investigation of pb(ii) adsorption onto pumice samples: application of optimization method based on...
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ORIGINAL PAPER
Investigation of Pb(II) adsorption onto pumice samples:application of optimization method based on fractional factorialdesign and response surface methodology
Tekin Sahan • Dilara Ozturk
Received: 21 May 2013 / Accepted: 12 September 2013 / Published online: 20 September 2013
� Springer-Verlag Berlin Heidelberg 2013
Abstract The adsorption of Pb(II) by pumice samples
collected from the Mount Ararat region, located in eastern
Turkey, was investigated in a batch system. The combined
and individual effects of operating parameters on adsorp-
tion were analyzed using a multi-step response surface
methodology. In the first step the most effective factors,
which are initial Pb(II) concentration, pH, and temperature,
were determined via fractional factorial design. Then the
steepest ascent/descent followed by central composite
design were used to interpret the optimum adsorption
conditions for the highest Pb(II) removal. The optimum
adsorption conditions were determined to be initial Pb(II)
concentration of 84.30 mg/L, pH of 5.75, and temperature
of 41.11 �C. At optimum conditions, the adsorption
capacity of pumice for Pb(II) was found to be 7.46 mg/g
according to a removal yield of 88.49 %. The obtained data
agreed with a second-order rate expression and fit the
Langmuir isotherm very well. The thermodynamic
parameters such as DH�, DS�, and DG� for the Pb(II)
adsorption were calculated at four different temperatures.
The present results indicate that pumice is a suitable
adsorbent material for adsorption of Pb(II) from aqueous
solutions.
Keywords Adsorption � Fractional factorial design �Isotherm � Optimization � Pumice � Response surface
methodology
Introduction
Being one of the important pollutants in wastewater, heavy
metals are a significant public health problem because they
are non-biodegradable and have a persistent nature. They
can accumulate through the food chain even at low con-
centrations, leading to a threat to aquatic life as well as to
animal and plant life and human health. Lead (Pb) is
among the potentially toxic heavy metals. Major sources
releasing Pb are industrial, such as Pb–acid batteries,
paints, wood production, fossil fuels, cables, plastics, pulp
industry, electroplating, and fertilizer industries. Lead
contamination is also due to effluents from vehicular traffic
and the mixing of roadside run-off (Sarı and Tuzen 2009).
The presence of Pb in drinking water, even in low
concentrations, may cause diseases such as anemia, hepa-
titis, nephrite syndrome, etc. (Lo et al. 1999). The biggest
issue with Pb, regarding human health, is long-term
chronic exposure causing developmental problems leading
to lower intelligence, lower earning potential, and lower
positive contributions to society. Severe exposure to Pb has
been associated with sterility, abortion, stillbirths, and neo-
natal deaths (Goel et al. 2005).
A number of processes exist for the removal of metal
pollutants, including chemical precipitation, ion exchange,
solvent extraction, ultrafiltration, reverse osmosis, electro-
dialysis, and adsorption. Of these, significant advantages of
adsorption are the regeneration of adsorbents, very effective
removal, high selectivity, lower operating costs, and envi-
ronmentally-friendly processes for wastewater treatment.
T. Sahan (&)
Department of Chemical Engineering, Faculty of Engineering
and Architecture, Yuzuncu Yil University, Campus,
65080 Van, Turkey
e-mail: [email protected]
D. Ozturk
Department of Environmental Engineering,
Faculty of Engineering and Architecture, Yuzuncu Yil
University, Campus, 65080 Van, Turkey
123
Clean Techn Environ Policy (2014) 16:819–831
DOI 10.1007/s10098-013-0673-8
In recent years, various adsorbents have been used for the
removal of Pb(II). A wide range of adsorbents, including
sawdust and modified peanut husk (Li et al. 2007), rice straw
fibers, cellulose derived from rice straw, and nanocellulose
fibers (Kardam et al. 2013), waste calcite sludge (Merrikh-
pour and Jalali 2012), Celtek clay (Sarı et al. 2007a), China
clay and wollastonite (Yadava et al. 1991), bentonite modi-
fied by 4-methylbenzo-15-crown-5 (Liu et al. 2006), olive
cake (Doyurum and Celik 2006), activated carbon prepared
from coconut shells (Sekar et al. 2004), siderite (Erdem and
Ozverdi 2005), montmorillonite, kaolin, tobermorite, mag-
netite, silica gel, and alumina (Katsumata et al. 2003), maize
bran (Singh et al. 2006), and zeolitic tuff (Karatas 2012),
have been studied for Pb(II) removal from aqueous systems.
However, new economic adsorbents, which are locally
available and have high adsorption capacity, are still needed.
Pumice is a light, porous igneous volcanic rock pro-
duced by the release of gases during the solidification of
lava (Hara et al. 1979; Panuccio et al. 2009). It has high
silica content (70.90 % SiO2). Silica gives durability to the
material, so it is suitable as an adsorbent against aggressive
external factors.
For adsorption of heavy metals from aqueous solutions,
traditional methods can be used but these methods include
changing one independent variable parameter, such as tem-
perature, pH, or heavy metal concentration, while maintaining
all others at a constant level. In this way, traditional methods
cause extra chemical consumption for each parameter, taking
excessive time and human power requirements. There are
many statistical programs to solve this problem. Among them,
response surface methodology (RSM) has been commonly
used in recent years (Sahan et al. 2010; Su et al. 2009; Yetil-
mezsoy et al. 2009). RSM is a collection of statistical and
mathematical techniques available for designing experiments,
building models, and analyzing the effects of several inde-
pendent variables. The main advantage of RSM is the
decreased number of experimental trials required to interpret
multiple parameters and their interactions. In order to deter-
mine a suitable polynomial equation for describing the
response surface, RSM can be employed to optimize the
process (Myers and Montgomery 2002).
To determine the optimum value and understand the
effect of one parameter in adsorption studies using tradi-
tional methods, the experimenter has to perform many
experiments by keeping other parameters constant at their
optimum values. If considered for each parameter in our
study, approximately two hundred experiments must be
carried out. However, in our study, using RSM only fifty
experiments were required.
The present work focuses on the removal of Pb(II) by
pumice samples collected at the foot of Mount Ararat in
batch experiments, and also determines the effect of
operating parameters (C0, pH, temperature, etc.) on the
adsorption of Pb(II) by RSM. In the first step, the most
significant parameters were evaluated by means of frac-
tional factorial design (FFD) and afterward the steepest
ascent/descent was utilized to determine the proximate
point to optimum, and central composite design (CCD) was
applied to locate the optimum points.
Our study differs from other studies in literature.
Although the experimental method used in this study is the
conventional batch method, pumice collected from the
Mount Ararat region located in eastern Turkey was first used
for heavy metal removal from an aqueous environment. In
addition, RSM as a statistical method, which we used to
optimize the adsorption conditions, has many advantages in
terms of cost and time, both increasingly important in recent
times. Moreover, we used RSM consisting of three steps
including FFD to determine the most effective parameters,
the steepest ascent to predict optimum vicinity, and CCD for
optimization. However, other adsorption studies in the lit-
erature used only RSM with a single step (Rashid et al. 2013;
Sugashini and Meera Sheriffa Begum 2013; Yetilmezsoy
et al. 2009; Zulkali et al. 2006).
Materials and methods
Adsorbent
Naturally obtained pumice samples were washed with
distilled water several times to remove impurities. Samples
were then dried in a drying oven at 100 �C for 24 h.
Afterward they were ground with a mill and sieved to
obtain the desired particle size and then stored in desic-
cators for further utilization. Native pH of the pumice was
9.01, determined by mixing the material in water with a
ratio of 1:2 and measuring pH with an electrode. The pore
volume, pore radius, and Bruner–Emmet–Teller (BET)
surface area for pumice samples were 0.007976 cm3/g,
185.86 A, and 2.33 m2/g, respectively. BET analyses were
made by Quanta Chrome Automated Pore-size Analyzer,
USA. Energy-dispersive X-ray spectroscopy (EDX) ana-
lysis (Oxford-X-Supreme EDX-Analyzer, UK) was per-
formed to determine the elemental content of pumice.
According to the results, the pumice used contained; SiO2:
70.90 %; Al2O3: 16.70 %; MgO: 1.4 %; Na2O: 3.9 %;
CaO: 5.20 %; K2O: 2.00 %.
X-ray diffraction (XRD) analysis of pumice was con-
ducted with a Philips—PW 3710 diffractometer with Cu–
Ka radiation. The results are shown in Fig. 1. For XRD
analysis, pumice samples were prepared in four different
ways: (a) random powder sample (2–70 2h range), (b) clay
fraction slide heated at 300 �C, (c) clay fraction slide
treated with ethylene glycol, and (d) air-dried oriented clay
fraction slide. The samples were dispersed in distilled
820 T. Sahan, D. Ozturk
123
water to enable clay fraction separation. Clay fraction
(\2 lm) was obtained from the clay/silt fraction suspen-
sion by sedimentation according to Stoke’s law and then
oriented specimens were prepared by pipetting a small
amount of the clay–water suspension onto a glass slide and
leaving to dry at room temperature. Diffraction patterns
were obtained from oriented mounts (after air-drying,
glycol saturation, and heating to 300 �C) in the 2–40� 2hrange, using a 1/2� divergence slit.
As can be seen from Fig. 1, XRD spectrograms show
that pumice consists mainly of silica glass (SiO2 %) with
smaller amounts of Al2O3, MgO, Na2O, CaO, and K2O
supported by EDX. In addition, since the background line
rose in the 20�–30� range and no characteristic peaks are
observed on the XRD curves, this finding points to pure
silica glass. As a result, the structure of the spectrograms,
shown in Fig. 1a–d, indicates that pumice consists of pure
glass material. Also, it can be seen that there was no clay
residue in pumice (Ersoy et al. 2010).
Pb(II) solutions
The Pb(II) stock solution (1,000 mg/L) was prepared by
dissolving Pb(NO3)2 (purity C99, Sigma-Aldrich) in dou-
ble-distilled water. The required dilutions were made from
the stock solution to prepare solutions in the range of
necessary concentrations. The solutions were adjusted to
the desired pH by adding negligible volumes of 0.10 M
HNO3 or NaOH (C97.0 %, pellets, Sigma-Aldrich) before
mixing the adsorbent into the solution. Experiments were
carried out with pH values up to 6 due to the fact that metal
precipitation occurred at higher pH values.
Batch adsorption experiments
The adsorption of Pb(II) on pumice was investigated by using
batch technique. All experiments were carried out in 250 ml
Erlenmeyer flasks containing 100 ml Pb(II) solution on a
temperature-controlled magnetic stirrer. The concentration of
unadsorbed Pb(II) in residual solutions was determined by a
flame atomic absorption spectrophotometer (THERMO Solar
AA Series spectrometer, USA) at wavelength 217 nm.
The adsorbed Pb(II) amount was calculated according to
the following equation.
qe ¼ðC0 � CeÞV
W; ð1Þ
where C0 and Ce are the initial and equilibrium concen-
trations (mg/L) of Pb(II) solution, respectively; V is the
volume of the medium (L); and W is the weight (g) of the
adsorbent, which was fixed at 10 g/L, used in the reaction
mixture.
Fig. 1 The XRD patterns of
a random powder, b heated at
300 �C of clay fraction,
c treated with ethylene glycol of
clay fraction, and d air-dried
clay fraction
Investigation of Pb(II) adsorption 821
123
After filtration, filter paper and glass were washed with
0.1 M HNO3 several times. The amount of Pb(II) in the
washing solution was determined with AAS and was found
to be lower than the detection limit of the AAS, which is
0.0001 mg L-1. This loss of Pb(II) was, therefore, ignored
in the measurements.
Experimental design and optimization
Parameter evaluation by FFD
FFD is an important statistical method for the efficient
exploration of the effects of several controllable factors on
a response of interest. The most significant adsorption
parameters affecting Pb(II) adsorption by pumice were
determined by FFD. For this purpose, six parameters,
which were C0, pH, temperature, contact time, agitation
speed, and particle size, were chosen. Each parameter was
represented at two levels, high and low, as shown in
Table 1. Design Expert 8.0.7.1 (trial version) was used for
regression analysis of the obtained experimental data. FFD
is expressed using the notation 2k-p, where 2 is the number
of levels of each factor investigated, k is the number of
factors which was 6 in this study, and p describes the size
of the fraction of the full factorial that is determined as 2
and finally the number of center points is selected as 5 to
estimate the experimental error. Consequently, a total of
twenty-one experiments, 26-2 ? 5, were carried out. Each
experiment for FFD was carried out in duplicate and data
presented are the arithmetical average values. The quality
of the fit of the first-order model equation was expressed by
the coefficient R2 and its statistical significance was
determined by an F test (Sahan et al. 2010). The signifi-
cance of the effect of each variable on Pb(II) adsorption
was obtained by a t test (Myers and Montgomery 2002).
Path of the steepest ascent (descent)
After important parameters were determined by FFD, the
next step was to explore the region around the current
operating conditions to decide what direction needed to be
taken to move toward the optimum region. The steepest
ascent was based on the results obtained from the FFD.
The parameters in the steepest ascent experiments move
in the same direction as the coefficients obtained from
FFD. This direction is parallel to the normal of the fitted
response surface (Myers and Montgomery 2002).
CCD
In RSM, CCD is the most popular choice for fitting a
second-order model. After FFD experiments, the number of
most important parameters was set as three (C0, pH, and
temperature). The total number of experiments for three
variables were 20 (=2k ? 2k ? 6), where k is the number
of independent variables. Fourteen experiments were added
to six replications at the center values to estimate the
experimental error (Sahan et al. 2010). The other parame-
ters, contact time, agitation speed, and particle size, were
kept constant at 120 min, 650 rpm, and 120 lm,
respectively.
In the optimization process, the response can be related
to chosen variables by linear or quadratic models. A qua-
dratic model is given in Eq. 2;
yn ¼ bo þX3
i¼1
bixi þX3
i¼1
biix2i þ
X3
i¼1
X3
j¼iþ1
bijxixj; ð2Þ
where yn is the response, b0 is the constant coefficient, xi
(i = 1–3) are non-coded variables, and bi is the linear, bii is
the quadratic, and bij (i and j = 1–3) is the second-order
interaction coefficients. The residuals, en, for each experi-
ment were computed as the difference between yn and yn,
which are the residual of the nth experiment, the observed
response and the predicted response, respectively.
The analysis of variance (ANOVA) data were computed
by Design Expert 8.0.7.1 (trial version) in order to obtain
the interaction between the processed variables and the
response. The quality of the fit of the polynomial model
was expressed by the coefficient of determination (R2) and
the statistical significance was checked by the F test using
the same program.
Determination of maximum points
The second-order model determined from Eq. 2 is adequate
for the optimal points. A general mathematical solution can
be obtained from Eq. 3 for the location of the stationary
point (Myers and Montgomery 2002). Writing the second-
order model in matrix notation, we have;
y ¼ bo þ x0bþ x
0Bxs; ð3Þ
where,
Table 1 Range of parameters studied in the FFD for Pb(II)
adsorption
Parameter no. Parameter Low level
(-1)
High level
(?1)
X1 Initial Pb(II) conc.
(C0, mg/L)
5 100
X2 pH 2 6
X3 Temperature (�C) 25 45
X4 Contact time (min) 10 120
X5 Agitation speed (rpm) 150 650
X6 Particle size (lm) 120 180
822 T. Sahan, D. Ozturk
123
Xs ¼X1
X2
Xk
24
35; b ¼
b1
b2
bk
24
35 and
B ¼b11 b12=2 b1k=2
Sym b22 b2k=2
Sym Sym bkk
24
35:
That is, b is a (kx1) vector of the first-order regression
coefficient and B is a (kxk) symmetric matrix whose main
diagonal elements are the pure quadratic coefficients (bii)
and whose off-diagonal elements are one half of the mixed
quadratic coefficients (bij, i = j). The stationary points
(Xs) are the solution of Eq. 4.
xs ¼ �1
2B�1b: ð4Þ
Results and discussion
Determination of the most effective parameters by FFD
According to FFD, the screening experiments were
designed to interpret the impact of six parameters, C0, pH,
temperature, contact time, particle size, and agitation
speed, while the adsorbed Pb(II) amount was selected as
the response. The levels of the tested parameters and the
observed responses are shown in Table 2. Based on the
statistical analysis of FFD results, C0, pH, and temperature
were determined to be the most important variables
affecting Pb(II) adsorption. The first-order model, Eq. 5,
obtained from FFD experiments represents the effect of the
parameters on the response.
Adsorbed Pb IIð Þ amount mg/gð Þ¼ �6:38þ 0:05 X1½ � þ 1:05 X2½ � þ 0:05 X3½ � þ 4:20E
� 004 X4½ � þ 7:31E� 03 X5½ � þ 1:75E� 05 X6½ �: ð5Þ
As seen in the coefficients from Eq. 5, C0 (X1), pH (X2), and
temperature (X3) are parameters with the most significant
effect on the response due to their high coefficients.
The method of the steepest ascent
Even though FFD is a significant tool for screening the
tested variables affecting the Pb(II) adsorption, it is unable
to predict the optimum vicinity of the variables. In these
cases, the steepest ascent method is useful to identify the
levels closest to the optimum point. Based on the coeffi-
cients in the first-order model equation obtained from
Eq. 5, the path of steepest ascent (descent) was applied to
detect the appropriate direction, increasing or decreasing
important variables determined by FFD according to their
signs in Eq. 5 while all the other variables were constant at
the center level of the FFD. Experimental design and the
Table 2 FFD for determination of the most important variables affecting the Pb(II) adsorption
Run C0 (X1,
mg/L)
pH (X2) Temperature
(X3, �C)
Contact
time (X4, min)
Agitation
speed (X5, rpm)
Particle size
(X6, lm)
Adsorbed amount
(mg/g)
1 52.50 4 35 65 400 150 3.25
2 5.00 6 25 10 650 180 0.29
3 100 2 45 120 150 120 1.50
4 52.50 4 35 65 400 150 3.20
5 5.00 6 45 10 150 120 0.37
6 100 6 25 10 650 120 5.10
7 100 2 45 10 650 120 0.70
8 5.00 2 25 120 650 120 0.00
9 100 6 25 120 150 120 5.70
10 5.00 6 25 120 150 180 0.31
11 5.00 6 45 120 650 120 0.42
12 52.50 4 35 65 400 150 3.51
13 100 2 25 120 650 180 0.11
14 52.50 4 35 65 400 150 3.52
15 100 6 45 120 650 180 6.90
16 5.00 2 25 10 150 120 0.00
17 52.50 4 35 65 400 150 3.30
18 5.00 2 45 120 150 180 1.25
19 100 6 45 10 150 180 6.50
20 5.00 2 45 10 650 180 1.01
21 100 2 25 10 150 180 0.00
Investigation of Pb(II) adsorption 823
123
corresponding responses are shown in Table 3. The
response achieved a maximum in the sixth experiment and
that is indicated in bold in Table 3. According to these
results, intervals of parameters for CCD should be
5.50–6.10 for pH, 77.50–87.50 mg/L for C0, and 39–43 �C
for temperature, respectively.
Optimization with CCD
The neighborhood of the optimum conditions were deter-
mined by the steepest ascent after FFD revealed that C0,
pH, and temperature are the most significant parameters.
CCD experiments for optimization of these parameters
were performed to locate the maximum removal of Pb(II)
by Design Expert 8.0.7.1 (trial version). The five coded
levels of each parameter were signed as -a, -1, 0, ?1, and
?a. Together with six replications conducted at the center
values to evaluate the pure error, fourteen experiments
were completed for optimization. Experimental design of
CCD and responses are shown in Table 4. The model
equation for uncoded (real) values of the quadratic model
fitting experimental results is shown in Eq. 6.
Adsorbed Pb IIð Þ amount mg/gð Þ ¼ �396:966
þ 3:27188 X1½ � þ 64:83068 X2½ � þ 3:89821 X3½ �� 0:1625 X1½ � X2½ � � 5:375E� 03 X1½ � X3½ �þ 0:14375 X2½ � X3½ � � 0:012554 X1½ �2� 4:96034 X2½ �2
� 0:051946 X3½ �2: ð6Þ
The statistical significance of the quadratic model was
evaluated by the analysis of variance (ANOVA) (data not
shown). The value of the determination of coefficient
(R2 = 0.84) indicates that 84 % of the variability in the
response is explained by the model.
Plot of observed removal of Pb(II) versus those obtained
from Eq. 6 is shown in Fig. 2. The figure proves that the
predicted response from the empirical model is in good
agreement with the observed data.
Figure 3 shows the simultaneous effects of temperature
and Co on the Pb(II) adsorption capacity of pumice. As seen
clearly from Fig. 3, temperature has a positive effect on
adsorption of Pb(II). Pb(II) adsorption capacity of pumice
increased with increasing temperature from 39 to 41 �C and
approximately reached a maximum value around 41 �C. The
increase in uptake of metal ions with temperature may be due
to the desolvation of the sorbing species and change in the
size of the pores (Raji and Anirudhan 1997). Also Mittal
et al. (2005) reported that increasing adsorption with an
increase in temperature indicates an increase in the mobility
of large metal molecules with increasing temperatures and
the ongoing adsorption process is endothermic. Ozer et al.
(2004) has suggested that increasing temperature may cause
a swelling effect within the internal structure of the adsor-
bent enabling metal ions to penetrate further. The adsorption
capacity of pumice rapidly increased with increasing C0
from 77.50 to 83.50 mg/L and roughly reached a maximum
at 84.00 mg/L. At low ion concentrations, the ratio of sur-
face active sites to the total metal ions in the solution is high
and hence all metal ions may interact with the adsorbent and
be removed from the solution. With increasing metal ion
concentration, there is an increase in the amounts of metal
ion adsorbed due to increasing driving force of the metal ions
toward the active sites on the adsorbent (Adebowale et al.
2006; Jiang et al. 2009). Similar observations are reported in
the literature (Bee et al. 2011; Bulut and Baysal 2006; Chen
et al. 2010; Li et al. 2009). When C0 was between 83.50 and
85.50 mg/L, the metal uptake reaches equilibrium and all
sites are saturated with metals. This phase is the gradual
adsorption stage and the rate of increment of adsorption
capacity became gradually slower with increasing C0 and
finally the metal uptake reached equilibrium. In the C0 range
from 85.50 to 87.50, the available pores become insufficient
to adsorb further metal ions and many ions are left in sus-
pension. Also, Jiang et al. (2009) reported that more Pb(II)
was left unadsorbed in solution due to the saturation of
binding sites at high concentration.
As can be seen in Fig. 4, although raising the pH of the
Pb(II) solution from 5.50 to 5.80 sharply increased adsorp-
tion capacity, above this pH adsorption capacity decreased
with increased pH. As the pH increases, the negative charge
density on the adsorbent surface increases due to deproto-
nation of the metal binding sites. Similar comments were
made by Anirudhan and Sreekumari (2011). They concluded
that the increase in metal removal with increase in pH can be
explained on the basis of a decrease in competition between
proton and the metal cations for the same functional groups
and by the decrease in positive charge of the adsorbent which
results in a lower electrostatic repulsion between the metal
cations and the surface. An adsorption mechanism for high
pH can be explained by Eqs. 7 and 8 (Sarı et al. 2007b). At
low pH values, the low adsorption is due to the increase in
Table 3 Experimental design of the steepest ascent and the corre-
sponding responses for Pb(II) adsorption
Run C0
(mg/L)
pH Temperature
(�C)
Ads. amount
[mg Pb(II)/g]
Center point 52.50 4.00 35 5.25
1 57.50 4.30 36 5.47
2 62.50 4.60 37 5.85
3 67.50 4.90 38 6.12
4 72.50 5.20 39 6.83
5 77.50 5.50 40 7.03
6 82.50 5.80 41 7.59
7 87.50 6.10 42 7.52
8 92.50 6.40 43 7.25
824 T. Sahan, D. Ozturk
123
positive charge density on the surface sites, and thus, elec-
trostatic repulsion occurs between the metal ions (M2?:
Pb2?) and the edge groups with positive charge (Si–OH2?)
on the surface following Eq. 9 (Sarı et al. 2007b).
�SiOHþ OH� $ �SiO� þ H2O, ð7Þ
�SiO� þM2þ $ �Si�O�M2þ; ð8Þ
�SiOHþ Hþ ! �Si�OHþ2 : ð9Þ
Surface charge density depends on pH of the media. The
charge from cations and anions are equal and total charge is
zero at PZC. Some previous studies have also indicated
that PZC is an important parameter for adsorption
(Caliskan et al. 2011; Perrott 1977; Sakurai et al. 1988;
Sakurai et al. 1989). The pHpzc was identified as the pH
where 0.1 M HNO3 titration curves of different adsorbent
masses (0.10, 0.20, and 0.30 g suspended in 0.03 M
NaNO3 at pH 12.0) converged with that of the reactive
blank solution (Miretzky et al. 2011). By this method, the
Table 4 Experimental design of CCD and responses
Run C0 (mg/L, X1) pH (X2) Temperature
(�C, X3)
Observed res.
[mg Pb(II)/g]
Predicted res.
[mg Pb(II)/g]
e (y0 - yp)
1 87.50 (?1) 5.50 (-1) 43.00 (?1) 6.75 6.87 -0.12
2 82.50 (0) 5.80 (0) 41.00 (0) 7.35 7.42 -0.07
3 82.50 (0) 5.80 (0) 41.00 (0) 7.38 7.42 -0.04
4 82.50 (0) 6.30 (?a) 41.00 (0) 6.40 6.05 0.35
5 82.50 (0) 5.80 (0) 37.64 (-a) 7.08 6.74 0.34
6 82.50 (0) 5.30 (-a) 41.00 (0) 6.53 6.27 0.26
7 87.50 (?1) 5.50 (-1) 39.00 (-1) 6.90 7.04 -0.14
8 87.50 (?1) 6.10 (?1) 43.00 (?1) 6.28 6.42 -0.14
9 77.50 (-1) 6.10 (?1) 43.00 (?1) 6.35 6.64 -0.29
10 77.50 (-1) 6.10 (?1) 39.00 (-1) 5.94 6.25 -0.31
11 82.50 (0) 5.80 (0) 41.00 (0) 7.38 7.42 -0.04
12 77.50 (-1) 5.50 (-1) 43.00 (?1) 5.88 6.12 -0.24
13 82.50 (0) 5.80 (0) 41.00 (0) 7.40 7.42 -0.02
14 90.91 (?a) 5.80 (0) 41.00 (0) 7.00 6.85 0.15
15 82.50 (0) 5.80 (0) 41.00 (0) 7.45 7.42 0.03
16 77.50 (-1) 5.50 (-1) 39.00 (-1) 5.78 6.07 -0.29
17 82.50 (0) 5.80 (0) 41.00 (0) 7.48 7.42 0.06
18 74.09 (-a) 5.80 (0) 41.00 (0) 6.68 6.22 0.44
19 82.50 (0) 5.80 (0) 44.36 (?a) 7.20 6.93 0.27
20 87.50 (?1) 6.10 (?1) 39.00 (-1) 6.05 6.24 0.35
e Corresponded error; y0, yp observed and predicted response, respectively
Actual value (mg/g)
Pre
dict
ed v
alue
(m
g/g)
5.50
6.00
6.50
7.00
7.50
5.50 6.00 6.50 7.00 7.50
Fig. 2 The observed Pb(II) uptake versus predicted Pb(II) uptake
capacity of adsorbent
39.00 40.00
41.00 42.00
43.00
77.50 79.50
81.50 83.50
85.50 87.50
6
6.5
7
7.5
8
Ads
orbe
d A
mou
nt (
mg/
g)
Initial Conc. (C
o, mg/L) Temperature (°C)
Fig. 3 Simultaneous effects of C0 and temperature on Pb(II) removal
at fixed pH of 5.80
Investigation of Pb(II) adsorption 825
123
pHpzc for pumice was found to be *4.00. When pH
increases, the high adsorption efficiency is probably due to
the fact that at pH above the pHpzc 4.00, the pumice surface
is mostly negatively charged, due to the deprotonated OH
surface sites.
Simultaneous effects of temperature and pH on Pb(II)
adsorption capacity are shown in Fig. 5.
Evaluation of the optimum adsorption conditions
The optimum points of the most important parameters to
maximize the adsorption of Pb(II) were evaluated by
application of Eq. 4. Xs, b, and B matrixes in Eq. 4 were
arranged by Eq. 6, which includes uncoded values of the
parameters. Xs, b, and B matrixes were formed as follows
(Myers and Montgomery 2002; Sahan et al. 2010).
Xs ¼X1
X2
X3
264
375; b¼
3:27188
64:83068
3:89821
264
375 and
B¼�0:012554 �0:1625=2 �5:375E� 03=2
�0:1625=2 �4:96034 0:14375=2
�5:375E � 03=2 0:14375=2 �0:051946
264
375
From the solution of the above matrixes with Eq. 4, the
optimum values for Pb(II) adsorption were 84.30 mg/L,
5.75, and 41.11 �C for C0, pH, and temperature, respectively.
Under optimum values, the adsorbed amount is 7.46 mg/g
and the corresponding removal efficiency of Pb(II) was
88.49 %. These results were confirmed with experiments.
Table 5 shows a comparison between the adsorption
capacity of pumice and other adsorbents in the literature.
Pumice is an abundant and inexpensive adsorbent in nature.
As can be seen from Table 5, it can be said that pumice is a
natural adsorbent having higher available capacity than
some other natural adsorbents. In addition, after adsorption,
pumice waste does not pose a danger to the environment.
Due to these features, it has the potential to remove Pb(II)
and other heavy metals from aqueous environments.
Additionally, an artificial wastewater representative of the
coating industry was prepared to investigate the adsorbent
sorption capacity for competitive adsorption. A multicompo-
nent artificial wastewater containing 80 mg/L Cu(II), 80 mg/L
Zn(II), 80 mg/L Cd(II), 80 mg/L Pb(II), and 80 mg/L Mn(II)
metal ions was prepared and several experiments were carried
out at the optimum pH and temperature. The treatment times
were held at 120 min. The average Pb(II) removal yield was
60.12 % mg/g while it was 28.99, 20.62, 20.60, and 20.52 %
for Cu(II), Cd(II), Zn(II), and Mn(II), respectively. The results
show that pumice has the highest adsorption capacity for Pb. It
can be concluded that pumice is a useful adsorbent not only for
wastewater containing lead, but also for wastewater contami-
nated by lead and other heavy metals.
In the above-mentioned competitive adsorption study, the
artificially prepared environment is very similar to real indus-
trial wastewater because the artificial wastewater included the
most abundant heavy metals together with Pb(II) ions in water
discharged from industries such as metal industry, lead–acid
battery, etc. The observed experimental data have shown that
the studied pumice has high adsorption capacity for Pb(II) in the
presence of other metal ions. Based on these results, it can be
said that pumice can be used for Pb(II) removal from both
artificially prepared wastewater and real industrial wastewaters.
Adsorption isotherm models
Adsorption isotherms are the basic requirements for
designing any sorption system. In this study, the most
common adsorption isotherm equations, including Langmuir
5.50 5.60
5.70 5.80
5.90 6.00
6.10
77.50 79.50
81.50 83.50
85.50 87.50
6
6.5
7
7.5
8
Ads
orbe
d A
mou
nt (
mg/
g)
Initial Conc. (C
o, mg/L) pH
Fig. 4 Simultaneous effects of C0 and pH on Pb(II) removal at fixed
temperature of 41 �C
39.00
40.00
41.00
42.00
43.00
5.50 5.60
5.70 5.80
5.90 6.00
6.10
6
6.5
7
7.5
8
Ads
orbe
d A
mou
nt (
mg/
g)
pH
Temperature (°C)
Fig. 5 Simultaneous effects of temperature and pH on Pb(II) removal
at fixed C0 of 82.50 mg/L
826 T. Sahan, D. Ozturk
123
(Langmuir 1918), Freundlich (Freundlich 1906), and Dubi-
nin–Radushkevich (D–R) (Dubinin and Radushkevich
1947), were tested to understand the nature of the adsorption
mechanism and the equilibrium conditions.
The Langmuir model assumes that adsorption occurs at
specific homogeneous sites on the adsorbent and is used
successfully in many monolayer adsorption processes.
The linearized Langmuir isotherm equation is repre-
sented by Eq. 10.
Ce
Qe
¼ 1
qmaxKL
þ 1
qmax
Ce; ð10Þ
where Qe (mg/g) is the adsorbed Pb(II) amount at equilibrium,
Ce (mg/L) is the supernatant concentration at the equilibrium,
and qmax (mg/g) and KL (L/mg) are constants representing the
maximum adsorption capacity and the Langmuir constant
related to the heat of adsorption, respectively. Figure 6a
illustrates the plot of Ce/Qe versus Ce. The qmax and KL
values were calculated as 7.98 mg/g and 1.41 L/mg from
Fig. 6a, respectively. RL constant, a dimensionless separation
factor, is used to predict whether an adsorption system is
‘‘favorable’’ or ‘‘unfavorable’’ (Ma et al. 2012). RL is defined as;
RL ¼1
1þ KLC0
; ð11Þ
where C0 (mg/L) is the highest C0 and KL (L/mg) is the
Langmuir constant. The RL value in range of 0–1 computed
from Eq. 11 shows that adsorption of Pb(II) onto pumice is
favorable.
The Freundlich model can be applied for non-ideal
adsorption on heterogeneous surfaces and multilayer
sorption. The linearized Freundlich equation is Eq. 12.
lnQe ¼ lnKf þ1
nlnCe; ð12Þ
where Kf (L/g) is a constant relating the adsorption
capacity and 1/n is an empirical parameter relating the
adsorption intensity. Figure 6b shows the plot of lnQe
versus lnCe. The values of 1/n and Kf were found to be 0.40
and 3.73 L/g, respectively.
The D–R isotherm model is used to determine the
adsorption type, physical or chemical. The linearized D–R
isotherm equation (Dubinin and Radushkevich 1947) is:
lnqe ¼ lnqm � be2; ð13Þ
where qe (mg/g) is the amount of metal ions adsorbed at
equilibrium, qm (mg/g) is the maximum adsorption
capacity, b is the activity coefficient related to mean
adsorption energy (mg2/J2), and e is the Polanyi potential
which is equal to
e ¼ RT lnð1þ 1
Ce
Þ; ð14Þ
where R (J/mol K) is the gas constant and T (K) is the
absolute temperature. The constant b gives an idea about
the mean adsorption free energy E (kJ/mol) which can be
calculated using the relationship.
E ¼ 1ffiffiffiffiffiffiffiffiffi�2bp : ð15Þ
From the intercept of the plot in Fig. 6c, qm was found
to be 5.43 mg/g. The E (kJ/mol) value gives information
about the adsorption mechanism, physical or chemical
(Lodeiro et al. 2006). The E value computed as 4.19 kJ/mol
Table 5 Comparison between
pumice and other adsorbents
discussed in the literature
Adsorbent Pb(II) adsorption (mg/g) References
Poly-2-hydroxyethyl methacrylate 3.04 Moradi et al. (2009)
Maple sawdust 3.19 Yu et al. (2001)
Sericite 4.70 Tiwari et al. (2007)
Rice husk 8.60 Zulkali et al. (2006)
China clay 0.289 Yadava et al. (1991)
Wollastonite 0.217 Yadava et al. (1991)
Tailored bentonite 58 Cadena et al. (1990)
Zeolite 155.4 Leppert (1990)
Magnetic p(AMPS-c-VI) 88.50 Ozay et al. (2010)
CS-co-MMB-co-PAA hydrogel 96.62 Paulino et al. (2011)
Purolite C100 9.64 Abo-Farhaa et al. (2009)
Granular activated carbon 10.77 Machida et al. (2005)
Zeolitic tuff 15.79 Karatas (2012)
Modified/unmodified kaolinite clay 20/4.20 Jiang et al. (2009)
Neem oil cake (NOC) 30 Rao and Khan (2007)
Bone powder 55.30 Abdel-Halim et al. (2003)
Pumice 7.59 This work
Investigation of Pb(II) adsorption 827
123
indicates that the adsorption of Pb(II) onto pumice is
physical. The E value predicted from the D–R isotherm
plot can be used to estimate the type of adsorption process.
If the E value is between 8 and 16 kJ/mol, the adsorption
process may correspond to a chemical ion exchange. If the
E value is less than 8 kJ/mol, physical adsorption will be
the most likely adsorption mechanism. The same
observations were reported by others (Karatas 2012;
Ngah and Fatinathan 2010). Finally, the Langmuir
isotherm best fitted the equilibrium data since it presents
the highest R2 value than those calculated from the
Freundlich and D–R isotherms (Merrikhpour and Jalali
2012). In all isotherm studies, temperature and pH were
kept constant at 41.11 �C and 5.75, respectively.
Adsorption kinetics
Various adsorption kinetic models have been used to
describe the uptake of adsorbate depending upon time. The
adsorption data were analyzed using pseudo-first-order,
pseudo-second-order, and Elovich kinetic models (Elovich
and Larinov 1962; Lagergren 1898) which are shown in
linear form by the following equations, respectively.
log(qe � qtÞ ¼ logqe �k1
2:303t; ð16Þ
t
qt
¼ 1
k2q2e
þ ð 1
qe
Þt; ð17Þ
qt ¼1
blnabþ 1
blnt; ð18Þ
where qt and qe (mg/g) are the amounts of the metal ions
adsorbed at t (min) and equilibrium, respectively; and k1
(1/min) and k2 (g/mg min) are the rate constants of the
pseudo-first-order and second-order, respectively. a and bare known as the Elovich coefficients. Figure 7 and the
results given in Table 6 show that the adsorption of Pb(II)
onto pumice follows the pseudo-second-order kinetic
model due to high R2.
Adsorption thermodynamics
To evaluate the temperature dependence of Pb(II) adsorp-
tion by pumice, the changes in the thermodynamic
parameters including free energy (DG�), enthalpy (DH�),
and entropy (DS�) needed to be computed using the fol-
lowing equations:
y = 0.1269x + 0.087R² = 0.9952
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
0.00 2.00 4.00 6.00 8.00 10.00
Ce/
Qe
Ce
y = 0.3944x + 1.3143R² = 0.9464
-0.50
0.00
0.50
1.00
1.50
2.00
2.50
-4.00 -2.00 0.00 2.00 4.00
lnQ
e
lnCe
y = -1218.5x + 1.6891R² = 0.8521
-0.50
0.00
0.50
1.00
1.50
2.00
2.50
0 0.0005 0.001 0.0015 0.002
lnQ
e
εε 2
(a) (b)
(c)
Fig. 6 Langmuir isotherm (a),
Freundlich isotherm (b), and D–
R isotherm (c) for the
adsorption of Pb(II) onto
pumice
828 T. Sahan, D. Ozturk
123
KC ¼CA
CS
; ð19Þ
DG� ¼ �RTlnKC; ð20Þ
DG� ¼ DH
� � T DS�� �; ð21Þ
lnKC ¼DS
�
R� DH
�
RT; ð22Þ
where KC is the equilibrium constant, CA (mg/L) is the
amount of adsorbed Pb(II) at equilibrium, CS (mg/L) is the
equilibrium concentration of unadsorbed Pb(II) in the solu-
tion, R is the gas constant (8.314 J/mol K), and T (K) is
temperature. The thermodynamic parameters were calcu-
lated from the slope and intercept of the plot of lnKC versus
1/T (Fig. 8). The results are given in Table 7. While the
negative DG� value indicates that the nature of the adsorption
is thermodynamically feasible and spontaneous, the decrease
in DG� values with increase in temperature shows a decrease
in feasibility of adsorption at higher temperatures (Sarı et al.
2007c). The positive DH� value designates that the adsorp-
tion has an endothermic nature (Bulut and Baysal 2006; Sarıet al. 2007c). The positive value of DS� shows the increased
randomness at the solid/solution interfaces during the
adsorption of metal ions on pumice and also reflects the
affinity of the adsorbent material for the metal ions. It has
also been suggested that the positive value of DS� indicates
some structural changes in the adsorbate and adsorbent
(Ajmal et al. 2003; Pandey et al. 1985).
y = -0.0115x + 0.2118R² = 0.8632
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0 20 40 60 80 100
log(
q e-q
t)
t (min.)
y = 0.133x + 0.251R² = 0.9999
0
2
4
6
8
10
12
0 20 40 60 80 100
t/q t
(min
.g/m
g)
t (min.)
y = 0.6376x + 4.7357R² = 0.9523
0
1
2
3
4
5
6
7
8
0 1 2 3 4 5
q t(m
g/g)
lnt
(a) (b)
(c)
Fig. 7 a Pseudo-first-order,
b pseudo-second-order, and
c Elovich kinetic models for
adsorption of Pb(II) ions onto
pumice
Table 6 The kinetic constants for the adsorption of Pb(II) on the pumice
T (K) qe(exp) Pseudo-first-order kinetic model Pseudo-second-order kinetic model Elovich kinetic model
qe(cal.) k1 R2 qe(cal.) k2 R2 a b R2
314 7.59 1.63 0.03 0.86 7.52 0.07 0.99 1681.4 0.64 0.95
Investigation of Pb(II) adsorption 829
123
Conclusions
The adsorption of Pb(II) by pumice samples that were
collected from the Mount Ararat region, located in eastern
Turkey, was investigated in a batch system. Also, a multi-
step RSM was used to optimize the adsorption conditions.
The optimum adsorption conditions for removal of Pb(II)
were evaluated to be 84.30 mg/L, 5.75, and 41.11 �C for
C0, pH, and temperature, respectively. Under these opti-
mum conditions, maximum adsorbed amount and removal
efficiency of Pb(II) were 7.46 mg Pb(II)/g pumice and
88.49 %, respectively. The Langmuir isotherm model fitted
equilibrium data better than the Freundlich and D–R iso-
therm models. The thermodynamic parameters indicated
that the adsorption of Pb(II) onto pumice was of a feasible,
endothermic, and spontaneous nature. By applying the
kinetic models to the experimental data, it was found that
the kinetics of Pb(II) adsorption followed the pseudo-sec-
ond-order rate equation. Taking into consideration the
results above, it can be concluded that RSM is a powerful
statistical method for optimization of experimental condi-
tions and that pumice is a suitable adsorbent for the
removal of Pb(II) from wastewaters due to high adsorption
capacity, natural and abundant availability, and low cost.
Acknowledgments This work was supported by the Yuzuncu Yil
University Research Fund with Grant # 2011-FBE-YL038.
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