could instead define S as the sensitivity of [Cu(AL) 2] and add a calculated [CuAL] to both
standard curve and titration, it is easier to calibrate both the standard curve and the corresponding
titration as a function of I[Cu(AL)x] for each [AL]. Since [Cu(AL)]dop: [Cu(AL) 2 ]drop is not
necessarily equal to [Cu(AL)]:[Cu(AL) 2], calibration of the standard curve in terms of
1[Cu(AL)]drop insures that one calibration step accounts for any unpredictable mercury drop
surface chemistry among AL, CuAL, and Cu(AL) 2. In this way, we determine an empirical
relationship between sensitivity and I[Cu(AL)x] in the sample.
Determining the sensitivity of copper titrations of samples with high DOM content is difficult
because hydrophobic portions of DOM may sorb to the mercury drop surface and decrease the
sensitivity by hindering the simultaneous sorption of Cu(AL)x. This phenomenon is called the
surfactant effect. An external calibration using a standard curve in a DOM-free sample will
overestimate the sensitivity of samples with a significant surfactant effect.
To avoid the surfactant effect, internal calibrations are often used to determine the sensitivity of
sample. For this method, one assumes that at the end of the titration, all strong natural ligands
that can compete successfully with AL for copper are already saturated with copper. Therefore,
at higher [CU]T, one assumes a situation analogous to the UV-oxidized standard curve, where
31
[CUladded is equal to X[Cu(AL)x]ormed after each copper addition. This assumption is based on
the fact that a titration with competitive ligand exchange between AL and one ligand of
comparable strength and concentration is parallel to the UV-oxidized standard curve when all the
sample ligands have been filled with copper (0, Fig. 2.1). An internal calibration assumes that
the straight portion of the titration is the true sensitivity of the ACSV in that water sample, and
thatonly surfactants cause any decrease in sensitivity.
However, if weaker ligands are able to compete with AL for copper, then the slope will be
underestimated by an internal calibration. This is demonstrated in Fig. 2.1 (*),where a weaker
ligand of high concentration continues to complex some of the added copper because it is not
saturated with copper. Therefore, although the titration appears linear at greater [Cu]T, the slope
remains less than that of the UV-oxidized (no natural ligands) standard curve. Therefore, the
decrease in signal in an internal calibration could be due to complexation of some added copper by
weaker natural ligands as well as to surfactant effects. Comparison of titrations of the same
sample at different detection windows is the only way to tell the difference between natural
ligand binding of copper and surfactant effects as causes of decreased slope during internal
calibrations.
We developed a third method of calibration to avoid the uncertainty between depression of signal
due to surfactant effects or copper complexation by natural ligands. This external calibration
method accounts for the surfactant effect by using a great enough concentration of added ligand to
outcompete even the strongest natural ligands for added copper. In other words, an "overload"
32
titration is designed so that SRC(AL) is much larger than the SRC of any of the natural ligands.
The successful overload titration would be a straight line whose slope would be either equal to or
less than the slope of the UV-oxidized standard curve, depending on the magnitude of the
surfactant effect. The success of the overload titration could be tested in lab by titrating natural
ligands at several different overload values of [AL]. Consistency among observed slopes of these
overload titrations would suggest that the natural ligands do not complex any added copper, and
that the constant slope is the true sensitivity. The "overload" titration does not produce copper
speciation data, so unlike for internal calibrations, additional titrations at lower SRC(AL) must be
performed on separate aliquots of the same sample.
33
CHAPTER 3. PROCEDURE AND METHODS DEVELOPMENT
3.1. Sample preparation.
Only Teflon@ bottles were used to minimize adsorption of copper and ligands to bottle surfaces
and leaching of phthalate plasticizers into the samples. The Teflon@ bottles were washed in
detergent, soaked in 10% HCl (Aldrich, Analytical Grade) for two days, and rinsed six times with
distilled deionized water (DDW).
The Suwannee River Humic Acid (SRHA) collection was conducted as follows (Averett 1989):
river water was passed through a 0.45 gm filter, acidified to pH < 2, and collected on a nonpolar
resin (XAD-8) column. The column was then rinsed with 0.1 M NaOH and about 95% of the
DOM on the column was released into solution and collected. This solution was reacidifed to
pH = 1, and the precipitate that subsequently formed was separated by centrifugation and
collected as humic acid and freeze dried.
Stock solutions of 100 mg/L SRHA were made fresh every month in our lab from freeze-dried
SRHA and DDW. SRHA dissolved quickly in water without any addition of base. Humic acid
stock solutions were stored in the refrigerator until use.
The sample matrix was buffered, UV oxidized Sargasso seawater (UV-SW) collected with trace
metal clean methods. Organic matter in Sargasso seawater was destroyed by ultraviolet light
oxidation with a mercury lamp (Ace Glass, 1000 W) for at least 8 hours. One mL of UV-
34
oxidized 1 M boric acid (EM Science, Suprapur@), adjusted to pH of 8.2 with 0.35 M ammonia,
was added to one liter of UV-SW for a final pH buffer concentration of 1 mM.
Primary copper standards were made fresh every day from a 1 mM acidified copper stock
solution diluted with DDW from an Aldrich atomic absorption standard solution. The primary
copper standard was diluted with seawater for the secondary copper stock solution of 1 gM Cu
and a pH of about 7.
The added ligand was either benzoylacetone (bzac), developed as a competing ligand for CLECSV by Moffett (1995), or salicylaldoxime (SA), developed by Campos and van den Berg (1994)
(Figure 3.1). Both were recrystallized from 10-3 M aqueous ethylenediamine-tetraacetic acid
(EDTA) in DDW, rinsed with cold DDW, and filtered. After recrystallization, they were
redissolved, recrystallized, and filtered twice in DDW to rinse the added ligand of EDTA.
Dissolution was increased by gentle heating below 650 C for bzac, and below 400 C for SA
because SA decomposes at higher temperature. Bzac was dissolved in methanol (J. T. Baker,
HPLC Solvent) for a final stock solution concentration of 10-2 M. SA was dissolved in DDW for
a final stock solution concentration of 10 -4M. SA and bzac stock solutions were replaced every
three months and refrigerated when not in use.
35
OH
a. Saliyhldoxirne (SA)
OH
CH
0
0
b. Benoyhcetan (bzac)
Figure 3.1. Molecular structures of (a) salicylaldoxime (SA) and (b) benzoylacetone
(bzac).
36
3.2. Titration set up and sample analysis.
At the start of the experiment, one half of buffered, UV-irradiated seawater solution (500 mL)
was separated for UV-SW standard curves and determination of total copper of UV-SW, and 0.5
mL of the humic acid stock solution was added to the other 500 mL for a humic acid
concentration of 1.0 mg/L. The 1.0 mg/L humic acid solution was equilibrated for at least several
hours at room temperature in the dark.
For competitive ligand exchange with SA, copper titrations were set up as one sample to which
aliquots of copper were sequentially added. The sample was a 10.0 mL aliquot of the 1.0 mg/L
humic acid solution pipetted directly into the electrode sample cup. SA was also added to the
sample at this time. Copper from the secondary standard was added to the sample after each
titration point analysis for concentrations of total added copper from 0 to 30 nM for 3 pM SA
titrations and from 10 to 150 nM for 1 pM SA titrations. Solutions were equilibrated for 5
minutes after each copper addition and before analysis of the corresponding titration point.
Longer equilibration times did not results in a change in [Cu(SA),] measure (see section 3.3). The
sensitivity remained constant with ten consecutive measurements of the same sample.
In contrast, sensitivity dropped significantly between repeated measurements of the same sample
with bzac as the competing ligand. Bzac is much less water soluble than SA and likely sorbs
rapidly to the discarded mercury drop at the bottom of the cup, significantly reducing its
concentration in solution. Because repeated measurements were not practical, bzac titrations
were conducted with individual titration point samples. Ten to twelve 10.0 mL aliquots of the
37
1.0 mg/L humic acid solution were pipetted into individual 30 mL Teflon@ bottles. Copper from
the secondary standard was added to each sample to make concentrations of total added copper
varying from 10 to 150 nM. The samples were equilibrated for two to three hours, and then bzac
from the 10 - M stock solution was added to each sample and allowed to equilibrate with copper
and humic acid ligands for an additional one or two hours before analysis. Overnight equilibration
periods with bzac titrations also caused a loss of apparent sensitivity, perhaps due to slow but
significant sorption of bzac copper complexes to the walls of the Teflon@ sample bottles.
The samples were analyzed using differential pulse adsorptive cathodic stripping voltammetry
(DP ACSV) with a PAR 303A static mercury drop electrode and a EG&G PAR 394 analyzer.
Instrument settings for both SA and bzac titrations were as follows: deposition potential,
-0.08 V (versus Ag/AgCl electrode); scan range, -8 to -600 mV; scan rate, 10 mV/s; drop time, 0.2
s; pulse height, 25 mV. This instrument setting protocol was adapted from that used for bzac
(Moffett 1995), and was also found to provide the best sensitivity for SA, so was used for SA
although another protocol has been suggested (Campos 1994). Stir bar speed setting was usually
slow to avoid stir bar knocking, and the mercury drop size setting was large to increase
sensitivity at short deposition times. The purge time was 5 minutes. Deposition times in general
were 10, 20, and 30 seconds. The rest period between deposition time and initialization of scan
was five seconds.
For both SA and bzac the reduction of Cu in the Cu(AL) 2 complex produces the dominant peak
in the potential scan (Fig. 3.2). Reduction of the copper added ligand complex (Cu(AL) 2) was
38
iI
-40.00
-35.00
-30.00
-25.00
> -20.00
-15.00
-500.0
I
-400.0
-300.0
EI
mV
-200.0
-100.0
-200.0
-100.0
El mV
-32.00
-28.00
-24.00
-20.00
>-16.00
-12.00
-8.00
-4.00
-500.0
-400.0
-300.0
E/ mV
Figure 3.2. Sample potential scans: solid lines are samples with 1 mg/L SRHA,
and dashed lines are for the samples run the same day without SRHA. A) 200
pM bzac, SRHA, and 40, 60, and 120 nM copper. UV-SW sample is with 60 nM
copper. B) 1 pM SA, SRHA, and 2, 20, and 40 nM copper. UV-SW sample is
with 20 nM copper.
39
defined and occurred at -290 mV for bzac copper complexes and -330 to -400 mV for SA copper
complexes. The average background noise was 0.5 nA; therefore, peaks below 1 nA in height
were discarded. At the shorter deposition times used (less than 30 s), there was no signal caused
by the reduction of adsorbed humic complexes in samples with or without added ligand near the
area of the measured peak (Fig. 3.1).
3.3. Kinetics of humic acid and SA competition.
To determine how quickly copper, strong SRHA binding sites, and SA equilibrate after mixing,
we conducted a series of measurements of a single sample of 1 gM SA and 10 nM copper before
and after the addition of 1 mg/L SRHA. The Cu(SA) 2 signal remains constant for addition of SA
to 10 nM copper after repeated measurements (0, Fig. 3.2). After addition of SRHA, the
analytical signal will decrease with time due to competitive uptake of copper by SRHA, if
equilibration time between species is on the order of minutes. After addition of 1 mg/L SRHA to
this sample, the signal decreased within 2.5 minutes to a value approximately 35% of the original
signal, and then did not change significantly within a time period between 5 minutes and 24 hours
later (M, Fig. 3.3). The quick decrease in signal after addition of SRHA is possibly the result of
the surfactant effect as well as quick exchange (< 2 minutes) of copper from SA to SRHA.
However, supporting results show that the surfactant effect is negligible with 30 second
deposition times (Section 3.6). Therefore, this experiment shows that equilibration between
copper, SA, and SRHA is quick.
40
10
O
m
8
no SRHA
no SRHA average
1 mg/L SRHA
1 mg/L SRHA average
6
signal (nA)
.L
2
:
LI
25
30
4
2
0
0
5
10
15
20
Equilibration Time (minutes)
Figure 3.3. Signal, or peak height, versus equilibration time for 1 pM SA and 10 nM
copper without (0) and with (0) 1 mg/L SRHA. The reduction in peak height after
addition of SRHA is mostly if not completely due to competitive complexation of
copper by SRHA (see discussion in text). The final two points for the SRHA times
series were taken 24 hours after addition of SRHA to SA and copper. Because no
change in peak height occurs between time 2.5 mintes and 24 hours after addition of
SRHA, we assume that SRHA equilibrates within 2.5 minutes. The lines represent
the average value for all points for each sample.
41
For bzac titrations, the experimental protocol was a three hour equilibration of humic acid with
added copper and a two to three hour equilibration of these samples with bzac before analysis
(Moffett 1997). Much longer equilibration times were not possible because bzac seems to sorb
to the surfaces of the Teflon@ bottles, resulting in noticeably lowered sensitivity after six to eight
hours.
3.4. True sensitivity of ACSV with 1 mg/L SRHA.
Our calculations using previous results of revious copper titrations of the weaker ligands in
SRHA (Hering 1988) show that the large concentration of weaker ligands in 1 mg/L SRHA may
interfere with obtaining the true sensitivity of titrations when using internal calibrations. With
MINEQL, we modeled the reported set of copper ligand bins over a range of values of [Cu]T and
SRC(AL). We compared the behavior of weaker ligands at higher [Cu]T with values of SRC(AL)
near that of the L1 class (10,000) and with SRC(AL) corresponding to the highest concentrations
of SA and bzac already calibrated in the literature (Campos 1994; Moffett 1995). Although the
relationship among titration points appears linear at [CulT
=
100 nM and greater, this linear
relationship does not represent the true sensitivity of the titration because weaker ligands are still
competing with AL for copper. Using the slope of the higher [Cu]T of the titration as the
sensitivity underestimates the sensitivity by as much as 40% with these detection windows (0,
Fig 3.4.b).
42
1.E-07
.. =..... uM SA
9.E-08
-1-3 uM SA
_
8.E-08
....o-5 uM SA
7.E-08
-. *-25 uM SA
6.E-08
--..
no SRHA
5.E-08
0
4.E-08
y
3.E-08
0.60x - 1.8E-08
ZE-08
1.E-08
O.E+00
O.E+00 1.E-08 2.E-08 3.E-08 4.E-08 5.E-08 6.E-08 7.E-08 8.E-08 9.E-08 1.E-07
[CulT (M)
1.E-07
-o-
100 uM bzac
9.E-08
8.E-08
7.E-08
x
6.E-08
.-..-. 300 uM bzac
-o-500 uM bzac
-
SRHA
5.E-08
u
4.E-08
3.E-08
y 0.66x - 2.E-08
2.E-08
1.E-08
0.E+00
0.E+00 1.E-08 2.E-08 3.E-08 4.E-08 5.E-08 6.E-08 7.E-08 8.E-08 9.E-08 1.E-07
[CUlT (M)
Figure 3.4. MINEQL models of 1 mg/L SRHA and several concentrations of a) SA and
b) bzac. The models show that the slope of the titration does not return to unity at
higher [CU]T The line fits are best fits through the linear portion of the titrations;
the slope of each line should be unity if it represented the sensitivity of the
titrations. Here, weak humic binding decreases the slope below unity, showing that
internal calibrations would underestimate the sensitivity except for very high
SCR(AL) (25 pM SA), in which case SA outcompetes all humic ligands for copper.
43
Because very high concentrations of SA (25 piM) can outcompete all ligands (Fig. 3.4.a), we used
overload titrations with 25 gM SA to determine the true sensitivity of humic acid titrations with
SA as the added ligand. We could not use overload titrations with bzac as the added ligand
because 500 gM bzac does not provide a strong enough detection window to outcompete all
humic ligands, and bzac is not completely soluble at concentrations much higher than 500 gM
(Table 3.1).
3.5. Change in Cu(SA), electrode sensitivity with SA speciation.
The overload titration as external calibration has a complication: the speciation of CuAL and
Cu(AL) 2 will change in the range of [AL] used for titration and standard curve. For example, at 1
gM SA, [CuSA] is slightly greater than [Cu(SA) 2], but at 25 gM SA, [Cu(SA) 2] is much greater
than [CuSA]. This change in AL speciation will affect the sensitivity because only Cu(SA)2 is
reduced at the mercury drop during the ACSV step, and the relationship between [Cu(SA) 2 ]drop
and I[Cu(SA)] in the sample will change with a change in [SA]. Campos et al. (1994) found
that the sensitivity of Cu(SA)x increased significantly with increased [SA] up to 25 pM SA.
They found that sensitivity was roughly correlated to the ratio of [Cu(SA) 2] oto I[Cu(SA)x],
which increases until Cu(SA) 2 approximately equal to I[Cu(SA),] at [SA]= 25 gM. Correlation
between sensitivity and [Cu(SA) 2]/I[Cu(SA)] led Campos et al. to suggest that SA speciation at
the drop is similar to that in solution.
44
[AL](gM)
100
200
300
400
500
SRC(bzac)
500
2000
4500
8000
12,500
SRC(SA)
1
3
5
25
4160
18,500
40,800
704,000
Table 3.1. Side reaction coefficients used in modeling and titration calculations. Bzac
calibration data taken from Moffett, 1995, where Kcu(bzac) is neglected and pCu(bzac)2 1010'7. SA calibration data taken from Campos and van den Berg, 1994, where Kcu(sA)=
109-7 and $Cu(SA)2
=
1015.
45
We too compared the slopes of standard curves obtained with 1 - 5 gM and 25 gM SA in UVSW to determine the sensitivity of the electrode at each SA concentration, and we obtained a
different relationship between sensitivity and [SA] (Fig. 3.5). We used a different deposition
potential than Campos et al. (-0.08 versus -1. lV), and Campos et al. observed that the sensitivity
increased dramatically with deposition potential especially above -0.8V. There is no guarantee
that the ratios of [Cu(SA)], [Cu(SA) 2], and [SA] adsorbed to the drop are linearly related to their
ratios in the sample solution because the physical conditions at the drop (charge,
hydrophobicity) may affect the rate of adsorption of each of the SA species differently.
Therefore the deposition potential may effect SA speciation on the drop in some unpredictable
but consistent fashion. Because we observed a reproducible relationship between sensitivity and
[SA] for different deposition times and different experiments, we used our analytical results
instead of those of Campos et al.
Comparisons of 25 pM SA sensitivities to 1 and 3 gM SA sensitivies give correction factors of
55% and 78%, respectively. These correction factors are used for all SA titrations presented. The
percent standard deviation for each value averaged over the three deposition times is
approximately 5%.
3.6. Surfactant effects of 1 mg/L SRHA on standard curves.
As discussed in Section 2.4, we have two external calibration methods to obtain the true
sensitivity of humic substance titrations. If the surfactant effect is negligible or well known, we
can use the slope of the UV standard curve (with correction factors if necessary) to monitor the
46
1.20
T -
1.00
1.5
0.80
081.0
o0
0.60
A
A
0
0.5
A
04)
0.40
I
-----
,0.0
30s
20 s
0.20
0
10
10 s
2
30
[SA] (gM)
0.00
0
10
20
30
[SA] (jIM)
Figure. 3.5. Relative sensitivity of Cu(SA) 2 reduction with increasing [SAJ. Inset is the
absolute sensitivity of the same samples, at deposition times of 10, 20, and 30 seconds.
47
analytical sensitivity. If we do not know the magnitude of the surfactant effect, we must add an
excess ("overload") concentration of AL to complex all added copper so that the slope of the line
of peak height versus
[Cu]added =
I[Cu(AL)x]ormed is the true sensitivity of the sample.
Do humic substances at concentrations typically found in natural systems significantly interfere
with the adsorption and reduction of Cu(AL) 2 during ACSV? To answer this question we
determined the magnitude of the surfactant effect of 1 mg/L SRHA in our samples. Standard
curves at 25 pM SA were conducted in UV-SW with and without 1 mg/L SRHA. Because 25
gM SA outcompetes all SRHA binding sites for added copper, any decrease in sensitivity
between samples with and without humic substances is due to humic interference at the electrode
drop. The deposition time should be as long as possible while avoiding large surfactant effects.
We observed very large and troublesome surfactant effects at one and two minute deposition
times with 1 mg/L SRHA; the surfactant effect caused a net loss in sensitivity (data not shown).
Because the surfactant effect is more pronounced at longer deposition times, a quick qualitative
check for its interference can be obtained from repeated analysis of the same sample with several
different deposition times to verify that a linear relationship between peak height and deposition
time exists. Because this method is quick, it can be used as a rough check for surfactant effects
before a sample titration. For example, the relationship between sensitivity and deposition time
was linear for deposition times of at least one minute for samples of 5 RM SA and 1 and 2 mg/L
humic acid (Fig. 3.6). The peak heights are the same for both concentrations of humic acid
because 5 gM SA has complexed most of the copper. It is worth noting for didactic and warning
48
purposes that a 1 mg/L solution of Suwannee River Fulvic Acid (SRFA), analyzed in the same
time period, exhibited a much larger surfactant effect than SRHA on several different occasions
(and new solutions.) Although we do not know why the surfactant effect was sometimes greater,
especially because SRFA is expected to be less hydrophobic than SRHA, we do know to check
the solution beforehand with this quick method to avoid wasting time and samples.
We used short deposition times of 10, 20, and 30 seconds to avoid net loss of sensitivity and
perhaps avoid the surfactant effect completely. We investigated a range of deposition times to
verify that increased magnitude of any surfactant effect corresponds to longer deposition time.
The standard curves and overload titrations are presented in Figure 3.7. Each graph represents a
single sample of UV-SW or UV-SW plus 1 mg/L SRHA, to which copper aliquots were added.
The three samples at each [Culadded value represent the same sample analyzed sequentially with
30, 20, and 10 second deposition times. The equilibration time between each copper addition was
5 minutes. The detection limit was about 2 nA. The precision of a measurement is
approximately 0.5 nA, based on repeated measurements of the same sample.
The slopes of the best fit lines were determined by Excel linear trendline fit. The average slopes
of the 30, 20, and 10 second deposition time points are 1.05, 0.84, and 0.55 nA/nM copper
(Table 3.2). Based on an uncertainty in peak heights of 0.5 nA (which includes both background
noise and 5% signal instability for these peak heights), the slopes have standard deviations of
anywhere from 10 to 25% of their absolute values for each standard curve. This suggests that
standard curves with only four to six data points are not accurate enough to determine the
49
15
*SRFA
QSRHA
OSRHA
'10
5
0
0
20
40
60
deposition time (s)
Figure 3.6. Peak height versus deposition time for 5 uM SA and 10 nM copper. The grey
triangles are for a separate 1 mg/L SRFA analyzed the same day. Because copper species
continue to deposit on the mercury during the 5 second rest step and the 20 second scan
from deposition potential to reduction potential, the relationship between deposition time
and slope has an intercept greater than zero.
50
10
L
I
.
0
5
10
[Cu] added (nM)
15
10
5
[Cu] added (nM)
15
0
5
10
[Cu] added (nM)
15
0
5
10
[Cu] added (nM)
15
15
06.28.99 no SRHA I
a 20 s
Xi0s
10
10
IN 'J
I
I
15
5-
0
.0
0
5
10
[Cu] added (nM)
15
0
5
10
[Cu] added (nM)
15
Figure 3.7. Standard curves of UV-SW and overload titrations of 1 mg/L SRHA with 25
gM SA and at 10, 20, and 30 second deposition times.
51
sensitivity with accuracy better than 10% of the value of the sensitivity. Confidence limits were
calculated assuming that the uncertainty in [Cu] is negligible compared to uncertainty in peak
height, and that the uncertainty in peak height is roughly constant at 0.5 nA (Bevington 1992)
(see Appendix). For comparison, the percent standard deviation of all slopes presented for each
deposition time (taken on different days) is about 3%to 13%,.(Table 3.2), with an average
percent standard deviation of 7%. Therefore, 10% day-to-day standard deviation is consistent
with the accuracy of the standard curves.
The values of the slopes of the overload titrations are within the range of uncertainty of the
slopes of the standard curves and show no trend of decreased sensitivity with addition of 1 mg/L
SRHA. The magnitude of any surfactant effects or copper binding by humic acid on the
analytical signal should be evident in the difference between the slopes of the UV-SW slopes and
the overload titrations of 1 mg/L SRHA conducted under the same conditions. However, several
attempts to compare standard curves and overload titrations provided inconsistent results. The
percent of the overload slope over the standard curved slope varied from 94% to 110% (Table
3.3). Although the 30 second overload titrations both have slopes less than those of the standard
curves taken the same day, the differences are within the average standard deviation of the
values of the slopes. In addition, the trend of decreased peak height for overload titrations
is not consistent for the 20 and 10 second deposition times, where with one exception the slopes
of the overload titrations are greater than the slopes of the standard curves. We conclude that 1
mg/L SRHA causes no consistently significant surfactant effect and that it does not measurably
bind copper in the presence of 25 pM SA under these conditions. These results support the use
52
Table 3.2. Slopes of UW-SW standard curves and overload titrations of 1 mg/L SRHA and
25 gM SA conducted over three days. The average slopes are for the four standard curves
and two overload titrations at each deposition time.
deposition time
date
06.27.99
06.28.99
06.28.99
06.29.99
average
relative standard
deviation (%)
UV-SW standard curve
slope (nA/nM)
30s
20s
los
1.04
1.09
1.08
0.99
1.05
5.3
0.80
0.83
0.85
0.88
0.84
2.7
0.52
0.58
0.57
0.52
0.55
5.8
1 mg/L SRHA overload
titration slope (nA/nM)
30s
20s
lOs
1.02
0.86
0.63
0.93
0.98
6.4
0.72
0.79
12.5
0.57
0.60
7.7
Table 3.3. Comparison of the surfactant effect for three
deposition times. Percentages are the slope of the
overload titration of 1 mg/L SRHA over the UV-SW
06.28.99
06.29.99
average
% overload slope of UV-SW slope
94
102
110
94
82
109
93
94
109
53
of UV-SW standard curves to determine the true sensitivity of speciation titrations. These
results also validate the theory that 25 pM SA will outcompete all humic binding sites for copper
and that overload titrations with 25 gM SA can be used if necessary to determine the true
sensitivity of speciation titrations.
3.7. Determination of total copper concentrations.
The average initial copper concentration, [Cu]T', was 1.2+/- 0.5 nM. This was determined by
the negative value of the x-intercept of standard curves of UV-SW conducted before each titration
(Fig. 3.8). The average [Cu]uv-sw did not include the x-intercept of the standard curve of
10.07.98, which indicates a UV-SW concentration of 3.4 nM, because the large value of the
analysis point at 20 nM [Cu]added seems to be the source of the large value of the x-intercept.
Without this outlier, the calculated concentration of copper in UV-SW from this standard curve is
1.7 nM. The value of [Cu]T of 1.6 nM compares favorably with previous measurements of 1 to
2 nM total copper in the Sargasso Sea surface waters, the source of the UV-SW (Moffett 1990).
We should expect slightly lower [Cu]T because some copper should sorb to the surfaces of the
quartz tubes during the UV irradiation step once all of the strong copper ligands are destroyed.
We expect negligible copper contribution from 1 mg/L SRHA to our samples. Previous
measurements of copper (Averett 1989) in SRHA suggest that there is 0.05 nanomole copper per
mg SRHA, for a final contribution of 0.05 nM copper in our samples. We added 2 mg/L SRHA to
UV-SW, irradiated with UV light for 24 hours, and found no measureable increase in [Cu]T,when
54
30
-10
10
30
50
[Cu] added (nM)
70
90
Figure 3.8. Standard curves corresponding to titrations presented. The sensitivities for
the two bzac titrations (conducted with 10 second deposition times) are different because
the capillary tube was changed between the two dates of the titrations.
55
compared to UV-SW samples without SRHA, which verifies the fact that the SRHA copper
contribution to the seawater copper is negligible.
Based on the reproducibility of standard curves and rarity of "outlier" data points, we conclude
that our methods to avoid copper contamination worked consistently during the time period of
the titrations presented. [Cu]T for the titrations shown was calculated as the concentration of
added copper for each titration point plus 1.6 nM, the concentration of copper in UV-SW.
56
CHAPTER 4. RESULTS AND DISCUSSION.
4.1. Titrations of 1 mg/L SRHA.
In this chapter, we present two bzac titrations, one with 100
sM bzac and one with 200 gM
bzac, and six SA titrations, four with 1 FM SA and two with 3 gM SA. In addition, the SA
titrations were conducted with 10 and 30 second deposition times to compare the effects of
increased sensitivity and any surfactant effect on the final data. These titrations were collected
with our developed methods described in Chapter 3.
All eight titrations are presented in Fig. 4.1 (A-E). For all titrations, the slope of the titration line
at higher [Cu]T is less than that of the "no SRHA" line, verifying that our expectation that weaker
binding sites in SRHA would be able to compete with AL at higher [Cu]T and suggesting that
either the external calibrations of sensitivity using a UV-SW standard curve or overload titrations
were needed to avoid underestimation of the binding strength of SRHA at higher [Cu]T.
The sizes of the error bars in Figure 4.1 are estimated standard deviations determined by the
uncertainty of the peak heights due to baseline noise (plus or minus 0.5 nA), and by the
combination of signal noise and uncertainty in the sensitivity (estimated as 10% relative standard
deviation). At low [Cu]T, the uncertainty caused by baseline noise affects the precision of the
small peak heights. At high [Culr the error in sensitivity is the greatest factor decreasing the
precision of values of [Cu(AL)].
57
250
A. 1 mg/L SRHA with bzac
-no
200
SRHA
+
100798, 200 uM bzac
X
110298, 100 uM bzac
150
CO
Z
d
100 -
50 -
0
50
100
150
200
250
[Cu]T (M)
Figure 4.1. Sample titrations of Suwannee River Humic Acid. (A) Titrations of SRHA
with 100 and 200 uM bzac with 10 second deposition times. (B) Titrations of SRHA with 1
and 3 gM SA conducted with 10 second deposition time. (C) Same data as B, but with
expanded scale. (D) Titrations conducted with the same samples as in B, but with 30
second deposition time. (E). Same data as D, but with expanded scale. Error bars in all
figures correspond to one standard deviation derived from uncertainties in peak height
and sensitivity, as discussed in the Appendix.
58
100
80
60
'-I
N 40
20
0
20
0
40
60
80
100
[Cu]T (M)
20
A
C. 1 mg/L SRHA and SA, 10 s
-no
15
-
SRHA
o
05.24.9 9, 1 uM SA
o
05.28.9 9, 1 uM SA
9 05.28.9 9, 3 uM SA
10-
5-
0-
0
5
10
[Cu]T (M)
59
15
20
100
-
D. I mg/L SR HA and SA, 30 s
SRHA
-no
80
-
3
05. 28.99, 1 uM SA
0
05. 28.99, 3 uM SA
A 06.28.99, 1 uM SA
60
-
40
-
N
20
-
0
0
20
60
40
80
100
[CulT (M)
20
15
10
5
0
0
5
10
[CulT (M)
1.
60
15
20
In Figures 4.l.C and 4.1.E, the titrations of 1 mg/L SRHA with 1 and 3 gM SA at very low
[Cu]T illustrate the relatively large effect of baseline noise on the small peak heights for these
data. These data are therefore of limited use for determination of the binding strength of the
ligands in this analytical window. The values of I[CuLi], the difference between [Cu]T and
I[Cu(SA)x], are also directly affected by the uncertainty represented by the error bars. Error
bars of size comparable to the difference between [Cu]T and Y[Cu(SA)] mean that the value of
I[CuLi] will have an uncertainty comparable to its magnitude. The relative uncertainty in the
calculated value of I[CuLi] for the 3 gM SA titration is greater than that of the 1 gM SA
titration at the same [Cu]T, due to decreased humic acid complexation of copper (smaller values
of I[CuLi]) at higher [SA]. Use of concentrations of SA greater than 3 RM to probe copper
speciation of 1 mg/L SRHA will not provide useful additional speciation data at comparable
deposition times (and with comparable uncertainty), because the values of E[CuLi] at each value
of [Cu]T would be much smaller and therefore much less precise than those of 3 gM SA.
Therefore, for titrations with our operating conditions, the detection window useful for probing
the strongest ligands in 1 mg/L SRHA has an SRC of about 20,000, that of 3 jM SA.
4.2. Modeling titrations of 1 mg/L SRHA.
Copper complexation by humic acid is likely controlled by a spectrum of binding sites with
decreasing binding strengths and increasing concentrations. However, modeling the copper
titrations as one or two bins of ligands with average strength and total concentration is useful for
comparison to other studies in which the final data reported are ligand class concentrations and
61
average binding strengths. This section compares Langmuir linearization plots of all titrations,
discusses differences between titrations, and presents ligand class average conditional binding
strengths and concentrations obtained from best fits of linear portions of the Langmuir plots.
Langmuir linearizations for two bzac titrations and four 1 gM SA titrations are shown in Fig. 4.2.
Titrations of I mg/L SRHA analyzed with two different deposition times and three values of
SRC(AL) roughly agree within the range of [Cu 2 +] of 10-13 to about 10-11, but the curvature in the
Langmuir linearizations for the SA titrations does not appear in those for the bzac titrations. The
lack of curvature in the bzac titrations at low [Cu 2 +] is likely due to the low resolution in [Cu 2 +]
and to the large error in I[CuLi] caused by low signal-to-noise ratios at low [CU]T values.
Some 1 gM SA titrations (Fig. 4.3 B-E) have curvature suggesting a stronger ligand class at low
[Cu 2 +] (10-12 M). Because all titrations were conducted with identical procedure (standard
additions of 2 nM copper until
[Cu]added
is 10 nM, with standard additions of 10 nM thereafter),
the fits of their Langmuir linearizations follow the same pattern. The first four points in Fig
4.3.B-E correspond to 4 through 10 nM added copper in each titration, and these four points
have a slope steeper than that of the remaining points. So, best fits for [La] and KcUa were
conducted through these four points for each of these titrations. In addition, best fits for La were
conducted for all points in the 3 pM SA titrations (Fig. 4.3. F and G). Titrations D and E were
analyzed with 10 instead of 30 second deposition times; the increased sensitivity at greater
deposition time allows more accuracy and precision at very low [Cu 2 +] (<
62
1012
M).
153-
-
G+4
T4E4
11.02.98, pbawl = 100 uM, 10 9
7+X
+
2E4
05E0
0
15-11
2E-11
3E11
-
511
Et-11
S5-11
4E-11
10.07.98. rbzac1=m 200 uM. 10 a
9.11
15-10
ICU21
P)
--
15-3
1F
C.
B.
05.24.98
B4
a
E
05.28.99
[SA] = 1 uM
10s
.E-4
[SA] =1 uM
10s
65.4
05.28.99
[SA] = 3uM
10.
45.4
454
4.5.4
2E-4
2E-4
21-4
154.
IJJ
2.E40
0E+0
0E+0
1&-11
0E+0
2E-II1
1-11
0E0
2-1
15-11
2-11
00
5E-13
15-12
[Cual PI)
15-3
15-3
a
F.
05.28.99
[SA =1 uM
30a
SE&4
E-4
I
05.21.99
[SA =1 uM
30 s
6E-4
854
454
454
254
254
0E40.
AM
0
+054
111
[cue
0E+0
2&11
42 Laall[l
tS)
1-1
2211
05.0
1-12
5E13
31ur
Figure 4.2. Langmuir linearizations of all titrations presented. Scales of axes are identical except for the 3 uM SA titrations.
4E-4
4E-4
4E-4
B.
05.24.98
[SA] = I uM
108s
3E-4
E
D.
06.28.99
. .uM
3E4
1E-4
-
05.28.99
[SA] = 3uM
10s
3E4 4
2E4
2E-4
IE-4
1E4
L=Tw
0E+0
E13
1E-12
2&-12
2E-12
0E+0
5E-13
1E-12
2-12
2&12
0E+0
5E-13
1E-12
2&12
2E-12
0 E+0
5E-13
1E-12
2E-12
2E-1
[Cual (M)
4E.4
4E-4
4E-4
F.
05.28.99
[SA] = 1 uM
30s
3E-4
06.28.99
[SAI=I uM
30a
3E-4
£r 2E4
0%
H.
05.28.99
[SA] = 3 uM
3E-4
30s
2E4
2E-4
1E-4
1E.4
1E4
0E+0
5-13
1-12
[Cul (M)
2-12
2-12
OE40
0E+0
OE+O
5-13
1E-12
(Cul1 (M)
2E-12
2E-12
0E+0
5-13
IE-12
2-12
2-12
[Cu"] (M)
Figure 4.3. Langmuir linearizations of all SA titrations presented. Scales of all axes are identical and expanded from Fig. 4.2.
Figure 4.3 is a magnified view of all SA titrations and compares 3 pM SA titrations with 1 gM
SA titrations. Comparison of titrations conducted at 1 and 3 gm SA (Figure 4.5) show that the 3
gM titrations show very strong binding at better resolution than the 1 pM SA titrations, from
[Cu 2 +] of 1 to 5 x 1013 M. This stronger binding is in agreement with trends of 1 pM SA
titrations although ligand fits of 1 gM SA titrations will represent copper binding at lower
[Cu 2 +]. Again, the 30 second deposition time titration data shows considerably better precision
than the 10 second deposition time data. Because the titration data for both 10 and 30 second
deposition times match, any surfactant effect for the 30 second deposition time data is negligible.
Despite the large uncertainty of these fits, given the error bars on the data points used for the
linerizations, we observed the trends expected when a continuous range of binding sites is probed
using different SRC(AL) values. Because humic substances contain a continuous range of binding
sites, the fits of the bzac titration data depend on the analytical window of the titration and the
value of SRC(AL). The bzac titrations probe ligand classes with average conditional copper
binding strengths of 1010.3 and 1010.6 and concentrations of 110 to 160 nM per mg/L SRHA.
Predictably, SRC(La) is correlated to SRC(bzac), which determines the center of the detection
window of the titration. The discrepancy in this pattern for 1 pM and 3 pM SA is explained by
the fact that only the first data points of the 1 gM SA titrations were modelled for La. Ranges of
[Cu]T* used to determine La are similar for both 1 and 3 uM SA titrations, so SRC(La) are also
similar. The variation in values of SRC(La) among supposedly identical titrations is relatively
large, which also makes comparisons difficult.
65
Table 4.1. Average conditional binding strengths and ligand concentrations of ligand
classes found in titrations A-H. La is the stronger ligand class in each titratioin, chosen
for best fit of curve in nonlinear Langmuir plots or as single ligand class for linear
Langmuir plots. No Lb was modeled from data points not used for La because of lack of
convergence of a two ligand model and/or insufficient data for Lb.
[La] (nM)
160
112
log KCuLa
10.3
10.6
SRC(La)
3200
4800
SRC(AL)
500
2000
B (1 gM SA)
12
12.0
11,400
4200
C (1 gM SA)
10
12.2
15,000
4200
D (1 gM SA)
E (1 jM SA)
9
14
12.2
12.1
13,500
18,200
4200
G (3jM SA)
H (3jM SA)
8
12.2
12,000
18,500
13
12.1
16,900
18,500
A (100 gM bzac)
A (200 gM bzac)
66
4200
The SA titrations show that strong copper binding by 1 mg/L SRHA can be fit with a ligand
class with an average conditional binding strength of 1011.9 to
1012.2
and an with an average [La] of
about 10 nM. These copper binding strengths are within the range of strengths of L1 reported in
coastal speciation studies (e.g., van den Berg 1990; Donat 1994; Moffett 1997). The values of
[La]
and KCuLa we report represent strong binding by 1 mg/L SRHA when probed with methods
and detection windows used to probe Li strength ligands in coastal field samples.
The values of [Lb] and KCuLb for the 1 pM SA titrations are not reported due to insufficient data
and lack of convergence of iterations when separating the contributions of La and
Lb
to humic
binding. The value of KLb for these titrations is roughly 10", only an order of magnitude
different from that of La-
4.3 Graphical presentation of the binding ability of 1 mg/L SRHA.
The results of all titrations of 1 mg/L SRHA are presented on a graph of log I[CuLi] versus log
[Cu 2 +] (Figure 4.4). Error bars show the precision of each titration point based on uncertainty in
measurement and sensitivity determination (Appendix). The titrations show the binding ability
of 1 mg/L SRHA from [Cu]T* values of 1 to 100 nM. This range of [Cu]T* spans the
concentrations of total copper found in coastal waters, from pristine to very polluted.
Figure 4.4 verifies that CLE-ACSV with bzac spans the range of I[CuLi] between that detectable
by CLE-ACSV with SA as the added ligand and that detectable by ASV. Titrations of SRHA and
67
1E-9 :1,
*
05.28.99, 3 uM SA, 30 s
/
O 05.28.99, 3 uM, 10 s
M 05.28.99, 1 uM SA, 30 s
1E-10
0
05.28.99, 1 uM SA, 10 s
A
06.28.99, 1 uM SA, 30 s
o 05.24.99, 1 uM SA, 10 s
-
+
10.07.98, 200 uM bzac, 10 s
x
11.02.98, 100 uM bzac, 10 s
'I
1 mg/L SRHA (Hering, 1988)
1E-11
I1E-12
1E-13
I
1E-14 P
1E- 10
1 E-9
1E-8
1 E-7
1 E-6
I[CuL] (M)
Figure 4.4. Plot of I[CuL] versus [Cu 2+] for all eight copper titrations of 1 mg/L SRHA,
plus ASV results of SRHA binding at high copper calculated from reported stability
.
constants and concentrations per mg/L SRHA. Calculation of the error bars is discussed
in the Appendix.
68
bzac and SA show that the use of larger SRC(AL) removes more copper from humic binding sites
and probes the binding ability of lower concentration sites as expected (van den Berg 1990).
Where the CLE-ACSV and ASV data overlap, the bzac data suggest that SRHA has a slightly
greater binding ability than the results of the ASV study suggest. Possible overestimation of
[Cu2 +] in the ASV study, due to quickly dissociating copper humic complexes contributing to
ASV-labile copper, could explain the slight difference between CLE-ACSV and ASV data.
At any given X[CuLi], [Cu2+] should be identical for all titrations if the ligands are at equilibrium
with copper. Within the expected error, all titrations agree well with each other; this verifies that
the five minute equilibration times for SA and the three hour equilibration times for bzac are
sufficient for complete equilbration between added ligand and humic binding sites and that
neglecting surfactant effects was justified.
Any titrations with SRC much greater than that of 3 pM SA (18,500) at a deposition time less
than 30 seconds would not give additional information about the strong binding ability of 1 mg/L
SRHA. As discussed in Section 4.2, the error associated with the 3 pM SA titration data is
almost as great as the value of [ICuLi]. However, 3 FM SA probes the strongest binding sites in
humic acid relevant to copper chemistry in coastal waters, as the lowest value of [Cu]T* is 1 nM.
Therefore these data for 1 mg/L SRHA could be used to estimate [Cu2+] from [Cu]T at values of
[CU]T as low as 1 nM, a concentration of copper typical of remote unpolluted waters.
69
4.4. Comparison of SRHA titrations to estuary field sample titrations.
Figure 4.5 contains several titrations of 1 mg/L SRHA and copper titration data from two field
samples taken off the coast of Cape Cod (Moffett, 1997). Data from multiple titrations from two
estuaries, Waquoit Bay, a high DOC harbor (25%o), and Vineyard Sound (32%o), a coastal water,
were titrated in the same range of copper with the same added ligands (SA and bzac) and at
comparable detection windows. The line corresponding to complexation of copper by only
hydroxide and carbonate species in marine waters is included for reference.
Figure 4.5 shows that copper titrations of 1 mg/L SRHA appear similar to copper titrations of
coastal field samples. Humic acid binding ability overlaps with field data at higher total copper
(10 to 100 nM), while some stronger binding by the field samples occurs at 1 to 10 nM total Cu.
Another study relevant to our findings is the analysis of copper speciation in a series of estuary
samples along a salinity transect in the Severn Estuary (Apte 1990). This field study found that
concentrations of strong ligands (Kcus = 1011.4 to 1012.8) were greatest near the mouth of the river
entering the estuary (low salinity) and decreased conservatively as salinity increased, from 200
nM in the river (S = 2%o) to 13 nM at near the coast (S = 33%o). Total DOC concentrations
were 2 to 4 mg/L and also decreased conservatively as salinity increased. Because 1 mg/L SRHA
has 10 nM strong ligands with average KcuLi of 1012, if one assumes that all terrestrial humic acids
have similar binding behavior to SRHA, humic acid could be responsible for a large fraction of the
strong ligands in the river carried through the Severn Estuary.
70
I E-8
]--Carbonate
and hydroxide complexation of copper (Byrne and Miller, 1985)
O Vineyard Sound raw data (Moffett, 1997)
A Waquoit Bay raw data (Moffett, 1997)
1E-9
-
X
XI
1 mg/L HA, 3 uM SA
mg/L HA, I uM SA
0
4. 1 mg/L HA, 100 uM bzac
O9
IE-10 -
+
1E-11
A6
13 M I A
1E-12
03
X
1E-13
X
X B
16
A
A
01
A
1F 114 I
|
1E-10
1E-9
1E-8
1E-7
1E-6
I[CuLi] (M)
Figure 4.5. Comparison of three copper titrations of 1 mg/L SRHA with
multiple copper titrations of two coastal samples (Vineyard Sound, MA, and
Waquoit Bay, MA.) Coastal sample data is from Moffett, 1995.
71
4.5. Suwannee River Humic Acid as Source of Copper Ligands
With an average molecular weight of 1100 Daltons, a 1 mg/L solution of SRHA has a
concentration of 900 nM molecules. Therefore about one in 90 molecules of SRHA is capable of
strongly binding copper with an average KCuL = 1012. This is a reasonable proportion of strong
binding sites to humic molecules.
Suwannee River humic acid may include DOM from microorganism sources that make an
unknown contribution to the total concentration of strong ligands in humic acid, if those ligands
are also collected on an XAD-8 column at a pH<2. Because the original concentration of the
humic acid in Okefenokee Swamp was about 40 mg/L, the swamp would have contained roughly
400 nM of LI strength ligand (or more, if it is collected less efficiently than humic acid on XAD
columns.) A concentration of L1 in the range of 400 nM is higher than concentrations of strong
ligands found in very eutrophic (high biological activity) freshwater lakes, which are in the range
of 100 to 200 nM (Xue 1999). In addition, [Cu]T is probably in the nanomolar range in the
Okefenokee Swamp; the very large excess of [Ll] over [Cu]r argues against biological production
if the presence of high [Cu 2 '] is needed to induce biological production of the ligand. These
arguments support our assumption that a large fraction of the strong ligands found in the 1 mg/L
SRHA solutions are derived not from microbial sources but from terrestrial plant sources.
72
CHAPTER 5. SUMMARY AND FUTURE RESEARCH
5.1. Results of methods development.
The surfactant effect and the effect of weak natural ligand binding on determining the sensitivity
with internal calibrations could affect titrations of field samples containing humic acid or any
other mixture of strong and weak ligands. The large concentrations of weaker binding sites in
humic acid cause an underestimation of the sensitivity of ACSV measurements, which causes an
underestimation of the concentration of natural ligands probed at the high [Cu]T end of titrations.
Even in field samples where strong ligands are also present, humic substances must be considered
when interpreting strong copper binding data.
For titrations with SA as the added ligand, we successfully developed a new method of external
calibration using "overload" concentrations of SA that outcompeted all humic ligands for copper
in our 1 mg/L SRHA samples. The overload titration is equivalent to an external calibration with a
ligand-free solution but accounts for surfactant effects. An extra "titration" of the sample is
necessary for this method, but this method would be useful in situations where weak ligands
likely affect determination of the sensitivity by internal calibration.
Because bzac, the other ligand used to probe SRHA, does not dissolve completely at
concentrations high enough to conduct an overload titration, for bzac titrations we used UV-SW
standard curves and low deposition times (10 seconds) to minimize surfactant effects. These
standard curves were successful because deposition times of 10 caused negligible surfactant
effects for 1 mg/L SRHA. That the surfactant effect was negligible at 10 seconds was checked
73
independently with comparisons of SA overload titrations and SA UV-SW standard curves, and
verified for bzac by the fact that the bzac titration data overlap with the SA titration data.
However, significant surfactant effects were associated with deposition times of greater than 30
seconds, so that use of the UV-SW standard curves is limited to very short deposition times.
5.2. Implications of binding ability of SRHA on coastal copper speciation.
Humic acid contributes to the pool of LI as well as L2 strength copper ligands in estuaries and
coastal areas with significant terrestrial organic matter inputs. A solution of 1 mg/L SRHA
contains 10 nM strong binding sites with with KCuL1=10 12 . SRHA also makes a significant
contribution to L2, with 140 nmole/mg binding sites with an average binding strength of KcuL2=
10105 probed in the two L2-centered detection windows used in this study.
The low detection limits of CLE-ACSV allowed us to probe stronger binding sites in 1 mg/L
SRHA from values of [Cu]T* of 1 to 100 nM. Probing of stronger binding sites in humic
substances with copper titrations and CLE-ACSV provides more information than studies which
examine the "copper binding capacity" of DOM at higher [Cu]r. Copper binding capacity, often
reported as micromolar concentrations of binding sites with average binding constants near 107 to
109, (Rozan 1999) may not be useful for calculations of free copper in systems with nanomolar
concentrations of total copper. Only a small fraction of total modeled sites bind copper in
natural systems, and we show that this small fraction of binding sites responsible for copper
binding has a much higher binding constant than the average binding constant of ligands probed at
[Cu]T greater than 50 nM.
74
Terrestrial ligands could decrease the toxic effects of copper on the coastal ecosystem without
metabolic cost to its organisms. For example, if [Cu]T were 5 nM, 1 mg/L SRHA alone could
reduce [Cu 2 +] to 1 pM, below the toxicity levels of all but the most copper sensitive organisms
(Table 1.1). At the EPA ambient water quality criterium copper concentration, 42 nM, 1 mg/L
SRHA alone could reduce [Cu 2+] to about 10 pM or 0.01 nM, still below toxicity levels for many
species. Humic acid could be largely responsible for the low concentrations of free copper
observed in both pristine and polluted estuaries and coastal areas.
The copper titrations of 1 mg/L SRHA reported in this paper suggest that humic acid controls
copper speciation in coastal waters with high terrestrial matter inputs. Humic acid concentrations
in estuaries are often near or above 1 mg/L. If all humic acids have roughly the same binding
ability as SRHA per mg, some of the strong copper binding observed in rivers, surface sediments,
and runoff and sewage, may be due to terrestrial humic acids. Interpretations of field research
should consider terrestrial humic acid as a possible contributor to both L1 and L2 observed in
coastal systems and recognize the role of terrestrial humic acid in reducing copper toxicity to
coastal ecosystems.
5.3. Future Research and Directions.
Comparison of the binding abilities of both Suwannee River Humic and Fulvic Acids (work now
in progress in our laboratory) will indicate whether the binding ability of terrestrial substances,
like their separation behavior, is related to their average molecular size and hydrophobicity.
Isolated humic and fulvic acids from other sources besides swamps, for example lakes and rivers,
75
should also be investigated. The Okefenokee Swamp may be a unique source of humic
substances, typical for swamps and blackwater rivers in the southern United States, but unlike
that of forests or freshwater lakes. Copper polluted coastal waters whose copper speciation has
already been explored, e.g. (Donat 1994; Moffett 1997; Wells 1998; van den Berg 1987; Apte
1990), are mostly located in the northern United States and Europe. We cannot assume that
humic acids from terrestrial systems in the northern United States or Europe have comparable
binding ability to SRHA. Comparison of the binding abilities of several humic acids will show
either that we can assume that all humic substances have roughly the same binding ability or that
the role of humic substances in copper speciation varies greatly with source.
Our results that terrestrial humic acid may contribute to coastal copper speciation would
logically lead to a project to correlate humic acid concentrations and strong binding ability in
estuarine field samples. However, the possibility of other sources of ligands in estuaries
complicates the interpretation of ligand data. The nanomolar concentrations of strong ligands in
estuaries are very small compared to the mg/L concentrations of humic acid. Therefore, even a
small microorganism input of strong ligand could make interpretations difficult when looking at
salinity transects or seasonal changes in strong ligand concentrations (Apte 1990; Rozan 1999).
Quantification of terrestrial humic acid is also difficult. Because humic acid is separated from
samples with a column that does not separate terrestrial humic acids from marine
(microorganism) humic acids, any attempts to correlate total humic acid concentrations to
terrestrial ligand signals in field samples would be difficult. Using color as a measure of terrestrial
humic acids is also risky because one wavelength will not represent all terrestrial humic acids, and
76
color may change (appear or disappear) across salinity transects. Therefore, investigation of
isolated terrestrial materials is so far the only feasible scientific pathway to investigate terrestrial
humic acid's role in coastal copper speciation.
The results of our study can also be used to justify continued research in other directions, such as
investigation of terrestrial sources including sediments, runoff, sewage effluents, and rivers as
sources of both ligand and copper to coastal areas. Comparison of strong ligand and copper
concentrations where freshwater sources empty into estuaries (before marine sources of ligands
mix with the freshwater ligands) show that much copper transported by runoff and rivers may
already be complexed by terrestrial substances (Apte 1990; Sedlak 1997). Here the issue of ligand
source is more general: do sources autochthonous to estuaries (e.g., marine microorganisms which
are produced at some cost to marine ecosystems) account for more copper binding in those
estuaries than allochthonous sources (e.g, humic acids which buffer marine ecosystems from
metal imputs)? In other words, are strong allochthonous ligands comparable to [CU]T in water
released to the estuaries? Or, is [Cu]T often greater than strong allochthonous ligand
concentrations, and therefore in general we depend on marine phytoplankton sources of ligands
to lower [Cu2 '] below concentrations toxic to microorganisms? More subtle questions, such as
whether allochthonous ligands release copper in saline waters due to cation competition and the
ionic strength effect, are also important. These questions are most relevant to current
environmental issues: how to regulate and monitor copper (and other metals) released into coastal
areas based on their effect on marine ecosystems, and how best to assess ecosystem health of
metal polluted coastal areas.
77
APPENDIX. ERROR ANALYSIS.
In this section we list and discuss all possible sources of error in the copper titrations, present
the results of error analysis, and discuss the relative magnitudes of random error and systematic
error and their effects in these titrations. This section does not include discussion of error
associated with best fits of binding constants and ligand concentrations.
Considered sources of error for our titrations are as follow:
1. Analytical variability: repeated measurements of the peak height of a single sample have both
an absolute variability of about 0.5 nA and a relative variability of about 3%. The absolute
error is caused by the background noise intrinsic to voltammetry that occurs at all voltages
and affects the smallest peaks measured at low values of [Cu]r. The absolute variability
converted to nM concentrations of Z[Cu(AL)x] is 0.4 nA divided by S, the sensitivity. The
3% relative variability of measurements is negligible compared to the error associated with
determination of the sensitivity, discussed below, so is not included in the calculation of error
bars.
2. Our estimate of the variability of the parameter S is based on the average relative standard
deviation (average 8%, range 2% to 17%) of the calibration slopes used for the titrations. The
standard deviation of the slopes each titration were calculated assuming that the only source
of error was the analytical noise, and
ar 2 = N *
e m
A
(A.1)
where a,,, is one standard deviation of the slope, aY is the error in the peak measurement, N is
78
the number of data points in the standard curve, and A' is
A'= Nx
-(x)2
(A.2)
This estimation doesn't take into effect the 3% variability in all points, but provides an
estimate of uncertainty associated with the standard curves. A greater number of data points
in the standard curve is associated with less uncertainty in S; at least ten data points are
needed for an uncertainty of 5% or less. For our calculations we used a realistic approximate
value of 10% uncertainty in the slopes of the standard curves and overload titrations
conducted with each titration.
3. Repeated analyses of [Cu]1 in UV-SW over several months based on standard curves in UVSW with 1 to 20 nM added copper give [Cu], of 1.2 ± 0.3 nM.
The error for I[Cu(AL)x] was calculated as following:
Because
E[Cu(AL)] = i/S,
(2.13)
the percent relative standard deviation of X[Cu(AL)x] depends on that of both i, the analytical
signal, and S the sensitivity, so that
%rsd(X[Cu(AL)j) =
%rsd(i)2 + %rsd(S)2 .
(A.3)
Because [Cu2+] is related to X[Cu(AL)x],
[Cu 2+] = E[Cu(A L)X]/SRC(AL)
(2.5)
the percent standard deviation of [Cu 2 +] is
%rsd([Cu2 +])
=
1%rsd{X[Cu(AL), ] }2 + %rsd[SRC(AL)]2 .
79
(A.4)
The error in SRC(AL) is not a negligible source of error; for example, the value of log SRC(AL)
for 1 gM SA is 3.68 ± 0.08 (Campos, 1994). However, we use visual comparisons of titrations
with bzac and SA to verify consistency of data probed with different SRC(AL), as any error in
SRC(AL) would appear as a systematic offset of titration datasets conducted with different
SRC(AL). Therefore, we neglect %rsd[SRD {AL] here and use
%rsd([Cu2"])
=
%rsd(E[Cu(AL)j).
(A.5)
Finally, from Eq. 2.8,
Z[CuLJ = [CulT - L[Cu(AL),],
and the standard deviation of X[CuLi] is therefore
sd(X[CuLJ) = lsd([Cu]T )2 + sd( [Cu(AL)x ])2 .
(A.6)
These values and relationships were used to calculate the error bars in Figures 4.1 through 4.4.
80
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