Monitoring the performance and microbial diversity dynamics of a full scale anaerobic wastewater treatment plant treating sugar factory wastewater.

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Downloaded by Drexel University on 17 March 2013 Published on 04 January 2013 on http://pubs.rsc.org | doi:10.1039/C2EM30597F

Cite this: Environ. Sci.: Processes Impacts, 2013, 15, 494

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Monitoring the performance and microbial diversity dynamics of a full scale anaerobic wastewater treatment plant treating sugar factory wastewater† N. Altınay Perendeci,*a F. Yesxim Ekincib and Jean Jaques Godonc Microbial community dynamics and the overall system performance of a real scale anaerobic wastewater treatment plant treating sugar industry wastewater were studied. Dominant bacterial and archaeal communities were monitored by 16S rRNA gene targeted PCR amplification and single strand conformation polymorphism analysis (SSCP). Chemical oxygen demand (COD) removal efficiency and operational parameters such as pH, temperature, alkalinity, volatile fatty acids in the methanogenic reactor remained within respective optimal ranges. All bacterial profiles in acidogenic and methanogenic reactors and in the lamella separator presented complex patterns; and the bacterial diversity, measured as SSCP profile richness and structure, was quite chaotic. In contrast to the results obtained

Received 23rd July 2012 Accepted 4th December 2012

for the bacterial community, archaeal 16S rDNA patterns in acidogenic and methanogenic reactors and the lamella separator remained relatively stable over the whole operation period of the anaerobic wastewater treatment plant. Evaluation of microbial community dynamics and overall system

DOI: 10.1039/c2em30597f rsc.li/process-impacts

performance using the Mantel test revealed that there was no correlation between the dynamics of the microbial communities and the abiotic parameters.

Environmental impact Water management in sugar factories is an important issue because of the need to limit water consumption and avoid the mass of pollutants discharged from inadequately treated wastewater. The problem may be even more severe in certain locations, where the factory's water intake and the discharge of wastewater have an adverse effect on the environment. Sugar beet factory wastewater with a high organic load has enormous potential to create serious environmental pollution problems if discharged without treatment. The major originality of this paper lies in the fact that the microbial community dynamics and overall system performance have been investigated for the rst time for a real scale anaerobic wastewater treatment plant treating sugar industry wastewater over the whole operation period.

1

Introduction

Water management in sugar factories is an important issue because of the need to limit water consumption and avoid the mass of pollutants discharged from inadequately treated wastewater.1 Sugar beet factory wastewater with a high organic load has enormous potential to create serious environmental pollution problems if discharged without treatment. Over the

a

Akdeniz University, Engineering Faculty, Environmental Engineering Department, 07058 Antalya, Turkey. E-mail: [email protected]; Fax: +90 242 3106306; Tel: +90 242 3106334

b

Yeditepe University, Faculty of Engineering and Architecture, Food Engineering Department, 34755 Istanbul, Turkey. E-mail: [email protected]; Tel: +90 216 5780000/3187

c INRA, UR50, Laboratoire de Biotechnologie de l'Environnement, Narbonne, F-11100, France. E-mail: [email protected]; Fax: +33 (0)4 68 42 51 60; Tel: +33 (0)4 68 42 51 51

† Electronic supplementary 10.1039/c2em30597f

information

(ESI)

available.

494 | Environ. Sci.: Processes Impacts, 2013, 15, 494–502

See

DOI:

past decades, anaerobic wastewater treatment has gained recognition in the management of sugar industry wastewater2–6 due to the following characteristics of anaerobic digestion processes: (i) high performance in COD removal; (ii) relatively low energy requirement; (iii) practically negligible production of excess sludge; (iv) low emission of odor; (v) need for a smaller surface area; and (vi) production of recoverable energy. The application of anaerobic processes in wastewater treatment requires careful monitoring of the conventional parameters such as pH, volatile fatty acids (VFA), alkalinity, temperature, etc. However, little attention has been paid to the activity of the microbial community in the process.7–11 Anaerobic digestion is carried out by a rich and diverse microbial community based on a synergistic relationship between the bacterial and archaeal populations. The composition (i.e. diversity and structure) of microbial communities, in turn, affects the performance of the anaerobic process. It is therefore important to monitor the microbial ecosystem to understand

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Paper the dynamics and functions of its microbial communities for the performance optimization of the anaerobic process. Molecular techniques, which take advantage of the small subunit (SSU) rRNA molecule, eliminate the dependence on the isolation of pure cultures and have been extensively used during the last decade to analyze community prole and activity.7–11 Results of advanced molecular methods show that considerable diversity is present in the anaerobic microbial community and that a link does exist between that diversity and functional stability of anaerobic reactors.12 Understanding the principles of the community prole and function and then correlating these results with the system's performance data give insight to designers and engineers seeking to optimize treatment plant performance. As stated by Ramirez and Steyer,13 very few studies14–17 of a real-scale anaerobic reactor have linked bacterial and archaeal community proles, dynamics and operating activities with the reactor performance data. A major objective in this study was to monitor bacterial and archaeal community proles and to present a relation between microbial community dynamics and reactor performance in a real scale anaerobic wastewater treatment plant treating sugar factory wastewater. Dominant bacterial and archaeal communities were monitored by 16S rRNA gene targeted PCR amplication and single strand conformation polymorphism analysis (SSCP), which allows the analysis of DNA fragments with similar size to be separated according to their conguration (secondary structure).18 Targeting the 16S rRNA V3 region, which permits phylogenetic discrimination of microbial species, makes it possible to monitor microbial community by one prole of peaks where each peak corresponds to a different sequence of 16S rRNA V3 region i.e. one bacterium.19–21 Also, the microbial community dynamics, their activity and their relationship with the performance of the reactor was evaluated using Principal Component Analysis (PCA) and the Mantel test.

2

Experimental

2.1

Process conguration

The sugar beet processing factory (Ere˘ gli, Turkey) has an actual processing capacity of 8000 tonnes of beet per day. The wastewater from the factory consists of two main streams: one is a mixture of ume (beet transportation water) and washing water, and the second is the process wastewater generated during sugar manufacture. Flume and washing water are used to convey the sugar beet into the factory and to remove the soil from the beet, respectively. Furthermore, some sugar is leached from the beet as it passes through the uming system. Flume water is separated from the beet and passed through a clarier, in which the mud slurry of the stream is thickened to 10–20 % (w/v). The clear water leaving the clarier passes into the “Elfa” system (beet unloading system working with water) for reuse as ume water in the uming system. The underow of the clarier is pumped to soil settlement lagoons. Supernatant water of the soil settlement lagoons is then pumped to the equalization basin for equalizing with process water. Wastewater from an equalization basin is pumped to the treatment plant for the removal of COD and nitrogenous


Environmental Science: Processes & Impacts compounds prior to reuse and discharging off-site. Equalized wastewater is treated in a full scale Anamet type anaerobic wastewater treatment plant (AWWTP) consisting of sequential anaerobic and aerobic biological treatment units. The anaerobic unit includes acidogenic (1170 m3) and methanogenic (8000 m3) reactors, totally mixed, and a lamella type sludge separation system. The Anamet plant is designed for a wastewater ow of 4680 m3 per day with a COD load of 37 500 kg per day. The process scheme of the anaerobic wastewater treatment plant can be found in Perendeci et al.22 2.2

Characteristics of wastewater

The characteristics of inuent wastewater fed to the wastewater treatment plant are presented in Table 1. The average values of inuent wastewater parameters in Table 1 are obtained from the daily composite of samples. 2.3

Analysis of wastewater

Volumetric wastewater ow rates were measured on-line by electromagnetic ow meters (Danfoss MagFlo). The gas ow rate was recorded by a Bailey Fischer Porter vortex ow-meter. The gas composition of the biogas was determined by an on-line Varian micro-gas chromatography. The pH of wastewater in acidogenic and methanogenic reactors was measured on-line with a Mettler-Toledo glass pH electrode. The temperature of wastewater, heated to 35 C using a plate heat exchanger before entering the Anamet plant, was measured in the acidogenic and methanogenic reactors with an on-line PT100 thermocouple. Programmable logic control (PLC) and TEOS 32 – supervisory control and data acquisition (SCADA) systems were used to control and monitor the anaerobic wastewater treatment plant. Every ve minutes, values of wastewater, gas ow rates, pH, temperature and CH4 content of biogas values were sampled from the TEOS 32 SCADA system to form average daily values of these variables. Samples were collected every two hours from various locations of the treatment plant to form daily composites for analysis. Volatile fatty acid (VFA), chemical oxygen demand (COD), total alkalinity concentration (TA), total suspended solids (TSS), volatile suspended solids (VSS) and calcium (Ca) analyses were Table 1 plant

Characteristics of influent wastewater fed to the wastewater treatment

Wastewater parameters

Minimum

Maximum

Average (St. dev.)

Qinf (m3 d1) COD (mg L1) pH VFA (mg HAc per L) Ca (mg L1) Ntot (mg L1) NH4-N (mg L1) Ptot (mg L1) PO4-P (mg L1) TSS (mg L1) VSS (mg L1)

180 356 4.62 320 54 1.2 0.25 0.1 0.03 33 25

3924 9705 8.59 3860 1047 18.8 17.2 5.8 3.3 753 607

2711 (1109) 5242 (1010.21) 6.13 (0.77) 2228 (602.75) 762 (216.17) 4.78 (2.98) 2.20 (2.62) 2.13 (1.57) 1.43 (0.77) 345 (112.05) 288 (90.31)

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Environmental Science: Processes & Impacts carried out off-line according to standard methods.23 Total nitrogen (Ntot), ammonium nitrogen (NH4-N), total phosphorus (Ptot) and orthophosphate phosphorus (PO4-P) analyses were also performed off-line according to the 2,6-dimethylphenol, Indophenol Blue (Analog DIN 38406 E 5-1, ISO 7150-1), Phosphomolybdenum Blue (Analog DIN 38405 D11-4, ISO 6878-11986) and Phosphomolybdenum Blue (Analog DIN 38405 D11-4, ISO 6878-1-1986) protocols, respectively.


2.4 Sludge sampling, extraction and purication of total genomic DNA Sludge samples for the molecular community analysis were taken from the acidogenic and methanogenic reactors and lamella separator during the operation period, when the plant was running under unsteady conditions. Sludge sampling was started ve weeks aer the beginning of the treatment period and repeated weekly for eleven weeks. Composite samples were prepared from sludge taken at four (1.5, 7.3, 13.1 and 18.9 m) and eight (1.4, 3.7, 5.9, 8.1, 10.3, 12.5, 14.7 and 16.9 m) different levels of the acidogenic and methanogenic reactors, respectively. Following separation at 17 500g for 10 min, the supernatants were removed and pellets were stored at 80 C until subsequent DNA analysis. Upon resuspension of the pellets in 2 mL 4 M guanidine thiocyanate, 0.1 M Tris–HCl, pH 7.5 and 600 mL N-lauroyl sarcosine 10% (v/v) (Sigma, Tauirchen, Germany), DNA extraction and purication were performed using the QIAmp DNA STOOL Kit (QIAGen) in accordance with the instructions of the manufacturer. 2.5 DNA amplication and single strand conformation polymorphism (SSCP) analysis The samples were analyzed for microbial community prole by the PCR-single strand conformation polymorphism (SSCP) technique. For the PCR step, primers for amplication of the V3 16S rDNA bacterial region and for nested amplication of the V3 16S rDNA archaeal region were used (Table 2). Each PCR reaction tube contained 10 Pfu Turbo DNA polymerase buffer (Stratagene, California, USA), 200 mmol L1 of each deoxynucleotide triphosphate (Promega, Madison, USA), 0.13 mg of primer W49-104 and W116-W104, 0.2 mg of primer W02-W17, 1.25 U of Pfu Turbo DNA polymerase (Stratagene, California, USA), in addition to 1 mL of total DNA previously diluted in water, adjusted to a total volume of 50 mL with

Table 2

Paper water. For bacteria, an initial 2 min denaturation step at 94 C was followed by 25 cycles of a three-stage program (30 s at 94 C, 30 s at 61 C, 30 s at 72 C) and a nal 10 min elongation at 72 C. For archaea, nested amplication was performed: for the rst amplication (primer W02-W17), a 2 min initial denaturation step at 94 C was followed by 25 cycles of a three-stage program (1 min at 94 C, 1 min at 50 C, 1 min at 72 C), and a nal 10 min elongation at 72 C. For the second amplication (primer W116-104), a 2 min initial denaturation step at 94 C was followed by 25 cycles of a three-stage program (30 s at 94 C, 30 s at 51 C, 30 s at 72 C) and a nal 10 min elongation at 72 C. PCR–SSCP products providing bands of proper size (approximately 200 bp) were conrmed on a 2% (w/v) agarose gel-electrophoresis. SSCP capillary electrophoresis with an ABI 3130 genetic analyzer (Applied Biosystems) was carried out in accordance with a previously described protocol.24 2.6

Evaluation of SSCP proles

Since visual comparison of SSCP proles would not be very informative due to the large number of peaks, the proles were evaluated using Statistical Analysis of Fingerprints25 (Safum, Ver. 1.47) (http://www.montpellier.inra.fr/narbonne/anglais/ researchunits/microbialecology.html) based on Principal Component Analysis (PCA) working under MATLAB (The Matworks Inc. Natick, MA). Relationships between the SSCP proles were inferred from PCA graphs.26 The Mantel test was performed using statistical R soware to determine the correlation between DNA ngerprinting data and process parameters.

3

Results and discussion

3.1

Evaluation of operational parameters

The rate of sugar production depends on the sugar beet harvesting time, as does the operation of the Anamet-type wastewater treatment plant (AWWTP). Consequently, there is no continuous operation of the AWWTP for a whole year; it is only operated for 3–4 months during the white sugar production period. Furthermore, it is impossible to feed the plant at the desired average inuent ow rate and run it with the optimal load throughout the whole operating period. Accordingly, at the full-scale AWWTP where this research was conducted, the inuent ow rate of 180 m3 d1 at start-up, gradually increased to reach a maximum of 3924 m3 d1, then maintained a steady

Sequences and target positions of primers

Primer

Sequence

Position in E.colia

Target

W49 W116b W17 W104b W02

ACGGTCCAGACTCCTACGGG HEX-TCCAGGCCCTACGGGG ATTCYGGTTGATCCYGSCRG FAM-TTACCGCGGCTGCTGGCAC GNTACCTTGTTACGACTT

F330 F333 F3 R533 R1492

16S rRNA bacteria 16S rRNA archaea 16S rRNA archaea 16S rRNA universal 16S rRNA universal

a The position corresponds to the 50 -end of the primers, using E. coli 16S rRNA as reference, Brosius et al.36 F and R correspond to forward and reverse primers, respectively. b These primers W116 and W104 are marked at the 50 end with uorophores HEX.



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Paper level of treatment performance for a time, and decreased towards the end of the 126 day operating period (Fig. 1a). The average inuent volumetric ow rate was calculated as 2711 m3 d1, which was less than the plant's design value of 4680 m3 d1. The average sludge recycle ow rate was also observed as 5229 m3 d1 practicing a 1.93 sludge recycling ratio during the treatment period. Oscillations throughout the operating period over a wide range were an indication of the dependence of the Anamet plant performance on the sugar beet intake rate and were consistent with the unsteady-state character of such plants.22 However, VFA and TA, as well as COD measurements were used as control variables to maintain stable performance and prevent the organic overloading of the methanogenic reactor and the plant did not experience any process failure during the last 8 years of operation. The average treatment efficiency based on COD removal of the anaerobic unit attained

Environmental Science: Processes & Impacts approximately 96% showing that process was in the bound of stable operating condition. Changes in the pH and temperature of inuent wastewater and in the acidogenic and methanogenic reactors are given in Fig. 1(b) and (c), respectively. The pH of the inuent wastewater varied between 4.6 and 8.6, possibly due to the quantity of ume and washing water coming from the soil settlement lagoon and the process wastewater. The pH of the methanogenic reactor remained between 6.9 and 7.7 during the operating period. Average pH values were within the range suggested in the plant's operational manual and the literature,27 and were 6.1, 6.1 and 7.1 in the inuent wastewater and acidogenic and methanogenic reactors, respectively. Inuent wastewater coming from the equalization basin at an average temperature of 15.2 C was heated by a plate heat exchanger to raise average temperatures in the acidogenic and methanogenic reactors to

Fig. 1 Time profile of parameters during the operation period; (a) flow rate, (b) pH, (c) temperature, (d) chemical oxygen demand, (e) volatile fatty acid, (f) total alkalinity, (g) organic loading rate, (h) COD removal efficiency, and (i) produced gas flow rate. Arrows above (a) correspond to DNA sampling time.



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Environmental Science: Processes & Impacts 39.7 C and 37.8 C, respectively. In the methanogenic reactor, both pH and temperature values stayed within the optimal ranges (pH: 6.5–8.2, 35–37 C) throughout the entire operating period. Fig. 1(d) and (e) highlight the frequent changes in COD and VFA concentrations in the inuent. The average values obtained for total COD concentrations were 5.2, 4.9, 0.2 and 0.2 g L1 for the inuent wastewater, outlets of acidogenic and methanogenic reactors, and the effluent, respectively. The average sCOD/ tCOD ratios of the inuent wastewater were calculated as 0.97 and 0.96 for the last two operation periods. The average VFA concentrations in the inuent wastewater, acidogenic and methanogenic reactors were also obtained as 2.2, 3.1 and 0.02 g

Fig. 2

Paper L1, respectively. VFA concentrations in the acidogenic reactor were higher than those in the inuent, indicating that acidogenesis occurred here. During the operation period, the average VFA value was measured as 3.1 g L1 in the acidogenic reactor. This value was acceptable from the point of design criteria supplied by plant management, in which steady state operational VFA concentration ranges and normal VFA values were given as 2.5–3.5 g L1 and 3 g L1 for the acidogenic reactor, indicating an acceptable level of acidication in the acidogenic reactor. Furthermore, the degree of acidication (DoA), or the acidication level, dened as VFA in COD basis/total COD of inuent28 was calculated for over the past ve years, in terms of total acidication (acidication which occurs in soil settlement

Bacterial diversity SSCP profiles of the acidogenic reactor (H), methanogenic reactor (A) and lamella separator (L) at days 38, 68 and 110.



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lagoons and in the acidogenic reactor) and acidication (acidication which occurs in the acidogenic reactor alone) values (for VFA equivalent COD, assume 1 g HAc per L ¼ 1.066 g COD per L). The average degree of total acidication and acidication levels were found to be 69% and 24%, respectively. Compared to the 77% total acidication suggested as optimal for a mesophilic chemostat as acidogenic reactor29 or to the 20–40 % DoA suggested as suitable for the enhancement of process performance for pre-acidication,30 the obtained values appeared to be within acceptable limits. Also, the average TA concentration in the methanogenic reactor was 1.9 g CaCO3 per L (Fig. 1f). The COD removal efficiency in the anaerobic unit was in the range of 94–99 % (Fig. 1h) at OLRs between 0.17 and 3.35 kg COD per m3 per day (Fig. 1g) during the operation period. An average COD removal efficiency was calculated as 95.99% at an average of 1.82 kg COD per m3 per day OLR. The anaerobic unit had been operated up to an OLR of 2.76 kg COD per m3 per day (average 1.38) up until day 17, with an average COD removal efficiency of 97.3%; thereaer the OLR was increased to approximately 3.35 kg COD per m3 per d (average 2.10), having an average COD removal efficiency of 95.96% between the day 18 and 98. Then, decrease in average OLR to 1.03 kg COD per m3 per day was applied, resulting in a average COD removal efficiency of 94.87% between the days 99 and 126. Since the OLR was strongly affected by the inuent ow rate (Qinf), it was not possible to feed the plant with designed average inuent ow rate and operate it at optimum load during the entire operation period.22 Since, Qinf values were low in the beginning and at the end of the treatment period, OLR values were also low in quantity. The reason for the considerably slight decrease in COD removal efficiency could be the OLRs applied to the anaerobic unit. An average calculated OLR value of 1.82 kg COD per m3 per day was much lower than the design OLR of 4.69 kg COD per m3 per day. The average biogas production was 8289 m3 d1 with 66% methane content (Fig. 1i). An average methane yield was calculated as 0.33 m3 CH4 per kg COD from the observed average data. Considering the theoretical CH4 production of 0.395 L methane per gram of COD removed at 35 C,27 the obtained result showed that effectiveness of the studied real scale anaerobic process converting the sugar factory wastewater into methane was at acceptable levels. In Fig. 1, manipulated process variables and state variables uctuated widely, showing the unsteady nature of the Anamet plant throughout the operating period, and indicating a dependence of plant performance on the sugar beet intake rate, consistent with the unsteady-state character of such plants. However, the term “stable” is used to dene process stability within a certain bound. Since state variables such as COD, VFA, total alkalinity and pH were dened in a certain range, it is assumed that the treatment plant works within certain bounds and that the plant is in stable mode.

38, 47, 54, 61, 68, 75, 82, 96, 103, 110 and 124. Bacterial and archaeal microbial diversities were assessed and compared by SSCP SSU rDNA ngerprint analysis. Out of a total of 34 bacterial and 32 archaeal SSCP proles obtained, only those pertaining to acidogenic and methanogenic reactors and lamella separator samples on days 38, 68, and 110 are presented for simplicity (Fig. 2). Since PCA graphs were used to infer the relationships within the whole sample, PCA analysis of all the proles have been incorporated in Fig. 3. Bacterial SSCP proles showed 12 to 23, 12 to 22 and 15 to 24 distinguishable peaks in the acidogenic and methanogenic reactors, and lamella separators, respectively. Over the operating period, SSCP proles from acidogenic reactor were different from those in methanogenic reactor and lamella separator proles (Fig. 2 and 3). Furthermore, at the same sampling time, the methanogenic reactor and lamella separator bacterial SSCP proles showed similarities (Fig. 2 and 3). PCA analysis of the acidogenic reactor proles showed a weak variation over time compared to methanogenic reactor and lamella separator proles (Fig. 3). The archaeal diversity of sludge samples taken from acidogenic and methanogenic reactors, and lamella separator were also assessed. SSCP proles from days 38, 68 and 110 and a PCA analysis of all the proles are presented in Fig. 4 and 5, respectively. All archaeal proles presented simple patterns compared with bacterial proles (Fig. 2). Archaeal SSCP proles exhibited 8 to 11, 3 to 6, and 3 to 6 distinguishable peaks in the acidogenic and methanogenic reactors, and lamella separator, respectively. The archaeal SSCP proles from methanogenic reactor and lamella separator were similar (Fig. 4 and 5); with the same ve dominant peaks being present throughout the operating

3.2

Fig. 3 PCA results of bacterial SSCP profiles. White dot, star and black dot correspond, respectively, to the acidogenic reactor (H), methanogenic reactor (A) and lamella separator (L). 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, and 11 correspond to days 38, 47, 54, 61, 68, 75, 82, 96, 103, 110 and 124, respectively.

Bacterial and archaeal community dynamics

Samples from the acidogenic and methanogenic reactors and the recycling line of the lamella separator were collected on days



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Fig. 4

Archaeal diversity SSCP profiles of the acidogenic reactor (H), methanogenic reactor (A) and lamella separator (L) at days 38, 68 and 110.

period. In contrast, the acidogenic reactor archaeal SSCP proles were different and more complex than methanogenic reactor and lamella separator proles. PCA analysis, based on the archaeal SSCP proles, showed similar results to the bacterial proles. The variation in the acidogenic reactor proles was less than those in both methanogenic reactor and lamella separator (Fig. 5), while the amplitude of the prole changes was less in the archaeal communities compared to that of the bacterial communities. Moreover, the bacterial and archaeal dynamics over time did not seem link to abiotic parameters. A Mantel test conrmed that there was no correlation between the SSCP pattern and process parameters (data not shown).


The Anamet-type plant was designed for a process incorporating an acidogenic and a methanogenic phase to take advantage of the separation between two groups of microorganisms to improve the COD removal efficiency. According to Bouallagui et al.,31 this separation enables each ecosystem to adapt to its own substrate, with optimization of both processes. The results of the present analysis, in accordance with this comment, also showed that both (acidogenic and methanogenic) reactors presented different microbial communities; but, contradicting it, revealed that both reactors presented bacterial and archaeal communities despite the unsuitable archaeal growth conditions in the acidogenic reactor. However, observed SSCP proles representing archaeal community in the


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Fig. 5 PCA results of archaeal SSCP profiles. White dot, star and black dot correspond, respectively, to the acidogenic reactor (H), methanogenic reactor (A) and lamella separator (L). 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, and 11 correspond to days 38, 47, 54, 61, 68, 75, 82, 96, 103, 110 and 124, respectively.

acidogenic reactor may come from the soil settlement lagoons, because of the long HRT in soil settlement lagoons. Determination of bacterial and archaeal community in acidogenic reactor revealed that acidication and even methanization should occur in soil settlement lagoons. However, our results do not provide additional data on the ratio between archaea and bacteria in acidogenic and methanogenic reactors. In terms of the dynamics, the bacterial communities in the acidogenic reactor appear to be relatively stable compared to those of the methanogenic reactor. Similarly, archaeal communities in the methanogenic reactor are more balanced than in the acidogenic reactor. Most of the microbial community studies related to anaerobic wastewater treatment in the literature were performed in the laboratory scale. Fernandez et al.32 monitored the community dynamics of bacteria and archaea in a functionally stable, continuously mixed anaerobic lab-scale reactor for 605 days and found differences in the levels of diversity and dynamics between the bacterial and archaeal domains. The population diversity within the bacterial domain was nearly four times higher than that in the archaeal domain, and the pattern of bacterial populations varied considerably over time. But, archaeal populations displayed less variability despite the stability of system functioning and environmental conditions. Similar population diversity results were found in several other methanogenic reactors.21,31,33–35 On the other hand, very limited studies investigated bacterial and archaeal community proles in the real-scale anaerobic reactor.14–17 Leclerc et al.14 characterized the diversity of archaeal community proles from industrial anaerobic digesters by SSCP. The 44 digesters were sampled from different types of reactors treating agricultural waste, the food processing and petro-chemical industries, pulp and paper plant, breweries, slaughterhouses and municipal


Environmental Science: Processes & Impacts waste. Four to six distinct archaeal peaks per digester were determined by SSCP without incorporating process performance data. Roest et al.15 investigated community analysis of a full-scale up ow anaerobic sludge blanket (UASB) reactor treating paper mill wastewater. Dominant phylotypes within complex microbial communities present in the anaerobic wastewater treatment system were determined by DNA ngerprinting combined with conventional microbiological methods. Keyser et al.17 also studied DNA ngerprinting of the methanogens present in three different types of UASB granules that had been used to treat winery, brewery and peach-lye canning effluents. But, in the Roest et al.15 and Keyser et al.17 studies, the results of population diversity were not evaluated with the reactor performance data. Only Akarsubasi et al.16 investigated the relationship between the performance (COD removal efficiency with OLR) of two distinct full-scale UASB reactors treating alcohol distillery wastewater with the acetoclastic methanogenic activity and archaeal composition by DNA ngerprinting. UASB reactors have shown quite similar archaeal compositions and pH, temperature and alkalinity have been assumed to remain within their desired ranges. However, the results of population diversity were not linked to performance data. Conclusively, the results of our study cannot be compared with the literature data obtained in a real scale anaerobic wastewater treatment plant due to the different variables, process types and sizes of reactor. However, this study was the rst of its kind conducted on an industrial-scale anaerobic system evaluating microbial community dynamics and overall system performance.

4

Conclusions

In this study, SSCP analysis was used to highlight changes in bacterial and archaeal community proles of an industrial scale anaerobic wastewater treatment plant. The bacterial communities in the acidogenic reactor exhibited great diversity compared to the methanogenic reactor while the diversity of archaeal communities appeared to be stable in both acidogenic and methanogenic reactors during the operational period. On the other hand, system performance variables such as COD, VFA, total alkalinity and pH were dened in certain ranges, suggesting that the plant worked within certain bound in stable mode. Evaluation of microbial community dynamic and overall system performance revealed that there was no direct link between the stability of the microbial communities and the abiotic parameters.

Acknowledgements This project (2003K121020/9) governed by Suleyman Demirel University was supported by Turkish Prime Ministry State Planning Organization. The authors would like to express their gratitude to Turkish Sugar Factories Cooperation Ere˘ gli Sugar factory for permission to take sludge samples and to use plant data; and to INRA, Laboratoire de Biotechnologie de l'Environnement for opening their laboratory facility, in-kind support


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Environmental Science: Processes & Impacts for the community analysis and technical guidelines. This study has also been supported by the Project Management Unit of Akdeniz University, Turkey.


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