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Variations of hydrogen production and microbial community with heavy metals during fermentative hydrogen production
Journal of Industrial and Engineering Chemistry 17 (2011) 340–345
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Journal of Industrial and Engineering Chemistry journal homepage: www.elsevier.com/locate/jiec
Variations of hydrogen production and microbial community with heavy metals during fermentative hydrogen production Yoona Cho, Taejin Lee * Department of Environmental Engineering, Seoul National University of Science & Technology, Seoul 139-743, Republic of Korea
A R T I C L E I N F O
Article history: Received 10 June 2010 Accepted 2 September 2010 Available online 2 March 2011 Keywords: Hydrogen production Heavy metals Microbial community
A B S T R A C T
The effects of heavy metals on fermentative hydrogen production were examined based on metal type and concentration. Hydrogen production was stimulated by low concentrations of Cd and Zn but decreased at concentrations of 40 and 1 mg/L, respectively. Hydrogen production was inhibited for the entire range of Cu tested. The order of toxic density was Cu > Zn > Cd at concentrations below 2 mg/L but was Zn > Cu > Cd at higher concentrations. The depression rates of hydrogen production were calculated to be 225.8 mL-H2/mg-Zn, 67.37 mL-H2/mg-Cu, and 13.39 mL-H2/mg-Cd. The presence of heavy metals caused a shift in microbial community. The presence of Clostridium genus bacteria, identified as Clostridium magum, Clostridium diolis, and Clostridium sp., resulted in active hydrogen production. Klebsiella genus bacteria were the most abundant of the class Gammaproteobacteria and also stimulated hydrogen production at relatively low concentrations of heavy metal. When Rhodocista pekingensis, Erwinia chrysanthemi strain 1015-1, Delftia sp. YF 31, or uncultured Klebsiella sp. clone F1 apr.32 were present, hydrogen production was seriously decreased. ß 2011 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved.
1. Introduction Global energy is mostly dependent on fossil fuels, leading to the foreseeable depletion of limited fossil energy resources. The use of fossil fuels is also causing global climate change mainly due to the emission of pollutants [1]. Therefore, the depletion of fossil energy resources combined with global environmental changes is increasing the need to develop energy substitutes, such as solar power, nuclear power, and bio-gases like methane and hydrogen [2]. Hydrogen gas has received the greatest attention due to its potential as a powerful fuel [3]. Hydrogen is regarded as a nonpolluting fuel since its combustion produces only water as the main byproduct. Hydrogen is exclusively produced by water electrolysis or by steam methane reformation. Although the biological production of hydrogen is not yet wide spread, it has become an exciting new area of technology that offers a chance to use hydrogen from a variety of renewable resources [1]. Hydrogen production by microorganisms can be divided into two main categories: hydrogen produced by photosynthetic organisms cultured under anaerobic light conditions, and that produced by anaerobic bacteria via fermentation metabolism [4]. Dark fermentation, which utilizes a variety of carbon sources
* Corresponding author. Tel.: +82 2 970 6614. E-mail address: [email protected] (T. Lee).
including waste, is not subject to oxygen limitation or light dependency [3]. There are many studies on the optimal fermentative conditions (inoculum type, pH, temperature, hydrogen partial pressure, etc.) for hydrogen production using mixed bacterial inoculum [2]. However, there are few studies on how heavy metals affect the generation of hydrogen. When using sludge or another biomass, one must know what poisonous materials the carbon source includes. The presence of heavy metals may result in the inhibition and failure of sewage digestion, for example. Several studies have demonstrated the toxic effects of heavy metals on anaerobic digestion [5,6]. These studies found that acidogenesis and methanogenesis could be adversely affected by the presence of heavy metals. Metabolism of hydrogenesis is similar to acidogenesis, except that its efficiency and variations of microbial community in the presence of heavy metals has been little reported. This study was conducted in order to investigate the effect of heavy metals on hydrogen production using anaerobic sewage sludge microflora in the presence of Cd, Cu, and Zn ions. For this purpose, the efficiency and toxicity density of hydrogen production were analyzed using the modified Gompertz equation. The characteristics of VFAs (volatile fatty acid) production in the presence of heavy metals were also investigated. In addition, a PCR-amplified v3 region of 16s rDNA was analyzed by denaturing gradient gel electrophoresis (DGGE) in order to investigate the microbial diversity involved in hydrogen production in the presence of heavy metals.
1226-086X/$ – see front matter ß 2011 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.jiec.2011.02.036
Y. Cho, T. Lee / Journal of Industrial and Engineering Chemistry 17 (2011) 340–345
2. Materials and methods 2.1. Seed microorganisms Seed microflora was obtained by the return of activated sludge from a municipal sewage treatment plant. Synthetic medium was placed into a serum bottle at an initial pH of 7.0. The medium was then purged with nitrogen gas for 5 min to obtain anaerobic conditions. The medium was inoculated with 6 mL of the settled sludge supernatant and then continuously shaken at 120 rpm on a mechanical shaker at 30 8C. The gas produced in the headspace of the serum bottle was collected and measured for the amount of hydrogen produced. Serial transfers of the cultures were conducted using fresh medium from the serum bottles in order to acquire the acclimated seed microflora. 2.2. PCR procedure To analyze the complexity of the hydrogen-producing microbial community, DNA was extracted from the microorganisms in the serum bottle. The 16s rDNA fragments were amplified by PCR using 341f and 518r as primers [7] followed by separation by DGGE. Each band on the DGGE profile represented a gene fragment of unique 16s rDNA sequences, each of which was analyzed using the BLAST program for identification of each species in the microbial community [8–10]. 2.3. Operation of batch reactor Hydrogen production experiments were conducted in a 1.5 L batch fermentor at 30 3 8C. Each liter of synthetic medium consisted of 15 g of sucrose, 2 g of NH4HCO3, 1 g of KH2PO4, 100 mg of MgSO47H2O, 10 mg of NaCl, 10 mg of NaMoO42H2O, 10 mg of CaCl22H2O, 15 g of MnSO47H2O, and 2.78 mg of FeCl2 [3]. The batch fermentor in Fig. 1 was stirred at a constant rate of 250 rpm by a magnetic stirrer, and the headspace was initially filled with nitrogen gas. The pH of the solution was automatically controlled by the addition of 3 N aqueous KOH solution via a peristaltic pump. The reactor was kept in the dark by wrapping with aluminum foil, thus preventing the growth of photosynthetic bacteria and algae. After the reactor was constructed, 15 mL of seed solution from the serum bottle was added. The initial concentration of volatile suspended solids (VSS) after addition of the seed solution to the reactor was approximately 6 mg/L. The produced
[()TD$FIG]
341
gas was collected by the biogas collector filled with 2% aqueous H2SO4 (v/v) solution. The gas volume was measured and 0.5 mL aliquots of gas were periodically withdrawn from the headspace of the reactor at regular intervals. 2.4. Analytical methods The composition of biogas in the headspace of the fermentor was determined using a gas chromatography apparatus (GOW Mac Series 350) equipped with a thermal conductivity detector (TCD). Chromatographic separation of the reactor headspace samples was achieved using a 60 1/800 stainless steel SS 350A Molsieve 13 (80/100 mesh) with nitrogen as the carrier gas at a flow rate of 30 mL/min. The operation temperatures of the injection port, oven, and detector were 80, 100, and 100, respectively. The concentration of VSS and chemical oxygen demand (COD) were determined according to standard procedures [11]. The carbohydrate concentration was measured following the phenol– sulfuric acid method [12]. Fatty acids (acetic acid, butyric acid, formic acid, and propionic acid) were analyzed using a highperformance liquid chromatography (HPLC) system (KNAUER, German) equipped with a UV detector operated at 210 nm along with a Supelcogel C-610H (130 mm 7.8 mm ID) column. 2.5. Model development A modified Gompertz equation (Eq. (1)) was used to fit the cumulative hydrogen production curves for each experiment [13,14]. Re M ¼ P ex p exp ðl tÞ þ 1 P
(1)
where M is the cumulative hydrogen production (mL), l is the lagphase time (h), P is the hydrogen production potentia l (mL), R is the maximum hydrogen production rate (mL/h), t is the incubation time (h), and e is the exp(1) = 2.718. Parameters were estimated using the curve fit function of Sigma Plot 2002 (SPSS Science, U.K.). The hydrogen production ratio (Rh) in Eq. (2) was used to calculate the amount of hydrogen production with heavy metals. Rh was defined as the fraction of hydrogen produced over 72 h by metal-dosed seed microflora compared to control. Rh ð%Þ ¼
Hm 100% Hc
(2)
where Hm and Hc denote the amount of hydrogen produced over 72 h by metal-dosed seed microflora and control, respectively. 3. Results and discussion 3.1. Effects of heavy metals on hydrogen production
Fig. 1. Schematic diagram of the batch reactor for anaerobic fermentative hydrogen production.
Fig. 2 shows the hydrogen production time course for Cd-dosed seed microflora. The accumulative amount of produced hydrogen was identified based on total biogas production and hydrogen gas content. A similar trend in hydrogen production was observed after dosing with Zn. Cd and Zn were found to stimulate hydrogen production at concentrations of 20 and 0.5 ppm, respectively, but not at higher concentrations. Cu was found to inhibit hydrogen production at all tested concentrations. For Cu, all volumes of metal-dosed hydrogen were less than those produced by the control (in the absence of heavy metals). The hydrogen production potential (P) and maximum rate of hydrogen production (R) of the control were calculated as 1207.61 mL and 73.63 mL/h, respectively. The H2 yield of control was calculated as 108.17 mL/g-COD based on the amount of sucrose consumed in the medium.
[()TD$FIG]
[()TD$FIG]
Y. Cho, T. Lee / Journal of Industrial and Engineering Chemistry 17 (2011) 340–345
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Fig. 2. Cumulative hydrogen production curve at various Cd concentrations. Regression of experimental data was performed by Sigma Plot of SPSS Science Inc. using the modified Gompertz equation.
Table 1 shows the ranges of hydrogen production potential (P) and maximum rate of hydrogen production (R) in the presence of Cu, Cd, and Zn. In all batches, hydrogen production data were satisfactorily fitted by Eq. (1). The R2 value was over 0.942. Peak hydrogen production potential values occurred at 0, 0.1 and 20 mg/ L for Cu, Zn, and Cd respectively. These values show inhibition (Cu) and stimulation (Zn, Cd) of hydrogen production in the presence of heavy metals. In contrast to hydrogen production potential, the maximum rates of hydrogen production in the presence of heavy metals did not exceed the value of the control. This implies that heavy metals within a certain range of concentrations stimulated hydrogen production, but the maximum rate was inhibited at all other concentrations. H2 yields in the presence of the heavy metals were identical with hydrogen production potential but not with max maximum rate of hydrogen production. 3.2. Metal toxicity comparison Fig. 3 shows the relationships between hydrogen production ratio (Rh) and metal concentration. Rh was metal concentrationdependent. Rh values exceeding 100%, such as 103% and 128%, occurred at 20 mg-Cd/L and 0.1 mg-Zn/L, respectively. Low Cd and Zn concentrations stimulated fermentative hydrogen production by mixed microflora. Reduced stimulation occurred when the Cd and Zn concentrations reached 40 and 1 mg/L, respectively. Metal dosages over these values resulted in Rh values lower than 100%, Table 1 Modified Gompertz equation parameter. Metal Control Cd (mg/L)
Cu (mg/L)
Zn (mg/L)
20 40 60 80 100 0.1 0.5 1.0 3.0 5.0 0.1 0.5 1.0 2.0 5.0
P (mL)
R (mL/h)
H2 yield (mL/g-COD)
R2
1207.61 1252.11 855.94 454.81 267.49 207.13 593.32 522.93 510.95 460.06 204.87 1547.55 1309.08 482.87 318.64 268.62
73.63 65.70 27.67 25.08 13.49 10.90 42.41 37.64 29.89 11.82 10.50 63.19 57.03 55.13 45.32 20.51
108.17 120.39 82.30 43.73 26.48 19.72 51.15 47.11 37.16 29.77 19.37 139.42 104.49 42.39 27.98 23.77
0.993 0.999 0.994 0.996 0.989 0.995 0.999 0.999 0.995 0.989 0.942 0.991 0.972 0.973 0.987 0.976
Fig. 3. Relationships between hydrogen production ratio (Rh) and metal concentration. Regression of experimental data was performed by Sigma Plot of SPSS Science Inc. using a linear equation.
indicating metal toxicity during hydrogen production. These metal concentrations were threshold concentrations. For the Cu dosage experiments, Rh differed from those of Cd or Zn. Rh values were always smaller than 100%, indicating inhibition of hydrogen production. Zn showed very high inhibition at its threshold concentration and above, with Rh decreased rapidly to below 37%. Zn was more toxic than Cu at a certain concentration range. The toxic effect of heavy metals varied depending on the metal concentration. For example, the order of toxic density was Cu > Zn > Cd at concentrations below 2 mg/L but Zn > Cu > Cd above it. Hydrogen gas production was reduced at 225.8 mL-H2/mg-Zn, 67.37 mL-H2/mg-Cu, and 13.39 mL-H2/mg-Cd. These values indicate that Zn toxicity was about 3.35 times higher than Cu. However, an inhibition study on the acidogenesis of dairy wastewater by Zn and Cu showed that Cu was 1.4–4.3-fold more toxic than Zn [15]. Another inhibition study on the hydrogenesis of glucose by Zn and Cu also showed that Cu was more toxic than Zn [16]. However, those results are not identical with our study. These differences might be due to variations in experimental setup, such as seeding microflora or the substrate used. 3.3. Production of volatile fatty acids (VFAs) Hydrogen formation was accompanied by the production of volatile fatty acids (VFAs) or solvent during the anaerobic digestion process. Therefore, the concentration distribution and fractions of VFA can be useful indicators for monitoring hydrogen production. The distribution of metabolites formed during hydrogen fermentation is often a crucial signal indicating the efficiency of hydrogenproducing cultures [13,17,18]. Soluble fermentation products included n-butyrate, acetate, lactate, ethanol, trace amounts of formate, propionate, isobutyrate, n-valeate, isovalerate, n-caproate, n-propanol, isopropanol, n-butanol, and sec-butanol. In the case of n-butyrate, 2 mol of H2 were produced together with 1 mol of n-butyrate from 1 mol of hexose, as shown in Eq. (3). C6 H12 O6 þ 2H2 O ! CH3 CH2 CH2 COO þ 2HCO3 þ 3Hþ þ 2H2 (3) According to the stoichiometry, 4 mol of hydrogen were produced with 2 mol of acetate from 1 mol of hexose, as shown in Eq. (4). C6 H12 O6 þ 4H2 O ! 2CH3 COO þ 2HCO3 þ 4Hþ þ 4H2
(4)
[()TD$FIG]
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343
mance of the hydrogen fermentor. In this study, HBu/HAc ratios ranged between 0.3 and 3.0 and were metal type- and concentration-dependent. As the inlet Cd concentration was increased from 20 to 40 mg Cd/L, the acetate composition increased from 6.0% to 33.0%. At the same time, the butyrate composition decreased from 54.6% to 1.7%. Fig. 4 shows the HBu/HAc ratios at different metal concentrations. The HBu/HAc ratio became higher as hydrogen production was increased, which corresponds to the results of previous studies [22]. Trends in the HBu/HAc ratio in Fig. 4 were dependent upon metal concentration and were similar with those of hydrogen production activities in Fig. 3. Therefore, the concentration of metal in the medium affected HBu/HAc ratio, and hydrogen production was metal concentration-dependent.
Fig. 4. Relationships between HBu/HAc ratio and metal concentration. Regression of experimental data was performed by Sigma Plot of SPSS Science Inc. using a linear equation.
From Eq. (4), more hydrogen can be generated by acetate production, but this was not always true. Increase acetate composition was accompanied with decreased H2 conversion in previous studies in which mixed cultures were used [19–21]. Acetogens, which produces acetate from H2 and CO2, as shown in Eq. (5), might be selected in this case. There was no significant production of propionate or methane, which means that acetate production in Eq. (5) was the main pathway of gaseous H2 consumption in this study. As shown in Eq. (5), acetogens could also utilize carbohydrates, thereby competing with H2-producing Clostridium sp. [21]. 4H2 þ 2HCO3 þ Hþ ! CH3 COO þ 4H2 O
(5)
From Eqs. (4) and (5), 1 mol of hexose produced 3 mol of acetate and hydrogen ion instead of hydrogen gas (Eq. (6)). Thus, production of n-butyrate and not acetate is more useful for hydrogen production. C6 H12 O6 ! 3CH3 COO þ 3Hþ
(6)
The ratio of n-butyrate/acetate (HBu/HAc) has been reported as [()TD$FIG] simple indicator showing the metabolic pathway and perfora
3.4. PCR-DGGE analysis of hydrogen-producing microflora (PCRDGGE profiles analysis) The experimental results have shown that hydrogen production activities or HBu/HAc ratios varied in the presence of heavy metals, supposedly due to changes in the microbial community caused by heavy metals in the medium. PCR-denaturing gradient gel electrophoresis (PCR-DGGE) analysis on 16s rDNA was attempted to investigate the effects of heavy metal on the microbial community responsible for hydrogen production. The number of bands in the gel corresponds to the relative diversity of the microbial community, whereas intensity of the bands indicates the degree of abundance of each microbial group [23]. PCR-DGGE profiles of each sample showed various band patterns in Fig. 5. Detailed analysis of each sample showed the presence of distinguishable patterns that revealed different bacterial genera or species. Based on band intensities, major bands A through G were present in the control. Bands H, I, J, K, L, M, N, and O were detected in the presence of Cd, indicating the presence of various microbial species. In the case of Cu, band P only appeared at low concentrations while the intensity of band O was increased at higher Cu concentrations. Bands B and E were analyzed at 0.1 and 0.5 mg/L of Zn. Bands I, Q, and R occasionally appeared in the presence of heavy metals. Taken together, bands B, E and G were associated with relatively higher hydrogen production, whereas bands A, C, D, and F appeared at all
Fig. 5. Denaturing gradient gel electrophoresis (DGGE) profile of PCR-amplified 16s rDNA extracted from the culture at various metal concentrations. Control is represented by the DGGE profile in the absence of heavy metals. The gradient concentration of the denaturant was 40–60%. Values below metals are the metal concentrations in mg/L.
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Table 2 Characteristics of DNA fragments obtained from DGGE with various Cd, Cu, and Zn concentrations. Band name
A B C D E F G H I J K L M N O P Q R
Gen bank search result
Taxonomic description (class)
Phylogenetically closest relative
Accession no.
Similarity
Uncultured bacterium clone H57L Clostridium sp. LYH 1 Pectobacterium wasabiae WPP163 Klebsiella sp. GSK Clostridium magnum strain FM5 Klebsiella sp. enrichment culture clone FD-4 Clostridium diolis strain W1 Uncultured klebsiella sp. clone F4 aug.12 Rhodocista pekingensis strain 3-P Uncultured citrobacter sp. clone F7 feb.77 Raoultella planticola strain R52 Phytobacter diazotrophicus strain LS8 Klebsiella pneumoniae strain SC Pectobacterium carotovorum subsp. Erwinia chrysanthemi strain 1015-1 Delftia sp. YF 31 Klebsiella oxytoca strain QD43 Uncultured klebsiella sp. clone F1 apr.32
EU560736.1 FJ158032.1 CP001790.1 GU066861.1 GU129927.1 GU167259.1 DQ831125.1 GQ418150.1 NR_028855.1 GQ416333.1 EF551363.1 DQ821583.2 FJ232952.1 GQ167303.1 GQ293897.1 GU143685.1 GU139690.1 GQ416844.1
100 100 100 100 94 100 100 100 96 99 86 98 100 100 99 95 100 94
concentrations, indicating heavy metals had no significant effects. Bands H, J, K, L, M, N, and Q mostly appeared in the presence of relatively low concentrations of Cd or Zn. Bands I, O, P, and R only appeared at relatively rates of low hydrogen production. To understand the differences in microbial diversity among samples, bands of interest were excised and sequenced. Microbes and accession number were identified from the 16s rDNA using the NCBI BLAST program. The closest affiliation along with percentage identity data of the bands based on the sequences is shown in Table 2. Three major bacterial taxa were identified, with 15 sequences related to the phylum Proteobacteria, 13 affiliated to the class Gammaproteobacteria, 1 to the class Alphaproteobacteria, and 1 to the class Betaproteobacteria. The remaining sequences were related to the phylum Firmicute. In the phylum Firmicute, Clostridium magum, Clostridium diolis, and Clostridium sp. were identified as a part of the Clostridium genus. The genus showed relatively higher hydrogen production, as shown by bands B, E, and G in Fig. 5, and was reported to produce hydrogen by dark fermentation [8,24–27]. The most abundant genus in the class Gammaproteobacteria was the Klebsiella genus, which also showed a relatively high rate of hydrogen production. It was noteworthy that hydrogen was produced by the Klebsiella genus even in the presence of heavy metals. The Klebsiella genus was present in bands D, F, H, M, Q, and R in Fig. 5 and has been reported in other studies on hydrogen production [28–32]. It is also affiliated with uncultured Klebsiella sp. clone F1 apr.32 in the presence of high heavy metal content, indicating that every Klebsiella genus does not always promote hydrogen generation. The four species identified in this study were Erwinia chrysanthemi strain 1015-1, Rhodocista pekingensis strain 3-P, Raoultella planticola strain R52, and Pectobacterium carotovorum subsp. Erwinia chrysanthemi was described as a plant pathogen that produces acrylhomoserine lactones [33]. Rhodocista pekingensis has been isolated as a cyst-forming phototrophic bacterium from a municipal wastewater [34]. Raoultella planticola strain was studied for phylogetic analysis with the Klebsiella genus and was amended as a part of the Klebsiella genus [35]. Pectobacterium carotovorum was described as a phytopathogenic entrobacteria capable of degrading plant cell walls [36]. Raoultella planticola and Pectobacterium carotovorum are associated with increased hydrogen production in contrast with Erwinia chrysanthemi strain 1015-1. Two of the remaining three species, the
g-Proteobacteria Firmicute g-Proteobacteria g-Proteobacteria Firmicute g-Proteobacteria Firmicute g-Proteobacteria a-Proteobacteria g-Proteobacteria g-Proteobacteria g-Proteobacteria g-Proteobacteria g-Proteobacteria g-Proteobacteria b-Proteobacteria g-Proteobacteria g-Proteobacteria
Phytobacter diazotrophicus strain and citrobacter sp. but not Delftia sp., are suspected of hydrogen production. Therefore, further studies based on independent incubation should be conducted to confirm the individual contribution of each species to hydrogen production.
4. Conclusions In this study, we demonstrated the effects of Cd, Cu, and Zn ions and VFAs (volatile fatty acids) on fermentative hydrogen production. The rate of hydrogen production and the toxic effects differed depending on metal type or concentration. The microbial species responsible also varied depending on which metal was present. Experimental results are listed below: (1) Cd and Zn were found to stimulate hydrogen production at concentrations lower than 20 and 0.5 ppm, respectively, but not at a higher concentration range. Cu was found to completely inhibit hydrogen production at tested concentrations. The toxic effects of metals varied depending on the metal concentration. For example, the order of toxic density was Cu > Zn > Cd at concentrations below 2 mg/L but was Zn > Cu > Cd at higher concentrations. (2) Hydrogen gas production was reduced to 225.8 mL-H2/mg-Zn, 67.37 mL-H2/mg-Cu, and 13.39 mL-H2/mg-Cd. These values indicate that the toxicity of Zn was about 3.35 times higher than that of Cu. (3) The HBu/HAc ratio became higher as hydrogen production increased. Trends in the HBu/HAc ratio were dependent upon metal concentration, similar to those of hydrogen production activities. Therefore, metal concentration in the medium affected the HBu/HAc ratio and hydrogen production. (4) Various species of microflora appeared in the presence of the heavy metals during fermentative hydrogen production. In the phylum Firmicute, Clostridium magum, Clostridium diolis, and Clostridium sp. were identified as a part of the Clostridium genus. The most abundant genus in the class Gammaproteobacteria was the Klebsiella genus, which appeared at low concentrations of heavy metal, resulting in stimulation of hydrogen production. Hydrogen production was decreased when Rhodocista pekingensis, Erwinia chrysanthemi strain 10151, Delftia sp. YF 31, or uncultured klebsiella sp. clone F1 apr.32 appeared. Thus, certain species belonging to class Gammapro-
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teobacteria and class Betaproteobacteria did not promote hydrogen production.
References [1] D. Das, T. Nejat Veziroglu, Int. J Hydrogen Energy 26 (2001) 13. [2] Y. Kawagoshi, N. Hino, A. Fujimoto, M. Nakao, Y. Fujita, S. Sugimura, K. Furukawa, J. Biosci. Bioeng. 100 (2005) 524. [3] Y.-S. Jun, S.-H. Yu, K.-G. Ryu, T.-J. Lee, J. Microbiol. Biotechnol. 18 (2008) 1130. [4] Y. Ueno, S. Haruta, M. Ishii, Y. Igarashi, Appl. Microbiol. Biotechnol. 57 (2001) 555. [5] C.-Y. Lin, Water Res. 27 (1993) 147. [6] O. Yenigun, F. Kizilgun, G. Yilmazer, Environ. Technol. 17 (1996) 1269. [7] M.S. Pedro, S. Haruta, M. Hazaka, R. Shimada, C. Yoshida, K. Hiura, M. Ishii, Y. Igarashi, J. Biosci. Bioeng. 91 (2001) 159. [8] J. Baek, E. Choi, Y. Yun, S. Kim, M. Kim, J. Microbiol. Biotechnol. 16 (2006) 1210. [9] B. Dabrock, H. Bahl, G. Gottschalk, Appl. Environ. Microbiol. 58 (1992) 1233. [10] O. Mizno, R. Dinsdale, F.R. Hawkes, D.L. Hawkes, T. Noike, Bioresour. Technol. 73 (2000) 59. [11] APHA, Standard Methods for the Examination of Water and Wastewater, American Public Health Association, Washington, DC, 2002. [12] M. Dubois, K.A. Gilles, J.K. Hamilton, P.A. Rebers, F. Smith, Anal. Chem. 28 (1956) 350. [13] Y.J. Lee, T. Miyahara, T. Noike, J. Chem. Technol. Biotechnol. 77 (2002) 694. [14] B.E. Logan, S.E. Oh, I.S.S Kim, V. Ginkel, Environ. Sci. Technol. 36 (2002) 2530. [15] H.Q. Yu, H.H.P. Fang, Environ. Technol. 22 (2001) 1459. [16] X.J. Zheng, H.Q. Yu, J. Environ. Sci. Health 39 (2004) 89. [17] G. Dinopoulou, T. Rudd, J.N. Lester, Biotechnol. Bioeng. 31 (1988) 958.
[18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36]
345
G.C. Cha, T. Noike, Water Sci. Technol. 36 (1997) 247. S.-E. Oh, S. Van Ginkel, B.E. Logan, Environ. Sci. Technol. 37 (2003) 5416. I. Hussy, F.R. Hawkes, R. Dinsdale, D.L. Hawkes, Biotechnol. Bioeng. 84 (2003) 619. H. Drake, K. Kusel, C. Matthies, Anton. Leeuw. 81 (2002) 203. C.C. Chen, C.Y. Lin, M.C. Lin, Appl. Microbiol. Biotechnol. 57 (2002) 224. T. Zhang, H.H.P. Fang, Biotechnol. Lett. 22 (2000) 399. M. Bomar, H. Hippe, B. Schink, FEMS Microbiol. Lett. (1991) 347. X. Wang, D. Hoefel, C.P. Saint, P.T. Monis, B. Jin, J. Appl. Microbiol. 103 (2007) 1415. U.-J. Chang, W.-E. Chen, S.-Y. Shih, S.-J. Yu, J.-J. Lay, F.-S. Wen, C.-C. Huang, Appl. Microbiol. Biotechnol. 70 (2006) 598. C.H. Hung, C.H. Cheng, L.H. Cheng, C.M. Liang, C.Y. Lin, Int. J. Hydrogen Energy 33 (2008) 1586. L. Minnan, H. Jinli, W. Xuijuan, C. Jinzao, L. Chuannan, Z. Fengzhang, X. Liangshu, Res. Microbiol. 156 (2005) 76. L. Brynhildsen, Bo.V Lundgren, B. Allard, T. Rosswall, Appl. Environ. Microbiol. 54 (1988) 1689. T. Chandresh, S. Bhosale, D. Ranade, J. Microbiol. Biotechnol. 16 (2006) 870. X. Chen, Y. Sun, Z. Xiu., X. Li, D. Zhang, Int. J Hydrogen Energy 31 (2006) 539. G. Kovtunovych, T. Lytvynenko, V. Negrutska, O. Lar, S. Brisse, N. Kozyrovska, Res. Microbiol. 154 (2003) 587. W. Nasser, M.L. Bouillant, G. Salmond, S. Reverchon, Mol. Microbiol. 29 (1998) 1391. D. Zhang, H. Yang, W. Zhang, Z. Huang, S.-J. Liu, Int. J. Syst. Evol. Microbiol. 53 (2003) 1111. M. Drancourt, C. Bollet, A. Carta, P. Rousselier, Int. J. Syst. Evol. Microbiol. 51 (2001) 925. W.-j. Lim, S.-k. Ryu, S.-r. Park, M.-k. Kim, C.-l. An, S.-y. Hong, E.-c. Shin, J.-y. Lee, Y.p. Lim, H.-d. Yun, J. Microbiol. Biotechnol. 15 (2005) 302.
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Journal of Industrial and Engineering Chemistry journal homepage: www.elsevier.com/locate/jiec
Variations of hydrogen production and microbial community with heavy metals during fermentative hydrogen production Yoona Cho, Taejin Lee * Department of Environmental Engineering, Seoul National University of Science & Technology, Seoul 139-743, Republic of Korea
A R T I C L E I N F O
Article history: Received 10 June 2010 Accepted 2 September 2010 Available online 2 March 2011 Keywords: Hydrogen production Heavy metals Microbial community
A B S T R A C T
The effects of heavy metals on fermentative hydrogen production were examined based on metal type and concentration. Hydrogen production was stimulated by low concentrations of Cd and Zn but decreased at concentrations of 40 and 1 mg/L, respectively. Hydrogen production was inhibited for the entire range of Cu tested. The order of toxic density was Cu > Zn > Cd at concentrations below 2 mg/L but was Zn > Cu > Cd at higher concentrations. The depression rates of hydrogen production were calculated to be 225.8 mL-H2/mg-Zn, 67.37 mL-H2/mg-Cu, and 13.39 mL-H2/mg-Cd. The presence of heavy metals caused a shift in microbial community. The presence of Clostridium genus bacteria, identified as Clostridium magum, Clostridium diolis, and Clostridium sp., resulted in active hydrogen production. Klebsiella genus bacteria were the most abundant of the class Gammaproteobacteria and also stimulated hydrogen production at relatively low concentrations of heavy metal. When Rhodocista pekingensis, Erwinia chrysanthemi strain 1015-1, Delftia sp. YF 31, or uncultured Klebsiella sp. clone F1 apr.32 were present, hydrogen production was seriously decreased. ß 2011 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved.
1. Introduction Global energy is mostly dependent on fossil fuels, leading to the foreseeable depletion of limited fossil energy resources. The use of fossil fuels is also causing global climate change mainly due to the emission of pollutants [1]. Therefore, the depletion of fossil energy resources combined with global environmental changes is increasing the need to develop energy substitutes, such as solar power, nuclear power, and bio-gases like methane and hydrogen [2]. Hydrogen gas has received the greatest attention due to its potential as a powerful fuel [3]. Hydrogen is regarded as a nonpolluting fuel since its combustion produces only water as the main byproduct. Hydrogen is exclusively produced by water electrolysis or by steam methane reformation. Although the biological production of hydrogen is not yet wide spread, it has become an exciting new area of technology that offers a chance to use hydrogen from a variety of renewable resources [1]. Hydrogen production by microorganisms can be divided into two main categories: hydrogen produced by photosynthetic organisms cultured under anaerobic light conditions, and that produced by anaerobic bacteria via fermentation metabolism [4]. Dark fermentation, which utilizes a variety of carbon sources
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including waste, is not subject to oxygen limitation or light dependency [3]. There are many studies on the optimal fermentative conditions (inoculum type, pH, temperature, hydrogen partial pressure, etc.) for hydrogen production using mixed bacterial inoculum [2]. However, there are few studies on how heavy metals affect the generation of hydrogen. When using sludge or another biomass, one must know what poisonous materials the carbon source includes. The presence of heavy metals may result in the inhibition and failure of sewage digestion, for example. Several studies have demonstrated the toxic effects of heavy metals on anaerobic digestion [5,6]. These studies found that acidogenesis and methanogenesis could be adversely affected by the presence of heavy metals. Metabolism of hydrogenesis is similar to acidogenesis, except that its efficiency and variations of microbial community in the presence of heavy metals has been little reported. This study was conducted in order to investigate the effect of heavy metals on hydrogen production using anaerobic sewage sludge microflora in the presence of Cd, Cu, and Zn ions. For this purpose, the efficiency and toxicity density of hydrogen production were analyzed using the modified Gompertz equation. The characteristics of VFAs (volatile fatty acid) production in the presence of heavy metals were also investigated. In addition, a PCR-amplified v3 region of 16s rDNA was analyzed by denaturing gradient gel electrophoresis (DGGE) in order to investigate the microbial diversity involved in hydrogen production in the presence of heavy metals.
1226-086X/$ – see front matter ß 2011 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.jiec.2011.02.036
Y. Cho, T. Lee / Journal of Industrial and Engineering Chemistry 17 (2011) 340–345
2. Materials and methods 2.1. Seed microorganisms Seed microflora was obtained by the return of activated sludge from a municipal sewage treatment plant. Synthetic medium was placed into a serum bottle at an initial pH of 7.0. The medium was then purged with nitrogen gas for 5 min to obtain anaerobic conditions. The medium was inoculated with 6 mL of the settled sludge supernatant and then continuously shaken at 120 rpm on a mechanical shaker at 30 8C. The gas produced in the headspace of the serum bottle was collected and measured for the amount of hydrogen produced. Serial transfers of the cultures were conducted using fresh medium from the serum bottles in order to acquire the acclimated seed microflora. 2.2. PCR procedure To analyze the complexity of the hydrogen-producing microbial community, DNA was extracted from the microorganisms in the serum bottle. The 16s rDNA fragments were amplified by PCR using 341f and 518r as primers [7] followed by separation by DGGE. Each band on the DGGE profile represented a gene fragment of unique 16s rDNA sequences, each of which was analyzed using the BLAST program for identification of each species in the microbial community [8–10]. 2.3. Operation of batch reactor Hydrogen production experiments were conducted in a 1.5 L batch fermentor at 30 3 8C. Each liter of synthetic medium consisted of 15 g of sucrose, 2 g of NH4HCO3, 1 g of KH2PO4, 100 mg of MgSO47H2O, 10 mg of NaCl, 10 mg of NaMoO42H2O, 10 mg of CaCl22H2O, 15 g of MnSO47H2O, and 2.78 mg of FeCl2 [3]. The batch fermentor in Fig. 1 was stirred at a constant rate of 250 rpm by a magnetic stirrer, and the headspace was initially filled with nitrogen gas. The pH of the solution was automatically controlled by the addition of 3 N aqueous KOH solution via a peristaltic pump. The reactor was kept in the dark by wrapping with aluminum foil, thus preventing the growth of photosynthetic bacteria and algae. After the reactor was constructed, 15 mL of seed solution from the serum bottle was added. The initial concentration of volatile suspended solids (VSS) after addition of the seed solution to the reactor was approximately 6 mg/L. The produced
[()TD$FIG]
341
gas was collected by the biogas collector filled with 2% aqueous H2SO4 (v/v) solution. The gas volume was measured and 0.5 mL aliquots of gas were periodically withdrawn from the headspace of the reactor at regular intervals. 2.4. Analytical methods The composition of biogas in the headspace of the fermentor was determined using a gas chromatography apparatus (GOW Mac Series 350) equipped with a thermal conductivity detector (TCD). Chromatographic separation of the reactor headspace samples was achieved using a 60 1/800 stainless steel SS 350A Molsieve 13 (80/100 mesh) with nitrogen as the carrier gas at a flow rate of 30 mL/min. The operation temperatures of the injection port, oven, and detector were 80, 100, and 100, respectively. The concentration of VSS and chemical oxygen demand (COD) were determined according to standard procedures [11]. The carbohydrate concentration was measured following the phenol– sulfuric acid method [12]. Fatty acids (acetic acid, butyric acid, formic acid, and propionic acid) were analyzed using a highperformance liquid chromatography (HPLC) system (KNAUER, German) equipped with a UV detector operated at 210 nm along with a Supelcogel C-610H (130 mm 7.8 mm ID) column. 2.5. Model development A modified Gompertz equation (Eq. (1)) was used to fit the cumulative hydrogen production curves for each experiment [13,14]. Re M ¼ P ex p exp ðl tÞ þ 1 P
(1)
where M is the cumulative hydrogen production (mL), l is the lagphase time (h), P is the hydrogen production potentia l (mL), R is the maximum hydrogen production rate (mL/h), t is the incubation time (h), and e is the exp(1) = 2.718. Parameters were estimated using the curve fit function of Sigma Plot 2002 (SPSS Science, U.K.). The hydrogen production ratio (Rh) in Eq. (2) was used to calculate the amount of hydrogen production with heavy metals. Rh was defined as the fraction of hydrogen produced over 72 h by metal-dosed seed microflora compared to control. Rh ð%Þ ¼
Hm 100% Hc
(2)
where Hm and Hc denote the amount of hydrogen produced over 72 h by metal-dosed seed microflora and control, respectively. 3. Results and discussion 3.1. Effects of heavy metals on hydrogen production
Fig. 1. Schematic diagram of the batch reactor for anaerobic fermentative hydrogen production.
Fig. 2 shows the hydrogen production time course for Cd-dosed seed microflora. The accumulative amount of produced hydrogen was identified based on total biogas production and hydrogen gas content. A similar trend in hydrogen production was observed after dosing with Zn. Cd and Zn were found to stimulate hydrogen production at concentrations of 20 and 0.5 ppm, respectively, but not at higher concentrations. Cu was found to inhibit hydrogen production at all tested concentrations. For Cu, all volumes of metal-dosed hydrogen were less than those produced by the control (in the absence of heavy metals). The hydrogen production potential (P) and maximum rate of hydrogen production (R) of the control were calculated as 1207.61 mL and 73.63 mL/h, respectively. The H2 yield of control was calculated as 108.17 mL/g-COD based on the amount of sucrose consumed in the medium.
[()TD$FIG]
[()TD$FIG]
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Fig. 2. Cumulative hydrogen production curve at various Cd concentrations. Regression of experimental data was performed by Sigma Plot of SPSS Science Inc. using the modified Gompertz equation.
Table 1 shows the ranges of hydrogen production potential (P) and maximum rate of hydrogen production (R) in the presence of Cu, Cd, and Zn. In all batches, hydrogen production data were satisfactorily fitted by Eq. (1). The R2 value was over 0.942. Peak hydrogen production potential values occurred at 0, 0.1 and 20 mg/ L for Cu, Zn, and Cd respectively. These values show inhibition (Cu) and stimulation (Zn, Cd) of hydrogen production in the presence of heavy metals. In contrast to hydrogen production potential, the maximum rates of hydrogen production in the presence of heavy metals did not exceed the value of the control. This implies that heavy metals within a certain range of concentrations stimulated hydrogen production, but the maximum rate was inhibited at all other concentrations. H2 yields in the presence of the heavy metals were identical with hydrogen production potential but not with max maximum rate of hydrogen production. 3.2. Metal toxicity comparison Fig. 3 shows the relationships between hydrogen production ratio (Rh) and metal concentration. Rh was metal concentrationdependent. Rh values exceeding 100%, such as 103% and 128%, occurred at 20 mg-Cd/L and 0.1 mg-Zn/L, respectively. Low Cd and Zn concentrations stimulated fermentative hydrogen production by mixed microflora. Reduced stimulation occurred when the Cd and Zn concentrations reached 40 and 1 mg/L, respectively. Metal dosages over these values resulted in Rh values lower than 100%, Table 1 Modified Gompertz equation parameter. Metal Control Cd (mg/L)
Cu (mg/L)
Zn (mg/L)
20 40 60 80 100 0.1 0.5 1.0 3.0 5.0 0.1 0.5 1.0 2.0 5.0
P (mL)
R (mL/h)
H2 yield (mL/g-COD)
R2
1207.61 1252.11 855.94 454.81 267.49 207.13 593.32 522.93 510.95 460.06 204.87 1547.55 1309.08 482.87 318.64 268.62
73.63 65.70 27.67 25.08 13.49 10.90 42.41 37.64 29.89 11.82 10.50 63.19 57.03 55.13 45.32 20.51
108.17 120.39 82.30 43.73 26.48 19.72 51.15 47.11 37.16 29.77 19.37 139.42 104.49 42.39 27.98 23.77
0.993 0.999 0.994 0.996 0.989 0.995 0.999 0.999 0.995 0.989 0.942 0.991 0.972 0.973 0.987 0.976
Fig. 3. Relationships between hydrogen production ratio (Rh) and metal concentration. Regression of experimental data was performed by Sigma Plot of SPSS Science Inc. using a linear equation.
indicating metal toxicity during hydrogen production. These metal concentrations were threshold concentrations. For the Cu dosage experiments, Rh differed from those of Cd or Zn. Rh values were always smaller than 100%, indicating inhibition of hydrogen production. Zn showed very high inhibition at its threshold concentration and above, with Rh decreased rapidly to below 37%. Zn was more toxic than Cu at a certain concentration range. The toxic effect of heavy metals varied depending on the metal concentration. For example, the order of toxic density was Cu > Zn > Cd at concentrations below 2 mg/L but Zn > Cu > Cd above it. Hydrogen gas production was reduced at 225.8 mL-H2/mg-Zn, 67.37 mL-H2/mg-Cu, and 13.39 mL-H2/mg-Cd. These values indicate that Zn toxicity was about 3.35 times higher than Cu. However, an inhibition study on the acidogenesis of dairy wastewater by Zn and Cu showed that Cu was 1.4–4.3-fold more toxic than Zn [15]. Another inhibition study on the hydrogenesis of glucose by Zn and Cu also showed that Cu was more toxic than Zn [16]. However, those results are not identical with our study. These differences might be due to variations in experimental setup, such as seeding microflora or the substrate used. 3.3. Production of volatile fatty acids (VFAs) Hydrogen formation was accompanied by the production of volatile fatty acids (VFAs) or solvent during the anaerobic digestion process. Therefore, the concentration distribution and fractions of VFA can be useful indicators for monitoring hydrogen production. The distribution of metabolites formed during hydrogen fermentation is often a crucial signal indicating the efficiency of hydrogenproducing cultures [13,17,18]. Soluble fermentation products included n-butyrate, acetate, lactate, ethanol, trace amounts of formate, propionate, isobutyrate, n-valeate, isovalerate, n-caproate, n-propanol, isopropanol, n-butanol, and sec-butanol. In the case of n-butyrate, 2 mol of H2 were produced together with 1 mol of n-butyrate from 1 mol of hexose, as shown in Eq. (3). C6 H12 O6 þ 2H2 O ! CH3 CH2 CH2 COO þ 2HCO3 þ 3Hþ þ 2H2 (3) According to the stoichiometry, 4 mol of hydrogen were produced with 2 mol of acetate from 1 mol of hexose, as shown in Eq. (4). C6 H12 O6 þ 4H2 O ! 2CH3 COO þ 2HCO3 þ 4Hþ þ 4H2
(4)
[()TD$FIG]
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mance of the hydrogen fermentor. In this study, HBu/HAc ratios ranged between 0.3 and 3.0 and were metal type- and concentration-dependent. As the inlet Cd concentration was increased from 20 to 40 mg Cd/L, the acetate composition increased from 6.0% to 33.0%. At the same time, the butyrate composition decreased from 54.6% to 1.7%. Fig. 4 shows the HBu/HAc ratios at different metal concentrations. The HBu/HAc ratio became higher as hydrogen production was increased, which corresponds to the results of previous studies [22]. Trends in the HBu/HAc ratio in Fig. 4 were dependent upon metal concentration and were similar with those of hydrogen production activities in Fig. 3. Therefore, the concentration of metal in the medium affected HBu/HAc ratio, and hydrogen production was metal concentration-dependent.
Fig. 4. Relationships between HBu/HAc ratio and metal concentration. Regression of experimental data was performed by Sigma Plot of SPSS Science Inc. using a linear equation.
From Eq. (4), more hydrogen can be generated by acetate production, but this was not always true. Increase acetate composition was accompanied with decreased H2 conversion in previous studies in which mixed cultures were used [19–21]. Acetogens, which produces acetate from H2 and CO2, as shown in Eq. (5), might be selected in this case. There was no significant production of propionate or methane, which means that acetate production in Eq. (5) was the main pathway of gaseous H2 consumption in this study. As shown in Eq. (5), acetogens could also utilize carbohydrates, thereby competing with H2-producing Clostridium sp. [21]. 4H2 þ 2HCO3 þ Hþ ! CH3 COO þ 4H2 O
(5)
From Eqs. (4) and (5), 1 mol of hexose produced 3 mol of acetate and hydrogen ion instead of hydrogen gas (Eq. (6)). Thus, production of n-butyrate and not acetate is more useful for hydrogen production. C6 H12 O6 ! 3CH3 COO þ 3Hþ
(6)
The ratio of n-butyrate/acetate (HBu/HAc) has been reported as [()TD$FIG] simple indicator showing the metabolic pathway and perfora
3.4. PCR-DGGE analysis of hydrogen-producing microflora (PCRDGGE profiles analysis) The experimental results have shown that hydrogen production activities or HBu/HAc ratios varied in the presence of heavy metals, supposedly due to changes in the microbial community caused by heavy metals in the medium. PCR-denaturing gradient gel electrophoresis (PCR-DGGE) analysis on 16s rDNA was attempted to investigate the effects of heavy metal on the microbial community responsible for hydrogen production. The number of bands in the gel corresponds to the relative diversity of the microbial community, whereas intensity of the bands indicates the degree of abundance of each microbial group [23]. PCR-DGGE profiles of each sample showed various band patterns in Fig. 5. Detailed analysis of each sample showed the presence of distinguishable patterns that revealed different bacterial genera or species. Based on band intensities, major bands A through G were present in the control. Bands H, I, J, K, L, M, N, and O were detected in the presence of Cd, indicating the presence of various microbial species. In the case of Cu, band P only appeared at low concentrations while the intensity of band O was increased at higher Cu concentrations. Bands B and E were analyzed at 0.1 and 0.5 mg/L of Zn. Bands I, Q, and R occasionally appeared in the presence of heavy metals. Taken together, bands B, E and G were associated with relatively higher hydrogen production, whereas bands A, C, D, and F appeared at all
Fig. 5. Denaturing gradient gel electrophoresis (DGGE) profile of PCR-amplified 16s rDNA extracted from the culture at various metal concentrations. Control is represented by the DGGE profile in the absence of heavy metals. The gradient concentration of the denaturant was 40–60%. Values below metals are the metal concentrations in mg/L.
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Table 2 Characteristics of DNA fragments obtained from DGGE with various Cd, Cu, and Zn concentrations. Band name
A B C D E F G H I J K L M N O P Q R
Gen bank search result
Taxonomic description (class)
Phylogenetically closest relative
Accession no.
Similarity
Uncultured bacterium clone H57L Clostridium sp. LYH 1 Pectobacterium wasabiae WPP163 Klebsiella sp. GSK Clostridium magnum strain FM5 Klebsiella sp. enrichment culture clone FD-4 Clostridium diolis strain W1 Uncultured klebsiella sp. clone F4 aug.12 Rhodocista pekingensis strain 3-P Uncultured citrobacter sp. clone F7 feb.77 Raoultella planticola strain R52 Phytobacter diazotrophicus strain LS8 Klebsiella pneumoniae strain SC Pectobacterium carotovorum subsp. Erwinia chrysanthemi strain 1015-1 Delftia sp. YF 31 Klebsiella oxytoca strain QD43 Uncultured klebsiella sp. clone F1 apr.32
EU560736.1 FJ158032.1 CP001790.1 GU066861.1 GU129927.1 GU167259.1 DQ831125.1 GQ418150.1 NR_028855.1 GQ416333.1 EF551363.1 DQ821583.2 FJ232952.1 GQ167303.1 GQ293897.1 GU143685.1 GU139690.1 GQ416844.1
100 100 100 100 94 100 100 100 96 99 86 98 100 100 99 95 100 94
concentrations, indicating heavy metals had no significant effects. Bands H, J, K, L, M, N, and Q mostly appeared in the presence of relatively low concentrations of Cd or Zn. Bands I, O, P, and R only appeared at relatively rates of low hydrogen production. To understand the differences in microbial diversity among samples, bands of interest were excised and sequenced. Microbes and accession number were identified from the 16s rDNA using the NCBI BLAST program. The closest affiliation along with percentage identity data of the bands based on the sequences is shown in Table 2. Three major bacterial taxa were identified, with 15 sequences related to the phylum Proteobacteria, 13 affiliated to the class Gammaproteobacteria, 1 to the class Alphaproteobacteria, and 1 to the class Betaproteobacteria. The remaining sequences were related to the phylum Firmicute. In the phylum Firmicute, Clostridium magum, Clostridium diolis, and Clostridium sp. were identified as a part of the Clostridium genus. The genus showed relatively higher hydrogen production, as shown by bands B, E, and G in Fig. 5, and was reported to produce hydrogen by dark fermentation [8,24–27]. The most abundant genus in the class Gammaproteobacteria was the Klebsiella genus, which also showed a relatively high rate of hydrogen production. It was noteworthy that hydrogen was produced by the Klebsiella genus even in the presence of heavy metals. The Klebsiella genus was present in bands D, F, H, M, Q, and R in Fig. 5 and has been reported in other studies on hydrogen production [28–32]. It is also affiliated with uncultured Klebsiella sp. clone F1 apr.32 in the presence of high heavy metal content, indicating that every Klebsiella genus does not always promote hydrogen generation. The four species identified in this study were Erwinia chrysanthemi strain 1015-1, Rhodocista pekingensis strain 3-P, Raoultella planticola strain R52, and Pectobacterium carotovorum subsp. Erwinia chrysanthemi was described as a plant pathogen that produces acrylhomoserine lactones [33]. Rhodocista pekingensis has been isolated as a cyst-forming phototrophic bacterium from a municipal wastewater [34]. Raoultella planticola strain was studied for phylogetic analysis with the Klebsiella genus and was amended as a part of the Klebsiella genus [35]. Pectobacterium carotovorum was described as a phytopathogenic entrobacteria capable of degrading plant cell walls [36]. Raoultella planticola and Pectobacterium carotovorum are associated with increased hydrogen production in contrast with Erwinia chrysanthemi strain 1015-1. Two of the remaining three species, the
g-Proteobacteria Firmicute g-Proteobacteria g-Proteobacteria Firmicute g-Proteobacteria Firmicute g-Proteobacteria a-Proteobacteria g-Proteobacteria g-Proteobacteria g-Proteobacteria g-Proteobacteria g-Proteobacteria g-Proteobacteria b-Proteobacteria g-Proteobacteria g-Proteobacteria
Phytobacter diazotrophicus strain and citrobacter sp. but not Delftia sp., are suspected of hydrogen production. Therefore, further studies based on independent incubation should be conducted to confirm the individual contribution of each species to hydrogen production.
4. Conclusions In this study, we demonstrated the effects of Cd, Cu, and Zn ions and VFAs (volatile fatty acids) on fermentative hydrogen production. The rate of hydrogen production and the toxic effects differed depending on metal type or concentration. The microbial species responsible also varied depending on which metal was present. Experimental results are listed below: (1) Cd and Zn were found to stimulate hydrogen production at concentrations lower than 20 and 0.5 ppm, respectively, but not at a higher concentration range. Cu was found to completely inhibit hydrogen production at tested concentrations. The toxic effects of metals varied depending on the metal concentration. For example, the order of toxic density was Cu > Zn > Cd at concentrations below 2 mg/L but was Zn > Cu > Cd at higher concentrations. (2) Hydrogen gas production was reduced to 225.8 mL-H2/mg-Zn, 67.37 mL-H2/mg-Cu, and 13.39 mL-H2/mg-Cd. These values indicate that the toxicity of Zn was about 3.35 times higher than that of Cu. (3) The HBu/HAc ratio became higher as hydrogen production increased. Trends in the HBu/HAc ratio were dependent upon metal concentration, similar to those of hydrogen production activities. Therefore, metal concentration in the medium affected the HBu/HAc ratio and hydrogen production. (4) Various species of microflora appeared in the presence of the heavy metals during fermentative hydrogen production. In the phylum Firmicute, Clostridium magum, Clostridium diolis, and Clostridium sp. were identified as a part of the Clostridium genus. The most abundant genus in the class Gammaproteobacteria was the Klebsiella genus, which appeared at low concentrations of heavy metal, resulting in stimulation of hydrogen production. Hydrogen production was decreased when Rhodocista pekingensis, Erwinia chrysanthemi strain 10151, Delftia sp. YF 31, or uncultured klebsiella sp. clone F1 apr.32 appeared. Thus, certain species belonging to class Gammapro-
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teobacteria and class Betaproteobacteria did not promote hydrogen production.
References [1] D. Das, T. Nejat Veziroglu, Int. J Hydrogen Energy 26 (2001) 13. [2] Y. Kawagoshi, N. Hino, A. Fujimoto, M. Nakao, Y. Fujita, S. Sugimura, K. Furukawa, J. Biosci. Bioeng. 100 (2005) 524. [3] Y.-S. Jun, S.-H. Yu, K.-G. Ryu, T.-J. Lee, J. Microbiol. Biotechnol. 18 (2008) 1130. [4] Y. Ueno, S. Haruta, M. Ishii, Y. Igarashi, Appl. Microbiol. Biotechnol. 57 (2001) 555. [5] C.-Y. Lin, Water Res. 27 (1993) 147. [6] O. Yenigun, F. Kizilgun, G. Yilmazer, Environ. Technol. 17 (1996) 1269. [7] M.S. Pedro, S. Haruta, M. Hazaka, R. Shimada, C. Yoshida, K. Hiura, M. Ishii, Y. Igarashi, J. Biosci. Bioeng. 91 (2001) 159. [8] J. Baek, E. Choi, Y. Yun, S. Kim, M. Kim, J. Microbiol. Biotechnol. 16 (2006) 1210. [9] B. Dabrock, H. Bahl, G. Gottschalk, Appl. Environ. Microbiol. 58 (1992) 1233. [10] O. Mizno, R. Dinsdale, F.R. Hawkes, D.L. Hawkes, T. Noike, Bioresour. Technol. 73 (2000) 59. [11] APHA, Standard Methods for the Examination of Water and Wastewater, American Public Health Association, Washington, DC, 2002. [12] M. Dubois, K.A. Gilles, J.K. Hamilton, P.A. Rebers, F. Smith, Anal. Chem. 28 (1956) 350. [13] Y.J. Lee, T. Miyahara, T. Noike, J. Chem. Technol. Biotechnol. 77 (2002) 694. [14] B.E. Logan, S.E. Oh, I.S.S Kim, V. Ginkel, Environ. Sci. Technol. 36 (2002) 2530. [15] H.Q. Yu, H.H.P. Fang, Environ. Technol. 22 (2001) 1459. [16] X.J. Zheng, H.Q. Yu, J. Environ. Sci. Health 39 (2004) 89. [17] G. Dinopoulou, T. Rudd, J.N. Lester, Biotechnol. Bioeng. 31 (1988) 958.
[18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36]
345
G.C. Cha, T. Noike, Water Sci. Technol. 36 (1997) 247. S.-E. Oh, S. Van Ginkel, B.E. Logan, Environ. Sci. Technol. 37 (2003) 5416. I. Hussy, F.R. Hawkes, R. Dinsdale, D.L. Hawkes, Biotechnol. Bioeng. 84 (2003) 619. H. Drake, K. Kusel, C. Matthies, Anton. Leeuw. 81 (2002) 203. C.C. Chen, C.Y. Lin, M.C. Lin, Appl. Microbiol. Biotechnol. 57 (2002) 224. T. Zhang, H.H.P. Fang, Biotechnol. Lett. 22 (2000) 399. M. Bomar, H. Hippe, B. Schink, FEMS Microbiol. Lett. (1991) 347. X. Wang, D. Hoefel, C.P. Saint, P.T. Monis, B. Jin, J. Appl. Microbiol. 103 (2007) 1415. U.-J. Chang, W.-E. Chen, S.-Y. Shih, S.-J. Yu, J.-J. Lay, F.-S. Wen, C.-C. Huang, Appl. Microbiol. Biotechnol. 70 (2006) 598. C.H. Hung, C.H. Cheng, L.H. Cheng, C.M. Liang, C.Y. Lin, Int. J. Hydrogen Energy 33 (2008) 1586. L. Minnan, H. Jinli, W. Xuijuan, C. Jinzao, L. Chuannan, Z. Fengzhang, X. Liangshu, Res. Microbiol. 156 (2005) 76. L. Brynhildsen, Bo.V Lundgren, B. Allard, T. Rosswall, Appl. Environ. Microbiol. 54 (1988) 1689. T. Chandresh, S. Bhosale, D. Ranade, J. Microbiol. Biotechnol. 16 (2006) 870. X. Chen, Y. Sun, Z. Xiu., X. Li, D. Zhang, Int. J Hydrogen Energy 31 (2006) 539. G. Kovtunovych, T. Lytvynenko, V. Negrutska, O. Lar, S. Brisse, N. Kozyrovska, Res. Microbiol. 154 (2003) 587. W. Nasser, M.L. Bouillant, G. Salmond, S. Reverchon, Mol. Microbiol. 29 (1998) 1391. D. Zhang, H. Yang, W. Zhang, Z. Huang, S.-J. Liu, Int. J. Syst. Evol. Microbiol. 53 (2003) 1111. M. Drancourt, C. Bollet, A. Carta, P. Rousselier, Int. J. Syst. Evol. Microbiol. 51 (2001) 925. W.-j. Lim, S.-k. Ryu, S.-r. Park, M.-k. Kim, C.-l. An, S.-y. Hong, E.-c. Shin, J.-y. Lee, Y.p. Lim, H.-d. Yun, J. Microbiol. Biotechnol. 15 (2005) 302.