Biosorption of Toxic Heavy Metals by Unmodified Marine Red Alga (Kappaphycus alvarezii): Kinetics...

Copyright © 2013 by Modern Scientific Press Company, Florida, USA

International Journal of Environment and Bioenergy, 2013, 7(2): 91-107

International Journal of Environment and Bioenergy

Journal homepage: www.ModernScientificPress.com/Journals/IJEE.aspx

ISSN: 2165-8951

Florida, USA

Article

Biosorption of Toxic Heavy Metals by Unmodified Marine Red

Alga (Kappaphycus alvarezii): Kinetics and Isotherm Studies

Sohail Rafiq 1, Majid K.M. Ali 2, Mahyar Sakari 1, Jumat Sulaiman 2, Suhaimi M. Yasir 3, *

1Water Research Unit (WRU), School of Science and Technology, Universiti Malaysia Sabah, 88400

Kota Kinabalu, Sabah 2Mathematics with Economics Programme, School of Science and Technology, Universiti Malaysia

Sabah, 88400 Kota Kinabalu, Sabah 3Seaweed Research Unit (UPRL), School of Science and Technology, Universiti Malaysia Sabah,

88400 Kota Kinabalu, Sabah

* Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.:

+6088-320000 ext: 5778.

Article history: Received 5 July 2013, Received in revised form 13 August 2013, Accepted 30 August

2013, Published 15 October 2013.

Abstract: Present study has been undertaken to propose an alternative use of unmodified

marine red alga (Kappaphycus alvarezii) (UMRA) as biosorbent for the removal of heavy

metal ions from aqueous solutions. The biosorption studies were conducted in batch

adsorption system as a function of contact time and initial metal ion concentration. The

adsorption system attained equilibrium after 150 min of contact time for Pb (II), Cu (II), Zn

(II) and Cd (II). The removal efficiency of red alga improved as metal ions concentrations

were lowered. The equilibrium sorption data was better explained by Langmuir isotherm

model suggesting that the adsorption of metal cations observed monolayer sorption pattern.

Pseudo-first-order model, pseudo-second-order model and intraparticle diffusion model

were utilised to test the sorption kinetics involved in the process. It was observed that

pseudo-first-order kinetic model could better describe the adsorption kinetics. A

comparison of maximum sorption capacity of several agro-based waste material showed

that red alga can be a suitable alternative to use as biosorbent in the removal of toxic heavy

metals from aqueous solutions.

Keywords: biosorption; unmodified; removal; isotherm; kinetics.

Int. J. Environ. Bioener. 2013, 7(2): 91-107


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1. Introduction

The heavy metal pollution has become a major environmental problem in the past few decades

after industrial revolution. With the rapid development of industries such as metal plating facilities,

mining operations, fertilizer industries, tanneries, batteries, paper industries and pesticides, etc., toxic

heavy metals are directly or indirectly are being discharged into the aquatic streams posing the genuine

environmental threat. Contrary to organic pollutants, heavy metals are non-biodegradable and tend to

accumulate in living bodies and some are carcinogenic in nature. Toxic heavy metals found in

industrial and domestic wastewaters including lead, copper, cadmium, zinc, nickel, mercury and

chromium. The toxic heavy metals in the aquatic world are extremely deleterious for fauna and flora.

Most of the research is diverted to developing cost-effective technologies for the removal of metal ions

from aqueous solutions. Common wastewater treatment technologies included membrane separation,

electrochemical precipitation, ion exchange, pre-concentration, flotation, membrane filtration,

ultrafiltration, coagulation-flocculation and adsorption. Adsorption is the most commonly used

technique because of its user friendliness and cost effectiveness. This process is found to be versatile

and effective when it comes to the operational cost and appropriate regeneration steps. Several recent

published works utilized locally available low cost adsorbents including sago waste (Quek et al.,

1998), neem bark (Naiya et al., 2008), orange peels (Xuan et al., 2006), Banana waste (Annadurai et

al., 2006), coffee residue (Boonamnuayvitaya et al., 2004), tree fern (Ho et al., 2004), tea waste

(Amarasinghe and Williams, 2007), rice husk (Feng et al., 2004), peanut hulls (Brown et al., 2000) and

barley straw (Larsen and Schierup, 1981).

Since UMRA is abundant, locally available, cost effective and readily available media which

can be utilized as biosorbent material for wastewater treatment processes. Present study focuses on the

potential utilization of locally available material to remove toxic heavy metals from waste water.

2. Materials and Methods

2.1. Sample Preparation

The UMRA samples were collected from the east coast of Sabah, Malaysia. The samples were

thoroughly washed with distilled water to remove the impurities and solar dried for 16 h. The samples

were grounded to obtain the 1-2 mm of particle size fraction for batch experiments. The samples were

stored in airtight glass containers without further treatment for prior analysis.

2.2. Solution Preparation



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The Pb (II), Cu (II), Zn (II) and Cd (II) ionic solutions were prepared by mixing the nitrate salts

with distilled water to get 1000 mg/L ionic concentration. Working solutions were prepared by diluting

the stock solutions with distilled water to get the desired ionic concentrations.

2.3. Surface Morphology Analysis

The surface morphology of UMRA was determined by using Carl/Zeiss Evo MA 10 scanning

electron microscope (SEM). The sample was mounted onto 1.27 cm diameter stub and was coated with

gold using Emitech K550X Sputter Coater at 20 mA current for 1 min duration. Images were captured

under electron acceleration voltage of 15 kv.

2.4. Fourier Transform Infrared Spectroscopy

The UMRA sample was grounded to powder by using mortar and pestle. The powdered sample

was dried in the drying oven for 30 min at 60 °C to get rid of water molecules. Perkin Elmer series

spectrum 100 was used to obtain the spectrum in the range of 4000 to 650 cm-1 in 4 cm-1 resolution.

2.5. Isotherm Experiments

Batch adsorption isotherm experiments were conducted by equilibrating 0.1 g of sorbent with

100 mL of each of four different metal ion solutions with known concentrations at pH 5 and 22 °C ± 2

°C in an orbital shaker for the known period of time. The mixtures were then removed, filtered and

analysed for left over heavy metal content with atomic absorption spectrophotometer (Perkin Elmer

4100).

2.6. Kinetic Studies

Kinetic study was conducted with known amount of sorbent with 100 mL of each metal ion

solutions at pH 5. The samples were shaken at an agitation rate of 150 rpm and then were taken out

after known time intervals, filtered and analysed for heavy metal content.

3. Results and Discussion

3.1. Surface Morphology

The SEM analysis of UMRA (Figure 1) shows protuberance and irregular surface. The

characteristic microstructure can be seen on surface of sorbent, which may be due to calcium and other

crystalloid salts. Characteristic porous cavities responsible for adsorption can be seen on the surface of

sorbent.



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Figure 1. Scanning electron micrograph (SEM) of UMRA

3.2. Fourier Transform Infrared Spectroscopy

The FTIR spectrum of the UMRA can be seen in figure 2. The FTIR spectroscopic analysis

indicated broad bands at 3377 cm−1, representing bonded –OH and –NH groups. The two

characteristic bands observed at 1633 and 1413 cm−1 are because of carboxylate ions and can be related

to strong asymmetrical stretching and weak symmetrical stretching, respectively. The bands at about

1236 cm−1, represents –SO3 stretching whereas bands around 1029 cm−1 assigned to the –C–O

stretching of alcoholic groups. Functional groups including carbonyl, hydroxyl, nitro and ethers are

responsible to adsorb heavy metals (Srivastava et al., 2006; Demirbas, 2008; Li et al., 2008).

4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 660.0

94.16

94.4

94.6

94.8

95.0

95.2

95.4

95.6

95.8

96.0

96.2

96.4

96.6

96.8

97.0

97.2

97.4

97.6

97.8

98.0

98.2

98.4

98.6

98.8

99.0

99.2

99.4

99.6

99.8

100.00

cm-1

%T

3377.03

1632.91 1029.26

887.88

1236.781413.21 818.50

Figure 2. FT-IR spectroscopy of UMRA



95

3.3. Effect of Initial Metal Concentration

The initial metal concentration has strong effect on adsorption capability of various sorbents.

Normally, removal capacity of sorbent increased with increased in initial metal ion concentration in

solution (Ahmaruzzaman, 2011). The initial concentration of metal ions provides an important driving

force to overcome all mass transfer resistance of heavy metals between the aqueous and solid phases

(Chowdhury and Saha, 2010). Results showed that increase in initial metal concentration from 5 mg/L

to 100 mg/L showed the decrease in percentage removal of Pb (II), Cu (II), Zn (II) and Cd (II) ions

from 26.8 % to 3.7 %, 22.4 % to 3.3 %, 20.8 % to 3.1 % and 19.0 % to 2.9 %, respectively (Figure 3).

However, the adsorptions in terms of mg/g were increased by increasing initial metal ion

concentration.

Figure 3. Effect of initial concentration of metal cations on adsorption at 22 °C ± 2 °C

3.4. Effect of Contact Time

The rate of adsorption is of the most important parameters to design batch adsorption

experiments. Consequently, it is important to establish the time dependence of such systems under

various process conditions. Metal ion adsorption capacities were determined as a function of time to

optimize the contact time for the adsorption of heavy metals on MRA. The variation of metal

adsorption with respect to time is shown in Figure 4. It can be concluded that the removal of metal ions

is increasing with increase in the contact time. Initially the adsorption was low but increases with the

passage of time. The metal removal of Pb(II), Cu (II), Zn (II) and Cd (II) at 90 min were found to be



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20.4 %, 14.7, 12.24 and 11.8 %, respectively. The optimum time required for UMRA to get the

maximum adsorption efficiency was found to be 150 min.

Figure 4. Effect of contact time on adsorption of metal cations

3.5. Isotherm Studies

Adsorption is generally described through adsorption isotherms, which is the relationship

between the amounts of a substance adsorbed at constant temperature (Ahmad et al., 2007). According

to Özcar et al. (2008) data collected from isotherm can be used to predict the maximum adsorption

capacity and to optimise the usage of sorbent. Two most commonly known adsorption isotherm

models, namely Langmuir (1918) and Freundlich (1906) were tested for fitting the experimental data.

3.5.1. Langmuir isotherm

The Langmuir adsorption isotherm relates the adsorption of adsorbate on a solid surface based

on the assumption that a maximum adsorption capacity corresponds to a saturated monolayer of solute

molecules on the adsorbent surface. There are three assumptions adapted for this isotherm, a)

adsorption is only occur for monolayer coverage; b) all the active sites on the surface of the sorbent are

same and can accommodate one sorbent specie and c) the ability of a specie to be adsorbed on a

random site is independent. The Langmuir adsorption isotherm can be represented in the equation

below.



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eL

eLme

CK

CKQQ

1 (1)

where, Qe (mg/g) is the amount of metal ions adsorbed at equilibrium, Qm (mg/g) is the maximum

adsorption capacity of the adsorbent, KL (L/mg) is the Langmuir constant and Ce (mg/L) is the

concentration of sorbate ions at equilibrium. The linearized Langmuir isotherm of Ce versus Ce/Qe

produced a straight line (Figure 5) with slope 1/Qm and intercept 1/QmKL. Table 1 lists the Langmuir

constant and maximum adsorption capacity Qm of different metal ions onto UMRA at 22 ± 2 °C.

Figure 5. Linearised Langmuir isotherm model for adsorption of metal cations

Table 1. Summary of Langmuir and Freundlich constants

Langmuir Qm

(mg/g)

KL

(L/mg) R2

Pb (II) 3.994 0.150 0.999

Cu (II) 3.534 0.138 0.999

Zn (II) 3.432 0.116 0.999

Cd (II) 3.230 0.115 0.999

Freundlich KF

(mg/g (L/mg)1/n) n R2

Pb (II) 1.101 3.250 0.910

Cu (II) 0.716 3.110 0.897

Zn (II) 0.535 2.957 0.925

Cd (II) 0.428 2.874 0.913



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The essential characteristics of the Langmuir isotherm can be expressed in terms of

dimensionless constant separation factor RL given by the following equation:

oL

LCK

R

1

1 (2)

where KL is the Langmuir constant and Co is the highest initial metal ion concentration (mg/L) used in

the experiment. According to the value of RL the isotherm shape can be interpreted as follows.

Value of RL Type of adsorption

RL > 1 Unfavourable

RL = 1 Linear

0 < RL < 1 Favourable

RL = 0 Irreversible

The values of RL (Figure 6) calculated were in range between 0 and 1 which indicate that the

adsorption is favourable at experimental conditions studied.

Figure 6. The separation factor RL derived from Langmuir model against different

initial metal concentrations

The reverse fitting graph of Langmuir isotherm (Figure 7) showed in agreement with

experimental and calculated data of adsorption of heavy metal cations onto UMRA.



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Figure 7. Reverse fitting graph of Langmuir and Freundlich model

Table 2. Comparison of maximum adsorption capacities reported with present work

Media

used

Qmax

(mg/g) Reference

Media

used

Qmax

(mg/g) Reference

Pb (II)

Cu (II)

Barley

straw 15.2

Larsen and Schierup,

1981

Wheat

shell 8.3 Basci et al., 2004

Rice husk 11.0 Chuah et al., 2005 Peanut

hull 8.0 Brown et al., 2000

Olive stone

waste 9.2 Fiol et al., 2006

Coffee

husks 7.5 Oliveira et al., 2008

Orange

peel 4.0 Annadurai et al., 2003

Banana


Hazel nut

shell 1.78 Cimino et al., 2000

Orange


UMRA 4.0 Present study UMRA 3.5 Present study

Zn (II) Cd (II)

Papaya

wood 13.5 Namane et al., 2005

Olive

cake 10.6 Doyurum and Çelik, 2006

Peanut

hull 9 Basci et al., 2004

Rice

polish 9.7 Singh et al., 2005

Coir 8.6 Conrad and Hansen,

2007 Rice husk 8.6

Kumar and Bandyopadhyay et al.,

2006

Coffee

husks 5.6

Oliveira et al.,

2008

Coffee

husks 6.9

Oliveira et al.,

2008

Cocoa

shell 2.9 Meunier et al., 2003

Wheat

bran 0.7 Singh et al., 2006

UMRA 3.4 Present study UMRA 3.2 Present study



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3.5.2. Freundlich isotherm

The Freundlich adsorption isotherm relates the adsorption of adsorbate ions on a solid surface

based on the assumption that adsorption occurs on heterogeneous surface. The Freundlich adsorption

isotherm can be represented in the equation as under;

/n

eFe CKQ1

(3)

where, Qe is the amount of metal ions adsorbed at equilibrium, KF is the Freundlich adsorption

capacity constant, Ce is the concentration of sorbate ions at equilibrium and n is the Freundlich

intensity constant. The lineraised plot of log Ce versus log Qe yields a straight line (Figure 8). The

slope 1/n ranging between 0 and 1 is a measure of adsorption intensity or surface heterogeneity,

becoming more heterogeneous as its value gets closer to zero. The Freundlich constants with

correlation coefficient are presented in Table 1.

The correlation coefficients obtained from Langmuir and Freundlich models suggest that the

Langmuir isotherm model explains better the experimental data. Moreover, the reverse fitting of

Langmuir model further confirmed the suitability of this model for the experimental data referring that

the adsorption takes place in monolayer fashion.

Figure 8. Freundlich isotherm model for the adsorption of metal cations

3.6. Adsorption Kinetics

In order to investigate the mechanism of adsorption, the pseudo-first-order, pseudo-second-

order and intraparticle diffusion equations were used to test the experimental data.



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3.6.1. Pseudo-first-order

The pseudo-first-order model is one of the most common kinetic models widely used to analyse

the adsorption of metal cations onto different adsorbent media. The pseudo-first-order model assumes

that the rate of adsorption on sorbent is proportional to the number of active sites available on to

adsorbent media. This model can be represented by the following equation:

tk

QQQ ete303.2

log)log( 1 (4)

where Qe and Qt are the amounts of metal ions adsorbed on adsorbent at equilibrium and at time t,

respectively (mg/g). The slope and intercept of plot of log (Qe-Qt) versus t were used to determine the

pseudo-first-order rate constant k1 (min-1) (Figure 9).

Figure 9. Pseudo-first-order kinetic model for the adsorption of metal cations

3.6.2. Pseudo-second-order

The pseudo-second-order model can be expressed as

eet Q

t

QkQ

t

2

2

1 (5)

where, k2 (g/mg min) is the pseudo second order rate constant. If the pseudo-second-order kinetics is

applicable, the plot t/Qt versus t shows a linear relationship. The k2 and Qe (mg/g) can be determined

from the intercept and slope of the graph (Figure 10). The summary of constants calculated from



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pseudo-first-order, pseudo-second-order and intraparticle diffusion model can be seen in Table 3. The

initial sorption rate h of Pb (II) ions was higher as compared to Cu (II), Zn (II) and Cd (II) ions.

Figure 10. Pseudo-second-order kinetic model for the adsorption of metal cations

Table 3. Summary of different constants calculated from kinetic models used for the study

Pseudo-first-order

Metal

ions

k1

(min-1)

Qe (mg/g)

calculated R2

Qe

(mg/g)

Pb (II) 0.019 2.445 0.988

2.294

Cu (II) 0.024 1.774 0.986

1.684

Zn (II) 0.021 1.498 0.955

1.498

Cd (II) 0.024 1.372 0.966

1.319

Pseudo-second-order

Metal

ions

k2

(g/mg min)

Qe (mg/g)

calculated R2

h

(mg/g min)

Qe

(mg/g)

Pb (II) 0.003 4.085 0.847 0.015 2.294

Cu (II) 0.002 3.508 0.713 0.007 1.684

Zn (II) 0.002 3.390 0.491 0.005 1.498

Cd (II) 0.002 3.479 0.336 0.003 1.319

Intraparticle diffusion

Metal

ions

Kid

(mg/g min 0.5)

C

(mg/g) R2

Pb (II) 0.225 -0.172 0.934

Cu (II) 0.170 -0.184 0.928

Zn (II) 0.151 -0.189 0.916

Cd (II) 0.136 -0.173 0.903



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3.6.3. Intraparticle diffusion

The intraparticle diffusion model can be expressed by the equation as follows:

CtKQ idt 5.0 (6)

where Qt (mg/g) is the amount of adsorbate adsorbed at time t, Kid (mg/g min 0.5) is the intraparticle

diffusion constant and C is the adsorption constant. The graph of Qt against t0.5 produce the linear line

and the intraparticle diffusion constants Kid and C can be determined from the slope gradient and

intercept (Figure 11). The value of C indicates the thickness of boundary layer whereas the value of Kid

indicates the enhancement of adsorption.

Figure 11. Intraparticle diffusion model for the adsorption of metal cations

The reverse fitting graph of three kinetic models with experimental data can be seen in the

Figure 12. The pseudo-first-order model is found to be in better agreement with experimental data

compared to pseudo-second-order and intraparticle diffusion. Additionally higher correlation

coefficient values (Table 2) obtained from pseudo-first-order model also suggested the suitability of

pseudo-first-order model for present study. Moreover, it can also be concluded from the reverse fitting

graph and correlation coefficient values that intraparticle diffusion also takes part in controlling the

rate of reaction.

The results found are in agreement with previous studies reported in literature (Kalyani et al.,

2004; Hansen et al., 2006) where pseudo-first-order model was the rate limiting step.



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Figure 12. Reverse fitting graph of different kinetic models used in this study

4. Conclusions

Present study showed that UMRA observed better sorption performance at low initial metal ion

concentration from 5 to 20 mg/L. After increasing the initial metal concentration from 20 to 100 mg/L

the removal efficiency decreased from 14.3 % to 3.7 %, 12.4 % to 3.3 %, 11.4 % to 3.1 % and 10.9 %

to 2.9 % for Pb (II), Cu (II), Zn (II) and Cd (II) ions, respectively. The biosorption of Pb (II) ions were

higher than other metal ions. The isotherm studies revealed that Langmuir model better explained the

experimental data compared to Freundlich model. Maximum monolayer calculated adsorption capacity

for Pb (II), Cu (II), Zn (II) and Cd (II) ions were 3.99, 3.53, 3.43 and 3.23 mg/g, respectively. The

biosorption kinetics was best described by pseudo-first-order model. Equilibrium was achieved after

150 min of contact time. The experimental data demonstrated UMRA to be suitable media for use as

biosorbent in the removal of heavy metals from aqueous solutions.

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