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Heavy metal removal from multicomponent system by sulfate reducing bacteria: Mechanism and cell surface characterization
Accepted Manuscript Title: Heavy metal removal from multicomponent system by sulfate reducing bacteria: mechanism and cell surface characterization Author: Gopi Kiran Mothe Kannan Pakshirajan Gopal Das PII: DOI: Reference:
S0304-3894(15)30302-2 http://dx.doi.org/doi:10.1016/j.jhazmat.2015.12.042 HAZMAT 17321
To appear in:
Journal of Hazardous Materials
Received date: Revised date: Accepted date:
20-8-2015 27-10-2015 22-12-2015
Please cite this article as: Gopi Kiran Mothe, Kannan Pakshirajan, Gopal Das, Heavy metal removal from multicomponent system by sulfate reducing bacteria: mechanism and cell surface characterization, Journal of Hazardous Materials http://dx.doi.org/10.1016/j.jhazmat.2015.12.042 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Heavy metal removal from multicomponent system by sulfate reducing bacteria: mechanism and cell surface characterization
M Gopi Kiran1, Kannan Pakshirajan1,2* and Gopal Das1,3
1
Centre for the Environment, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India.
2
Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India. 3
Department of Chemistry, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India.
*Corresponding Author Dr. Kannan Pakshirajan Professor Department of Biosciences and Bioengineering Indian Institute of Technology Guwahati Guwahati 781039, Assam, India. Tel: +91-361- 2582210 Fax: +91-361- 2690762 Email: [email protected]
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Highlights Heavy metal bioremoval from mixture using sulfate reducing bacteria was evaluated Sulfate reduction to sulfide resulted in metal precipitation for removal More than 95% removal was obtained at low initial concentration combination of the metals Characterization of the bio-precipitates confirmed metal sulfides formation on the bacteria
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Abstract This study evaluated the combined effect of Cd(II), Cu(II), Ni(II), Fe(III), Pb(II) and Zn(II) on each other removal by anaerobic biomass under sulfate reducing condition. Statistically valid Plackett-Burman design of experiments was employed to carry out this mixture study. The results obtained showed a maximum removal of Cu(II) (98.9 %), followed by Ni(II) (97 %), Cd(II) (94.8 %), Zn(II) (94.6 %), Pb(II) (94.4 %) and Fe(III) (93.9 %). Analysis of variance (ANOVA) of the sulfate and chemical oxygen demand (COD) reduction revealed that the effect due to copper was highly significant (P value <0.05) on sulfate and COD removal. To establish the role of sulfate reducing bacteria (SRB) in the metal removal process, surface morphology and composition of the metal loaded biomass were analyzed by transmission electron microscopy (TEM) equipped with energy dispersive spectroscopy (EDS) and by field emission scanning electron microscopy (FESEM) integrated with energy dispersive X-ray spectroscopy (EDX). The results obtained revealed that the metal precipitates are associated with the outer and inner cell surface of the SRB as a result of the sulfide generated by SRB.
Abbreviations
AMD = acid mine drainage APHA = American public health association COD = chemical oxygen demand FESEM-EDX = field emission scanning electron microscopy equipped with energy dispersive X-ray spectroscopy FTIR = Fourier transforms infrared spectrometer 3
g = gravitational acceleration MLVSS = mixed liquor volatile suspended solids SRB = sulfate reducing bacteria TEM-EDS = transmission electron microscopy with energy dispersive spectroscopy ANOVA = Analysis of Variance EDTA = ethylene diamine tetraacetic acid BESA= bromo ethane sulfonic acid
Keywords: Heavy metals, sulfate reducing bacteria, multicomponent system, anaerobic biomass, Plackett-Burman design.
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1. Introduction Effluent from metallurgical industries and acid mine drainage (AMD) from mining activities are, in general, acidic and rich in sulfates and dissolved metals, such as cadmium (Cd), copper (Cu), nickel (Ni), iron (Fe), lead (Pb) and zinc (Zn) (1). Discharge of such heavy metal containing wastewater results in a serious problem towards the environment and living things (2). Heavy metals can be classified as toxic and non-biodegradable inorganic pollutants as they tend to accumulate in the food chain and get absorbed by living organisms, including humans, which result in severe health issues (3). Moreover, they influence the aesthetic quality of potable water (4). Besides heavy metals, high sulfate content is predominant in waste streams from metal leaching operations (5). Several problems exist due to the discharge of sulfate rich wastewaters, e.g., sulfate reduction to hydrogen sulfide (H2S), often leading to corrosion of pipe materials (6). Hence, it is of utmost importance to remove heavy metals and sulfate from wastewater prior to its discharge into the environment. Therefore, there is a continuous search for economical, effective and ecofriendly processes for the removal of heavy metals from wastewater, which would ensure their presence in the environment below permissible limits (7). Metal precipitation aided by biological sulfate reduction to S2-, HS-, H2S, etc. offers attractive advantages over the conventional chemical precipitation techniques. For instance, biological sulfate reduction technique for heavy metal removal produces a low amount of secondary sludge for further disposal. Thus, it is realized to be an efficient technique to remove heavy metals from wastewater at low initial concentrations (8). This process utilizes sulfate reducing bacteria (SRB) that belong to a group of morphologically diverse and anaerobic microorganisms with an ability to use sulfate as the terminal electron acceptor and simple organic / inorganic compounds as
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electron donors (9). SRB under anaerobic conditions reduce sulfate to different forms of sulfide and in this way not only reduction of soluble sulfate takes place but also leads to the formation of insoluble metal sulfides, thus aiding in both sulfate and heavy metal removal form wastewater (10, 11). Hence, anaerobic reduction of sulfate to sulfide by SRB is the determining step for the removal of both sulfate and heavy metals from wastewater (12, 13). Several authors have reported the effect of individual heavy metals on the sulfate reduction by SRB (14). Pollutants such as heavy metals never exist single in wastewater; they rather coexist with other inorganic species. Industrial effluents, particularly AMD, usually contain more than one metal along with high sulfate content. Thus, it is important to analyze and characterize the metal removal from multicomponent system by SRB. Therefore, this study was aimed at investigating heavy metals precipitation from multicomponent system and also their effect on sulfate and chemical oxygen demand (COD) reduction by SRB. One way to study the combined effect of heavy metals and sulfate on cultures of SRB is by using statistically designed experiments. For instance, individual main effect of factors on a given response can be investigated employing Plackett-Burman design, which is an efficient statistical experimental design technique (15). Analysis of the results is accomplished through analysis of variance (ANOVA) and Student’s t test. Hence, in this study, different combinations of high and low concentration levels of six heavy metals, viz. Cd(II), Cu(II), Ni(II), Fe(III), Pb(II) and Zn(II), were chosen using the PlackettBurman screening design. ANOVA and Student’s t test were then applied for statistical analysis of the results to interpret the significance and effect of these metals on each other’s removal as well as on sulfate and COD removal in the study. The SRB biomass from the experiments was
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further characterized using Fourier transform infrared (FTIR) spectrometer, transmission electron microscope equipped with energy dispersive spectroscopy (TEM-EDS) and field emission scanning electron microscope equipped with energy dispersive X-ray spectroscopy (FESEMEDX). 2. Materials and Methods 2.1 SRB source and culture conditions Anaerobic biomass containing mixed consortia of SRB was collected from a lab scale upflow anaerobic packed bed reactor treating sulfate rich wastewater whose microbial community characterization showed the presence of SRB, Desulfovibrio species in the biomass (16, 17). The medium composition for the biomass growth was as follows (g/L): 0.5 KH2PO4, 1 NH4Cl, 1.47 Na2SO4, 0.1 CaCl2∙2H2O, 0.1 ascorbic acid, 0.2 tri-sodium citrate, 0.2 ethylene diamine tetraacetic acid (EDTA) (18), 0.15 FeSO4·7H2O, 0.2 bromo ethane sulfonic acid (BESA) (19) and 1 yeast extract (modified Postgate medium) (20). The pH of the solution was adjusted to 7 using 1N NaOH. 10% v/v of the anaerobic biomass was added to 100 mL serum bottle containing the pH adjusted medium. The bottles were purged with nitrogen gas, before and after inoculation, incubated in an orbital shaker set at 30oC temperature and 120 rpm agitation speed for seven days. 60% v/v of sodium lactate was used as the carbon source for culturing the biomass. Such freshly grown biomass was designated as the maintenance culture for carrying out subsequent metal removal experiments. All chemicals and reagents used in this study were of analytical grade. 2.2 Heavy metal precipitation from multicomponent system by SRB To study the metal removal, sulfate reduction and COD from multicomponent system by SRB, a Plackett-Burman design consisting of 12 experimental runs with different combination levels of 7
Cd(II), Cu(II), Ni(II), Fe(III), Pb(II) and Zn(II) was applied (Table 1). The low and high concentration levels of each of Cd(II), Ni(II), Pb(II) and Zn(II) were chosen as 5 and 10 mg/L, respectively. Whereas, for Cu(II), these were 25 and 50 mg/L. In the case of Fe(III), 10 and 25 mg/L were chosen as the low and high initial levels, respectively. All these initial levels of the heavy metals were based on a previous single component study using the same anaerobic biomass containing SRB (17). Individual metal stock solutions of Cu(II), Cd(II), Ni(II), Fe(III), Pb(II) and Zn(II) of 10,000 mg/L concentration each were prepared using CuCl2·2H2O, Cd(NO3)2·4H2O, NiCl2·6H2O, FeCl3·6H2O, PbNO3 and ZnCl2, respectively. The modified Postgate medium as mentioned earlier was added with the corresponding metal stock solution so as to obtain a desired concentration of the heavy metals in each of the experimental runs. The statistical software Minitab (Version 16, PA, USA) was used for statistical analysis of the results obtained. All the experiments in this study were performed using 100 mL serum bottles. These bottles were purged with nitrogen gas before and after inoculation with 10% v/v (754 mg/L) anaerobic biomass measured as mixed liquor volatile suspended solids (MLVSS). The bottles were then incubated in an orbital shaker set at 30oC temperature and 120 rpm agitation speed. Bottles without any added metals but containing only the media, carbon source and the biomass, served as the control in these experiments. Liquid samples were taken from the bottles at regular intervals during the experiments to determine conductivity, pH, sulfate, COD, MLVSS, metal and sulfide (soluble) concentrations in the samples. 2.3 Characterization of the metal bio-precipitates Characterization of the metal bio-precipitates formed due to the SRB was carried out by FTIR spectroscopy, TEM-EDS and FESEM-EDX. FTIR spectrum was obtained to describe the changes in the biomass functional groups due to sulfate reduction and heavy metal precipitation. For FTIR 8
analysis, the control and metal treated SRB biomass were centrifuged (8000×g) for 5 min, washed twice with distilled water and the pellets obtained were vacuum dried and analyzed using a FTIR spectroscope (Perkin Elmer, Spectrum Two, Singapore). Similarly, for TEM analysis, biomass sample from the experimental run 1 (Table 1) was centrifuged as it yielded a very high metal removal efficiency among the different experimental runs, and the pellet obtained was loaded on a copper grid coated with carbon for observation under TEM (JOEL, JEM2100, Japan) at 200 kV integrated with EDS. For FESEM analysis, the same precipitates obtained from experimental run 1 were oven dried at 80 oC for 2 h and gold coated using a sputter coater (21). The precipitates were then analyzed for morphology and elemental composition using FESEM-EDX (Zeiss, Sigma, Germany). 2.4 Analytical methods Soluble metal concentration in samples was determined using an atomic absorption spectrometer (Varian, AA240, The Netherlands) as per the American Public Health Association (APHA) (22). Biomass was estimated as MLVSS according to the APHA method (22). Sulfate and COD in the samples was quantified according to the methods described in APHA (22). Sulfide concentration was measured according to the method described by Cord Rowish (23).
2. Results 3.1 Heavy metal precipitation from multicomponent system
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Table 1 presents the simultaneous removal of Cd(II), Cu(II), Ni(II), Fe(III), Pb(II) and Zn(II) by SRB in the multicomponent system, which reveals that the removal efficiency of each of these metals varied depending upon their combination level in their respective mixtures. The results obtained shows a maximum removal for Cu(II) (98.9 %), followed by Ni(II) (97 %), Cd(II) (94.8 %), Zn(II) (94.6 %), Pb(II) (94.4 %) and Fe(III) (93.9 %). A maximum metal removal was achieved at a low concentration combination of these metals, i.e. at 5 mg/L each of Ni(II), Cd(II), Zn(II) and Pb(II), at 25 mg/L Cu (II) and 10 mg/L Fe (III) in the experimental run 1 (Table 1). These results clearly indicate the dependence of metal precipitation by SRB on the metals and their concentration combination in the mixture. An overall removal efficiency of more than 75 % for each metal was achieved except for nickel (72. 4 %) (Table 1).
Insert < Table 1 > Here
Sulfate and COD reduction efficiencies corresponding to the different experimental runs are presented in Table 1. Experimental run 1 resulted in maximum sulfate reduction efficiency (92.5%), followed by that in experimental runs 2 and 11. Maximum COD reduction efficiency (91.2%) was observed in experimental run 1 followed by that in runs 2 and 8 (Table 1). Both sulfate and COD reduction efficiencies were the minimum in the experimental run 12 with metals added at their respective high initial levels.
For a better interpretation and assessment of the significance of the individual heavy metals on each other removal as well as on the sulfate and COD reduction by SRB, the results obtained were analyzed statistically in terms of Student’s t test and analysis of variance (ANOVA). Table 2 presents the ANOVA of metal removal; ANOVA of sulfate and COD removal is presented in
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Table 3. In these ANOVA tables, a high Fisher’s F value and a low probability P value of the regression model indicate the model precision in explaining the variations in the results.
Insert < Tables 2 and 3 > Here
Values of the statistical parameters Fisher’s F, P, standard error (S), coefficient of determination (R2) and adjusted R2, collectively illustrate whether the level means are significantly different from each other or not. These values also clearly show goodness of fit of the respective regression models used to explain the experimental results (24). A lower value of the parameter S represents an accurate prediction capability of these models. R2 and adjusted R2 values describe the amount of variation in the observed response values. Hence, a minimum value of S and a maximum value of R2 represent an accurate prediction capability of the model. Tables 2 and 3 also present the accuracy and precision of the models, in terms of R2, adjusted R2, S and predicted residual error sum of squares (PRESS). These values suggest that the models were quiet efficient in distinguishing the results accurately. Best results were however, obtained for the models used to describe Fe(III), Pb(II) and Zn(II) removal by SRB (Table 2).
Student t test, which is used as a common tool to verify the significance of the coefficients of the regression model parameters on a given response, was applied to understand the effect of these metals on each other removal in the multicomponent system. The estimated coefficients of individual effect of these metals are described in Table 4 in which the associated T and P values were used to specify their significance. From Table 4, an increase in initial Cu(II) concentration showed significant negative effect with a P value of 0.04 on its own removal, whereas Cd(II) showed a significant positive effect on Cu(II) removal. Ni(II) showed significant negative effect
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on its own removal with a P value of 0.016, whereas Cd(II), Fe(III) and Zn(II) showed positive effect on Ni(II) removal.
Insert < Table 4 > Here
Both Fe(III) and Ni(II) showed a very high significant negative effect on Fe(III) removal with P values of 0.008 and 0.036, respectively. Both Cd(II) and Zn(II) showed a positive effect on Fe(III) removal. In case of Pb(II) removal, the influence due to Pb(II), Ni(II) and Cu(II) was strongly negative with P values of 0.001, 0.006 and 0.028, respectively; whereas Zn(II) and Fe(III) showed positive effect on Pb(II) removal. Zn(II) showed negative effect with a P value of 0.008, whereas Cd(II) and Fe(III) showed positive effect on Zn(II) removal. Cu(II) seemed to inhibit both sulfate and COD removal with P values of 0.01 and 0.042, respectively; whereas Fe(III) exhibited a positive effect on sulfate removal (Table 5). All these results were in agreement with the literature on student’s t test (25).
Insert < Table 5 > Here
All these effects of different heavy metals on each other’s removal in the multicomponent system are better illustrated in the form of Pareto charts, depicted in Fig. 1(a–g). Horizontal bars in these charts represent the effect due to the individual metals, and those extending past the reference line (vertical line on the chart) represent the significant ones (α=0.05). In summary, an increase in Cu(II), Ni(II) and Zn(II) concentration level in the mixture was inhibitory to their own removal (Fig. 1a, 1b and 1e). Fe(III) and Ni(II) inhibited Fe(III) removal (Fig 1c ) whereas Pb(II), Ni(II) and Cu(II) showed inhibitory effect on Pb(II) removal in the multicomponent system (Fig. 1d). Among the different metals, Cu(II) inhibited both sulfate and COD reduction by SRB (Fig. 1f and 1g). 12
Insert < Figure 1 > Here
3.2 Characterization of metal bioprecipitates
TEM-EDS, FESEM-EDX and FTIR analyses of the bio-precipitates were carried out to understand the morphology and elemental composition of the metal precipitates formed by SRB in this study. Figs. 2a and Fig. 3a display clear TEM images of the bacterial cell with metal sulfides precipitated on its surface. The presence of different metals as sulfides is confirmed by the EDS spectrum taken from a spot on the outer cell surface of the bacteria (Fig. 2b and Fig. 3b). Metal precipitates in the vicinity of the bacterial cell surface are clearly visible from Fig. 3. Insert to Fig. 4a represents the FESEM image of the control biomass in which the solid biomass appears as a coalescent material; its elemental composition was confirmed by EDX spectrum. Similarly, insert to Fig. 4b shows the FESEM image of the biomass taken from experimental run 1, which also shows EDX spectrum of the biomass. A comparison between these figures confirms metal sulfide precipitation by SRB together with the presence of other elements, which constituted the Postgate medium.
Insert < Figures 2, 3 and 4 > Here
The presence of sulfur peak in the spectra (Fig. 2b, Fig. 3b and Fig. 4b) is attributed to the metal sulfide precipitation as a result of SRB activity. The precipitates formed are predominantly amorphous form of sulfide salts corresponding to the different metals (Figs. 4a and 4b). Sulfate and COD reduction along with metal sulfide formation confirmed that sulfidogenesis is the governing mechanism for heavy metal removal by SRB (19). Overall, the results from TEM and FESEM revealed that the metals were mainly removed by sulfide precipitation and are associated with cell surface.
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Insert < Figure 5 > Here
FTIR spectra of the heavy metal loaded anaerobic biomass from the experiments were obtained to verify the interaction between the metal ions and the functional groups present on the bacterial surface (Fig. 5). The major stretching in the spectrum was in the range 468 - 3696 cm-1 for the control biomass and 467- 3468 cm-1 for biomass obtained from the experimental run 1. The spectra were characterized by the much sharper peaks in the different wave regions 599 - 618 cm-1, 1018 - 1043 cm-1, 1535 - 1544 cm-1 and 1638 - 1642 cm-1, respectively. Occurrence of neutral C=O complex was indicated by the stretching modes at 1644 cm-1 in control biomass and a minor shift to 1642 cm-1 in the heavy metal loaded biomass. Bands in the range 1199 - 800 cm-1 are associated with C–O–C and C–O–P stretching. These stretching vibrations involve oligo and polysaccharides present in the bacteria (26). Bands corresponding to the stretching mode from 1525 to 1558 cm-1 are mainly due to -NH stretching and can be attributed to protein amide I and amide II bands (27). Bands corresponding to the range from 3748 to 3764 cm-1 indicate either N-H stretching of amine group or O-H stretching of hydroxyl group. An earlier study on the FTIR spectra of SRB has reported that sulfate ions exhibit five bands centered around 1230 cm-1, 1130 cm-1, 1070 cm-1, 1000 cm-1 and 610 cm-1 (28).
3. Discussion
Metal effect on sulfate reduction by SRB is well reported in the literature (8). However, there is a very less understanding on the combined effect of more than one metal in mixture on simultaneous sulfate and COD reduction as well as on each other metal removal in multicomponent system. It 14
can be observed from Table 1 that the results corresponding to heavy metal removal and the sulfate reduction correlated with each other, confirming heavy metal precipitation by SRB through sulfate reduction. Among the different heavy metals in the multicomponent system, copper removal was maximum (98.9 %) due to its low solubility product with sulfide. Similar observation can be made on the removal of the other metals by the SRB in this study. Thus, the order of removal of these metals as sulfide salts is attributed to their respective solubility product constant values (29).
The results shown in Table 1 further suggest that metals at a low initial concentration level (experimental run 1) in the multicomponent system did not inhibit the growth of SRB. This can be explained based on the combined results of metal and sulfate removal (Table 1). However, a high initial metal concentration level (experimental run 12) resulted in a reduced activity of the SRB and, therefore, its sulfate reduction efficiency (72.6 %). Due to the low sulfate reduction efficiency, metal removal was also less compared to those in other experimental runs (Table 1).
At a low initial metal concentration, precipitation of the insoluble metal due to sulfide produced by SRB avoids any toxic effect of the metal on SRB. This could be the major mechanism of metal precipitation and metal tolerance by SRB (30). On the other hand, at an elevated concentration, these metals tend to be toxic to microorganisms due to their enhanced bioavailability, thus resulting in denaturation and deactivation of enzymes, rupture of cell organelles and membrane integrity, etc. (31). Such toxic effects of copper on SRB in the medium has been reported by Kadukova and Vircikova (32). The heavy metal resistance due to SRB also varies with respect to different metal species, as presented in Table 6 (12). Fig. 1 depicts that Cu(II), Ni(II) and Pb(II) showed significant inhibitory effect on Pb(II) removal; Ni(II) and Fe(III) resulted in an inhibitory effect on Fe(III) removal. All these effects of heavy metals on each other removal from mixture can be attributed
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to the solubility product constant of the corresponding metal sulfide salts (CuS, PbS, NiS, ZnS, FeS and CdS); the values are 6×10–37, 3×10–28, 3×10–19, 2×10–25, 6×10–19 and 8×10–28, respectively (29).
Insert < Table 6 > Here
Table 1 showed that the removal of all the metals was the maximum at a low concentration combination of these metals. At a high concentration combination of these metals, the metalremoval efficiency was slightly lower. Owing to the increase in the metal sulfide (M/S2−) ratio, reduction in the metal removal efficiency was observed at a high concentration combination of metals (33). At a lower value of M/S2− ratio (<1), sulfide formed due to sulfate reduction is high, which ensures a high metal sulfide precipitation for the metal removal. Also, any toxic effect due to the low residual metal ions in solution on the sulfate reduction process becomes insignificant. At an M/S2− ratio greater than 1, the residual metal concentration shows toxic effect on the organism, thereby reducing the sulfate reduction efficiency and, therefore, its own removal (Table 1) (33).
In order to confirm the heavy metal removal mechanism by the SRB through sulfate reduction in the multicomponent system, FTIR spectra, FESEM-EDX and TEM-EDS spectra of the biomass were examined. The TEM images (Figs. 2a and 3a) confirmed the ability of these SRB to grow in presence of the metals in the mixture. The images also revealed the presence of a layer or shell like structure on cell wall of the bacteria. The TEM images further reveal that metal sulfide is associated with outer layer of the bacterial cell surface (Figs. 2b and 3b). From FESEM-EDX spectra of the precipitates (Fig. 4), it is clear that the metals were precipitated as metal sulfides (12). Different metals precipitated in the experimental run 1 were highlighted with circle in Fig. 16
4b. Among the peaks due to the different elements, only the peak due to sulfide is significant indicating that the metals were present as sulfides in the precipitate (Figs. 2, 3 and 4). All other forms such as M(OH)x, MCO3, etc. were not significant.
FTIR spectra (Fig. 5) of the biomass used in this multicomponent system showed bands corresponding to the sulfate ions, which clearly indicate sulfate reduction for its growth and metabolism (34, 35). FTIR analysis confirmed a high degree of similarity of the functional groups corresponding to SRB in the present study with several other reported species of SRB such as Desulfovibrio vietnamensis DSM 10520, Desulfovibrio gigas ATCC 19364, Desulfovibrio gabonensis DSM 10636 and Desulfovibrio indonesiensis NCIMB 13468 (36).
4. Conclusions
This study demonstrated the simultaneous precipitation of different heavy metals from the multicomponent system by SRB. A very high removal of the different metals was achieved at both low and high initial concentrations. However, at a high metal concentration combination, sulfate and COD reduction were inhibited, thus resulting in a slightly reduced removal of these metals by SRB. FTIR spectroscopy analysis of the biomass confirmed the presence of functional groups in the SRB that were similar to that of an earlier reported SRB Desulfovibrio species. TEM-EDS and FESEM-EDX analyses of the bacteria following metal removal further confirmed that the metal precipitates formed were associated with the cell surface and the heavy metal removal mechanism was attributed to the capability of the SRB to reduce sulfate to sulfide, thereby resulting in precipitation of the metals as sulfide salts. Overall, this study proved very good potential of SRB in the anaerobic biomass for metal precipitation with a high tolerance towards different metals in the multicomponent system. 17
Acknowledgements
Authors thank the Central Instruments Facility (CIF), IIT Guwahati, for FESEM-EDX and TEMEDS analyses of the samples. Authors also thank Mr. Prahlad Kumar Baruah and Mr. Partha Protim Bakal for their assistance in TEM and FTIR analysis. Authors also thank all the reviewers for their valuable suggestions in the review process.
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C. Rubio, C. Ott, C. Amiel, I. Dupont-Moral, J. Travert L. Mariey, Sulfato/ thiosulfato reducing bacteria characterization by FT-IR spectroscopy: a new approach to biocorrosion control, J Microbiol Methods. 64 (2006) 287–296.
27.
Quan. Hong’en, Bai. He, H. Yang, Y. Kong, S. Jiao, Removal of Cu(II) and Fe(III) from aqueous solutions by dead sulfate reducing bacteria, Front Chem Sci Eng. 7(2) (2013) 177–184.
28.
K. Nakamoto, Infrared Spectra of Inorganic and Coordination Compounds, Wiley, Newyork (1970).
29.
J. W. Hill, R. H. Petrucci, T. W. McCreary, & S. S. Perry, General Chemistry. 4th Edition, (2005), Pearson Prentice Hall, Upper Saddle River.
30.
C. White, G. M Gadd, Copper accumulation by sulfate-reducing bacterial biofilms, FEMS Microbiol Lett. 183 (2000) 313–318.
31.
M. Alexandrinoa, F. Macías, R. Costa, N.C. Gomesc, A.V. Canárioa, MC. Costa, A bacterial consortium isolated from an Icelandic fumarole displays exceptionally high levels of sulfate reduction and metals resistance, J Hazard Mater. 137 (2011) 162–370.
20
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J. Kadukova, E. Vircikova, Comparison of differences between copper bioaccumulation and biosorption, Environ Int. 31 (2005) 227–232.
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D. K. Villa-Gomez, K. Pakshirajan, R. Maestro, S. Mushi, P. N. L. Lens, Effect of process variables on the sulfate reduction process in bioreactors treating metalcontaining wastewaters: Factorial design and response surface analyses, Biodegradation. 26 (2015) 299–311.
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Rajesh. Singh, Anil. Kumar, Anita. Kirrolia, Rajender. Kumar, Neeru.Yadav, Narsi R. Bishnoi, Rajesh K. Lohchab, Removal of sulphate, COD and Cr ( VI ) in simulated and real wastewater by sulphate reducing bacteria enrichment in small bioreactor and FTIR study, Bioresour Technol. 102 (2) (2011) 677–682. Available from: http://dx.doi.org/10.1016/j.biortech.2010.08.041
35.
Zaina. Nor Azimah Mohd, Suhaimi. Mohd Suardi, Ani. Idris, Development and modification of PVA– alginate as a suitable immobilization matrix, Process Biochem. 40 (2011) 2122–2129.
36.
J. Feio Maria, Vitaly Zinkevich, Iwona B. Beech, Enric L. Brossa, Peter Eaton, Jurgen Schmitt, Jean Guezennec, Desulfovibrio alaskensis sp. nov., a sulphatereducing bacterium from a soured oil reservoir, Int J Syst Evol Microbiol. 54 (2004) 1747–1752.
21
List of Figures with captions Fig.1. Pareto chart showing the effect of different heavy metals on each other’s removal, sulfate and COD reduction by SRB: a) Cu(II) removal, b) Ni(II) removal, c) Fe(III) removal, d) Pb(II) removal, e) Zn(II) removal, f) sulfate removal and g) COD removal (vertical line shows significance cutoff at P value less than 0.05).
Fig.2. a) TEM image of a metal loaded bacterial cell from the experimental run #1, b) EDS spectrum from a spot on the bacterial cell surface. Fig.3. a) TEM image of a metal loaded bacterial cell from the experimental run #1 showing intact metal precipitate on SRB cell surface, b) EDS spectrum from a spot on the bacterial cell surface. Fig.4. EDX spectrum of a) control biomass, b) metal loaded biomass from experimental run #1. Insert to these figures show the image of the respective biomass. Fig.5. FTIR spectra of control biomass and heavy metal loaded biomass from experimental run # 1 in the study.
22
2.571
Cu
Ni
Zn
Cu
Fe
Zn
Metal
Metal
2.571
Pb
Cd
Ni
Pb 0
1
2
3
4
5
Standardized Effect
a
Ni
Cu
Cu
Metal
Metal
Ni
Pb
Fe
Zn
Zn 2
3
4
c
5
0
Zn
Ni
Pb
Metal
Fe
Cu
5
2
3
4
5
2.571
Cu
Pb
4
Standardized Effect
Zn
Cd Ni
Cd
Fe 0
1
2
3
4
5
0
Standardized Effect
e
f
2.571
Cu Fe Ni Zn Pb Cd 0
g
1
d
2.571
3
Cd
Cd
Standardized Effect
2 2.571
Pb
1
1
Standardized Effect
Fe
0
Metal
0
b
2.571
Metal
Fe
Cd
1
2
3
4
5
Standardized Effect
Fig. 1.
23
1
2
3
4
Standardized Effect
5
Fig.2.
24
Fig.3.
25
Fig.4.
26
%T
70 65 60 55 50 45 40 35 30 25 20 15 10 5
Control 694
1641 1091 1031 610
1199
Experimental run 1
3500
3000
1642
1018 1094 1135 1123
2500 2000 1500 -1 Wave Number (Cm )
Fig.5.
27
1000
500
List of tables with captions Table 1 Plackett-Burman experimental design matrix showing different combination levels of the heavy metals in the study along with the removal of metals, sulfate and COD Table 2 Analysis of variance of heavy metal removal from a multicomponent system by SRB Table 3 Analysis of variance of sulfate and COD removal in the presence different heavy metals in the study Table 4 Significance of different heavy metals on each other metal removal in the study in terms of Student t test Table 5 Significance of different heavy metals on sulfate and COD removal in terms of Student t test Table 6 Minimum inhibitory concentration (MIC) range of different heavy metals on mixed consortia of SRB
28
Table 1 Exp
Cda
Cub
Nia
Fec
Pba
Zna
Run
a
Sulfate
COD
Metal Removal (%)e
Removald
Removald
Cda
Cub
Nia
Fec
Pba
Zna
(%)
(%)
1
5
25
5
10
5
5
92.5
91.2
94.8
98.9
97
93.9
94.4
94.6
2
10
25
10
10
5
5
85.8
87.7
86.4
98.0
83.2
93.5
92.0
86.6
3
5
25
10
25
10
5
84.6
70.8
84.4
95.0
85
78.9
87.2
89.3
4
5
50
5
10
5
10
77.3
71.7
81.8
91.6
92.2
91.9
93.1
81.3
5
5
50
10
25
5
10
75.5
66.5
92.4
92.2
81.4
78.3
90.8
83.1
6
5
50
10
10
10
5
73.2
65
92.6
91.1
72.4
77.4
85.5
84
7
10
50
5
25
10
5
81.1
74.8
79.1
93.0
95.2
77.3
88.3
92.6
8
10
25
5
10
10
10
78.4
86
84
94.6
93.4
93.9
88.4
80.2
9
10
25
10
25
5
10
83.5
69
79
92.9
92.5
78
91.4
85.0
10
10
50
5
25
5
5
73.7
63
83.9
93.6
86.2
84.6
92.4
92
11
5
25
5
25
10
10
85.2
66.8
91.9
92.5
94
84.3
93.2
85.1
12
10
50
10
10
10
10
72.6
62.5
81.5
93.8
83.2
83.5
83.3
82.2
Cd: Cadmium concentration level (5 and 10 mg/L); Ni: Nickel concentration level (5 and 10 mg/L); Pb: Lead
concentration level (5 and 10 mg/L) and Zn: Zinc concentration level (5 and 10 mg/L) b
Cu: Copper concentration level (25 and 50 mg/L);
c
Fe: Iron concentration level (10 and 25 mg/L);
d
Average of sulfate and COD removal (duplicates) in the presence of combination of all these heavy metals
e
Average of metal removal (duplicates)
29
Table 2 Source Cd(II)a Main effects Cd Cu Ni Fe Pb Zn Residual Error Total Cu(II)b Main Cd Cu Ni Fe Pb Zn Residual Error Total Ni(II)c Main Cd Cu Ni Fe Pb Zn Residual Error Total Fe(III)d Main Cd Cu Ni Fe Pb Zn Residual Error Total Pb(II)e Main Cd Cu Ni Fe Pb Zn
DF1
Seq SS2
Adj MS3
F4
P5
6 1 1 1 1 1 1
188.434 161.26 7.038 0.042 8.687 1.849 9.559
31.406 161.26 7.038 0.042 8.687 1.849 9.559
1.02 5.26 0.23 0 0.28 0.06 0.31
0.5 0.07 0.652 0.972 0.617 0.816 0.601
5 11
153.387 341.821
30.677
6 1 1 1 1 1 1 5
47.3236 1.7787 22.6875 0.1045 6.3365 4.296 12.1203 15.7949
7.8873 1.7787 22.6875 0.1045 6.3365 4.296 12.1203 3.159
2.5 0.56 7.18 0.03 2.01 1.36 3.84
0.167 0.487 0.044 0.863 0.216 0.296 0.107
11
63.1185
6 1 1 1 1 1 1 5
460.785 11.408 99.187 303.007 13.868 7.207 26.107 120.337
76.797 11.408 99.187 303.007 13.868 7.207 26.107 24.067
3.19 0.47 4.12 12.59 0.58 0.3 1.08
0.112 0.522 0.098 0.016 0.482 0.608 0.345
11
581.122
6 1 1 1 1 1 1 5
481.629 4.248 77.521 104.312 239.771 54.955 0.822 63.976
80.271 4.248 77.521 104.312 239.771 54.955 0.822 12.795
6.27 0.33 6.06 8.15 18.74 4.29 0.06
0.031 0.589 0.057 0.036 0.008 0.093 0.81
11
545.605
6 1 1 1 1 1 1
121.968 5.936 14.301 31.818 3.63 66.27 0.013
20.328 5.9361 14.3008 31.8176 3.63 66.27 0.0133
13.37 3.9 9.4 20.92 2.39 43.57 0.01
0.006 0.105 0.028 0.006 0.183 0.001 0.929
30
Residual Error Total Zn(II)f Main Cd Cu Ni Fe Pb Zn Residual Error Total
5
7.604
11
129.572
6 1 1 1 1 1 1 5
206.329 0.124 2.651 20.489 28.213 6.871 147.982 41.77
11
248.099
1.5209
34.388 0.124 2.651 20.489 28.213 6.871 147.982 8.354
a
4.12 0.01 0.32 2.45 3.38 0.82 17.71
0.071 0.908 0.598 0.178 0.126 0.406 0.008
(S= 5.5 PRESS= 885.0 R2= 55.12%) (S= 1.7 PRESS= 90.80 R2= 75.13%) c (S= 4.9 PRESS= 693.14 R2= 79.2%) d (S= 3.3 PRESS= 331.5 R2= 89.09%) e (S= 1.2 PRESS= 44.19 R2= 94.1%) f (S= 2.8 PRESS= 240.59 R2= 83.1%) 1 Degree of freedom; 2 Sum of squares; 3 Mean sum of squares; 4 Fisher’s value; 5 Probability a Cadmium; b Copper; c Nickel; d Iron; e Lead; f Zinc; S: standard error; PRESS: predicted residual error sum of squares; R 2: coefficient of determination b
31
Table 3 Source % Sulfate Removala Main Cd Cu Ni Fe Pb Zn Residual Total % COD Removalb Main Cd Cu Ni Fe Pb Zn Residual Total
DF1
Seq SS2
Adj MS3
F4
P5
6 1 1 1 1 1 1 Error 11
340.702 14.747 268.03 14.077 1.266 14.286 28.296 5 422.639
56.784 14.747 268.03 14.077 1.266 14.286 28.296 81.937
3.47 0.9 16.36 0.86 0.08 0.87 1.73 16.387
0.097 0.386 0.01 0.397 0.792 0.393 0.246
6 1 1 1 1 1 1 Error 11
836.46 10.08 385.33 85.33 235.85 44.85 75 5 1096.96
139.41 10.08 385.33 85.33 235.85 44.85 75 260.5
2.68 0.19 7.4 1.64 4.53 0.86 1.44 52.1
0.15 0.678 0.042 0.257 0.087 0.396 0.284
a
(S= 4.04 PRESS= 471.95 R2= 80.61%) (S= 7.21 PRESS= 1500.48 R2= 76.25%) 1 2 Degree of freedom; Sum of squares; 3 Mean sum of squares; 4 Fisher’s value; 5 Probability S: standard error; PRESS: predicted residual error sum of squares; R 2: coefficient of determination b
32
Table 4 Term aEffect Coef T P bEffect Coef T P cEffect Coef T P dEffect Coef T P eEffect Coef T P fEffect Coef T P
a
f
Cd(II) -7.3 -3.6 -2.29 0.07 0.77 0.385 0.75 0.48 1.95 0.97 0.69 0.522 1.1 0.5 0.5 0.5 -1.4 -0.7 -1.9 0.1 0.20 0.102 0.1 0.9
Cu(II) -1.5 -.076 -0.48 0.652 -2.7 -1.3 -2.68 0.044 -5.7 -2.8 -2.03 0.098 -5.0 -2.5 -2.46 0.057 -2.1 -1.0 -3.0 0.02 -0.9 -0.4 -0.5 0.5
Ni(II) 0.118 0.059 0.04 0.972 -0.18 -0.09 -1.42 0.216 -10.0 -5.0 -3.55 0.016 -5.8 -2.9 -2.8 0.036 -3.2 -1.6 -4.5 0.006 -2.6 -1.3 -1.5 0.17
Fe(III) -1.7 -0.85 -0.53 0.617 -1.45 -0.72 -1.42 0.216 2.15 1.0 0.76 0.48 -8.9 -4.4 -4.33 0.008 1.1 0.55 1.5 0.18 3.0 1.5 1.8 0.12
Pb(II) -0.785 -0.39 -0.25 0.816 -1.19 -0.59 -1.17 0.29 -1.55 -0.7 -0.5 0.6 -4.28 -2.1 -2.0 0.093 -4.7 -2.3 -6.6 0.001 -1.5 -0.7 -0.9 0.4
Zn(II) -1.7 -0.89 -0.56 0.601 -2.01 -1.00 -1.19 0.107 2.9 1.4 1.04 0.3 0.52 0.26 0.25 0.81 0.06 0.03 0.09 0.9 -7.0 -3.5 -4.2 0.008
(For Cd(II) removal); b(For Cu(II) removal); c(For Ni(II) removal); d(For Fe(III) removal); e(For Pb(II) removal);
(For Zn(II) removal); Effect; Coeff: Coefficient; T: T value; P: probability
33
Table 5 Term Effect Coef T Sulfate removal Constant 80.307 1.169 0 Cd -2.217 -1.109 -0.95 Cu -9.452 -4.726 -4.04 Ni -2.166 -1.083 -0.93 Fe 0.65 0.325 0.28 Pb -2.182 -1.091 -0.93 Zn -3.071 -1.536 -1.31 COD removal Constant 72.917 2.084 0 Cd 1.833 0.917 0.44 Cu -11.33 -5.667 -2.72 Ni -5.333 -2.667 -1.28 Fe -8.867 -4.433 -2.13 Pb -3.867 -1.933 -0.93 Zn -5 -2.5 -1.2 Effect; Coeff: Coefficient; T: T value; P: probability
P
0.386 0.01 0.397 0.792 0.393 0.246
0.678 0.042 0.257 0.087 0.396 0.284
34
Table 6
a
Metals
Cd
Cu
Ni
Fe
Pb
Zn
MICa (mg/L)
4-54
2-50
10-20
>60
75-125
13-40
Minimum inhibitory concentration
35
S0304-3894(15)30302-2 http://dx.doi.org/doi:10.1016/j.jhazmat.2015.12.042 HAZMAT 17321
To appear in:
Journal of Hazardous Materials
Received date: Revised date: Accepted date:
20-8-2015 27-10-2015 22-12-2015
Please cite this article as: Gopi Kiran Mothe, Kannan Pakshirajan, Gopal Das, Heavy metal removal from multicomponent system by sulfate reducing bacteria: mechanism and cell surface characterization, Journal of Hazardous Materials http://dx.doi.org/10.1016/j.jhazmat.2015.12.042 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Heavy metal removal from multicomponent system by sulfate reducing bacteria: mechanism and cell surface characterization
M Gopi Kiran1, Kannan Pakshirajan1,2* and Gopal Das1,3
1
Centre for the Environment, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India.
2
Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India. 3
Department of Chemistry, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India.
*Corresponding Author Dr. Kannan Pakshirajan Professor Department of Biosciences and Bioengineering Indian Institute of Technology Guwahati Guwahati 781039, Assam, India. Tel: +91-361- 2582210 Fax: +91-361- 2690762 Email: [email protected]
1
Highlights Heavy metal bioremoval from mixture using sulfate reducing bacteria was evaluated Sulfate reduction to sulfide resulted in metal precipitation for removal More than 95% removal was obtained at low initial concentration combination of the metals Characterization of the bio-precipitates confirmed metal sulfides formation on the bacteria
2
Abstract This study evaluated the combined effect of Cd(II), Cu(II), Ni(II), Fe(III), Pb(II) and Zn(II) on each other removal by anaerobic biomass under sulfate reducing condition. Statistically valid Plackett-Burman design of experiments was employed to carry out this mixture study. The results obtained showed a maximum removal of Cu(II) (98.9 %), followed by Ni(II) (97 %), Cd(II) (94.8 %), Zn(II) (94.6 %), Pb(II) (94.4 %) and Fe(III) (93.9 %). Analysis of variance (ANOVA) of the sulfate and chemical oxygen demand (COD) reduction revealed that the effect due to copper was highly significant (P value <0.05) on sulfate and COD removal. To establish the role of sulfate reducing bacteria (SRB) in the metal removal process, surface morphology and composition of the metal loaded biomass were analyzed by transmission electron microscopy (TEM) equipped with energy dispersive spectroscopy (EDS) and by field emission scanning electron microscopy (FESEM) integrated with energy dispersive X-ray spectroscopy (EDX). The results obtained revealed that the metal precipitates are associated with the outer and inner cell surface of the SRB as a result of the sulfide generated by SRB.
Abbreviations
AMD = acid mine drainage APHA = American public health association COD = chemical oxygen demand FESEM-EDX = field emission scanning electron microscopy equipped with energy dispersive X-ray spectroscopy FTIR = Fourier transforms infrared spectrometer 3
g = gravitational acceleration MLVSS = mixed liquor volatile suspended solids SRB = sulfate reducing bacteria TEM-EDS = transmission electron microscopy with energy dispersive spectroscopy ANOVA = Analysis of Variance EDTA = ethylene diamine tetraacetic acid BESA= bromo ethane sulfonic acid
Keywords: Heavy metals, sulfate reducing bacteria, multicomponent system, anaerobic biomass, Plackett-Burman design.
4
1. Introduction Effluent from metallurgical industries and acid mine drainage (AMD) from mining activities are, in general, acidic and rich in sulfates and dissolved metals, such as cadmium (Cd), copper (Cu), nickel (Ni), iron (Fe), lead (Pb) and zinc (Zn) (1). Discharge of such heavy metal containing wastewater results in a serious problem towards the environment and living things (2). Heavy metals can be classified as toxic and non-biodegradable inorganic pollutants as they tend to accumulate in the food chain and get absorbed by living organisms, including humans, which result in severe health issues (3). Moreover, they influence the aesthetic quality of potable water (4). Besides heavy metals, high sulfate content is predominant in waste streams from metal leaching operations (5). Several problems exist due to the discharge of sulfate rich wastewaters, e.g., sulfate reduction to hydrogen sulfide (H2S), often leading to corrosion of pipe materials (6). Hence, it is of utmost importance to remove heavy metals and sulfate from wastewater prior to its discharge into the environment. Therefore, there is a continuous search for economical, effective and ecofriendly processes for the removal of heavy metals from wastewater, which would ensure their presence in the environment below permissible limits (7). Metal precipitation aided by biological sulfate reduction to S2-, HS-, H2S, etc. offers attractive advantages over the conventional chemical precipitation techniques. For instance, biological sulfate reduction technique for heavy metal removal produces a low amount of secondary sludge for further disposal. Thus, it is realized to be an efficient technique to remove heavy metals from wastewater at low initial concentrations (8). This process utilizes sulfate reducing bacteria (SRB) that belong to a group of morphologically diverse and anaerobic microorganisms with an ability to use sulfate as the terminal electron acceptor and simple organic / inorganic compounds as
5
electron donors (9). SRB under anaerobic conditions reduce sulfate to different forms of sulfide and in this way not only reduction of soluble sulfate takes place but also leads to the formation of insoluble metal sulfides, thus aiding in both sulfate and heavy metal removal form wastewater (10, 11). Hence, anaerobic reduction of sulfate to sulfide by SRB is the determining step for the removal of both sulfate and heavy metals from wastewater (12, 13). Several authors have reported the effect of individual heavy metals on the sulfate reduction by SRB (14). Pollutants such as heavy metals never exist single in wastewater; they rather coexist with other inorganic species. Industrial effluents, particularly AMD, usually contain more than one metal along with high sulfate content. Thus, it is important to analyze and characterize the metal removal from multicomponent system by SRB. Therefore, this study was aimed at investigating heavy metals precipitation from multicomponent system and also their effect on sulfate and chemical oxygen demand (COD) reduction by SRB. One way to study the combined effect of heavy metals and sulfate on cultures of SRB is by using statistically designed experiments. For instance, individual main effect of factors on a given response can be investigated employing Plackett-Burman design, which is an efficient statistical experimental design technique (15). Analysis of the results is accomplished through analysis of variance (ANOVA) and Student’s t test. Hence, in this study, different combinations of high and low concentration levels of six heavy metals, viz. Cd(II), Cu(II), Ni(II), Fe(III), Pb(II) and Zn(II), were chosen using the PlackettBurman screening design. ANOVA and Student’s t test were then applied for statistical analysis of the results to interpret the significance and effect of these metals on each other’s removal as well as on sulfate and COD removal in the study. The SRB biomass from the experiments was
6
further characterized using Fourier transform infrared (FTIR) spectrometer, transmission electron microscope equipped with energy dispersive spectroscopy (TEM-EDS) and field emission scanning electron microscope equipped with energy dispersive X-ray spectroscopy (FESEMEDX). 2. Materials and Methods 2.1 SRB source and culture conditions Anaerobic biomass containing mixed consortia of SRB was collected from a lab scale upflow anaerobic packed bed reactor treating sulfate rich wastewater whose microbial community characterization showed the presence of SRB, Desulfovibrio species in the biomass (16, 17). The medium composition for the biomass growth was as follows (g/L): 0.5 KH2PO4, 1 NH4Cl, 1.47 Na2SO4, 0.1 CaCl2∙2H2O, 0.1 ascorbic acid, 0.2 tri-sodium citrate, 0.2 ethylene diamine tetraacetic acid (EDTA) (18), 0.15 FeSO4·7H2O, 0.2 bromo ethane sulfonic acid (BESA) (19) and 1 yeast extract (modified Postgate medium) (20). The pH of the solution was adjusted to 7 using 1N NaOH. 10% v/v of the anaerobic biomass was added to 100 mL serum bottle containing the pH adjusted medium. The bottles were purged with nitrogen gas, before and after inoculation, incubated in an orbital shaker set at 30oC temperature and 120 rpm agitation speed for seven days. 60% v/v of sodium lactate was used as the carbon source for culturing the biomass. Such freshly grown biomass was designated as the maintenance culture for carrying out subsequent metal removal experiments. All chemicals and reagents used in this study were of analytical grade. 2.2 Heavy metal precipitation from multicomponent system by SRB To study the metal removal, sulfate reduction and COD from multicomponent system by SRB, a Plackett-Burman design consisting of 12 experimental runs with different combination levels of 7
Cd(II), Cu(II), Ni(II), Fe(III), Pb(II) and Zn(II) was applied (Table 1). The low and high concentration levels of each of Cd(II), Ni(II), Pb(II) and Zn(II) were chosen as 5 and 10 mg/L, respectively. Whereas, for Cu(II), these were 25 and 50 mg/L. In the case of Fe(III), 10 and 25 mg/L were chosen as the low and high initial levels, respectively. All these initial levels of the heavy metals were based on a previous single component study using the same anaerobic biomass containing SRB (17). Individual metal stock solutions of Cu(II), Cd(II), Ni(II), Fe(III), Pb(II) and Zn(II) of 10,000 mg/L concentration each were prepared using CuCl2·2H2O, Cd(NO3)2·4H2O, NiCl2·6H2O, FeCl3·6H2O, PbNO3 and ZnCl2, respectively. The modified Postgate medium as mentioned earlier was added with the corresponding metal stock solution so as to obtain a desired concentration of the heavy metals in each of the experimental runs. The statistical software Minitab (Version 16, PA, USA) was used for statistical analysis of the results obtained. All the experiments in this study were performed using 100 mL serum bottles. These bottles were purged with nitrogen gas before and after inoculation with 10% v/v (754 mg/L) anaerobic biomass measured as mixed liquor volatile suspended solids (MLVSS). The bottles were then incubated in an orbital shaker set at 30oC temperature and 120 rpm agitation speed. Bottles without any added metals but containing only the media, carbon source and the biomass, served as the control in these experiments. Liquid samples were taken from the bottles at regular intervals during the experiments to determine conductivity, pH, sulfate, COD, MLVSS, metal and sulfide (soluble) concentrations in the samples. 2.3 Characterization of the metal bio-precipitates Characterization of the metal bio-precipitates formed due to the SRB was carried out by FTIR spectroscopy, TEM-EDS and FESEM-EDX. FTIR spectrum was obtained to describe the changes in the biomass functional groups due to sulfate reduction and heavy metal precipitation. For FTIR 8
analysis, the control and metal treated SRB biomass were centrifuged (8000×g) for 5 min, washed twice with distilled water and the pellets obtained were vacuum dried and analyzed using a FTIR spectroscope (Perkin Elmer, Spectrum Two, Singapore). Similarly, for TEM analysis, biomass sample from the experimental run 1 (Table 1) was centrifuged as it yielded a very high metal removal efficiency among the different experimental runs, and the pellet obtained was loaded on a copper grid coated with carbon for observation under TEM (JOEL, JEM2100, Japan) at 200 kV integrated with EDS. For FESEM analysis, the same precipitates obtained from experimental run 1 were oven dried at 80 oC for 2 h and gold coated using a sputter coater (21). The precipitates were then analyzed for morphology and elemental composition using FESEM-EDX (Zeiss, Sigma, Germany). 2.4 Analytical methods Soluble metal concentration in samples was determined using an atomic absorption spectrometer (Varian, AA240, The Netherlands) as per the American Public Health Association (APHA) (22). Biomass was estimated as MLVSS according to the APHA method (22). Sulfate and COD in the samples was quantified according to the methods described in APHA (22). Sulfide concentration was measured according to the method described by Cord Rowish (23).
2. Results 3.1 Heavy metal precipitation from multicomponent system
9
Table 1 presents the simultaneous removal of Cd(II), Cu(II), Ni(II), Fe(III), Pb(II) and Zn(II) by SRB in the multicomponent system, which reveals that the removal efficiency of each of these metals varied depending upon their combination level in their respective mixtures. The results obtained shows a maximum removal for Cu(II) (98.9 %), followed by Ni(II) (97 %), Cd(II) (94.8 %), Zn(II) (94.6 %), Pb(II) (94.4 %) and Fe(III) (93.9 %). A maximum metal removal was achieved at a low concentration combination of these metals, i.e. at 5 mg/L each of Ni(II), Cd(II), Zn(II) and Pb(II), at 25 mg/L Cu (II) and 10 mg/L Fe (III) in the experimental run 1 (Table 1). These results clearly indicate the dependence of metal precipitation by SRB on the metals and their concentration combination in the mixture. An overall removal efficiency of more than 75 % for each metal was achieved except for nickel (72. 4 %) (Table 1).
Insert < Table 1 > Here
Sulfate and COD reduction efficiencies corresponding to the different experimental runs are presented in Table 1. Experimental run 1 resulted in maximum sulfate reduction efficiency (92.5%), followed by that in experimental runs 2 and 11. Maximum COD reduction efficiency (91.2%) was observed in experimental run 1 followed by that in runs 2 and 8 (Table 1). Both sulfate and COD reduction efficiencies were the minimum in the experimental run 12 with metals added at their respective high initial levels.
For a better interpretation and assessment of the significance of the individual heavy metals on each other removal as well as on the sulfate and COD reduction by SRB, the results obtained were analyzed statistically in terms of Student’s t test and analysis of variance (ANOVA). Table 2 presents the ANOVA of metal removal; ANOVA of sulfate and COD removal is presented in
10
Table 3. In these ANOVA tables, a high Fisher’s F value and a low probability P value of the regression model indicate the model precision in explaining the variations in the results.
Insert < Tables 2 and 3 > Here
Values of the statistical parameters Fisher’s F, P, standard error (S), coefficient of determination (R2) and adjusted R2, collectively illustrate whether the level means are significantly different from each other or not. These values also clearly show goodness of fit of the respective regression models used to explain the experimental results (24). A lower value of the parameter S represents an accurate prediction capability of these models. R2 and adjusted R2 values describe the amount of variation in the observed response values. Hence, a minimum value of S and a maximum value of R2 represent an accurate prediction capability of the model. Tables 2 and 3 also present the accuracy and precision of the models, in terms of R2, adjusted R2, S and predicted residual error sum of squares (PRESS). These values suggest that the models were quiet efficient in distinguishing the results accurately. Best results were however, obtained for the models used to describe Fe(III), Pb(II) and Zn(II) removal by SRB (Table 2).
Student t test, which is used as a common tool to verify the significance of the coefficients of the regression model parameters on a given response, was applied to understand the effect of these metals on each other removal in the multicomponent system. The estimated coefficients of individual effect of these metals are described in Table 4 in which the associated T and P values were used to specify their significance. From Table 4, an increase in initial Cu(II) concentration showed significant negative effect with a P value of 0.04 on its own removal, whereas Cd(II) showed a significant positive effect on Cu(II) removal. Ni(II) showed significant negative effect
11
on its own removal with a P value of 0.016, whereas Cd(II), Fe(III) and Zn(II) showed positive effect on Ni(II) removal.
Insert < Table 4 > Here
Both Fe(III) and Ni(II) showed a very high significant negative effect on Fe(III) removal with P values of 0.008 and 0.036, respectively. Both Cd(II) and Zn(II) showed a positive effect on Fe(III) removal. In case of Pb(II) removal, the influence due to Pb(II), Ni(II) and Cu(II) was strongly negative with P values of 0.001, 0.006 and 0.028, respectively; whereas Zn(II) and Fe(III) showed positive effect on Pb(II) removal. Zn(II) showed negative effect with a P value of 0.008, whereas Cd(II) and Fe(III) showed positive effect on Zn(II) removal. Cu(II) seemed to inhibit both sulfate and COD removal with P values of 0.01 and 0.042, respectively; whereas Fe(III) exhibited a positive effect on sulfate removal (Table 5). All these results were in agreement with the literature on student’s t test (25).
Insert < Table 5 > Here
All these effects of different heavy metals on each other’s removal in the multicomponent system are better illustrated in the form of Pareto charts, depicted in Fig. 1(a–g). Horizontal bars in these charts represent the effect due to the individual metals, and those extending past the reference line (vertical line on the chart) represent the significant ones (α=0.05). In summary, an increase in Cu(II), Ni(II) and Zn(II) concentration level in the mixture was inhibitory to their own removal (Fig. 1a, 1b and 1e). Fe(III) and Ni(II) inhibited Fe(III) removal (Fig 1c ) whereas Pb(II), Ni(II) and Cu(II) showed inhibitory effect on Pb(II) removal in the multicomponent system (Fig. 1d). Among the different metals, Cu(II) inhibited both sulfate and COD reduction by SRB (Fig. 1f and 1g). 12
Insert < Figure 1 > Here
3.2 Characterization of metal bioprecipitates
TEM-EDS, FESEM-EDX and FTIR analyses of the bio-precipitates were carried out to understand the morphology and elemental composition of the metal precipitates formed by SRB in this study. Figs. 2a and Fig. 3a display clear TEM images of the bacterial cell with metal sulfides precipitated on its surface. The presence of different metals as sulfides is confirmed by the EDS spectrum taken from a spot on the outer cell surface of the bacteria (Fig. 2b and Fig. 3b). Metal precipitates in the vicinity of the bacterial cell surface are clearly visible from Fig. 3. Insert to Fig. 4a represents the FESEM image of the control biomass in which the solid biomass appears as a coalescent material; its elemental composition was confirmed by EDX spectrum. Similarly, insert to Fig. 4b shows the FESEM image of the biomass taken from experimental run 1, which also shows EDX spectrum of the biomass. A comparison between these figures confirms metal sulfide precipitation by SRB together with the presence of other elements, which constituted the Postgate medium.
Insert < Figures 2, 3 and 4 > Here
The presence of sulfur peak in the spectra (Fig. 2b, Fig. 3b and Fig. 4b) is attributed to the metal sulfide precipitation as a result of SRB activity. The precipitates formed are predominantly amorphous form of sulfide salts corresponding to the different metals (Figs. 4a and 4b). Sulfate and COD reduction along with metal sulfide formation confirmed that sulfidogenesis is the governing mechanism for heavy metal removal by SRB (19). Overall, the results from TEM and FESEM revealed that the metals were mainly removed by sulfide precipitation and are associated with cell surface.
13
Insert < Figure 5 > Here
FTIR spectra of the heavy metal loaded anaerobic biomass from the experiments were obtained to verify the interaction between the metal ions and the functional groups present on the bacterial surface (Fig. 5). The major stretching in the spectrum was in the range 468 - 3696 cm-1 for the control biomass and 467- 3468 cm-1 for biomass obtained from the experimental run 1. The spectra were characterized by the much sharper peaks in the different wave regions 599 - 618 cm-1, 1018 - 1043 cm-1, 1535 - 1544 cm-1 and 1638 - 1642 cm-1, respectively. Occurrence of neutral C=O complex was indicated by the stretching modes at 1644 cm-1 in control biomass and a minor shift to 1642 cm-1 in the heavy metal loaded biomass. Bands in the range 1199 - 800 cm-1 are associated with C–O–C and C–O–P stretching. These stretching vibrations involve oligo and polysaccharides present in the bacteria (26). Bands corresponding to the stretching mode from 1525 to 1558 cm-1 are mainly due to -NH stretching and can be attributed to protein amide I and amide II bands (27). Bands corresponding to the range from 3748 to 3764 cm-1 indicate either N-H stretching of amine group or O-H stretching of hydroxyl group. An earlier study on the FTIR spectra of SRB has reported that sulfate ions exhibit five bands centered around 1230 cm-1, 1130 cm-1, 1070 cm-1, 1000 cm-1 and 610 cm-1 (28).
3. Discussion
Metal effect on sulfate reduction by SRB is well reported in the literature (8). However, there is a very less understanding on the combined effect of more than one metal in mixture on simultaneous sulfate and COD reduction as well as on each other metal removal in multicomponent system. It 14
can be observed from Table 1 that the results corresponding to heavy metal removal and the sulfate reduction correlated with each other, confirming heavy metal precipitation by SRB through sulfate reduction. Among the different heavy metals in the multicomponent system, copper removal was maximum (98.9 %) due to its low solubility product with sulfide. Similar observation can be made on the removal of the other metals by the SRB in this study. Thus, the order of removal of these metals as sulfide salts is attributed to their respective solubility product constant values (29).
The results shown in Table 1 further suggest that metals at a low initial concentration level (experimental run 1) in the multicomponent system did not inhibit the growth of SRB. This can be explained based on the combined results of metal and sulfate removal (Table 1). However, a high initial metal concentration level (experimental run 12) resulted in a reduced activity of the SRB and, therefore, its sulfate reduction efficiency (72.6 %). Due to the low sulfate reduction efficiency, metal removal was also less compared to those in other experimental runs (Table 1).
At a low initial metal concentration, precipitation of the insoluble metal due to sulfide produced by SRB avoids any toxic effect of the metal on SRB. This could be the major mechanism of metal precipitation and metal tolerance by SRB (30). On the other hand, at an elevated concentration, these metals tend to be toxic to microorganisms due to their enhanced bioavailability, thus resulting in denaturation and deactivation of enzymes, rupture of cell organelles and membrane integrity, etc. (31). Such toxic effects of copper on SRB in the medium has been reported by Kadukova and Vircikova (32). The heavy metal resistance due to SRB also varies with respect to different metal species, as presented in Table 6 (12). Fig. 1 depicts that Cu(II), Ni(II) and Pb(II) showed significant inhibitory effect on Pb(II) removal; Ni(II) and Fe(III) resulted in an inhibitory effect on Fe(III) removal. All these effects of heavy metals on each other removal from mixture can be attributed
15
to the solubility product constant of the corresponding metal sulfide salts (CuS, PbS, NiS, ZnS, FeS and CdS); the values are 6×10–37, 3×10–28, 3×10–19, 2×10–25, 6×10–19 and 8×10–28, respectively (29).
Insert < Table 6 > Here
Table 1 showed that the removal of all the metals was the maximum at a low concentration combination of these metals. At a high concentration combination of these metals, the metalremoval efficiency was slightly lower. Owing to the increase in the metal sulfide (M/S2−) ratio, reduction in the metal removal efficiency was observed at a high concentration combination of metals (33). At a lower value of M/S2− ratio (<1), sulfide formed due to sulfate reduction is high, which ensures a high metal sulfide precipitation for the metal removal. Also, any toxic effect due to the low residual metal ions in solution on the sulfate reduction process becomes insignificant. At an M/S2− ratio greater than 1, the residual metal concentration shows toxic effect on the organism, thereby reducing the sulfate reduction efficiency and, therefore, its own removal (Table 1) (33).
In order to confirm the heavy metal removal mechanism by the SRB through sulfate reduction in the multicomponent system, FTIR spectra, FESEM-EDX and TEM-EDS spectra of the biomass were examined. The TEM images (Figs. 2a and 3a) confirmed the ability of these SRB to grow in presence of the metals in the mixture. The images also revealed the presence of a layer or shell like structure on cell wall of the bacteria. The TEM images further reveal that metal sulfide is associated with outer layer of the bacterial cell surface (Figs. 2b and 3b). From FESEM-EDX spectra of the precipitates (Fig. 4), it is clear that the metals were precipitated as metal sulfides (12). Different metals precipitated in the experimental run 1 were highlighted with circle in Fig. 16
4b. Among the peaks due to the different elements, only the peak due to sulfide is significant indicating that the metals were present as sulfides in the precipitate (Figs. 2, 3 and 4). All other forms such as M(OH)x, MCO3, etc. were not significant.
FTIR spectra (Fig. 5) of the biomass used in this multicomponent system showed bands corresponding to the sulfate ions, which clearly indicate sulfate reduction for its growth and metabolism (34, 35). FTIR analysis confirmed a high degree of similarity of the functional groups corresponding to SRB in the present study with several other reported species of SRB such as Desulfovibrio vietnamensis DSM 10520, Desulfovibrio gigas ATCC 19364, Desulfovibrio gabonensis DSM 10636 and Desulfovibrio indonesiensis NCIMB 13468 (36).
4. Conclusions
This study demonstrated the simultaneous precipitation of different heavy metals from the multicomponent system by SRB. A very high removal of the different metals was achieved at both low and high initial concentrations. However, at a high metal concentration combination, sulfate and COD reduction were inhibited, thus resulting in a slightly reduced removal of these metals by SRB. FTIR spectroscopy analysis of the biomass confirmed the presence of functional groups in the SRB that were similar to that of an earlier reported SRB Desulfovibrio species. TEM-EDS and FESEM-EDX analyses of the bacteria following metal removal further confirmed that the metal precipitates formed were associated with the cell surface and the heavy metal removal mechanism was attributed to the capability of the SRB to reduce sulfate to sulfide, thereby resulting in precipitation of the metals as sulfide salts. Overall, this study proved very good potential of SRB in the anaerobic biomass for metal precipitation with a high tolerance towards different metals in the multicomponent system. 17
Acknowledgements
Authors thank the Central Instruments Facility (CIF), IIT Guwahati, for FESEM-EDX and TEMEDS analyses of the samples. Authors also thank Mr. Prahlad Kumar Baruah and Mr. Partha Protim Bakal for their assistance in TEM and FTIR analysis. Authors also thank all the reviewers for their valuable suggestions in the review process.
18
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21
List of Figures with captions Fig.1. Pareto chart showing the effect of different heavy metals on each other’s removal, sulfate and COD reduction by SRB: a) Cu(II) removal, b) Ni(II) removal, c) Fe(III) removal, d) Pb(II) removal, e) Zn(II) removal, f) sulfate removal and g) COD removal (vertical line shows significance cutoff at P value less than 0.05).
Fig.2. a) TEM image of a metal loaded bacterial cell from the experimental run #1, b) EDS spectrum from a spot on the bacterial cell surface. Fig.3. a) TEM image of a metal loaded bacterial cell from the experimental run #1 showing intact metal precipitate on SRB cell surface, b) EDS spectrum from a spot on the bacterial cell surface. Fig.4. EDX spectrum of a) control biomass, b) metal loaded biomass from experimental run #1. Insert to these figures show the image of the respective biomass. Fig.5. FTIR spectra of control biomass and heavy metal loaded biomass from experimental run # 1 in the study.
22
2.571
Cu
Ni
Zn
Cu
Fe
Zn
Metal
Metal
2.571
Pb
Cd
Ni
Pb 0
1
2
3
4
5
Standardized Effect
a
Ni
Cu
Cu
Metal
Metal
Ni
Pb
Fe
Zn
Zn 2
3
4
c
5
0
Zn
Ni
Pb
Metal
Fe
Cu
5
2
3
4
5
2.571
Cu
Pb
4
Standardized Effect
Zn
Cd Ni
Cd
Fe 0
1
2
3
4
5
0
Standardized Effect
e
f
2.571
Cu Fe Ni Zn Pb Cd 0
g
1
d
2.571
3
Cd
Cd
Standardized Effect
2 2.571
Pb
1
1
Standardized Effect
Fe
0
Metal
0
b
2.571
Metal
Fe
Cd
1
2
3
4
5
Standardized Effect
Fig. 1.
23
1
2
3
4
Standardized Effect
5
Fig.2.
24
Fig.3.
25
Fig.4.
26
%T
70 65 60 55 50 45 40 35 30 25 20 15 10 5
Control 694
1641 1091 1031 610
1199
Experimental run 1
3500
3000
1642
1018 1094 1135 1123
2500 2000 1500 -1 Wave Number (Cm )
Fig.5.
27
1000
500
List of tables with captions Table 1 Plackett-Burman experimental design matrix showing different combination levels of the heavy metals in the study along with the removal of metals, sulfate and COD Table 2 Analysis of variance of heavy metal removal from a multicomponent system by SRB Table 3 Analysis of variance of sulfate and COD removal in the presence different heavy metals in the study Table 4 Significance of different heavy metals on each other metal removal in the study in terms of Student t test Table 5 Significance of different heavy metals on sulfate and COD removal in terms of Student t test Table 6 Minimum inhibitory concentration (MIC) range of different heavy metals on mixed consortia of SRB
28
Table 1 Exp
Cda
Cub
Nia
Fec
Pba
Zna
Run
a
Sulfate
COD
Metal Removal (%)e
Removald
Removald
Cda
Cub
Nia
Fec
Pba
Zna
(%)
(%)
1
5
25
5
10
5
5
92.5
91.2
94.8
98.9
97
93.9
94.4
94.6
2
10
25
10
10
5
5
85.8
87.7
86.4
98.0
83.2
93.5
92.0
86.6
3
5
25
10
25
10
5
84.6
70.8
84.4
95.0
85
78.9
87.2
89.3
4
5
50
5
10
5
10
77.3
71.7
81.8
91.6
92.2
91.9
93.1
81.3
5
5
50
10
25
5
10
75.5
66.5
92.4
92.2
81.4
78.3
90.8
83.1
6
5
50
10
10
10
5
73.2
65
92.6
91.1
72.4
77.4
85.5
84
7
10
50
5
25
10
5
81.1
74.8
79.1
93.0
95.2
77.3
88.3
92.6
8
10
25
5
10
10
10
78.4
86
84
94.6
93.4
93.9
88.4
80.2
9
10
25
10
25
5
10
83.5
69
79
92.9
92.5
78
91.4
85.0
10
10
50
5
25
5
5
73.7
63
83.9
93.6
86.2
84.6
92.4
92
11
5
25
5
25
10
10
85.2
66.8
91.9
92.5
94
84.3
93.2
85.1
12
10
50
10
10
10
10
72.6
62.5
81.5
93.8
83.2
83.5
83.3
82.2
Cd: Cadmium concentration level (5 and 10 mg/L); Ni: Nickel concentration level (5 and 10 mg/L); Pb: Lead
concentration level (5 and 10 mg/L) and Zn: Zinc concentration level (5 and 10 mg/L) b
Cu: Copper concentration level (25 and 50 mg/L);
c
Fe: Iron concentration level (10 and 25 mg/L);
d
Average of sulfate and COD removal (duplicates) in the presence of combination of all these heavy metals
e
Average of metal removal (duplicates)
29
Table 2 Source Cd(II)a Main effects Cd Cu Ni Fe Pb Zn Residual Error Total Cu(II)b Main Cd Cu Ni Fe Pb Zn Residual Error Total Ni(II)c Main Cd Cu Ni Fe Pb Zn Residual Error Total Fe(III)d Main Cd Cu Ni Fe Pb Zn Residual Error Total Pb(II)e Main Cd Cu Ni Fe Pb Zn
DF1
Seq SS2
Adj MS3
F4
P5
6 1 1 1 1 1 1
188.434 161.26 7.038 0.042 8.687 1.849 9.559
31.406 161.26 7.038 0.042 8.687 1.849 9.559
1.02 5.26 0.23 0 0.28 0.06 0.31
0.5 0.07 0.652 0.972 0.617 0.816 0.601
5 11
153.387 341.821
30.677
6 1 1 1 1 1 1 5
47.3236 1.7787 22.6875 0.1045 6.3365 4.296 12.1203 15.7949
7.8873 1.7787 22.6875 0.1045 6.3365 4.296 12.1203 3.159
2.5 0.56 7.18 0.03 2.01 1.36 3.84
0.167 0.487 0.044 0.863 0.216 0.296 0.107
11
63.1185
6 1 1 1 1 1 1 5
460.785 11.408 99.187 303.007 13.868 7.207 26.107 120.337
76.797 11.408 99.187 303.007 13.868 7.207 26.107 24.067
3.19 0.47 4.12 12.59 0.58 0.3 1.08
0.112 0.522 0.098 0.016 0.482 0.608 0.345
11
581.122
6 1 1 1 1 1 1 5
481.629 4.248 77.521 104.312 239.771 54.955 0.822 63.976
80.271 4.248 77.521 104.312 239.771 54.955 0.822 12.795
6.27 0.33 6.06 8.15 18.74 4.29 0.06
0.031 0.589 0.057 0.036 0.008 0.093 0.81
11
545.605
6 1 1 1 1 1 1
121.968 5.936 14.301 31.818 3.63 66.27 0.013
20.328 5.9361 14.3008 31.8176 3.63 66.27 0.0133
13.37 3.9 9.4 20.92 2.39 43.57 0.01
0.006 0.105 0.028 0.006 0.183 0.001 0.929
30
Residual Error Total Zn(II)f Main Cd Cu Ni Fe Pb Zn Residual Error Total
5
7.604
11
129.572
6 1 1 1 1 1 1 5
206.329 0.124 2.651 20.489 28.213 6.871 147.982 41.77
11
248.099
1.5209
34.388 0.124 2.651 20.489 28.213 6.871 147.982 8.354
a
4.12 0.01 0.32 2.45 3.38 0.82 17.71
0.071 0.908 0.598 0.178 0.126 0.406 0.008
(S= 5.5 PRESS= 885.0 R2= 55.12%) (S= 1.7 PRESS= 90.80 R2= 75.13%) c (S= 4.9 PRESS= 693.14 R2= 79.2%) d (S= 3.3 PRESS= 331.5 R2= 89.09%) e (S= 1.2 PRESS= 44.19 R2= 94.1%) f (S= 2.8 PRESS= 240.59 R2= 83.1%) 1 Degree of freedom; 2 Sum of squares; 3 Mean sum of squares; 4 Fisher’s value; 5 Probability a Cadmium; b Copper; c Nickel; d Iron; e Lead; f Zinc; S: standard error; PRESS: predicted residual error sum of squares; R 2: coefficient of determination b
31
Table 3 Source % Sulfate Removala Main Cd Cu Ni Fe Pb Zn Residual Total % COD Removalb Main Cd Cu Ni Fe Pb Zn Residual Total
DF1
Seq SS2
Adj MS3
F4
P5
6 1 1 1 1 1 1 Error 11
340.702 14.747 268.03 14.077 1.266 14.286 28.296 5 422.639
56.784 14.747 268.03 14.077 1.266 14.286 28.296 81.937
3.47 0.9 16.36 0.86 0.08 0.87 1.73 16.387
0.097 0.386 0.01 0.397 0.792 0.393 0.246
6 1 1 1 1 1 1 Error 11
836.46 10.08 385.33 85.33 235.85 44.85 75 5 1096.96
139.41 10.08 385.33 85.33 235.85 44.85 75 260.5
2.68 0.19 7.4 1.64 4.53 0.86 1.44 52.1
0.15 0.678 0.042 0.257 0.087 0.396 0.284
a
(S= 4.04 PRESS= 471.95 R2= 80.61%) (S= 7.21 PRESS= 1500.48 R2= 76.25%) 1 2 Degree of freedom; Sum of squares; 3 Mean sum of squares; 4 Fisher’s value; 5 Probability S: standard error; PRESS: predicted residual error sum of squares; R 2: coefficient of determination b
32
Table 4 Term aEffect Coef T P bEffect Coef T P cEffect Coef T P dEffect Coef T P eEffect Coef T P fEffect Coef T P
a
f
Cd(II) -7.3 -3.6 -2.29 0.07 0.77 0.385 0.75 0.48 1.95 0.97 0.69 0.522 1.1 0.5 0.5 0.5 -1.4 -0.7 -1.9 0.1 0.20 0.102 0.1 0.9
Cu(II) -1.5 -.076 -0.48 0.652 -2.7 -1.3 -2.68 0.044 -5.7 -2.8 -2.03 0.098 -5.0 -2.5 -2.46 0.057 -2.1 -1.0 -3.0 0.02 -0.9 -0.4 -0.5 0.5
Ni(II) 0.118 0.059 0.04 0.972 -0.18 -0.09 -1.42 0.216 -10.0 -5.0 -3.55 0.016 -5.8 -2.9 -2.8 0.036 -3.2 -1.6 -4.5 0.006 -2.6 -1.3 -1.5 0.17
Fe(III) -1.7 -0.85 -0.53 0.617 -1.45 -0.72 -1.42 0.216 2.15 1.0 0.76 0.48 -8.9 -4.4 -4.33 0.008 1.1 0.55 1.5 0.18 3.0 1.5 1.8 0.12
Pb(II) -0.785 -0.39 -0.25 0.816 -1.19 -0.59 -1.17 0.29 -1.55 -0.7 -0.5 0.6 -4.28 -2.1 -2.0 0.093 -4.7 -2.3 -6.6 0.001 -1.5 -0.7 -0.9 0.4
Zn(II) -1.7 -0.89 -0.56 0.601 -2.01 -1.00 -1.19 0.107 2.9 1.4 1.04 0.3 0.52 0.26 0.25 0.81 0.06 0.03 0.09 0.9 -7.0 -3.5 -4.2 0.008
(For Cd(II) removal); b(For Cu(II) removal); c(For Ni(II) removal); d(For Fe(III) removal); e(For Pb(II) removal);
(For Zn(II) removal); Effect; Coeff: Coefficient; T: T value; P: probability
33
Table 5 Term Effect Coef T Sulfate removal Constant 80.307 1.169 0 Cd -2.217 -1.109 -0.95 Cu -9.452 -4.726 -4.04 Ni -2.166 -1.083 -0.93 Fe 0.65 0.325 0.28 Pb -2.182 -1.091 -0.93 Zn -3.071 -1.536 -1.31 COD removal Constant 72.917 2.084 0 Cd 1.833 0.917 0.44 Cu -11.33 -5.667 -2.72 Ni -5.333 -2.667 -1.28 Fe -8.867 -4.433 -2.13 Pb -3.867 -1.933 -0.93 Zn -5 -2.5 -1.2 Effect; Coeff: Coefficient; T: T value; P: probability
P
0.386 0.01 0.397 0.792 0.393 0.246
0.678 0.042 0.257 0.087 0.396 0.284
34
Table 6
a
Metals
Cd
Cu
Ni
Fe
Pb
Zn
MICa (mg/L)
4-54
2-50
10-20
>60
75-125
13-40
Minimum inhibitory concentration
35