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Optimization of Cd2+ removal by the cyanobacterium Synechocystis pevalekii using the response surface methodology
Process Biochemistry 44 (2009) 118–121
Contents lists available at ScienceDirect
Process Biochemistry journal homepage: www.elsevier.com/locate/procbio
Short communication
Optimization of Cd2+ removal by the cyanobacterium Synechocystis pevalekii using the response surface methodology J.I.S. Khattar *, Shailza Department of Botany, Punjabi University, Patiala 147002, India
A R T I C L E I N F O
A B S T R A C T
Article history: Received 9 May 2008 Received in revised form 10 September 2008 Accepted 22 September 2008
Response surface methodology (RSM) has been used to optimize the critical parameters responsible for higher Cd2+ removal by a unicellular cyanobacterium Synechocystis pevalekii. A three-level Box–Behnken factorial design was used to optimize pH, biomass and metal concentration for Cd2+ removal. A coefficient of determination (R2) value (0.99), model F-value (86.40) and its low p-value (F < 0.0001) along with lower value of coefficient of variation (5.61%) indicated the fitness of response surface quadratic model during the present study. At optimum pH (6.48), biomass concentration (0.25 mg protein ml1) and metal concentration (5 mg ml1) the model predicted 4.29 mg ml1 Cd2+ removal and experimentally, 4.27 mg ml1 Cd2+ removal was obtained. ß 2008 Elsevier Ltd. All rights reserved.
Keywords: Cadmium removal Optimization Response surface methodology Box–Behnken design Synechocystis Cyanobacterium
1. Introduction Heavy metals release in the environment is increasing continuously as a result of industrial activities and technological development [1]. This contamination poses a serious threat to the environment and human health as these metals are non-biodegradable, are toxic even at low concentration and enter in to the food chain [2,3]. Cadmium contributes markedly to environmental pollution and is relatively more toxic than other heavy metals [4]. Several physico-chemical methods like chemical precipitation, ion-exchange, electrolysis, filtration, etc., are used to treat polluted water but these are inefficient when the concentration of metal is low and are expensive [5,6]. Thus, there is an urgent need for cost effective and efficient technologies for the treatment of heavy metal contaminated waste water [7]. The ability of different groups of microorganisms has been tested to sorb heavy metals so as to select superior organisms for bioremediation purposes [8–10]. Among all the microbes, microalgae are able to concentrate heavy metals in significant amounts even from aqueous solutions with very low concentration of these metals [11,12]. Microalgae are better candidates for heavy metal removal as these are easy to culture, easy to handle, have relatively large surface area, are photosynthetic and have very simple nutritional requirements. It
* Corresponding author. Tel.: +91 175 304 6265; fax: +91 175 228 3073. E-mail address: [email protected] (J.I.S. Khattar). 1359-5113/$ – see front matter ß 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.procbio.2008.09.015
has been shown that the metal removal efficiency is strongly influenced by pH, biomass concentration and metal concentration [13–15]. All studies on metal removal by microalgae have been done by taking one variable at one time. It is laborious and time consuming to perform such experiments and accurate optimum conditions are not obtained. It is also not possible to detect the frequent interactions occurring between two or more factors [16,17]. Response surface methodology (RSM) helps to study the effect of several factors influencing the responses by varying these factors simultaneously. In RSM studies, limited number of experiments is required to be performed and one is able to determine accurate optimum values of test variables [18]. During the present investigation, optimum values of pH, biomass concentration and metal concentration have been determined by RSM for removal of Cd2+ by Synechocystis pevalekii. 2. Materials and methods 2.1. Test organism The test organism Synechocystis pevalekii, a unicellular cyanobacterium, was isolated from polluted water of Satluj river, which is being polluted by industrial wastes and sewage of Ludhiana city in Punjab state of India. 2.2. Culture conditions The stock cultures of the organism were propagated in Chu-10 medium (as modified by Safferman and Morris) [19] enriched with 10 mM KNO3 in a culture room maintained at 28 2 8C. Cultures were illuminated for 14 h a day with cool white fluorescent lights (9.8 W m2 at the surface of culture vessels).
J.I.S. Khattar, Shailza / Process Biochemistry 44 (2009) 118–121 2.3. Experimental design
2.5. Protein estimation 2+
The Box–Behnken factorial design was used to optimize the Cd removal efficiency of the test organism. This design consists of three levels (low, medium and high coded as 1, 0, and +1). The complete design consisted of 17 runs and these were performed in duplicate to optimize the levels of selected variables, i.e., pH, biomass concentration and metal concentration. For statistical calculations the three independent variables were designated as X1, X2 and X3, respectively, and were coded according to the following equation: xi ¼
Xi X0 DX i
Y ¼ b0 þ
Protein content of the cells was determined following Lowry et al. [21].
3. Results and discussion In the preliminary experiments the organism removed 3.79 mg Cd2+ from 5 mg ml1 solution at pH 6.0 and biomass concentration 0.15 mg ml1.
(1)
3.1. Optimization of parameters for Cd2+ removal
where xi is the coded value of an independent variable, Xi is the real value of an independent variable, X0 is the real value of an independent variable at the centre point and DXi is the step change value [20]. The lowest and highest levels of the variables were pH 5 and 7, biomass concentration 0.025 and 0.3 mg protein ml1 and metal concentration 3 and 7 mg ml1. The range of variables selected is based on preliminary experiments in which broad ranges were used and the range for each variable selected during the present study is the one in which metal removal was maximum. The metal removal efficiency of S. pevalekii was multiply regressed w.r.t. the different parameters by the least square methods as follows: X
119
bi xi þ
X
bii x2i þ
X
bi j x i x j
(2)
where Y is the predicted response variable; b0, bi, bii and bij are constant regression coefficients of the model; xi and xj (i = 1, 3; j = 1, 3, i 6¼ j) represent the independent variables in the form of coded values. The accuracy and fitness of the above model was evaluated by coefficient of determination (R2) and F value. Table 1 gives the Box–Behnken design matrix along with experimental and predicted values for Cd2+ removal in coded terms. The predicted values for Cd2+ removal were obtained by applying quadratic model (Design Expert software version 7.1.3, Stat ease). The optimum values of the variable factors for metal removal were obtained by solving the regression equation, by analyzing the response surface contour plots and constraints for the variable factors using the same software. The goal fixed for the metal removal was maximum metal removal. For pH and biomass concentration the goal was set in range and for metal concentration it was on target, i.e., 5 mg ml1. 2.4. Cd2+ removal in batch systems To study Cd2+ removal by the test organism, 15 days old stock cultures were harvested and washed by centrifugation (10,000 g, 10 min). A known amount of washed biomass was inoculated in 20 ml imidazole-HCl buffer (0.2 M) containing known concentration of Cd2+ and incubated at 28 2 8C under continuous light. After 1 h (during this time system attains equilibrium as optimized earlier) a known volume of cell suspension were withdrawn and rapidly centrifuged. Residual metal content in the supernatant was determined using acetylene–air atomic absorption spectrophotometer. Amount of metal removed by the test organism was determined by subtracting the residual metal concentration from the initial metal concentration in the solution. The data are average of two independent experiments. Table 1 Box–Behnken design matrix in coded terms along with experimental and predicted values for Cd2+ removal. variables
Cd2+ removal (mg ml1)
Runs
Independent (coded) x1
x2
x3
Experimental
Predicted
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
1 0 0 1 1 1 0 0 1 1 0 0 0 0 1 0 1
1 0 1 0 0 1 0 0 1 0 0 1 0 1 1 1 0
0 0 1 1 1 0 0 0 0 1 0 1 0 1 0 1 1
3.68 0.05 3.88 0.03 1.72 0.03 2.93 0.05 1.93 0.07 2.16 0.06 3.87 0.04 3.85 0.06 4.33 0.04 3.84 0.05 3.86 0.02 0.82 0.05 3.87 0.03 2.09 0.07 1.14 0.05 4.14 0.02 2.00 0.05
3.71 3.9 1.84 2.92 1.98 2.18 3.9 3.94 4.29 3.93 3.9 0.76 3.9 2.24 1.17 4.16 2.07
x1 = pH, x2 = biomass concentration, and x3 = metal concentration.
Table 1 shows that there was a considerable variation in the Cd2+ removal by S. pevalekii at different values of selected parameters. The data obtained was analyzed by applying multiple regression analysis method based on Eq. (2). The predicted response Ym for Cd2+ removal was obtained and is given as: Y m ¼ 3:87 þ 0:33x1 þ 1:05x2 þ 0:72x3 0:093x1 x2 þ 0:21x1 x3 þ 0:29x2 x3 0:28x21 0:76x22 0:91x23
(3)
In this equation Ym is the predicted response variable, i.e., the Cd2+ removal (mg ml1), x1, x2 and x3 are the independent variables in coded units, i.e., pH, biomass concentration and metal concentration, respectively. The data obtained from Eq. (3) are significant. It is verified by Fvalue and the analysis of variance (ANOVA) by fitting the data of all independent observations in response surface quadratic model. Significance of each coefficient of Eq. (3) was determined by applying t-test and p-values of each are listed in Table 2. Since the p-values of the all the coefficients, except for x1x2, are p < 0.05, it implies that these are significant. The linear effect of coefficients x1, x2, and x3, i.e., pH (p < 0.0008), biomass concentration (p < 0.0001) and metal concentration (p < 0.0001) is significant. Similarly, the interactive effects of pH and metal concentration (p < 0.03), biomass and metal concentration (p < 0.01) are also significant, but the interactive effect of pH and biomass concentration (p > 0.3) is insignificant. The coefficients of the quadratic terms had negative effects. p-Values of the quadratic terms, i.e., pH ðx21 Þ (p < 0.01), biomass ðx22 Þ (p < 0.0001) and metal concentration ðx23 Þ (p < 0.0001) are more significant. Thus, statistical analysis of data shows that small variations in the values of the selected variables alter the Cd2+ removal efficiency of the test organism. In this model x1 ; x2 ; x3 ; x1 x3 ; x1 x2 ; x2 x3 ; x21 ; x22 ; and x23 are significant model terms. Analysis of variance (ANOVA) for response surface quadratic model gave F-value 86.40, R2 value 0.99, probability < 0.0001 and coefficient of variation (C.V. = 5.61%) signifying that model is highly significant and experiments are highly accurate and reliable (Table 2). Experimental and predicted values for Cd2+ removal lie within narrow interval (4%). This also shows the excellent degree of fitness for the model equation. Response surface contour plots help to understand the relationship between the response and experimental levels of each variable. These plots also show the type of interaction between test variables and help to obtain the optimum conditions [22]. Fig. 1 shows Cd2+ removal as a result of interaction between pH and biomass concentration with one variable, i.e., metal concentration, maintained at constant value. As pH increased the Cd2+ removal efficiency of the organism also increased but at higher pH values, the efficiency slightly decreased. It is well known that both the cell surface metal binding sites and availability of metal ions in solution are affected by pH. At low pH, the cell surface sites are closely linked to the H+ ions, therefore, these sites become unavailable for other cations. However, with an increase in pH, there is an increase in ligands with negative charges which results
J.I.S. Khattar, Shailza / Process Biochemistry 44 (2009) 118–121
120
Table 2 Analysis of variance (ANOVA), regression coefficient estimate and test of significance for Cd2+ removal (response surface quadratic model). Factor
Sum of squares
Mean squares
Coefficient estimated S.E.
Intercept (Model) x1 x2 x3 x1x2 x1x3 x2x3 x21 x22 x23 Residual
21.28 0.88 8.82 4.19 0.03 0.18 0.33 0.33 2.44 3.51 0.19
2.36 0.88 8.82 4.19 0.03 0.18 0.33 0.33 2.44 3.51 0.027
+3.87 0.07 +0.33 0.05 +1.05 0.05 +0.72 0.05 0.09 0.08 +0.21 0.08 +0.29 0.08 0.28 0.08 0.76 0.08 0.91 0.08
Corrected total
21.47
d.f.
F-value
Probability (p) > F
9 1 1 1 1 1 1 1 1 1 7
86.40 32.07 322.24 153.10 1.25 6.44 12.08 11.89 88.97 128.23
<0.0001 0.0008 <0.0001 <0.0001 0.3 0.03 0.01 0.01 <0.0001 <0.0001
16
R2 = 0.99, Adj. R2 = 0.97, Pred. R2 = 0.85, and C.V. = 5.61%.
in increased binding of cations [15,23,24]. At very high pH, metal ions get precipitated and thus are not available for binding [25]. Maximum metal is thus removed at optimum pH due to the availability of sufficient negatively charged binding sites. It is also clear from the figure that metal removal efficiency increased with increase in biomass but after certain extent metal removal was saturated or slightly decreased. This may be due to the limited availability of metal ions, increased electrostatic interactions, interference between binding sites and reduced mixing at higher biomass densities [14,23,24]. Interactions between pH versus metal concentration and biomass versus metal concentration are shown in Figs. 2 and 3. Fig. 2 shows that Cd2+ removal efficiency of test organism increased with increase in metal concentration up to certain level beyond which efficiency declined slightly. This may be due to the reason that binding sites are fully occupied at certain metal concentration and beyond that removal slowed down due to the unavailability of sufficient binding sites. It was concluded that metal removal efficiency of the biomass increases with increase in metal concentration in solution and then becomes saturated after a certain concentration of metal [26]. A circular contour plot represents that the interaction between the corresponding variables is negligible while elliptical contour
plot indicates significant interactions [27]. Elliptical contour plots obtained from the data of the present study clearly show that the mutual interactions between the variables are significant (Figs. 1– 3) and Cd2+ removal by S. pevalekii in batch experiments is sensitive to minor changes in test variables. Final optimized conditions can be obtained by solving the inverse matrix (from Eq. (3)) and through statistical analysis of the constraints. By both means, the optimum values of the test variables in uncoded (natural) units obtained were pH 6.48, biomass concentration = 0.25 mg protein ml1 and metal concentration = 5 mg ml1. At these optimized conditions, the model predicted 4.29 mg Cd2+ removal from each ml of test solution. In a recent review, Mehta and Gaur [15] have given a comparative account of the metal removal capabilities of a large number of algae in which metal removal efficiencies have been given in terms of % removal. It is always better to compare metal removal efficiencies in terms of actual amounts of metal removed. Sandau et al. [28] reported that Cd2+ removal efficiency of Spirulina platensis and Chlorella vulgaris varied with initial metal concentration. S. platensis and C. vulgaris removed 12.08 and 12.45 mg Cd2+ mg1 biomass, respectively, from 20 mg Cd2+ ml1 solution when 1.06 mg ml1 biomass was used. The organism employed during the present study was able to remove 4.27 mg Cd2+ from 5 mg ml1 solution
Fig. 1. Response surface contour plots showing effect of biomass (mg protein ml1) and pH on cadmium removal (mg ml1) by S. pevalekii (metal concentration: 5 mg ml1). The points on the corners and center of the figure represent experimental design points. The point with number 5 in the centre indicates that contour plots have been drawn when the value of the fixed variable (metal concentration) is at the mid point of lowest and highest selected levels.
Fig. 2. Response surface contour plots showing effect of metal concentration (mg ml1) and pH on cadmium removal (mg ml1) by S. pevalekii (biomass concentration: 0.16 mg protein ml1). The points on the corners and center of the figure represent experimental design points. The point with number 5 in the centre indicates that contour plots have been drawn when the value of the fixed variable (biomass concentration) is at the mid point of lowest and highest selected levels.
J.I.S. Khattar, Shailza / Process Biochemistry 44 (2009) 118–121
121
References
Fig. 3. Response surface contour plots showing effect of biomass (mg protein ml1) and metal concentration (mg ml1) on cadmium removal (mg ml1) by S. pevalekii (pH: 6.0). The points on the corners and center of the figure represent experimental design points. The point with number 5 in the centre indicates that contour plots have been drawn when the value of the fixed variable (pH) is at the mid point of lowest and highest selected levels.
when 0.25 mg protein ml1 biomass was used and this comes to 17.08 mg Cd2+ removed mg1 protein (85% efficiency) which is also a very good efficiency. 3.2. Verification of the model Verification of the calculated optimum conditions for Cd2+ removal was done by performing the experiments at optimized conditions. Under these conditions, S. pevalekii removed 4.27 mg Cd2+ ml1 (85.4% metal removal) which is in agreement with the predicted value suggesting that Eq. (3) was valid for Cd2+ removal. 4. Conclusion Conventional methods that use one factor at a time are time consuming and expensive. During the present investigation response surface methodology based on three-factor-three-level Box–Behnken design was used to solve these problems. This design helped in locating the optimum levels of the most significant factors which contribute to the maximum metal removal. RSM, the selected method, resulted not only in increased metal removal efficiency by the test organism but also showed that it is simple, efficient, time and material saving. The test organism is quite efficient in Cd2+ removal from solution and thus has the potential to be exploited for treatment of cadmium containing industrial effluents before their discharge in to water bodies. Acknowledgements The authors are grateful to the Head, Department of Botany, Punjabi University, Patiala and Programme Coordinators, DRS (Special Assistance Programme) and ASIST Programme of U.G.C., New Delhi for necessary laboratory facilities.
[1] Bahadir T, Bakan G, Altas L, Buyukgungor H. The investigation of lead removal by biosorption: an application at storage battery industry wastewaters. Enz Microbial Technol 2007;41:98–102. [2] Gavrilescu M. Removal of heavy metals from the environment by biosorption. Eng Life Sci 2004;4:219–32. [3] Sheng PX, Ting YP, Chen JP, Hong L. Separation of lead, copper, cadmium, zinc and nickel by marine algal biomass: characterization of biosorption capacity and investigation of mechanisms. J Colloid Interf Sci 2004;275:131–41. [4] Kunjan P, Prell A, Safar H, Sobotka M, Rezanka T, Holler P. Use of the industrial yeast Candida utilis for cadmium sorption. Folia Microbiol 2006;51:257–60. [5] Leusch A, Holan ZR, Volesky B. Biosorption of heavy metals (Cd, Cu, Ni, Pb, Zn) by chemically-reinforced biomass of marine algae. J Chem Technol Biotechnol 1995;62:279–88. [6] Rangsayatorn N, Pokethitiyook P, Upatham ES, Lanza GR. Cadmium biosorption by cells of Spirulina platensis TISTR 8217 immobilized in alginate and silica gel. Environ Int 2004;30:57–63. [7] Eccles H. Treatment of metal-contaminated wastes: why select a biological process? TIBTECH 1999;17:462–5. [8] Gabriel J, Baldrian P, Hladikova K, Hakova M. Copper sorption by native and modified pellets of wood rotting basidiomycetes. Lett Appl Microbiol 2001;32: 194–8. [9] Phoenix VR, Martinez RE, Konhauser KO, Ferris FG. Characterization and implications of the cell surface reactivity of Calothrix sp. strain KC97. Appl Environ Microbiol 2002;681:4827–34. [10] Tangaromusk J, Pokethitiyook P, Kruatrachue M, Upatham ES. Cadmium biosorption by Sphingomonas paucimobilis biomass. Biores Technol 2002;85: 103–5. [11] Gekeler N, Grill E, Winnackear EL, Zenk MH. Algae sequester metal complexes via synthesis of phytochelatin complexes. Arch Microbiol 1998;158:197–202. [12] Andrade AD, Rollemberg MCE, Nobrega JA. Proton and metal binding capacity of the green freshwater alga Chaetophora elegans. Process Biochem 2005;40: 1931–6. [13] Cordero B, Lodeiro P, Herrero R, Sastre de Vicente ME. Biosorption of cadmium by Fucus spiralis. Environ Chem 2004;1:180–7. [14] Fraile A, Penche S, Gonzalez F, Blazquez ML, Munoz JA, Ballester A. Biosorption of copper, zinc, cadmium and nickel by Chlorella vulgaris. Chem Ecol 2005;21: 61–75. [15] Mehta SK, Gaur JP. Use of algae for removing heavy metal ions from wastewater: progress and prospects. Crit Rev Biotechnol 2005;25:113–52. [16] Moyo S, Gashe BA, Collison EK, Mpuchane S. Optimizing growth conditions for the pectinolytic activity of Kluyveromyces wickerhamii by using response surface methodology. Int J Food Microbiol 2003;85:87–100. [17] Liu BL, Tzeng YM. Optimization of growth medium for the production of spores from Bacillus thuringiensis using response surface methodology. Bioproc Eng 1998;18:413–8. [18] Adinarayana K, Ellaiah P. Response surface optimization of the critical medium components for the production of alkaline protease by a newly isolated Bacillus sp.. J Pharm Sci 2002;5:272–6. [19] Safferman RS, Morris I. Growth characteristics of the blue green algae virus LPP-1. J Bacteriol 1964;88:771–5. [20] Doddapaneni KK, Tatineni R, Potumarthi R, Mangamoori LN. Optimization of media constituents through response surface methodology for improved production of alkaline proteases by Serratia rubidaea. J Chem Technol Biotechnol 2007;82:721–9. [21] Lowry OH, Rosebrough NJ, Farr AL, Randall RJ. Protein measurement with Folin-phenol reagent. J Biol Chem 1951;193:265–75. [22] Myers RH, Montgomery DC. Surface Methodology: Process and Product Optimization Using Designed Experiments, 1st ed., New York: Wiley-Interscience; 1995. [23] Gong R, Ding Y, Liu H, Chen Q, Liu Z. Lead biosorption and desorption by intact and pretreated Spirulina maxima biomass. Chemosphere 2005;58:125–30. [24] Khattar JIS, Sarma TA, Sharma A. Optimization of chromium removal by the chromium resistant mutant of the cyanobacterium Anacystis nidulans in a continuous flow bioreactor. J Chem Technol Biotechnol 2007;82:652–7. [25] Barriada JL, Herrero R, Prada-Rodriguez D, Sastre de Vicente ME. Waste spider crab shell and derived chitin as low cost materials for cadmium and lead removal. J Chem Technol Biotechnol 2007;82:39–46. [26] Donmez GC, Aksu Z, Ozturk A, Kutsal T. A comparative study on heavy metal biosorption characteristics of some algae. Process Biochem 1999;34:885–92. [27] Muralidhar RV, Chirumamila RR, Marchant R, Nigam P. A response surface approach for the comparison of lipase production by Candida cylindracea using two different carbon sources. Biochem Eng J 2001;9:17–23. [28] Sandau E, Sandau P, Pulz O. Heavy metal sorption by microalgae. Acta Biotechnol 1996;16:227–35.
Contents lists available at ScienceDirect
Process Biochemistry journal homepage: www.elsevier.com/locate/procbio
Short communication
Optimization of Cd2+ removal by the cyanobacterium Synechocystis pevalekii using the response surface methodology J.I.S. Khattar *, Shailza Department of Botany, Punjabi University, Patiala 147002, India
A R T I C L E I N F O
A B S T R A C T
Article history: Received 9 May 2008 Received in revised form 10 September 2008 Accepted 22 September 2008
Response surface methodology (RSM) has been used to optimize the critical parameters responsible for higher Cd2+ removal by a unicellular cyanobacterium Synechocystis pevalekii. A three-level Box–Behnken factorial design was used to optimize pH, biomass and metal concentration for Cd2+ removal. A coefficient of determination (R2) value (0.99), model F-value (86.40) and its low p-value (F < 0.0001) along with lower value of coefficient of variation (5.61%) indicated the fitness of response surface quadratic model during the present study. At optimum pH (6.48), biomass concentration (0.25 mg protein ml1) and metal concentration (5 mg ml1) the model predicted 4.29 mg ml1 Cd2+ removal and experimentally, 4.27 mg ml1 Cd2+ removal was obtained. ß 2008 Elsevier Ltd. All rights reserved.
Keywords: Cadmium removal Optimization Response surface methodology Box–Behnken design Synechocystis Cyanobacterium
1. Introduction Heavy metals release in the environment is increasing continuously as a result of industrial activities and technological development [1]. This contamination poses a serious threat to the environment and human health as these metals are non-biodegradable, are toxic even at low concentration and enter in to the food chain [2,3]. Cadmium contributes markedly to environmental pollution and is relatively more toxic than other heavy metals [4]. Several physico-chemical methods like chemical precipitation, ion-exchange, electrolysis, filtration, etc., are used to treat polluted water but these are inefficient when the concentration of metal is low and are expensive [5,6]. Thus, there is an urgent need for cost effective and efficient technologies for the treatment of heavy metal contaminated waste water [7]. The ability of different groups of microorganisms has been tested to sorb heavy metals so as to select superior organisms for bioremediation purposes [8–10]. Among all the microbes, microalgae are able to concentrate heavy metals in significant amounts even from aqueous solutions with very low concentration of these metals [11,12]. Microalgae are better candidates for heavy metal removal as these are easy to culture, easy to handle, have relatively large surface area, are photosynthetic and have very simple nutritional requirements. It
* Corresponding author. Tel.: +91 175 304 6265; fax: +91 175 228 3073. E-mail address: [email protected] (J.I.S. Khattar). 1359-5113/$ – see front matter ß 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.procbio.2008.09.015
has been shown that the metal removal efficiency is strongly influenced by pH, biomass concentration and metal concentration [13–15]. All studies on metal removal by microalgae have been done by taking one variable at one time. It is laborious and time consuming to perform such experiments and accurate optimum conditions are not obtained. It is also not possible to detect the frequent interactions occurring between two or more factors [16,17]. Response surface methodology (RSM) helps to study the effect of several factors influencing the responses by varying these factors simultaneously. In RSM studies, limited number of experiments is required to be performed and one is able to determine accurate optimum values of test variables [18]. During the present investigation, optimum values of pH, biomass concentration and metal concentration have been determined by RSM for removal of Cd2+ by Synechocystis pevalekii. 2. Materials and methods 2.1. Test organism The test organism Synechocystis pevalekii, a unicellular cyanobacterium, was isolated from polluted water of Satluj river, which is being polluted by industrial wastes and sewage of Ludhiana city in Punjab state of India. 2.2. Culture conditions The stock cultures of the organism were propagated in Chu-10 medium (as modified by Safferman and Morris) [19] enriched with 10 mM KNO3 in a culture room maintained at 28 2 8C. Cultures were illuminated for 14 h a day with cool white fluorescent lights (9.8 W m2 at the surface of culture vessels).
J.I.S. Khattar, Shailza / Process Biochemistry 44 (2009) 118–121 2.3. Experimental design
2.5. Protein estimation 2+
The Box–Behnken factorial design was used to optimize the Cd removal efficiency of the test organism. This design consists of three levels (low, medium and high coded as 1, 0, and +1). The complete design consisted of 17 runs and these were performed in duplicate to optimize the levels of selected variables, i.e., pH, biomass concentration and metal concentration. For statistical calculations the three independent variables were designated as X1, X2 and X3, respectively, and were coded according to the following equation: xi ¼
Xi X0 DX i
Y ¼ b0 þ
Protein content of the cells was determined following Lowry et al. [21].
3. Results and discussion In the preliminary experiments the organism removed 3.79 mg Cd2+ from 5 mg ml1 solution at pH 6.0 and biomass concentration 0.15 mg ml1.
(1)
3.1. Optimization of parameters for Cd2+ removal
where xi is the coded value of an independent variable, Xi is the real value of an independent variable, X0 is the real value of an independent variable at the centre point and DXi is the step change value [20]. The lowest and highest levels of the variables were pH 5 and 7, biomass concentration 0.025 and 0.3 mg protein ml1 and metal concentration 3 and 7 mg ml1. The range of variables selected is based on preliminary experiments in which broad ranges were used and the range for each variable selected during the present study is the one in which metal removal was maximum. The metal removal efficiency of S. pevalekii was multiply regressed w.r.t. the different parameters by the least square methods as follows: X
119
bi xi þ
X
bii x2i þ
X
bi j x i x j
(2)
where Y is the predicted response variable; b0, bi, bii and bij are constant regression coefficients of the model; xi and xj (i = 1, 3; j = 1, 3, i 6¼ j) represent the independent variables in the form of coded values. The accuracy and fitness of the above model was evaluated by coefficient of determination (R2) and F value. Table 1 gives the Box–Behnken design matrix along with experimental and predicted values for Cd2+ removal in coded terms. The predicted values for Cd2+ removal were obtained by applying quadratic model (Design Expert software version 7.1.3, Stat ease). The optimum values of the variable factors for metal removal were obtained by solving the regression equation, by analyzing the response surface contour plots and constraints for the variable factors using the same software. The goal fixed for the metal removal was maximum metal removal. For pH and biomass concentration the goal was set in range and for metal concentration it was on target, i.e., 5 mg ml1. 2.4. Cd2+ removal in batch systems To study Cd2+ removal by the test organism, 15 days old stock cultures were harvested and washed by centrifugation (10,000 g, 10 min). A known amount of washed biomass was inoculated in 20 ml imidazole-HCl buffer (0.2 M) containing known concentration of Cd2+ and incubated at 28 2 8C under continuous light. After 1 h (during this time system attains equilibrium as optimized earlier) a known volume of cell suspension were withdrawn and rapidly centrifuged. Residual metal content in the supernatant was determined using acetylene–air atomic absorption spectrophotometer. Amount of metal removed by the test organism was determined by subtracting the residual metal concentration from the initial metal concentration in the solution. The data are average of two independent experiments. Table 1 Box–Behnken design matrix in coded terms along with experimental and predicted values for Cd2+ removal. variables
Cd2+ removal (mg ml1)
Runs
Independent (coded) x1
x2
x3
Experimental
Predicted
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
1 0 0 1 1 1 0 0 1 1 0 0 0 0 1 0 1
1 0 1 0 0 1 0 0 1 0 0 1 0 1 1 1 0
0 0 1 1 1 0 0 0 0 1 0 1 0 1 0 1 1
3.68 0.05 3.88 0.03 1.72 0.03 2.93 0.05 1.93 0.07 2.16 0.06 3.87 0.04 3.85 0.06 4.33 0.04 3.84 0.05 3.86 0.02 0.82 0.05 3.87 0.03 2.09 0.07 1.14 0.05 4.14 0.02 2.00 0.05
3.71 3.9 1.84 2.92 1.98 2.18 3.9 3.94 4.29 3.93 3.9 0.76 3.9 2.24 1.17 4.16 2.07
x1 = pH, x2 = biomass concentration, and x3 = metal concentration.
Table 1 shows that there was a considerable variation in the Cd2+ removal by S. pevalekii at different values of selected parameters. The data obtained was analyzed by applying multiple regression analysis method based on Eq. (2). The predicted response Ym for Cd2+ removal was obtained and is given as: Y m ¼ 3:87 þ 0:33x1 þ 1:05x2 þ 0:72x3 0:093x1 x2 þ 0:21x1 x3 þ 0:29x2 x3 0:28x21 0:76x22 0:91x23
(3)
In this equation Ym is the predicted response variable, i.e., the Cd2+ removal (mg ml1), x1, x2 and x3 are the independent variables in coded units, i.e., pH, biomass concentration and metal concentration, respectively. The data obtained from Eq. (3) are significant. It is verified by Fvalue and the analysis of variance (ANOVA) by fitting the data of all independent observations in response surface quadratic model. Significance of each coefficient of Eq. (3) was determined by applying t-test and p-values of each are listed in Table 2. Since the p-values of the all the coefficients, except for x1x2, are p < 0.05, it implies that these are significant. The linear effect of coefficients x1, x2, and x3, i.e., pH (p < 0.0008), biomass concentration (p < 0.0001) and metal concentration (p < 0.0001) is significant. Similarly, the interactive effects of pH and metal concentration (p < 0.03), biomass and metal concentration (p < 0.01) are also significant, but the interactive effect of pH and biomass concentration (p > 0.3) is insignificant. The coefficients of the quadratic terms had negative effects. p-Values of the quadratic terms, i.e., pH ðx21 Þ (p < 0.01), biomass ðx22 Þ (p < 0.0001) and metal concentration ðx23 Þ (p < 0.0001) are more significant. Thus, statistical analysis of data shows that small variations in the values of the selected variables alter the Cd2+ removal efficiency of the test organism. In this model x1 ; x2 ; x3 ; x1 x3 ; x1 x2 ; x2 x3 ; x21 ; x22 ; and x23 are significant model terms. Analysis of variance (ANOVA) for response surface quadratic model gave F-value 86.40, R2 value 0.99, probability < 0.0001 and coefficient of variation (C.V. = 5.61%) signifying that model is highly significant and experiments are highly accurate and reliable (Table 2). Experimental and predicted values for Cd2+ removal lie within narrow interval (4%). This also shows the excellent degree of fitness for the model equation. Response surface contour plots help to understand the relationship between the response and experimental levels of each variable. These plots also show the type of interaction between test variables and help to obtain the optimum conditions [22]. Fig. 1 shows Cd2+ removal as a result of interaction between pH and biomass concentration with one variable, i.e., metal concentration, maintained at constant value. As pH increased the Cd2+ removal efficiency of the organism also increased but at higher pH values, the efficiency slightly decreased. It is well known that both the cell surface metal binding sites and availability of metal ions in solution are affected by pH. At low pH, the cell surface sites are closely linked to the H+ ions, therefore, these sites become unavailable for other cations. However, with an increase in pH, there is an increase in ligands with negative charges which results
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Table 2 Analysis of variance (ANOVA), regression coefficient estimate and test of significance for Cd2+ removal (response surface quadratic model). Factor
Sum of squares
Mean squares
Coefficient estimated S.E.
Intercept (Model) x1 x2 x3 x1x2 x1x3 x2x3 x21 x22 x23 Residual
21.28 0.88 8.82 4.19 0.03 0.18 0.33 0.33 2.44 3.51 0.19
2.36 0.88 8.82 4.19 0.03 0.18 0.33 0.33 2.44 3.51 0.027
+3.87 0.07 +0.33 0.05 +1.05 0.05 +0.72 0.05 0.09 0.08 +0.21 0.08 +0.29 0.08 0.28 0.08 0.76 0.08 0.91 0.08
Corrected total
21.47
d.f.
F-value
Probability (p) > F
9 1 1 1 1 1 1 1 1 1 7
86.40 32.07 322.24 153.10 1.25 6.44 12.08 11.89 88.97 128.23
<0.0001 0.0008 <0.0001 <0.0001 0.3 0.03 0.01 0.01 <0.0001 <0.0001
16
R2 = 0.99, Adj. R2 = 0.97, Pred. R2 = 0.85, and C.V. = 5.61%.
in increased binding of cations [15,23,24]. At very high pH, metal ions get precipitated and thus are not available for binding [25]. Maximum metal is thus removed at optimum pH due to the availability of sufficient negatively charged binding sites. It is also clear from the figure that metal removal efficiency increased with increase in biomass but after certain extent metal removal was saturated or slightly decreased. This may be due to the limited availability of metal ions, increased electrostatic interactions, interference between binding sites and reduced mixing at higher biomass densities [14,23,24]. Interactions between pH versus metal concentration and biomass versus metal concentration are shown in Figs. 2 and 3. Fig. 2 shows that Cd2+ removal efficiency of test organism increased with increase in metal concentration up to certain level beyond which efficiency declined slightly. This may be due to the reason that binding sites are fully occupied at certain metal concentration and beyond that removal slowed down due to the unavailability of sufficient binding sites. It was concluded that metal removal efficiency of the biomass increases with increase in metal concentration in solution and then becomes saturated after a certain concentration of metal [26]. A circular contour plot represents that the interaction between the corresponding variables is negligible while elliptical contour
plot indicates significant interactions [27]. Elliptical contour plots obtained from the data of the present study clearly show that the mutual interactions between the variables are significant (Figs. 1– 3) and Cd2+ removal by S. pevalekii in batch experiments is sensitive to minor changes in test variables. Final optimized conditions can be obtained by solving the inverse matrix (from Eq. (3)) and through statistical analysis of the constraints. By both means, the optimum values of the test variables in uncoded (natural) units obtained were pH 6.48, biomass concentration = 0.25 mg protein ml1 and metal concentration = 5 mg ml1. At these optimized conditions, the model predicted 4.29 mg Cd2+ removal from each ml of test solution. In a recent review, Mehta and Gaur [15] have given a comparative account of the metal removal capabilities of a large number of algae in which metal removal efficiencies have been given in terms of % removal. It is always better to compare metal removal efficiencies in terms of actual amounts of metal removed. Sandau et al. [28] reported that Cd2+ removal efficiency of Spirulina platensis and Chlorella vulgaris varied with initial metal concentration. S. platensis and C. vulgaris removed 12.08 and 12.45 mg Cd2+ mg1 biomass, respectively, from 20 mg Cd2+ ml1 solution when 1.06 mg ml1 biomass was used. The organism employed during the present study was able to remove 4.27 mg Cd2+ from 5 mg ml1 solution
Fig. 1. Response surface contour plots showing effect of biomass (mg protein ml1) and pH on cadmium removal (mg ml1) by S. pevalekii (metal concentration: 5 mg ml1). The points on the corners and center of the figure represent experimental design points. The point with number 5 in the centre indicates that contour plots have been drawn when the value of the fixed variable (metal concentration) is at the mid point of lowest and highest selected levels.
Fig. 2. Response surface contour plots showing effect of metal concentration (mg ml1) and pH on cadmium removal (mg ml1) by S. pevalekii (biomass concentration: 0.16 mg protein ml1). The points on the corners and center of the figure represent experimental design points. The point with number 5 in the centre indicates that contour plots have been drawn when the value of the fixed variable (biomass concentration) is at the mid point of lowest and highest selected levels.
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References
Fig. 3. Response surface contour plots showing effect of biomass (mg protein ml1) and metal concentration (mg ml1) on cadmium removal (mg ml1) by S. pevalekii (pH: 6.0). The points on the corners and center of the figure represent experimental design points. The point with number 5 in the centre indicates that contour plots have been drawn when the value of the fixed variable (pH) is at the mid point of lowest and highest selected levels.
when 0.25 mg protein ml1 biomass was used and this comes to 17.08 mg Cd2+ removed mg1 protein (85% efficiency) which is also a very good efficiency. 3.2. Verification of the model Verification of the calculated optimum conditions for Cd2+ removal was done by performing the experiments at optimized conditions. Under these conditions, S. pevalekii removed 4.27 mg Cd2+ ml1 (85.4% metal removal) which is in agreement with the predicted value suggesting that Eq. (3) was valid for Cd2+ removal. 4. Conclusion Conventional methods that use one factor at a time are time consuming and expensive. During the present investigation response surface methodology based on three-factor-three-level Box–Behnken design was used to solve these problems. This design helped in locating the optimum levels of the most significant factors which contribute to the maximum metal removal. RSM, the selected method, resulted not only in increased metal removal efficiency by the test organism but also showed that it is simple, efficient, time and material saving. The test organism is quite efficient in Cd2+ removal from solution and thus has the potential to be exploited for treatment of cadmium containing industrial effluents before their discharge in to water bodies. Acknowledgements The authors are grateful to the Head, Department of Botany, Punjabi University, Patiala and Programme Coordinators, DRS (Special Assistance Programme) and ASIST Programme of U.G.C., New Delhi for necessary laboratory facilities.
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