CHAPI'ER III

COPPER COMPLEXATION CAPACITY

Introduction

Among the various approaches in trace metal speciation studies,

investigations through understanding the complexation reactions of

organic and inorganic lig~nds in the aquatic system constitute an

important area. Recent publications on chemical, speciation studies have

highlighted the importance and need of understanding metal organic

interactions (Mantoura, 1982) or complexation characteristics of natural

waters (Kramer and Duinker 1984).

The scenario of metal-organic interactions in a natural aquatic

system is ·highly complex. On the one side there is competition between

major cations (eg. Ca, Mg, etc.) and others in traces (eg., transition metal

ions) for forming more stable complexes with ligands. On the other side

many potential ligands or chelators compete for one trace element.

The broad area of studies on metal-organic interactions in an aquatic

system, comprises the following : (1) the direct determination of species

belonging to metal-organic complexes like tetrabutyl tin, tetramethyl lead

and methyl or ethyl mercury compounds,(2) functional group

determinations of ligands (like RCOO-, R2NH-, RS-), (3) studies on nature

and complexation of well defined organic compounds, (amino acids,

67

carbohydrates, glycols etc.), experimentally defined groups. of organic

matter, (humics and fulvics) and model compounds (like EDTA, NTA etc.)

and (4) investigation of total complexing capacity of natural waters.

Chau (1973) defined complexing capacity of natural waters as their

ability to take up ionic metals into non-labile complexes as measured ·by

using Anodic Stripping Voltammetry (ASV). This is a measure of the·

concentration of organic ligands capable of binding ionic metal into non­

labile complexes.

Heavy metal ions added to a natural aquatic system get complexed or

adsorbed by particulates or dissolved organics before ultimately getting

incorporated into the sediments (Sanchez and Lee 1973). This "buffering"

action or conventionally termed as the complexing capacity of the natural

water body is extremely important to the aquatic biota. Since copper is one

of the most toxic common metal ions present in aquatic bodies and the one

which forms very strong complexes with natural organic ligands;

1 · · · c C 2 + . 11 d (Fl comp exation capacities 10r u Ions are usua y measure orence

1982).

The study of the overall complexation capacity of a natural sample

gives an idea, not only on the types of metal-organic interactions but also on

the influence of organisms in chemical speciation as their excreta and cell

exudates (Extracelluar Metal Binding Organics-EMBO) contribute potential

ligands in the aquatic system.

68

As the species distribution in a natural sample is affected by the total

assemblage of all inorganic and organic ligands, the net binding effect has

been studied through measuring the apparent complexation capacity for

copper, since copper-organic chelates are the strongest complexes formed

when competing inorganic reactions are considered. Analysis of

environmental samples (natural waters) for estimating complexation

capacity, thus involves reactions with several ligands. The stability and

rate constants thus obtained do not then reflect the interaction with a single

ligand but the complex multi-ligand system. The copper complexation

capacity has been considered as a measure of the concentration of organic

ligands (L) capable of binding free metal ions to form non-labile complexes.

In natural systems part of the complexation capacity might be caused by

colloidal material. A variety of complexing agents like fulvic, humic and

tanniG acids, lignin and colloidal particles of Fe2o3, Al20 3 and Mn02 are

normally present in natural waters. Polluted waters will contain additional

natural and synthetic compounds. Generally the concentration of the

ligands is well in excess of the metals present. This excess of concentration

of ligands which is responsible for the complexation capacity of the aquatic

system, is an important water quality parameter as it is an indicator of the

concentration of heavy metals that can be discharged to a waterway before

free metal ions can appear. (Florence and Batley, 1980; Florence 1982,

Plavsic et. al. 1982, Hart, 1981). Several methods are in practice to estimate

the copper complexation capacity and conditional stability constants.

· Bioassays •. ion-exchange on resins or Mn02 , ion-selective electrode

69

potentiometry, Cu-salt solubilization, amperometry, and voltammetry are

the commonly used methods for measuring complexation capacity.

Methods of Estimation

Complexation capacity being a measure of the total ligand

concentration, both inorganic and organic, methods of measurement

should be capable of dealing with the difficult situation presented by the

infinite variety of organic ligands. Inorganic iigands like C03 2-, Cr, P04 a­

etc. that can interact with metals are easily measured and their

interactions with the metals are well characterized. However, the situation

is quite different for organic ligands especially because of their relatively

undefined mode of interaction with the metals (Reuter and Perdue, 1977).

In surface waters the major part of organic matter comes from

natural sources like decaying plant and animal bodies, animal excreta etc.

A series of transformations taking place in the aquatic environment,

convert this organic matter into a class of compounds of highly complexing

nature namely humic and fulvic acids. They have been termed as the

natural scavengers of metals in aquatic systems. Also present are the low

molecular weight compounds like tannins (Martin et. al., 1976) and

siderochromes (Murphy et. al., 1976) which also complex with metals. Gel

chromatographic fractionation of natural organics (Gjessing and Lee 1967)

have shown compounds having molecular weights greater than 50,000

(Daltons) also to be associated with metals.

70

With all these complex nature and variety of very similar and

dissimilar compounds, the multi-ligand system represented by a natural

aquatic body is difficult to deal with a single method of complexation

capacity measurement. Various methods have been in use for the past

decade or so. However, each of them suffers from one drawback or the other

and some of them give either too low or quite exaggerated values. This

stalemate can easily be overcome by considering the complex nature of the

ligands, theoretical considerations of their complexation behaviour,

method of measurement; and finally the nature of inherent interferences of

the analytical method or probe. Several methods like DPSV, CSV, MN02-

adsorption, ligand exchange - EDTA, ISE potentiometry, Dialysis, Gel

filtration, Fluorescence quenching titrations are used to estimate

complexation capacity.

Solubilization Method

The method is based on the fact that solubility of copper reaches a

minimum in the pH range 10-11 where the copper concentration is

approximately 2.4 X 10-7M (-l5JlgCull). Any increase of solubility of copper

at this pH range will be due to complexation of the metal by other

constituents of water. The experimental procedure requires the addition of

excess copper in soluble form with the pH of solution adjusted between 9.8

and 10.2 with Na2co3. The solution is heated for 1 hour, and finally filtered

through a 0.45 Jlm filter. The soluble copper is measured by ato~ic

absorption spectrophotometry or any other sensitive technique. Many

researchers have used this method in the past, for tap waters, creek

71

waters, sewage waters, rain waters etc. (Kunkel and Manahan, 1973, Elder

et.al., 1976),

However, serious drawbacks have been pointed out (Campbell, et.al.,

1977) for the copper hydroxide solubilization method. First~y, the alkaline

pH( - 10) required for this method differs greatly from the pH of natural

waters and hence the complexation capacity estimates may not have much

relevance to the aquatic system dealt with. Theoretical calculations

indicated that weak complexing agents like glycine, salicylic acid, citric

acid etc. would get further weakened at higher pHs. Further, the method

has been shown inherently insensitive to many weak ligands of biological

origin. Apart from the above, an appreciable amount of organic ligands

gets adsorbed on to the Cu(OH)2 formed. Similarly the solubility being

operationally defined by filtration through a 0.45 Jlm filter, the estimated

soluble copper includes the peptized portion also, which brings in an

appreciable error. The procedure of boiling the solution at an alkaline pH

also alters seriously the chemical nature of the complexing agents and

species like NH3 gets expelled during the same, which otherwise are

capable of complexing the metal ions.

Instead of solubilization of Cu(OH)2 other salts of copper like copper

phosphate, (Huber, 1980) copper carbonate (Avnimelech and Raveh, 1982)

and copper sulphide (Kerr and Quinn 1980) also had been used. However,

those methods also have many of the above said drawbacks. The copper

complexation capacity estimated with solubilization technique ranged from

72

0.147 mol/1 for a sanitarylandfillleachate (Avnimelech and Raveh 1982) to

500 Jl mol/1 for an oilfield brine (Kunkel and Manahan, 1973)

Biological Methods

Several studies on toxic effects of copper to aquatic organisms like

algae, crustaceans, diatoms etc. showed a direct relation not only with the

concentration of totai metal ions but also with the nature and concentration

of other organic and inorganic constituents. (Sunda and Guillard 1976,

Gillespie and Vaccaro 1978, Lewis et.al. 1971, Sunda et.al. 1978,

Zevenhuizen et.al 1979). These studies suggested that "biologically

available" form is the free metal ions. Toxicity to the organisms get reduced

to the extent the metal ions are complexed in the aquatic system. This fact

has been explored by many researchers to measure complexation capacity

through biological methods.

In these methods, growth or a related metabolic parameter is

measured as a function of added copper ion, and then the fraction of bio

available copper i.e. the· concentration of free metal ions in solution is

calculated. A titration-like curve results from the data points and the end

point can be detected. One of the studies conducted with the purpose of

developing a technique for the biological measurement of copper binding

capacity used the growth depression of the Thalassiosira pseudonana

(Davey et.al. 1973). A similar response of the copper ion specific electrode

and the growth response of T. pseudonana in copper bioassays was also

observed (Erickson 1972). This similarilty suggested that the T. pesudonana

73

copper bioassay can be used as an end point detector for the measurement

of copper binding capacity of sea water. Davey et. al. (1973) examined both

unaltered sea water and sea water spiked with EDTA or Histidine for

complexation capacity measurements. The decreased growth rate of the

diatom when plotted against the copper concentration yielded a sigmoidal

titration curve. The point of 50 per cent growth reduction corresponded to a

specific amount of chelants spiked into the system. In a similar way

unspiked sea water samples were also treated.

However, this has been proposed only as a semi-quantitative method,

for lack of sufficient proof through the related experiments of copper

bioassays ~f sea water with organics and without organics (i.e. after UV

oxidation). It was observed that the UV light treatment did not alter the

bioassay results except in case of sea water with high organic content.

Simultaneously, it was confirmed that no release of Cu, Ph, Cd or Zn had

taken place during the oxidation by UV light. Hence it was suggested that

the natural organics got incompletely oxidized and becam~ toxic. The

attempts to remove organics by charcoal treatment and then conducting

Cu-bioassays also could not bring any definite results as both sea water and

artificial sea water (ASW) showed a reduction in the growth of T.

pseudonana. Thus experiments designed to establish the relation of

organic chelants and copper toxicity were not fully successful. The first

experiment which was conducted to show the effect of presence or absence

of organic chelants on copper toxicity failed as the UV oxidation resulted in

some toxics. In the second experiment, the charcoal column method of

74

refining the organics resulted in reduction in growth of T. pesudonana in

sea water and even in ASW supplemented with vitamin B12. It is possible

that charcoal treatment removed some essential constituents or added up

some undesirable ones for the growth ofT. pseudonana. However, the

studies with ASW spiked and unspiked with chelants suggested that

chelants suppress toxicity and their concentrations get reflected in the Cu­

bioassay results and hence this can be used as a semiquantitative method

for estimating complexation capacity (Davey et. al. 197'3).

In another set of experiments the rate of metabolism of c14 glucose

by bacterial cultures was followed as a function of copper concentrations

(Gillespie and Vaccaro 1978). The radio-labelled glucose made the

measurements possible in one hour.

Similarly c14 labelled sodium carbonate was used to follow the rate

of c14 fixation and thereby to assess the effect of free copper ions on

phytoplanktons (Gachter et.al. 1978). Their study revealed two classes of

ligands which complex copper. One group is present in low concentration

(3.7 X 10-8 M) but with high formation constant (log K::: 10.1) and another

class of compounds present at higher concentration (2.6 X 10-6 M) but has a

much lower formation constant (log K = 7.4). The biological methods are

quite sensitive and do not require any modification in the natural

conditions, i.e. in the chemical environment of the system, and therefore

are quite meaningful as the data generated will be directly amenable to

biological interpretation.

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However, the drawbacks are many. Chief among them is that other

substances, besides the metal of interest can exhibit toxic effect on

organisms. Thus, it is difficult to examine samples of different

compositions. Also nutrient requirements of the organism may limit the

types of samples that can be tested. Finally the analysis times are lengthy

as metabolic rates are being monitored; The higher concentration of algae

or similar micro-organisms affects the concentration of dissolved copper

due to sorption on their cell surfaces. The adsorption of copper on to

phytoplankton and macrophytes has been well documented (Hunt and

Fitzgerald, 1983).

Ion-Exchange Method

The method of determining metal-ligand stability constant by using

cation exchangers was developed more than 40 years ago. (Schubert, 1948).

The cation exchanger acts as a second ligand and metal ions will partition

themselves between the ion exchanger and the complexing ligands,

satisfying the following equations,

MR

K M + xL -------~-- ML

X

[MRJ . Kd = ---------­

[M][R]

K = s

[ML] X

----------[M][L]x

where Kd is the distribution constant and Ks, the stability constant of the

, complex.

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From the above equation it is evident that the extent of complexation

then depends on the magnitude of the stability and distribution constants

and on the concentrations of the ligand and resin. The ligand concentratien

can then be comp·uted by measuring the distribution constant in the

presence and absence of ligands, with a fixed resin concentration.

The above method was simplified later. for mononuclear complexes

(Martell and Calvin 1952). The treatment can be summarized by the

following equation.

. Ao log(--- -1) =log K + x log [L]

A . s

A0 and A are the ratios of ion-exchanged to unbound i.e. [MR]/[M] in the

absence and presence of ligand respectively. Ks is the stability constant, x is

the number of moles of ligand reacting per mole of metal and [L] is the

concentration of ligand. By plotting log (A01A - 1) vs log [L], the stability

constant an~ the stoichiometry of the complax can be determined.

However, in the determination of stability constants of the mixture of

ligands, non-integer ratios of ligand-to-metal entities have been frequently

found. The problem has been approached through a theory of polynuclear '

complex formation (Clark and Turner 1969) and a. modified method of

evaluation of stabili-ty constants have been developed (Stevensen and

Ardakani 1972). The applicability of this method in the presence of multiple

ligands, some of which do not follow 1 : 1 complexation has been· verified

(MacCarthy.1977, Crosser and Allen 1977)

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In a modified method (Crosser and Allen 1977) a plot of soluble metal

M vs exchanged metal M enables the calculation of the ligand s r

conce·ntration, ligand number and conditional stability constant. Ligands

which strongly complex transition metals (like EDTA, NTA etc.) can be

accurately dealt with by this method but it fails in c&se of the weak ligands

such as glycine. Also if a mixture of ligands is present, the concentration of

each of them can be determined only if their stability constants are

sufficiently different.

An ion-exchange method in which a weak inorganic ion-exchanger

Mn02 was employed in place of synthetic resins to study the complexing

capacity of ligands in natural waters has been developed (van den Berg and

Kramer 1979). The advantage of this method is that weak exchanger

permits the determination of weaker ligands in solution. Also active

dispersion of Mn02 can be very conveniently effected volumetrically with

greater reproducibility and accuracy and hence permits the use of small

amount of Mn02 compared to ion-exchange resins. This advantage makes

it useful for systems with low concentration of complexants.

However, problems are met with Mn02 also, because of inadequacies

of methods to produce Mn02 with a consistent character, its inability to

exchange metals in a linear fashion at low concentrations of metal

loadings and finally its capacity to adsorb ligands and metals. These cause

appreciable errors in the estimations (Davis and Leckie 1978, Guy et.al.

1975).

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As evident from the above discussion, this method has a number of

limitations regarding the basic assumptions made for the derivation of the

equation quoted above. The assumptions that th~ system must (i) be at

equilibrium (ii) exhibit no sorption of metal complexes or ligands to the ion­

exchange resin (iii) form only mono-nuclear complexes and (iv) have free

ligand and resin concentrations relatively constant during the course of

titration, are never met with in experimental conditions. Still in the past

years the method has been widely used for complexation capacity

measurements.

Voltammetric Methods

A number of voltammetric techniques like Pulse Polarography (PP),

Differential Pulse Polarography (DPP), Anodic Stripping Voltammetry

(ASV)J Differential Pulse Anodic Stripping Voltammetry (DPASV) etc. are

available to measure very small quantities of uncomplexed metal ions in

solution. These methods measure the free form of a metal and not

necessarily its total concentration. The free form of the metal includes the

pure ionic form and weak complexes which are electrochemically labile

I.e., reducible at the electrode surface. Thus, electrochemical

measurements of complexation capacities are operationally constrained by

the deposition potential selected. The polarographic methods measure

labile metal by monitoring the current produced while reducing (or

oxidizing) the uncomplexed metal. An electro-chemical probe like

DME(Dropping Mercury Electrode), HMDE(Hanging Mercury Drop

Electrode) or SMDE(Static Mercury Drop Electrode) along with a standard

79

electrode will measure the current, when a suitable metal ion is titrated

with the sample which contains the complexing materials. A plot of

current vs concentration of metal added in 11 mols will readily give the

complexation capacity by drawing a perpendicular line to concentration

axis from the point of inflection Fig. III-I. At the initial stages of titration

the slope changes slowly and after the end point i.e., when all the ligands

are complexed slope change occurs drastically.

A portion of the ionic copper gets complexed with the organic ligands

into non-labile complexes, resulting in a lower slope than in the calibration

plot. When no free ligands capable of complexing copper are left, then the

slope will be identical to the calibration plot.' Extrapolation of the plot can

give the copper complexation capacity.

The most commonly used electrochemical techniques are ASV and

DPASV and both offer high sensitivity and ease of analysis. The stability

constant has been estimated by two different approaches from electro­

chemical measurements. The shift of the redox potential of an

electrochemically labile and reversible complex in presence of an excess of

ligand is the most widely used one.

The relationship

2.303 RT 2.303 RT E1 _ 2 = ----;;F----- log KMLx + x ----;;F ____ log [L] .

80

4.0

ct ~ "'3.0

CD .. -c: CD '-~ 2. ()

.:tt 0 CD a..

Concentration of copper added I AIM

Fig. III-1

ASV peak-height-concentration plot

Source: Kramer C. J. M. (1985)

81

where, E 1_2 is the shift in peak potential, [L] is the excess ligand

concentration and 'x' is the number of ligands required to complex one

mole of the metal, can be used for estimating the stability constant. The

method has been successfully applied for hydroxy and carbonate complexes

(Ernst et. al. 1975, Bilinski et. al. 1976) but can not be applied for humic

acids or other organic ligands because of the electrochemically irreversible

decomposition of the complexes and adsorption of organics on electrode

surface.

The conditional stability constant can be determined from the plot of

ASV peak current generated when a metal titrant is added to the system vs

the concentration of added metal. The detailed theoretical treatment has

been given earlier (Shuman and Woodward, 1973). In this method the ratio

of free to bound metal is plotted against free metal concentration and the

slope of the straight line obtained determines the metal binding capacity of

the system (i.e. 1/CL) and the intercept gives the value for 1/KsCL' where

CL and Ks are complexation capacity and stability constant, respectively.

Several techniques of voltammetry have been used for measuring

apparent complexation capacity. DPASV was one of the most often used

technique, with a choice for a variety of electrodes (Duinker and Kramer,

1977, Srna et.al. 1980, Plavsic et. al., 1982; Kramer and Duinker, 1984,

Kramer, 1985). A comparison of sensitivities of electrode types like Rotating

Mercury Film Electrode (RMFE) (in situ and pre formed), HMDE, and Jet

Stream Mercury Film Electrode (JSMFE) showed that JSMFE is the most

82

sensitive. Speciation of metals and the complexation capacity of natural

fresh waters and sewage effluents have been measured by many authors.

(Matson, 1968, Allen et. al. 1970, Chau and Lum-Shue-Chan, 197 4;

Gardiner and Stiff, 1975).

DPASV titrations performed by Chau et. al. (1974) showed that

complexes having stability constants of the order of at least 1010 were

required to measure the copper complexation capacity accurately. They

examined both natural and synthetic waters (constituted natural waters)

for reaching these conclusions.

ASV titration, one of the widely used methods consists of adding

aliquots of standard Cu(II) solution to the sample and measuring the ASV

peak current. Several additions are made one after the other and ASV peak

currents are measured until the slope of the plot of peak current vs Cu

concentration attains the slope value of the blank titration curve. Assuming

1 : 1 complex formation to take place the complexation capacity (CCcu) and

apparent stability constant Ks can be calculated from the relationship.

However, there are several problems associated with determining

complexing capacity by ASV titration viz., (i) some Cu complexes dissociate

extensively in the diffusion layer, though they are thermodynamically

stable. This brings additional amount of free ions which had originally

83

remained in the complexed form eg., (Cu- NTA complex). The error due to

additional current in the peak current measurement, contributed by the

kinetic lability of the complex, can be corrected to some extent but methods

of corrections are very complex,(ii) the ASV peak current gets depressed by

the organic layer adsorbed on the electrode as it hinders the

electrodeposition step in the ASV analysis. This may mislead one to infer

the formation of some complex though in reality no complex has formed .

(Florence, 1986), (iii) formation of the Cu-organic complex may be slow and

it may take several hours to complete complexation reactions.

The use of linear scan voltammetry at a rapidly Dropping Mercury

Electrode or Streaming Mercury Electrode has been suggested to minimize

the electrode fouling by organics, if samples have sufficiently high

complexing capacity. Interference from adsorbed organics increase in the

order DPP (DME) <linear scan ASV (TMFE) < DPASV (HMDE) (Varney et.

al. 1984). In anodic stripping voltammetry the electrode is exposed to

organic matter for the period of the deposition time, whereas in

polarography it lasts only for the drop time. Also in linear scan

voltammetry the metal ion has to cross the adsorbed organic layer only

once, while· in differential pulse technique the ion has to undergo many

crossings of the adsorbed layer as multiple redox reactions are involved.

But with streaming Mercury Electrode, where the rapid renewal of

electrode takes place, adsorptive processes and metal complexation with

slow dissociation kinetics have little effect on diffusion current. Hence SME

84

has been considered specially useful for complexation capacity titrations.

Another promising technique for the elimination of interference by

adsorption is to cover the thin mercury film electrode with an ultra

filtration or cellulose acetate membrane.Electrodes coated with Nafion,

cellulose acetate. etc. have been found useful for ASV titrations, giving

sharper break in the titration curve, as the coatings are able to minimize

the interference from non-complexing surface active organics (Florence,

1989).

It has always been felt that whatever be the method chosen for

measuring complexation capacity, the measured value should correspond

well with values obtained by bioassay techniques. Only such correlations

will bring forth the ecotoxicological significance of complexation capacity

studies.

With 'all the drawbacks discussed above and many of the

improvements suggested yet to be incorporated, electro-chemical methods

of estimation of complexation capacity have been widely used and plenty of

examples are available in the literature.

Ion Selective Electrode Potentiometry

Basically Ion Selective Electrode (ISE) potentiometry also belongs to

electrochemical method of analysis. But a major difference exists between

the ISE potentioriletry and the other electrochemical method, namely

polarographic analysis. In polarographic analysis the current produced is

85

measured when the labile species is oxidised or reduced, while ion selective

electrodes measure the potential of the system during which no oxidation

or reduction happens. Also as explained earlier, the ·polarographic

analysis is "dynamic" in nature and cannot possibly measure the

"natural" species as the measurement itself disturbs the equilibrium. In

ion selective electrode· potentiometry, the equilibrium conditions are

retained to a greater extent. Also ion selective electrodes measure the

activity of free, hydrated metal ions and no other species. When a Cu ion­

selective electrode calibrated with a standard CuSO 4 solution in a non­

complexing media like nitrate or perchlorate is used, the electrode will not

respond to simple complexes like CuCl+, Cu(OH)2 + and CuC03. Since

complexation capacity has been defined as the measure of the amount of

heavy metal ions that can be added to an aquatic system before free metal

ions appear, ion-selective electrodes which can sense only free ions will be

one of the best analytical probes suitable for complexation capacity

measurements. However, some reviews (Florence, 1986; Hart 1981;

Neubecker and Allen, 1983) point out that complexation capacity

determined by ion-selective electrodes are much higher in magnitude than

by ASV or DPASV methods, as simple complexes like CUCl+, Cu(OH)2 and

CuC03 also contribute tawards the complexation capacity when measured

by ISE. However, since toxicity due to metals have been shown mainly

related to the concentration of free metal ions, complexation capacity

measured by ion-selective electrodes will have much significance in

ecotoxicological terms. Bioassay studies conducted in conjunction with ISE

measurements are quite in agreement with this argument (Davey, et.al.

86

1973). Comparison of bio-assay results with concentration of ASV labile­

metal species are also available in literature but good correlations have

been obtained in few cases only. Hence complexation capacity

measurements estimated by ion-selective electrodes are important in metal

speciation and aquatic toxicity studies.

It must be mentioned, however, that most of the available electrodes

are not sufficiently sensitive for measuring the low concentration of metals

which is generally encountered while titrating majority of the natural

water samples. Hence their use has been limited to waste waters or waters

containing high concentration of humic acids or other complexing agents.

Periodic cleaning of electrodes against poisoning by other metal ions (eg.

Hg2+) or organics can however, retain the electrodes in good condition.

The electrodes are used to measure the fraction of the metal in the

free state (i.e. ionic form). This is done by measuring the potentials during

the course of titration of the sample with the metal ions. In the absence and

presence of ligands, the difference in potential and the fraction of the

complexed metal ion (0 will be related as

f = [M]total X ioCEO - E)/p

2.303RT where p = -------------

nF

There are many studies employing ISE method reported in

literature. For example, Ottawa river in Canada had been studied with a·

87

special interest on the effect of ligand size .on complexation capacity for

several metals, like Hg(II), Pb(II), Cu(II) and Cd(II) using the respective

ion-selective electrodes (Ramamoorthy and Kushner 1975).

Complexation capacities for Cu(II) and .Pb(II) were studied with

pond water and river water using ion-selective electrodes and the data was

fitted into Nernst equation, to estimate the complexation capacity, mean

molecular weight of the ligands, stability of the constituents, stoichiometry

of the complex and pH dependence of the stability constant. (Buffie et.al.

1977).

In another study computer modelling calculations of cupric ion

activities were done using a standard program REDEQL (Morel and

Morgan 1972) for each Cu(II) spikes in a titration. Using ISE,

measurement of cupric ion activity was also done ,in conjunction with the

same. The difference has been attributed to the complexation capacity of

sample (McCrady and Chapman 1979).

Many other authors have also made use of ISE potentiometry for

complexation capacity measurements using Cu, Cd and Pb ISEs (Giesy et.

al. 1978) and some have compared this method with other non-conventional

methods like spectrofluorometry (Saar and Weber 1980b),dialysis etc.

Dialysis

Dialysis technique has also been employed by some authors (Truitt

and Weber 1981). Using this technique complexation capacities estimated

for sea waters have been reported as 0.11 - 1.6 ll mol Cull for south eastern

88

Atlantic Bight (Wood and Evans, 1980) and 0.6 - 0.9 ~ mol Cti/1 for Boston

Harbour (Stolzberg and Rosin, 1977).

Dialysis technique has the advantage of not disturbing natural

equilibrium of metals and ligands. It allows the metal to be analysed by any

convenient method. Also it has the advantage of its ability to be employed for

·in situ measurements of metal ions (Benes and Steinnes 197 4). Though the

method requires longer time for dialysis and larger samples, it is sensitive

to micro molar quantities of complexing agents. The copper and cadmium

complexation capacities of New Hampshire surface waters were. found to

range from 1.1 to 15.1 ~ mol!l and 0 to 9. 7 ~ mol!l respectively by this

method (Truitt and Weber 1981). The estimated complexation capacities

showed good correlation with dissolved organic carbon, pH, hardness,

conductance and UV absorbance.

The dialysis method becomes successful only when the dialysis

membrane is able to exclude completely the complexed metal ions and

uncomplexed ligands from the diffusate solution and the membrane allows

unhindered diffusion of uncomplexed metal ions, to attain equilibrium

between external and internal solutions in terms of free metal ion

concentration. In a situation different from the above, estimated

complexation capacity will deviate very much from the actual value.

Diffusion of complexed metal ions or uncomplexed ligands from the

retentate to the internal solution will lead to an underestimation of

complexation capacity.

89

The condition that the concentration of·uncomplexed metal ions in

retentate and diffusate should be the same, is an essential requirement as

the metal ion concentration of the diffusate is taken as equilibrium "free"

ion concentration for calculations. Secondary experiments conducted to

verify whether the above conditions are fulfilled O!" not, revealed that

around 10 per cent of the complexing capacity had permeated through the

dialysis membrane. The study also indicated that permeation of the

complexed metal to diffusate is much less compared to uncomplexed

ligands. Apart from these, diffusion was found hindered at higher pHs (6

and 7) in case of Cu2+ titration and has been attributed to the soluble

species like Cu2(0H)2 +(Truitt and Weber, 1981).

McCrady and Chapman (1979) have explained the non-unity slopes of

Cu2+ titrations of natural waters due to inorganic complexation. Other

factors which may bring errors in dialysis titration may be the growth of

micro-organisms on the dialysis membrane and the inhomogeneity of

pores in dialysis membrane.

However, the method can be employed for a wide range of metal ions

unlike the Electron Paramagnetic Resonance (EPR) or fluorescence studies

where only para-magnetic metal ions can be considered. Also limitations of

low sensitivity as in the case of ISE measurements and the small range of

selection of metal ions as in voltammetric determinations do not handicap

this method from using for any kind of sample or metal ions (Truitt and

Weber 1981).

90

Fluorescence Quenching Method

Diehert (1910) observed the fluorescence of natural waters, but for

nearly five decades this property had not been exploited for understanding

natural waters chemistry (Smart et. al. 1976). Natural waters contain a

variable ratio of humic and fulvic acids and may affect intensity of

. fluorescence. Christman (1970) showed that fluorescence of many natural

waters are. remarkably. similar, though some difference are possible as

evident by the studies of molecular size separation (Ghass~mi and

Christman, 1968). They observed that high molecular weight components

fluoresce less compared to low molecular weight components~

The literature on fluorescence of natural waters indicates that a

relationship exists between fluorescence and organic matter in water.

Smart et. al. (1976) studied the effect of temperature, pH and natural

pigments like chlorophyll on fluorescence of natural waters. A strong

temperature dependence was observed for all the samples examined, but

the degree of dependence varied. However, a general formula was derived

by which temperature correction terms could be calculated. Two distinct

types of pH dependent-fluorescence also were observed. One type showed

fluorescence maximum at pH 5-6 and other one was sigmoid with little

change in fluorescence between pH 6-8. Hence it can be inferred that

though pH has an effect on fluorescence, between pH 5-8, the variations are

relatively limited.

Transition metals when complexed with fluorescing organic

molecules, bring a tremendous decrease in the fluorescing property. This

91

quenching of fluorescence is brought about by the non-radiative paths of

relaxation facilitated by the co-ordinate bonded transition metal ions like

Cu2+, Co2+, Ni2+ etc. (Seitz 1981). This method has been used to estimate

the stability constants of Cu- humic and Cu-fulvic acid complexes by Sarr

and Weber (1980a, b). The same experiments were monitored by copper-ion

selective electrode method also and very similar titration results were

obtained. The method has been found quite sensitive to very low humic and

fulvic acid concentrations. In their study attention was also given to the

extra cellular metal binding organic matter (EMBO) released by the

organisms due to the stress from the excess uncomplexed metal or the

deficient micro-nutrients.

Ryan and Weber (1982a), extended this method for estimating copper

complexation capacity of natural waters. This method has various

advantages over other methods of estimation. While almost all the other

methods· estimate the concentration of bound metal ions by indirect

methods, fluorescence quenching method enables one to estimate the

concentration of bound metal ions directly. The indirect methods account

for the bound metal ion concentration as the difference between the total

and estimated free metal ion concentrations and are prone to several

errors. When the organic ligand is in excess, the free metal ion

concentration will be extremely low and will be difficult to measure

correctly as lower detection limits are reached under such circumstances.

When metal ion is in excess, an error gets introduced as the small

difference of two large numbers are involved in calculations.

92

Fluorescence quenching measurement however, gtves a direct

measurement of the unbound ligand. The complexation with paramagnetic

ion quenches the fluorescence, as it enhances the relaxation processes

through non-radiative processes. The correlation between concentrations

and fluorescense intensities is given by

[ML]

= (1)

where [ML] is the equilibrium concentration of metal ligand complex, CL is

the total ligand concentration or complexation capacity, I, is the

fluorescence intensity at any point in the titration and IL and IML are

fluorescence intensities of free and bound ligand respectively. But this

equation applied to a simple system (Ryan and Weber 1982b) will not be

applicable to natural system comprising of a complex mixture of ligands.

Hence a stability function or product s1 is defined for 1 : 1 binding (Ryan

and Weber 1982a)

N i!i [M][(Li)]

s1 = ------------------ C2) N

[M] i~1 [Li]

where N is the number of ligand species present in the mixture and [Li],

[M(Li)] are the equilibrium concentration of the ith ligand and its metal

complex respectively and [M] is the free metal ion concentration.

93

Therefore

N l: [M(L.)J

i=l 1

------------ = ----------------- ( 3)

where

N N N CL = i~l [Li] + i:l [M(Li)] = i~l [Li] + [M(Li)] (4)

.N IL = l: IL.

i=l 1

N 1ML = i;1

1M(Li)

N I = 1L + 1ML = i~1 (ILi + 1M(Li)

Combining equation (2) and (4) we get

- 4S12cL CM)l/2] + 100

(5)

This is identical with the equation for conditional stability constant

Ki derived for fluorescent quenching titration of fluorescing ligands

forming 1 : 1 metal-ligand complexes (Rayn and Weber 1982a) with the

exception of s1 in place of K1. The experiments were conducted for variety

of water samples. 50 to 25 ml of aliquots were subjected to deareation by

purging with moist N2 and the pH adjusted with 0.01 M KOH or HN03. The

samples were sent to the cuvette and back to titration cell for several

minutes. The fluorescence intensities were measured at an emission

94

wavelength of 430. nm and excitation wavelength of 350 nm. Approximately

15 min elapsed between successive additions of the titrant while adequate

stirring was maintained with magnetic beads. Rayleigh Scattering was

also measured keeping the excitation and emission at 400 nm to indicate an

end point to the titration, as the scattering becomes very large when

precipitates are formed. The fluorescence data are discarded once the

scattering value is double the initial value.

The titration data has been subjected to non-linear regression by a

program NONREG, (which is a part of the statistical programmes

developed by University of North Carolina, Chapel Hill, USA) and Ks or s1

and IML calculated. The values calculated revealed various problems

which cannot be explained by the chemistry of the water sample. The

negative correlation of K and CL rather points out to an artifact of the data s .

treatment which is a major limitation of this method. Also, it may not be

true that all the complexing agents will fluoresce or all those fluoresce are

of complexing nature. Hence the method though looks quite simple has

inherent draw backs when applied to natural systems.

However the advantage is that the titrations performed with

fluorescence as the means of detection directly study the effect of metal ion

binding on ligands, instead of relying on a measurement of difference

between free and total metal ions.

Chelex Method

Strong cation exchange resins, like Chelex - 100 also have been used

to estimate the strong complexing agents. (Stolzberg and Rosin 1977). The

95

chelex resin strips off metal ions from the weak complexes and excess

metal ions in the sample and allows the strong complexes to pass. Thereby

it provides a method to estimate the strong ligands. Since Chelex-100 is, a

very strong cation exchanger, weak complexing agents are not included in·

the estimation. Besides this, the equlibration time taken for the resin and

the sample has a profound influence over the amount of metal exchanged

with the resin (Figura and McDuffie 1980).

Mackey and Higgins (1988) measured copper complexation capacity

of sea water by extending the method of Stolzberg and Rosin (1977). The

CCcu values were determined by spiking the sea water with GuSO 4 to a '

final concentra~ion of 8000 nM, leaving the samples for a period of 3-6

months, filtering. and removing the excess cupric ions by passing through

chelex-100, destroying the organic matter by UV photo-oxidation and

analyzing the resultant solution of copper.

Copper-organic chelates are the strongest complexes formed when

competing inorganic reactions are considered and therefore copper can

displace most of the other metals from the ligands of interest. However, the

procedure will not be able to measure ligands present as inert complexes,

as they wil~ not associate with the copper spikes;

Furthermore, when the contact time of the sample with the resin

was varied through manipulations of the length of the column or flow rate,

the copper concentration in the column effiuent varied in the case of

artificial sea water (ASW) samples containing NT A but remained more or

96

less constant in the case of EDTA. However, in case of one set of natural

samples the recovery of spiked EDT A was found to be only 55 per cent in the

effiuent and was attributed to the irreversible adsorption of EDTA to

particulates as the samples were neither filtered or centrifuged. Another

set of samples pre-concentrated six times with rotary vacuum evaporator,

also showed similar results. Naturally occurring ligands may be weaker

than EDTA or NTA. The strong influence of flow rate and column length

on the effectiveness of the complexed copper uptake by resin indicated a

thermodynamic and kinetic control operating over the uptake of complexed

metal by chelex- 100.

The number of ligand sites available becomes an important factor in

deciding the immobilization of the ligand on resin as chelex-M-L. Larger

bidentate ligands are less likely to form such mixed complexes while

unidentate or bidentate ligands may get immobilized. Hence the method . measures an operational complexation capacity, which is dependent only

on the complete detection of polydentate ligands forming complexes with

the spiked metal ions and having stability con~Stants of certain critical

values. The critical value is being decided by the stability constant of the

metal-resin complex, pH of the sample, column length, flow rate etc. To

tackle these problems modification using shorter columns, high flow rates

etc were tried out. However, leakage of uncomplexed metal, metal sorbed

on to organic and inorganic colloidal particles etc. have been identified to

introduce 'errors in the estimation. Also ligands which are slow to

exchange their metal for the spiked Cu2+ ions will not be detected unless

sufficient time is given for equilibration.

97

Cu-Catechol Complexing

Catechol-Cathodic Stripping Voltammetry is one of the recent

developments in voltammetric method applied to complexation capacity

measurements. van den Berg (1984), used this method to measure the

complexation capacity of estuarine waters. Very recently the method with

some modifications has been applied to fresh waters (Jones and Hart, 1989).

van den Berg (1984) has described ligand exchange- cathodic

stripping voltammetry to tackle the problem of dissociation of Cu-complexes

in the diffusion layer. The method is based oil the competition between the

na:tural ligands and catechol for Cu(II) ions and the subsequent cathodic

stripping of the adsorbed Cu-catechol complex. However, because of the

high stability of the Cu - catechol complex, the method would measure only

those ligands which form relatively stable complexes with Cu (Florence,

1986).

The Catechol - CSV method (van den Berg, 1984) is based on the

competition between natural ligands in a water sample and the added

ligand catechol, for the cupric ions introduced. The copper-catechol

complex formed is selectively adsorbed as a thin film on a hanging

mercury drop electrode (HMDE), the concentration of which is determined

then by CSV (Cathodic Stripping Voltametery). The free copper

concentrations are then obtained through calculations involving stability

constants of copper-catechol complex. Further calculations lead to the total

ligand concentration and conditional stability constant.

98

When cupric ions are added to a sample containing catechol (Cat),

acetate buffer and competing natural ligands, the distribution of cupric

ions can be represented as

Cut = [Cu'] + [Cu Cat] + [Cu L]

where Cut = total copper concentration

Also,

[Cu'] = concentration of copper ions and copper acetate species

[CuCat] = concentration of copper-catechol complex

[CuL] = concentration of copper complexes of natural ligands

Cu2+ + L ------ CuL and

* [CuL] K = ----------- further

[Cu] [L]

represents the complexation reactions of natural ligands.

The above equation for conditional stability constant can be written as

[Cu] 1 Cu --------- = ----------- + -------- (Ruzic 1982) [CuL] K*[Lt] [Lt]

This equation can be written in the form (van den Berg, 1984)

where

S = the slope of 1 versus [Cu] at higher [Cu] ' p .

lp = measured peak stripping current

a'= aCu + aCu Cat·

qq

Assuming that 1 : 1 CuL complexes are only formed a plot of

lp/(S[Cut]- lp) vs lp/S will give linear curve with a slope equal to 1/[Lt] anc!

* y-intercept equal to a '/K [Lt]. By calculating a' for the catechol

* concentrat~on used Lt and K can be estimated.

When applied to fresh waters the modification made was mainly in

selecting an optimum catechol concentration, a catechol concentration

corresponding to half the peak current i.e. lp (m/2) obtained for untreated,

filtered water rather than that obtained for UV irradiated water. However,

a method has not been evolved yet to select an optimum concentration of

catechol that is universally acceptable and this has been indicated as a

significant limitation of this method as of now.

Data on complexation capacities and conditional stability constants

estimated by ASV and CSV greatly differ (Jones 1987, Hart and Jones 1984).

It is suggested that the complexing ligands determined by catechol - CSV

method could be a sub set of those found out by ASV method (Jones and

Hart 1989) and is analogous to the two ligand model suggested earlier (Hart

and Jones 1984).

While in ASV method kinetic contribution to stripping current due to

the dissociation of the metal organic complex is a major problem, CSV

method does not suffer from this, since cathodic stripping relies on the

adsorptive accumulation of the copper-catechol complex followed by the

Faradaic reduction of the cupric ions. However, CSV method is not free

100

from the interferences due to the adsorption of organics on mercury drop as

in ASV method (van den Berg 1984). The complexing capacity detected by

ASV is due to a group of ligands which forms non-ASV labile copper

complexes for either thermodynamic or kinetic reasons, while CSV method

estimates those copper complexes which remain undissociated when

catechol is added. The dissociation of complex in presence of catechol will

depend upon factors such as catechol concentration, ligand concentr~tion

and stability constant of the respective complexes. Hence it is not clear

whether the ligands detected by CSV method are a sub-set of ASV-detected

ligands or they are a separate group of ligands (Jones and Hart, 1989).

Each method seems to speciate the ligands or binding sites. Hence

different methods under simil?r conditions or almost similar conditions,

give a _speciation of complexation capacity. One can draw valuable

informations from these differences rather than looking at the differences

as drawbacks of a particular method in comparison to the another. For

example, the ASV method has been shown to measure a different "s~t" of

copper binding ligands than the ISE method (Hart and Jones 1984).

If these observations are considered from a: different angle it seems

that each method of estimation of complexation capacity speciates the

complexing·ligands or the binding sites into different binding strengths by

virtue of the methodology involved. Hence instead of looking at the

complexation capacity parameter as the total complexing capacity of the

system, it must be speciated into a group of complexing capacities by the

101

various methods available. The drawbacks identified with each method will

then minimise and the complexation capacity will be resolved into different

sets of complexing abilities, defined by each method.

Treatment ofTitration Data

Most of the methods for measuring complexation capacity and

conditional stability constants involve titration cf ligands present in the

water sample with a metal ion. The titration curves give a variety of

information on the aquatic samples by the different types of data treatments

that can be adopted. The following paragraphs describes in detail the

various methods and theories involved.

WhiJe using methods like DPASV, ISE etc. two different ways of

estimations are possible - they are the direct titration and equilibration

methods.

In the direct titration method the voltammetric measurement is

made immediately after spiking and continued for several subsequent

spikes. Advantages of this method for complexation capacity

measurements are that the time required for the analysis is very short and

the volume of sample required could be as small as 5 cm3 . However, slow

complexation reactions are not included in this method of estimation.

In equilibration method the spiked sub-samples with different

amount of ionic metals are allowed to equilibrate for hours or days. After a

102

sufficient equilibration period the voltammetric measurements are made.

Since sufficient time is given to attain the equilibrium, the complexation

capacity estimations are more representative of the natural conditions.

Ruzic (1984) suggested four methods of treatment of data to calculate

copper complexation capacity (CCcu) and apparent stablity constants (K8

)

Kramer (1985) used Scatchard and van den Berg methods of linearisation.

In the present study also Scatchard and van den Berg methods are made

use of.

In van den Berg plot the ratio of the free to bound metal concentration

is plotted against free metal concentration (i.e. M_FMB vs MF) Fig. III-2a,

while in Scatchard plot, the ratio of bound to free metal concentration is

plotted against bound metal concentration (i.e. MB/MF vs MB) Fig. III-2b.

Kramer(1985) observed distortion of the Scatchard plots for estimating

CCcu and Ks by direct titration method and attributed this to kinetic

effects. The distortion did not allow reliable CCcu and Ks calculations to be

made. These observations agreed with the theoretical predictions by Ruzic

(1984).

The various theoretical aspects of the chemical equilibrium related to

the complexation reactions resulting during the titration have been

examined in detail and suitable data treatment methods have been

proposed (Ruzic 1982)~ The theories and procedure proposed in literature

for interpreting the direct titration curves, to estimate the metal binding

capacity of natural waters suffer from various limitations.

103

-"0

S3D 0

,.0 ..... cu Cl .:: 2.0 -.~ -0 0:

-cu Q) 2.0 .... -..... , c: :::1 0 .0 -0 1.0 -0 Ct:

0 50 100 150 . Free metal cone . .

Fig.III-2a van den Berg Plot

0 20 40 Bound metal cone.

Fig. III-2b Scatchard Plot

Source : Kramer C. J. M. (19R5)

104

A simple model which assumes 1 : 1 complexation of the metal and

the unknown ligand can be represented as follows

eM 2 [ M ] = CL - [L]

and

K = (eM - [M])[M][L] = eeL - [L])!(M][L]

where eM, eL are the total concentration of trace metal and unknown

ligand L, [M] and [L] are free metal and free ligand concentration and K,

the stability constant of the complex Mv

In case of direct titration with additional amount of detectable metal

speciesllm, the equilibrium constant K can be expressed as

K

and hence

[ML]

= [M] [L]

eM+ run- [MJ == ----------------------

.1m =

[M] [L]

eL ----------------- - eM + [M] 1 + (1/K[M])

The equation represents the titration curve where .1m is the.

additional amount of detectable form of trace metal. Several authors

proposed many methods of interpretation of the resulting titration curve,

mainly by extrapolation of experimental results from the region of large

values of added titran1j.1m (Shuman and Woodward, 1977).

105

In such a case the equation

.1m = -eM+ [MJ ... 0)

1 + (1/K(M])

becomes nearly equal to eL - eM + [M] Since K[M]

. becomes very large.

Further, .1m

[M)->0

Hence, with known values of CM before the titration, information on

total concentration of unknown ligand can be obtained from the intercept on

.1m axis.

Shuman and Woodward (1977) noted that with small amounts of

titrant the lower range of titration curve can be used to estimate apparent

stability constant K' of the complex (ML) if the K'eL value does not exceed

1000. Under this condition value of [M] will be very small and hence the

equation, (1)

becomes

K[M] =

== -----------

106

where MT is the total concentration of metal originally present plus added

(i.e. eM + .1m). The plot of

MT VS [M]

enables one to estimate the stability constant of complex formed, from the

slope of the curve. If the concentration of metal (eM) prior to the titration is

negligibly small, the amount of titrant added ·will be a good measure of the

trace metal concentration.

and

.1m K[M] =

eL- .1m

The approximations made in reaching these equations are however not

ideal and also, successive approximation bring significant errors. Initially

the equation was derived with the approximation that when .1m is very

large, K[M] is very high and the reduced form

.1m = eL - CM + [M] is reached.

The above equation is approximated

to .1m- eL- eM when [M] -->0

This ~s further subjected to another approximation of eM being very

negligible prior to the titration and hence .1m- eL. It has been pointed out

that even the first approximation can lead to significant error in the

estimation of metal binding capacity of the system if KeL values are less

than 500 (Ruzic, 1982).

107

However, a better way of estimating the binding capacity and stability

constant emerges out of the earlier equations, with a slight rearrangement.

We know

I.e.

CL Llrrl == ------------------- - c1Vl ~ [1VIJ

1 ~ ( 1/K[1Vl])

[1VI]

([1Vl] ~ 1/K) = -----------------

Thus by plotting :M/(1VlT- 1Vl) (Ratio of free to bound metal) vs free metal [1VI],

a straight line is obtained. From the slope and intercept the stability

constant and complexation capacity are calculated. This method enables us

to avoid the approximations used in earlier cases. Also, it is applicable to

the whole titration curve and not to a part of it as was the case before

(Shuman and Woodward, 1977). It is also possible to have a qualitative idea

on metal-binding capacity and stability of complex by a mere look at the

titration curve. The metal-binding capacity will be high if the slope of the

curve is small and stability of the complex will be high if the intercept on

the ordinate is small. However; this 1 : 1 complex formation model may not

be able to distinguish the Langmuir type of adsorption which can also

operate under the experimental conditions as colloiual particles of size less

than 0.45 J..l.m will be present in the samples even after the filtration

HlA

(Stumm and Morgan, 198]). A very similar equation can be obtained for

Langmuir adsorption on colloidal particles also.

Besides these, non-linearity is observed frequently for titration curves

of natural water samples. It can be due to the formation of complexes other

than 1 : 1, formation of more than a single 1 ': 1 complex of different

stabilities (either due to different ligands or different functional groups on

the same ligand), adsorption different from Langmuir type or formation of

a single 1 : 1 complex together with Langmuir adsorption.

A system containing several ligands which can form a series of 1 : 1

complexes differing sufficiently in their stabilities can be understood

clearly from the nature of the titration curve. Cases where the stability

constants of complexes are relatively similar and ligands can be divided

into two different groups, using the 1 : 1 model, the situation can be treated

as below

CM- [M] = CLl- [L1] + CL2- [L2]

A direct titration of the system with an additional amount of titrant

.b.m results in an equation given as below :

[M]

where

109

The above equation reduces to

* [MJICM:f- M) = ([M] + l/Kl )/(eLl+ eL2) M --> oo

where eLl + CL2 is total binding capacity and K1 * is the overall stability

constant. However, at the lower values of the added titrant (i.e. at initial

stages of the titration) the plot of the ratios of free to bound metal vs free

metal concentration deviates from a straight line. This non-linearity can be

further examined through plotting the inverse of ·the difference of

extrapolated values and actual ordinates against the free metal

concentration. And this results in two slopes and two intercepts, enabling

one to estimate the two different stability constants K1 and~ related to the

ligands Ll and L2 and their respective concentration e 11 and e 12.

A very similar picture emerges in a situation where a single

complex is formed and simultaneous Langmuir adsorption is taking place

on colloidal particles. In that case, the earlier equation has to be modified

only by replacing CL2 and K2 for the maximum surface concentration of

metal adsorbable on available surface of the colloidal particles per unit

volume of the system (A'tMN) and B, the adsorption equilibrium constant.

Another instance in which the direct titration curve can deviate from

linearity will be when more than one metal takes part in complexation,

even when it is 1 : 1 type (Ruzic 1982). For copper complexation capacity

determinations, titration of copper ions with natural waters which are

11 0

significantly polluted with various trace metals will show non-linearity for

the plots of ratio of free to bound against free metal concentration. However,

the non-linearly will be different from the case of one trace metal and two

ligands model. This can be understood by examining the respective ·,

equations of the direct titration curves for two ligands and one meta,

[M]

=

One ligand and two metals

MT1- [M1]

= [M1] + 1/K1 + CM2·Kz'K1 (1 + ~(~1- [M1])/K1[Ml])}CL

The non-linearity observed in the case of two metals forming two

complexes with one ligand, can be analysed in a somewhat easier way than

in the case of one metal forming two complexes with two ligands.

Mathematically speaking this is due to the less· number of unknowns and

the relatively simple expression in the former case.

There are many other instances, when non-linearity of the direct

titration curve is observed. A case is of one metal forming two complexes

with two ligands, when one of the two ligands is in excess concentration.

However, the non-linearty becomes measurable only when the difference in

the concentration of the two ligands is comparable to the difference in their

111

stability constant i.e. CLl K1 - CL2K2. In other cases one of the complexes

formed in favour of the other determines the form of the titration curve.

Similarly when one of the metal is in excess and complex formation is

studied for two metals and one ligand J the titration curve will show non­

linearity. Further, when one of the metals forms a very strong complex,

titration with the metal forming the weaker complex, masks part of the

binding capacity of the system.

Apart from all the above cases where only 1 : 1 complexes have been

considered, there are cases where the complexes of type MaLb are found.

The non-lin~arity of titration curves in such cases can be readily

differentiated from the non-linearity resulting from several 1 : 1 complexes.

This is clear from the equation representing the direct titration curve of

such a case.

[M] ------------- == [M]/(CM + ~m- [M])

The non-Langmuir adsorption if operating, the non-linearity

resulting will be easily distinguishable from the non-linearity due to the

formation of several 1 : 1 complexes, but it is difficult to differentiate it from

cases where complexes other than 1 : 1 type is formed. And further no non­

langmuir adsorption can be considered to fit in a simple straight line curve.

11 2

In short, direct titration of natural waters can provide valuable and

precise information through the plots of the ratio of free to bound against

free metal concentration as long as the system behaves analogous to a

model of 1 : 1 complex formation. But if such diagrams are not linear and

non-linearity is observed at the initial stages of titration suitable models of 1

: 1 complexes with several ligands or more than a single 1 : 1 complex with

one ligand or simultaneous operation of adsorptions of Langmuir or non­

Langmuir type etc. should be considered. Though such models increase the

complexities for interpretation of results, they are very useful to get a better

idea of the state of affairs. It further points out the need of understanding

the individualligand.s within the system and their combined effect.

To sum up, the non-linearity of the direct titration curve can result

from the following situations.

1. There are number of ligands in the system which forms several 1 : 1 ., .

complexes, or the same ligand forms complexes of different stabilities due

to different centres of binding.

2. The complex formed between one ligand and one metal could be other

than a 1 : 1 type complex.

3. More than one metal forms complexes with one type of ligand.

4. Langmuir type of adsorption of metal ions on colloidal matter along

with the adsorption of the complex formed.

5. Non-Langmuir type adsorption of metal ions on collodial matter.

11 3

Keeping these facts in mind a suitable model can be selected for the

data analysis and interpretation of the system of interest.

Methodolog))Results and Discussion

In the present study, the copper complexation capacity of the river

Yamuna (CCcu) has been estimated by ion selective electrode

potentiometry. The "direct titration" procedure has been adopted for the

estimations.

Immediately after the collection of samples, they were filtered

through 0.45 )lm membrane filters (Whatman) and preserved for speciation

studies as per the accepted procedures (Florence and Batley 1980). 50 ml of

the samples were pi petted out into metal free beakers and to each, 1· ml of

ISA (Ion Str.ength Adjustor) (Orion Cat.No.94-000~ 5M NaN03) was added.

The pH of the samples were brought to 7.0 as at pH 7.5 and above

precipitation of Cu(OH)2 can occur during titration (Orion Research Inc.,

1979) and also as pH 7.0 is a value very near to the average value of pH of the

samples. pH was monitored by a gel electrode (Ingold USA) attached to a

Control Dynamics pH meter. The pH was maintained stable to ±0.01 units

by adding 0.01 N NaOH or 0.01 HCl drops during titration as stable pH has

been identified as an essential requirement for the measurement of

potentials by ISE. All measurements were made at a temperature of 28°C (±

0.1).

The cupnc 1on selective electrode (Orion - model 94-29) was kept

immersed in 10-3 M EDTA for several minutes to facilitate the cleaning of

11 4

the membrane sensing surface and thereby decreasing the time to reach

equilibrium (McCrady and Chapman, 1979) i.e., to attain a stable potential

reading. Cross contamination between samples was prevented by keeping

the other two electrodes also (pH electrode and Orion single junction

reference electrode) in 10·3 M EDTA, to remove any adsorbed copper. The

electrodes were then washed with deionized distille9 water and introduced

into the sample in a beaker, which was kept stirred at a constant rate by a

Teflon coated magnetic bead. Cupric ion solution cio·4 M) prepared from

standard cupric solution (Orion cat. no.-942906) was added to it (100 JJ.l or

200 JJ.D and electrode potentials were noted when readings became stable

after each addition. For initial additions to reach equilibrium 30-45 minutes

were taken .. For subsequent additions the reading became stable within 10-

15 minutes. Care was taken to keep the intensity of light reaching the

experimental set up constant as cupric ion selective electrode sensing .

membrane (CuS/Ag2S) Is light sensitive.

Apparent complexation capacity and conditional stability constants

of copper organic chelates were then calculated by van den Berg (van den

Berg and Kramer, 1979) and Scatchard methods (Scatchard et.al. 1957)

using the computer programme COMCAPl and COMCAP2 respectively

(See Appendix - III). These programmes were written in Fortran 77 and

run on VAX 11/780 (Digital Equipment Corporation, USA) computer with a

VMS operating system. Complexation capacity values of the river at the six

sampling sites are presented in Tables III-1 to III-6.

115

TABLE 111-1

RAM GHAT

-----------------------------------------------------------------------------------------------------------ABS ABS ABS COMPLEX STAB+ COMPLEX STAB +LOGK COR (254) (464) (665) CAP(VB) CON(VB) CAP(SD) CON(SD)(VB) COE -----------------------------------------------------~---~-~---------~-----------·-------------------------

·0.030 0.005 0.004 8.016 17.196 6.933 26.288 6.235 0.987 0.033 0.007 0.005 9.108 19.571 7.573 31.925 6,292 0.981 0.031 0.006 0.004 8.523 16.845 7.128 27.475 6.226 0.984 0.035 0.008 0.006 9.557 22.830 7.916 37.211 6.359 0.979 0.032 0.004 0.003 8.654 12.884 7.780 16.473 6.110 0.990 0.016 0.004 0.003 2.484 21.264 3.109 8.985 6.328 0.979 0.017 0.004 0.003 2.793 18.295 3.184 11.133 6.262 0.985 0.024 0.003 0.002 5.109 11.451 4.905 13.003 6.059 0.997 0.027 0.004 0.003 5.588 14.939 5.107 20.910 6.174 0.995 0.029 0.004 0.003 7.192 13.829 6.124 22.357 6.141 0.989 0.029 0.004 0.003 7.264 11.078 6.636 13.895 6.044 0.993 0.028 0.004 0.003 8.022 11.773 7.239 145.051 6.071 0.991

-----------------------------------------------------------------------------------------------------------

TABLEffi-2

NAJAFGARH DRAIN

-----------------------------------------------------------------------------------------------------------ABS ABS ABS COMPLEX STAB+ COMPLEX STAB +LOGK COR (254) (464) (665) CAP(VB) CON(VB) CAP(SD) CON(SD) (VB) COE -----------------------------------------------------------------------------------------------------------0.053 0.019 0.016 8.800 10.873 7.962 13.538 6.036 0.990 0.055 0.015 0.013 9.047 8.498 8.241 10.292 5.929 0.987 0.057 0.023 0.019 9.496 12.187 8.548 15.237 6.086 0.989 0.059 0.023 0.021 9.950 13.419 8.932 16.809 6.128 0.988 0.048 0.024 0.017 9.602 12.442 8.638 15.564 6.095 0.989 0.036 0.011 0.009 6.798 7.676 6.366 8.955 5.885 0.993 0.035 0.019 0.017 7.011 15.158 6.168 22.735 6.181 0.991 0.035 0.017 0.014 7.028 11.843 6.430 14.916 6.073 0.995 0.035 0.016 0.014 7.136 11.914 6.517 15.047 6.076 0.995 0.037 0.018 0.015 7.648 12.400 6.933 15.827 6.093 0.994 0.043 0.026 0.021 8.871 14.833 7.939 19.205 6.171 0.990 0.052 0.030 0.024 8.277 18.084 7.133 27.726 6.257 0.986

-----------------------------------------------------------------------------------------------------------

11 6

TABLEill•3

OLD YAMUNA BRIDGE

-----------------------------------------------------------------------------------------------------------ABS ABS ABS COMPLEX STAB+ COMPLEX STAB +LOGK COR (254) (464) (665) CAP(VB) CON(VB) CAP(SD) CON(SD) (VB) COE

-----------------------------------------------------------------------------------------------------------0.043 0.008 0.007 10.669 3.746 10.432 3.888 5.574 0.988 0.063 0.008 0.007 11.755 3.446 12;621 3.126 5.537 0.979 0.050 0.012 0.010 13.943 4.865 13.543 5.084 5.687 0.986 0.071 0.010 0.009 14.320 3.788 15.570 3.401 5.578 0.960 0.075 0.016 0.013 15.102 5.901 14.630 6.186 5.771 0.986 0.015 0.004' 0.003 3.332 5.110 4.537 2.848 5.708 0.965 0.029 0.005 0.003 4.284 4.183 5.399 2.794 5.622 0.969 0.024 0.006 0.005 5.492 6.636 5.307 7.229 5.822 0.995 0.038 0.007 0.006 5.711 7.373 5.628 7.692 5.868 0.994 0.037 0.005 0.004 7.750 3.503 7.683 3.566 5.544 0.990 0.038 0.010 0.009 8.091 7.489 7.632 8.465 5.874 0.993 0.045 0.019 0.016 10.531 11.100 9.528 13.484 6.045 0.986

TABLEID-4

YAMUNA BARRAGE

-----------------------------------------------------------------------------------------------------------ABS ABS ABS COMPLEX STAB COMPLEX STAB LOGK COR (254) (46,4) (665) CAP(VB) CON(VB) CAP(SD) CON(SD) (VB) COE

-----------------------------------------------------------------------------------------------------------0.072 0.009 0.008 10.678 3.949 11.743 3.454 5.597 0.974 0.071 0.009 0.008 14.291 3.455 16.180 2.942 5.538 0.954 0.083 0.015 0.013 17.628 4.696 19.553 4.122 5.672 0.955 0.075 0.019 0.017 19.384 6.130 21.330 5.442 5.787 0.955 0.051 0.011 0.010 15.315 3.719 17.214 3.202 5.570 0.955 0.034 0.005 0.004 6.886 2.830 9.106 1.898 5.452 0.933 0.039 0.006 0.005 7.802 3.765 8.963 3.053 5.576 0.971 0.042 0.006 0.005 8.566 3.753 9.696 3.125 5.574 0.972 0.042 0.005 0.004 8.600 2.679 10.787 1.962 5.428 0.935 0.046 0.007 0.006 9.301 3.784 10.405 3.215 5.578 0.973 0.047 0.007 0.005 9.673 2.655 11.856 2.019 5.424 0.935 0.051 0.006 0.005 10.244 2.660 12.428 2.055 5.425 0.936

117

TABLEID-5

NIZAMUDDEEN BRIDGE

-----------------------------------------------------------------------------------------------------------ABS ABS ABS COMPLEX STAB COMPLEX STAB LOGK COR (254) (464) (665) CAPCVB) CON(VB) CAP(SD) CONCSD) (VB) COE -----------------------------------------------------------------------------------------------------------0.037 0.005 0.004 7.800 3.736 8.900 3.053 5.572 0.978 0.042 0.007 0.006 8.060 5.401 8.197 5.258 5.732 0.995 0.042 0.021 0.017 8.041 19.644 6.605 36.544 6.293 0.984 0.044 0.007 0.005 8.720 3.724 9.788 3.134 5.571 . 0.979 0.058 0.018 0.011 11.956 4.184 12.951 3.742 5.622 0.982 0.026 0.006 0.004 5.500 5.742 5.875 5.054 5.759 0.993 0.029 0.005 0.003 5.965 5.559 6.291 5.035 5.745 0.994 0.043 0.014 0.012 6.702 16.326 5.631 30.031 6.213 0.990 0.035 0.004 0.003 7.085 3.797 8.215 3.011 5.579 0.977 0.056 0.015 0.014 7.468 17.713 6.186 32.879 6.248 0.987 0.047 0.006 0.005 7.355 5.357 7.549 5.125 5.729 0.995 0.037 0.010 0.008 7.790 18.681 6.421 34.731 6.271 0.986 -----------------------------------------------------------------------------------------------------------

TABLEID-6

OKHLA

ABS ABS ABS COMPLEX STAB COMPLEX STAB LOGK COR (254) (464) (665) CAP(VB) CON(VB) CAP(SD) CON(SD) (VB) COE

-----------------------------------------------------------------------------------------------------------0.053 0.047 0.040 10.397 46.153 8.778 71.273 6.664 0.977 0.060 0.021 0.018 12.409 17.917 11.329 21.112 6.253 0.984 0.060 0.022 0.018 12.418 18.796 11.372 22.021 6.274 0.985 0.061 0.019 0.015 12.755 18.542 11.666 21.745 6.268 0.983 0.061 0.028 0.019 12.741 18.416 11.653 21.597 6.265 0.983 0.048 0.019 0.017 8.841 20.792 7.568 32.015 6.318 0.984 0.046 0.013 0.010 8.583 19.393 7.369 29.810 6.288 0.985 0.044 0.022 0.019 9.430 25.527 8.024 39.397 6.407 0.981 0.050 0.023 0.017 9.910 32.352 8.397 49.965 6.510 0.979 0.051 0.036 0.030 10.029 34.813 8.490 53.769 6.542 0.979 0.052 0.029 0.018 10.214 39.612 8.635 61.180 6.598 0.978 0.050 0.033 0.030 10.039 35.048 8.498 54.132 6.545 0.979

--------------------------------------------------------------------------·-----------·-------·------------ABS (254), ABS (465) & ABS (665) =ABSORBANCE MEASURED AT THE RESPECTIVE WAVE LENGTHS (NM)

COMPLEX CAP (VB) & COMPLEX CAP (SD) = COMPLEXATION CAPACITY CALCULATED BY VAN DEN BERG PLOTS (VB) AND SCATCHARD PLOTS (SD) (IN MICROMOLES)

STAB CON(VB) AND STAB CON (SD) = STABLIITY CONSTANTS BY (VB) & (SD) METHODS (x1E+5)

11 8

The van den Berg plots and Scatchard plots of the experimental data

for all the six sites over a period of 12 months arc shown in Figs. III-3 - III-

14. The monthly variation of complexation capacities of each of the six sites

over a period of one year has been shown in Fig.III-15 and III-16. The

plotting has been done with the help of PLOTXY progl'amme (Geevan, 1990)

written in FORTRAN-GKS and run on Micro Vax computer.

The dissolved organic carbon of the water samples was ascertained

by measuring the absorbance at 254 nm. The method has been found highly

successful to monitor dissolved organics in natural waters if turbidity do

not exceed certain limits (Dobbs et. al. 1972) and hence was adopted.

Also the humic and fulvic acid fractions were ascertained by

absorbance measurements at 465 nm and 665 nm as absorbances at 465 run

and 665 nm are dependent on the concentration of humic substances (Chen

et. al. 1977). Absorbances were measured using a UV-VIS

spectrophotometer (Bausch and Lomb, Spectronic 1001).

Using the LOTUS 1-2-3, correlations were ascertained for

complexation capacity and absorbance at 254 nm Fig. III-17. Also

correlations of stability constants vs ratios of absorbances

(ABSA.4651ABSA.254), and (ABSA.665!ABSA.254) were examined Figs. III-18

and III-19.

Copper complexation capacity values estimated showed a minimum

of 2.484 ~ mol/1 and maximum of 19.384 ~ mol/1 by Vanden Berg method of

11 9

OJ50 ~

c::: :::> = -= ---

U3B

c.> c.> "-

: 1

O.ltH/ !tree metal cone) 0.2~H5

-= c::: :::> = -= ---c.> cu L

:2

0.92l Otl !tree metal cone) O.!!E-05

0.3~2

-= c: ::.:> = -= ---cu cu c._

:5

O.ttHI !free metal cone) 0.2tH5

3.~50

-= c:: :::> = -= ---cu C1> <-

:6 -------71 . I

0.019 0.059 0.312 0.7ff-OB (free metal cone) O.t~E-05 0.22£-07 (free metal cone) 0.21-05 O.!ff-07 (free metal cone) 0.70£-05

2.915 :7

----'------x

O.lXi O.BIH? (free metal cone) 0.6BE-05

. 10 o.~~- F_. ------

= . c:: :::> = -= ---

0.0~3

c.> «>.> <-

O.I7H7 (free metal cone) OJOH5

0.552 ~ = :::> = -= ---

0.0~

cu C1> c_

: 11

0.31£-07 (free metal cone) 0.3!-115

Fig. III-3

van den Berg Plots

RAM GHAT

120

0.872 ~ c:: :::> = -= ---- .. «>.> «>.> c_ -

0.072

:9 --------

0.29H7 (free metal cone) 0.4!-115

o.m -= c:: :::> = -=

----Q.l

0.070

«>.> c...

0.2ff-117 (free metal cone) 0.27H5

1.372 "'0 c:: => = ..<::1 -QJ QJ

J::

: 1 O.H9 "'0 c:: :::J = ..<::1 - -QJ QJ

J::

0.291

0.067 0.088 0.055 O.l£~7 !free metal concl 0.2!H5 UH7 (free metal cone) 0.2iH5 0.21H7 !free 111etal concl 0.2!H5

0.2U :4 0.200 :5

0.769 :6

0.~7 0.053 Ufi O.UHl !free metal cone! 0.11£~5 0.20H7 !free metal cone! 0.20£~ O.HH7 (free 111etal cone) HOE~

0.520 "'0 c:: => = = --QJ cu c... ......

0.051 0.21H7 !free metal concl 0.31f~5

: 10

0.072 0.2iH7 !free metal cone) 0.2H5

0.~ :9

0.082 0.31£-07 !free metal cone) Hf-o5 0.30H7 !free metal cone) 0.3!-115

1.312

HH7 (free metal cone) 0.21H5 HH7 (free metal cone) O.?.d-65

Fig. III-4

van den Berg Plots

NAJAFGARH DRAIN

121

: 1 o.526 r --- -------------:a

c:: :::::1 C> ..0 -Q.> Q.>

!::

0.226 0.74H7 !free metal cone! 0.31r-05

:4

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2.1~ -;:::; = ::::> C> ..0 -Q.> Q.)

"--0. 728

:7

0.11Eil6 (free metal concl 0.6!£-05

0.898 "0 c:: ::::> C> ..0 -­Q.> Q.)

!::

0.311

: 10 ~-----·-----

O.!OE--116 !free metal cone] Hf--115

0.514 :2

0.91fil7 !free metal cone] 0.31fil5

0.2n8 '0 c:: ~ = ..0 --

0.101

..., cu "-

:5

0.37H7 (free metal cone] O.lff--115

·a 1.171= f:_ _________ /1 § I

_g ! --- i

Q.) '

~ i

0.2U- _j 0.70Eil7 (free metal cone] Uf--115

Hill '0 c:: :::::1 = ..0 -C1.l Q.)

!::

0.122

: 11

~_.__.____._~j 0.44Eil7 (free metal cone] 0.31-05

Fig. III-5

van den Berg Plots

OLD YAMUNA BRIDGE

122

0.47H7 (free metal cone] 0.20Eil5

2.~ '0 c:: ::::> = ..0 -..., cu "--

0.~

:6

0.21fil6 !free 11etal cone] H,'H5

:9 1.079 ----- ------·--:71

I '0 c:: :::> = ..0 -­Q.)

Q.> "--

I

D.6~il7 (free metal cone] U!H5

. 12 0.249= r=-----------7 = . ::::> . = '

..0 '

~ ! CU I

"- I i

D.057 J D.2ail7 !free metal cone] O.Hi--115

O.!flft : 1 0.397 :2 0.220

0.2~ 0.226 0.~

:3 ------·---?1 I I

0.7f-1l7 !free metal cone) 0.3!~5 O.l!H7 (free metal cone) 0.2fH5 0.50H7 (free metal cone) O.IIH5

D.HB :4 -~----------

0.091 0.3fH7 (free metal cone) 0.12£-llS

•7

·-~lr~·-71 !;~ !

O.lift- I

0.11H6 (free metal cone) O.HHS

0.719

I I I

0 ~no __.__.......____.........___,J, ___ ..__..._ ___ ,___j

,JU

0.91H7 (free metal cone) 0.3BH5

O.ll'i :5 ..---:----=-----

0.195 O.fi9H7 (free meta 1 cone) 0.21~

. 8 ' u~r~--~

~~ I c._ I ..!!:=. :

0.332 0.99£-07 (free 111ctal cone) UlHS

6.872 : 11 --·-·----r<

0.443 0.14H6 (free metal cone) UH5

Fig.III-6

·van den Berg Plots

YAMUNA BARRAGE

123

1.371

0.616

o .ant

0,415

:6 --71 i

i I

.__._....-..,~..__._~~........J

0.17H6 (free metal eoncl UI~

HH6 !free metal cone) o.~6H5

: 12 -~/~

0.1I-1l6 (free metal cone) UOHS

Uff-QB !free metal cone) 0.6BE-o6

0.106 :4 -···· ----··/-!

,/' 0.~

O.llH7 !free metal concl O.BJF-06

0.272

\

0.031 u_._~~ .. _.__._ •• _...__._j

O.!I-o7 lfree metal concl o.~f-05

0.113 -:a

c::: ~ = = -~.

: 10

~ i .. ~:::: i

0.01~ .........._~~,__j,-~~--~-J

0.6:£-QB !free meta 1 cone) 0.93E-06

:2

O.t2f-G7 (f~ee metal cone) O.!l4t-o6

0.106 :5

0.02.9 O.llH? !free !!letal cone! O.Bi-G6

D.173 :8

--- .. -----~

/ I

I

0.021 -~-- •--'----'--~~J

0.91£-oB !free metal cone) O.!I-D5

. 11 o.o96 r-· ------------~

If/ l ~ tl. ! c... ' ~' i

U\2 If - · ·· · · _j

0.54HB [free metal cone) O.BOE-o6

Fig.III-7

van den Berg Plots

NIZAMUDDEEN BRIDGE

124

:3 0.110 -~

/

i 0.030 ..__._~~~......_~. ~ _j

H£-o7 (free llletal cone) 0.9!H6

0.211 :6 - -- ------·----- -

0.02.8 _.....,_.__.__,~--· J O.t!f-ol !free metal cone) D. !If .fl5

:9 0.12~ -------~

g ,/ I = -~ / ~ (

0.016 -~c-~~ ~ .. _. _ _j

0.6HB !free metal cone) O.IOHS

0.112 -:a c::: ~ ·= = -Q.> ..., c... -

D. 014

: 12

0.6?£-08 (free metal cone) 0.9Lf-o6

/l I

uoo -;:; c :::r C>

.&::I -cu cu "---

:2 0.306 '0 c :::r C>

.&::I -cu cu

£

:3

7l / !

! U06 0.226 0.02!

HH6 (free metal cone] 0.45H5 0.74£..07 (free metal cone] 0.37E-05 0.84HB (free metal cone] 0.2lH5

0.812 -;:; c :::r C>

.&::I -cu cu "---

:4

'0 c:; :::r C)

.&::I -cu cu "---

:5 1.299

o c:; :::r C>

.&::I -cu cu !::

:6

71 i i

I '

Uil 0.222 0.339 0.10H6 [free metal cone] 0.41H5 0.72H7 (free metal cone] 0.2&:-os O.!OE-06 (free metal cone) 0.5!H5

~~lr 0.315- IL-t ~· ~o.-J-..o..__.___,~

O.!I£H7 (free metal cone] OA!IH5

U.l3 -;:;

c:; :::r C>

.&::I -cu Q..l "---

0.026

: 10

O.!OH7 (free metal cone) 0.2ff-05

0.532 -;:; c:; :::r C)

..C> -0.032

cu Q..l "--

:8

/i I

HH7 (free metal cone] UH5

0 11 0.82~ r--' --

=I· Jt = r Q.l ' "- ~

..::!::.~

0.~

o.B1H7 (free metal cone] o.41f·-o5

Fig.III-8

van den Berg Plots

OKIUA

125 .

1.115

D.«B

O.:Ml

0.023

:9

UH6 (free metal cone] Ulf~5

:172 ' .

.

' ' l I

__.____~~ ....... _~_ ......... _ ~..-J

UH9 (free metal cone] HHS

/

15.~ : 1 ·---~

Ci! cu ~ -~ -= c::

::::::0 0 .e -~~ 2.5

O.lfi-07 (Bound metal cone) 0.24E-Q5

:4

l

0.7&:-oa !Bound ret a l concl HlE-o5

:7 3.1!17 ·-······ --- -·---, c;; I

11>

-~ ., I

~·~ OJ40

O.B!E-07 (Bound metal cone) 0.6&:115

. ~ 0 ?2.00~ r ~ .... ~:. ------- --·- -:

... . .i: . ·- . :

~f~i m r i

2.012 L~-~--~-1

O.i!f-o? laounc :ne~al cone! OJOf-05

:2 42.102 ---·---~

Ci! cu c:.. ~

I ....... _ = c:: ::::::0 = £9

U.~

O.!la:-o8 (Bound metal cone) 0.17E-o5

16.1!12

2.916 O.M-87 !Bound metal cone) 0.2I-o5

·s 7.666 ~--· - . -- --- .. !

Ci) : cu ' c... ' • .....__ I j

] f\ i o.896 L~

o.~rt-o! !Sound metal cone) UIE-o5

·H H.842 -~ ~ - · -- ---- ---,

LB!~l~=~ 0.3![-G/ !Bound :netal cone) UH5

Fig.III-9

Scatchard Plots

RAM GHAT

12 6

33.7J :3 ------~

cu ' cu I c:..

I -......_ = c:: I ::::::0 0

!9. I !

lBi O.!!H7 !Bound metal cone) 0.2\HS

3.197 :6 ---

c.~

0.9!H7 !Bound metal cone) O.IOE-o5

.. 9 13.~ ~ _:__. -- -----· .. ·- -- - -;

f ~~ i

1 r<..____ . 1.U5- L.~-~~_j

0.2!H7 (aound retal cone) HH5

tUt7 : 12 --------- ---··---; '

0.2fdi !Bound metal cone) 0.2!t-o5

1Ut5 '"iii" ... "---"CC c::: = ~

2.1il

: 1 -------------, I I

0.25H7 !Bound meta 1 cone) U!f-05

al.!Hi '"iii" ... "---"CC c::: :::J m

:LJ

11.!i6 :2 --------,

2.112 · Uf-D7 !Bound metal cone) 0.27Ei15

18.627 Ci> c:u .f= -"CC c::: ::::!:1 = m

:5

18.031 :3

3.61 0.21f-07 !Bound metal cone) 0.21H5

U02 :6

u~ J. 5li9 t. JJO

O.llf-D7 IBounc metal cone) O.!BE-o5 0.20H7 IBounc metal cone) 0.20H5 o.~lE-07 IBounc metal cone) o.~Of-o5

19.2~ :7 -------,

0.21E-o7 !Bouno 11eta I cone) 0.31H5

13.~

'"iii" ... "---"CC c::: = = .IE.

2.131

: 10

. 0.27E-o7 !Bound metal cone) 0.&-os

H.IJ.i2 cu c:u "---= c: ::::!:1

.!!§

:B

0.31H7 (Bound metal cone) 0.3I-o5

: 11

~.~ ------l =

3.12 as ...._._~~~-~~ o .19f-D7 (Bound meta 1 cone) 0.21H5

Fig.III-10

Scatchard· Plots

NAJAFGARH DRAIN

127

12.1~ :9

1.7!1

! \ I I

UJH7 !Bound metal cone) 0.3I-o5

: 12 '"iii"

c:u .~ -"CC

c:: = ~

3.197

O.ii-97 !Bound metal cone) 0.2d~

3.748 Cii' IU c... --= r:::::

"' m

7.533 -cu IU c... --'CI

§ m

1.181 1.943 3.!il

:3

I I

Uif-06 !9ounc nee a! toncl 0.60£-95 0.31H6 !Bound eetal concl 0.60£--t!i -0.1H6 !Bound 1etal tone) D. /If f5

5.ZJ 9.01 Cii' IU c... --= .::: :::1 .e

1.179 Cii'

a.> c... --'CI .:::

~ 2.&1 U83 I.B

O.lf-06 (9ound tetal cone) 0.6H - 0.3ff-o6 !Bound metal concl 0.7i-t5 D.2llf-oti (Bound .etal tone) Hlf f.i

un Cii' IU c.__ --= .:::

j

:.8 5.2lill

U7G Ui3 · 8.9

:9 -·---· ---

0.2!-ll6 !Bouna metal cone) D.2f-o5 O.Dt-06 (Bound metal cone) Uf-05 0.3f-o6 !BorJnd metal cone) Hlf-115

Hili : 10 ·----....... , !

8.1~ : 11 -------- -----l 17.311

Cii' ... c.. ---'CI .::: :::1

&

1.112 Li!l U12 0.30E-06 (Bound metal cone) OAE-95 O.li-o6 !Bound ~etal cone) o.~ UE-86 (Bound aetal cone) O.i'I~

Fig.III-11

Scatchard Plots

OLD YAMUNA BRIDGE

17R

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0.3~-Gfi l3ouno netal cone) o.~~

:~

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: 7 ------------, I

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0.&~ !Bou~d metal cone) OATc-GS

. 10 3.32J_ K=------- . ------- -l

-~r \v~ , ~L ~~ ~ . I

1. Bl -~ J._~____.__._

· O.l!d6 IBouno metal. cone) 0.5! -o5

Cii' .... .£::: -= -c: ::I

d_il

2.515

--l Uf-«6 !Bound metal cone) D.6!H5

: 5

OJI~ IBouno metal cone) D.61H5

. 8 . 1087 .... f_. --------- ·--l !:f~:

. -g /. . \J' g 1- ; .ea ·- i

I. l.ZU I ' . I '

O.Jnf-«6 !Bound metal cone) 0.50H5

. 11

1.~ Lv:;';~ -~ :.!fJ~ . ~-~~--'-'-~-...._\1

0.2fd6 laouno metal cone) Ui-05

Fig.III-12

Sea tchard Plots

YAMUNA BARRAGE

129

7.5D -----~

'

O.!Hii !Bound lftetal cone) 0./i-GS

o.a-fli !Bound metal cone) U8E-f5

·g

1.1!~ r~--~

a.~~L ._\ o.~ !Bound metal cone! v~-os

: 12

1.247

-~~ 0.27£-«li !Bound metal cone) 0.50.----t5

2.461 : 1 - ----- ------- --, :2 ----------------,

I !

~.281 :3 ----- ----- ---l

I

!

1.833 --- 1.42/ 3.22 0.21-"-DO (9ouno 'l!eral cone! D.4ff115 o.n--o6 !Bound metal cone) D.5:H5 G.H !Bound aetal cone) Uot-t5

2.m --a:>

I1J

"­-­...... _ ~

§ &

1.230

0.2~H6 {Bound ~eta1 cone) O.SOF-05

"'iU cu "---=c;-r= :::J e

G.IE7

: 7 -----------, I

I

I !

l O.JIE-66 !Bound metal cone! UH5

. 10 1I.2!P · r . __ :-_ · - - --- ----- ·1

~ PI ;

-~ [t_\ I :.3 ~ .03 . i

2.542 -~--~·____.___.___ '

O.lf.-06 (Bound :ne~al cone) 0.63:--DS

0.3I--G6 (Bound meta 1 cone! D.6I~

:8 ll.!I06 ··--------- ---l -cu·

I I

Q.) I

c. \

~ -\ ~:-~

!.876 O.JH6 !Bound metal cone) 0.5H5

. 1 ~

mJ' r< . ··.l ·:gl~----,~ :_, j

~ :

1.213 .• -~~ -~~ ~ '

U!-06 !Bound metal cone I D.5f fi

Fig.III-13

Scatchard Plots

NIZAMUDDEEN BRIDGE

130

2.9i :6 ------, "'iU

~L-~ = c: ~ .

a.7'iJ~ ~----UHii !Bound metal cone) OAH

2.2ll :9 ---- ----·-··-· ·- ----

D.lE--66 !Bound me~al cone! O.IJ 05

. 12 42.41i r ----

a:;- 11

r: ~\ 1.91i1L~ ~

UI~ !Dound ~etal cone) 0.&&.:--as

!i.!5f . I • ! 31.652

:2 ------~

12.8 8.654 0.1f.gfi (3ounc ne~al concl D.a.te-o5 O.!Hi !Bound 111etal cone) 0.81HI5

3t211 Cii

.~ ..::0 r=

j

9.a

:4 --------l I

UH (Bouno metal cone) 0.112E-o5

'7 ~·; r-·---------_ -, "' ~ "\ I l.il6lL~I

Uf-o!i (3cund ne~al CD1cl O.?~H5

. 10 ~~ r-~---!: _____ --- ----~

cu · I ~ \ I

~ -~ : j: ~ i

8.~ l-~-·-~~ Uf{l6 (douno metal cone! O.ac:~

:5 33.~ -------l-~ \ - I

l~j 9.348

U.H6 (Bound metal cone) UH5

·a ~.mcu r------------ ---l

cu : L r . ~- ... I -:a ~- i ~ L . -:-::» 0 .

m 1-

5.710 L --~----- --'~ D.1f-o6 (3oJno tetal cone) 0.7?H5

II.~

Cii .... ... ~ -------~ r::' -:;;o

~

1Ui1

:11 .-----~

I .

Uf~ !Bound metal cone) Ul-o5

Fig.III-14

&atchard Plots

OKHLA

1 31

:3

UHi lBound aetal concl O.&I-o5

l!.m :6 --------, I

Uf-Dli (Bound aetal cone) O.li-o5

fil.'lll Cii cu .f= ;;;

J 1 .•

:9 -- ·-· ---------l ! !

Uf-1 (Bound metal cone) D.Bh: fj

67.~ : 12

Ull UHI (Bound metal cone) UH5

115.0

a c. u ... .!

o.o

,.. ..., ... u • I u g ... ..., • tC

~

I

[ t

COPPER COMPLEXATION CAPACITY (VB8)

Fig 111-15

! I . L .. _ J

12 Manth•

132

2!1.0

-1"4 ...... a : ... I a & ... .! ,.. .., ... ~ I u g ... .., I ... !

0.0

COPPER COMPLEXATION CAPACITY(SCO)

Fig 111-16

t2 MDnthe

133

calculatiol).s. Corresponding values by Scatchard method were 3.109 and

21.330 J.1 mol/1. The complexation capacity of unfiltered samples of river

Yamuna reported earlier were much higher (Banerjee and Jagadeesh,

1990). The unfiltered samples contained large amounts of suspended

particulates and colloidal clay and organic matter, which might have

adsorbed a major portion of the cupric ions added during titration and

hence showed higher values. In the present study the filtered samples were

used for titration in order to make the comparable and multiple

determination.

The values of copper complexation capacity varied from site to site

and month to month. However, a general trend was seen in the monthly

variation of copper complexation capacity at all sites Fig. III-15 and III-16.

High values of copper complexation capacity were observed during the

summer months reaching a peak in June for most of the cases and

decreasing thereafter. A sharp drop in the CCcu values at all sites

immediately after June can be attributed to the flushing because of rains

and consequent dilution Fig. III-15 and III-16 of dissolved organics.

As mentioned earlier, both van den Berg and Scatchard methods

were used to calculate the apparent copper complexation capacity values

and stability constants of Cu-organic chelates. Kramer (1985) r~ported all

the CCcu values calculated by Scatchard method were higher than those by

van den Berg method. In present study also most of the estimations agreed

with the above observation.

134

The' various reasons for non-linearity of direct titration curves have

been explained elsewhere. In the present study the non-linearities of

titration curires were minimum for sample of Ramghat site (site with

minimum human interference) while other sites showed appreciable

deviations from linearity. It is quite possible that sewage and industrial

effiuents that reach the river after Ramgaht contributed many colloidal

materials of organic and inorganic origin, which adsorb the metal ions.

Also many ligands of anthropurgic origin which might form complexes

other than 1 : 1 types also compete for the metal ions, and can bring in non­

linearities.

A larger scatter has been noted for the Scatchard plots compared to

the van den Berg plots for the same set of data obviously due to some

mathematical artifacts. The slope of the Scatchand plot is mainly

dependent on a few high (MT - MF)/MF values representing the small

additions of copper ions and is less reliable while opposite is true when van

den Berg plots are used. (Kramer, 1985).

However, the larger scatter in Scatchard plots magnifies the slight

deviation in non-linearity to a greater extent and hence enables one to

decide about the number of different ligands or binding sites from the

nature of the plots qualitatively and to some extent quantitatively. The

conventional F-test and Runs test (Sunda and Hanson, 1979) have been

found to produce unreliable results for the different types of ligand sites or

ligands, when the scatter in the plots have been influenced by factors like

135

. non-Langmuir adsorption, and complexations other 1 : 1 types operate.

Also since the synthetic ligands like anionic surfactants involved in Cu(II)

complexation by sewage sludge or sewage sludge fulvic acids do not behave

as isolated independent ligands, but instead participate as co-ligands with

other a-containing functional groups or as moities incorporated into fulvic

acid structure (Senesi and Sposito, 1987) the possibilities for non-linearities

in the plots are more probable. In such cases electrode response gives few

freak values at the initial stages of titration and influences largely the

conventional test results. Scatchard plots help to avoid such freak values to

appear in the calculations. Hence the present study recommends the van

den Berg method of estimation for apparent complexation capacity and

stability constant, and Scatchand plot method for estimating the minimum

number of ligand types or binding sites as well as subsequent estimation of

their concentration.

Correlations of complexation capacities and· stability constants with

parameters related to dissolved organics showed highly significant results.

The earlier studies have revealed that complexation capacities increase

sharply with the increase of absorbance at 254 nm for sites with minimum

human interference in comparison with the sites of tremendous human

. interference. The correlation coefficients were also high in the former

cases (Banerjee and Jagadeesh 1989). The present study with filtered

samples confirmed the earlier results and even yielded higher correlation

coefficients. In a similar study (van den Berg and Karmmer 1979) the data

scatter observed in the correlation plot of CCcu and organic absorbance

136

suggested that only part of the absorption is caused by complexing

materials and the rest is caused by other organic matter. The study of Sma

et. al. (1980) suggested that it is not always possible to assume that the

quantity of carbon present in water is indicative of the quantity of

complexing organic compounds contained in the sample. The ratio of

complexing capacity to dissolved organic matter showed tremendous

variation from site to site, indicating the variation' in the composition of the

organic matter (Srna et. al. 1980). The present study however, showed a

good correlation of organic absorbance and complexation capacity and

further indicated higher slope and significant correlation at sites with

minimum interference. This could be possibly due to some portion of

complexation capacity at down stream sites being controlled by some strong

synthetic ligands present in sewage or industrial effiuents, which are also

non-absorbing at 1..254 nm. The stability constant when plotted against the

ratio of absorbance at 1..465, 1..254 and 1..665, 1..254 nm showed significant

correlations and higher slopes at upstream sites only, suggesting that the

stability constants mainly depend on the natural ligands like humic and

fulvic fractions at upstream sites. At down stream site the influence of

natural ligands is less. The log stability constants ranged from 5.424 to

6.664 and are well in agreement with the values reported earlier (van den

Berg and Kramer 1979, Shuman and Woodward 1977, Banerjee and

Jagadeesh 1990). The Scatchard plot suggested that a minimum of two

ligand types or binding sites are operating during the complexation of Cu2+

with the river water studied. Studies on Newport river and Neuse River

(Sunda and Hanson 1979) revealed three types of ligands or binding sites.

137

COMPLEXATION CAPACITY VS ABSORBANCE (A 254)

30~----------------------~------------------.

-~~ ::J 0

~22 ...J 0

~I -

z 0

~ 6 X w ...J Q. ~ 0 0 0.01 0.03 0.05 0.07 0.09

ABSORBANCE (A 25'4)

Fig.III-17

138

STABILITY CONSTANT VS ABSORBANCE ·RATI0(~465/A254)

50

-· 1{)0

X -t-z <( t-(/) z

20 0 u >-t-_J

m 10 <( t-(/)

0.1 0.3 0.5 0.7 0.9

RAT I 0(465/254)

Fig.III-18

139

STABILITY CONSTANT VS ABSORBANCE RATIO( ,\665/ A254)

so·

6

- 40 /5 lf)

0 X ·-1-z 30 <{.

1-(/) z 0 (.)

20 >-1-_J -co <[ 1- 10 (/)

0.1 0.3 0.5 0.7 0.9

. RATIO (665/254)

Fig.III-19

140

Bresnahan et. al. (1978) observed a doubling of binding sites of fresh water

fulvic acids available to cupric ions, when the pH changed from 4. 7 to 6.0.

Wilson and Weber (1977) recognized two types of functional group sites

equal in number for fresh water fulvic acids. In the present study a

minimum of two binding sites for upstream samples and the possibilities of

three or more binding sites or ligand types in certain samples were

indicated in the Scatchand plots. This however, needs further investigation

in the light of discussions earlier on factors responsible for such scatter.

From the present study, the following inferences (a) in general and (b) in -.

particular regarding the river Yamuna can be drawn :

(a) 1. The van den Berg methods gives reasonably linearised plots.

The apparent complexation capacity values and conditional stability

constant values estimated from this method are reliable.

2. The enlarged scatter in the Scatchard plots help to judge the

minimum number of ligands or binding sites from the shape of the

curve and can be used as a preliminary tool for further

investigations. Hence the Scatchard plots also should be studied

simultaneously with the former, for this purposa.

3. The ratios, Abs 465/Abs 254 and Abs 665/Abs 284 are in good

correlation with stability constants at a site where the human

interference is minimum i.e. sewage sludge and industrial effluents

are minimum or absent. Sites at which natural ligands are the

predominant group, better correlations are obtained. Hence checking

the correlation of stability constant with the above said ratios, can

reveal the presence of synthetic ligands, and hence might be

considered towards a new pollution index, for natural waters.

4. The filtration through 0.45 Jlm membrane filter can remove an

appreciable amount of organics and hence the complexation capacity

may get underestimated. But the filtration is also essential to remove

the suspended particulates materials as it can interfere in the

complexation capacity estimation and absorbance measurements.

Solutions to the above problems may be sought by measuring the

optical absorbance of dissolved organic matter using the thermal

lense effect (Power and Langford 1988) and then estimating the

complexation capacity of the samples before and after the complete

photooxidation of organics through the recent method of Martin­

Goldberg and Shuman (1989).

(b) 1. The apparent copper complexation capacity of river Yamuna

varied from 2.484 Jl mol Cull to 19.384 Jl mol Cull, which is well

within the range of values reported, by similar method for many

other natural water bodies. River Yamuna being a 'regulated

stream' by the few barrages over her, water level keeps fluctuating.

Also, effluents from the many non-point sources reaching Yamuna

change the concentration of ligands responsible for complexation,

from time to time. These factors obviously lead to the variation of

142

copper complexation capacities of the nver at different sites.

However, the complexation capacities rea·ched the peak values at all

sites at the same period, namely in the month of June.

2. The log stability constants of Cu-organic chelates in river

Yamuna varied from 5.424 to 6.664. The values reported for natural

waters ranged from 5.0 to 11.0 (Shuman and Woodward 1977;

Neubecker and Allen 1983).

143