Understanding Short-Chain Fatty Acids Accumulation Enhanced in

Nov 24, 2010 - Environmental and Material Engineering, Yantai University,. Yantai Shandong 264025, China. Received August 20, 2010. Revised manuscript...
0 downloads 0 Views 4MB Size
Download PDF
Environ. Sci. Technol. 2010, 44, 9343–9348

Understanding Short-Chain Fatty Acids Accumulation Enhanced in Waste Activated Sludge Alkaline Fermentation: Kinetics and Microbiology P E N G Z H A N G , †,‡ Y I N G U A N G C H E N , * ,† QI ZHOU,† XIONG ZHENG,† XIAOYU ZHU,† AND YUXIAO ZHAO† State Key Laboratory of Pollution Control and Resources Reuse, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China, and School of Environmental and Material Engineering, Yantai University, Yantai Shandong 264025, China

Received August 20, 2010. Revised manuscript received November 7, 2010. Accepted November 12, 2010.

Most of the studies on sewage sludge treatment in literature were conducted for methane generation under acidic or near neutral pH conditions. It was reported in our previous studies that the accumulation of short-chain fatty acids (SCFAs), the preferredcarbonsourceofbiologicalwastewaternutrientremoval, was significantly enhanced when sludge was fermented under alkaline conditions, but the optimal pH was temperaturedependent (pH 10 at ambient temperature, pH 9 at mesophilic, and pH 8 at thermophilic), and the maximal SCFAs yields were in the following order: thermophilic pH 8 > mesophilic pH 9 > ambient pH 10 > ambient uncontrolled pH. In this study the kinetic and microbiological features of waste activated sludge fermented in the range of pH 7-10 were investigated to understand the mechanism of remarkably high SCFAs accumulation under alkaline conditions. The developed sludge alkaline fermentation model could be applied to predicate the experimental data in either batch or semicontinuous sludge alkaline fermentation tests, and the relationships among alkaline pH, kinetic parameters, and SCFAs were discussed. Further analyses with fluorescence in situ hybridization (FISH) and PCR-based 16S rRNA gene clone library indicated that both the ratio of bacteria to archaea and the fraction of SCFAs producer accounting for bacteria were in the sequence of thermophilic pH 8 > mesophilic pH 9 > ambient pH 10 > ambient uncontrolled pH, which was in correspondence with the observed order of maximal SCFAs yields.

Introduction Biological production of short-chain fatty acids (SCFAs) from waste sludge fermentation has gained increasing interest due to the fact that waste sludge generated in wastewater treatment plant can be reused, and the produced SCFAs are the preferred carbon sources for biological nutrient removal microbes (1-3). It was reported in our previous studies that * Corresponding author phone: 86-21-65981263; fax: 86-2165986313; e-mail: [email protected]. † Tongji University. ‡ Yantai University. 10.1021/es102878m

 2010 American Chemical Society

Published on Web 11/24/2010

compared to the acidic or pH uncontrolled treatment more SCFAs were accumulated when waste activated sludge (WAS) was fermented under alkaline conditions, and the optimum pH for maximal SCFAs accumulation in ambient, mesophilic, and thermophilic fermentation was respectively pH 10, pH 9, and pH 8 (4, 5). Kinetic model and microbiology assay have been proven to be useful tools for the description, prediction, and evaluation of the performance of anaerobic treatment system. However, as sludge treatment is usually conducted for methane generation under acidic or near neutral pH conditions, most of the reported kinetic models (6-11) and microbial community information (12-15) focus on the improvement of methane production. The kinetics and microbiology regarding sludge alkaline fermentation for SCFAs production have not been investigated. Several stages (i.e., hydrolysis, acidification, and methane formation) are involved in sludge anaerobic treatment. In order to understand how the alkaline pH affects these stages, especially SCFAs accumulation, in this paper sludge alkaline fermentation kinetic model was developed and tested, and the effects of alkaline pH on kinetic parameters and SCFAs accumulation were discussed. Finally, the microbiology during sludge alkaline treatment for maximal SCFAs accumulation at different temperatures was investigated to dig out the reasons for maximal SCFAs accumulation coming out in the following sequence: thermophilic pH 8 > mesophilic pH 9 > ambient pH 10 > ambient uncontrolled pH.

Materials and Methods Source of WAS. The WAS used in this study was obtained from the secondary sedimentation tank of a municipal wastewater treatment plant in Shanghai, China, and was concentrated by settling at 4 °C for 24 h before use. The main characteristics of the concentrated sludge for model parameters estimation in batch tests are TSS (total suspended solids) 14.33 ( 0.56 g/L, VSS (volatile suspended solids) 10.02 ( 0.21 g/L, total protein 9.65 ( 0.16 kgCOD/m3, total carbohydrate 2.32 ( 0.15 kgCOD/m3, and lipid and oil 0.14 ( 0.01 kgCOD/m3. The same sludge was used for model validation in both batch and semicontinuous experiments, but its TSS was respectively 15.02 ( 0.47 g/L and 12.51 ( 1.62 g/L. Batch Experiments Setup. The batch tests of WAS alkaline treatment were carried out in a series of identical reactors, which were made of plexiglass and each had a liquid volume of 5.0 L. All reactors were mechanically stirred at 100 rpm (rotations per minute), and the alkaline pH was controlled by adding 2 M sodium hydroxide (NaOH). All the following experiments were duplicated. Twelve batch reactors with different pHs (7, 8, 9 and 10) and temperatures (ambient (21 ( 1 °C), mesophilic (35 ( 2 °C), and thermophilic (55 ( 2 °C)) were run to get the kinetic parameters. For model validation the same reactors were operated, but a different sludge concentration was used. Semicontinuous Reactors Operation. The 16S rRNA gene clone library and FISH (fluorescence in situ hybridization) analyses and the model validation were conducted in four semicontinuous reactors (i.e., every day the WAS was wasted one time from the reactors and the same amount of fresh WAS was added according to the sludge retention time (SRT)), which were operated at pH 10 (21 ( 1 °C), pH 9 (35 ( 2 °C), pH 8 (55 ( 2 °C), and uncontrolled pH (21 ( 1 °C), respectively. The pH values used in this study were the optimum ones reported for maximal SCFAs accumulation in ambient, mesophilic, and thermophilic alkaline fermentation, respecVOL. 44, NO. 24, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

9343

tively (4, 5). According to our previous studies the SRT in the above four reactors was maintained respectively at 8d, 5d, 9d, and 8d (4, 5). After the SCFAs concentration in all reactors did not change significantly with time, the 16S rRNA gene clone library and FISH analyses were started. Analytic Methods. The analyses of SCFAs, methane, carbohydrate, protein, lipid, COD, TSS, and VSS were the same as described in our previous publication (16). The following probes were used for FISH analysis: Bacteria EUB338all (a combination of EUB338 and EUB338+), targeting most of Bacteria, and Archaea ARC915 targeting most of Archaea (17). Oligonucleotides were synthesized and fluorescently labeled with FAM for EUB338/EUB338+ and TAMRA for ARC915. Therefore, in obtained images, all bacteria appear green (acidogens and acetogens), and methanogenic bacteria appear yellow. The anaerobic biomass withdrawn from the reactors was fixed in 4% freshly prepared paraformaldehyde solution for 3-4 h at 4 °C. A 10 µL fixed sample was mounted on a glass slide using a micropipet. It was then prehybridized in 5×SSC, 0.1% N-lauroylsarcosine, 0.02% SDS, 1% blocking reagent, and formamide for 0.5 h at 50 °C. After removal of the prehybridization solution, 20 µL of hybridization buffer (0.9 M NaCl, 20 mM Tris-HCl (pH 7.2), 0.01% SDS) with 5 ng probes were applied to the sample. Hybridizations were performed in a hybridization incubator (ThermoBrite, America) at 46 °C at least 1.5 h (18). The hybridization stringency was adjusted by adding 20% formamide to the hybridization buffer. Hybridization was followed twice by a stringent washing buffer (20 mM TrisHCl (pH 7.2), 0.01% SDS, 250 mM) (18). The washing buffer was removed by rinsing the slides with distilled water, and the slides were air-dried. The slides were mounted to avoid bleaching and examined with epifluorescence microscope (Nikon, Japan). At least 15 microscopic fields were randomly acquired to obtain statistically valid determinations. The ratio of bacteria to archaea was determined with image analysis system (Image-Pro Plus, V6.0, Media Cybernetics). Before microbial community analysis, the digested biomass samples (0.5 g, wet weight) were collected on days 105 from the semicontinuous reactors. DNA extraction from biomass samples, PCR of bacteria 16S rRNA genes (using primers 27F (5′- AGAGTTTGATCCTGGCTCAG-3′) and 1492R (5′-GGTTACCTTGTTACGACTT- 3′)), DNA purification, ligation and transformation, construction of clone libraries, and phylogenetic analysis were performed the same as described by Feng et al. (19). The sequences were checked for chimera formation by using the Chimera check program of the Ribosomal Database Project II database. Accession Numbers. The nucleotide sequences reported in this paper have been deposited in the GenBank, EMBL and DDBL nucleotide database under the accession numbers GU454862-GU455376. Model Description. Hydrolysis, acidogenesis, and methogenesis are usually involved in organic wastes anaerobic treatment. Thus kinetic model presented here describes the following three phases: hydrolysis of sludge particulate organic matter (SP) and dead biomass into hydrolysate (Sh) (soluble protein, carbohydrate, and amino acids, etc.); fermentation of the hydrolysate into SCFAs (Sv) by acidogenic bacteria (Xh), and transformation of SCFAs into methane (SCH4) by methanogens (Xv). The production and transformation of hydrogen were not considered in this study due to very low hydrogen production observed (lower than 0.021 kg COD/m3) in all experiments. These three stages can be summarized as follows Particulate organic matter (SP) + dead biomass f Hydrolysate (Sh) f SCFAs (Sv) f Methane (SCH4) The kinetic model includes four substrates degradation and two biomasses (acidogenic bacteria and methanogens) 9344

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 24, 2010

decay. The hydrolysis step can be described by the first order reaction kinetics (8, 20). Both the conversion of soluble hydrolysate to SCFAs and the transformation of SCFAs to methane follow the Monod type kinetic models (8, 21). The biomass decay has been reported to be first-order kinetics, and the dead biomass is supposed to be remained in the system as degradable particulate organic matter (8). The effect of noncompetitive inhibition functions of ammonia on biomass was negligible in this study because the inhibiting ammonia concentration was reported in the range of 1.7 to 14 g/L (22), whereas its concentration in this study was less than 0.4 g/L. However, the SCFAs inhibitions on acidogenic bacteria and methanogens were included in the kinetic model due to significant SCFAs accumulated during WAS alkaline fermentation (4, 5), and the Haldane inhibition kinetics was applied to evaluate the inhibitions of SCFAs (23). According to the above discussion, sludge alkaline fermentation can be described by the following equations

dSv ) dt

dSP ) -k · SP + kd,h · Xh + kd,v · Xv dt

(1)

dSh ) k · SP dt

(2)

km,h · Xh KS,h Sv 1+ + Sh KI,h

km,v km,h · Xh · Xv KS,h Sv KS,v Sv 1+ + 1+ + Sh KI,h Sv KI,v dSCH4 ) dt

km,v · Xv KS,v Sv 1+ + Sv KI,v

dXh ) Yh · dt

km,h · Xh - kd,h · Xh KS,h Sv 1+ + Sh KI,h

dXv ) Yv · dt

km,v · Xv - kd,v · Xv KS,v Sv 1+ + Sv KI,v

(3)

(4)

(5)

(6)

where k is the hydrolysis rate of particulate organic matter (d-1), km,h is the maximum specific utilization of hydrolysate (kg COD/kg COD · d-1), km,v is the maximum specific utilization of SCFAs (kg COD/kg COD · d-1), Ks,h is the half-saturation constant of acidogenic bacteria growth (kg COD/m3), Ks,v is the half-saturation constant of methanogens growth (kg COD/m3), KI,h is the inhibition constant of SCFAs on acidogenic bacteria growth (kg COD/m3), KI,v is the inhibition constant of SCFAs on methanogens growth (kg COD/L), Yh is the yield of acidogenic bacteria (kg COD/kg COD), Yv is the yield of methanogens (kg COD/kg COD), kd,h is the decay rate of acidogenic bacteria (d-1), and kd,v is the decay rate of methanogens (d-1). All concentrations of particulate organic matter (SP), hydrolysate (Sh), SCFAs (Sv), methane (SCH4), and acidogenic bacteria and methanogenus (Xh and Xv) were expressed as kg COD/m3. Like many references the initial concentrations of Xh and Xv (0.30 and 0.35 kgCOD/m3, respectively), which were same for different fermentation conditions due to the same sludge concentration used, were determined by fitting the curves of SP, Sh, Sv, and SCH4 obtained in batch tests. The sludge biodegradable COD was obtained by subtracting the nonbiodegradable COD from sludge COD. The sludge nonbiodegradable COD was obtained by running four of the above-described batch tests (i.e., pH 10 plus 21 ( 1 °C, pH 9 plus 35 ( 2 °C, pH 8 plus 55 ( 2 °C, and

TABLE 1. Kinetic Parameters of KS,h, KS,v, KI,h, KI,v, Yh, Yv, and kd,v under Ambient, Mesophilic, and Thermophilic Conditions parameters

ambient

mesophilic

thermophilic

Ks,h (kg COD/m3) Ks,v (kg COD/m3) KI,h (kg COD/m3) KI,v (kg COD/m3) Yh (kg COD/kg COD) Yv (kg COD/kg COD) kd,v (d-1)

0.01 0.01 1.50 1.50 0.05 0.05 0.01

0.05 0.05 1.50 1.50 0.05 0.05 0.03

0.2 0.3 1.50 1.50 0.05 0.05 0.2

uncontrolled pH plus 21 ( 1 °C) for more than 30 d until the particulate COD did not change significantly, and the final concentration of COD in particulate organic matter was considered the sludge nonbiodegradable COD. The sensitivities were quantified in terms of the average of absolute differences between simulation results with prior determined parameters values suggested in the literature (8) and with parameters with a relative change of target parameter, as presented in the following equation

Sensitivity index )

∑ |C

STD(t)

- CSENS(t)|

N

where N is the number of data (simulation time), and CSTD and CSENS are the simulation results with suggested parameter values and the parameters with a relative change of target parameter, respectively. The sensitivity analysis of kinetic parameters for four components was carried out by changing

the value of a target parameter from -50% to 50% with respect to their suggested values (24).

Results and Discussion The Kinetics of Sludge Alkaline Treatment. Sensitivity analysis has been widely applied to reduce the complexity of parameter estimation procedure, determine the significance of model parameters, and identify the dominant parameters (25). As the parameters KI,h, KI,v, Yh, and Yv showed low variability in anaerobic system (7, 26), their values reported by Siegrist et al. (8) were adopted in this study (Table 1). The sensitivity analyses of the other seven parameters were then conducted, and their initial values were referred to ref 8. According to the sensitivity analysis results (Table S1, Supporting Information), it can be seen that k showed the highest sensitivity to almost all components, km,h, km,v and kd,h showed higher sensitivity, and kS,h, kS,v, and kd,v were the parameters that would not be very important in the model. An iterative procedure suggested by Blumensaata and Kellerb (26) was applied to optimize the kinetic parameters used in this study. The parameters, KS,h, KS,v, and kd,v with low sensitivity on model output, were used directly without modification in this study, and their values are presented in Table 1. Other parameters, k, km,v, km,h, and kd,v showing significant impact on model output, were estimated according to the batch experimental data. Their final values are shown in Figure 1 after calibration (Figure S1, Supporting Information). The developed model was then directly validated in both batch and semicontinuous reactors (the sludge concentration was different with that in model development), and the results are illustrated in Figure 2. Apparently, the developed model can be used to predict the experimental data in both batch and semicontinuous tests.

FIGURE 1. Effect of pH on the most sensitive kinetic parameters at ambient, mesophilic, and thermophilic temperatures (a: hydrolysis rate of particulate organic matter (k); b: maximum specific utilization of hydrolysate (km,h); c: maximum specific utilization of SCFAs (km,v); d: decay rate of acidogenic bacteria (kd,h)). VOL. 44, NO. 24, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

9345

FIGURE 2. Comparison of the measured data and simulated values in batch and semicontinuous experiments during model validation. 9346

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 24, 2010

The data in Figure 1a indicated that increasing pH from 7 to 10 caused the improvement of hydrolysis rate of particulate organic matter (k) significantly at any temperature investigated (see Table S2 for statistical analysis). More sludge hydrolysis at higher pH indicates that more hydrolysate will be provided for acid-forming bacteria to generate greater SCFAs. The data in Figure 1b showed that the value of maximum specific utilization of hydrolysate (km,h) at ambient temperature increased with pH, but both mesophilic and thermophilic km,h first increased with pH and then decreased with further pH increase (the maximal mesophilic and thermophilic km,h appeared at pH 9 and pH 8, respectively). The effect of pH on km,h was similar to that on SCFAs accumulation observed in our previous publications (Figure S2, Supporting Information). The enhancement of km,h usually results in improved SCFAs accumulation if the SCFAs were not utilized by SCFAs consumers such as methanogens. As seen in Figure 1c, the maximum specific utilization of SCFAs (km,v) decreased with pH significantly at all temperatures investigated (statistical analyses are shown in Table S2). Although Figure 1d indicated that the decay rate of acidogenic bacteria (kd,h) increased slightly with pH at all temperatures investigated, the effect of pH on kd,h was insignificant (Table S2). Also, the microbiological study in the following text showed that the alkaline pH caused the increase of the percentage of acidogenic bacteria. Thus, at room temperature the maximal SCFAs accumulation should occur at pH 10, which was in correspondence with our previous observation (4). Nevertheless, the maximal SCFAs was respectively at pH 9 and pH 8 instead of at pH 10 under mesophilic and thermophilic conditions (5), which might be due to the km,h values being much greater than k and km,v, and the maximal km,h appeared at pH 9 and pH 8, respectively. The Microorganisms of Sludge Alkaline Fermentation. Under the conditions of maximal SCFAs accumulation, three semicontinuous flow reactors were operated respectively at pH 10 (ambient temperature), pH 9 (mesophilic temperature), and pH 8 (thermophilic temperature) for microbiology study. Also, a control reactor was run at uncontrolled pH and ambient temperature. After operating for 30 d it was observed that the SCFAs concentration in four reactors reached relatively stable (Figure S3, Supporting Information), and the average SCFAs concentration in four reactors was 0.23 (ambient uncontrolled pH), 1.44 (ambient pH 10), 1.96 (mesophilic pH 9), and 2.67 (thermophilic pH 8) kg COD/ m3, which suggested that production of SCFAs could be accomplished continuously. Then the microbial community in these four reactors was assayed by FISH (Figure 3), and the results were analyzed with image analysis system. It was found that the ratio of bacteria to archaea in these four semicontinuous reactors was 20:1 (ambient uncontrolled pH), 68:1 (ambient pH 10), 101:1 (mesophilic pH 9), and 177:1 (thermophilic pH 8). Apparently, the order of bacteria to archaea ratio in these four semicontinuous reactors was in line with their observed average SCFAs concentrations. It is well-known that not all bacteria can produce SCFAs. The bacterial community diversity in four reactors was further analyzed by PCR-based 16S rRNA gene clone library. There were significant differences of microbial community diversity among four reactors (Figure S4, Supporting Information). According to the four clone libraries of 16S rRNA (Table S3, Supporting Information), it seems that the dominant microorganisms in three alkaline treatment reactors were the class Clostridia of the phylum Firmicutes, which accounted for 75.8%, 77.6%, and 89.6% in the reactors of ambient pH 10, mesophilic pH 9, and thermophilic pH 8, respectively. Many species of Clostridia have been reported to be able to produce organic acids under anaerobic conditions (27, 28). Further analysis showed that in the ambient pH 10 reactor, 46.0% of the Clostridia was closely related to Clostridium

FIGURE 3. FISH images of digested sludge sampled randomly from reactor of ambient pH 10 (a), mesophilic pH 9 (b), thermophilic pH 8 (c), and ambient uncontrolled pH (d). Greens bacteria; yellowsarchaea. spp. (Figure S4a), which has been reported to be the dominant microorganism at higher pH in mixed culture fermentation (29) and be able to produce acetate under obligately alkaliphilic (pH 8-10.2) conditions (30). In the reactors of mesophic pH 9 and thermophilic pH 8, 42.4% and 55.1% of the Clostridia were close to the genus Anaerobranca (Figures S4b and S4c). The growth of Anaerobranca requires Na+ (31). As 2 M sodium hydroxide was used to control the alkaline conditions there was a large number of Na+ in three alkaline reactors, which was one reason for Anaerobranca spp. predominant in the reactors of mesophic pH 9 and thermophilic pH 8. The genus Anaerobranc is only a few species among anaerobic bacteria capable of anaerobically converting protein and carbohydrates to acetate as major end product at pH 6.0-10.5 and temperature 30-67 °C (31-33). Also, the genus Anaerobranca was a moderately thermophilic microorganism, and their optimum temperature for growth was 50-57 °C (32, 33), which was consistent with more Anaerobranca spp. observed in thermophilic pH 8 reactor of this study. In the blank reactor (ambient uncontrolled pH), the highest level of diversity was β-Proteobacteria (43.4% of all clones). On the basis of the analysis of the phylogenetic relationships of OTUs, the majority of β-proteobacteria clones were represented by OTU1 (11/122), OTU2 (1/122), OTU3 (1/122), and OTU5 (2/122), which were closely related to the Thauera spp., a denitrifying bacteria (34-36). It is therefore easily understood that the SCFAs accumulation in four semicontinuous reactors was in the following sequence: thermophilic pH 8 > mesophilic pH 9 > ambient pH 10 > ambient uncontrolled pH.

Acknowledgments This work was financially supported by the National HiTech Research and Development Program of China (863) VOL. 44, NO. 24, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

9347

(2007AA06Z326) and the Foundation of State Key Laboratory of Pollution Control and Resource Reuse (PCRRK09002).

Supporting Information Available Tables S1-S3 and Figures S1-S4. This material is available free of charge via the Internet at http://pubs.acs.org.

Literature Cited (1) Moser-Engeler, R.; Udert, K. M.; Wild, D.; Siegrist, H. Products from primary sludge fermentation and their suitability for nutrient removal. Water Sci. Technol. 1998, 38, 265–273. (2) Elefsiniotis, P.; Wareham, D. G.; Smith, M. O. Use of volatile fatty acids from an acid-phase digester for denitrification. J. Biotechnol. 2004, 114, 289–297. (3) Tong, J.; Chen, Y. Enhanced biological phosphorus removal driven by short-chain fatty acids produced from waste activated sludge alkaline fermentation. Environ. Sci. Technol. 2007, 41, 7126–7130. (4) Yuan, H.; Chen, Y.; Zhang, H.; Jiang, S.; Zhou, Q.; Gu, G. Improved Bioproduction of Short-Chain Fatty Acids (SCFAs) from Excess Sludge under Alkaline Conditions. Environ. Sci. Technol. 2006, 40, 2025–2029. (5) Zhang, P.; Chen, Y.; Zhou, Q. Waste activated sludge hydrolysis and short-chain fatty acids accumulation under mesophilic and thermophilic conditions: effect of pH. Water Res. 2009, 43, 3735– 3742. (6) Angelidaki, I.; Ellegaard, L.; Ahring, B. K. A comprehensive model of anaerobic bioconversion of complex substrates to biogas. Biotechnol. Bioeng. 1999, 633, 363–372. (7) Batstone, D. J.; Keller, J.; Angelidaki, I.; Kalyuzhnyi, S.; Pavlostathis, S. G.; Rozzi, A.; Sanders, W.; Siegrist, H.; Vavilin, V. IWA Task Group on Modelling of Anaerobic Digestion Processes. Anaerobic Digestion Model No. 1 (ADM1); IWA Publishing: London, 2002. (8) Siegrist, H.; Vogt, D.; Garcia-Heras, J. L. Gujer, Willi.; Mathematical model for meso- and thermophilic anaerobic sewage sludge digestion. Environ. Sci. Technol. 2002, 36, 1113–1123. (9) Pavlostathis, S. G.; Gossett, J. M. A kinetic model for anaerobic digestion of biological sludge. Biotechnol. Bioeng. 2004, 28, 1519– 30. (10) Ramirez, I.; Mottet, A.; Carre`re, H.; De´le´ris, S.; Vedrenne, F.; Steyer, J. P. Modified ADM1 disintegration/hydrolysis structures for modeling batch thermophilic anaerobic digestion of thermally pretreated waste activated sludge. Water Res. 2009, 43, 3479–3492. (11) Tomei, M. C.; Braguglia, C. M.; Cento, G.; Mininni, G. Modeling of Anaerobic Digestion of Sludge. Crit. Rev. Environ. Sci. Technol. 2009, 39, 1003–1051. (12) Zhang, T. C.; Noike, T. Influence of retention time on reactor performance and bacterial trophic populations in anaerobic digestion processes. Water Res. 1994, 28, 27–36. (13) Codina, J. C.; Ascensio´n Mun ˜ oz, M.; Cazorla, F. M.; Pe´rez-Garcı´a, A.; Morin ˜ igo, M. A.; de Vicente, A. The inhibition of methanogenic activity from anaerobic domestic sludges as a simple toxicity bioassay. Water Res. 1998, 32, 1338–1342. (14) van Lier, J. B.; Tilche, A.; Ahring, B. K.; Macarie, H.; Moletta, R.; Dohanyos, M.; Pol, L. W. H.; Lens, P.; Verstraete, W. New perspectives in anaerobic digestion. Water Sci. Technol. 2001, 43, 1–18. (15) Hwang, K.; Shin, S. G.; Kim, J. Methanogenic profiles by denaturing gradient gel electrophoresis using order-specific primers in anaerobic sludge digestion. Appl. Microbiol. Biotechnol. 2008, 80, 269–276. (16) Chen, Y.; Yuan, H.; Zhou, Q.; Gu, G. Hydrolysis and acidification of waste activated sludge at different pHs. Water Res. 2007, 41, 683–689. (17) Chen, C. L.; Horng, J. H.; Liu, W. T. Identification of important microbial populations in the mesophilic and thermophilic phenol-degrading methanogenic consortia. Water Res. 2008, 42, 1963–1976.

9348

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 24, 2010

(18) Merkel, W.; Manz, W.; Szewzyk, U.; Krauth, K. Population dynamics in anaerobic wastewater reactors: modelling and in situ characterization. Water Res. 1999, 33, 2392–2402. (19) Feng, L.; Chen, Y.; Zheng, X. Enhancement of waste activated sludge protein conversion and volatile fatty acids Aaccumulationduring waste activated sludge anaerobic fermentation by carbohydrate substrate addition: the effect of pH. Environ. Sci. Technol. 2009, 43, 4373–4380. (20) Gavala, N. H.; Angelidaki, I.; Ahring, B. K. Kinetic and modeling of anaerobic digestion process. Adv. Biochem. Eng. Biotechnol. 2002, 81, 57–93. (21) Martin, A.; Borja, R.; Banks, C. J. Kinetic model for substrate utilisation and methane production during the anaerobic digestion of olive mill wastewater and condensation water waste. J. Chem. Technol. Biotechnol. 1994, 60, 7–16. (22) Chen, Y.; Cheng, J. J.; Creamer, K. S. Inhibition of anaerobic digestion process: A review. Bioresour. Technol. 2008, 99, 4044– 4064. (23) Bernard, O.; Hadj-Saddok, Z.; Dochain, D.; Genovesi, A.; Steyer, J. P. Dynamical model development and parameter identification for an anaerobic wastewater treatment process. Biotechnol. Bioeng. 2001, 75, 424–438. (24) Jeong, H. S.; Suh, C. W.; Lim, J. L.; Lee, S. H.; Shin, H. S. Analysis and application of ADM1 for anaerobic methane production. Bioprocess. Biosyst. Eng. 2005, 27, 81–89. (25) Tartakovsky, B.; Mu, S. J.; Zeng, Y.; Lou, S. J.; Guiot, S. R.; Wu, P. Anaerobic digestion model no. 1-based distributed parameter model of an anaerobic reactor. II. Model validation. Bioresour. Technol. 2008, 99, 3676–3684. (26) Blumensaata, F.; Kellerb, J. Modeling of two-stage anaerobic digestion using the IWA Anaerobic Digestion Model No. 1 (ADM1). Water Res. 2005, 39, 171–183. (27) Mitchell, W. J. Physiology of carbohydrates to solvent conversion by clostridia. Adv. Microbiol. Physiol. 1998, 39, 31–130. (28) Cangenella, F.; Weigel, J. The potential of thermophilic clostridia in biotechnology. In The clostridia and biotechnology; Woods, D. R., Ed.; Butterworth-Heinenmann: MA, USA, 1993; pp 393429. (29) Temudo, M. F.; Muyzer, G.; Kleerebezem, R.; van Loosdrecht, M. C. M. Diversity of microbial communities in open mixed culture fermentations: impact of the pH and carbon source. Appl. Microbiol. Biotechnol. 2008, 80, 1121–1130. (30) Zhilina, T. N.; Kevbrin, V. V.; Turova, T. P.; Lysenko, A. M.; Kostrikina, N. A.; Zavarzin, G. A. Clostridium alkalicellum sp. nov.; an obligately alkaliphilic cellulolytic bacterium from a soda lake in the Baikal region. Mikrobiologiia 2005, 74, 642–53. (31) Engle, M.; Li, Y.; Woese, C.; Wiegel, J. Isolation and characterization of a novel alkalitolerant thermophile, Anaerobranca horikoshii gen. nov.; sp. nov. Int. J. Syst. Bacteriol. 1995, 45, 454–461. (32) Prowe, S.; Antranikian, G.; Anaerobranca, G. Gottschalkii sp. nov.; a novel thermoalkaliphilic bacterium that grows anaerobically at high pH and temperature. Int. J. Syst. Evol. Microbiol. 2001, 51, 457–465. (33) Gorlenko, V.; Tsapin, A.; Namsaraev, Z.; Teal, T.; Tourova, T.; Engler, D.; Mielke, D.; Nealson, K. Anaerobranca californiensis sp. nov.; an anaerobic, alkalithermophilic, fermentative bacterium isolated from a hot spring on Mono Lake. Int. J. Syst. Evol. Microbiol. 2004, 54, 739–743. (34) Gallus, C.; Gorny, N.; Ludwig, W.; Schink, B. Anaerobic degradation of alpha-resorcylate by a nitrate reducing bacterium Thauera aromatica strain AR-1. Syst. Appl. Microbiol. 1997, 20, 540–544. (35) Song, B.; Palleroni, N. J.; Kerkof, L. J.; Haggblom, M. M. Characterization of halobenzoate-degrading denitrifying Azoarcus and Thauera isolates and description of Thauera chlorobenzoica sp. nov. Int. J. Syst. Bacteriol. 2001, 1, 589–602. (36) Mechichi, T.; Stackebrandt, E.; Gad’on, N.; Fuchs, G. Phylogenetic and metabolic diversity of bacteria degrading aromatic compounds under denitrifying conditions, and description of Thauera phenylacetica sp nov.; Thauera aminoaromatica sp nov.; and Azoarcus buckelii sp nov. Arch. Microbiol. 2002, 178, 26–35.

ES102878M