CC BY-NC-ND
0Journal of Advanced Research (2015) xxx, xxx-xxx
Cairo University Journal of Advanced Research
ORIGINAL ARTICLE
Treatment of an automobile effluent from heavy metals contamination by an eco-friendly montmorillonite
Kovo G. Akpomie a,b,% Folasegun A. Dawodu a
Department of Chemistry (Industrial), University of Ibadan, Ibadan, Nigeria
' Projects Development Institute (PRODA), Federal Ministry of Science and Technology, Enugu, Nigeria
ARTICLE INFO
ABSTRACT
Article history: Received 2 October 2014 Received in revised form 1 December 2014
Accepted 8 December 2014 Available online xxxx
Keywords:
Automobile effluent
Montmorillonite
Sorption
Isotherm
Kinetic
Thermodynamics
Unmodified montmorillonite clay was utilized as a low cost adsorbent for the removal of heavy metals from a contaminated automobile effluent. Fourier transform infrared spectroscopy, X-ray diffraction and scanning electron microscopy were used to characterize the adsorbent. Batch sorption experiments were performed at an optimum effluent pH of 6.5, adsorbent dose of 0.1 g, particle size of 100 im and equilibrium contact time of 180 min. Thermodynamic analysis was also conducted. Equilibrium data were analyzed by the Langmuir, Freundlich, Temkin and Dubinin-Radushkevich models. A heterogeneous surface of the adsorbent was indicated by the Freundlich model. The Langmuir maximum adsorption capacity of the montmorillonite for metals was found in the following order: Zn (5.7mg/g) > Cu (1.58 mg/g) > Mn (0.59 mg/g) > Cd (0.33 mg/g) > Pb (0.10 mg/g) " Ni (0.10 mg/g). This was directly related to the concentration of the metal ions in solution. The pseudo-first order, pseudo-second order, intraparticle diffusion and liquid film diffusion models were applied for kinetic analysis. The mechanism of sorption was found to be dominated by the film diffusion mechanism. The results of this study revealed the potential of the montmorillonite for treatment of heavy metal contaminated effluents.
© 2014 Production and hosting by Elsevier B.V. on behalf of Cairo University.
Introduction
Corresponding author. Tel.: +234 8037617494. E-mail address: kovoakpmusic@yahoo.com (K.G. Akpomie). Peer review under responsibility of Cairo University.
The pollution of the environment with toxic substances has been on the increase in recent years as a result of the rapid growth of industries. Most industries such as automobile, mining, electroplating, iron-steel and battery industries utilize substances containing heavy metals [1]. Subsequently, these heavy metals are discharged into the environment from the effluents obtained from the industries. Although small amounts of some heavy metals are necessary for the normal development of
http://dx.doi.org/10.1016/j.jare.2014.12.004
2090-1232 © 2014 Production and hosting by Elsevier B.V. on behalf of Cairo University.
biological cycles, most of them are toxic at high concentrations [2]. The release of heavy metals into the environment poses a serious health threat to aquatic lives, plants and humans due to their persistence, non-biodegradability and bio-accumulation in the food chain. Therefore, the removal of these metals from industrial effluents is necessary to maintain environmental quality [3]. Several techniques have been utilized for heavy metal removal, which include solvent extraction, filtration, ion-exchange, coagulation, sedimentation, oxidation and activated carbon adsorption [4]. However, these techniques have the disadvantages of high cost, low removal efficiency and the problem of secondary contamination. As a result, many researchers have utilized low cost, eco-friendly and highly efficient adsorbents for removal of heavy metals from effluents. These adsorbents include biomass materials, clays, charcoal, sludge ash, microorganisms and lateritic materials, just to mention a few [5].
Montmorillonite has been found to be suitable for heavy metals adsorption through cation exchange mechanism in the interlayer and the formation of inner sphere complexes through Si-O and Al-O groups at the clay particle edges [68]. However, most studies in literature have not been focused on the use of montmorillonite for adsorption of heavy metals from automobile effluent in particular. This study is therefore focused on the removal of heavy metals from a contaminated automobile effluent unto a montmorillonite. The automobile industry (Innoson) is located in Nnewi, Anambra State, Nigeria and was chosen due to the reasonably high metal concentration. Similarly, the montmorillonite was utilized because it is found in an abundant amount in Nigeria and can be utilized as a low cost and eco-friendly adsorbent.
The montmorillonite was used without any modification (chemical or physical) in order to keep the process cost low. The effect of various experimental conditions such as pH, adsorbent dose, contact time and particle size was investigated. Equilibrium, kinetic and thermodynamic analyses were also determined to understand the adsorption mechanism.
Experimental
Processing of the montmorillonite
The montmorillonite was collected from Oji river local government area of Enugu State, Nigeria. It was immersed in excess distilled water in a pretreated plastic container and stirred uniformly for proper mixing. Thereafter, the mixture was passed through a mesh sieve of size 500 im to get rid of plant materials and other suspended particles. The obtained filtrate was kept for 24 h to settle after which excess water was decanted. The residue was sundried and then dried in an oven at 378 K for 4 h. The dried clay was then pulverized and passed through different mesh sieves of sizes 100-500 im to obtain the unmodified montmorillonite clay (UMC).
Physicochemical analysis
The automobile effluent was collected from the discharge outlet of Innoson automobile industry located in Nnewi, Anambra State, Nigeria. The effluent was stored at 277 K in a refrigerator. Sample collection was based on the technique described [9]. The physicochemical analysis of the effluent was determined
using standard methods [10]. Heavy metal concentration in the effluent was analyzed by the use of the Atomic absorption spectrophotometer (AAS) (Buck scientific model 210VGP). All the reagents used were of analytical grade, obtained from Sigma Aldrich (Steinheim, Germany) and used without further purification. Chemical composition of UMC was determined by the AAS after digestion of the sample with nitric acid. The cation exchange capacity (CEC) of the adsorbent was obtained by the ammonium acetate method [11], while the pH point of zero charge was determined by the method described [12]. The slurry pH was obtained as described previously [5]. X-ray diffractometer (Randicon MD 10 model) was utilized for the X-ray diffraction (XRD) analysis. The Fourier transform infrared (FTIR) of UMC was taken by the help of the Fourier transform infrared spectrophotometer (Shimadzu FTIR 8400s). BET surface area and pore properties of UMC were obtained by nitrogen adsorption-desorption isotherms by the use of the micromeritics ASAP 2010 model analyzer. Scanning electron microscopy (SEM) (Hitachi S4800 model) was used to determine the morphology of the adsorbent.
Effluent treatment
The automobile effluent was treated by the batch adsorption technique by contacting 0.1 g of the adsorbent with 50 ml of effluent solution in 100 ml plastic bottles under optimum conditions as described: The effect of pH was studied by adjusting the pH of the effluent from 2 to 8 by the drop wise addition of 0.1 M NaOH or 0.1 M HCl when required before the addition of the adsorbent, this was performed using an adsorbent particle size of 100 im, contact time 180 min and temperature of 300 K. The influence of adsorbent dose was performed using different doses of 0.1-0.5 g, effluent pH 6.5, adsorbent particle size 100 im and contact time of 180 min at temperatures of 300, 313 and 323 K. Temperature regulation was performed by the use of a thermostat water bath. By varying the particle size of the adsorbent from 100 to 500 im the influence of adsorbent particle size on adsorption was investigated, this was performed at pH 6.5, adsorbent dose 0.1 g, contact time 180 min and temperature of 300 K. Finally, the effect of contact time on adsorption was performed at an effluent pH of 6.5, particle size 100 im at temperatures of 300, 313 and 323 K by varying the contact time of experiment from 10 to 300 min.
At the end of the given contact time for each experiment, the solution was filtered and the concentration of heavy metals remaining in the filtrate was determined by the AAS. The adsorption capacity of UMC for heavy metals was determined by the mass balance equation given as:
qe = v(Ci - Ce)/m (1)
where qe (mg/g) is the amount of heavy metal adsorbed per unit weight of UMC, CI (mg/L) is the initial concentration of heavy metals in the effluent, Ce (mg/L) is the equilibrium concentration of heavy metals remaining in the effluent, v (L) is the volume of effluent solution used and m (g) is the mass of UMC utilized for adsorption.
Isotherm modeling
The equilibrium isotherm model analysis was studied by the application of the Langmuir, Freundlich, Temkin and
Dubinin-Radushkevich models. The Langmuir isotherm describes a monolayer adsorption unto a homogenous adsorbent surface. The linear form of the Langmuir equation is given as follows [13]:
Ce/qe = 1/qL Kl + Ce/qL (2)
where qL (mg/g) is the Langmuir maximum monolayer adsorption capacity, KL (L/mg) is the Langmuir adsorption constant. A dimensionless constant or separation factor (RL) of the Langmuir isotherm was utilized to describe the adsorption and was calculated from the equation:
RL = 1/[1 + KLCI] (3)
The value of RL indicates the type of adsorption to be irreversible (Rl = 0), favorable (0 < RL < 1), linear (RL = 1) or unfavorable (RL > 1).
The Freundlich isotherm model is based on a multilayer adsorption onto a heterogeneous surface. The linear form of the Freundlich equation is given as follows [14]:
log qe = log Kf + [1 /n] log Ce (4)
where KF (L/g) is the Freundlich adsorption constant related to the adsorption capacity of the adsorbent, while n is a dimen-sionless constant related to the adsorption intensity.
The Temkin isotherm model is based on the assumption that the free energy of adsorption is a function of the surface coverage and the linear form is presented as follows [15]:
qe = B ln A + B lnCe (5)
where A (L/mg) is the equilibrium binding constant, the constant B = RT/bT (mg/g) is related to the heat of adsorption, R is the ideal gas constant (8.314 J/mol K), T (K) is the absolute temperature and bT is the Temkin isotherm constant.
The Dubinin-Radushkevich (D-R) isotherm was applied to identify the nature of adsorption as either physical or chemical process. The linear form of the isotherm is given as follows [16]:
ln qe = ln qm
where qm (mg/g) is the D-R maximum adsorption capacity, b (mol2/J2) is related to the mean free energy of adsorption and £ (kJ/mol) is the Polanyi potential [£ = RTln(1 + 1/Ce)]. The constant b is related to the energy of adsorption E (kJ/mol) by the following equation:
E = 1/(2b)1/2 (7)
When the value of E falls in the range of 8-16 kJ/mol the adsorption is said to be chemically controlled and it is dominated by physical mechanism if E <8 kJ/mol [17].
Kinetic modeling
The kinetic mechanism of the adsorption process was investigated by application of the pseudo-first order, pseudo-second order, intraparticle diffusion and liquid film diffusion model rate equations.
The pseudo-first order kinetic model or Largergren equation is expressed as follows [18]:
log(qe - qt) = logqe - (K,/2.303)t (8)
where qe (mg/g) and qt (mg/g) are the amount adsorbed at equilibrium and time t (min), respectively. K (min-1) is the pseudo-first orders rate constant of adsorption.
The pseudo-second order kinetic model is given as follows
t/qt = 1/K2qe2 + t/qe (9)
where K2 (g/mg/min) is the pseudo second order rate constant. The initial sorption rate h (mg/g/min) was calculated from the equation:
h = K2qe2
Metal ions are transported from the aqueous phase to the surface of an adsorbent and can diffuse into the interior if favorable. The intraparticle diffusion equation would then be applicable and is given as follows [20]:
qt = Kdt1/2 + C
where Kd (mg/g min1/2) is the intraparticle diffusion rate constant and C represents the intercept. If the plot of qt versus t1/2 is linear then the intraparticle diffusion is involved in the mechanism. Also, if the plot passes through the origin (C = 0) then intraparticle diffusion is the sole rate controlling step of the adsorption process [17].
When the transport of the adsorbate from the liquid phase to the solid phase boundary plays the most significant role in adsorption, then the liquid film diffusion model can be applied [20]:
ln(1 - F) = -Kfdt + Y
where F = qt/qe is the fractional attainment of equilibrium, Kfd (mg/g min) is the film diffusion adsorption rate constant and Y is the intercept. A linear plot of —ln(1 — F) versus t suggest that the kinetics of adsorption involves a film diffusion mechanism. Furthermore, if the plot is linear with (Y = 0) then film diffusion is the sole rate controlling mechanism.
Adsorption thermodynamics
The thermodynamic parameters such as the standard free energy (AG0), Enthalpy change (AH0) and entropy change (AS0) were determined to evaluate the feasibility of adsorption by the application of the following equations [5]:
DG0 = -RT ln Kc
ln Kc = — AH°/RT) + (AS0/R)
where Kc = Ca/Ce is the distribution coefficient, Ca (mg/L) and Ce (mg/L) represent the concentration of heavy metals adsorbed and that remaining in solution at equilibrium, respectively. T (K) is the absolute temperature and R is the gas constant. AH0 and AS0 were evaluated from the slope and intercept of the linear plot of lnKc versus 1/T.
Results and discussion
Physicochemical characteristics
The physicochemical characterization of the automobile effluent is shown in Table 1. pH is a simple parameter but is extremely important, since most of the chemical reactions in aquatic environment are controlled by any change in its value. Aquatic organisms are sensitive to pH changes and biological treatment requires pH control or monitoring. Also the toxicity of heavy
Table 1 Physicochemical characterization of the automobile effluent.
Parameter Value
pH 6.5
Temp (K) 303
Dissolved oxygen (mg/L) 4.6
Biochemical oxygen demand (mg/L) 82.5
Chemical oxygen demand (mg/L) 189.3
Lead (mg/L) 2.91
Zinc (mg/L) 19.38
Copper (mg/L) 14.50
Cadmium (mg/L) 6.09
Nickel (mg/L) 2.04
Manganese (mg/L) 9.81
Chromium (mg/L) 0.46
metals also gets enhanced at particular pH values. This shows the importance of the pH in deciding the quality of a wastewa-ter and anything higher or lower than the World Health Organization (WHO) limit could be harmful to the environment. From the result it is seen that the pH of the effluents obtained was slightly acidic which may be due to the use of acid in car batteries in the automobile industry. However, the pH is within the World Health Organization (WHO) limit of 6.58.5 [21], which implies that there could be no danger on the receiving environment. Temperature is one of the most important ecological features, which controls certain behavioral characteristics of organisms and the solubility of salts and gases in water. The basis of all life functions is a complicated set of biological reactions that are influenced by temperature. Disease resistance is also linked to temperature. Siyanbola et al. [22] reported that bio-chemical reactions of aquatic organisms are temperature dependent and increase in temperature of water body will promote chemical reactions in the water resulting in bad odor and taste due to non solubility of gases such as oxygen [23]. Furthermore, temperature increase may become barrier to fish migration and in this way seriously affect reproduction of species. The temperature of the automobile effluent was within the WHO limit of 294305 K which is environmental friendly [21].
The most important measure of water quality is the dissolved oxygen (DO) [24]. Hydrogen sulfide is formed under conditions of deficient oxygen in the presence of organic materials and sulfate. The effluent waste discharge to surface water source is largely determined by the oxygen balance of the system and its presence is essential in maintaining life within a system. Dissolved oxygen concentration in unpolluted water normally range between 8 to 10 mg/L and the concentration below 5 mg/L adversely affect aquatic life [25] because the organisms become stressed, suffocate and die. The DO values obtained for the effluent were below the recommended WHO standard (5 or more) [21]. Similarly, the Biochemical Oxygen Demand (BOD) and chemical oxygen demand (COD) are very useful parameters in accessing the quality of an effluent. The consequences of high BOD and COD are the same as low DO; both parameters affect directly the amount of DO. The greater the BOD and COD the more rapidly oxygen is depleted in the water; this means a corresponding decrease in the DO value and the less oxygen available to aquatic life. BOD is simply a measure of the amount of oxygen required by bacteria or
microorganisms for breaking down to simpler substances of the decomposable organic matter present in any water, waste-water or treated effluent [26]. The greater the decomposable organic matter the greater the oxygen demand and the greater the BOD [27]. The COD is a measure of the oxygen equivalent of that portion of the organic matter in a sample that is susceptible to oxidation by a strong chemical oxidant. Both BOD and COD are used to measure the concentration of organic matter present in any water. The BOD and COD values of the effluent are much higher than the WHO recommended limit of 15 and 40 mg/L respectively, which may lead to a corresponding increase in their receiving water bodies and consequently have an adverse effect on aquatic lives [21]. The high values obtained could be attributed to an increase in the addition of both organic and inorganic contaminants entering the systems from the industrial processes.
Most importantly, the heavy metals concentration of Pb, Zn, Cu, Cd, Ni and Mn exceeded the maximum WHO permissible limits of 0.05, 5.0, 1.0, 0.005, 0.05 and 0.1 mg/L respectively [21]. This indicates a harmful effect to the environment and aquatic life, since the effluents are capable of having a corresponding increase in the concentration of these metals in their receiving water bodies. Chromium however recorded a lower concentration than the WHO limit of 0.05 mg/L [21]. Due to the high concentrations of heavy metals in the automobile effluent, it was therefore necessary to investigate the potential of UMC as an adsorbent for the removal of these metals from solution.
Data obtained from the physicochemical characterization of UMC are shown in Table 2. The montmorillonite recorded a BET surface area (SBET) of 55.76 m2/g, a total pore volume (TPV) of 0.0688 cm3/g and an average pore diameter (APD) of 49.35 A [8]. As discussed previously [8], the SBET is higher than that reported by Guo et al. [7] of 26.33 m2/g but close to that (61 ± 2 m2/g) reported by Macht et al. [28]. Many factors contribute to the differences in SBET values of montmorillonites which include the purity and type of montmorillonite, the saturating cation, the out gassing temperature and the preparatory treatment received by the sample [8].
Table 2 Physicochemical characterization of the montmorillonite.
Parameter Value
SiO2 (%) 47.32
AhOs(%) 25.91
Fe2O3 (%) 2.14
CaO (%) 3.39
K2O (%) 1.07
Na2O (%) 2.86
MgO (%) 3.14
TiO2 (%) 0.12
MnO (%) 0.43
LOI (%) 13.56
Si/Al ratio 1.83
Sbet (m2/g) 55.76
TPV (cm3/g) 0.0688
APD (A) 49.35
CEC (meq/100 g) 90.78
pHpzc 3.7
Slurry pH 4.2
The nature of the functional groups on the surface of UMC before and after adsorption was determined by the FTIR spectra as shown in Fig. 1. Absorption bands at 3697.66 cm-1 and 3620.51 cm-1 are assigned to the inner surface -OH stretching of kaolinite, which suggest the presence of kaolinite in the sample [8]. The outer -OH stretching vibration was indicated by the bands at 3441.12 cm-1 and 3410.26 cm-1. Absorption at 1627.97 cm-1 represents the -COO- symmetric stretching vibration. The smectite structure was indicated by the occurrence of the outer -OH stretching and the symmetric -COO-stretching vibration [29]. Absorptions at 1114.891006.88 cm-1 correspond to the Si-O bending vibration while the stretching vibrations were observed at 796.63 cm-1 and 694.4 cm-1 [30]. The Al-O bending vibration was observed at 912.36 cm-1, and the Al-O-Si skeletal vibration at 536.23, 470.65 and 430.14 cm-1 [8]. After the adsorption of heavy metals from the automobile effluent, there were bands shifts in the surface groups which indicate the active participation of the surface functional groups in the adsorption process. This was inferred by the outer surface -OH band shift from 3441.12 to 3439.19 cm-1, the occurrence of bands at 2360.95 and 2000.25 cm-1 may have been acquired from adsorption of some organic containing substances in the effluent. The
involvement of the symmetric -COO- groups was indicated by absorption shifts from 1627.97 and 1629.9 cm-1. Bands shift from 1097.53 to 1095.6 cm-1, 1031.95 to 1033.88 cm-1 and 1006.88 cm-1 to 1008.8 cm-1 were observed for the Si-O bending vibration. The Al-O-Si linkages were also involved as indicated by the shifts from 536.23 to 538.16 cm-1 and 430.14 to 426.28 cm-1.
The d-spacing values presented by the XRD spectra of UMC in Fig. 2 showed 2h values of 23.74 and 24.58, indicating montmorillonite as the major constituent and also the presence of quartz as minute mineral. Finally, the SEM image of UMC as shown in Fig. 3 revealed a porous structure which is a requirement for any potential adsorbent.
Influence of pH
The initial pH of a solution is a very important factor to be considered in adsorption studies as its affects the surface charge of an adsorbent. As a result, characterization of the effect of pH on adsorption was investigated in this study. The result of pH on the removal of heavy metals from the automobile effluent is shown in Fig. 4. An increase in the adsorption of metals with increase in effluent
Fig. 1 The Fourier transform infrared spectra of the montmorillonite (A) before adsorption (B) after adsorption of metal ions.
I ^ I ч 11111 ^ il 1111111111111111111111| n 11111 111111111111111 nj I il 11111 i^j 11
Fig. 2 XRD spectra of the montmorillonite.
Fig. 4 Influence of pH on the adsorption of heavy metals from the automobile effluent.
pH was observed. Considering the affinity of UMC for metal ions, the following trend was obtained: Zn > Cu > Mn > Cd > Pb > Ni. It was noticed that this trend corresponds to the metals concentrations in the effluent with the highest concentration obtained for Zn and the lowest for Ni (Table 1). It has been reported that the differences in
Fig. 5 Influence of adsorbent dose on the removal of heavy metals from the effluent at (A) 300 K, (B) 313 K and (C) 323 K.
adsorption trend of the metal ions may be attributed to differences in the behaviors among these metals or their ions in solution [31]. This is mainly due to differences in the properties such as ionic size, electro-negativity, strength of acidity of the metals and the pKOH (negative log of hydrolysis constant) [31]. However, in this case the initial metal concentration in the effluent dominates the above mentioned factors in determining the amount of metal ions adsorbed. Such factors are usually significant, if the metal ions in solution are presented in the same concentration. We have reported in our previous study that the initial concentration of metal ions in solution plays a major role in determining the amount of metal ions adsorbed [5]. This is because higher concentration generates a greater driving force for more metals to undergo an interaction with the adsorbent. In fact it also implies the availability of more metal ions in solution to be adsorbed. Therefore if there are changes in the effluent, metals concentration would change, resulting in a change in the adsorption capacity of the adsorbent for each metal depending on its concentration. The low adsorption capacity of UMC for Pb(II) and Ni(II) is simply due to the very low concentration of these metals in the effluent. Also, significant adsorptions of metal ions were achieved at higher pH values greater than the pHpzc of 3.7 at which the surface of the adsorbent acquired a negative charge. The low adsorption of metal ions at lower pH values is due to the high concentration of H+ ions in solution which competes with the metals for the active sites of the adsorbent. However, as the pH of the solution increases, the number of H+ ions decreases, thereby making more active sites available for metal sorption due to decreased competition with the protons. At an effluent pH of 5.0, Cd ion showed a decrease; also slight decrease in adsorption of Mn and Cu was obtained at 6.0. The natural pH of the effluent of 6.5 was used in this study for all subsequent experiments because optimum removal of all metal ions was achieved at this pH range and higher values were avoided to prevent metal precipitation as hydroxides.
Influence of adsorbent dose
The adsorbent weight is an important factor in determining the capacity of the adsorbent for a given adsórbate concentration and volume of solution. The influence of the weight of UMC on heavy metal removal from the effluent was studied at temperatures of 300, 313 and 323 K. The result is shown in Fig. 5. It can be seen that for all temperatures studied, a decrease in the equilibrium adsorption capacity per unit mass of UMC for all metal ions with increase in the weight of adsorbent was recorded. The decrease in adsorption capacity is attributed to the higher UMC dose providing more active adsorption sites, which results in the sites remaining unsaturated during the adsorption process. It can also be as a result of the decrease in the specific surface area of the adsorbent as a result of the cohesive property of montmorillonite, which results in the coming together of adsorbent particles leading to the blockage of some of the active sites [29].
Equilibrium isotherm analysis
In order to effectively analyze and design an adsorption process it is important to understand the equilibrium isotherm application. Adsorption isotherms provide basic physicochem-ical data for evaluating the applicability of the adsorption process as a unit operation. In this regard, the Langmuir, Freundlich, Temkin and D-R isotherms were applied to the experimental evaluation. The regression coefficient (R2) was utilized to determine the best fitted model and the closer the R2 values to 1, the best the model fit. However, due to the inherent bias resulting from linearization, the mechanistic conclusion from the good fit (R2) of the models alone should be avoided when deciding the applicability of an isotherm model [32]. Another very important tool for evaluating the applicability of an isotherm model is the non-linear chi-square test (v2).
Table 3 Equilibrium isotherm parameters obtained for the treatment process.
Isotherm model Zn(II) Cu(II) Mn(II) Cd(II) Pb(II) Ni(II)
Langmuir model
q-L (mg/g) 5.7 1.58 0.59 0.33 0.10 0.10
KL (L/mg) 0.062 0.129 0.148 0.273 0.386 1.711
R2 0.639 0.971 0.987 0.772 0.548 0.099
X2 12.82 32.70 24.63 19.823 5.915 0.036
Freundlich model
Kf (L/g) 0.25 0.06 0.015 0.035 0.038 0.062
1/n 0.642 2.435 2.958 3.387 3.260 0.460
R2 0.915 0.977 0.968 0.916 0.818 0.650
X2 0.124 0.105 0.049 0.030 0.026 0.031
Temkin model
A (L/g) 0.381 0.359 0.324 0.536 0.762 2.998
B (mg/g) 4.662 5.619 3.651 2.925 0.877 0.051
R2 0.801 0.867 0.871 0.977 0.915 0.076
X2 1.006 0.426 0.175 0.133 0.023 0.028
D-R model
qm (mg/g) 5.601 7.652 5.613 6.653 2.149 0.101
B (mol2/J2) 3 x 10~6 4 x 10~6 6 x 10~6 3 x 10~6 2 x 10~6 2 x 10-7
E (kJ/mol) 0.408 0.353 0.288 0.408 0.5 1.581
R2 0.749 0.914 0.926 0.901 0.816 0.039
X2 0.996 0.415 0.133 0.032 0.192 0.033
The chi-square test is a statistical tool necessary for the best fit of an adsorption model. It is the sum of the squares of the differences between the experimental data and the data obtained by calculating from the models. It is expressed mathematically as:
V2 = [(qeexp - qeca02/qecal]
where qeexp (mg/g) is the experimental data of the equilibrium capacity and qecal (mg/g) is the equilibrium capacity obtained by calculating from the model. y2 would be a smaller number if the data from the model are similar to the experimental one and would be a larger number if they are different. The equilibrium isotherm parameters calculated are presented in Table 3. It was observed that the Langmuir isotherm is not applicable to the adsorption process due to the low R2 and large y2 recorded. This suggests that the adsorption process may not be attributed to a monolayer adsorption unto a homogenous adsorbent. The RL values obtained for the metal ions at all temperatures were between 0.223 and 0.962, which indicates that the adsorption of the metals unto UMC is a favorable process. The Freundlich model was found to present the best fit among all the isotherms considering the high R2 and low y2 values obtained. The only exception was observed for Pb(II) and Ni(II), where the Temkin isotherm was found to be more applicable. The conformity of the data to the Freundlich model suggests a multilayer adsorption of metal ions unto a heterogeneous surface of UMC. Furthermore, considering the energy E obtained from the D-R isotherm for all metals ions, the values are very much less than 8 kJ/mol. This indicates that the metal ions adsorption from the automobile effluent unto UMC is a physical process. A physical adsorption is desirable as the energy barrier to be overcome by metal ions is low and also facilitates easy desorption of metals during regeneration of the adsorbent.
Influence of particle size
The particle size of an adsorbent can have a significant effect on the adsorption capacity of the adsorbent. Therefore it was investigated using adsorbent sizes in the range 100500 im. The influence of particle size of UMC on the removal of heavy metals from the automobile effluent at 300 K is shown in Fig. 6. A decrease in the adsorption of all the metal ions with increase in particle size of UMC was recorded. This decrease is attributed to a decrease in the specific surface area of the adsorbent with increase in particle size [31]. The break-
ing of larger particles helps to open up more sites and channels on the surface of UMC resulting in more accessibility for metal ions owing to better diffusion [33]. An insignificant adsorption of Pb and Ni ions was recorded at larger particle sizes due to the extremely low concentration of these metals in the effluent as stated earlier. Furthermore, it has been reported that smaller particle sizes move faster in the solution compared to larger particles resulting in a faster rate of sorption [34]. For optimum sorption the particle size of 100 im was chosen and utilized in this study.
Influence of contact time
The time it takes metal ions and adsorbent to reach equilibrium is of considerable importance in adsorption experiment because it depends on the nature of the system used and can provide information on the process mechanism. The effect of contact time on the adsorption of heavy metals from the effluent was studied at temperatures of 300, 313 and 323 K and the results are presented in Fig. 7. It was observed that the rate of removal of metal ions was rapid initially (with the exception of Pb and Ni) and became gradual until an equilibrium time beyond
Fig. 6 Influence of adsorbent particle size on the removal of heavy metals from the effluent.
Fig. 7 Effect of contact time on the removal of heavy metal from the effluent at (A) 300 K, (B) 313 K, and (C) 323 K.
which there was no significant increase in the rate of removal. The initial rapid uptake is due to the presence of abundant active sites on UMC at this stage which becomes used up with time attaining saturation [29]. Although Pb and Ni ions still had a lower adsorption due to their lower concentrations, Ni reached equilibrium faster (around 90 min) compared to Pb(II) ions which attained equilibrium around 180 min. Similarly, the adsorption of the other metal ions on UMC at all temperatures presented various equilibrium times of 90 min for Cu, 120 min for Zn, 150 min for Mn and around 180 min for Cd ions. This implies that the concentration of the metal ion in solution was not the major determining factor in the rate of adsorption. Also, the solution temperature did not significantly affect the rate of removal although it affected the overall adsorption capacity of UMC for the metals. The different equilibrium times observed for the metal ions may be explained by considering the hydrated ionic radii of the metal ions. The ionic radii of the metals are given as Ni (0.69 A) < Cu (0.72 A) < Zn (0.74 A) < Mn (0.80 A) < Cd (0.97 A) < Pb (1.20 A). This implies that the smaller the ionic radii the faster the rate of adsorption [15]. Although the ionic radii of Cu and Zn are very close, their rates of adsorption were quite different. This might be due to differences in other properties of the metals such as their electronegativity, strength of acidity and the pKOH values of the metal hydroxides in solution [31].
Kinetic model analysis
The ability to predict the rate of adsorption for a given system is one of the most important factors in adsorption system design, as the system kinetics determines adsorbate residence time and the reactor dimensions. In this regard, the pseudofirst order, pseudo second order, intraparticle diffusion and liquid film diffusion rate equations were used to analyze the kinetic data. The calculated constants obtained from these models are given in Table 4. It was observed from the linear regression coefficient (R2) values that the adsorption process showed a greater conformity to the pseudo-second order
model than the pseudo-first order model. The opposite was observed for Pb and Ni ions. Similarly, the y2 values of the pseudo second order model showed good agreement between the experimental qe values and the calculated ones than the first order model, as the values for the former were lower. A reverse trend was also obtained again for Pb. It has been observed that adsorption unto a multi-metal ion system is a complex one. The behavior of each metal ion in a multi-metal ion system depends strongly on the concentration as well as the physical and chemical properties of the adsorbate and adsorbent [33]. We could conclude that the pseudo second order model best represents the kinetics of adsorption for Zn, Cu, Mn and Cd while the pseudo first order became more applicable for the lower concentrations of Pb and Ni ions. Many researchers have obtained the best fit for kinetic data with the pseudo second order model [35].
The pseudo-first order and pseudo-second order models could not identify the diffusion mechanism therefore the kinetic result was further analyzed with the intraparticle diffusion model and the liquid film diffusion model. The adsorption mechanism is usually controlled by the film diffusion or intraparticle diffusion or both [36]. From Table 4, the low R2 values presented by the intraparticle diffusion model suggest that the intraparticle diffusion mechanism did not play a major role in the overall sorption of the metal ions at all temperatures. Also, the occurrence of the intercepts (C „ 0) indicates the involvement of surface phenomenon. This was supported by the good R2 values presented by the film diffusion model. This revealed very important information that the rate controlling mechanism of adsorption is external liquid surface layer diffusion, although it was not the sole rate controlling mechanism due to the occurrence of the intercept (Y).
Thermodynamic analysis
The determination of thermodynamic parameters (AH0, AS0, and AG0) is important to evaluate the feasibility, spontaneity and heat change of the adsorption process. The calculated val-
Table 4 Kinetic model parameters obtained for the adsorption process.
Model/metals Zn(II) Cu(II) Mn(II) Cd(II) Pb(II) Ni(II)
Pseudo-first order model
qe (mg/g) 9.616 12.11 3.39 2.64 0.61 0.51
KI (min-1) 0.025 0.041 0.021 0.018 0.005 0.018
R2 0.934 0.945 0.970 0.917 0.5 0.931
X2 10.843 15.678 21.27 15.916 0.689 2.19
Pseudo-second order model
qe (mg/g) 7.09 5.0 2.93 2.39 0.04 0.52
h (mg/g min) 0.148 0.162 0.038 0.018 0.0001 0.0007
K2 (g/mg min) 0.003 0.006 0.004 0.003 0.072 0.003
R2 0.952 0.965 0.927 0.953 0.208 0.044
X2 0.475 0.396 0.154 0.032 27.448 0.135
Intraparticle diffusion model
K (mg/g min1/2) 0.319 0.204 0.137 0.105 0.042 0.009
C 1.351 1.566 0.217 0.074 0.306 0.013
R2 0.675 0.589 0.759 0.808 0.730 0.580
Film diffusion model
Kfd (mg/g min) 0.04 0.039 0.023 0.018 0.006 0.18
Y 1.17 0.8 0.566 0.544 0.376 0.72
R2 0.919 0.963 0.976 0.922 0.911 0.833
Table 5 Thermodynamic parameters obtained at different adsorbent doses for the adsorption process.
Parameter/adsorbent dose 0.1 g 0.2 g 0.3 g 0.4 g 0.5 g
Zn(II)
AH° (kJ/mol) -4.55 -2.72 3.97 10.39 8.28
AS0 (J/mol K) -10.97 -2.09 21.57 44.83 40.88
300 K (AG0) (kJ/mol) -1.097 -1.995 -2.494 -3.117 -4.05
313 K (AG0) (kJ/mol) -1.483 -2.290 -2.810 -3.513 -4.39
323 K (AG0) (kJ/mol) -0.778 -1.907 -2.980 -4.162 -4.99
Cu(II)
AH0 (kJ/mol) -5.36 -3.76 4.94 7.98 9.16
AS0 (J/mol K) -13.58 -6.38 24.47 35.35 40.95
300 K (AG0) (kJ/mol) -1.147 -1.745 -2.419 -2.669 -3.142
313 K (AG0) (kJ/mol) -1.431 -2.004 -2.706 -2.993 -3.617
323 K (AG0) (kJ/mol) -0.778 -1.558 -2.980 -3.491 -4.081
Mn(II)
AH0 (kJ/mol) -6.25 -1.01 10.64 10.37 13.54
AS0 (J/mol K) -21.28 -2.44 37.85 37.93 50.02
300 K (AG0) (kJ/mol) -0.399 -0.199 -0.723 -1.144 -1.471
313 K (AG0) (kJ/mol) -0.104 -0.442 -1.197 -1.197 -2.134
323 K (AG0) (kJ/mol) -0.966 -0.107 -1.584 -2.068 -2.605
Cd(II)
AH0 (kJ/mol) -9.96 -3.33 26.21 36.06 39.18
AS0 (J/mol K) -32.05 -7.28 57.99 124.04 135.60
300 K (AG0) (kJ/mol) -0.074 -0.897 -1.222 -1.222 -1.596
313 K (AG0) (kJ/mol) -0.936 -1.665 -1.874 -2.680 -3.148
323 K (AG0) (kJ/mol) -0.993 -0.618 -2.551 -4.055 -4.699
Pb(II)
AH0 (kJ/mol) 28.16 24.12 24.19 24.19 35.57
AS0 (J/mol K) 76.09 78.98 78.98 117.06 119.95
300 K (AG0) (kJ/mol) 2.369 1.197 0.474 0.474 0.474
313 K (AG0) (kJ/mol) 0.494 0.494 -0.546 -0.546 -1.275
323 K (AG0) (kJ/mol) 0.510 -0.564 -1.316 -1.316 -2.148
Ni(II)
AH0 (kJ/mol) 45.58 65.45 48.83 75.86 66.60
AS0 (J/mol K) 136.27 201.03 152.15 241.11 214.92
300 K (AG0) (kJ/mol) 5.013 5.013 2.544 3.218 1.397
313 K (AG0) (kJ/mol) 2.029 2.654 2.654 0.988 0.937
323 K (AG0) (kJ/mol) 2.095 0.429 -1.182 -2.363 -3.786
ues obtained at adsorbent doses of 0.1-0.5 g are given in Table 5. An exothermic adsorption process was indicated at doses of 0.1 and 0.2 g for Zn, Cu, Mn and Cd while an endothermic adsorption was obtained at higher doses of 0.3-0.5 g. The adsorption of Pb and Ni was found to be endothermic at all doses (0.10.5 g). The exothermic adsorption recorded for some of the metals is simply due to higher concentration and the existence of limited number of active sites due to smaller adsorbent doses. Therefore as the temperature increases the adsorbed metal ions on the saturated surface of UMC acquires more kinetic energy for desorption into the bulk phase [37]. On the other hand, at higher doses which present more active sites, the acquired kinetic energy is utilized for the movement of the metal ions to the unsaturated active sites. Pb and Ni are present in lower concentrations, so increase in temperature enhances their movement to the surface of UMC (due to higher kinetic energy) in a competitive system where metals with higher concentration are present. This result was corroborated with the AS0 values being negative at lower doses of 0.1-0.2 g for Zn, Cu, Mn, and Cd, indicating a decrease in randomness at the solid-solution interface [37], and also the positive values for Pb and Ni indicating an increase in randomness at the interface [29]. A feasible and
spontaneous process was observed at all temperatures for Zn, Cu, Mn and Cd as negative AG0 values were obtained. A more complex situation was observed for Pb and Ni in which a non-spontaneous process accounted for some of the adsorptions due to the positive AG0 values. This might be due to the very low concentration of these metals as stated earlier. However, the energy acquired by the metals with increase in temperature and increase in the number of active sites with adsorbent dose tend to make the process spontaneous. This supports our discussion on the AH0 and AS0 values obtained.
Conclusions
The adsorption of heavy metals from an automobile effluent on a montmorillonite as a low cost adsorbent was carried out using batch sorption technique. The montmorillonite proved to be suitable for significant removal of all the metals ions from the effluent despite been used in its unmodified form. This adsorbent which is present in abundant amount in Eastern Nigeria can be utilized for treatment of effluents containing high concentration of heavy metals thereby reducing the toxic effects posed by these metals to the environment.
Conflict of interest
The authors have declared no conflict of interest.
Compliance with Ethics Requirements
This article does not contain any studies with human or animal
subjects.
References
[1] Gulnaz O, Kaya A, Dincer S. The reuse of dried activated sludge for adsorption of reactive dye. J Hazard Mater 2006;134:190-6.
[2] Hani H. The analysis of inorganic and organic pollutants in soil with special regard to their bioavailabilty. Int J Environ Anal Chem 2009;39(2):197-208.
[3] Celekli A, Yavuz-Atmaca M, Bozkurt H. An eco-friendly process: predictive modellig of copper adsorption from aqueous solution on Spirulina platensis. J Hazard Mater 2010;173:123-9.
[4] Liang S, Guo X, Feng N, Tian Q. Isotherms, kinetics and thermodynamic studies of adsorption of Cu(II) from aqueous solution by Mg/K type orange peel adsorbents. J Hazard Mater 2010;174:756-62.
[5] Dawodu FA, Akpomie KG. Simultaneous adsorption of Ni(II) and Mn(II) ions from aqueous solution unto a Nigerian kaolinite clay. J Mater Res Technol 2014;3(2):129-41.
[6] Bhattacharyya KG, Gupta SS. Kaolinite, montmorillonite and their modified derivatives as adsorbents for removal of Cu(II) from aqueous solution. Sep Purif Technol 2006;50:388-97.
[7] Guo Z, Li Y, Zhang S, Niu H, Chen Z, Xu J. Enhanced sorption of radiocobalt from water by Bi(III) modified montmorillonite: a novel adsorbent. J Hazard Mater 2011;192:168-75.
[8] Akpomie KG, Dawodu FA. Efficient abstraction of nickel(II) and manganese(II) ions from solution onto an alkaline-modified montmorillonite. J Taibah Univ Sci 2014;8:343-56.
[9] Pearson HW, Mara DD, Bartone CR. Guidelines for the minimum evaluation of the performance of full scale waste stabilization ponds. Water Res 1987;21(9):1067-75.
[10] AOAC (Association of Official Analytical Chemist). Official methods of analysis. 20th ed. Maryland (USA): Gaithersburg, Md; 2005.
[11] Rhoades JD. Cation exchange capacity. In: Methods of soil analyst. Hadison (WI, USA): American Society of Agronomy/ Soil Science of America; 1982.
[12] Dawodu FA, Akpomie KG. Abstraction of Zn(II) ions from solution on a Nigerian bentonite. Pacific J Sci Technol 2014;15(1):257-67.
[13] Langmuir I. The adsorption of gases on plane surfaces of glass, mica and platinum. J Am Chem Soc 1918;40:1361-403.
[14] Freundlich HMF. Over the adsorption. J Phys Chem 1906;57:385-471.
[15] Temkin MI, Phzhev V. Kinetics of ammonia synthesis on promoted ion catalyst. Acta Physicochem 1940;12:217-22.
[16] Dubinin MN, Zaverina ED, Radushkevich LV. Sorption and structure of activated carbons: adsorption of organic vapors. J Phys Chem 1947;21:351-1362.
[17] Guler UA, Sarioglu M. Single and binary biosorption of Cu, Ni and methylene blue by raw and pretreated Spirogyra sp.: equilibrium and kinetic modeling. J Environ Chem Eng 2013;1:269-377.
[18] Lagergren S. About the theory of so-called adsorption of soluble substances. Ksven Vetenskapsakad Handl 1898;24(4):1-39.
[19] Ho YS, Mckay G. The kinetics of sorption of basic dyes from aqueous solution by sphagnum moss peat. Can J Chem Eng 1998;76:822-7.
[20] Taffarel SR, Rubio J. On the removal of Mn ions by adsorption onto natural and activated Chilean zeolites. Mineral Eng 2009;22:336-43.
[21] World Health Organization (WHO). The world report: shaping the future. Geneva 27 (Switzerland): World Health Organization, 1211; 2003.
[22] Siyanbola TO, Ajanaku KO, James OO, Olugbuyiro JAO, Adekoya JO. Physicochemical characteristics of industrial effluents in Lagos State, Nigeria. Int J Multidis Sci Eng 2011;1:49-54.
[23] Akan JC, Abdulrahman FL, Dimari GA, Ogugbuaja VO. Physicochemical determination of pollutants in wastewater and the vegetable samples along the Jakara wastewater channel in Kano metropolis, Kano State, Nigeria. Eur J Sci Res 2008;23:122-33.
[24] Peirce JJ, Weiner RF, Vesilind PA. Environmental pollution and control. 4th ed. Woburn (MA, USA): Butter worthHeinemann; 2010, p. 373-4.
[25] Rao PV. Textbook of environmental engineering. Eastern economy ed. New Delhi: Prentice-Hall of India Private Limited; 2005, p. 280-8.
[26] Sankpal ST, Naikwade PV. Physicochemical analysis of effluent discharge of fish processing industries in Ratnagiri, India. Bioresource Discov 2012;3(1):107-11.
[27] Ademoroti CMA. Standard methods for water and effluent analysis. Ibadan (Nigeria): Foludex Press Ltd; 1996, p. 44-54.
[28] Macht F, Eusterhues K, Pronk GJ, Totsche KU. Specific surface area of clay minerals: comparison between atomic force microscopy measurement and bulk-gas (N2) and liquid (EGME) adsorption methods. Appl Clay Sci 2011;53:20-6.
[29] Li Y, Xia B, Zhao O, Liu F, Zhang P, Du Q, Wang D, Li D, Wang Z, Xia Y. Removal of copper ions from aqueous solution by calcium alginate immobilized kaoline. J Environ Sci 2011;23(3):404-11.
[30] Njoya A, Nkoumbou C, Grosbois C, Njopwouo D, Njoya D, Courtin-Nomade A, et al. Genesis of Mayouom kaolin deposit (western Cameroon). Appl Clay Sci 2006;32:125-40.
[31] Barka N, Abdennouri M, El-Makhfouk M, Qourzal S. Biosorption characteristics of cadmium and lead onto eco-friendly dried cactus (Opuntia ficus indica) cladodes. J Environ Chem Eng 2013;1:144-9.
[32] Foo KY, Hameed BH. Insights into the modeling of adsorption isotherms systems. Chem Eng J 2010;156:2-10.
[33] Hui KS, Chao CYS, Kot CS. Removal of mixed metal ions in wastewater by zeolite 4A and residual products from recycled coal fly ash. J Hazard Mater 2005;127:89-101.
[34] Kooli F, Yan L, Al-Faze R, Al-Sehimi A. Removal enhancement of basic blue 41 brick waste from an aqueous solution. Arab J Chem 2014. http://dx.doi.org/10.1016/ arabjc.2014.04.003.
[35] Gautam RK, Mudhro A, Lofrano G, Chattopadhyaya MC. Biomass-derived biosorbent for metal ions sequestration: adsorbent modification and activation methods and adsorbent regeneration. J Environ Chem Eng 2014;2:239-59.
[36] Yu L, Luo YM. The adsorption mechanism of anionic and cationic dyes by Jerusalem artichoke stalk-based mesoporous activated carbon. J Environ Chem Eng 2014;22:220-9.
[37] Meitei MD, Prasad MNV. Lead(II) and cadmium(II) biosorption on Spirodela polyrhiza (L) Schleiden biomass. J Environ Chem Eng 2013;1:200-7.
