Cadmium, Copper and Zinc Biosorption Study by Non ...

Water Air Soil Pollut DOI 10.1007/s11270-009-9987-x

Cadmium, Copper and Zinc Biosorption Study by Non-Living Egeria densa Biomass Juliana M. T. de A. Pietrobelli & Aparecido N. Módenes & Márcia R. Fagundes-Klen & Fernando R. Espinoza-Quiñones

Received: 9 October 2008 / Accepted: 15 January 2009 # Springer Science + Business Media B.V. 2009

Abstract In this work, the potential removal of Cd, Cu, and Zn ions by non-living macrophytes Egeria densa has been studied. The adsorption kinetic and equilibrium experiments of these three metals on E. densa were performed in batch systems with controlled temperature and constant shaking. It was observed that all metal adsorption rates have increased when the pH was increasing. A pH threshold of 5 was established for use in adsorption experiments in order to avoid the metal precipitation. For adsorption kinetic tests, the equilibrium times for all metals were around 45 to 60 min. The equilibrium data at pH 5 were better described by the Langmuir isotherm than the Freundlich one, with the adsorption rate and maximum metal content values of 0.43 L g−1 and 1.25 mequiv g−1 for Cd, 4.11 L g-−1 and 1.43 mequiv g−1 for Cu, and 0.83 L g−1 and 0.93 mequiv g−1 for Zn. These adsorption parameters for E. densa resemble or are better than those for other biosorbents already studied, suggesting that the macrophytes E. densa as a biosorbent has a good metal removal potential for applications in effluent treatment systems. J. M. T. d. A. Pietrobelli : A. N. Módenes : M. R. Fagundes-Klen : F. R. Espinoza-Quiñones (*) Department of Chemical Engineering- Postgraduate Program, West Parana State University, Campus of Toledo, rua da Faculdade 645, Jd. La Salle, 85903-000 Toledo, Paraná, Brazil e-mail: [email protected]

Keywords Biosorption . Metal removal . Egeria densa . Cadmium . Copper . Zinc

1 Introduction The use of natural resources without any control has caused a serious global contamination problem in the ecosystem water quality, mainly due to the organic and inorganic chemical pollution. The development of new technologies for toxic substance cleanup is therefore of significant environmental interest. In special, many attempts have been made to remove and recover metallic elements from effluents polluted with high heavy metal concentrations, in order to reduce the metal content to below the allowed metal concentration limits, by the international and Brazilian environmental norms and to avoid the pollution of underwater resources and the water body. Many efforts have been directed toward the use of conventional treatment of metal-contaminated wastewater, including chemical precipitation, chemical oxidation or reduction, electrochemical treatment, reverse osmosis, solvent extraction, ion exchange and evaporation (Ozcan et al. 2005), and sorption processes (Davis et al. 2003; Martinez et al. 2006). However, when most of these methodologies are applied in low metal concentration effluent treatments, they could have high chemical costs, low removal efficiencies, low selectivities, high energy requirements, and secondary toxic slurries generation.

Water Air Soil Pollut

A promising technological proposal, called biosorption, for the metal removal is the use of living and dead biomass in order to remove the metallic ions of low concentrations from aqueous solution (Ahluwalia and Goyal 2007). The biosorption phenomenon involves a series of passive and active transport mechanism combinations, beginning with the metallic ion diffusion into the biomaterial. Therefore, it contains many passive processes of accumulation such as adsorption, ion exchange, covalent bonds, complexation, chelation, and micro-precipitation process. The industrial potential of the biosorption depends on many factors, such as biosorption capacity, efficiency and facility of metal recovery, and mainly, equivalence with the performance and cost of traditional processes (Liu et al. 2007). For the use in industrial applications and its corresponding equipment design, the biosorbent performance must be investigated and compared with other technologies. This kind of study has been reported for many biological materials such as bacteria (Chubar et al. 2008), seaweed (Cruz et al. 2004; Chojnacka et al. 2005; Fagundes-Klen et al. 2007; Romera et al. 2008), green algae (Al-Rub et al. 2006), fungus (Ozsoy et al. 2008), and aquatic macrophytes (Saygideger et al. 2005). In this work, the metal removal potential by the non-living aquatic macrophytes Egeria densa as biosorbent was investigated. In order to seek the best experimental conditions, preliminary tests, such as metal precipitation, particle size among others, adsorption kinetic, and equilibrium tests, were performed for the cadmium, copper, and zinc ions. Based on the best experimental conditions for each metal, the adsorption equilibrium and kinetic parameters for E. densa were estimated by two models.

knife electrical mill and were stored for preliminary tests and biosorption experiments. 2.2 Chemicals All chemicals used were of analytical reagent grade. Deionized waters were used as a dilution media. Stock solutions of Cd, Cu, and Zn ions were also prepared, ranging from 3.0 to 10.0 mequiv L−1, from their water-soluble metallic salts (CdCl 2 ·H 2 O, CuCl2·2H2O, and ZnCl2) and stored in volumetric flasks for preliminary and biosorption tests. Solutions of NaOH (1 M) and HCl (1 M) were used for pH adjustment. Cadmium-, zinc-, and copper-certified standard solutions (1.0 g L−1 for AAS, Merck) were used to obtain the calibrated curve for atomic absorption spectrometer. 2.3 AAS Measurements

2 Materials and Methods

For metal concentration measurements, after each test or biosorption experiment, the liquid phase was separated from the adsorbent by a vacuum filtration system using 0.45-μm membranes. An absorption atomic spectrometer (AAS), model AA 932-GBC (Analitica), was used. For cadmium analysis, eight diluted standard solutions ranging from 0.004 to 1.8 mg L−1 were used in order to calibrate the ASS, while for zinc and copper, the AAS calibration was made using eight diluted standard solutions ranging from 0.005 to 1.6 mg L−1 and from 0.01 to 4 mg L−1, respectively. The calibration coefficients (r2) obtained from four calibration standards for all analyses were 0.995 or better. The wavelengths (nm) used for the metals were the following: Cu, 224.7; Cd, 228.8; and Zn, 213.8. After the AAS calibration, sometimes, samples were diluted to fit in the calibration range. Confidence intervals of 95% were calculated for the results.

2.1 Biomass Sampling

2.4 Preliminary Tests

Aquatic macrophytes E. densa were collected from artificial ponds in the Aquiculture Advanced Research Center, which is located near Toledo City in the Brazilian Paraná state west region. Afterwards, aquatic plants were washed in tap water, rinsed many times using deionized water, and dried at room temperature. All samples were grounded using steel-

Several liquid solutions supplied with 6.23, 4.52, and 3.34 mequiv L−1 for Cd, Cu, and Zn, respectively, were used in order to determine the pH threshold value as the metal precipitation is beginning to occur. The metal-doped solution was distributed in a set of nine small aliquots, and their pH was adjusted to range from 3 to 8 by the addition of aliquots of NaOH


and HCl. Each metal precipitation test was made by duplicate, without any shaking and at room temperature by 48 h. Afterwards, the amount of metal in each sample was measured by AAS. In order to improve the biosorption kinetic and experimental equilibrium conditions, the temperature effect on the plant drying was tested using two temperatures, 30°C and 50°C, while the solution temperature effect was evaluated at four controlled temperatures, ranging from 25°C to 45°C and using as a biosorbent only those plants at 30°C. For both tests, a volume of 50 mL single metal solution was added to 250 mg dry plant in 125-mL Erlenmeyer flask. The mixtures were shaken on a rotary shaker for 12 h for each temperature setting, and then, the quantity of metal in each sample was measured by AAS. The biosorbent grain size effect was also studied, using three fractional grain sizes between 0.1 to 0.6 mm and their mixture as well. This test was carried out using the same procedure for the temperature effect test, but in this case, a 30°C controlled temperature metal solution and 12 h contact time were used. 2.5 Kinetic Test

value, 50 mL metal-supplied solution was added to the dry biomass, ranging from 20 to 550 mg, in a set of 125-mL Erlenmeyer flasks. All experiments were repeated with constant shaking and controlled temperature of 30°C, monitoring regularly the pH and performing the required adjustments, during the 12 h contact time. Afterwards, initial and final metal concentrations in each sorption experiment were determined by AAS.

3 Results and Discussion 3.1 Preliminary Test The precipitation data of cadmium, zinc, and copper were fitted by logistic-type functions, with correlation coefficient of 0.9968, 0.9959, and 0.9783, respectively, as can be seen in Fig. 1, suggesting that the same pH threshold can be assigned as being 5.3 for the metal precipitation, with cadmium, zinc, and copper concentration reductions of about 0.5%, 2.7%, and 1.1%, respectively, which are below the experimental standard deviations. Consequently, all the following sorption experiments were performed at pH below this threshold, avoiding thus the unacceptable pH range.

In order to obtain the equilibrium time, single standard solutions of Cd, Cu, and Zn were prepared of 7.92, 4.52, and 4.18 mequiv L−1. For each metal, an aliquot of 50 mL with pH adjusted at 5 was added to a 250-mg dry biomass in a 125-mL Erlenmeyer flask, and then it was agitated on a shaker at 30°C constantly controlled temperature. The solution pH was regularly monitored during the whole time of sorption experiments, and its pH was adjusted at 5, as required. The adsorption experimental times were set at several contact times, ranging from 0 to 120 min. Afterwards, the initial and final metal concentrations in each sorption experiment were determined by AAS. 2.6 Equilibrium Concentration Equilibrium experiments were carried out at two pH values (4 and 5) for each studied metal, using the E. densa as biosorbent, in order to seek the best response to the equilibrium concentration parameters to be obtained by adsorption isotherm models. At each pH

Fig. 1 Precipitation effect of cadmium (square), zinc (triangle), and copper (circle) with the increase of pH in the solution. Solid, dashed, and dotted lines represent the logistic-type fitting for Cd, Zn, and Cu data, respectively. Standard deviations are less than the symbol size for all metals


In both dry temperatures tested, the same metal removal of 70% was achieved, suggesting that the biosorbent treatment does not increase the metal removal significantly. Consequently, in this work, a room temperature (30°C) was used for all metal sorption experiments and other tests in order to simplify the plant drying procedure. In the interval of 25°C to 45°C controlled temperatures, the highest metal removal percentage was observed to occur at 30°C of the solution temperature, corresponding to 70% metal removal, which was approximately 15% above the other tested temperatures. Based on the previous results, the 30°C solution temperature was set up for all the following metal sorption experiments. On the other hand, the metal-sorption experiments based on the E. densa fractional grain sizes have shown small differences (less 5%) as the cadmium, copper, and zinc ion removal were compared among different grain sizes tested. For practical purpose, the grain size effect might be considered insignificant, and consequently, grains, without a previous size separation after the grounding process, were chosen, obtaining thus a 70% mean metal removal.

Cu, and Zn removal rates were obtained by using Sargassum filipendula (Fagundes-Klen et al. 2007) at an equilibrium time between 1 and 2 h. Thus, the biosorbent tested in this study exhibits a quick adsorption behavior. 3.3 Adsorption Kinetic For all the sorption data, obtained below pH 5, the metal concentration in solid phase in time, labeled as qt, was calculated from the initial concentration (C0, in mequiv L−1) and the final concentration (Ct, in

3.2 Kinetic Test The dry E. densa biomass-based biosorption kinetic data, using Cd2+, Cu2+, and Zn2+-doped solutions at pH 5, are shown in Fig. 2a–c, respectively, where logistic-type functions are used to fit the metal biosorption process with a good correlation coefficient and χ2 was achieved. Approximately 70% removal was achieved for all metals. The biosorption of each metal has increased sharply at short contact and slowed gradually as equilibrium was approached. Figure 2a–c shows the quick biosorption behavior of all metals with the equilibrium times around 45 to 60 min. This kind of behavior is typical for metal biosorption characterized by no energy exchange reaction, and thus, the metal removal is governed by a purely physical–chemical interaction between the biosorbent and the metal-supplied liquid phase (Cruz et al. 2004). In another paper (Lodeiro et al. 2005), 50% of Cd removal rates were obtained using Bifurcaria bifurcata, Saccorhiza polyschides, Pelvetia caniculata, Ascoplyllum nodosum, and Laminaria ochroleuca, with around 3-h equilibrium time, while 70% of Cd,

Fig. 2 Effect of the contact time on a cadmium, b copper, and c zinc sorption for E. densa, using an initial concentration of 7.92, 4.52, and 4.18 mequiv. L−1, respectively, at pH 5, 12 h equilibrium time and 30°C. Standard deviations are less than the symbol size for all metals. All metal sorption data were fitted by logistic-type functions


kinetics, which allows the determination of adsorption rate constant (K1) (Ozsoy et al. 2008). log qeq qt ¼ log qeq

K1 t 2; 303

ð2Þ

where K1 is the rate constant of the pseudo first-order adsorption process (min−1), qeq and qt (in mequiv g−1) are the metal adsorption quantities in equilibrium and in time, respectively. On the other hand, the second-order rate equation is described by Eq. 3. For this case, the experimental data was convenient to plot as t/qt against t, which showed a linear tendency of the data and allowed the determination of metal adsorption quantities in equilibrium (qeq) and the sorption rate constant, called K2, from the linear parameters of Eq. 3. t 1 1 ¼ þ t qt K2 q2eq qeq

Fig. 3 The adsorption kinetic data of a cadmium, b copper, and c zinc for E. densa, at pH 5, 12 h equilibrium time and 30°C. Standard deviations are less than the symbol size for cadmium and copper. All metal adsorption kinetic data were also fitted by linear-type functions, according to the pseudo second order model (Eq. 3)

ð3Þ

where K2 is the rate constant of sorption (g meq−1 min−1). The experimental data, at 30°C and pH 5, were found to follow better the second-order kinetic model, based on the value obtained from the correlation coefficient. Both experimental and modeling results are shown in Fig. 3. Hence, based on the second-order kinetic model, it was possible to determine the adjustable qeq parameter and the sorption rate constant (K2) values as shown in Table 1. 3.4 Equilibrium Concentration

−1

mequiv L ) in time, accumulated on the E. densa mass, using the Eq. 1.

qt ¼

V ðC0 Ct Þ ms

ð1Þ

where V is the volume of the solution and ms the biosorbent mass (dry weight). In order to analyze the sorption kinetics, both the pseudo-first order (Ho and McKay 1998; Bhattacharyya and Sharma 2005) and pseudo-second order (Ho and McKay 2000; Ho 2005) kinetic models were used. A plot of log(qeq −qt) versus t would provide a straight line for the first-order adsorption

Despite the original development, the Langmuir adsorption isotherm has traditionally been extended to describe empirically the equilibrium relationships between a liquid bulk phase and a solid phase, in

Table 1 The equilibrium uptake concentration (qeq) and sorption rate constant (K2) of cadmium, copper, and zinc for E. densa, at pH 5 and 30°C, obtained from the linearity of the adsorption kinetic experimental data, according to the Eq. 3 Metal Cadmium Copper Zinc

qeq (mequiv g−1)

k2 (g mequiv−1 min−1)

1.025±0.002 0.678±0.002 0.644±0.039

0.826±0.051 0.792±0.202 0.510±0.396


On the other hand, the empirical Freundlich isotherm has also been employed to quantify the equilibrium biosorption system of metals. This type of isotherm is currently described by Eq. 5. n ð5Þ q* ¼ k C * where k and n are empirically determined constants, with k being related to the maximum binding capacity, and n is related to the affinity or binding strength. 3.5 Sorption Isotherm The typical favorable-type Langmuir and Freundlich isotherms, represented by the Eqs. 4 and 5 with two

Fig. 4 Sorption isotherm of a cadmium, b copper, and c zinc for E. densa at pH 4, 12 h equilibrium time and 30°C. All metal sorption isotherms were fitted according to the Langmuir and Freundlich equations

order to quantify and compare the performance of different biosorbents (Davis et al. 2003). The traditional form of the Langmuir adsorption isotherm is represented by Eq. 4. q* ¼ qmax

bC * 1 þ bC *

! ð4Þ

where q* is the metal uptake (mequiv g−1), qmax is the maximum metal uptake per biosorbent mass (mequiv g−1), C* is the metal concentration (mequiv L−1) at the equilibrium, and b is an adjustable parameter (mequiv L−1) corresponding to the ratio between the metal adsorption and desorption rates.

Fig. 5 Sorption isotherm of a cadmium, b copper, and c zinc for E. densa at pH 5, 12 h equilibrium time and 30°C. All metal sorption isotherms were fitted according to the Langmuir and Freundlich equations

Water Air Soil Pollut Table 2 Metal adsorption modeling parameters for Langmuir and Freundlich isotherm models and their respective fitting statistical parameters, for metal adsorption data obtained at 30°C and 12 h equilibrium time Isotherms

Parameters

Metals Cadmium

Langmuir

Freundlich

qmax b r2 χ2 k n r2 χ2

Copper

Zinc

pH 4

pH 5

pH 4

pH 5

pH 4

pH 5

0.96 (5) 0.39 (6) 0.9896 0.0007 0.29 (2) 0.45 (4) 0.9713 0.0029

1.25 (5) 0.43 (5) 0.9854 0.0014 0.42 (3) 0.42 (4) 0.9440 0.0051

0.78 (2) 3.10 (28) 0.9914 0.0004 0.54 (2) 0.25 (4) 0.9000 0.0025

1.43 (4) 4.11 (42) 0.9910 0.0020 1.05 (3) 0.34 (3) 0.9150 0.010

0.68 (14) 0.50 (20) 0.9427 0.0009 0.23 (1) 0.56 (7) 0.9183 0.0008

0.93 (10) 0.83 (2) 0.9569 0.0021 0.40 (2) 0.46 (4) 0.9565 0.0022

adjustable parameters, were assumed to model the E. densa-based metal equilibrium biosorption data. The metal adsorption modeling parameters for each isotherm type were obtained by a minimization procedure employing the Origin® software, version 8.0. The adsorption experimental data for all metals on dry biomass of E. densa corresponding to pH 4 and 5 are shown in Figs. 4 and 5, respectively, including both Langmuir and Freundlich isotherms. From the correlation coefficient and reduced χ2 values of each model, as shown in Table 2, the data were fitted better with the Langmuir isotherm. Although the Freundlich model is often employed for metal adsorption in liquids, this isotherm exhibits rather higher relativeto-data difference as compared with the Langmuir one in order to characterize the E. densa biomass. The fitting curves drawn in Figs. 4 and 5 were made using a couple of adjustable Langmuir (qmax, b) and Freundlich (k, n) parameters. The metal adsorption rate and the maximum metal content values for all the metals are summarized in Table 2 for both pH 4 and 5. In comparison between Langmuir parameters for both pH, the maximal cadmium, copper, and zinc contents have reached the highest values when pH threshold was used for all metal adsorption experiments. At pH 5, their values correspond to 1.25 ± 0.05, 1.43 ± 0.04, and 0.93 ± 0.10 mequiv g−1 for cadmium, copper, and zinc, respectively. Nonetheless, similar result was obtained by Fagundes-Klen et al. (2007), using the marine algae S. filipendula, whose maximum metal content values were 1.26 and 1.28 mequiv g−1 for Cd and Zn

divalent ions, respectively. In comparison, this is an almost identical value for that corresponding to the adjustable Langmuir parameter associated with the E. densa, with the same experimental conditions. In another paper (Dang et al. 2009), lower adsorption capacity values (0.26 and 0.36 mequiv g−1) were reported for the Cd and Cu divalent ions using the Tricticum aestvum as biosorbent. Based on the low ratio between the adsorption and desorption rate values, which is one of the adjustable Langmuir parameters, the low equilibrium time and the quick metal removal rate, suggests that the E. densa-based metal removal could not be only associated with one main mechanism, but it might be a combination of several mechanisms within this new kind of biosorbent, such as exchange ion, complexation, quelation, inorganic precipitation, and coordination, along with the adsorption mechanism. The low equilibrium (approximately 45 to 60 min for Cd, Cu, and Zn), the quick metal removal rate, and the high metal uptake demonstrated that the E. densa as biosorbent can be used in the treatment of effluent systems due to great adsorption potential.

4 Conclusions In this work, the biosorption-based removal of Cd, Cu, and Zn divalent ions by non-living aquatic macrophytes E. densa was studied as a possible metal cleanup from industrial effluents. The kinetic test showed its short equilibrium time, indicating that the E. densa as a biosorbent could be used in continuous


treatment system. With its good characteristics for removal of all metal ions studied such as low equilibrium time, high metal removal rate, and maximal metal uptake similar or better than other biosorbent, the E. densa can be considered as an efficient biosorbent for the implementation of biosorption technology in industrial and environmental remediation. Acknowledgments We thank the Araucaria Foundation and the CNPq for financial support of this study. We also thank both Scientific and Technological Research Centers located in Cascavel (Fundetec) and in Toledo (Funtec) cities in the Brazilian Paraná State for the technical support in AAS technique.

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