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15226514.2017.1374335

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International Journal of Phytoremediation
ISSN: 1522-6514 (Print) 1549-7879 (Online) Journal homepage: http://www.tandfonline.com/loi/bijp20
An ecofriendly approach for bioremediation of
contaminated water environment: Potential
contribution of a coastal seaweed community to
environmental improvement
Fatih Deniz & Elif Tezel Ersanli
To cite this article: Fatih Deniz & Elif Tezel Ersanli (2017): An ecofriendly approach for
bioremediation of contaminated water environment: Potential contribution of a coastal seaweed
community to environmental improvement, International Journal of Phytoremediation, DOI:
10.1080/15226514.2017.1374335
To link to this article: http://dx.doi.org/10.1080/15226514.2017.1374335
Accepted author version posted online: 20
Oct 2017.
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An ecofriendly approach for bioremediation of contaminated water environment: Potential
contribution of a coastal seaweed community to environmental improvement
Fatih Deniz a,*, Elif Tezel Ersanli b
a
Department of Environmental Protection Technologies, Bozova Vocational High School,
Harran University, 63850 Bozova/Sanlıurfa, Turkey
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b
Department of Biology, Faculty of Arts and Science, Sinop University, 57000 Sinop, Turkey
* Corresponding author. Tel.: +90 414 3183000; fax: +90 414 3183260
E-mail address: f_deniz@outlook.com (F. Deniz)
Abstract
High levels of heavy metals like copper ions in many industrial based effluents lead to serious
environmental and health problems. Biosorption is a potential environmental biotechnology
approach for biotreatment of aquatic sites polluted with heavy metal ions. Seaweeds have
received great attention for their high bioremediation potential in recent years. However, the coapplication of marine macroalgae for removal of heavy metals from wastewater is very limited.
Thus, for the first time in literature, a coastal seaweed community composed of Chaetomorpha
sp., Polysiphonia sp., Ulva sp. and Cystoseira sp. species was applied to remove copper ions
from synthetic aqueous medium in this study. The biosorption experiments in batch mode were
conducted to examine the effects of operating variables including pH, biosorbent amount, metal
ion concentration and contact time on the biosorption process. The biosorption behavior of
biosorbent was described by various equilibrium, kinetic and thermodynamic models. The
biosorption of copper ions was strongly influenced by the operating parameters. The results
indicated that the equilibrium data of biosorption were best modeled by Sips isotherm model.
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The values of mean free energy of biosorption computed from Dubinin-Radushkevich isotherm
model and the standard Gibbs free energy change indicated a feasible, spontaneous and physical
biotreatment system. The pseudo-second-order rate equation successfully defined the kinetic
behavior of copper biosorption. The pore diffusion also played role in the control of biosorption
process. The maximum copper uptake capacity of biosorbent was found to be greater than those
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of many other biosorbents. The obtained results revealed that this novel biosorbent could be a
promising material for copper ion bioremediation implementations.
Keywords
Seaweed community; Water environment; Heavy metals; Bioremediation
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1. Introduction
Numerous anthropogenic activities such as mining, metal plating, leather tanning, chemical,
battery, pesticide, electronic goods and fertilizer manufacturing frequently causes water quality
degradation. Among the major pollutants present in the effluents discharged from these sources
into the water bodies, heavy metal ions generate serious environmental and health concerns
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(Morosanu et al. 2016). These elements are detrimental to living beings due to their persistence,
toxicity, accumulation and non-biodegradability (Yang et al. 2015a). Copper is considered as one
of most harmful heavy metals owing to its frequent occurrence in wastewaters and negative
effects on human and other organisms. Copper is an essential trace element required by living
beings for its significant functions in metabolic activities but acute doses cause metabolic
disorders (da Silva et al. 2016; Deniz et al. 2011; Hu et al. 2015). Therefore, the related
industries are required to diminish the contents of copper ions in their effluents to acceptable
levels before discharged into the receiving water bodies.
Different technologies such as chemical precipitation, coagulation, ion exchange,
electrochemical reduction and membrane separation are applied to control the copper pollution.
However, these processes have their own inherent limitations including secondary pollution,
high energy requirement, incomplete metal removal, high capital and operational costs (Hu et al.
2015; Ucun et al. 2009). Biosorption is one of the most promising alternative technologies
involved in the removal of copper ions from industrial waste streams. As a subcategory of
adsorption and a potential remediation technique, biosorption is the process of binding pollutants
on the surface of biological material due to the presence of characteristic functional groups and is
a cost effective strategy that uses readily available biomass from nature (Areco and dos Santos
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Afonso 2010; Podstawczyk et al. 2015). The natural biomaterials and certain biological waste
products from industrial and agricultural operations are economic and ecofriendly as they are
available in abundance, renewable, non-toxic and low in cost. The major advantages of
biosorption by these biosorbents for water pollution control are less investment in terms of both
initial cost and operational cost, simple design, easy operation and negligible effect of toxic
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substances (Morosanu et al. 2016; Yargıç et al. 2015).
Seaweeds are multicellular macroscopic algae, attached to the bottom in relatively shallow
coastal water. They grow in the intertidal, shallow and deep sea areas up to 180 m depth and also
in estuaries, backwaters on the solid substrate such as rocks, dead corals, pebbles, shells and
other plant materials (Vijayan et al. 2016). They play very important roles in sustaining the
biodiversity and ecological functions in marine ecosystems (Yang et al. 2015b). Marine
macroalgae possess various bioactive compounds such as polysaccharides, proteins, lipids,
polyphenols, carotenoids and vitamins. These phycochemicals have different functional groups
including carboxyl, hydroxyl, phosphate and amine that can bind the heavy metal ions (Areco
and dos Santos Afonso 2010; Sanjeewa et al. 2016). Annual primary production rates of
macroalgae are very high when compared to other biological groups. Every year about 8 million
tons of wet seaweeds are being produced along the coastal regions worldwide. This biomass of
macroalgae has important advantages due to its high capacity of heavy metal uptake and
macroscopic structure (Areco and dos Santos Afonso 2010; Vijayan et al. 2016).
Turkey is surrounded by seas on the three sides, by Black Sea in the north, the Mediterranean
Sea in the south and the Aegean Sea in the west. In the northwest, there is also an internal sea,
the Sea of Marmara, between the straits of the Dardanelles and the Bosporus, which are
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important waterways that connect the Black Sea with the rest of the world. The coastline of
Turkey is 8333 kilometers. Numerous species of seaweeds belonging to green algae, brown algae
and red algae groups exist in these coastal regions of Turkey. Most of seaweeds biomass are
collected in the beaches due to eutrophication and harmful effects to the environment and are
treated as a waste. Since macroalgae growth is high in the coastal areas of Turkey, their waste
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biomass can be economically utilized for the biosorption of heavy metals from aqueous
environment. On the other hand, previous studies emphasized on the removal of metal pollutants
focusing on just one seaweed species (Anastopoulos and Kyzas 2015; Zeraatkar et al. 2016).
Very little attention has been paid to the co-utilization of marine macroalgae that aimed to make
a more effective biosorbent and save our environment from their detrimental effects. Thus, for
the first time in the literature, a coastal seaweed community composed of Chaetomorpha sp.,
Polysiphonia sp., Ulva sp. and Cystoseira sp. that inhabit the coast of Black Sea in Sinop
(Turkey) was applied to remove copper ions from synthetic aqueous solution in the present
study. The biosorption studies were performed under different experimental conditions in order
to optimize the efficiency of bioremediation process in batch system. The copper removal
behavior of biosorbent by biosorption was described by using the related equilibrium, kinetic and
thermodynamic models.
2. Materials and methods
2.1. Preparation of biosorbent material
The seaweeds, Chaetomorpha sp. (green algae), Polysiphonia sp. (red algae), Ulva sp. (green
algae) and Cystoseira sp. (brown algae), were collected from Black Sea coast in Sinop, Turkey.
The harvested fresh biomasses were washed with tap water, followed by several washings with
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distilled water to remove extraneous materials. They were dried at 80 °C in an oven until a
constant weight was achieved. The dried macroalgae biomasses were crushed in a laboratory
blender and sieved through a 0.5 mm (pore size) standard sieve. For the pretreatment of these
biomaterials, the samples of 1 g of biomasses were treated with 0.3 M solution of NaOH (100
mL) for 24 h under slow stirring. The pretreated biomasses were washed several times with
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distilled water to remove excess chemical substance and then dried as mentioned above. The
obtained final products were thoroughly mixed in equal weight ratios and kept in a glass bottle
for the biosorption studies.
2.2. Preparation of copper solution
All chemicals used were of analytical reagent grade and were purchased from Sigma-Aldrich.
A stock solution of copper (1 g L-1) was prepared from Cu(SO4).5H2O dissolution in distilled
water. Necessary dilutions were made from the stock solution to prepare the working solutions in
the concentration range of 10-30 mg L-1. The initial pH of each solution was adjusted to the
desired value with HCl and NaOH solutions (0.1 mol L-1) before mixing the biosorbent.
2.3. Biosorption studies
The biosorption experiments in batch mode were implemented in a set of conical flasks
containing 100 mL of copper solutions to observe the effects of pH (2-6), biosorbent dosage (1030 mg), initial metal ion concentration (10-30 mg L-1) and contact time (0-120 min) on the
pollutant removal. The flasks were agitated on an orbital shaker at 200 rpm and 28 °C for the
desired contact times. After the experiments, the suspensions were centrifuged to remove the
biomasses from the biosorption media and then the residual copper ion concentrations in the
solutions were analyzed.
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2.4. Heavy metal quantification
The concentration of copper element remaining in the biosorption medium was measured by
Inductively Coupled Plasma-Mass Spectrometer (ICP-MS, Thermo X Series II). The amount of
copper ion bound by the composite biosorbent (q, mg g-1) was calculated using the following
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mass balance equations (Farooq et al. 2010):
qt 
qe 
 C0  Ct V
M
 C0  Ce V
M
…………………….. (1)
…………………….. (2)
where C0, Ct and Ce (mg L-1) are the concentrations of heavy metal at the initial, a time t and
equilibrium, respectively. V (L) is the volume of aqueous solution and M (g) is the mass of
biosorbent. q is equal to qt and qe at a time t and equilibrium, respectively.
2.5. Process modeling
The biosorption isotherms and kinetics for the copper removal process were investigated at
the determined optimal environmental conditions for various ion concentrations in the range of
10-30 mg L-1. The biosorption equilibrium data were modeled by using Freundlich (Freundlich
1906), Langmuir (Langmuir 1918), Sips (Sips 1948) and Dubinin-Radushkevich (Dubinin and
Radushkevich 1947) isotherm models. The pseudo-first-order (Lagergren 1898), pseudo-secondorder (Ho 2006), Elovich (Chien and Clayton 1980) and intra-particle diffusion (Weber and
Morris 1963) equations were used to model the experimental data of copper removal kinetics.
The equations of all used models are presented in Table 1. The isotherm and kinetic parameters
were obtained by using the nonlinear curve fitting tool of SigmaPlot software package. The
determination coefficient (R2) and root mean square error (RMSE) analysis methods were used
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to determine the best fit models. A high R2 and low RMSE values indicate the closer agreement
of model with the experimental data.
2.6. Characterization techniques
The surface morphological features of unloaded and copper loaded biosorbent samples were
visualized by using a Scanning Electron Microscope (SEM, Zeiss Evo Ls 10) with an
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accelerating voltage of 5 kV. The infrared spectral studies were performed to determine the
major functional groups involved in the biosorption of copper ions. The spectra of raw and heavy
metal treated biosorbents were obtained by Fourier Transform Infrared Spectroscopy (FTIR,
PerkinElmer Spectrum 400) using KBr pellets method in the wavelength range of 4000-400 cm-1.
3. Results and discussion
3.1. Optimization of environmental conditions
The biosorption of heavy metal ions from aqueous medium is influenced by various
environmental factors. Thus, the effects of operating variables including pH, biosorbent amount,
initial metal ion concentration and contact time on the copper biosorption by the biosorbent were
investigated in the first step of the study.
3.1.1. Effect of pH
The ambient pH affects the heavy metal binding sites on the biosorbent surface and the ion
chemistry in solution. In the present study, to avoid the precipitation of copper metal, the effect
of pH on the copper ion biosorption capacity of the composite biosorbent was studied at initial
pH values ranging from 2 to 6 and the response plot is shown in Fig. 1(a). The maximum ion
uptake was obtained at pH 6. At low values of pH, the hydrogen ions competitively inhibited the
binding of cationic copper ions on the functional sites of the biosorbent. In contrast, at higher pH
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values, the repulsive forces between the hydrogen ions and the heavy metal cations decreased
and this enhanced the copper ion removal by the biosorbent. A similar pH trend was reported for
the biosorption of various heavy metal species onto the different biomasses of macroalgae by
other authors (Anastopoulos and Kyzas 2015; Zeraatkar et al. 2016).
3.1.2. Effect of biosorbent quantity
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The biosorption capacity of biosorbent material for a certain initial metal ion concentration is
defined by biosorbent dosage. The effect of composite biosorbent amount on the copper uptake
was examined by changing biosorbent quantity at the range from 10 to 30 mg as illustrated in
Fig. 1(b). The biosorption capacity decreased with increasing amount of the biosorbent. This
decrease in the copper removal potential of biosorbent was mainly due to the biosorption sites
remaining unsaturated during the biosorption reaction. Another reason might be the aggregation
of biosorbent particles at higher biosorbent amount. Such an aggregation results in a decrease in
the effective surface area of biosorbent for the biosorption of copper ions and an increase in
diffusional path length (Özer et al. 2009; Yargıç et al. 2015). Therefore, the optimum biosorbent
amount was selected as 10 mg for the copper removal in this study.
3.1.3. Effect of initial copper ion concentration
The biosorption of heavy metals is particularly dependent on the initial concentration of ions
in the solution phase. Fig. 1(c) indicates the effect of initial copper concentration on the
biosorption capacity of biosorbent for the metal concentration range of 10-30 mg L-1. It was clear
that the copper ion removal potential of biosorbent increased as the initial metal ion
concentration increased. The observed results could be explained by the enhancement in the
motivating gradient force dependent on the initial copper ion concentration. An increase of the
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heavy metal concentration for a specific amount of biosorbent makes a greater driving force to
transport the metal ions from the biosorption medium to the biosorbent surface which leads to
increase the biosorption capacity of biosorbent (Morosanu et al. 2016; Yargıç et al. 2015).
Consequently, the copper ion dosage of 30 mg L-1 was selected for further investigations.
3.1.4. Effect of contact time
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The contact time is one of the most important operating parameters influencing the
biosorption process. The effect of contact time on the copper biosorption onto the composite
biosorbent was studied for the time range varying from 0 to 120 min and the obtained results are
depicted in Fig. 1(d). The copper uptake capacity of biosorbent increased with increasing contact
time. A contact time of 120 min was found to be sufficient to achieve the biosorption equilibrium
and after this equilibrium period, the biosorption amount did not significantly change with
further time. This behavior was due to the fact that initially a large number of vacant active
binding sites on the biosorbent surface were available for the biosorption of copper ions and the
heavy metal concentration was high. After a certain contact time period, the binding sites were
occupied by the metal ions, the biosorbent surface became saturated and the ion concentration
gradient between the aqueous phase and the solid phase decreased. All these slowed down the
copper biosorption rate. Similar observation was reported in many literature studies (Akbari et al.
2015; Hu et al. 2015; Verma et al. 2016).
3.2. Modeling studies of biosorption process
In order to achieve the maximum biosorption performance of biosorbent for the removal of
copper ions, the optimum values of environmental variables like pH, biosorbent amount, initial
metal ion concentration and contact time were discussed in the previous section of the present
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study. The optimum operating conditions were determined as pH of 6, biosorbent quantity of 10
mg, initial metal ion concentration of 30 mg L-1 and contact time of 120 min for efficient copper
ion biosorption. In this step, the equilibrium and kinetics of biosorption process were studied at
these optimum environmental conditions for different initial concentrations of copper metal and
the obtained biosorption data were modeled by various isotherm and kinetic equations.
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3.2.1. Biosorption isotherm modeling
The biosorption isotherms explain the distribution relation of heavy metal ions in the aqueous
solution and on the biosorbent material at the equilibrium condition and constant temperature,
and also describe the nature of interaction between the pollutant ions and the biosorbent. They
are very important for the design of biosorption system, definitions of biosorption process
mechanism and surface characteristics of biosorbent and evaluation of biosorption capacities of
different biosorbents for heavy metal ions. Thus, experimentally obtained equilibrium data for
the removal of copper by the composite biosorbent were modeled by using Freundlich
(Freundlich 1906), Langmuir (Langmuir 1918), Sips (Sips 1948) and Dubinin-Radushkevich
(Dubinin and Radushkevich 1947) isotherm models. The equations of these models and the
description of their parameters are listed in Table 1. The nonlinear curve fitting tool of SigmaPlot
software was used to determine the parameters of used models. The best fit isotherm equation
was chosen on the basis of R2 and RMSE analysis values for the equilibrium models.
To mention briefly these isotherm models, Freundlich model is entirely an empirical equation,
and according to this model, the surface heterogeneity of biosorbent and multilayer biosorption
of metal ions onto this heterogeneous surface are considered. Langmuir model assumes that the
biosorption takes place onto the homogeneous surface of biosorbent and a monolayer biosorption
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occurs on the biosorbent surface containing finite number of identical binding sites. This
isotherm model is widely applied to evaluate the maximum biosorption capacity of biosorbent
and describe the biosorption process in aspect of thermodynamics. Sips isotherm equation is a
combination of Langmuir and Freundlich models and can be more competent to describe the
biosorption equilibrium. On the other hand, Dubinin-Radushkevich equation is an empirical
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isotherm model and extensively used for the estimation of the nature of biosorption process
through Gaussian energy distribution onto the heterogeneous surface of biosorbent.
Table 2 shows the parameters and statistical analysis results for all the isotherm models.
Besides, the comparison of these models with the experimental data of biosorption is illustrated
in Fig. 2(a). From the analysis values of R2 and RMSE of the isotherm models, it was noted that
the biosorption equilibrium data for the biosorption of copper ions onto the biosorbent was best
modeled by Sips equation. As mentioned above, this isotherm model incorporates the features of
both Langmuir and Freundlich isotherm models. The nS exponential constant of Sips model
shows the heterogeneity degree of biosorbent binding surface. For the present study, the value of
nS parameter was found to be close to unity, which indicates that the copper biosorption occurred
on the homogeneous surface of composite biosorbent in monolayer form. The statistical data
obtained for Langmuir and Freundlich isotherm models also confirmed this finding. Similar
results were reported by various literature studies (Amirnia et al. 2016; Oubagaranadin and
Murthy 2010).
A dimensionless constant, RL can be derived based on Langmuir isotherm model. This
constant is usually named as equilibrium parameter or separation factor and can be calculated
from the following equation (Yang et al. 2015a):
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RL 
1
…………………….. (3)
1  K L C0
where C0 (mg L-1) is the initial concentration of heavy metal ions and KL (L mg-1) is Langmuir
equilibrium constant related to the biosorption energy. RL displays the nature of biosorption
process which can be unfavorable (RL > 1), linear (RL = 1), favorable (0 < RL < 1) or irreversible
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(RL = 0). The values of RL for this study were obtained between 0.76 and 0. 90, which show a
favorable biosorption system. For the removal of copper ions from aqueous solution, the
suitability of this biosorption system was also verified by the value of nF constant obtained from
Freundlich isotherm model which was calculated between 0 and 10.
In order to determine the physical or chemical nature of the heavy metal biosorption process,
the mean free energy of biosorption, E (kJ mol-1) can be computed from Dubinin-Radushkevich
isotherm model by using the following equation (Areco and dos Santos Afonso 2010):
E
1
…………………….. (4)
2B
where B (mol2 kJ-2) is a constant related to the biosorption energy. A value of E below 8 kJ mol-1
shows physical biosorption while a value between 8 and 16 kJ mol-1 indicates chemical
biosorption. The value of E for the biosorption of copper ions onto the biosorbent was found to
be 0.26 kJ mol-1. This energy value suggests that a physical interaction occurred between the
active sites of biosorbent surface and copper ions. Besides, the standard Gibbs free energy
change (ΔG°) was evaluated to confirm the biosorption reaction nature and also assess the
thermodynamic feasibility of biotreatment process according to the following equation (Gholami
et al. 2016):
G  RT ln KD …………………….. (5)
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where R (J mol-1 K-1) is the universal gas constant and T (K) is the absolute temperature. KD
(Cs/Ce) is the distribution coefficient. Cs and Ce (mg L-1) are the equilibrium metal ion
concentrations on the biosorbent and in the biosorption medium, respectively. The values of ΔG°
were found between -5.64 and -5.81 kJ mol-1 for the removal of copper ions. These negative
ΔG° values indicate the feasibility and spontaneity of the copper ion biosorption. Furthermore,
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the value of ΔG° in the range of 0 to -20 kJ mol-1 is typical for the physical biosorption while for
the chemical biosorption, it is in the range of -80 to -400 kJ mol-1. Thus, it could be concluded
that the biosorption reaction of copper metal ions involved physical interactions.
3.2.2. Biosorption kinetic modeling
The kinetic study of heavy metal biosorption provides valuable data on the rapidity of
biosorption, process mechanism and potential rate controlling step(s). The kinetics of biosorption
is very important for the design of biosorption system and the prediction of optimum operating
conditions in real-scale biosorption process. Thus, the pseudo-first-order (Lagergren 1898),
pseudo-second-order (Ho 2006), Elovich (Chien and Clayton 1980) and intra-particle diffusion
(Weber and Morris 1963) equations were used to model the kinetics of copper ion biosorption
onto the composite biosorbent.
According to the pseudo-first-order kinetic model, the rate of occupation of biosorption sites
by the heavy metal ions is proportional to the number of vacant binding sites on the biosorbent.
In the pseudo-second-order model, this rate is proportional to the square of the number of empty
active sites on the biosorbent. Elovich model is another rate equation based on the biosorption
capacity and describes the kinetics of chemical biosorption onto the heterogeneous biosorbent.
All these models are reaction based kinetic equations which only explain the interaction of active
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sites of biosorbent material with heavy metal ions. However, the biosorption of metal ions from
aqueous solution onto the biosorbent involves several stages (Hu et al. 2015; Özer et al. 2009):
(1) the movement of heavy metal ions from the bulk liquid to the boundary layer (liquid film)
surrounding the biosorbent particles (bulk diffusion),
(2) the transfer of ions from the liquid film to the external surface of biosorbent (external or film
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diffusion)
(3) the diffusion of metal ions from the biosorbent surface to the pores of biosorbent material
(pore or intra-particle diffusion)
(4) the biosorption reaction on the pore surface sites of biosorbent (ion biosorption).
The first stage can be neglected under sufficient agitation condition. The last step is a very rapid
process and also this stage can be ignored. Thus, in order to determine the potential contribution
of remaining biosorption steps to the copper ion biosorption process, Weber and Morris diffusion
based kinetic model (intra-particle diffusion) was also used in this study. The equations of used
kinetic models and the description of their parameters are presented in Table 1. In order to
determine the parameters of kinetic models, again the nonlinear curve fitting tool of SigmaPlot
software was used. The best fit kinetic equation was selected on the basis of R2 and RMSE
analysis values of used kinetic models. The values of kinetic parameters and the statistical data
are given in Table 2. Furthermore, the plots of all kinetic models are depicted in Fig. 2(b) and
(c). According to the statistical results, it could be concluded that the pseudo-second-order model
was more suitable to describe the kinetic behavior of the copper biosorption process. This
finding showed that the biosorption of copper ions onto the biosorbent was likely based on the
chemical surface reaction. On the other hand, the high R2 and low RMSE values for the pseudo-
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first-order kinetic model indicated that the physical interactions might also play a role in the
surface reaction. The pseudo-second-order model was successfully applied for the prediction of
biosorption behaviors of various metal ions onto different biosorbents (Akbari et al. 2015; Areco
and dos Santos Afonso 2010; Hu et al. 2015; Yargıç et al. 2015).
As shown in Fig. 2(c), the intra-particle diffusion plot for the removal of copper ions from
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aqueous medium by biosorption process possessed a few linear regions. The initial segment is
attributed to the external surface biosorption. The second region is related to the pore diffusion of
metal ions and the last portion is correspond to the biosorption equilibrium stage. Thus, different
mechanisms like the external diffusion and intra-particle diffusion might play a role in the
control of the biosorption rate of copper ions. Similar kinetic characteristics for the biosorption
of various heavy metals were reported in literature (Areco and dos Santos Afonso 2010; Tabaraki
et al. 2014).
3.3. Characterization of biosorbent material
Scanning Electron Microscope (SEM) was used for the visualization of physical surface
morphology of the composite biosorbent. Fig. 3 shows the surface properties of biosorbent
before (a) and after (c) the biosorption of copper ions. The textural structure of biosorbent was
irregular and rough. This structure provides a larger biosorption surface for the biosorption of
copper ions. After the binding of metal cations, the surface of biosorbent became dull because of
the coating of biosorbent particles with the copper ions. Besides, based on the monolayer
biosorption capacity, the specific surface area of biosorbent (S, m2 g-1) can be estimated
according to the following equation (Ho et al. 2002; Sarat Chandra et al. 2015):
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S
qm N A A
…………………….. (6)
Mw
where qm (mg g-1) is the maximum metal ion biosorption capacity of biosorbent and NA
(molecules mol-1) is the number of Avogadro (6.02 × 1023). A (m2) and Mw (g mol-1) are the
cross sectional area and the molecular weight of heavy metal ion, respectively. The values of the
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cross sectional area and molecular mass for copper metal are 1.58 Å2 and 63.55 g mol-1,
respectively (Ho et al. 2002). Thus, the specific surface area of composite biosorbent was
calculated as 26.99 m2 g-1. The obtained value was higher than those of various other biosorbents
(Murphy et al. 2009; Özer et al. 2009; Yang et al. 2015a).
The study of Fourier Transform Infrared Spectroscopy (FTIR) was performed to identify the
functional groups of biosorbent material and their potential interaction with copper ions. The
spectra of FTIR for the biosorbent before (b) and after (d) the copper biosorption are presented in
Fig. 3. The spectral analysis indicated that many active groups especially observed at the band
positions of 3335.11, 2913.99, 1621.61, 1543.53, 1423.53, 1234.06 and 822.48 cm-1 might
contribute the biosorption of copper cations. These peaks correspond to O-H, C-H, C=O, C=C
and C-O functional groups (Legan et al. 2016; Mwangi and Ngila 2012; Verma et al. 2016).
3.4. Comparison study
Up to present, numerous studies on the biosorption of copper metal from aqueous
environment have been reported. The maximum biosorption capacities of different biosorbents
for copper ions are presented in Table 3. As can be seen, the maximum copper ion uptake
capacity of composite biosorbent was higher than those of many other biosorbents. This
comparison indicated the great potential of biosorbent for the bioremoval of copper ions from
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aqueous solution. The differences in the metal ion biosorption capacities of biosorbents might be
due to the properties of biosorbent materials and different operational conditions. On the other
hand, the integration of this bioremediation system with biofuel production from the seaweed
community will makes the macroalgae biomass an extremely promising candidate for sustainable
waste management and renewable energy production.
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4. Conclusions
A batch experimental study for the removal of copper ions from synthetic aqueous solution by
the biosorbent composed of four different seaweed biomasses was first presented in this paper.
The biosorption of copper ions was strongly influenced by the operating parameters such as pH,
biosorbent quantity, copper cation concentration and contact time. The experimental data of
copper biotreatment system were successfully modeled by the pseudo-second-order and Sips
equations. The maximum biosorption capacity of biosorbent for copper ions was obtained as
180.36 mg g-1. These findings showed that the coastal seaweed community has great
bioremediation potential for water environments polluted with copper ions.
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Table 1. Models of biosorption isotherm and kinetic.
Model type
Equation
Parameter description
Freundlich
qe  K FCe1/ nF
qe (mg g-1) is the metal ion biosorption capacity of
biosorbent at the equilibrium. Ce (mg L-1) is the metal
ion concentration at equilibrium. KF (mg g-1 (L mg1 1/n
) F) and nF (-) are the isotherm constants related to the
biosorption capacity and intensity, respectively.
Langmuir
qe 
qm K LCe
1+K LCe
qm (mg g-1) is the maximum metal ion biosorption
capacity of biosorbent. KL (L mg-1) is the equilibrium
constant related to the biosorption energy.
Sips
q ( K C )1/ nS
qe  m S e 1/ nS
1+( KSCe )
KS (L mg-1)1/nS is the equilibrium constant and nS (-) is
the model exponent.
DubininRadushkevich
qe  qm e B
B (mol2 kJ-2) is a constant related to the mean free
energy of biosorption. Ɛ is Polanyi potential which is
equal to RT ln (1 + (1/Ce)). R (J mol-1 K-1) is the
universal gas constant and T (K) is the absolute
temperature.
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Isotherm models
2
Kinetic models
Pseudo-firstorder
qt  qe (1  e k1t )
qt and qe (mg g-1) are the biosorption capacity of heavy
metal at a time t and the equilibrium, respectively. k1
(min-1) is the biosorption rate constant.
Pseudo-secondorder
k q 2t
qt  2 e
1  k2 qet
k2 (g mg-1 min-1) is the rate constant of biosorption.
Elovich model
1
qt  ln(1+ t )

α (mg g-1 min-1) is the initial biosorption rate and β (g
mg-1) is the desorption constant.
Intra-particle
diffusion
qt  kp t1/2  C
kp (mg g-1 min-1/2) is the rate constant and C (mg g-1) is a
constant providing information about the thickness of
boundary layer.
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Table 2. Modeling data of biosorption equilibrium and kinetic.
Isotherm Models
Model
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Parameter
Freundlich
Langmuir
Sips
Dubinin-Radushkevich
KF (mg g1 (L mg1)1/nF)
12.78
qm (mg g1)
181.11
qm (mg g1)
180.36
qm (mg g1)
182.69
nF (-)
1.11
KL (L mg1)
0.01
KS (L mg1)1/nS
0.06
E (kJ mol1)
0.26
R2
0.995
R2
0.997
nS (-)
0.72
R2
0.952
RMSE
5.46
RMSE
4.32
R2
1.000
RMSE
16.95
RMSE
-
Kinetic Models
Model
Pseudo-first-order
Pseudo-second-order
Parameter
qe (mg g1)
115.45
qe (mg g1)
151.76
α (mg g1 min1)
23.19
C (mg g1)
4.91
k1 (min1)
0.03
k2 (g mg1 min1)
0.0002
β (g mg1)
0.04
kp (mg g1 min1/2)
11.51
R2
0.996
R2
0.998
R2
0.917
R2
0.979
RMSE
2.61
RMSE
2.01
RMSE
11.52
RMSE
5.80
24
Elovich
Intra-particle diffusion
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Table 3. Comparison of copper ion biosorption capacities of different biosorbents.
Biosorbent material
Maximum biosorption (mg g-1)
Reference
Enteromorpha prolifera
57.14
(Özer et al. 2009)
Chlorella minutissima
16.16
(Yang et al. 2015a)
Caulerpa serrulata
5.27
(Mwangi and Ngila 2012)
Posidonia oceanica
85.78
(Izquierdo et al. 2010)
Tomato waste
34.48
(Yargıç et al. 2015)
Acidosasa edulis shoot shell
2.51
(Hu et al. 2015)
Acer saccharum leaves
126.21
(Amirnia et al. 2016)
Pine cone powder
26.32
(Ofomaja et al. 2010)
Composite seaweed biomass
180.36
This study
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(a)
(b)
(c)
(d)
Fig. 1. Effects of pH (a), biosorbent amount (b), cation concentration (c) and contact time (d).
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(a)
(b)
(c)
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Fig. 2. Curves of isotherm (a), kinetic (b) and pore diffusion models (c).
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(a)
(b)
(c)
(d)
Fig. 3. SEM images and FTIR spectra of biosorbent before (a, c) and after (b, d) copper
biosorption.
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