Journal of Environmental Management 80 (2006) 222–229
www.elsevier.com/locate/jenvman
Comparison of zinc complexation properties of dissolved natural
organic matter from different surface waters
Tao Cheng *, Herbert E. Allen
Center for the Study of Metals in the Environment, Department of Civil and Environmental Engineering, University of Delaware, Newark, DE 19716, USA
Received 20 January 2005; received in revised form 23 July 2005; accepted 5 September 2005
Available online 9 December 2005
Abstract
The zinc binding characteristics of natural organic matter (NOM) from several representative surface waters were studied and compared. NOM
samples were concentrated by reverse osmosis. The samples were treated in the laboratory to remove trace metals. Square wave anodic stripping
voltammetry (SWASV) was used to study zinc complexing properties of those NOM samples at fixed pH, ionic strength, and dissolved organic
carbon (DOC) concentrations. Experimental data were compared to the predictions from the Windermere Humic Aqueous Model (WHAM)
Version VI. At the same pH, ionic strength, and temperature, the zinc titration curves for NOM samples from different surface water sources tested
in our study almost overlapped each other, indicating similarity in zinc binding properties of the NOM. A discrete two-site model gave good fits to
our experimental titration data. Non-linear fitting by FITEQL 4.0 shows that the conditional zinc binding constants at the same pH are similar for
NOM from different sources, indicating that zinc complexation characteristics of the NOM used in our study do not depend on their origin and one
set of binding parameters can be used to represent Zn-NOM complexation for NOM samples from those different surface water sources
representing geographically diverse locations. In addition, the total ligand concentrations (L1,T, L2,T, and LT) of all NOM show no observable
gradation with increasing pH (L1,TZ2.06G0.80 mmol/g carbon; L2,TZ0.12G0.04 mmol/g carbon; LTZ2.18G0.78 mmol/g carbon), while the
c
Þ show a linear increase with increasing pH ðlog K1c ðpHZ 6:0ÞZ 4:69G0:25;
conditional binding constants of zinc by NOM ðlog KZnL
log K1c ðpHZ 7:0ÞZ 4:94G0:10; log K1c ðpHZ 8:0ÞZ 5:25G0:006; log K2c ðpHZ 6:0ÞZ 6:29G0:13; log K2c ðpHZ 7:0ÞZ 6:55G0:08; log K2c ðpH
Z8:0ÞZ 6:86G0:023Þ with a slope of ca. 0.28, indicating the zinc-NOM complexes become more stable at higher pH. The WHAM VI predicted
free zinc ion activities at high zinc concentrations agree with our experimental results at pH 6.0, 7.0, and 8.0. However, the zinc binding of these
NOM samples is over estimated by WHAM VI at zinc concentrations below 10K6 M at pH 8.0.
q 2005 Elsevier Ltd. All rights reserved.
Keywords: Zinc complexation; Natural organic matter; Anodic stripping voltammetry
1. Introduction
Zinc (Zn) is a common element occurring naturally in the
environment and it is widely used by humans for domestic and
industrial purposes. Zinc is an essential element
and micronutrient required for normal growth by plants and
animals. At both high and low concentrations zinc can be
detrimental to organisms. In uncontaminated waters zinc
concentration is usually very low and can span a wide range
from 10K10 to 10K6 M (Stumm and Morgan, 1996). Both
human activity and natural processes have inevitably increased
the level of zinc concentrations in some natural water systems
and high concentrations of zinc that are toxic or even lethal to
* Corresponding author. Tel.: C1 626 395 4385; fax: C1 626 395 2940.
E-mail address: tcheng@ce.udel.edu (T. Cheng).
0301-4797/$ - see front matter q 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jenvman.2005.09.007
organisms have been observed, which has caused great
environmental concern.
The speciation of zinc in natural waters is a critical factor to
consider when assessing the environmental impact of zinc. The
bioavailability, toxicity, transport and fate of zinc in the aquatic
environment, and water quality criteria have been recognized
as a function of water chemistry (Allen and Hansen, 1996).
Complexation of zinc by natural organic matter (NOM) has
important influence on the speciation of zinc in various natural
waters. The complexation of Zn by NOM in natural waters can
markedly lower the free Zn2C activity relative to total
dissolved Zn, leaving only a small fraction of the total zinc
as ‘free’ zinc, which is considered to be bioavailable, or toxic
(Allen and Hansen, 1996). Therefore, in order to understand
zinc toxicity in water bodies, we need to understand the
characteristics of zinc binding by NOM.
Modeling is an essential tool in quantifying the speciation of
metals in the environment. There are a number of models
T. Cheng, H.E. Allen / Journal of Environmental Management 80 (2006) 222–229
available that predict metal complexation with inorganic and
organic compounds with defined chemical nature. NOM has
not been very well characterized due to its complicated nature,
although the main functional groups that bind to metals are
known. There exist a few models that model metal binding to
NOM (WHAM, NICA) (Tipping, 1994; 1998; Benedetti et al.,
1995). The Biotic Ligand Model (BLM), which uses WHAM
to compute organic speciation, was recently proposed to
predict acute toxicity of metals to aquatic organisms (Di Toro
et al., 2001; Santore et al., 2001). The key assumption of all the
above models is that metal complexation characteristics of
NOM do not depend on their origin (Tipping, 1994; 1998;
Benedetti et al., 1995; Di Toro et al., 2001; Santore et al., 2001;
Lu and Allen, 2002). Although NOM exhibit site specificity in
metal binding (Sarathy, 2002), there is evidence that copper
binding properties of NOM from surface water sources over
large spatial and temporal scales are similar (Lu and Allen,
2002). This is probably because the main mechanisms of metal
binding (metal complexation with carboxylic groups and
phenolic groups) in these NOM are similar. However, only
very limited literature is available on zinc binding by NOM. In
recent years, zinc binding by NOM has been studied by a range
of techniques including Donnan membrane (Oste et al., 2002),
cation ion-exchange (Fortin and Campbell, 1998), resin
equilibrium (Christensen and Christensen, 1999; 2000), and
voltammetry (Jansen et al., 1998; Xue and Sigg, 1994).
However, to our knowledge, studies that compare binding
characteristics of NOM from different surface water sources
have not been reported for Zn. This paper reports data from
experiments conducted on NOM samples from three surface
water sources to determine zinc complexing properties. The
data are analyzed to ascertain if all organic matters can be
generalized to behave in a similar fashion to complex zinc.
2. Materials and methods
All reagents used were analytical grade except the acids,
which were Optima grade. Unless otherwise mentioned, all
reagents were obtained from Fisher Scientific (Pittsburgh; PA;
USA) ‘Better’ buffers (Kandegedara and Rorabacher, 1999) of
MES (for pH 6.0), MOPS (for pH 7.0) and PIPBS (for pH 8.0)
were used in zinc titrations to keep the pH constant. The buffers
were added to samples to achieve a 0.01 M concentration of the
buffers. During the titrations, 0.1 M NaOH or 0.1 M HNO3 was
added as required to keep the pH change withinG0.1 pH unit.
Distilled de-ionized water was used in all experiments, for all
dilutions, and for blanks.
In order to test whether a single model of metal-NOM
complexation is adequate, or whether typically observed
variations in the characteristics of NOM samples from different
sources are sufficient to require site-specific chemical
characterizations or models, three sites were chosen as sources
of NOM in order to include geographically diverse locations,
ones that are likely to provide NOM samples that vary in
composition and chemical behavior. NOM was sampled from
the Big Moose Lake, a high elevation system in the Adirondack
Mountains of New York State, in May 2000; from the Edisto
223
River, a typical receiving water in South Carolina with a much
larger watershed and longer residence time, in March 2001;
and from the Suwannee River, Georgia, in June 1997.
Procedures used are described in detail in an earlier publication
(Ma et al., 2001). The source water was filtered through a
0.45 mm pore size filter and the samples were concentrated in
the field using a reverse osmosis (RO) unit (Model PROS/2S,
RealSoft, Norcross, GA) (Serkiz and Perdue, 1990). The
samples were stored in coolers with ice in the field and in a
refrigerator at 4 8C in the laboratory. The concentrated NOM
samples were passed through a HC-saturated cation-exchange
resin (Dowex 50WX8, Fluka Chemical Co., Milwaukee, WI)
column to remove both trace metals and major cations. To
avoid losing the humic acid (HA) fraction of the NOM on the
resin due to the strongly acidic condition, HA was separated in
advance by acidic precipitation (pHz1) and was later
recombined with the material that passed through the cationexchange column. The NOM samples thus treated were used in
all the subsequent experiments.
2.1. Characterization of NOM
We determined the concentration of the DOC (Dissolved
Organic Carbon) and the DIC (Dissolved Inorganic Carbon) of
the concentrated NOM samples using a Tekmar-Dohrman DC190 TOC analyzer. The metal concentration of the diluted
samples was analyzed using Inductively Coupled Plasmaoptical emission spectroscopy (ICP-OES) (Spectro Analytical
Instrument, Kleve, Germany). The DOC, DIC and the metal
concentrations of the NOM samples are reported in Table 1.
2.2. Zinc titrations
The NOM samples were also titrated against zinc using
square wave anodic stripping voltammetry (SWASV) over
a range of total zinc concentrations ranging from 10K7 to
Table 1
Metal concentrations, dissolved organic carbon (DOC), dissolved inorganic
carbon (DIC), and percentage of fulvic and humic acids of NOM
Conc., (mg/L)
(with 10 mg
DOC/L)
Na
K
Ca
Mg
SC-NOM
NY-NOM
GA-NOM
74.5
121.5
0.081
0.775
0.597
0.565
0.1071
0.0813
0.0372
0.045
0.017
!0.0018
Conc. (mg/L)
(with 10 mg
DOC/L)
Ni
Cu
Zn
Cd
Pb
SC-NOM
NY-NOM
GA-NOM
!2.01
!2.01
!2.01
2.30
3.62
3.79
1.24
0.43
!0.10
!0.68
!0.68
!0.68
!0.5
!0.5
!0.5
DOC (mg/L)
SC-NOM
NY-NOM
GA-NOM
564.1
331.8
931.3
IC (mg/L)
2.322
1.006
0.932
Percentage of HA and FA (%)
HA
FA
19
10
5
81
90
95
224
T. Cheng, H.E. Allen / Journal of Environmental Management 80 (2006) 222–229
10K4 M. Voltammetric measurements were performed using
an Analytical Instrument Systems AIS Model DLK-100A
electrochemical analyzer with an EG&G Princeton Applied
Research PARC 303A static mercury drop electrode in the
hanging mercury drop electrode (HMDE) mode. The mercury
drop size was ‘large’ with a surface area of 2.83 mm2. The
reference electrode was Ag/AgCl/3M KCl. For each measurement, a new mercury drop was extruded and the sample
solution to be measured was purged with ultrapure (grade 5.0)
N2 for 4 min to eliminate the possible interference of O2. In
addition, the head space of the voltammetric cell was filled
with nitrogen after purging so that possible dissolution of CO2
from the atmosphere into the solution during the measurement
was minimized. SWASV mode was used to measure labile
(electro-active) Zn concentration. For each SWASV measurement the deposition time was 30 s and deposition potential was
K1.500 V without stirring; an equilibration time of 15 s
followed; for the stripping step, the square wave mode was
used, the pulse height was 0.025 V and the potential scan began
from K1.500 V and ended at K0.800 V at a scan rate of
50 mV/s. SC, NY, and GA NOM were titrated with Zn at 3 pHs
K6.0, 7.0, and 8.0. ‘Better’ buffers were used to control the pH
to the required value. These ‘better’ buffers do not complex
metal ions and thus do not interfere with the titrations
(Kandegedara and Rorabacher, 1999). For each titration,
10 mg/L of the dissolved organic carbon (DOC) was prepared
by dilution of the concentrated NOM. One M NaNO3 solution
was added to adjust the ionic strength to 0.02 M. Titrations
were conducted in a clean room at a constant temperature of
w22 8C. The reactions were allowed to stabilize after each
addition of zinc for at least 4 min. Our experiments on ZnNOM complexation kinetics show that the complexation
reactions between Zn and NOM reach equilibrium within
4 min under our experimental conditions (data not shown).
De Jong and van Leeuwen, 1987a,b,c):
2.3. Computation of free zinc activities
2.4. Models
The peak current measured by SWASV, Ip, which is
proportional to the labile fraction of metal in the voltammetric
measurement, is a weighted average of the diffusion of all
metal species (freeCcomplexed). For a fully labile system
(that is, a system in which all the relevant metal species (Zn and
ZnL in our system) are electroactive), the peak current Ip is
expressed as (van Leeuwen et al., 1989; De Jong and van
Leeuwen, 1987a,b,c):
To compute speciation of zinc in aquatic media that contain
NOM, a chemical equilibrium model is used. The simplest
metal complexation with homogeneous ligands can be
represented by:
½ZnL
c
Zn2C C L Z ZnL KZnL
Z
(6)
½Zn2C½L
where Zn represents the ‘simple’ (or more exactly, hydrated)
zinc ion, L is the ligand, ZnL is the complex formed between
c
Zn2C and L. The conditional zinc binding constant KZnL
is only
valid at constant pH and ionic strength. To simplify the
discussion, only the 1:1 ZnL complex is considered. While
other stoichiometry (other than 1:1) between Zn and ligand (L)
is possible, it is generally valid to assume the stoichiometry of
the complexation reaction between Zn and NOM is 1:1, since it
has been shown that the majority of the bond formed between
metal ion and NOM is monodentate and it has been shown by a
number of studies that this assumption is reasonable in
modeling metal ion and NOM complexation (Tipping, 1998;
Bugarin et al., 1994; Pinheiro et al., 1994).
Ip ZKpK1=2 nFAD 1=2 CM;T
tK1=2
(1)
where F is the Faraday constant, A is the electrode surface area,
n is the number of moles of electrons transferred per mole of
metal oxidized or reduced, CM;T
is the total soluble metal
concentration in the bulk solution, t is characteristics time,
which is constant in our experiments, and D is the weighted
average of the diffusion coefficient of all metal species (freeC
complexed), which is expressed as (van Leeuwen et al., 1989;
½M
½ML
D Z DM C DML
CM;T
CM;T
(2)
where [M]* is the free metal ion concentration in bulk solution,
[ML]* is the complexed metal concentration in bulk solution,
DM and DML are the diffusion coefficients of the free and
complexed metal ion.
The mass balance of metal in bulk solution is,
½M C ½ML Z CM;T
(3)
Defining normalized current F as the ratio of peak current in
the presence of ligands to that of a ligand-free reference,
1=2
IpL
D
FZ Z
(4)
DM
Ip
where F is the normalized current, IpL is the peak current in the
presence of ligands and Ip is that of the ligand-free reference.
Combining Eqs. (2)–(4), the free metal ion activity in bulk
solution is expressed as,
IpL 2
DML
K DM
Ip
CM;T
½M Z (5)
1K DDML
M
Eq. (5) is used to compute free zinc ion concentration for fully
labile Zn-NOM systems in our experiments. Normalized
current is obtained by comparing the peak current in the
presence of ligands and that of the ligand-free reference. Total
zinc concentration was determined by ICP. For a fully labile
system, the value of DML/DM can be estimated under
conditions when the ligand concentration is in large excess
of the total zinc concentration so that [M]*/[ML]* and the
weighted average D tends to DML (Eq. (2)).
T. Cheng, H.E. Allen / Journal of Environmental Management 80 (2006) 222–229
225
However, in order to represent the complexation reaction of
zinc with NOM, the heterogeneity property of NOM must been
taken into consideration. To this end, discrete multi-site models
are usually used. The simplest case of the discrete multi-site
model is the two-site model (van den Berg, 1984), represented
by:
Zn2C C Li Z ZnLi
c
KZnL
Z
i
Li;T Z Li C ZnLi
½ZnLi ½Zn2C½Li (7)
(8)
c
where KZnL
is the conditional stability constant, valid only at
i
constant pH and ionic strength, Li is free binding sites which
means the sites are not bound by zinc, Li,T is total binding sites.
iZ1, 2 is an index, representing two distinguishable ligands
present in NOM. Usually one is carboxyl group and the other
one is the phenolic group. The discrete two-site model (or
discrete multi-site model) has been successfully applied to
describe metal interaction with organic matter in a number of
conditions (van den Berg, 1984; Hering and Morel, 1988).
A fitting model, FITEQL 4.0 (Herbelin and Westall, 1999)
was used to calculate stability constants of ligand-zinc
complexes, and ligand concentrations. For the zinc titration
data, a 2-site model gives a good fit so it was adopted in our
modeling approach.
WHAM (Windermere Humic Aqueous Model) Version VI
(Tipping, 1998) was used to calculate zinc complexation with
NOM. The Zn binding constant of fulvic acid is log KMAZ1.6,
and the Zn binding constant of humic acid is log KMAZ1.5 in
WHAM Version VI. The free zinc data predicted by WHAM
were compared to experimental data to determine if WHAM
gave an accurate description of Zn-NOM complexation.
3. Results and discussion
DOC was determined on samples both before and after the
precipitation of the humic acid. All the organic matter that is
not humic acid is considered fulvic acid when calculating
speciation using WHAM. The DOC, IC (Inorganic Carbon)
and percentage of HA (Humic Acids) and FA (Fulvic Acids)
thus measured are reported in Table 1. The NOM from Edisto
River, SC, Big Moose Lake, NY and the Suwannee River, GA
were found to contain 81, 90 and 95% fulvic acid, respectively.
3.1. Zinc titrations
Concentrated SC, NY, and GA-NOM were added to a
solution with a fixed total Zn concentration of 7.37!10K7 M
buffered at pHZ7.0. For each addition of NOM, a voltammetric measurement was made and normalized current and
peak potential was plotted against DOC concentration. The
titration curve for GA-NOM is shown in Fig. 1. With the first
addition of NOM (DOCz20 mg/L), the normalized current
dropped from 1.0 to about 0.45, indicating a large fraction of
zinc was complexed by NOM and the diffusion coefficient of
the NOM complexed zinc was much lower than that of the free
Fig. 1. Estimation of DZnL/DZn value by titration of Zn with GA-NOM.
Titration of total Zn concentration of 7.33!10K7 M at pHZ7.0, IZ0.02 M,
TZ25 8C. The estimated DZnL/DZn value was 0.014.
zinc ion (Eq. (4)). When more NOM was added, however, the
decrease in the normalized current became more gradual and
the normalized current attained a limiting value at high DOC
concentrations. During the same titration the peak potential
tended to more negative values with addition of NOM
(At DOCZ0, the peak potential was K1.16 V, at DOCZ
300 mg/L, the peak potential was K1.22 V, while at DOCZ
600 mg/L, the peak potential was K1.26 V). This systematic
shift in peak potential indicates that the complex formed
between zinc and NOM (ZnL) is labile and the decrease in peak
current is due to a lower diffusion coefficient of the labile
complex (ZnL) compared to that of the free zinc ion (DZnL!
DZn), not due to the presence of inert (non-labile) complexes
(Jansen et al., 1998; Cleven and Leeuwen, 1986; van Leeuwen
et al., 1989). The DZnL/DZn values which were determined for
the NOM samples using Eq. (4) are: SC-NOM, 0.04; NYNOM, 0.06; GA-NOM, 0.014. These values of DZnL/DZn are
close to the reported value of 0.05 (Jansen et al., 1998). In
addition, for the purpose of computing free Zn ion, Jansen et al.
(1998) showed that the influence of the value of DZnL/DZn on
the resulting free Zn ion is very small. This was also confirmed
by our calculation using Eq. (5).
Zinc was added to SC, NY, and GA-NOM samples having
10 mg/L DOC and the labile zinc ion was determined
following each change in total zinc. The titration curve of the
SC-NOM is shown in Fig. 2. For the same total zinc
concentration, a decrease in peak current, which is in
proportional to the labile zinc concentration (Eq. (1)), was
observed with increase in pH. It was demonstrated by
MINTEQCcalculation that under our experimental conditions
free zinc ion is the dominant inorganic zinc species and the
complexes formed between zinc and inorganic ligands (mainly
OHK, ClK, and NOK
3 ) are negligible. It was also demonstrated
by MINTEQCthat no zinc solid formed under our experimental conditions. So the decrease in peak current was due to
formation of zinc-NOM complexes, not due to formation of
zinc solids or zinc inorganic complexes. As discussed
previously, the zinc-NOM complexes in our system were
labile; so the free zinc activities in our titration can be
226
T. Cheng, H.E. Allen / Journal of Environmental Management 80 (2006) 222–229
Fig. 2. Zn titration curves for the SC-NOM sample at pH 6.0, 7.0 and 8.0.
DOCZ10 mg/L; IZ0.02 M; TZ25 8C.
computed using Eq. (5). Comparison of zinc titrations
expressed as free zinc ion against total soluble zinc
concentrations for NY, SC and GA-NOM at pH 6.0, 7.0 and
8.0 are shown in Fig. 3. All the curves with the same pH and
ionic strength almost overlap each other. This indicates that
NOM from different surface water sources are similar in
complexation of zinc. To determine whether the three sets of
data are similar or different, we based our judgment on the
difference in log [Zn2C], not [Zn2C], considering the wide
range of the metal concentrations involved (several orders of
magnitude) and the complicated nature of the NOM. In
addition, in fitting the metal-NOM binding parameters in
WHAM, the relative error of the log of metal concentration, not
metal concentration, is used to estimate the goodness of fitting
(Tipping, 1998). In modeling metal complexation with NOM,
an error of a factor of 3–4 in the free metal ion activity is
acceptable (Christensen and Christensen, 2000).
Fig. 3. Zn titration curves of NOM samples from different sources at pH 6.0, 7.0, and 8.0. DOCZ10 mg/L; IZ0.02 M; TZ25 8C.
T. Cheng, H.E. Allen / Journal of Environmental Management 80 (2006) 222–229
Fig. 4 shows plots that compare the WHAM VI
predictions with our experimental data. For the NOM from
different sources, WHAM predictions account for the free
zinc very well (in terms of the relative error of log [Zn2C])
at high total Zn concentrations for all pH tested in our
titrations. However, WHAM under predicts the free zinc ion
activity at low total soluble Zn concentrations at pH 8.0. This
indicates that the strong binding sites of low concentration
for zinc complexation in NOM implied by WHAM VI may
not exist. Very limited literature is available on comparison
of experimentally measured Zn species in natural waters with
WHAM simulations. Christensen and Christensen (1999,
2000) reported that WHAM Version V tends to over estimate
Zn-NOM complexation. They suggested the default Zn-NOM
stability constant in WHAM Version V, which is the ‘best
average’ from a limited number of published data, is
overestimated. By using a Zn-NOM reaction constant
227
of 1.7, instead of the default value of 1.3; they found good
agreements between their experimentally measured free Zn
activities and WHAM Version V prediction. It should be
noted that the Zn-organic matter binding constants reported
by Christensen and Christensen (2000) are for organic
matters that originate from leachate of solid waste disposal,
which presumably are different with respect to metal binding
compared to organic matters from surface water sources. In a
recent study, it was reported that the metal binding
characteristics of organic matters from the effluents of
municipal wastewater treatment plants were very different
from those of organic matters from surface water sources
(Sarathy, 2002). Metal binding sites other than carboxylic
and phenolic groups in those organic matters from leachate
and wastewater effluent might account for the different metal
binding properties compared to those of NOM from surface
water sources.
Fig. 4. Comparison of WHAM VI simulation and experimental Zn titration curves for SC, NY, and GA-NOM at pH 6.0, 7.0 and 8.0. DOCZ10 mg/L; IZ0.02 M;
TZ25 8C. The symbols represent experimental measurement (open circles (B): pH 6.0; open squares (,): pH 7.0; open diamonds (,): pH 8.0). The lines represent
WHAM simulation.
228
T. Cheng, H.E. Allen / Journal of Environmental Management 80 (2006) 222–229
Table 2
Conditional stability constants and site densities of Zn-NOM complexation obtained by 2-site model fit with FITEQL 4.0 (SDZstandard deviation)
Sample
pH
c
log KZnL;1
(mol/L)K1
c
log KZnL;2
(mol/L)K1
L1,T
(mmol/g C)
L2,T
(mmol/g C)
LT
(mmol/g C)
Percent of
Li,T/LT
1
2
SC-NOM
6.0
7.0
8.0
6.0
7.0
8.0
6.0
7.0
8.0
6.0
4.66
4.82
5.24
4.95
4.99
5.25
4.46
5.01
5.25
4.69
0.25
5.3
4.94
0.10
2.1
5.25
0.006
0.1
6.21
6.46
6.87
6.44
6.59
6.87
6.22
6.59
6.83
6.29
0.13
2.0
6.55
0.08
1.1
6.86
0.023
0.3
1.69
2.54
1.83
1.33
2.04
1.84
3.99
1.82
1.48
0.07
0.11
0.16
0.10
0.13
0.16
0.07
0.12
0.15
1.76
2.65
1.99
1.43
2.17
2.00
4.04
1.94
1.63
0.96
0.96
0.92
0.93
0.94
0.92
0.98
0.94
0.91
0.04
0.04
0.08
0.07
0.06
0.08
0.02
0.06
0.09
2.06
0.80
39
0.12
0.04
32
2.18
0.78
36
0.94
0.023
2.4
0.06
0.023
38
NY-NOM
GA-NOM
Avg
SD
Relative SD,
Avg
SD
Relative SD,
Avg
SD
Relative SD,
Avg.
SD
Relative SD,
%
7.0
%
8.0
%
%
3.2. Zinc ligand binding constants
c
Þ
Table 2 lists the conditional binding constant ðlog KZnL;i
values and the concentrations of the ligands for SC, NY, and
GA-NOM obtained by FITEQL 4.0 using a 2-site model. Since
temperature and ionic strength were held constant, these binding
constants varied only with pH. Our fitting results showed that the
c
Þ values increased with
conditional binding constant ðlog KZnL;i
pH, indicating that the zinc-ligand complexes become more
stable at higher pH. This is what we would expect since the
decrease in competing protons at higher pH results in higher
stability for the complexes. It was also observed that the
total ligand concentrations (L1,T, L2,T, and LT) showed
no gradation with pH. Plots of log of conditional stability
c
Þ versus pH (Fig. 5) for NOM samples illustrate
constants ðKZnL;i
c
that log KZnL;i is linearly pH dependent with slopes close to
0.283 (equations shown in Fig. 5). The slopes (0.280 and 0.285)
of our linear regression of the log of conditional stability
constants and pH are close to the slope (0.276) reported by
Christensen and Christensen (2000). They reported a linear
relationship between the log of conditional stability constants
and pH as:
c
log KZnL
Z 0:276pH C 2:581
(9)
for Zn-NOM complexation by two leachate dissolved
organic carbon samples with similar ionic strength and pH
range (IZ0.056 and 0.023 M, the pH range is 5.0–8.0).
4. Conclusions
Fig. 5. Conditional stability constants obtained by the 2-site model for NOM
c
samples versus pH. Averaged log KZnL;i
of SC, NY, and GA-NOM obtained
from the 2-site model was plotted against pH.
At the same pH, ionic strength, and temperature, the zinc
titration curves for NOM samples from different surface water
sources tested in our study almost overlap each other,
indicating that zinc complexation characteristics of the surface
water NOM used in our study do not depend on their origin.
This is probably because the main mechanisms of metal
binding (metal complexation with carboxylic groups and
phenolic groups) in these NOM are similar. These observations
added strength to the assumption that one set of binding
constants and ligand concentrations can be used to represent
Zn-NOM complexation for NOM from dissimilar surface
water sources. Clearly, however, more data on zinc binding by
NOM from other sources are required to determine whether the
zinc complexation characteristics of NOM depend on their
origin, or whether the NOM studied here happen to have
similar Zn binding affinity. Titrations of the type presented here
T. Cheng, H.E. Allen / Journal of Environmental Management 80 (2006) 222–229
on a wider range of natural water NOM would be useful in
investigating this issue further. Comparison of titration curves
with those predicted by WHAM VI shows a good fit in the case
of all NOM at high zinc concentrations for pH 6.0, 7.0 and 8.0,
and a poor fit in the case of low zinc concentrations at pH 8.0.
Zinc binding by NOM may be over estimated in the current
version of WHAM, especially at low zinc concentrations and
high pH.
Acknowledgements
The support from the International Lead Zinc Research
Organization for this research is gratefully acknowledged.
References
Allen, H.E., Hansen, D.L., 1996. The importance of trace metal speciation to
water quality criteria. Water Environ. Res. 68, 42–54.
Benedetti, M.F., Milne, C.J., Kinniburgh, D.G., van Riemsdijk, W.H., Koopal,
L.K., 1995. Metal ion binding to humic substances: application of the nonideal competitive adsorption model. Environ. Sci. Technol. 29, 446–457.
Bugarin, M.G., Mota, A.M., Pinheiro, J.P., Goncalves, M.L.S., 1994. Influence
of metal concentration at electrode surface in different pulse anodic
stripping voltammetry in the presence of humic matter. Anal. Chim. Acta
294, 271–281.
Christensen, J.B., Christensen, T.H., 1999. Complexation of Cd, Ni, and Zn by
DOC in polluted groundwater: a comparison of approaches using resin
exchange, aquifer material sorption, and computer speciation model
(WHAM and MINTEQA2). Environ. Sci. Technol. 33, 3857–3863.
Christensen, J.B., Christensen, T.H., 2000. The effect of pH on the
complexation of Cd, Ni, and Zn by dissolved organic carbon from
leachate-polluted groundwater. Water Res. 34, 3743–3754.
Cleven, R.F.M.J., Leeuwen, H.P., 1986. Electrochemical analysis of the heavy
metal/humic acid interaction. Int. J. Environ. Anal. Chem. 27, 11–28.
De Jong, H.G., van Leeuwen, H.P., 1987a. Voltammetry of metal complex
systems with different diffusion coefficients of the species involved. Part I.
Analytical approaches to the limiting current for the general case including
association/dissociation kinetics. J. Electroanal. Chem. 234, 1–16.
De Jong, H.G., van Leeuwen, H.P., 1987b. Voltammetry of metal complex
systems with different diffusion coefficients of the species involved. Part II.
Behaviour of the limiting current and its dependence on association/dissociation kinetics and lability. J. Electroanal. Chem. 234, 17–29.
De Jong, H.G., van Leeuwen, H.P., 1987c. Voltammetry of metal complex
systems with different diffusion coefficients of the species involved. Part III.
The current–potential relation for the general case including association/
dissociation kinetics. J. Electroanal. Chem. 235, 1–10.
Di Toro, D.M., Allen, H.E., Bergman, H.L., Meyer, J.S., Paquin, P.R., Santore,
R.C., 2001. Biotic ligand model of the acute toxicity of metals. I. Technical
basis. Environ. Toxicol. Chem. 20, 2383–2396.
229
Fortin, C., Campbell, P.G.C., 1998. An ion-exchange technique for free-metal
ion measurements (Cd2C, Zn2C): applications to complex aqueous media.
Int. J. Environ. Anal. Chem. 72, 173–194.
Herbelin, A.L., Westall, J.C., 1999. FITEQL. A Computer Program for
Determination of Chemical Equilibrium Constants from Experimental
Data, Version 4.0, Report 99-01. Department of Chemistry, Oregon State
University, Corvallis, OR.
Hering, J.G., Morel, F.M.M., 1988. Humic acid complexation of calcium and
copper. Environ. Sci. Technol. 22, 1234–1237.
Jansen, R.A.G., van Leeuwen, H.P., Cleven, R.F.M.J., van den Hoop,
M.A.G.T., 1998. Speciation and lability of zinc (II) in river waters.
Environ. Sci. Technol. 32, 3882–3886.
Kandegedara, A., Rorabacher, D.B., 1999. Noncomplexing tertiary amines as
‘better’ buffers covering the range of pH 3–11. Temperature dependence of
their acid dissociation constants. Anal. Chem. 71, 3140–3144.
Lu, Y., Allen, H.E., 2002. Characterization of copper complexation with
natural dissolved organic matter (DOM) — link to acidic moieties of DOM
and competition by Ca and Mg. Water Res. 36, 5083–5101.
Ma, H., Allen, H.E., Yin, Y., 2001. Characterization of isolated fractions of
dissolved organic matter from natural waters and a wastewater effluent.
Water Res. 35, 985–996.
Oste, L.A., Temminghoff, E.J.M., Lexmond, T.M., van Riemsdijk, W.H., 2002.
Measuring and modeling zinc and cadmium binding by humic acid. Anal.
Chem. 74, 856–862.
Pinheiro, J.P., Mota, A.M., Goncalves, M.L.S., 1994. Complexation study of
humic acids with cadmium(II) and lead(II). Anal. Chim. Acta. 284,
525–537.
Santore, R., Di Toro, D.M., Paquin, P.R., Allen, H.E., Meyer, J.S., 2001. A
biotic ligand model of the acute toxicity of metals. 2. Application to acute
copper toxicity in freshwater fish and daphnia. Environ. Toxicol. Chem. 20,
2397–2402.
Sarathy V., 2002. Comparison of Copper Complexation Properties of
Dissolved Organic Matter from Surface Waters and Wastewater Effluents.
Master Thesis. University of Delaware, Newark, DE, USA.
Serkiz, S.M., Perdue, E.M., 1990. Isolation of dissolved organic matter from
the Suwannee River using reverse osmosis. Water Res. 24, 911–916.
Stumm, W., Morgan, J.J., 1996. Aquatic Chemistry: Chemical Equilibria and
Rates in Natural Waters, third ed. Wiley, New York.
Tipping, E., 1994. WHAM— a chemical equilibrium model and computer code
for waters, sediments, and soils incorporating a discrete site electrostatic
model of ion-binding by humic substances. Comput. Geosci. 20, 973–1023.
Tipping, E., 1998. Humic ion-binding model VI: an improved description of the
interactions of protons and metal ions with humic substances. Aquat.
Geochem. 4, 3–48.
van den Berg, C.M.G., 1984. Determination of the zinc complexing capacity in
seawater by cathodic stripping voltammetry of zinc–APDC complex ions.
Mar. Chem. 16, 122–130.
van Leeuwen, H.P., Cleven, R., Buffle, J., 1989. Voltammetric techniques for
complexation measurements in natural aquatic media: role of the size of
macromolecular ligands and dissociation kinetics of complexes. Pure Appl.
Chem. 61, 255–274.
Xue, H.B., Sigg, L., 1994. Zinc speciation in lake waters and its determination
by ligand-exchange with EDTA and differential-pulse anodic-stripping
voltammetry. Anal. Chim. Acta 284, 505–515.