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Environmental Processes

, Volume 5, Supplement 1, pp 43–57 | Cite as

Evaluation of Ammonia Oxidizing Bacterial Community Structure of a Municipal Activated Sludge Plant by 454 High-Throughput Pyrosequencing

  • Oluyemi Olatunji AwolusiEmail author
  • Sheena Kumari
  • Faizal Bux
Original Article
  • 122 Downloads

Abstract

The ammonia oxidizing bacteria (AOB) community structure in a municipal wastewater treatment plant (WWTP) was monitored over the winter and summer periods. The AOB distribution in the full-scale WWTP was investigated using barcoded 454-pyrosequencing targeting the ammonia monooxygenase alpha subunit (amoA) gene. Using the quantitative polymerase chain reaction (qPCR) technique, the AOB population was quantified over 237 days. The plant operational parameters and its nitrification performance were also monitored. As revealed by pyrosequencing, majority of the identified AOB during the study were related to uncultured ammonia oxidizing bacteria, and Nitrosomonas oligotropha. Furthermore, it revealed higher AOB abundance during the summer which was 6 times more compared to winter. Substantial percentages of the reads from both seasons could not be assigned to any phylum, which suggests that vast population of novel, ecologically significant AOB species still inhabit the complex activated sludge communities unexploited. A significant seasonal variation in temperature (α = 0.05; P = < 0.0001) was recorded in the reactor with maximum temperature amplitude of 10.2 °C. The average nitrification rate recorded during the summer was 0.09 ± 0.03 g N-NH4+/g MLSS/d, whereas it was 0.04 ± 0.02 g N-NH4+/g MLSS/d during the winter. The nitrification rate of the plant had significant correlation with AOB population abundance (r = 0.70; P = 0.01) and temperature (α = 0.05; P = 0.0018). This study indicates that AOB diversity and population abundance were contributory factors to efficient nitrification in activated sludge system.

Keywords

454-pyrosequencing AOB WWTP Nitrification qPCR Nitrifiers 

1 Introduction

Ammonia oxidizing bacteria (AOB) are responsible for the first rate limiting ammonia-oxidizing step during nitrification (Ramond et al. 2015). These AOB are chemolithoautotrophic which are influenced by operational and environmental factors including: dissolved oxygen concentration (DO), temperature, pH, chemical oxygen demand (COD), alkalinity, substrate concentration (Kim et al. 2011; Mousavi et al. 2017). Traditionally, efficient nitrification has been reported at a pH ranging from 7.5 to 8.5 (Fulweiler et al. 2011; Sajuni et al. 2010). Seasonal temperature changes have been identified as one of the major factors that significantly affect the nitrifiers and their nitrification efficiency in full-scale wastewater treatment plants (WWTPs) (Awolusi et al. 2016; Kim 2013). In a reactor treating synthetic wastewater, Kim et al. (2008) found that when the temperature increased from 20 to 30 °C, oxidation of ammonia proceeded from 0.253 to 1.33 g N/g VSS/d (5.3-fold increase). The AOB performance can also be impacted by heterotrophic bacteria, since their growth rate is slower than that of heterotrophs and at short retention times (SRT), they can be washed out with the suspended solids (Yan and Hu 2009).

Understanding the complex microbial community in wastewater treatment plant (WWTP) is important in designing functionally stable and effective treatment systems. Biological interactions have been found to be dominant drivers in determining the bacterial community assembly in WWTPs, whereas environmental conditions partially explain phylogenetic and quantitative variances (Ju and Zhang 2015b). According to Keuter et al. (2017), engineered bioreactors depend on their microbial community physiology. Knowledge of these microbial community members will be useful for improving the construction and operation of such bioreactors. Wang et al. (2010) further noted that a better understanding of the microbial ecology of AOB in bioreactors could enhance the treatment performance and control of the WWTP. Various authors also indicated that early detection of a change in the nitrifier population may indicate the performance inhibition, and this can allow plant operators to take action in preventing washout of these essential bacteria (Siripong and Rittmann 2007; Wang et al. 2010).

Traditional microbiological methods have been previously relied upon for identifying and quantifying microbes in wastewater, however, the advent of molecular techniques has revealed their inadequacies since many important microbial taxa remained unnoticed (Xia et al. 2010). The different Sanger-sequencing based molecular approaches, i.e., polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE), terminal restriction fragment length polymorphism (T-RFLP), temperature-gradient gel electrophoresis (TGGE) and cloning, have been used in profiling the microbial diversity harboured in WWTPs (Gomez-Silvan et al. 2010; McMahon et al. 2009). Fluorescent in situ hybridization (FISH), among others, has also been used in microbial diversity profiling of engineered wastewater treatment systems with some degrees of success (Awolusi et al. 2015). The shortcoming of FISH as a molecular quantification tool is well documented (Awolusi et al. 2015), however, quantitative qPCR has been reported as a more efficient technique of quantifying microbial populations in activated sludge (Aydin et al. 2015; Kim et al. 2013; Tvrdik et al. 2012). Furthermore, it has been shown that the traditional Sanger-sequencing still grossly underestimates the communities of the complex environmental samples due to the hundreds or thousands of important sequences that go unnoticed when employing this method (Shokralla et al. 2012). A major shortcoming of this technique is that it requires in vivo amplification of DNA fragments in bacterial hosts (cloning) prior to sequencing. Cloning is labour-intensive, tediously long and subject to bacterial host bias (Morozova and Marra 2008).

Due to the thousands of DNA templates usually present in wastewater samples, there is need for a technique that is capable of simultaneous reading from different DNA templates (Shokralla et al. 2012). The next generation sequencing (NGS) approach offers a speedy and extensive data production, offering the opportunity of investigating the microbial ecology on a larger scale and with more detail (Ju and Zhang 2015a). Next generation sequencing offers the advantage of direct sequencing from environmental samples without cloning into a bacterial host before sequencing, as obtained in the traditional Sanger approach. The NGS has revolutionized the genomic and metagenomic research with different platforms being commercially available.

In this study, the variation in community structure of the AOB over the winter and summer was evaluated using the 454-pyrosequencing technique and quantitative polymerase chain reaction (qPCR). Ammonia oxidation has been identified as the rate-limiting step in nitrification, hence, the distribution of the AOB in this system was examined to understand their role in nitrification efficiency. The wastewater characteristics and operational parameters including the plant nitrification performance were also monitored during summer and winter periods.

2 Materials and Methods

2.1 Wastewater Treatment Plant Selection and Sampling

The full-scale WWTP selected for this study is situated in the midlands of KwaZulu-Natal province, South Africa. It has a modified Johannesburg process configuration with primary settling, pre-anoxic, anaerobic, anoxic, and aerobic zones. As shown in Fig. 1, the effluent from the primary settling tank is distributed to the pre-anoxic and anaerobic tanks. An internal recycle is pumped from the last part of the aerobic units to the anoxic zone. There was a return activated sludge (RAS) recycled from the secondary settler to the pre-anoxic zone to allow phosphate removal and denitrification. The plant receives a discharge of 82,880 ± 20,832 m3/d (average dry weather flow) consisting of 90% domestic and 10% industrial wastewaters, including textile dyes, waxes and oils. Wastewater characteristics and operational parameters for the plant are shown in Table 1. Composite sludge samples (from the aeration tank), influent and effluent water samples were collected fortnightly for a period of 237 days (May–July 2012 and November 2012–March 2013). Sterile sampling bottles were used in collecting the samples and all samples were maintained at 4 °C while in transit to the lab.
Fig. 1

Schematic representation of wastewater treatment plant

Table 1

Average (winter and summer) wastewater characteristics and operational parameters of the selected plant as observed during the study period

Parameter

Winter

Summer

Rainfall (mm)

26.0 ± 18.6

116.8 ± 32.0

Temperature

16.5 ± 2.1

22.8 ± 2.7

pH

7.3 ± 0.2

7.2 ± 0.1

DO (mg/L)

0.6 ± 0.3

0.6 ± 0.1

MLSS (mg/L)

6157 ± 783.3

4728 ± 1282.0

Chemical Oxygen Demand (mg/L)

1156 ± 976.1

684.7 ± 258.9

Ammonia (NH3 mg N/L)

31.6 ± 6.0

24.4 ± 4.5

Electrical Conductivity (mS/m)

83.0 ± 5.0

62.3 ± 4.6

Flow Rate (ML/Day)

62.0 ± 2.2

96.8 ± 14.5

HRT (h)

6.3 ± 0.2

4.3 ± 1.0

OLR (kg-COD/m3.d)

4.0 ± 1.1

4.5 ± 1.8

ALR (g-NH4/m3.d)

121 ± 22.0

144 ± 29.0

F/M (g-COD/g-MLSS.d)

0.6 ± 0.1

0.9 ± 0.3

COD removal (%)

97.5 ± 1.3

94.1 ± 2.6

Ammonia Removal (%)

60.0 ± 18.0

83.0 ± 13.0

COD chemical oxygen demand; HRT hydraulic retention time; OLR organic loading rate; ALR ammonia loading rate; F/M food to micro-organisms ratio

2.2 Process Monitoring and Chemical Analysis

The collected samples were filtered with MN 85/90 filter papers (Macherey – Nagel, Germany) and the supernatant were analysed for nitrogen species (NH4+-N, NO2-N, NO3-N) using Thermo Scientific™ Gallery™ Automated Photometric Analyser (Vantaa, Finland). Samples for chemical oxygen demand (COD) measurement were first digested according to standard method 5220D (APHA 1998) – closed reflux colorimetric method – in a microwave digester (Milestone Start D, Sorisole, Italy) at 150 °C for 1 h in COD vials containing the digestion solution (Awolusi 2016). The COD concentration in the resulting digest was measured using Gallery™ Automated Photometric Analyser. Temperature, dissolved oxygen (DO) concentrations and pH measurements were done on-site using a portable YSI meter (YSI 556 Multiprobe System). The mixed liquor suspended solids (MLSS) was also measured using standard methods (APHA 1998).

2.3 Nitrification Rate Calculation

Nitrification rates in the plant were calculated based on the parameters monitored in the plant and the information supplied by the plant operators. The nitrification rate was calculated according to the following equation:

$$ {R}_{nitrification}=\frac{Q_{in}\left({\left[N{H}_4^{+}\hbox{--} N\right]}_{in}\hbox{--} {\left[N{H}_4^{+}\hbox{--} N\right]}_{out}\right)}{V_{reactor}{\left[ MLSS\right]}_{reactor}} $$
where:
R nitrification

rate of plant’s nitrification (g NH4+-N/g MLSS/d)

Q in

influent flowrate (ML/d)

\( {\left[N{H}_4^{+}\hbox{--} N\right]}_{in} \)

Influent ammonia nitrogen (mg/L)

\( {\left[N{H}_4^{+}\hbox{--} N\right]}_{out} \)

Effluent ammonia nitrogen (mg/L)

[MLSS]reactor

Mixed liquor suspended solids of the reactor (mg/L) and

V reactor

reactor working volume (ML).

2.4 Sample Preparation and DNA Extraction

The distribution of ammonia oxidizing bacteria was assessed using high-throughput pyrosequencing approach. Composite sludge samples (from the aeration tank) taken fortnightly between the 1st and 78th day represented the winter period (May–July 2012) whilst the samples from 79th through 237th day (November 2012–March 2013) represented the summer. The extracted DNA samples for the winter months were pooled together in equimolar quantities to make up the winter template DNA sample, whilst the same was done for the summer months. These resulted in two separate template DNA samples used for pyrosequencing analysis. The freeze-thaw DNA extraction was according to modified Briese (2002) protocol.

2.5 Quantitative PCR (qPCR)

Serial 10-fold dilutions of purified 16S rRNA gene fragments (target DNA) obtained from PCR-amplified AOB (amoA-1F and amoA-2R) were used to generate standard curves as described by Lienen et al. (2014). The concentrations (μg/μL) of these templates were determined using NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, USA). These concentrations were used in calculating their copy by considering their molecular weight and Avogadro’s number. Ten-fold serial dilutions of the target DNA were prepared from 108 to 102 copy numbers. The real time PCR quantification was carried out according to the modification of the method described by Steinberg and Regan (2009) using Bio-Rad C1000 Touch Thermal Cycler-CFX96 Real-Time System (BIO-RAD, USA). The cell numbers of AOB were then calculated from the quantified copy numbers of the amoA genes (obtained from qPCR) by dividing it by a factor of two as AOB has been reported to possess two copies of amoA genes within their genome (Harms et al. 2003; McTavish et al. 1993).

2.6 Sequencing and Analysis

The amoA locus of the AOB in the activated sludge was amplified using the primer set: amoA-1F/amoA-2R. The PCR was performed in a total reaction volume of 50 μL containing 10 ng of DNA template. The final concentrations of the different components in the reaction mix (200 μM of DNTPs, 1.5 mM of MgCl [Taq buffer with initial MgCl concentration of 20 mM], 2.5 U of Taq DNA polymerase [Thermo Scientific, Lithuania] and 0.5 μM of each primer) were according to modified protocols from Degrange and Bardin (1995). The PCR products were purified and end-repaired. The amoA amplicons generated from the PCR were sent to Inqaba Biotechnical Industries (Pty), South Africa, where the composition of the amplicon of amoA targeted locus was determined by pyrosequencing. The barcodes for multiplexing were incorporated between the forward primers (amoA-1F) and the 454-adapter sequence for pyrosequencing using Roche 454 FLX Titanium sequencing platform (Roche, USA) (Schloss et al. 2009). The resulting sequence was processed according to the method of Klindworth et al. (2013). Afterwards, the unique tags obtained were aligned with the 16S rRNA database with the aid of the BLASTN programme (Sekar et al. 2014). The tag redundancy was eliminated and sequences were assigned into OTU based on similarities of greater than 90%. With the aid of MEGA6 software, representative sequences were aligned using the ClustalW programme. The neighbour-joining method was employed for the phylogenetic analysis (Tamura et al. 2013). The raw pyrosequencing .sff file has been deposited into the NCBI sequence Read Archive.

2.7 Short-Read Archive Accession Numbers

The raw reads for the pyrosequencing have been deposited into NCBI Sequence Read Archive under the accession numbers SRP053412.

2.8 Statistical Analysis

GraphPad Prism version 5.00 for Windows (GraphPad Software, San Diego California USA) was used in carrying out Pearson correlation and unpaired t-test. The Pearson correlation was employed for testing the relationship between two variables, whilst the unpaired t-test was for comparing means.

3 Results and Discussion

3.1 Plant Performance and AOB Quantification

In this study, the plant’s highest nitrification rate was recorded during the summer with an average of 0.09 ± 0.03 g N-NH4+/g MLSS/d, whereas the rate was 0.04 ± 0.02 g N-NH4+/g MLSS/d during the winter (Fig. 2). The nitrification rate of the plant ranged from 0.02 to 0.07 g N-NH4+/g MLSS/d during the winter whilst it was between 0.05 and 0.14 g N-NH4+/g MLSS/d during the summer (Fig. 3). The nitrification rates of the plant during the investigated period is shown in Fig. 2. At the peak of winter (days 0–79), when the lowest temperature (Fig. 3) of 14 °C was recorded on day 64, the nitrification rate was 0.02 N-NH4+/g MLSS/d (Fig. 2) which was the lowest recorded during the study. Seasonal temperature variation has been implicated as one of the environmental factors affecting nitrification rates in wastewater treatment facilities (Arévalo et al. 2014; Kim 2013). In this study, a significant seasonal variation in temperature (α = 0.05; P = < 0.0001) was observed in the reactor, with 10.2 °C as the maximum temperature amplitude recorded. The temperature observed in the aeration basins ranged from 14.2 °C to 25.1 °C during the study (Fig. 3). The plant’s nitrification rate was found to correlate significantly with seasonal temperature fluctuation (α = 0.05; P = 0.0018) and the trend of this finding is comparable with the observation of Kim et al. (2008) who noted a significant correlation between temperature and ammonia oxidation in their study.
Fig. 2

Copy number of AOB and nitrification rate variation with time in the WWTP

Fig. 3

DO, pH and temperature variation with time in the WWTP

The qPCR efficiency was within the 90 to 110% range and the standard curves were linear over six orders of magnitude (R2 > 0.99). The AOB population abundance was quantified using the primer set targeting the amoA (ammonia monooxygenase) gene locus. The AOB abundance was within the range of 7.8 × 107–8.2 × 109 cells/μL. The AOB average was (5.02 ± 1.37)×108 cells/μL during the winter and (4.06 ± 0.97)×109 cells/μL during the summer. The variation in AOB population size during this study is shown in Fig. 2. The calculated nitrification rate of the plant showed a significant correlation with AOB copy number (r = 0.70; P = 0.01). A significant correlation was also found between the AOB (amoA gene) copy numbers and temperature in the reactors (r = 0.52; P = 0.05). The lowest AOB abundance was recorded during the winter as shown in Fig. 2. This finding is in line with earlier studies that linked AOB population decrease with reduced nitrification rate (LaPara and Ghosh 2006; Zhang et al. 2009).

There was a significant variation in the influent ammonia concentration of the plant. The average influent ammonia concentration was 24.5 ± 4.6 mg/L during the summer, and 31.7 ± 6.1 mg/L in the winter. An average mixed liquor suspended solids (MLSS) of 6157 ± 783 mg/L was recorded during the winter, whereas it was 5070 ± 1172 mg/L in the summer. The plant was operated at average hydraulic retention time (HRT) of 4.3 ± 1.0 h during the summer and 6.3 ± 0.2 h in the winter. Although there was about 30% increase in the influent ammonia concentration during the winter, there was a 19.0% reduction in ammonia loading rate (ALR) resulting from the winter rainfall (Table 1). Despite the lower COD during the summer, an increased flow rate during this season resulted in higher organic loading rate (OLR) of about 4.5 ± 1.8 kg-COD/m3.d on the average. The fluctuations in OLR could be attributed to the variation in the type of influent as a result of commercial and industrial wastes. Generally, the plant recorded a steady COD removal rate of above 90% (Table 1) during the two seasons. The influent wastewater had a higher conductivity of 83.0 ± 5.0 mS/m during the winter compared to 62.3 ± 4.6 mS/m during the summer. The variation in substrate concentration and electrical conductivity of wastewater could have been caused by the dilution with rain which was higher in the summer (116.8 ± 32.0 mm) as indicated in Table 1. Breakdown of the surface aerators within the aerobic tanks was a common observation during the sampling which resulted in low DO concentrations in the reactor with the exception of days 105 to 136 when the aerators were fully functional. The average DO concentration in the aeration tank during the summer and the winter were 0.60 ± 0.10 and 0.66 ± 0.40 mg/L, respectively (Fig. 3). Although theoretically, 1.7 mg/L has been reported as average DO concentration for complete nitrification (Ruiz et al. 2003), different authors have equally reported complete and stable nitrification at DO concentrations between 0.12–0.37 mg/L (Liu and Wang 2013; Park and Noguera 2004). Park and Noguera (2004) further noted that the efficient nitrification at low DO conditions correlated with the presence of a significant fraction of AOB belonging to the Nitrosospira and Nitrosomonas oligotropha lineages in a full-scale WWTP. Incidentally, in our study, AOB related to Nitrosomonas oligotropha lineages was observed during the summer period when higher nitrification rate was recorded. On the other hand, the reactor had a relatively stable pH range (6.53–7.39), which was close to neutral during the study period (Fig. 3), and showed no statistical correlation (r = 0.15; P = 0.59) with the nitrification rate of the plant. This was in line with the pH range (6.45–8.95) earlier reported to support complete nitrification by Ruiz et al. (2003). This indicates that other parameters, including temperature and AOB population abundance, were responsible for the fluctuations in the nitrification rates of this plant and not pH.

3.2 High-Throughput Pyrosequencing of the AOB amoA Genes

The AOB distribution for the different seasons was revealed by 454-pyrosequencing using the amoA-1F and amoA-2R primer sets (with multiplex barcodes inserted between the forward primers and the 454 adapter sequence). With the aid of MEGA6 software, representative sequences were aligned using the ClustalW programme, and the neighbour-joining method was employed for the phylogenetic analysis (Figs. 4 and 5). A total record of 212 and 1192 effective sequences were obtained from the winter and summer samples, respectively. After comparing them with the NCBI database, substantial percentages (72% in winter and 78% in summer samples) of the read from the two samples returned no hits and could not be assigned to any phylum (Fig. 6).
Fig. 4

Phylogenetic tree of some selected ammonia oxidizing bacteria OTUs based on amoA gene locus pyrosequencing reads using BLASTN and MEGAN for samples collected during summer

Fig. 5

Phylogenetic tree of some selected ammonia oxidizing bacteria OTUs based on amoA gene locus pyrosequencing reads using BLASTN and MEGAN for samples collected during winter

Fig. 6

AOB diversity as revealed by pyrosequencing (a) during summer, and (b) during winter

The identified AOB populations during the study were related to uncultured ammonia oxidizing bacteria, uncultured bacterium, and Nitrosomonas oligotropha (Fig. 6). Among these identified AOB populations, the uncultured ammonia oxidizing bacteria were the most dominant throughout the study with 97 and 95% during the summer and winter seasons, respectively, whilst the uncultured bacterium was 2 and 5% during the summer and winter, respectively. The Nitrosomonas oligotropha were about 1% of the AOB population during the summer, and was not detected during the winter (Fig. 6). Figures 4 and 5 depict the phylogenetic affiliations for AOB sequences retrieved from the winter and summer samples. The AOB abundance was 6 times higher during the summer than the winter (Fig. 6) when a higher nitrification rate and temperature were recorded (Table 1). The AOB sequences related to the uncultured bacterium and uncultured AOB showed increase of 133 and 360%, respectively, when the season changed from winter to summer (Fig. 6).

The plant had significant variation in the influent wastewater composition, and the prevailing operational/environmental conditions (Table 1), which resulted in changes in the microbial community structure of the reactor. This variation in microbial population affects the plant’s seasonal nitrification performance, as earlier reported (Miura et al. 2007; Rowan et al. 2003). The highest nitrification rate was recorded during the summer, when a higher AOB population density (cell/μL) was found in the reactor (Figs. 2 and 6). This indicates that AOB population abundance was contributory factors to efficient ammonia removal in the activated sludge.

Based on the pyrosequencing analysis, higher AOB abundance was observed in the summer when there was comparatively higher ammonia removal efficiency (Table 1). This was in agreement with the qPCR analysis which also indicated higher cells/μL during the summer. The Nitrosomonas oligotropha were only detected in the summer sample which indicated a possible higher diversity during the summer compared to the winter period (Fig. 6). Furthermore, the AOB abundance was 6 times higher during the summer compared to the winter when a higher nitrification rate and temperature were recorded. Using pyrosequencing, Zhang et al. (2015) also observed higher AOB population in the summer compared to the winter when monitoring three different WWTP. Niu et al. (2016) reported a significant decrease in bacterial amoA genes copy numbers during the winter, in a water purification plant. In this study, the AOB sequences related to uncultured bacterium and uncultured AOB showed increase of 133 and 360%, respectively, when the season changed from winter to summer. Nitrosomonas oligotropha-like sequence, which was detected from summer samples (1%), was absent in winter samples (Fig. 6). Earlier research efforts have documented a similar trend in different environments. Temperature has a major influence on AOB diversity and population structure with lower species richness correlating to lower temperatures (Urakawa et al. 2008). Similarly, Ju et al. (2014) observed a higher abundance of Nitrosomonas in activated sludge during the summer. Faulwetter et al. (2013) also reported seasonal impact on AOB community structure in a constructed wetland with higher diversity during the summer compared to the winter. A higher diversity of amoA gene was also recorded during the summer in the tidal wetland investigated by Zheng et al. (2013).

Overall, the summer period harboured larger AOB abundance compared to the winter, as revealed in this study. A significant proportion of the effective sequences (72% in winter and 78% in summer samples) termed “no hits” could not be successfully classified into any known bacterial 16S rRNA sequences, since they showed no similarity to the available sequences in NCBI database. This may suggest yet unidentified populations and vast unexploited AOB diversity. This corroborates a recent report (Abe et al. 2017) in which phylogenetic analysis of the pure strains isolated from a domestic WWTP revealed a deeply branching, previously unknown lineage and diversity within the genus Nitrosomonas. Yang et al. (2011) earlier on had reported that unassigned or unclassified bacteria usually consist a higher proportion in activated sludge compared to other environments, such as soil, because activated sludge have more complex microbial communities. In a study by Shi et al. (2013), occurrence of unclassified bacteria sequences in chlorination and clear water tanks was reported. They noted that these unclassified operational taxonomic units (OTU) from the different samples were closely clustered in the phylogenetic tree. Moreover, Fitzgerald et al. (2015), in a study of low DO reactor, reported that nitrification was not associated with any known ammonia-oxidizing prokaryote. Fitzgerald et al. (2015) further concluded that current understanding of aerobic nitrification is incomplete, and more work is needed to unravel the microbiological puzzle of nitrification especially at low DO.

4 Conclusions

The AOB community structure of the full-scale WWTP at different seasons was investigated using qPCR and 454-pyrosequencing targeting the ammonia monooxygenase alpha subunit gene. The AOB population density in terms of cell/μL increased when the average temperature of the WWTP increased in the summer. Pyrosequencing also revealed higher abundance of AOB in the reactor during the summer that was characterized by higher temperature. Furthermore, N. oligotropha and uncultured Nitrosospira sp. were only identified during the summer. This indicates that higher temperature elicited increased AOB diversity. The AOB abundance was 6 times higher during the summer than the winter when a higher nitrification rate and temperature were recorded. The AOB sequences related to uncultured bacterium and uncultured AOB showed increase of 133 and 360%, respectively, when the season changed from winter to summer. This suggests that higher AOB diversity and population density resulted in increased nitrification in activated sludge. This finding suggests that a vast diversity of novel, ecologically significant AOB species, which remain unexploited, still inhabit the complex activated sludge communities. Therefore, future research should target characterization of the nitrifying populations in wastewater, as pyrosequencing revealed a large percentage of the microbial community that did not match any of the known sequences in the existing database.

Notes

Acknowledgements

This work was supported financially by Durban University of Technology and the National Research Foundation SARChI Chair. An initial shorter version of the paper has been presented at the 10th World Congress of the European Water Resources Association (EWRA2017) “Panta Rhei”, Athens, Greece, 5-9 July, 2017 624 (http://ewra2017.ewra.net/).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflicts of interest.

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute for Water and Wastewater TechnologyDurban University of TechnologyDurbanSouth Africa

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