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Study of heavy metal removal from heavy metal mixture using the CCD method
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JIEC-1359; No. of Pages 9 Journal of Industrial and Engineering Chemistry xxx (2013) xxx–xxx
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Study of heavy metal removal from heavy metal mixture using the CCD method S. Demim a, N. Drouiche b,*, A. Aouabed c, T. Benayad d, M. Couderchet e, S. Semsari a a
Laboratoire de ge´nie chimique, De´partement de Chimie Industrielle, Universite´ Saad Dahlab, BP 270, Route de Soumaa, 09000 Blida, Algeria Centre de Recherche en Technologie des Semi-conducteurs pour l’Energe´tique, 2, Bd Frantz Fanon, BP 140, Alger-7-merveilles, 16027 Algiers, Algeria c Laboratoire d’Analyse Fonctionnelle des Proce´de´s Chimiques, De´partement de Chimie Industrielle, Universite´ Saad Dahlab, BP 270, Route de Soumaa, 09000 Blida, Algeria d Laboratoire Centrale de Police Scientifique, De´partement Se´curite´ Alimentaire/Environnement, 1, Rue Abdelaziz Boukhalfa, 16000, Algeria e Laboratoire Plantes Pesticides et De´veloppement Durable (PPDD), URVVC-SE EA 2069, Universite´ de Reims Champagne-Ardenne, BP 1039, 51687 Reims Cedex 2, France b
A R T I C L E I N F O
Article history: Received 23 February 2013 Received in revised form 29 April 2013 Accepted 8 May 2013 Available online xxx Keywords: Heavy metals Lemna gibba Phytoremediation CCD
A B S T R A C T
The central composite design (CCD) technique was used to study the effect of the native species Lemna gibba on the removal heavy metals from the mixture of heavy metals, and understand their impact on the process. The effects of Cd, Cr, Cu, Zn, and Ni cations, incubation period and fronds number on heavy metals removal (Cd, Cr, Cu, Zn, and Ni) were studied, and the results were statistically analyzed using JMP 9.0.2 (SAS Institute) software. The analysis aimed at giving a mathematical model that shows the influence of each variable. Each factor has a distinct effect on heavy metal removal. High correlation was found between the experimental and predicted results, reflected by R2 (coefficient of determination). ß 2013 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved.
1. Introduction Water pollution by heavy metals has become an important issue over the last two decades [1]. Significant amounts of heavy metals are discharged into aquatic environment due to industrial and human activities [1,2]. These heavy metals are not biodegradable and their presence in streams and lakes leads to bioaccumulation in living organisms, thereby causing health problems in animals, plants [2–6] and human beings [4,6,7]. Considering its effects on human beings and other aquatic organisms, appropriate treatment of the heavy metals from the waste water is of utmost importance [7]. The technologies used for their treatments are reverse-osmosis, ion-exchange, electrodialysis, adsorption, coagulation–flocculation, oxidation with ozone/ hydrogen peroxide, photocatalytic degradation, flotation, etc. [6,8– 11]. However, the effectiveness of these methods can change according to different metals and they can be very expensive especially if large volumes, low metal concentration and high
* Corresponding author at: Technology of Semi-conductor for the Energetic Research Center, Department of Environmental Engineering, 2, Bd Frantz Fanon, BP 140, Alger-7-merveilles, Algiers, Algeria. Tel.: +213 21 827631; fax: +213 21 433511. E-mail address: [email protected] (N. Drouiche).
standards of cleaning are required [12–14]. Contrary to this biological technique, like phytoremediation, may be advantageous choice in several situations, being considered an effective, low cost and preferred cleanup option for areas moderately polluted by metals [15,16]. This technology is based on the ability of plants to absorb and accumulate metal contaminants in their tissues and eliminate of high amount of these elements from water or groundwater [13]. The bio removal processing using aquatic plants contains two uptake processes: biosorption which is an initial fast, reversible, metal binding process and bioaccumulation, a slow, irreversible, ion sequestration step [13,17]. Some plants can accumulate high concentrations of heavy metals and have been used in experimental assays for the phytoremediation of contaminated in water and wastewater, such as Pistia stratiotes, Spirodela polyrrhiza, Eichhornia crassipes, Lemna minor, Potamogeton pusillus, Salvinia natans and Lemna gibba [2,3,18–26]. In aquatic phytoremediation systems, aquatic plants can be either floating on the water surface or submerged into the water. The floating aquatic hyper accumulating plants absorb or accumulate contaminants by its roots, while submerged plants accumulate metals by their whole body [27]. The selection of aquatic plant was based on consideration such a high growth rate and tolerance on exposure to high elements concentrations [21]. In this context, plants with a high colonization
1226-086X/$ – see front matter ß 2013 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jiec.2013.05.010
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rate can be viewed as excellent tools for phytoremediation [14,28]. Metal accumulation by plants has been extensively investigated using L. gibba as a model plant from duckweed plants. L. gibba is commonly found in wetlands and is fast growing, adapts easily to various aquatic conditions, and plays an important role in the extraction and accumulation of toxic elements from surface waters [22]. The aim of this study was to conduct a central composite design analysis to significant factors that influenced the removal heavy metals from aqueous solution by native species L. gibba and understand their impact on the process. The effect of some operating variables – initial concentration of Cd2+, initial concentration of Cr6+, initial concentration of Cu2+, initial concentration of Zn2+, initial concentration of Ni2+, incubation period and fronds number (biomass) on heavy metal removal was studied by using a CDD, which gives a mathematical model that shows the influence of each variable. In the statistical design, the factors involved in an experiment are being simultaneously changed. The most important advantages are that not only the effects of individual parameters but also their relative importance in a given process are evaluated and that the interaction of two or more variables can also be derived. This is not possible in a classical one factor at a time experiment [29]. 2. Materials and methods 2.1. Plant material Fronds of duckweed (L. gibba) were subcultured from the original stocks which were maintained in the research laboratory (Chemical Engineering Laboratory), Saad Dahlab University, since 2009. The stock cultures were maintained in polyvinyl chloride (PVC) aquariums containing 2000 mL of inorganic growth medium (pH 6.5) adapted from Chollet [30]. This medium consisted of (mg/l): KNO3: 202; KH2PO4: 50.3; K2HPO: 427.8; K2SO4: 17.4; MgSO47H2O: 49.6; CaCl2: 11.1; FeSO47H2O: 6; H3BO3: 5.72; MnCl24H2O: 2.82; ZnSO4: 0.6; (NH4)MoO244H2O: 0.043; CuCl22H2O: 0.008; CoCl26H2O: 0.054. The light regime for culturing and for all experiment tests was 16 h light:8 h darkness at temperature 22 2 8C. Plants were sub cultured twice a week. The same light and temperature conditions were applied as describe above. 2.2. Heavy metal experiments Growth conditions during the tests concerning temperature, light and nutriment medium were the same as during precultivation. However, 500 mL plastic cups were used, filled with 250 mL growth medium and heavy metals (Cd2+, Cr6+, Ni2+, Zn2+, Cu2+) were added and the plastic cups were covered with watch glasses
Table 1 Experimental ranges and levels of the studied factors. Factor
Coded symbol
Initial concentration of Cd2+ (mg/l) Initial concentration of Cr6+ (mg/l) Initial concentration of Cu2+ (mg/l) Initial concentration of Zn2+ (mg/l) Initial concentration of Ni2+ (mg/l) Incubation period (days) Fronds number
X1 X2 X3 X4 X5 X6 X7
Range level
and
-1
+1
0 0 0 0 0 1 50
0.2 0.1 3 5 5 3 120
half-way to prevent evaporation. The duckweeds fronds were randomly selected as inoculums, and were exposed to contaminants (heavy metals) at specific time periods (Tables 1 and 2). The following metal compound was used for experiments: CdSO48/3H2O, K2Cr2O7, CuSO45H2O, ZnSO47H2O and NiSO47H2O. All reagents used were analytical grade. 2.3. Experimental design A central composite design (CDD) of response surface methodology was applied to predict the effects of initial concentration of Cd2+, initial concentration of Cr6+, initial concentration of Cu2+, initial concentration of Zn2+, initial concentration of Ni2+, incubation period and fronds number (biomass) on heavy metal removal (RCd2þ , RCr6þ , RCu2þ , RZn2þ , and RNi2þ ). In order to determine the factors that influence the response and to investigate the interaction effects of various parameters, a tow level was designed. Each factor was studied at tow levels low level and high level (Table 1). To analyze the factorial design, the original measurement units for the experimental factors (encoded units) were transformed into coded units [36]. The factor levels were coded 1 (low) and +1 (high). The seven independent variables were coded X1, X2, X3, X4, X5, X6 and X7 and varied at two levels as shown in Table 1. The experimental design for heavy metals mixtures which, was generated by JMP 9.0.2 (SAS Institute). A total of 12 experimental runs were needed, the order of the experimental run was completely randomized. 2.4. Removal of heavy metals by L. gibba Removal was determined by quantifying the concentration of metal left in the medium after incubation with plants and initial concentration of metal in the medium before incubation. Concentration of heavy metals was determined by flame atomic absorption (Perkin-Elmer) using a mixture of air/acetylene as carrier gas after acidification of the sample with 2% HCl. Removal of metals was calculated according to Tanhan et al. [31].
Table 2 Coded design table for the factors and responses. Run
1 2 3 4 5 6 7 8 9 10 11 12
Factor
Responses
X1
X2
X3
X4
X5
X6
X7
RCd2þ
RCr6þ
RCu2þ
RZn2þ
RNi2þ
1 1 1 1 1 1 1 1 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0
30.09 100 100 13.97 100 26.45 35.29 100 29.78 27.59 29.31 26.86
17.34 22.16 100 100 34.5 100 22 100 28.28 28.53 26.93 27.64
13.92 16.68 26.03 97.39 95.61 28.87 94.09 89.57 17.03 14.66 14.05 13.24
100 21.05 24.68 24.93 100 100 26.91 100 12.48 11.78 11.72 11.03
16.78 88.82 23.99 31.68 39.87 94.13 94.23 90.68 26.44 26.53 27 25.65
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Table 3 Statistical parameter for RCd2þ , RCr6þ , and RCu2þ . RCr6þ
Factor
Constant X1 X2 X3 X4 X5 X6 X7 X1X1 R2 R2 (adj)
RCd2þ
RCu2þ
Effect
Coef
SE coef
Effect
Coef
SE coef
Effect
Coef
SE coef
56.77 73.55 6.24 1.82 1.82 4.42 6.24 4.42 69.68
28.385 36.775 3.12 0.91 0.91 2.21 3.12 2.21 34.84
0.692 0.489 0.489 0.489 0.489 0.489 0.489 0.489 0.848
55.69 4.33 76 4.25 1.92 1.92 4.33 4.25 68.31
27.845 2.165 38 2.125 0.96 0.96 2.165 2.125 34.155
0.357 0.253 0.253 0.253 0.253 0.253 0.253 0.253 0.438
29.49 1.595 5.39 72.79 1.555 0.935 3.735 6.76 86.05
14.745 0.7975 2.695 36.395 0.7775 0.4675 1.8675 3.38 43.025
0.815 0.576 0.576 0.576 0.576 0.576 0.576 0.576 0.998
0.99 0.94
0.99 0.99
3. Results and discussion
0.99 0.99
RZn2þ ¼ 11:7525 þ 0:76375X 1 0:20625X 2 0:76375X 3 37:80375X 4 þ 0:20625X 5 0:70125X 6
3.1. Experimental design
þ 0:70125X 7 þ 50:44375X 1 X 1 : The design matrix of coded values for the factors and the response in terms of the percent removal of Cd2+, Cr6+, Cu2+, Zn2+, and Ni2+ for all experimental runs are shown in Table 2. The results were analyzed using JMP release 9.0.2, produced by SAS Institute, and the main effects were determined. The model coefficients for the removal, the effects and standardized effects of the factors are shown in Tables 3 and 4 for RCd2þ , RCr6þ , RCu2þ , RZn2þ , and RNi2þ , respectively. The effect of a factor is defined as a change in response produced by a change in level of the factor [32]. The codified mathematical model employed for central composite (response surface) was [33,34]: X X X y ¼ b0 þ bi X i þ bii Xi2 þ bi j X i X j (1) where y is the predicted response, b0 is the constant, bi is the slope or linear effect of the input factor Xi, bii is the quadratic effect of input factor Xi, bij is the linear by linear interaction effect between the input factor Xi. Substituting the coefficient bi, bii and bij in Eq. (1) by their values from Tables 3 and 4, we got: RCd2þ ¼ 28:385 36:775X 1 þ 3:12X 2 þ 0:91X 3 0:91X 4 2:21X 5 3:12X 6 þ 2:21X 7 þ 34:84X 1 X 1 :
(2)
RCr6þ ¼ 27:845 2:165X 1 38X 2 2:125X 3 0:96X 4 þ 0:96X 5 þ 2:165X 6 þ 2:125X 7 þ 34:155X 1 X 1 :
(3)
RCu2þ ¼ 14:745 þ 0:7975X 1 2:695X 2 36:395X 3 þ 0:7775X 4 þ 0:4675X 5 þ 1:8675X 6 þ 3:38X 7 þ 43:025X 1 X 1 :
(4)
(5)
RNi2þ ¼ 26:405 0:8175X 1 0:0975X 2 4:0925X 3 0:3425X 4 31:9425X 5 þ 3:6025X 6 þ 3:0325X 7 þ 33:6175X 1 X 1 :
(6)
The main effects (X1, X2, X3, X4, X5, X6 and X7) represent deviations of the average between high and low level for each one of them. In the case of RCd2þ , an increase in X2, X3 and X7 from low to high level resulted in 6.24, 1.82, and 4.42% increase in removal efficiency, whereas increase in X1, X4, X5, and X6 resulted in decreasing removal efficiency by 73.55, 1.82, 4.42, and 6.24%, respectively. In the case of RCr6þ , an increase in X5, X6, and X7 from low to high level resulted in 1.92, 4.33, and 4.25% increase in removal efficiency, whereas increase in X1, X2, X3 and X4 resulted in decreasing removal efficiency by 4.33,76, 4.25, and 1.92%, respectively. In the case of RCu2þ , an increase in X1, X4, X5, X6 and X7 from low to high level resulted in 1.595, 1.555, 0.935, 3.735 and 6.76% increase in removal efficiency, whereas increase in X2, and X3 resulted in decreasing removal efficiency by 5.39, and 72.79%, respectively. In the case of RZn2þ , an increase in X1, X5 and X7 from low to high level resulted in 1.5275, 0.4125, and 1.4025% increase in removal efficiency, whereas increase in X2, X3, X4 and X6 resulted in decreasing removal efficiency by 0.412, 1.5275, 75.6075 and 1.4025%, respectively. In the case of RNi2þ , an increase in X6, and X7 from low to high level resulted in 7.205, and 6.065% increase in removal efficiency, whereas increase in X1, X2, X3, X4, and X5 resulted in decreasing removal efficiency by 1.635, 0.195, 8.185, 0.685 and 63.885%, respectively.
Table 4 Statistical parameter for RZn2þ , and RNi2þ . Factor
RZn2þ
RNi2þ
Effect
Coef
SE coef
Effect
Coef
SE coef
Constant X1 X2 X3 X4 X5 X6 X7 X1X1
23.504 1.5275 0.412 1.5275 75.6075 0.4125 1.4025 1.4025 100.8875
11.752 0.76375 0.206 0.76375 37.80375 0.20625 0.70125 0.70125 50.44375
0.296 0.209 0.209 0.209 0.209 0.209 0.209 0.209 0.362
52.81 1.635 0.195 8.185 0.685 63.885 7.205 6.065 67.235
26.405 0.8175 0.0975 4.0925 0.3425 31.9425 3.6025 3.0325 33.6175
0.28 0.198 0.198 0.198 0.198 0.198 0.198 0.198 0.342
R2 R2 (adj)
0.99 0.99
0.99 0.99
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Table 5 Statistical parameter for RCd2þ , RCr6þ , and RCu2þ . Source
Statistics SS
df
RCd2þ X1 X2 X3 X4 X5 X6 X7 X1X1
10,819.20 77.87 6.62 6.62 39.07 77.87 39.07 3236.87
1 1 1 1 1 1 1 1
Error Total
5.76 14,308.98
3 11
RCr6þ X1 X2 X3 X4 X5 X6 X7 X1X1
37.50 11,552 36.125 7.37 7.37 37.50 36.12 3110.84
Error Total
1.538 14,826.37
3 11
RCu2þ X1 X2 X3 X4 X5 X6 X7 X1X1
5.09 58.10 10,596.77 4.84 1.75 27.90 91.39 4936.40
1 1 1 1 1 1 1 1
Error Total
7.977 15,730.22
3 11
1 1 1 1 1 1 1 1
It can be concluded that the positive values of effects meant that an increase in their levels led to an increase in the response; on the other hand, the negative values of the effects led to a diminution of the responses, when their levels were increased.
MS
F0
P
10,819.20 77.87 6.62 6.62 39.07 77.87 39.07 3236.87
5635.69 40.56 3.45 3.45 20.35 40.56 20.35 1686.07
<0.0001 0.0078 0.1602 0.1602 0.0204 0.0078 0.0204 <0.0001
73.16 22537.56 70.48 14.38 14.38 73.16 70.48 6069.94
0.0034 <0.0001 0.0035 0.0322 0.0322 0.0034 0.0035 <0.0001
1.91 21.85 3985.49 1.82 0.66 10.49 34.37 1856.60
0.2605 0.0185 <0.0001 0.2702 0.4768 0.0479 0.0099 <0.0001
1.92
37.50 11,552 36.125 7.37 7.37 37.50 36.12 3110.84 0.51 5.09
5.09 58.10 10,596.77 4.84 1.75 27.90 91.39 4936.40 2.66
3.2. Analyze of variance (ANOVA) After estimating the factor main effects, the determination of the significant factor affecting the response was followed by
Table 6 Statistical parameter for RZn2þ and RNi2þ . Source
Statistics SS
df
RZn2þ X1 X2 X3 X4 X5 X6 X7 X1X1
4.67 0.34 4.67 11,432.99 0.34 3.93 3.93 6785.52
1 1 1 1 1 1 1 1
Error Total
1.053 18,237.45
3 11
RNi2þ X1 X2 X3 X4 X5 X6 X7 X1X1
5.35 0.08 133.99 0.94 8162.59 103.82 73.57 3013.70
Error Total
0.94 11,494.97
1 1 1 1 1 1 1 1 3 11
MS
F0
P
4.67 0.34 4.67 11,432.99 0.34 3.93 3.93 6785.52
13.29 0.97 13.29 32,570.30 0.97 11.21 11.21 19,330.60
0.0356 0.3974 0.036 <0.0001 0.3974 0.0441 0.0441 <0.0001
17.05 0.24 427.21 2.99 26,025.89 331.04 234.57 9608.98
0.0258 0.6562 0.0002 0.1821 <0.0001 0.0004 0.0006 <0.0001
0.35
5.35 0.08 133.99 0.94 8162.59 103.82 73.57 3013.70 0.31
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Fig. 1. Effects Pareto for (a) RCd2þ ; (b) RCr6þ ; (c) RCu2þ ; (d) RZn2þ ; (e) RNi2þ .
performing an analysis of variance (ANOVA). Tables 5–6 show the sum of squares used to estimate the factors effects and the F-ratio F0 defined as the ratio of the respective mean square effect and the mean square error. Since F0.05, 1, 8 = 5.32, all effects presenting F0 higher 5.32 have statistical significance. Furthermore, because the factor in this study has two levels, each ANOVA main effect and interaction have one degree of freedom [35]. From the P values defined as the smallest level of significance leading rejection of the null hypothesis [34], it appears that the main effect of each factor and the interaction effects are statistically significant when P < 0.05 [36]. For RCd2þ , the effects X1, X2, X5, X6, X7 and X1X1 present the higher statistical significance. Tow effects were not statistically significant: X3 and X4. For RCr6þ , all effects was statistically significant: X1, X2, X3, X4, X5, X6, X7 and X1X1. For RCu2þ , the effects X2, X3, X6, X7 and X1X1 present the higher statistical significance. The effects without significance are X1, X4 and X5. For RZn2þ , the effects X1, X3, X4, X6, X7, and X1X1 present the higher statistical significance. Two effects were not statistically significant: X2 and X5. For RNi2þ , the effects X1, X3, X5, X6, X7 and X1X1 present the higher statistical significance. Two effects were not statistically significant are X2 and X4.
3.3. Student’s t-test In order to determine whether calculated effects were significantly different from zero, Student’s t-test was employed. It was observed that for 95% confidence level and 8 freedom degrees, the t-value was equal to 2.306. Those evaluations are illustrated by means of Pareto charts in Fig. 1. The vertical line indicates minimum statistically significant effect magnitude for a 95% confidence level. Values shown in the horizontal columns are Student’s t-test values for each effect. Based on F-test and Student’s t-test, some effects were discarded, because they did not exhibit any statistical significance. Resultant models can be represented by: RCd2þ ¼ 28:385 36:775X 1 þ 3:12X 2 2:21X 5 3:12X 6 þ 2:21X 7 þ 34:84X 1 X 1 :
(7)
RCu2þ ¼ 14:745 2:695X 2 36:395X 3 þ 1:8675X 6 þ 3:38X 7 þ 43:025X 1 X 1 :
(8)
RZn2þ ¼ 11:7525 þ 0:76375X 1 0:76375X 3 37:80375X 4 0:70125X 6 þ 0:70125X 7 þ 50:44375X 1 X 1 :
(9)
RNi2þ ¼ 26:405 0:8175X 1 4:0925X 3 31:9425X 5 þ 3:6025X 6 þ 3:0325X 7 þ 33:6175X 1 X 1 :
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(10)
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Table 7 Analysis of variance–reduce model fit for RCd2þ and RCu2þ . Statistics SS
df
RCd2þ X1 X2 X5 X6 X7 X1X1
10,819.20 77.87 39.07 77.87 39.07 3236.87
1 1 1 1 1 1
Lack of fit Pure error Total
13.25 5.76 14,308.98
2 3 11
RCu2þ X2 X3 X6 X7 X1X1
58.10 10,596.77 27.90 91.39 4936.40
1 1 1 1 1
Lack of fit Pure error Total
11.67 7.98 15,730.22
3 3 11
MS
F0
P
10,819.20 77.87 39.07 77.87 39.07 3236.87
2845.83 20.48 10.28 20.48 10.28 851.41
<0.0001 0.0062 0.0238 0.0062 0.0238 <0.0001
3.45
0.1668
17.74 3235.81 8.52 27.91 1507.37
0.0056 <0.0001 0.0267 0.0019 <0.0001
1.46
0.3810
6.62 1.92
58.10 10,596.77 27.90 91.39 4936.40 3.89 2.66
For RCd2þ , R2 = 0.99, R2 (adj) = 0.99. For RCu2þ , R2 = 0.99, R2 (adj) = 0.99.
Based on Eqs. (7)–(10), the model was recalculated, eliminating the effect of insignificant factors. Tables 7 and 8 show the analysis of variance for the reduced model. Comparing F0 values with the values of F-test, the lack of fit of the model can be evaluated. The lack of fit associated with elimination of the factor X3 and X4 for RCd2þ produced F0 = 3.45. Since this value is 2.77 time lower than tabulation F0.05, 2, 3 = 9.55, this factor did not have statistical significance. The lack of fit associated with elimination of the factor X1, X4, and X5 for RCu2þ produced F0 = 1.46. Since this value is 6.35 time lower than tabulation F0.05, 3, 3 = 9.28, this factor did not have statistical significance. The lack of fit associated with elimination of the factor X2 and X5 for RZn2þ produced F0 = 0.97. Since this value is 9.84 times lower
than tabulation F0.05, 2, 3 = 9.55, this factor did not have statistical significance. The lack of fit associated with elimination of the factor X2, and X4 for RNi2þ produced F0 = 1.62. Since this value is 5.89 time lower than tabulation F0.05, 2, 3 = 9.55, this factor did not have statistical significance. 3.4. Residual plot The observed results from experiments were verified by comparing with the calculated data from model (predicted values). The difference between the observed and predicted values from regression defined the residuals [34]. Those evaluations are illustrated in Fig. 2. The plot between individual
Table 8 Analysis of variance–reduce model fit for RZn2þ and RNi2þ . Statistics SS
df
RZn2þ X1 X3 X4 X6 X7 X1X1
4.67 4.67 11,432.99 3.93 3.93 6785.52
1 1 1 1 1 1
Lack of fit Pure error Total
0.68 1.05 18,237.45
2 3 11
RNi2þ X1 X3 X5 X6 X7 X1X1
5.35 133.99 8162.59 103.82 73.57 3013.70
Lack of fit Pure error Total
1.01 0.94
1 1 1 1 1 1 2 3 11
MS
F0
P
4.67 4.67 11,432.99 3.93 3.93 6785.52
13.46 13.46 32,972.80 11.34 11.34 19,569.49
0.0145 0.0145 <0.0001 0.0199 0.0199 <0.0001
0.34 0.35
0.97
5.35 133.99 8162.59 103.82 73.57 3013.70
13.67 342.61 20,871.91 265.48 188.12 7706.09
0.50 0.31
1.62
For RZn2þ , R2 = 0.99, R2 (adj) = 0.99. For RNi2þ , R2 = 0.99, R2 (adj) = 0.99.
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0.4734
0.0140 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.3338
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Fig. 2. Residuals vs. predicted values for (a) RCd2þ ; (b) RCr6þ ; (c) RCu2þ ; (d) RZn2þ ; (e) RNi2þ .
residual values and in the predicted value shows that all the residuals are scattered randomly about the zero i.e. the errors have a constant variance. 3.5. Responses surface plot The 3D response surface, which is a three dimensional graphic representation was used to determine the individual and cumulative effect of the variable and the mutual interaction between the variable and the dependent variable. The response surface analyzes the geometric nature of the surface, the maxima and minima of the response and the significance of the coefficients of the canonical equation [37]. The response surface plots represented in Fig. 3 were obtained by varying two factors while keeping the other constant. The surface plot (Fig. 3(a)) where Cd removal was represented by varying simultaneously X1 from 1 to +1 and X2 from 1 to +1. From this response surface plot this is that Cd removal to 50% X1 should be 0.4 and X2 should be +0.444.
The surface plot (Fig. 3(b)) where Cr removal was represented by varying simultaneously X1 from 1 to +1 and X2 from 1 to +1. From this response surface plot this is that Cr removal to 50% X1 should be 0.8 and X2 should be nearly 0.533. The surface plot (Fig. 3(c)) where Cu removal was represented by varying simultaneously X2 from 1 to +1 and X3 from 1 to +1. From this response surface plot this is that Cu removal to 50% X2 should be 0.8 and X3 should be 0.911. The surface plot (Fig. 3(d)) where Zn removal was represented by varying simultaneously X1 from 1 to +1 and X4 from 1 to +1. From this response surface plot this is that Zn removal to 50% X1 should be 0.467 and X4 should be nearly 0.733. The surface plot (Fig. 3(e)) where Ni removal was represented by varying simultaneously X1 from 1 to +1 and X5 from 1 to +1. From this response surface plot this is that Ni removal to 50% X1 should be 0.444 and X5 should be nearly 0.556. The surface plot also describing individual and cumulative effect of these two test variable and test their subsequent effect on the response.
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Fig. 3. 3D surface plot of the combined effect on the removal of Cd2+ (a); Cr6+ (b); Cu2+ (c); Zn2+ (d) and Ni2+ (e).
It was not possible to make a direct comparison between the results obtained by Miretsky et al. [12], Dirilgen [1] and Monferra´n et al. [2] with our work, since the studied factors differed in each case. Nevertheless, it is possible to compare trends. Miretsky et al. [12] studied the simultaneous removals several heavy metals by of P. stratiotes, S. intermedia and L. minor, and concluded that high
metal removal percentage were obtained for the three species and metals and noted that the rate of metal uptake was dependent on the metal concentration for the 3 species studied. However, L. minor did not survive the conditions of the experiment. Dirilgen [1] studied the accumulation of Pb and Hg by L. minor, and concluded that an antagonistic interaction was observed on the metal
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accumulation efficiency. Also, he concluded that the significance of the joint effect (metal type concentration) on metal accumulation was more important for Hg than it was for Pb. Monferra´n et al. [2] studied the simultaneous removal of Cr6+ and Cu2+ by P. pusillus, and concluded that the bioaccumulation of Cr6+ was significantly enhanced in the presence of Cu2+, showing a synergic effect on Cr6+ removal. In another study, it was reported that L. minor removed Pb and Ni under the laboratory condition, and the multiple metal experiments showed neither synergistic nor antagonistic effects in term of metal removal [38]. These results were in good agreement with our current conclusions, i.e. the joint effect (metal type concentration) on heavy metal removal on the one hand, and on the other the heavy metal removal was influenced by other heavy metals present in medium (synergistic and/or antagonistic effects). According to Mishra et al. [3] and Dhir and Srivastava [24] the capacity for accumulation varied for each metal, which could be explained on the basis of differential affinity towards a metal and competition between metal ions during uptake. Khellaf and Zerdaoui [13] have reported that the metal uptake in plants has been influenced by the initial metal concentration present in the medium, amount of plant biomass and cations present in medium. Another factor may influence the heavy metal removal was population density. Demirezen et al. [39] studied the effect of population densities on nickel accumulation capacity of L. gibba. They concluded that the metal accumulation capacity of L. gibba significantly affected by plant density, high population densities causes decreasing nickel accumulation by L. gibba. However, our work for heavy metal removal showed a positive effect for fronds number, on limit values studied. According to Srivastava et al. [40], Monferra´n et al. [41] and Monferra´n et al. [2], the accumulation heavy metals by macrophytes was a concentration-time dependent manner, who noticed the most significant increase after 48 h exposure, although heavy metal content continued increasing gradually till 7 days. In our work, the heavy metal removal showed a positive effect for incubation time, in the case from Cr, Cu and Ni and negative effect for fronds number in case Cd and Zn. 4. Conclusion The central composite design is a good technique for studying the influence of major parameters on heavy metals removal in heavy metal mixture by L. gibba, allowing to significantly reducing the number of experiments and hence forth, saving time, energy and money. The selected variables – initial concentration of Cd2+, initial concentration of Cr6+, initial concentration of Cu2+, initial concentration of Zn2+, initial concentration of Ni2+, incubation period, and fronds number – showed to influence the uptake process, but their importance varies according the response. The equations’ describing the relation between the response and the variables allows identifying the statistically significant variables and evaluating quantitatively the effect of each one on the responses. The most significant effect for Cd2+ removal was ascribed to isolate effects of X1, X2, X5, X6, and X7. For Cr6+ removal, all effects were most significant. For Cu2+ removal, the most isolate effects were X2, X3, X6, and X7. For Zn2+ removal, the most isolate effects were X1, X3, X4, X6, and X7. Finally, the significant effect for Ni2+ removal was ascribed to isolate effects of X1, X3, X5, X6, and X7. For
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all responses, we found no interaction between variables. Also, the R2 value of regression models equations shows a good fit of the models with experimental data. These statements are valid within the lower limits of the factors: initial concentration of Cd2+ [0–0.2 mg/l], initial concentration of Cr6+ [0–0.1 mg/l], initial concentration of Cu2+ [0–3 mg/ l], initial concentration of Zn2+ [0–5 mg/l], initial concentration of Ni2+[0–5 mg/l], incubation period [1–3 d], and fronds number [50– 120].
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JIEC-1359; No. of Pages 9 Journal of Industrial and Engineering Chemistry xxx (2013) xxx–xxx
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Study of heavy metal removal from heavy metal mixture using the CCD method S. Demim a, N. Drouiche b,*, A. Aouabed c, T. Benayad d, M. Couderchet e, S. Semsari a a
Laboratoire de ge´nie chimique, De´partement de Chimie Industrielle, Universite´ Saad Dahlab, BP 270, Route de Soumaa, 09000 Blida, Algeria Centre de Recherche en Technologie des Semi-conducteurs pour l’Energe´tique, 2, Bd Frantz Fanon, BP 140, Alger-7-merveilles, 16027 Algiers, Algeria c Laboratoire d’Analyse Fonctionnelle des Proce´de´s Chimiques, De´partement de Chimie Industrielle, Universite´ Saad Dahlab, BP 270, Route de Soumaa, 09000 Blida, Algeria d Laboratoire Centrale de Police Scientifique, De´partement Se´curite´ Alimentaire/Environnement, 1, Rue Abdelaziz Boukhalfa, 16000, Algeria e Laboratoire Plantes Pesticides et De´veloppement Durable (PPDD), URVVC-SE EA 2069, Universite´ de Reims Champagne-Ardenne, BP 1039, 51687 Reims Cedex 2, France b
A R T I C L E I N F O
Article history: Received 23 February 2013 Received in revised form 29 April 2013 Accepted 8 May 2013 Available online xxx Keywords: Heavy metals Lemna gibba Phytoremediation CCD
A B S T R A C T
The central composite design (CCD) technique was used to study the effect of the native species Lemna gibba on the removal heavy metals from the mixture of heavy metals, and understand their impact on the process. The effects of Cd, Cr, Cu, Zn, and Ni cations, incubation period and fronds number on heavy metals removal (Cd, Cr, Cu, Zn, and Ni) were studied, and the results were statistically analyzed using JMP 9.0.2 (SAS Institute) software. The analysis aimed at giving a mathematical model that shows the influence of each variable. Each factor has a distinct effect on heavy metal removal. High correlation was found between the experimental and predicted results, reflected by R2 (coefficient of determination). ß 2013 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved.
1. Introduction Water pollution by heavy metals has become an important issue over the last two decades [1]. Significant amounts of heavy metals are discharged into aquatic environment due to industrial and human activities [1,2]. These heavy metals are not biodegradable and their presence in streams and lakes leads to bioaccumulation in living organisms, thereby causing health problems in animals, plants [2–6] and human beings [4,6,7]. Considering its effects on human beings and other aquatic organisms, appropriate treatment of the heavy metals from the waste water is of utmost importance [7]. The technologies used for their treatments are reverse-osmosis, ion-exchange, electrodialysis, adsorption, coagulation–flocculation, oxidation with ozone/ hydrogen peroxide, photocatalytic degradation, flotation, etc. [6,8– 11]. However, the effectiveness of these methods can change according to different metals and they can be very expensive especially if large volumes, low metal concentration and high
* Corresponding author at: Technology of Semi-conductor for the Energetic Research Center, Department of Environmental Engineering, 2, Bd Frantz Fanon, BP 140, Alger-7-merveilles, Algiers, Algeria. Tel.: +213 21 827631; fax: +213 21 433511. E-mail address: [email protected] (N. Drouiche).
standards of cleaning are required [12–14]. Contrary to this biological technique, like phytoremediation, may be advantageous choice in several situations, being considered an effective, low cost and preferred cleanup option for areas moderately polluted by metals [15,16]. This technology is based on the ability of plants to absorb and accumulate metal contaminants in their tissues and eliminate of high amount of these elements from water or groundwater [13]. The bio removal processing using aquatic plants contains two uptake processes: biosorption which is an initial fast, reversible, metal binding process and bioaccumulation, a slow, irreversible, ion sequestration step [13,17]. Some plants can accumulate high concentrations of heavy metals and have been used in experimental assays for the phytoremediation of contaminated in water and wastewater, such as Pistia stratiotes, Spirodela polyrrhiza, Eichhornia crassipes, Lemna minor, Potamogeton pusillus, Salvinia natans and Lemna gibba [2,3,18–26]. In aquatic phytoremediation systems, aquatic plants can be either floating on the water surface or submerged into the water. The floating aquatic hyper accumulating plants absorb or accumulate contaminants by its roots, while submerged plants accumulate metals by their whole body [27]. The selection of aquatic plant was based on consideration such a high growth rate and tolerance on exposure to high elements concentrations [21]. In this context, plants with a high colonization
1226-086X/$ – see front matter ß 2013 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jiec.2013.05.010
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rate can be viewed as excellent tools for phytoremediation [14,28]. Metal accumulation by plants has been extensively investigated using L. gibba as a model plant from duckweed plants. L. gibba is commonly found in wetlands and is fast growing, adapts easily to various aquatic conditions, and plays an important role in the extraction and accumulation of toxic elements from surface waters [22]. The aim of this study was to conduct a central composite design analysis to significant factors that influenced the removal heavy metals from aqueous solution by native species L. gibba and understand their impact on the process. The effect of some operating variables – initial concentration of Cd2+, initial concentration of Cr6+, initial concentration of Cu2+, initial concentration of Zn2+, initial concentration of Ni2+, incubation period and fronds number (biomass) on heavy metal removal was studied by using a CDD, which gives a mathematical model that shows the influence of each variable. In the statistical design, the factors involved in an experiment are being simultaneously changed. The most important advantages are that not only the effects of individual parameters but also their relative importance in a given process are evaluated and that the interaction of two or more variables can also be derived. This is not possible in a classical one factor at a time experiment [29]. 2. Materials and methods 2.1. Plant material Fronds of duckweed (L. gibba) were subcultured from the original stocks which were maintained in the research laboratory (Chemical Engineering Laboratory), Saad Dahlab University, since 2009. The stock cultures were maintained in polyvinyl chloride (PVC) aquariums containing 2000 mL of inorganic growth medium (pH 6.5) adapted from Chollet [30]. This medium consisted of (mg/l): KNO3: 202; KH2PO4: 50.3; K2HPO: 427.8; K2SO4: 17.4; MgSO47H2O: 49.6; CaCl2: 11.1; FeSO47H2O: 6; H3BO3: 5.72; MnCl24H2O: 2.82; ZnSO4: 0.6; (NH4)MoO244H2O: 0.043; CuCl22H2O: 0.008; CoCl26H2O: 0.054. The light regime for culturing and for all experiment tests was 16 h light:8 h darkness at temperature 22 2 8C. Plants were sub cultured twice a week. The same light and temperature conditions were applied as describe above. 2.2. Heavy metal experiments Growth conditions during the tests concerning temperature, light and nutriment medium were the same as during precultivation. However, 500 mL plastic cups were used, filled with 250 mL growth medium and heavy metals (Cd2+, Cr6+, Ni2+, Zn2+, Cu2+) were added and the plastic cups were covered with watch glasses
Table 1 Experimental ranges and levels of the studied factors. Factor
Coded symbol
Initial concentration of Cd2+ (mg/l) Initial concentration of Cr6+ (mg/l) Initial concentration of Cu2+ (mg/l) Initial concentration of Zn2+ (mg/l) Initial concentration of Ni2+ (mg/l) Incubation period (days) Fronds number
X1 X2 X3 X4 X5 X6 X7
Range level
and
-1
+1
0 0 0 0 0 1 50
0.2 0.1 3 5 5 3 120
half-way to prevent evaporation. The duckweeds fronds were randomly selected as inoculums, and were exposed to contaminants (heavy metals) at specific time periods (Tables 1 and 2). The following metal compound was used for experiments: CdSO48/3H2O, K2Cr2O7, CuSO45H2O, ZnSO47H2O and NiSO47H2O. All reagents used were analytical grade. 2.3. Experimental design A central composite design (CDD) of response surface methodology was applied to predict the effects of initial concentration of Cd2+, initial concentration of Cr6+, initial concentration of Cu2+, initial concentration of Zn2+, initial concentration of Ni2+, incubation period and fronds number (biomass) on heavy metal removal (RCd2þ , RCr6þ , RCu2þ , RZn2þ , and RNi2þ ). In order to determine the factors that influence the response and to investigate the interaction effects of various parameters, a tow level was designed. Each factor was studied at tow levels low level and high level (Table 1). To analyze the factorial design, the original measurement units for the experimental factors (encoded units) were transformed into coded units [36]. The factor levels were coded 1 (low) and +1 (high). The seven independent variables were coded X1, X2, X3, X4, X5, X6 and X7 and varied at two levels as shown in Table 1. The experimental design for heavy metals mixtures which, was generated by JMP 9.0.2 (SAS Institute). A total of 12 experimental runs were needed, the order of the experimental run was completely randomized. 2.4. Removal of heavy metals by L. gibba Removal was determined by quantifying the concentration of metal left in the medium after incubation with plants and initial concentration of metal in the medium before incubation. Concentration of heavy metals was determined by flame atomic absorption (Perkin-Elmer) using a mixture of air/acetylene as carrier gas after acidification of the sample with 2% HCl. Removal of metals was calculated according to Tanhan et al. [31].
Table 2 Coded design table for the factors and responses. Run
1 2 3 4 5 6 7 8 9 10 11 12
Factor
Responses
X1
X2
X3
X4
X5
X6
X7
RCd2þ
RCr6þ
RCu2þ
RZn2þ
RNi2þ
1 1 1 1 1 1 1 1 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0
30.09 100 100 13.97 100 26.45 35.29 100 29.78 27.59 29.31 26.86
17.34 22.16 100 100 34.5 100 22 100 28.28 28.53 26.93 27.64
13.92 16.68 26.03 97.39 95.61 28.87 94.09 89.57 17.03 14.66 14.05 13.24
100 21.05 24.68 24.93 100 100 26.91 100 12.48 11.78 11.72 11.03
16.78 88.82 23.99 31.68 39.87 94.13 94.23 90.68 26.44 26.53 27 25.65
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Table 3 Statistical parameter for RCd2þ , RCr6þ , and RCu2þ . RCr6þ
Factor
Constant X1 X2 X3 X4 X5 X6 X7 X1X1 R2 R2 (adj)
RCd2þ
RCu2þ
Effect
Coef
SE coef
Effect
Coef
SE coef
Effect
Coef
SE coef
56.77 73.55 6.24 1.82 1.82 4.42 6.24 4.42 69.68
28.385 36.775 3.12 0.91 0.91 2.21 3.12 2.21 34.84
0.692 0.489 0.489 0.489 0.489 0.489 0.489 0.489 0.848
55.69 4.33 76 4.25 1.92 1.92 4.33 4.25 68.31
27.845 2.165 38 2.125 0.96 0.96 2.165 2.125 34.155
0.357 0.253 0.253 0.253 0.253 0.253 0.253 0.253 0.438
29.49 1.595 5.39 72.79 1.555 0.935 3.735 6.76 86.05
14.745 0.7975 2.695 36.395 0.7775 0.4675 1.8675 3.38 43.025
0.815 0.576 0.576 0.576 0.576 0.576 0.576 0.576 0.998
0.99 0.94
0.99 0.99
3. Results and discussion
0.99 0.99
RZn2þ ¼ 11:7525 þ 0:76375X 1 0:20625X 2 0:76375X 3 37:80375X 4 þ 0:20625X 5 0:70125X 6
3.1. Experimental design
þ 0:70125X 7 þ 50:44375X 1 X 1 : The design matrix of coded values for the factors and the response in terms of the percent removal of Cd2+, Cr6+, Cu2+, Zn2+, and Ni2+ for all experimental runs are shown in Table 2. The results were analyzed using JMP release 9.0.2, produced by SAS Institute, and the main effects were determined. The model coefficients for the removal, the effects and standardized effects of the factors are shown in Tables 3 and 4 for RCd2þ , RCr6þ , RCu2þ , RZn2þ , and RNi2þ , respectively. The effect of a factor is defined as a change in response produced by a change in level of the factor [32]. The codified mathematical model employed for central composite (response surface) was [33,34]: X X X y ¼ b0 þ bi X i þ bii Xi2 þ bi j X i X j (1) where y is the predicted response, b0 is the constant, bi is the slope or linear effect of the input factor Xi, bii is the quadratic effect of input factor Xi, bij is the linear by linear interaction effect between the input factor Xi. Substituting the coefficient bi, bii and bij in Eq. (1) by their values from Tables 3 and 4, we got: RCd2þ ¼ 28:385 36:775X 1 þ 3:12X 2 þ 0:91X 3 0:91X 4 2:21X 5 3:12X 6 þ 2:21X 7 þ 34:84X 1 X 1 :
(2)
RCr6þ ¼ 27:845 2:165X 1 38X 2 2:125X 3 0:96X 4 þ 0:96X 5 þ 2:165X 6 þ 2:125X 7 þ 34:155X 1 X 1 :
(3)
RCu2þ ¼ 14:745 þ 0:7975X 1 2:695X 2 36:395X 3 þ 0:7775X 4 þ 0:4675X 5 þ 1:8675X 6 þ 3:38X 7 þ 43:025X 1 X 1 :
(4)
(5)
RNi2þ ¼ 26:405 0:8175X 1 0:0975X 2 4:0925X 3 0:3425X 4 31:9425X 5 þ 3:6025X 6 þ 3:0325X 7 þ 33:6175X 1 X 1 :
(6)
The main effects (X1, X2, X3, X4, X5, X6 and X7) represent deviations of the average between high and low level for each one of them. In the case of RCd2þ , an increase in X2, X3 and X7 from low to high level resulted in 6.24, 1.82, and 4.42% increase in removal efficiency, whereas increase in X1, X4, X5, and X6 resulted in decreasing removal efficiency by 73.55, 1.82, 4.42, and 6.24%, respectively. In the case of RCr6þ , an increase in X5, X6, and X7 from low to high level resulted in 1.92, 4.33, and 4.25% increase in removal efficiency, whereas increase in X1, X2, X3 and X4 resulted in decreasing removal efficiency by 4.33,76, 4.25, and 1.92%, respectively. In the case of RCu2þ , an increase in X1, X4, X5, X6 and X7 from low to high level resulted in 1.595, 1.555, 0.935, 3.735 and 6.76% increase in removal efficiency, whereas increase in X2, and X3 resulted in decreasing removal efficiency by 5.39, and 72.79%, respectively. In the case of RZn2þ , an increase in X1, X5 and X7 from low to high level resulted in 1.5275, 0.4125, and 1.4025% increase in removal efficiency, whereas increase in X2, X3, X4 and X6 resulted in decreasing removal efficiency by 0.412, 1.5275, 75.6075 and 1.4025%, respectively. In the case of RNi2þ , an increase in X6, and X7 from low to high level resulted in 7.205, and 6.065% increase in removal efficiency, whereas increase in X1, X2, X3, X4, and X5 resulted in decreasing removal efficiency by 1.635, 0.195, 8.185, 0.685 and 63.885%, respectively.
Table 4 Statistical parameter for RZn2þ , and RNi2þ . Factor
RZn2þ
RNi2þ
Effect
Coef
SE coef
Effect
Coef
SE coef
Constant X1 X2 X3 X4 X5 X6 X7 X1X1
23.504 1.5275 0.412 1.5275 75.6075 0.4125 1.4025 1.4025 100.8875
11.752 0.76375 0.206 0.76375 37.80375 0.20625 0.70125 0.70125 50.44375
0.296 0.209 0.209 0.209 0.209 0.209 0.209 0.209 0.362
52.81 1.635 0.195 8.185 0.685 63.885 7.205 6.065 67.235
26.405 0.8175 0.0975 4.0925 0.3425 31.9425 3.6025 3.0325 33.6175
0.28 0.198 0.198 0.198 0.198 0.198 0.198 0.198 0.342
R2 R2 (adj)
0.99 0.99
0.99 0.99
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Table 5 Statistical parameter for RCd2þ , RCr6þ , and RCu2þ . Source
Statistics SS
df
RCd2þ X1 X2 X3 X4 X5 X6 X7 X1X1
10,819.20 77.87 6.62 6.62 39.07 77.87 39.07 3236.87
1 1 1 1 1 1 1 1
Error Total
5.76 14,308.98
3 11
RCr6þ X1 X2 X3 X4 X5 X6 X7 X1X1
37.50 11,552 36.125 7.37 7.37 37.50 36.12 3110.84
Error Total
1.538 14,826.37
3 11
RCu2þ X1 X2 X3 X4 X5 X6 X7 X1X1
5.09 58.10 10,596.77 4.84 1.75 27.90 91.39 4936.40
1 1 1 1 1 1 1 1
Error Total
7.977 15,730.22
3 11
1 1 1 1 1 1 1 1
It can be concluded that the positive values of effects meant that an increase in their levels led to an increase in the response; on the other hand, the negative values of the effects led to a diminution of the responses, when their levels were increased.
MS
F0
P
10,819.20 77.87 6.62 6.62 39.07 77.87 39.07 3236.87
5635.69 40.56 3.45 3.45 20.35 40.56 20.35 1686.07
<0.0001 0.0078 0.1602 0.1602 0.0204 0.0078 0.0204 <0.0001
73.16 22537.56 70.48 14.38 14.38 73.16 70.48 6069.94
0.0034 <0.0001 0.0035 0.0322 0.0322 0.0034 0.0035 <0.0001
1.91 21.85 3985.49 1.82 0.66 10.49 34.37 1856.60
0.2605 0.0185 <0.0001 0.2702 0.4768 0.0479 0.0099 <0.0001
1.92
37.50 11,552 36.125 7.37 7.37 37.50 36.12 3110.84 0.51 5.09
5.09 58.10 10,596.77 4.84 1.75 27.90 91.39 4936.40 2.66
3.2. Analyze of variance (ANOVA) After estimating the factor main effects, the determination of the significant factor affecting the response was followed by
Table 6 Statistical parameter for RZn2þ and RNi2þ . Source
Statistics SS
df
RZn2þ X1 X2 X3 X4 X5 X6 X7 X1X1
4.67 0.34 4.67 11,432.99 0.34 3.93 3.93 6785.52
1 1 1 1 1 1 1 1
Error Total
1.053 18,237.45
3 11
RNi2þ X1 X2 X3 X4 X5 X6 X7 X1X1
5.35 0.08 133.99 0.94 8162.59 103.82 73.57 3013.70
Error Total
0.94 11,494.97
1 1 1 1 1 1 1 1 3 11
MS
F0
P
4.67 0.34 4.67 11,432.99 0.34 3.93 3.93 6785.52
13.29 0.97 13.29 32,570.30 0.97 11.21 11.21 19,330.60
0.0356 0.3974 0.036 <0.0001 0.3974 0.0441 0.0441 <0.0001
17.05 0.24 427.21 2.99 26,025.89 331.04 234.57 9608.98
0.0258 0.6562 0.0002 0.1821 <0.0001 0.0004 0.0006 <0.0001
0.35
5.35 0.08 133.99 0.94 8162.59 103.82 73.57 3013.70 0.31
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Fig. 1. Effects Pareto for (a) RCd2þ ; (b) RCr6þ ; (c) RCu2þ ; (d) RZn2þ ; (e) RNi2þ .
performing an analysis of variance (ANOVA). Tables 5–6 show the sum of squares used to estimate the factors effects and the F-ratio F0 defined as the ratio of the respective mean square effect and the mean square error. Since F0.05, 1, 8 = 5.32, all effects presenting F0 higher 5.32 have statistical significance. Furthermore, because the factor in this study has two levels, each ANOVA main effect and interaction have one degree of freedom [35]. From the P values defined as the smallest level of significance leading rejection of the null hypothesis [34], it appears that the main effect of each factor and the interaction effects are statistically significant when P < 0.05 [36]. For RCd2þ , the effects X1, X2, X5, X6, X7 and X1X1 present the higher statistical significance. Tow effects were not statistically significant: X3 and X4. For RCr6þ , all effects was statistically significant: X1, X2, X3, X4, X5, X6, X7 and X1X1. For RCu2þ , the effects X2, X3, X6, X7 and X1X1 present the higher statistical significance. The effects without significance are X1, X4 and X5. For RZn2þ , the effects X1, X3, X4, X6, X7, and X1X1 present the higher statistical significance. Two effects were not statistically significant: X2 and X5. For RNi2þ , the effects X1, X3, X5, X6, X7 and X1X1 present the higher statistical significance. Two effects were not statistically significant are X2 and X4.
3.3. Student’s t-test In order to determine whether calculated effects were significantly different from zero, Student’s t-test was employed. It was observed that for 95% confidence level and 8 freedom degrees, the t-value was equal to 2.306. Those evaluations are illustrated by means of Pareto charts in Fig. 1. The vertical line indicates minimum statistically significant effect magnitude for a 95% confidence level. Values shown in the horizontal columns are Student’s t-test values for each effect. Based on F-test and Student’s t-test, some effects were discarded, because they did not exhibit any statistical significance. Resultant models can be represented by: RCd2þ ¼ 28:385 36:775X 1 þ 3:12X 2 2:21X 5 3:12X 6 þ 2:21X 7 þ 34:84X 1 X 1 :
(7)
RCu2þ ¼ 14:745 2:695X 2 36:395X 3 þ 1:8675X 6 þ 3:38X 7 þ 43:025X 1 X 1 :
(8)
RZn2þ ¼ 11:7525 þ 0:76375X 1 0:76375X 3 37:80375X 4 0:70125X 6 þ 0:70125X 7 þ 50:44375X 1 X 1 :
(9)
RNi2þ ¼ 26:405 0:8175X 1 4:0925X 3 31:9425X 5 þ 3:6025X 6 þ 3:0325X 7 þ 33:6175X 1 X 1 :
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(10)
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Table 7 Analysis of variance–reduce model fit for RCd2þ and RCu2þ . Statistics SS
df
RCd2þ X1 X2 X5 X6 X7 X1X1
10,819.20 77.87 39.07 77.87 39.07 3236.87
1 1 1 1 1 1
Lack of fit Pure error Total
13.25 5.76 14,308.98
2 3 11
RCu2þ X2 X3 X6 X7 X1X1
58.10 10,596.77 27.90 91.39 4936.40
1 1 1 1 1
Lack of fit Pure error Total
11.67 7.98 15,730.22
3 3 11
MS
F0
P
10,819.20 77.87 39.07 77.87 39.07 3236.87
2845.83 20.48 10.28 20.48 10.28 851.41
<0.0001 0.0062 0.0238 0.0062 0.0238 <0.0001
3.45
0.1668
17.74 3235.81 8.52 27.91 1507.37
0.0056 <0.0001 0.0267 0.0019 <0.0001
1.46
0.3810
6.62 1.92
58.10 10,596.77 27.90 91.39 4936.40 3.89 2.66
For RCd2þ , R2 = 0.99, R2 (adj) = 0.99. For RCu2þ , R2 = 0.99, R2 (adj) = 0.99.
Based on Eqs. (7)–(10), the model was recalculated, eliminating the effect of insignificant factors. Tables 7 and 8 show the analysis of variance for the reduced model. Comparing F0 values with the values of F-test, the lack of fit of the model can be evaluated. The lack of fit associated with elimination of the factor X3 and X4 for RCd2þ produced F0 = 3.45. Since this value is 2.77 time lower than tabulation F0.05, 2, 3 = 9.55, this factor did not have statistical significance. The lack of fit associated with elimination of the factor X1, X4, and X5 for RCu2þ produced F0 = 1.46. Since this value is 6.35 time lower than tabulation F0.05, 3, 3 = 9.28, this factor did not have statistical significance. The lack of fit associated with elimination of the factor X2 and X5 for RZn2þ produced F0 = 0.97. Since this value is 9.84 times lower
than tabulation F0.05, 2, 3 = 9.55, this factor did not have statistical significance. The lack of fit associated with elimination of the factor X2, and X4 for RNi2þ produced F0 = 1.62. Since this value is 5.89 time lower than tabulation F0.05, 2, 3 = 9.55, this factor did not have statistical significance. 3.4. Residual plot The observed results from experiments were verified by comparing with the calculated data from model (predicted values). The difference between the observed and predicted values from regression defined the residuals [34]. Those evaluations are illustrated in Fig. 2. The plot between individual
Table 8 Analysis of variance–reduce model fit for RZn2þ and RNi2þ . Statistics SS
df
RZn2þ X1 X3 X4 X6 X7 X1X1
4.67 4.67 11,432.99 3.93 3.93 6785.52
1 1 1 1 1 1
Lack of fit Pure error Total
0.68 1.05 18,237.45
2 3 11
RNi2þ X1 X3 X5 X6 X7 X1X1
5.35 133.99 8162.59 103.82 73.57 3013.70
Lack of fit Pure error Total
1.01 0.94
1 1 1 1 1 1 2 3 11
MS
F0
P
4.67 4.67 11,432.99 3.93 3.93 6785.52
13.46 13.46 32,972.80 11.34 11.34 19,569.49
0.0145 0.0145 <0.0001 0.0199 0.0199 <0.0001
0.34 0.35
0.97
5.35 133.99 8162.59 103.82 73.57 3013.70
13.67 342.61 20,871.91 265.48 188.12 7706.09
0.50 0.31
1.62
For RZn2þ , R2 = 0.99, R2 (adj) = 0.99. For RNi2þ , R2 = 0.99, R2 (adj) = 0.99.
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0.4734
0.0140 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.3338
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Fig. 2. Residuals vs. predicted values for (a) RCd2þ ; (b) RCr6þ ; (c) RCu2þ ; (d) RZn2þ ; (e) RNi2þ .
residual values and in the predicted value shows that all the residuals are scattered randomly about the zero i.e. the errors have a constant variance. 3.5. Responses surface plot The 3D response surface, which is a three dimensional graphic representation was used to determine the individual and cumulative effect of the variable and the mutual interaction between the variable and the dependent variable. The response surface analyzes the geometric nature of the surface, the maxima and minima of the response and the significance of the coefficients of the canonical equation [37]. The response surface plots represented in Fig. 3 were obtained by varying two factors while keeping the other constant. The surface plot (Fig. 3(a)) where Cd removal was represented by varying simultaneously X1 from 1 to +1 and X2 from 1 to +1. From this response surface plot this is that Cd removal to 50% X1 should be 0.4 and X2 should be +0.444.
The surface plot (Fig. 3(b)) where Cr removal was represented by varying simultaneously X1 from 1 to +1 and X2 from 1 to +1. From this response surface plot this is that Cr removal to 50% X1 should be 0.8 and X2 should be nearly 0.533. The surface plot (Fig. 3(c)) where Cu removal was represented by varying simultaneously X2 from 1 to +1 and X3 from 1 to +1. From this response surface plot this is that Cu removal to 50% X2 should be 0.8 and X3 should be 0.911. The surface plot (Fig. 3(d)) where Zn removal was represented by varying simultaneously X1 from 1 to +1 and X4 from 1 to +1. From this response surface plot this is that Zn removal to 50% X1 should be 0.467 and X4 should be nearly 0.733. The surface plot (Fig. 3(e)) where Ni removal was represented by varying simultaneously X1 from 1 to +1 and X5 from 1 to +1. From this response surface plot this is that Ni removal to 50% X1 should be 0.444 and X5 should be nearly 0.556. The surface plot also describing individual and cumulative effect of these two test variable and test their subsequent effect on the response.
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Fig. 3. 3D surface plot of the combined effect on the removal of Cd2+ (a); Cr6+ (b); Cu2+ (c); Zn2+ (d) and Ni2+ (e).
It was not possible to make a direct comparison between the results obtained by Miretsky et al. [12], Dirilgen [1] and Monferra´n et al. [2] with our work, since the studied factors differed in each case. Nevertheless, it is possible to compare trends. Miretsky et al. [12] studied the simultaneous removals several heavy metals by of P. stratiotes, S. intermedia and L. minor, and concluded that high
metal removal percentage were obtained for the three species and metals and noted that the rate of metal uptake was dependent on the metal concentration for the 3 species studied. However, L. minor did not survive the conditions of the experiment. Dirilgen [1] studied the accumulation of Pb and Hg by L. minor, and concluded that an antagonistic interaction was observed on the metal
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accumulation efficiency. Also, he concluded that the significance of the joint effect (metal type concentration) on metal accumulation was more important for Hg than it was for Pb. Monferra´n et al. [2] studied the simultaneous removal of Cr6+ and Cu2+ by P. pusillus, and concluded that the bioaccumulation of Cr6+ was significantly enhanced in the presence of Cu2+, showing a synergic effect on Cr6+ removal. In another study, it was reported that L. minor removed Pb and Ni under the laboratory condition, and the multiple metal experiments showed neither synergistic nor antagonistic effects in term of metal removal [38]. These results were in good agreement with our current conclusions, i.e. the joint effect (metal type concentration) on heavy metal removal on the one hand, and on the other the heavy metal removal was influenced by other heavy metals present in medium (synergistic and/or antagonistic effects). According to Mishra et al. [3] and Dhir and Srivastava [24] the capacity for accumulation varied for each metal, which could be explained on the basis of differential affinity towards a metal and competition between metal ions during uptake. Khellaf and Zerdaoui [13] have reported that the metal uptake in plants has been influenced by the initial metal concentration present in the medium, amount of plant biomass and cations present in medium. Another factor may influence the heavy metal removal was population density. Demirezen et al. [39] studied the effect of population densities on nickel accumulation capacity of L. gibba. They concluded that the metal accumulation capacity of L. gibba significantly affected by plant density, high population densities causes decreasing nickel accumulation by L. gibba. However, our work for heavy metal removal showed a positive effect for fronds number, on limit values studied. According to Srivastava et al. [40], Monferra´n et al. [41] and Monferra´n et al. [2], the accumulation heavy metals by macrophytes was a concentration-time dependent manner, who noticed the most significant increase after 48 h exposure, although heavy metal content continued increasing gradually till 7 days. In our work, the heavy metal removal showed a positive effect for incubation time, in the case from Cr, Cu and Ni and negative effect for fronds number in case Cd and Zn. 4. Conclusion The central composite design is a good technique for studying the influence of major parameters on heavy metals removal in heavy metal mixture by L. gibba, allowing to significantly reducing the number of experiments and hence forth, saving time, energy and money. The selected variables – initial concentration of Cd2+, initial concentration of Cr6+, initial concentration of Cu2+, initial concentration of Zn2+, initial concentration of Ni2+, incubation period, and fronds number – showed to influence the uptake process, but their importance varies according the response. The equations’ describing the relation between the response and the variables allows identifying the statistically significant variables and evaluating quantitatively the effect of each one on the responses. The most significant effect for Cd2+ removal was ascribed to isolate effects of X1, X2, X5, X6, and X7. For Cr6+ removal, all effects were most significant. For Cu2+ removal, the most isolate effects were X2, X3, X6, and X7. For Zn2+ removal, the most isolate effects were X1, X3, X4, X6, and X7. Finally, the significant effect for Ni2+ removal was ascribed to isolate effects of X1, X3, X5, X6, and X7. For
9
all responses, we found no interaction between variables. Also, the R2 value of regression models equations shows a good fit of the models with experimental data. These statements are valid within the lower limits of the factors: initial concentration of Cd2+ [0–0.2 mg/l], initial concentration of Cr6+ [0–0.1 mg/l], initial concentration of Cu2+ [0–3 mg/ l], initial concentration of Zn2+ [0–5 mg/l], initial concentration of Ni2+[0–5 mg/l], incubation period [1–3 d], and fronds number [50– 120].
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