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: tsahan@gmail.com

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.

References

Abdel-Halim SH, Shehata AMA, El-Shahat MF (2003) Removal of

lead ions from industrial wastewater by different types of natural

materials. Water Res 37:1678–1683

Abo-Farhaa SA, Abdel-Aala AY, Ashourb IA, Garamona SE (2009)

Removal of some heavy metal cations by synthetic resin purolite

C100. J Hazard Mater 169:190–194

Adebowale KO, Unuabonah IE, Olu-Owolabi BI (2006) The effect of

some operating variables on the adsorption of lead and cadmium

ions on kaolinite clay. J Hazard Mater 134:130–139

Ajmal M, Rao RAK, Anwar S, Ahmad J, Ahmad R (2003) Adsorption

studies on rice husk: removal and recovery of Cd(II) from

wastewater. Bioresour Technol 86:147–149

Anirudhan TS, Sreekumari SS (2011) Adsorptive removal of heavy

metal ions from industrial effluents using activated carbon derived

from waste coconut buttons. J Environ Sci 23:1989–1998

Bee A, Talbot D, Abramson S, Dupuis V (2011) Magnetic alginate

beads for Pb(II) ions removal from wastewater. J Colloid

Interface Sci 362:486–492

Bulut Y, Baysal Z (2006) Removal of Pb(II) from wastewater using

wheat bran. J Environ Manage 78:107–113

Cadena F, Rizvi R, Peters RW (1990) Feasibility studies for the

removal of heavy metals from solution using tailored bentonite.

In: Hazardous and industrial wastes, Proceedings of the twenty-

second mid-Atlantic industrial waste conference, Drexel Uni-

versity, pp 77–94

Caliskan N, Kul AR, Alkan S, Gokirmak Sogut E, Alacabey I (2011)

Adsorption of Zinc(II) on diatomite and manganese-oxide-

modified diatomite: a kinetic and equilibrium study. J Hazard

Mater 193:27–36

Chen H, Zhao J, Dai G, Wu J, Yan H (2010) Adsorption

characteristics of Pb(II) from aqueous solution onto a natural

biosorbent, fallen Cinnamomum camphora leaves. Desalination

262:174–182

Doyurum S, Celik A (2006) Pb(II) and Cd(II) removal from aqueous

solutions by olive cake. J Hazard Mater 138:22–28

Dubinin MM, Radushkevich LV (1947) Equation of the character-

istics curve of activated charcoal. Chem Zent 1:875

Elovich SY, Larinov OG (1962) Theory of adsorption from solutions

of non electrolytes on solid (I) equation adsorption from

solutions and the analysis of its simplest form (II), verification

of the equation of adsorption isotherm from solutions. Izv Akad

Nauk SSSR Otd Khim Nauk 2:209–216

Erdem M, Ozverdi A (2005) Lead adsorption from aqueous solution

onto siderite. Sep Purif Technol 42:259–264

Ersoy B, Sariisik A, Dikmen S, Sariisik G (2010) Characterization of

acidic pumice and determination of its electrokinetic properties

in water. Powder Technol 197:129–135

Freundlich HMF (1906) Uber die adsorption in losungen. Z Phys

Chem (Leipzig) 57A:385–470

Goel J, Kadirvelu K, Rajagopal C, Garg VK (2005) Removal of

lead(II) by adsorption using treated granular activated carbon:

batch and column studies. J Hazard Mater 125:211–220

Hara T, Endo T, Ikeda K, Inaba N (1979) Concentration of heavy

metal ions by use of synthetic pumice and its related adsorbents.

Sci Eng Rev Doshisha Univ 20:1625

Jiang M, Wang Q, Jin X, Chen Z (2009) Removal of Pb(II) from

aqueous solution using modified and unmodified kaolinite clay.

J Hazard Mater 170:332–339

Karatas M (2012) Removal of Pb(II) from water by natural zeolitic

tuff: kinetics and thermodynamics. J Hazard Mater 199–200:

383–389

Kardam A, Raj KR, Srivastava S, Srivastava MM (2013) Nanocel-

lulose fibers for biosorption of cadmium, nickel, and lead ions

0

0.5

1

1.5

2

2.5

0.0

lnK

c

00315 0

y = -4693.1xR² = 0.

0.0032 0

x + 17.278.8172

0.00325

1/T (K-

0.0033-1)

0.00335 0.0034

Fig. 8 Plot of lnKc versus 1/T for the prediction of thermodynamic

parameters for the adsorption of Pb(II) ions onto pumice

Table 7 Thermodynamic parameters for adsorption of Pb(II) on

pumice

T (K) lnKc DG� (kJ/mol) DH� (kJ/mol) DS� (J/mol K)

298 1.38 -3.79

303 1.96 -4.51 39.02 143.65

308 2.11 -5.23

313 2.14 -5.94

830 T. Sahan, D. Ozturk

123

from aqueous solution. Clean Techn Environ Policy. doi:10.

1007/s10098-013-0634-2

Katsumata H, Kaneco S, Inomata K, Itoha K, Funasaka K, Masuyama

K, Suzuki T, Ohta K (2003) Removal of heavy metals in rinsing

wastewater from plating factory by adsorption with economical

viable materials. J Environ Manage 69:187–191

Lagergren S (1898) Zur theorie der sogenannten adsorption geloster stoffe.

Kungliga Svenska Vetenskapsakademiens, Handlingar, pp 1–39

Langmuir I (1918) The adsorption of gases on plane surfaces of glass,

mica and platinum. J Am Chem Soc 40:1361–1403

Leppert D (1990) Heavy metal sorption with clinoptilolite zeolite:

alternatives for treating contaminated soil and water. Min Eng

42:604–608

Li Q, Zhai J, Zhang W, Wang M, Zhou J (2007) Kinetic studies of

adsorption of Pb(II), Cr(III) and Cu(II) from aqueous solution by

sawdust and modified peanut husk. J Hazard Mater 141:163–167

Li Y, Zhang FS, Xiu FR (2009) Arsenic(V) removal from aqueous

system using adsorbent developed from a high iron-containing

fly ash. Sci Total Environ 407:5780–5786

Liu Y, Shen X, Xian Q, Chen H, Zou H, Gao S (2006) Adsorption of

copper and lead in aqueous solution onto bentonite modified by

4-methylbenzo-15-crown-5. J Hazard Mater 137:1149–1155

Lo W, Chua H, Lam KH, Bi SH (1999) A comparative investigation

on the biosorption of lead by filamentous fungal biomass.

Chemosphere 39:2723–2736

Lodeiro P, Barriada JL, Herrero R, Sastre de Vicente ME (2006) The

marine macroalga Cystoseira baccata as biosorbent for cad-

mium(II) and lead(II) removal: kinetic and equilibrium studies.

Environ Pollut 142:264–273

Ma X, Li L, Yang L, Su C, Wang K, Yuan S, Zhou J (2012) Adsorption

of heavy metal ions using hierarchical CaCO3–maltose meso/

macroporous hybrid materials: adsorption isotherms and kinetic

studies. J Hazard Mater 209–210:467–477

Machida M, Aikawa M, Tatsumoto H (2005) Prediction of simultaneous

adsorption of Cu(II) and Pb(II) onto activated carbon by conven-

tional Langmuir type equations. J Hazard Mater 120:271–275

Merrikhpour H, Jalali M (2012) Waste calcite sludge as an adsorbent

for the removal of cadmium, copper, lead, and zinc from aqueous

solutions. Clean Technol Environ Policy 14:845–855

Miretzky P, Munoz C, Cantoral-Uriza E (2011) Cd2? adsorption on

alkaline-pretreated diatomaceous earth: equilibrium and thermo-

dynamic studies. Environ Chem Lett 9:55–63

Mittal A, Krishnan L, Gupta VK (2005) Removal and recovery of

malachite green from wastewater using agricultural waste

material, de-oiled soya. Sep Purif Technol 43:125–133

Moradi O, Aghaie M, Zare K, Monajjemi M, Aghaie H (2009) The

study of adsorption characteristics Cu2? and Pb2? ions onto

PHEMA and P(MMA-HEMA) surfaces from aqueous single

solution. J Hazard Mater 170:673–679

Myers RH, Montgomery DC (2002) Response surface methodology.

Wiley, New York, p 43

Ngah WSW, Fatinathan S (2010) Adsorption characterization of Pb(II)

and Cu(II) ions onto chitosan-tripolyphosphate beads: kinetic,

equilibrium and thermodynamic studies. J Environ Manage 91:

958–969

Ozay O, Ekici S, Baran Y, Kubilay S, Aktas N, Sahiner N (2010)

Utilization of magnetic hydrogels in the separation of toxic metal

ions from aqueous environments. Desalination 260:57–64

Ozer A, Ozer D, Ozer A (2004) The adsorption of copper(II) ions on to

dehydrated wheat bran (DWB): determination of the equilibrium

and thermodynamic parameters. Process Biochem 39:2183–2191

Pandey KK, Prasad G, Singh VN (1985) Copper removal from

aqueous solutions by fly ash. Water Res 19(7):869–873

Panuccio MR, Sorgona‘ A, Rizzo M, Cacco G (2009) Cadmium

adsorption on vermiculite, zeolite and pumice: batch experi-

mental studies. J Environ Manage 90:364–374

Paulino AT, Belfiore LA, Kubota LT, Muniz EC, Almeida VC,

Tambourgi EB (2011) Effect of magnetite on the adsorption

behavior of Pb(II), Cd(II), and Cu(II) in chitosan-based hydro-

gels. Desalination 275:187–196

Perrott KW (1977) Surface charge characteristics of amorphous

aluminosilicates. Clay Clay Miner 25:417–421

Raji C, Anirudhan TS (1997) Chromium(VI) adsorption by sawdust:

kinetics and equilibrium. Indian J Chem Technol 4:228–236

Rao RAK, Khan MA (2007) Removal and recovery of Cu(II), Cd(II)

and Pb(II) ions from single and multimetal systems by batch and

column operation on neem oil cake (NOC). Sep Purif Technol

57:394–402

Rashid K, Suresh Kumar Reddy K, Shoaibi AA, Srinivasakannan C

(2013) Sulfur-impregnated porous carbon for removal of mer-

curic chloride: optimization using RSM. Clean Technol Environ

Policy. doi:10.1007/s10098-012-0564-4

Sahan T, Ceylan H, Sahiner N, Aktas N (2010) Optimization of

removal conditions of copper ions from aqueous solutions by

Trametes versicolor. Bioresour Technol 101:4520–4526

Sakurai K, Ohdate Y, Kyuma K (1988) Comparison of salt titration

and potentiometric titration methods for the determination of

zero point of charge (ZPC). Soil Sci Plant Nutr 34(2):171–182

Sakurai K, Ohdate Y, Kyuma K (1989) Factors affecting zero point of

charge (ZPC) of variable charge soils. Soil Sci Plant Nutr 35(1):21–31

Sarı A, Tuzen M (2009) Kinetic and equilibrium studies of

biosorption of Pb(II) and Cd(II) from aqueous solution by

macrofungus (Amanita rubescens) biomass. J Hazard Mater

164:1004–1011

Sarı A, Tuzen M, Soylak M (2007a) Adsorption of Pb(II) and Cr(III)

from aqueous solution on Celtek clay. J Hazard Mater 144:41–46

Sarı A, Tuzen M, Cıtak D, Soylak M (2007b) Equilibrium, kinetic and

thermodynamic studies of adsorption of Pb(II) from aqueous

solution onto Turkish kaolinite clay. J Hazard Mater 149:283–291

Sarı A, Tuzen M, Cıtak D, Soylak M (2007c) Adsorption character-

istics of Cu(II) and Pb(II) onto expanded perlite from aqueous

solution. J Hazard Mater 148:387–394

Sekar M, Sakthi V, Rengaraj S (2004) Kinetics and equilibrium

adsorption study of lead (II) on activated carbon prepared from

coconut shell. J Colloid Interface Sci 279:307–313

Singh KK, Talat M, Hasan SH (2006) Removal of lead from aqueous

solutions by agricultural waste maize bran. Bioresour Technol

97:2124–2130

Su S, Nie H, Zhu L, Chen T (2009) Optimization of adsorption

conditions of papain on dye affinity membrane using response

surface methodology. Bioresour Technol 100:2336–2340

Sugashini S, Meera Sheriffa Begum KM (2013) Optimization using

central composite design (CCD) for the biosorption of Cr(VI)

ions by cross linked chitosan carbonized rice husk (CCACR).

Clean Technol Environ Policy 15:293–302

Tiwari D, Kim H, Lee S (2007) Removal behavior of sericite for

Cu(II) and Pb(II) from aqueous solutions: batch and column

studies. Sep Purif Technol 57:11–16

Yadava KP, Tyagi BS, Singh VN (1991) Effect of temperature on the

removal of lead(II) by adsorption on China clay and wollaston-

ite. J Chem Technol Biotechnol 51:47–60

Yetilmezsoy K, Demirel S, Vanderbei RJ (2009) Response surface

modeling of Pb(II) removal from aqueous solution by Pistacia

vera L.: Box–Behnken experimental design. J Hazard Mater

171:551–562

Yu B, Zhang Y, Shukla A, Shukla SS, Dorris KL (2001) The removal

of heavy metals from aqueous solutions by sawdust adsorption

removal of lead and comparison of its adsorption with copper.

J Hazard Mater 84:83–94

Zulkali MMD, Ahmad AL, Norulakmal NH (2006) Oryza sativa L.

husk as heavy metal adsorbent: optimization with lead as model

solution. Bioresour Technol 97:21–25

Investigation of Pb(II) adsorption 831

123