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Sorption capacity of six different algae (green, red and brown) was evaluated in the recovery of cadmium, nickel, zinc, copper and lead from aqueous solutions.

Bioresource Technology 98 (2007) 3344–3353

Comparative study of biosorption of heavy metals using different types of algae E. Romera, F. Gonza´lez *, A. Ballester, M.L. Bla´zquez, J.A. Mun˜oz Dpto. Ciencia de los Materiales e Ingenierı´a Metalu´rgica, Facultad de C. Quı´micas, Universidad Complutense, Ciudad Universitaria, 28040 Madrid, Spain Received 11 July 2006; received in revised form 11 September 2006; accepted 17 September 2006 Available online 10 July 2007

Abstract Sorption capacity of six different algae (green, red and brown) was evaluated in the recovery of cadmium, nickel, zinc, copper and lead from aqueous solutions. The optimum sorption conditions were studied for each monometallic system. The optimum pH was 6 for the recovery of Cd, Ni and Zn, and less than 5 for Cu and Pb. The best results were obtained with the lowest biomass concentration used (0.5 g/L). Experimental data fitted a Langmuir model very well according to the following sequence of the sorption values: Pb > Cd P Cu > Zn > Ni. The brown algae achieved the lowest metal concentration levels in solution; the best results were obtained with Fucus spiralis. Finally, a software computer program was used to simulate the process by comparison of theoretical with experimental results and show minimum differences between both types of data. Ó 2006 Published by Elsevier Ltd. Keywords: Algae; Biosorption; Heavy metals; Simulation

1. Introduction Biosorption is an innovative technology that employs inactive and dead biomass for the recovery of heavy metals from aqueous solutions. As an alternative to traditional methods, its promising results are now being considered for application by the scientific community. In this context, research and development of new biosorbent materials has focused especially on algae, due to its high sorption capacity and its availability in almost unlimited amounts (Klimmek et al., 2001). However, the latest publications in this field have considered mainly other types of biomass (especially fungi and bacteria) instead of algae. It seems likely that algae do not form a homogeneous group within the vegetal kingdom. They are divided into several evolutionary pathways completely independent: a ‘‘red pathway’’ with red algae (Rhodophyta), a ‘‘brown pathway’’ with brown algae (inter alia, Chromophyta) and

*

Corresponding author. Tel.: +34 91 394 43 35; fax: +34 91 394 43 57. E-mail address: [email protected] (F. Gonza´lez).

0960-8524/$ - see front matter Ó 2006 Published by Elsevier Ltd. doi:10.1016/j.biortech.2006.09.026

a ‘‘green pathway’’ that includes green algae (Chlorophyta) along with mosses, ferns and several plants. Differences between these types of algae are mainly in the cell wall, where sorption takes place. Research in the field of biosorption has mostly concerned itself with brown algae (Holan et al., 1993; Chong and Volesky, 1995; Matheickal and Yu, 1999; Matheickal et al., 1999; Yu et al., 1999; Leusch et al., 1995) and to a less extent with green (Do¨nmez et al., 1999; Aksu et al., 1999, 1997) and red algae (Holan and Volesky, 1994). The cell walls of brown algae generally contain three components: cellulose, the structural support; alginic acid, a polymer of mannuronic and guluronic acids and the corresponding salts of sodium, potassium, magnesium and calcium; and sulphated polysaccharides. As a consequence, carboxyl and sulphate are the predominant active groups in this kind of algae. Red algae also contain cellulose, but their interest in connection with biosorption lies in the presence of sulphated polysaccharides made of galactanes (agar and carragenates). Green algae are mainly cellulose, and a high percentage of the cell wall are proteins bonded to polysaccharides to form glycoproteins. These compounds

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contain several functional groups (amino, carboxyl, sulphate, hydroxyl, . . .) which could play an important role in the biosorption process. The aim of the present work was to evaluate the sorption capacity of six different algae, Codium vermilara, Spirogyra insignis, Asparagopsis armata, Chondrus crispus, Fucus spiralis and Ascophyllum nodosum, in respect of five different heavy metals: cadmium, nickel, zinc, copper and lead. 2. Methods Two algae from each main division (green, brown and red) were selected for the experiments. Table 1 shows the identification of each alga (Cabioch et al., 1995; Van den Hoek et al., 1995; Graham and Wilcox, 1998). All biomass were marine except S. insignis, harvested from fresh water. The samples of C. vermilara, C. crispus, F. spiralis and A. nodosum were collected on the northern Atlantic coast of Spain and the red alga A. armata on the Mediterranean coast of Malaga, in southern Spain. Finally, S. insignis was collected at Valmayor dam (Madrid, Spain). Sample preparation consisted in a preliminary visual cleaning of impurities followed by several washes with distilled water. The overflow collected in each wash contained small particles of biomass substantially more difficult to settle, and it was centrifuged at 5000 rpm for 10–15 min. Then, after removal of the clear liquid, the pellet was mixed with previously-washed biomass to avoid large losses of it, and the whole system dried to constant weight in an oven at 60 °C. Once dry, samples were ground to the adequate particle size for biosorption tests ( Cu P Cd > Ni > Zn. This sequence was very similar to the order of affinities of the biomass for the metal (given by Langmuir constant b: Pb > Cd P Cu > Ni > Zn) and also decreased for each of the studied biomass expressed in L/mmol in Table 4. Only in those cases where values were very similar was that order of affinities altered to any degree (C. crispus and A. nodosum). In addition, for the same equilibrium metal concentration, the sorption capacity of each biomass (q) also followed a similar sequence in almost all cases, in accordance with the descending order of qmax for each biosorbent (Table 4). These results corroborate the hypothesis that the binding of metal to active sites of the cell wall is closely related to some intrinsic metal property, such as ionic radii and electronegativity of atoms (Chong and Volesky, 1996; Tobin et al., 1984). On the other hand, brown algae (F. spiralis and A. nodosum) showed higher sorption capacity than any other algae (Table 4). At worst they were able to retain twice as much metal as any of the other tested algae. The presence of alginates in the cell wall of brown algae could be responsible for such behaviour by anchoring the metal to the biomass (Fourest and Volesky, 1997; Davis et al., 2000). The sequence obtained as a function of the type of alga was: brown > red > green. Of the red algae, A. armata, with an intermediate sorption capacity, behaved similarly to green algae, whereas C. crispus was closer to brown algae. This relates to the fact that the latter contains carragenates in its composition, which behave similarly to the alginates in brown algae and are responsible for metal uptake by the biomass. From this it would seem that, besides the biomass itself or the type of alga used, the sorption phenomenon fundamentally depends on the type of metal employed.

Table 3 Regression coefficients for each Langmuir isotherm

Codium vermilara Spirogyra insignis Asparagopsis armata Chondrus crispus Ascophyllum nodosum Fucus spiralis

Pn

2

Nickel

Zinc

Copper

Lead

0.99 0.99 0.96 0.99 0.99 0.99

0.99 0.97 0.99 0.99 0.99 0.99

0.96 0.95 0.99 0.99 0.99 0.99

0.99 0.97 0.99 0.99 0.99 0.99

0.99 0.99 0.99 0.98 0.99 0.99

2

R ¼ 1  Pi¼1 n

ðY obsi  Y cali Þ

i¼1 ðY obsi

where Yobs Y obs Ycalc

Cadmium

ð5Þ

 Y obs Þ2

values of qe obtained experimentally average values of qe obtained experimentally values of qe calculated from the model

The closer the value is to 1, the better the fit to the model. The data shown in Table 3 indicate an almost perfect fit. Table 4 shows the values of constants qmax and b in moles to allow comparison of sorption data for each metal. Fig. 4 shows, in general, that for the five metals tested, when the metal concentration equilibrium increased, the value of sorption capacity also increased, but only up to a limiting value. That value represents the maximum amount of metal that the biomass can retain when all bond sites have been occupied. The metal that was best taken up by the biomass was lead. S. insignis, C. vermilara and A. armata were able to reduce the equilibrium metal concentration for each metal to a similar extent to other biomass, but they attained a higher sorption capacity. For each metal, the value of Ce decreased, following the same sequence for all the biomass, as follows:

Table 4 Values of Langmuir constants for the five monometallic systems tested with each biomass: Codium vermilara (1), Spirogyra insignis (2), Asparagpsis armata (3), Chondrus crispus (4), Ascophyllum nodosum (5) and Fucus spiralis (6) Algae

Cadmium (mg/g)

Values of qmax 1 21.8 2 22.9 3 32.3 4 75.2 5 87.7 6 114.9

Nickel (mmol/g) 0.19 0.20 0.29 0.67 0.78 1.02

Cadmium (L/mg) Values of b 1 2 3 4 5 6

0.10 0.12 0.09 0.06 0.15 0.11

Zinc

(mg/g)

(mmol/g)

(mg/g)

13.2 17.5 17.1 37.2 43.3 50.0

0.22 0.30 0.29 0.63 0.74 0.85

23.8 21.1 21.6 45.7 42.0 53.2

Nickel (L/mmol) 11.15 13.59 10.61 6.37 17.34 12.67

(L/mg) 0.09 0.04 0.12 0.05 0.13 0.13

Copper (mmol/g) 0.36 0.32 0.33 0.70 0.64 0.81

Zinc (L/mmol) 5.34 2.57 7.23 2.83 7.90 7.92

(L/mg) 0.03 0.04 0.07 0.07 0.22 0.11

(mg/g) 16.9 19.3 21.3 40.5 58.8 70.9

Lead (mmol/g) 0.27 0.30 0.33 0.64 0.93 1.12

Copper (L/mmol) 1.80 2.58 4.92 4.63 14.38 6.94

(L/mg) 0.14 0.09 0.13 0.04 0.16 0.17

(mg/g)

(mmol/g)

63.3 51.5 63.7 204.1 178.6 204.1

0.30 0.25 0.31 0.98 0.86 0.98

Lead (L/mmol)

(L/mg)

(L/mmol)

8.92 5.51 8.37 2.47 10.39 10.87

0.11 0.57 0.04 0.01 0.09 0.13

23.45 117.87 9.10 2.08 19.15 26.72

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3.4. Behaviour of algae in comparison with other types of biomass A comparative overall analysis of the results obtained with each biomass was carried out after quantifying the biosorbent capacity of the six algae under study. This study took into consideration data given in literature relating to other types of more widely-investigated biomass, such as bacteria and fungi (Kapoor and Viraraghavan, 1995; Pillichshammer et al., 1995; Pagnanelli et al., 2003; Veit et al., 2005; Yan and Viraraghavan, 2003; Zouboulis et al., 2004). Figs. 5 and 6 show graphically the values of

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qmax and b obtained from Langmuir isotherms and the average reference values of bacteria and fungi. In all cases, algae equalled or improved the best results of qmax obtained with fungi and bacteria. Brown algae (F. spiralis and A. nodosum) and the red alga C. crispus attained the highest values of qmax for all metals, well above those obtained with any of the other algae. In the former case, recovery values of 1 mmol metal/g biomass were obtained, while in the latter case values scarcely reached 0.3 mmol/g. The similar behaviour of C. crispus and brown algae has already been explained in terms of their carragenates

Fig. 5. Values of qmax for the six algae studied and average values of fungi and bacteria.

Fig. 6. Values of b for the six algae studied.

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content, which are gelifying compounds that enhance processes of this kind. A. armata (red alga), S. insignis and C. vermilara (green algae) behaved similarly in terms of maximum sorption capacity, but worse than C. crispus and brown algae. One of the reasons why green algae show lower levels of metal recovery could be that there is less probability of having two adjacent carboxylic groups at the right distance to allow the metal bond between them, such as happens with alginates (Schiewer and Wong, 2000). Fig. 6 represents biomass affinity (expressed by the value of b) versus different metal cations. All algae showed similar affinities for each metal tested, except for lead where each biomass seemed to behave differently. This could be explained by a different biomass bonding mechanism than in the other metals. In any event, the affinity for lead was much higher than for any other metal, especially with green and brown algae. This difference would have been more significant if the value for S. insignis with lead (117 L/mmol) had been considered, but this value was left out for a better comprehension of the other results. Singh et al. (2000) obtained a greater uptake of Pb using Spirogyra as biosorbent and dissolutions of Pb2+, Cd2+, Zn2+ and Cu2+ and suggested that lead anchored to the biomass due to its high affinity for certain active sites, which in turn were different to those taking up Cu2+ or Zn2+. Infrared spectroscopy revealed less functional groups on the surface than with other algae. This could account for the fact that qmax was not as high as expected from the high value of b, the same as occurred in the present work. The reason why some biomass show high affinity for a given metal and low sorption capacity, or vice versa, may be related to the degree of affinity of a specific biomass for each metal. Although the total amount of metal anchored on its surface will also depend on the number of active sites present and on how easily they can be accessed (Hashim and Chu, 2004). Therefore, when selecting the most appropriate biosorbent for a concrete situation it would be advisable to establish working conditions. It may be of more interest to recover the metal irrespective of the equilibrium concentration reached, or on the contrary the priority may be to reduce effluent pollution levels to within the legally-permitted range. In addition, if the interest was the recovery of large amounts of metal while achieving low equilibrium concentrations at the same time, the biosorbent used should have a high sorption capacity compatible with high values of b. However, if the idea was to recover the maximum amount of metal regardless of the equilibrium concentration reached, the biomass with the highest value of qmax should be selected even although the value of b was lower; in our case, this would be achieved with C. crispus. In any case, the ideal solution would be to find biosorbents with high sorption capacity and high values of b simultaneously (Langmuir, 1918; Holan et al., 1995; Davis et al., 2003). In this sense, brown algae seem to be ahead in

processes of this kind since they generally present higher or at least acceptable values of both parameters. In the present study, for instance, F. spiralis was the alga with the best sorption capacity of all the metals tested and consequently was the most effective of the six biosorbents used. 3.5. Simulation of the process The good fit of the experimental data to a Langmuir model makes it possible to set up metal-biomass equilibria of the type Sorbent ðBÞ þ Metal ðMeÞ () Sorbent–Metal ðBMeÞ Equilibrium B Me BMe ðqmax  qÞ

Ce

q ð6Þ

where B

Me BMe

final sorbent amount at equilibrium (number of bond sites not occupied by the metal which are vacant) final metal amount in solution once equilibrium is reached metal amount bonded with the sorbent at equilibrium (number of bond sites occupied by metal at equilibrium)

The equilibrium constant for such a reaction coincides with Langmuir affinity constant (b). Therefore, once equilibrium was defined, it was attempted to predict the biomass behaviour versus each metal. The idea was to find out what actually happens when the biomass is brought into contact with a metal solution under specific conditions. The known data for each system were: the equilibrium constant or affinity constant (b) of the Langmuir model and the maximum biomass uptake or total number of active sites available for the metal on the biomass surface. Data corresponding to the equilibrium conditions were calculated and compared with experimental data using a chemical speciation computer program PHREEQCI 6.2 (Charlton et al., 1997). The value of the metal concentration left in solution at equilibrium is entered and the program calculates both the amount of metal retained by the biomass (qe or number of active sites available for that metal occupied under such conditions) and the number of unoccupied sites at equilibrium (qmax  q). This assumption is of particular interest since government restrictions are constantly being tightened to reduce pollution levels in sewage. To that end the reactions between the biomass and each metal with its corresponding equilibrium constant (b), as determined from the Langmuir model, were uploaded to the program database. For instance, the simulation reactions for the brown alga F. spiralis (Fucusspi) and heavy metals cadmium and copper were as follows:

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Table 5 Comparison between experimental data and the estimated by the program for each monometallic system under study C0 (mg/L) (qe  qp)100/qmax (%) Cadmium 10 25 50 100 150

Spirogyra insignis

Codium vermilara

Asparagopsis armata

Chondrus crispus

Ascophyllum nodosum

Fucus spiralis

19.6 1.3 5.6 0.3 5.4

9.6 2.3 0.3 7.2 3.0

7.3 8.3 7.9 10.0 10.6

0.6 2.7 7.8 3.8 6.1

0.3 3.9 2.4 5.6 1.8

5.1 1.8 2.1 1.8 6.1

Nickel

10 25 50 100 150

12.2 4.9 3.5 1.3 11.1

7.7 5.7 7.0 5.3 1.4

2.7 3.2 1.3 4.8 1.7

0.7 4.6 7.5 2.4 6.6

2.0 1.0 7.5 4.9 1.4

8.1 3.2 0.4 0.8 3.8

Zinc

10 25 50 100 150

16.7 6.8 1.0 1.8 15.8

10.6 2.9 4.6 14.7 8.7

8.3 4.9 0.2 8.6 4.6

2.4 5.9 1.1 10.5 4.8

5.4 2.0 9.0 3.9 1.4

15.8 10.9 0.9 4.2 8.6

Copper

10 25 50 100 150

25.3 10.0 4.7 4.3 11.6

4.5 4.6 2.2 5.9 2.7

3.5 7.0 1.0 6.3 2.9

2.9 5.1 7.1 5.4 10.6

2.0 0.1 10.5 2.2 3.8

9.2 6.5 8.9 1.8 3.9

Lead

10 25 50 100 150

28.0 3.5 4.6 1.9 0.9

11.4 0.2 9.5 2.4 2.1

2.0 2.9 0.1 0.0 4.6

0.4 0.0 1.7 4.1 4.1

3.2 2.5 2.9 0.3 3.0

1.0 6.5 1.7 0.8 3.5

Fucusspi þ Cd þ 2 ¼ FucusspiCd þ 2 log k 4:1026

#ðk ¼ 12; 667Þ

Fucusspi þ Cu þ 2 ¼ FucusspiCu þ 2 log k 4:0363 #ðk ¼ 10; 872Þ By specifying the equilibrium conditions, the program was able to determine the values of Fucusspi, FucusspiCu+2 and FucusspiCd2+. Thus the program was able to reproduce the experimental results very well, with a good correlation between experimental data and data calculated by the program. The program also required specification of the physicochemical parameters of the solution (pH, temperature, equilibrium metal concentration, ionic species in solution, etc.) and the characteristics of the biomass itself (qmax, specific surface area and mass used). Table 5 shows the difference between the percentage of active sites occupied by the metal [(qe/qmax) Æ 100] in experiments with different initial concentrations for each metal and the percentage simulated by the program [(qp/ qmax) Æ 100] for each biomass tested. Of course, the smaller the difference between the two values, the better the program’s predictive accuracy. In general, there was a remarkable similarity between experimental data and those obtained using the program even though identical data were compared. In the most unfavourable cases, all coinciding with low initial metal concentrations, deviations were around 10% of the estima-

tion of biomass active sites occupied by metal species, which is not a significant figure in the simulation of a process. This may be the result of stronger competition by protons for the active sites on the algae cell walls, since the presence of metallic species in solution was hardly significant in these cases. Thus, the program furnished excellent data and came very close to the actual situation when an effluent contaminated with each of the five metals studied is treated with this type of biomass. This therefore confirms the viability of the program and its ability to estimate the value of q or the percentage of active sites occupied by the metal at equilibrium, with good reliability for all the biomass assayed. 4. Conclusions The effectiveness of the selected algae as biosorbent material was confirmed. Sorption capacity depended on the pH and the biomass concentration. The optimum pH value for recovery of Cd, Ni and Zn was 6 for the six algae under study. The optimum sorption pH for Cu ranged from 4 to 5 and for Pb from 3 to 5. Reducing the biomass concentration increased the sorption capacity, and in many cases values were higher at the lowest concentration (0.5 g/ L). Experimental data fitted a Langmuir equation very well. The regression coefficient was higher than 0.95 for the thirty monometallic systems studied; therefore, the

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equilibrium reactions between each biomass and the different metals are probably B þ Me () B  Me where the b constant is the equilibrium constant of such model. In all cases sorption capacity was greater with lead followed by cadmium. The sorption values for nickel, copper and zinc were very similar and the general sequence was: lead > copper P cadmium > zinc > nickel. Green and red algae, without carragenates in their composition, presented similar values of qmax for all metals. In all cases, these values were much lower than those registered with C. crispus and brown algae, which besides being very high for all metals reduced the metal equilibrium concentration to very low levels. In any case, the best results were achieved with F. spiralis. All algae showed similar affinities for each metal tested, but lead behaved differently in each case because the mechanism of bonding to the biomass was also different from the other metals. The sequence of affinities between each biomass and the different metals was as follows: lead > cadmium P copper > nickel > zinc. In general, the six algae studied achieved more effective biosorption of the five metals than the average values registered for bacteria and fungi. The PHREEQCI program proved to be a very useful tool for predicting the behaviour of the biomass once equilibria were defined. Acknowledgements The authors wish to express their gratitude to the Spanish Ministry of Science and Technology for funding this work. Thanks are also given to Eduardo Costas from Complutense University of Madrid and Antonio Flores from University of Malaga. References ¨ ., Kutsal, T., 1997. Application of multicomponent Aksu, Z., Acikel, U adsorption isotherms to simultaneous biosorption of iron (III) and chromium (VI) on C. vulgaris. J. Chem. Tech. Biotechnol. 70, 368–378. ¨ ., Kutsal, T., 1999. Investigation of simultaneous Aksu, Z., Acikel, U biosorption of copper (II) and chromium (VI) on dried Chlorella vulgaris from binary metal mixtures: application of multicomponent adsorption isotherms. Separ. Sci. Technol. 34, 501–524. Akthar, M.N., Sastry, K.S., Mohan, P.M., 1996. Mechanism of metal ion biosorption by fungal biomass. BioMetals 9, 21–28. Cabioch, J., Floch, J.Y., Le Toquin, A., Boudouresque, C.F., Meinesz, A., Verlaque, M., 1995. Guı´a de las algas de los m ares de Europa: Atla´ntico y Mediterra´neo. Omega, Barcelona. Charlton, S.R., Macklin, C.L., Parkhurst, D.L., 1997. PHREEQCI – a graphical user interface for the geochemical computer program PHREEQC. US Geological Survey. Water-Resources Investigations Report 97-4222, Lakewood, CO. Chong, K.H., Volesky, B., 1995. Description of two-metal biosorption equilibria by Langmuir-type models. Biotechnol. Bioeng. 47, 451–460. Chong, K.H., Volesky, B., 1996. Metal biosorption equilibria in a ternary system. Biotechnol. Bioeng. 49, 629–638.

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