- Email: [email protected]
Efficient nitrous oxide production and metagenomics-based analysis of microbial communities in denitrifying systems acclimated with different electron acceptors
International Biodeterioration & Biodegradation 138 (2019) 92–98
Contents lists available at ScienceDirect
International Biodeterioration & Biodegradation journal homepage: www.elsevier.com/locate/ibiod
Efficient nitrous oxide production and metagenomics-based analysis of microbial communities in denitrifying systems acclimated with different electron acceptors
T
Yingfen Zhao1, Danfei Zeng1, Guangxue Wu∗ Guangdong Province Engineering Research Center for Urban Water Recycling and Environmental Safety, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Nitrous oxide Free nitrous acid Electron acceptor Metagenomics analysis
Effects of electron acceptors and free nitrous acid (FNA) concentrations on the denitrification performance and nitrous oxide (N2O) generation were investigated in denitrifying systems acclimated with nitrate (SBRA) and nitrite (SBRI). Denitrifying genes and functional microbial communities were also analyzed. Under the same influent organic carbon to nitrogen ratio, the nitrate removal percentage in SBRA was 75.2%, while that of nitrite was 99.8% in SBRI. The highest N2O conversion ratio of 89.9% was obtained when nitrite was applied to SBRA due to the insufficient electrons for N2O reductase (Nos). Higher N2O generation was attributed to the severe FNA inhibition on Nos when NO2eN was used as the electron acceptor, rather than the relative abundance of gene nos. The dominant denitrifier was Acidovorax with the abundance of 21.9% in SBRA, while Thauera of 11.3% in SBRI.
1. Introduction Nitrous oxide (N2O) is produced during biological nitrogen removal, and its greenhouse effect is about 265–298 times that of CO2; on the other hand, N2O is a kind of oxidant which can be used in propulsion or automotive applications (Drewer et al., 2017; Yamamoto and Tachibana, 2018). N2O emission and mitigation have long been the focus in wastewater treatment (Frison et al., 2015; Mannina et al., 2018). However, the production of N2O during wastewater treatment is an important strategy for energy recovery in the form of nitrogen, especially from ammonia-rich wastewater, such as livestock wastewater. The combustion of 1 mole methane (CH4) and N2O produces about 30% more heat than 1 mole CH4 and oxygen (O2) (Lin et al., 2018). Scherson et al. (2014) found that the energy production capacity increased by 5.7–7.3% by adding N2O and O2 to the biogas combustion reactor, with the similar production rate of N2O during practical wastewater treatment. Therefore, it is of great significance to intensify the generation of N2O during wastewater treatment to increase the energy utilization potential. Autotrophic nitrification and heterotrophic denitrification are two main pathways responsible for N2O generation during biological nitrogen removal (Wunderlin et al., 2012). During nitrification, N2O can
be produced as a byproduct via nitrifier denitrification and/or hydroxylamine oxidation. In addition, N2O is an obligatory intermediate of denitrification. Denitrification involves sequential reduction from nitrate (NO3−-N) to nitrite (NO2−-N), nitric oxide (NO), N2O and nitrogen gas (N2), usually with organic carbon as the electron donor. However, denitrification performance varies when NO3−-N or NO2−-N is served as the electron acceptor, and the regulation of the electron acceptor is important to promote N2O production. Zeng et al. (2003) successfully acclimated denitrifying glycogen accumulating organisms in an anaerobic-anoxic system, and N2O rather than N2 was the main denitrification product. According to Zhao et al. (2018), the conversion ratio of NO3−-N to N2OeN was 56% during denitrification with glucose as the electron donor. Gao et al. (2017) proposed that using acetate/ propionate as the organic carbon and NO2−-N as the electron acceptor to acclimate denitrifying polyphosphate-accumulating organisms (DNPAOs), the NO2−-N to N2OeN conversion ratio reached to 70%–80%. A similar NO2−-N to N2OeN conversion ratio was also obtained by Myung et al. (2015) with polyhydroxybutyrate (generated by methanotrophs from CH4) as the electron donor. Therefore, effects of electron acceptors during denitrification on N2O production should be further investigated so as to maximize the N2O generation. Free nitrous acid (FNA) is an important factor affecting N2O
∗
Corresponding author. E-mail address: [email protected] (G. Wu). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.ibiod.2019.01.007 Received 30 October 2018; Received in revised form 6 January 2019; Accepted 12 January 2019 0964-8305/ © 2019 Elsevier Ltd. All rights reserved.
International Biodeterioration & Biodegradation 138 (2019) 92–98
Y. Zhao et al.
2.2. SBR cyclic experiments
generation through the regulation of microbial activity and metabolic pathways. During biological nitrogen removal, N2O generation increased with the accumulation of NO2−-N (Du et al., 2016), which was mainly caused by the FNA inhibition on N2O reduction. The possible reason was that FNA could combine with the active site of the Cucontained N2O reductase (Nos), resulting in competitive inhibition of N2O reduction. Wang et al. (2018) illustrated that the inhibition of N2O reduction was reversible when the FNA concentration was below 0.0828 mg/L. In a denitrifying system with NO2−-N as the electron acceptor and starch as the carbon source, Miao et al. (2017) obtained that the maximum N2OeN conversion ratio (62.7%) occurred at the FNA concentration of 0.1875 mg/L. Gao et al. (2016) investigated the inhibition of FNA on Pseudomonas aeruginosa PAO1, and found that at the FNA concentration of 0.1 mg/L, the NO reductase (Nor) activity increased while the transcription level of N2O reduction gene decreased, providing a reasonable explanation for N2O accumulation. Based on above results, further exploration of the influence mechanism of FNA on the microbial activity and N2O generation is necessary for the realization of energy recovery during denitrification. Types of denitrifiers and their functional gene abundances are important factors determining the efficiency of biological nitrogen removal and N2O production. Therefore, a comprehensive understanding of the microbes (especially functional microbes) is beneficial for performance improvement of wastewater treatment processes. The metagenomics techniques based on high-throughput sequencing analysis, such as 454 pyrosequencing and Illumina, can provide information of microbial taxonomy, functional genes and metabolic pathways. For instance, Liu et al. (2018) deduced the metabolic models for different phylogenetic clades of Achromobacter and Agrobacterium, and found the varied denitrifying potentials based on the comparative genome analysis. Metagenomics analysis helps provide a deeper understanding of the mechanisms for microbial interactions in wastewater treatment processes, which may contribute to the explanation of N2O generation during denitrification subjected to different electron acceptors and FNA concentrations. This study aims to investigate the effects of electron acceptors and FNA concentrations on denitrification and N2O generation, in combination with the metagenomics analysis of microbial community and functional genes. The study not only clarified denitrification mechanisms from aspects of biological kinetics and microbial interactions, but also provided strategies for biofuel N2O generation in the denitrification process.
The cyclic experiments were carried out in a conical bottle with an effective volume of 2 L. The applied condition was the same as the parent SBR. Glucose was also used as the carbon source with the corresponding COD concentration of 800 mg/L; NaNO3 and NaNO2 were applied as the electron acceptors, respectively, with the nitrogen concentration of 200 mg/L. The sealed silicone tube and microelectrode were connected to the rubber stopper for liquor sampling and aqueous N2O detection, respectively. The bottle was placed in 25 °C water bath and magnetic stirrer was adopted to ensure complete mixing. Each test lasted for 170 min. Samples were taken for the detection of NO3−-N, NO2−-N and gaseous N2OeN. 2.3. Denitrification under different electron acceptors and FNA concentrations Batch tests were carried out under different electron acceptors and FNA concentrations. Glucose was the carbon source, and the initial COD concentration was 400 mg/L under all conditions. For activated sludges taken from SBRA and SBRI, NO3−-N or NO2−-N was used as the electron acceptor, respectively, with the initial concentrations of 100, 200 and 400 mg/L. Experiments were conducted using 1 L silk mouth bottles in 25 °C water bath. Each test lasted for 60 min, with gas and liquid samples taken at the interval of 10 min. pH and aqueous N2O were measured online. Before each reaction, 20 mL mixed liquor was withdrawn for the test of volatile suspended solids (VSS) concentrations. 2.4. Analytical methods VSS, NO3−-N and NO2−-N were measured according to standard methods (APHA, 1999). The NO3−-N, NO2−-N and N2OeN reduction rates were calculated through linear regression of measured profiles. The specific NO3−-N, NO2−-N and N2OeN reduction rates (rNO3-N, rNO2-N, rN2OeN) were determined by division of the corresponding VSS. FNA was calculated by the following equation (Anthonisen et al., 1976):
FNA (mg HNO2 /L) =
47 CNO2 −N × 14 [exp( −2300/(273 + T )) × 10 pH ] + 1
where T is the temperature (°C), and CNO2eN is the concentration of NO2−-N (mg/L). The N2O conversion ratio during denitrification was calculated as follows:
2. Materials and methods
N2 O conversion ratio (%) = 2.1. System setup and operation
rN2O−N r or N2O−N rNO2 −N rNO3 −N
where rN2OeN is the production rate of N2OeN (mg N/g VSS·h), rNO2-N is the reduction rate of NO2−-N when NO2−-N is the electron acceptor (mg N/g VSS·h), and rNO3-N is the reduction rate of NO3−-N when NO3−-N is the electron acceptor (mg N/g VSS·h). Nitrogen balance was calculated using a modified equation (Zhang et al., 2011, 2012):
Two sequencing batch reactors (SBRs), with the effective volume of 6 L, were adopted to acclimate denitrifiers at 25 °C. The seed sludge was taken from a wastewater treatment plant in Shenzhen, Guangdong, China. Each SBR cycle consisted of 4 h, including 10 min of filling, 160 min of anoxic mixing, 20 min of aeration, 35 min of settling and 15 min of discharging. 600 mL mixed liquor was discharged at the end of the aerobic phase to maintain the sludge retention time (SRT) at 10 d. The influent and effluent were manipulated by timer-controlled peristaltic pumps. Microporous aerators were used for aeration during the aerobic phase. The carbon source was glucose, with the chemical oxygen demand (COD) concentration of 800 mg/L. NaNO3 and NaNO2 were used to provide electron acceptors for each reactor (noted as SBRA and SBRI) with the nitrogen concentration of 200 mg/L. The COD/N ratio of the influent wastewater was 4. Other components of both reactors were the same of 200 mg/L NaHCO3, 250 mg/L NH4Cl, 25 mg/L Na2HPO4, 45 mg/L CaCl2, 100 mg/L MgSO4, 15 mg/L yeast extract, and 0.2 mL/L trace elements (Smolders et al., 1994).
TNIn = TNE + TND (N2, NO) + TND (N2 Og ) + TND (N2 Oaq) where TNIn is the influent nitrogen concentration (mg N/L), TNE is the effluent nitrogen concentration (mg N/L), TND (N2, NO) is the nitrogen removed via N2 and NO gases during denitrification (mg N/L), TND (N2Og) is the nitrogen removed via gaseous N2O (mg N/L), and TND (N2Oaq) is the nitrogen in the form of dissolved N2O (mg N/L). The specific electron consumption rates (mmol e−/g VSS·h) of nitrate reductase (Nar), nitrite reductase (Nir), Nor and Nos, and the electron distribution among different reductases were calculated according to Pan et al. (2013) and Zhao et al. (2018). The gaseous N2O was analyzed by the Agilent 6280 Gas Chromatograph (Agilent Technologies, USA) with an electron capture 93
International Biodeterioration & Biodegradation 138 (2019) 92–98
Y. Zhao et al.
detector and a HP-PLOT/Q column (J&W GC Columns, Agilent Technologies, USA). Temperatures during testing were 50 °C for the injection port, 50 °C for the oven, and 300 °C for the detector. Nitrogen gas was used as the carrier gas at the flow rate of 15 mL/min. Pure N2O gas was used as the standard for calibration. The aqueous N2O was measured online using microelectrode (N2O-500-9707, Unisense, Denmark). All the experiments were conducted with duplicates. 2.5. 16S rRNA and metagenomic sequencing Total DNA in sludge samples was extracted by TruSeq DNA Sample Prep Kit (Illumina Inc, USA). The 16S rRNA gene was amplified with universal primers 515F (GTGCCAGCMGCCGCGGTAA) and 806R (GGACTACHVGGGTWTCTAAT), and then high-throughput 16S rRNA gene sequencing was performed to analyze microbial community (Yin et al., 2017). The raw data of 16S rRNA genes was deposited in the NCBI SRA database (https://www.ncbi.nlm.nih.gov/sra) with the project ID of PRJNA503920. The metagenomic sequencing was performed on the Illumina Hiseq platform. Raw DNA reads were quality filtered to get the clean reads, which were further assembled into contigs using the SOAPdenovo software (Li et al., 2008). Then open reading frames (ORF) were predicted based on the assembled contigs using the MetaGene software (Noguchi et al., 2006). All the ORFs were clustered using the CD-HIT software (95% identity, 90% coverage) (Fu et al., 2012) to establish the non-redundancy gene set. Using the SOAPaligner software (Li et al., 2008), the high quality reads of each sample were compared with the non-redundancy gene set (95% identity) so as to count the gene abundance in the samples. BLASTP (Altschul et al., 1997) was applied to compare the gene set with the NR database (non-redundancy amino acid sequence database) to gain the species annotation and the taxonomic classification. The Evolutionary Genealogy of Genes_Nonsupervised Orthologous Groups database and the Kyoto Encyclopedia of Genes and Genomes database were used to annotate the function and metabolic pathways of the genes by the BLASTP program at the E value of 10−5. The metagenomic raw data was deposited in NCBI with the project ID of PRJNA504741. 3. Results and discussion 3.1. Long-term system performance and cyclic experiments Fig. S1 illustrates the effluent NO3−-N and NO2−-N concentrations in SBRA and the effluent NO2−-N concentrations in SBRI during the long-term operation. Both SBRs gradually reached to steady state after 3 SRTs, and VSS concentrations of SBRA and SBRI under steady state were 6.3 and 5.9 g/L, respectively. Compared with SBRA, SBRI achieved complete denitrification, with the average effluent NO2−-N concentration of 0.2 mg/L and the removal percentage of 99.8%. The average effluent NO3−-N and NO2−-N concentrations of SBRA were 24.8 and 22.3 mg/L, and the NO3−-N removal percentage was 75.2%. Miao et al. (2017) also found that the denitrification efficiency was high when NO2−-N was used as the electron acceptor and easily biodegradable organic carbon as the electron donor. Fig. 1 and Fig. S2 show dynamics of nitrogen concentrations, COD concentrations and nitrogen balance within typical cycles of SBRA and SBRI. During the initial 10 min of the reaction in SBRA, NO3−-N concentrations dropped rapidly, while NO2−-N and dissolved N2OeN concentrations swiftly increased. The electron consumption rates of Nar, Nir, Nor and Nos in the initial 10 min were 5.0, 1.6, 1.6 and 0.1 mmol e−/g VSS·h, respectively. The corresponding electron distribution ratio was 59.4%, 19.5%, 19.5% and 1.5%, respectively. At this stage, organic carbon was relatively sufficient to generate reducing power, which could provide adequate electrons for denitrification. According to the electron consumption rate of four denitrification reductases, Nar possessed the competitive advantage, and the electron
Fig. 1. Denitrification performance, N2O generations (a–b) and nitrogen balance (c–d) in typical SBR reaction cycles of SBRA and SBRI.
consumption rate of Nar was 3 times that of Nir and 38 times that of Nos. Therefore, NO3−-N was degraded rapidly, while NO2−-N and N2OeN accumulated in the initial 10 min. After 30 min of the reaction in SBRA, nitrogen concentrations showed a linear reaction trend, with the corresponding rNO3-N, rNO2-N and rN2OeN of −3.5, −0.5 and 2.5 mg N/g VSS·h, respectively. The electron consumption rates of Nar, Nir, Nor and Nos were 0.5, 0.3, 0.3 and 0.1 mmol e−/g VSS·h, and the electron distribution ratio was 42.3%, 24.2%, 24.2% and 9.3%, respectively. The organic carbon was gradually consumed and the ability to provide reducing power for denitrification was deteriorated significantly. As a result, the electron consumption rate decreased for all denitrification reductases. Notably, the electron consumption rate of Nar decreased remarkably, indicating that Nar was more susceptible to carbon availability than other three denitrification reductases. The electron consumption rate of Nos maintained at a low level during the whole process, suggesting the lowest electron competition ability of Nos. Therefore, N2O was continuously accumulated until it reached to the maximum concentration of 49.3 mg/L at the end of the reaction. The N2OeN to NO3−-N ratio
94
International Biodeterioration & Biodegradation 138 (2019) 92–98
Y. Zhao et al.
was 65.8%, and the NO3−-N concentration was 29.9 mg/L, with the denitrification efficiency of 71.4%. The NO2−-N concentration was 15.5 mg/L. The gaseous N2OeN production was 1.6 mg/L and was generated at a low rate. At the end of the reaction in SBRI, the NO2−-N concentration was 0 mg/L, dissolved N2OeN concentration was 15.1 mg/L, and the conversion ratio from NO2−-N to N2OeN was 15.4%. Similar to SBRA, NO2−-N degraded rapidly in the initial 10 min, and the dissolved N2OeN accumulated to 24.9 mg/L. Nir consumed electrons at a higher rate than Nos, which was manifested by the fact that the electron consumption ratio of Nir to Nos was 5. This could explain the N2OeN accumulation. During 10–120 min, the degradation rate of NO2−-N was 6.9 mg N/g VSS·h, and the N2OeN accumulation rate decreased to 1.1 mg N/g VSS·h. After that, the dissolved N2OeN concentration gradually decreased, which was possibly due to the low NO2−-N concentration (4.9 mg/L). The availability of organic carbon is an important factor affecting nitrogen removal, including N2O emission (Spinelli et al., 2018). Based on the above analysis, in SBRA and SBRI, the nitrogen concentrations decreased rapidly at the initial stage. Ample carbon sources provided adequate reducing power for denitrification. As a result, even though the electron capture capacity was different among Nar, Nir, Nor and Nos, adequate electrons could supply them independently. The threestep denitrification in SBRI afforded more electrons to Nos than the four-step denitrification in SBRA, resulting in less N2O generations in SBRI. Similarly, when the organic carbon was limited, the electron competition was more severe among the four denitrification reductases in SBRA than in SBRI. The weak electron competition ability of Nos further contributed to more N2OeN generation in SBRA (Pan et al., 2013). In SBRI, however, the NO2−-N concentration was too low to accept numerous electrons, so N2O was served as the major electron acceptor and the dissolved N2OeN concentration decreased to a low level.
Fig. 2. Aqueous N2O production rates with different electron acceptors in SBRA and SBRI. Maximal rate refers to the rapid production rate at the initial stage of denitrification (0–10 min). Average rate refers to the average rate in the whole process of denitrification (0–60 min).
production rate in SBRI was 1.6 times as much as in SBRA in the initial stage. While the average N2O production rate in SBRI was lower than in SBRA. It demonstrated that SBRI had been adapted to high NO2−-N concentrations. In the initial stage, owing to the long-term acclimation in SBRI, the activity of Nir could be stimulated and high competitive existed in SBRI. Then fewer electrons were obtained by Nos in SBRI, which caused rapid generations of N2O. However, with decreasing concentrations of NO2−-N, Nir demanded fewer electrons so that Nos received more. Compared with SBRA, Nos in SBRI had been adapted to abundant NO2−-N in the long-term operation, so as to readily obtain the electrons and reduce N2OeN. Thus, less N2O production on average was observed in SBRI.
3.2. Effects of electron acceptors on N2O generation during denitrification As shown in Fig. S3, when NO2−-N was the electron acceptor, a similar trend in SBRA and SBRI was observed that N2O production firstly increased, and then slowed down or declined under different oxidized nitrogen (NOX-N) concentrations. By contrast, N2O was increasingly generated with NO3−-N as the electron acceptor. Fig. 2 demonstrates the NOX-N degradation rates in SBRA and SBRI with NO3−-N and NO2−-N (100 mg/L) as the electron acceptor, respectively. In the initial stage with NO2−-N as the electron acceptor, N2O was rapidly produced at the rate of 18.0 mg N/g VSS·h in SBRA, whereas 28.9 mg N/ g VSS·h in SBRI. The average N2O production rates in SBRA and SBRI were 12.4 and 6.4 mg N/g VSS·h, respectively. In contrast, N2O production rates with NO3−-N as the electron acceptor in SBRA and SBRI were 7.4 and 0.1 mg N/g VSS·h, respectively. Obviously, the production of N2O utilizing NO2−-N as the electron acceptor was much higher than utilizing NO3−-N. A previous study reported that denitrification via NO2−-N as the electron acceptor produced more N2O than via NO3−-N (Du et al., 2016). This can be explained by a modified model of electron transfer chain during denitrification (Zeng et al., 2018). Electrons from organics metabolism are transported by two pathways, one in priority supplying the electrons for Nar, and the other one supplying the electrons shared by Nir, Nor and Nos. Electron competition exists among Nir, Nor and Nos. In this study, when NO3−-N was the electron acceptor, NO2−-N was gradually produced, resulting in less electron consumption of Nir. Hence, enough electrons for Nos ensured the reduction of N2O. However, when NO2−N was the electron acceptor, high concentrations of NO2−-N required more electrons for Nir and accordingly fewer electrons were reserved for Nos. As a result, N2O production increased when NO2−-N was the electron acceptor. In addition, when NO2−-N was the electron acceptor, the N2O
3.3. Effect of FNA on N2O generation during denitrification The relationship between the maximal FNA concentration and the N2O production rate is shown in Fig. 3 under different initial NO3−-N concentrations. For SBRA, the FNA concentration ranges were 0.0004–0.0078, 0.0003–0.0044 and 0.0004–0.0053 mg/L under initial NO3−-N concentrations of 100, 200 and 400 mg/L, respectively. The corresponding final concentrations of N2OeN were 18.0, 27.1 and 26.1 mg/L. However, the N2OeN generation was significantly enhanced with NO2−-N as the electron acceptor. At the end of the reaction, N2OeN productions were 37.2, 45.2 and 30.4 mg/L under initial NO2−-N concentrations of 100, 200 and 400 mg/L, with FNA concentrations of 0.0022–0.0319, 0.0038–0.0459 and 0.0061–0.1527 mg/ L, respectively. Especially, the N2OeN conversion ratios were 89.9% and 84.7% at initial NO2−-N concentrations of 100 and 200 mg/L, which were notably higher than those with NO3−-N as the electron acceptor. By comparison, when NO2−-N was used as the electron acceptor, the maximal FNA concentrations were 4–28 times as much as NO3−-N, which had a stronger inhibition on Nos and significantly increased the N2OeN generation. For SBRI, when NO2−-N was used as the electron acceptor, the dissolved N2O concentration increased rapidly in the initial 10 min, and then subsequently slowed down and reached to the maximum concentrations of 17.1, 31.1 and 27.6 mg/L, respectively, under the initial NO2−-N concentrations of 100, 200 and 400 mg/L. The corresponding N2OeN conversion ratios were 38.3%, 52.0% and 42.7%. The FNA concentrations were 0.0023–0.0071, 0.0029–0.0448 and 95
International Biodeterioration & Biodegradation 138 (2019) 92–98
Y. Zhao et al.
Fig. 4. Relative gene abundances encoding denitrification enzymes in SBRA and SBRI. Fig. 3. Aqueous N2O production rates under different FNA concentrations in SBRA and SBRI.
abundance of nar α, encoding the active catalytic subunit of Nar, it was 0.07% in SBRA while 0.03% in SBRI. Therefore, the relative gene abundances in SBRI were all lower whether in terms of the Nar encoding gene or the active catalytic site nar α encoding gene than those in SBRA. This provided a possible explanation for the lower ability of microbes acclimated to NO2−-N to utilize NO3−-N. On the other hand, the expression of the denitrification genes required suitable conditions, such as the existence of NO3−-N or other denitrification intermediates. For example, NO3−-N, NO2−-N and N2O were found to be the triggers for the expressions of their respective denitrification genes (Baek et al., 2005). The gene abundance encoding Nir was 4232, accounting for 24.2% of the denitrification genes, and 0.06% of the total genes in the sequenced sample of SBRA. The percentages of nirK and nirS genes were 0.03% and 0.02%, respectively. In SBRI, the Nir encoding gene abundance was 1278, which made up 25.3% of the denitrification genes and 0.04% of the total genes. Accordingly, the ratios of nirK and nirS genes were 0.01% and 0.03%, respectively. These two types of Nir possess the same function, albeit different distributions among diverse microbial species. Therefore, different types of Nir might be complementary for microbes, especially in varied environments so as to ensure the efficient reduction of NO2−-N (Zhang et al., 2017). By analysis of the Nir encoding gene, the gene abundances did not differ much in SBRA and SBRI. In addition, the Nir encoding gene tended to be highly expressed to ensure denitrification since NO2−-N was accumulated in SBRA. Therefore, microbes in SBRA had relatively strong ability to reduce NO2−-N when it was the electron acceptor. Nos is encoded by the gene nos, which generally consists of three transcriptional subunits, i.e. nosZ, nosR and nosDFYL. Among them, the nosZ gene encodes the catalytic subunit NosZ, which has two conservative regions of CuA (as the electron entrance) and CuZ (as the catalytic site) (Pomowski et al., 2011). Based on the metagenomics analysis, the relative gene abundances encoding nosZ were similar, i.e. 0.029% in SBRA and 0.022% in SBRI. Zhang et al. (2017) pointed out that the ratio of (nirK + nirS)/nosZ might influence N2O accumulation. In this study, the ratios in SBRA and SBRI were 1.98 and 1.93, respectively. Therefore, N2O generations during denitrification were not directly relevant to the gene abundance of nos.
0.0136–0.0245 mg/L. In contrast, under different NO3−-N concentrations, the N2OeN generation in SBRI was much lower. The final N2OeN concentrations were 0.2, 0.2 and 0.5 mg/L, with the N2OeN conversion ratios of 1.1%, 0.9% and 2.2%, respectively. The lowest FNA concentrations were observed, i.e. 0.0002–0.0004, 0.0006–0.0009 and 0.0009–0.0014 mg/L, respectively, which might be the main reason for the low N2OeN generation. Therefore, relatively weak inhibition of FNA with NO3−-N as the electron acceptor resulted in a low N2OeN generation in SBRI. Generally, higher concentrations of FNA induced more N2O production. Nonetheless, although with less FNA in SBRI, SBRI tended to produce more N2O than SBRA at the initial stage of denitrification. This was attributed to the high activity of Nir in SBRI owing to the long-term acclimation to NO2−-N. High concentrations of NO2−-N further stimulated Nir and caused inadequate electrons for Nos. Thus, more N2OeN was generated in SBRI than SBRA at the initial stage of denitrification. In this study, the highest N2O conversion ratio was obtained at 89.9% when NO2−-N was applied to SBRA. In previous studies, Van Doan et al. (2013) proposed that approximately 70% of NO3−-N was converted into N2OeN in a bioelectrochemical denitrification system, and Myung et al. (2015) obtained the conversion ratio from NO2−-N to N2O was around 70% with the accumulated polyhydroxybutyrate as the electron donor. Therefore, the present study provided a valuable method for N2O generation with high efficiency. 3.4. Metagenomics analysis of denitrification genes Two types of dissimilated nitrate reductases existed in microbes, categorized as membrane-bound nitrate reductase (Nar) and cytoplasmic nitrate reductase (Nap) (Philippot, 2002). The abundance of the gene encoding Nar (EC: 1.7.99.4) was 8442 in SBRA, accounting for 48.2% of total denitrification genes and 0.1% of total genes of the sequenced sample. In SBRI, the Nar (EC: 1.7.99.4) coding gene abundance was 2076, and the corresponding percentages were 41.1% of denitrification genes and 0.1% of total genes. The relative subunit gene abundances of Nar were higher in SBRA than those in SBRI (Fig. 4). Some denitrifiers, such as Paracoccus denitrificans, reduced NO3−-N mainly by Nar under anoxic conditions, while Nap was responsible for the catalytic reduction of NO3−-N during denitrification under oxic conditions. In SBRA, the Nar/(Nar + Nap) ratio was 0.11%, higher than that in SBRI of 0.04%. In addition, in perspective of the relative
3.5. Microbial community and relevant functions Proteobacteria and Bacteroidetes were the dominant microbes at the phylum level in SBRA and SBRI (Fig. S4), which was consistent with results of Kondaveeti et al. (2014). The relative abundance of Proteobacteria in SBRA was 61.6%, and it was 62.1% in SBRI. For Bacteroidetes, 96
International Biodeterioration & Biodegradation 138 (2019) 92–98
Y. Zhao et al.
could use NO3−-N as the electron acceptor for anaerobic metabolism (Reinhold-Hurek and Hurek, 2006). Paracoccus was capable of complete denitrification, which could express genes encoding four types of denitrification reductases (Zumft, 1997). The relative abundance of Paracoccus in SBRI (4.3%) was higher than SBRA (0.9%). Paracoccus had three known paralogs, which regulated the transcription in denitrification such as the NarR sensitive protein, and could participate in the NO3−-N reduction (Wood et al., 2001). 4. Conclusions Complete nitrogen removal was achieved when NO2−-N was the electron acceptor in the long-term acclimation. The N2O production was higher with NO2−-N as the electron acceptor, and it was generally improved with increasing FNA concentrations. The highest N2O conversion ratio of 89.9% was obtained when NO2−-N was applied to SBRA, because the abrupt electron demand from Nir caused insufficient electrons for Nos. The disparate abilities for microbes to degrade NO3−N and NO2−-N in SBRA and SBRI were mainly caused by different gene abundances encoding Nar and Nir. FNA concentrations, rather than the gene abundance of nos, impacted on the N2O generation. The dominant denitrifier in SBRA was Acidovorax, while Thauera was the most abundant one in SBRI.
Fig. 5. Relative abundances of denitrifiers at the genus level in SBRA and SBRI.
the relative abundances in SBRA and SBRI were 18.9% and 18.3%, respectively. At the genus level, Acidovorax was the dominant microbes in SBRA, with the percentage of 21.9%. Other microbes of relative high abundances were Rubrivivax (5.7%), a genus in Bacteroidetes (8.0%) and Dechloromonas (2.3%). In SBRI, the most abundant three species were Thauera (11.3%), Acidovorax (4.7%), and Candidatus_Accumulibacter (4.1%). Thauera was capable of complete denitrification, and the relative abundance was 2.5% in SBRA. Acidovorax belongs to denitrifier, and the abundance of Nar encoding gene in Acidovorax accounted for 49.6%. This might prove that Acidovorax was the main species in SBRA to reduce NO3−-N. Regarding to the genes encoding Nir, Nor and Nos in Acidovorax, the respective abundances took up 63.8%, 32.4% and 32.4% of the total Nir, Nor and Nos encoding genes. It indicated that Acidovorax was a complete denitrifier and played a major role in denitrification in SBRA. In SBRI, however, the relative abundance of Acidovorax was 4.7%, and the Nar encoding gene abundance was 138, accounting 5.5% of the total Nar encoding gene. Accordingly, the abundances of Nir, Nor and Nos encoding genes were 74, 58 and 30, with the respective percentage of 22.4%, 5.6% and 4.5%. Fig. 5 illustrates the relative abundances of different denitrifiers in the two reactors. The total relative abundance of all denitrifiers in SBRA was 32.6%, with the most abundant species of Acidovorax. In SBRI, the percentage of all denitrifiers was 26.9%, and Thauera was the dominant species. It was worth mentioning that not all denitrifiers had the ability of complete denitrification. Some microbes, lacking of N2O reductases, only conduct the incomplete denitrification and emit N2O as the final product (Jha et al., 2017). Some microorganisms in the phylum of Proteobacteria were acclimated during denitrification, such as Burkholderia, Ralstonia, Azoarcus and Thauera. All of them had the typical nosZ gene and were complete denitrifiers (Coyotzi et al., 2017). Acidovorax had the most Nos encoding gene, accounting for 35.1% of the total Nos encoding gene in SBRA, followed by Thauera (8.4%) and Dechloromonas (6.9%). In SBRI, however, most Nos encoding genes were detected in Thauera, with the relative gene abundance of 35.7%, followed by Rhodobacter (4.8%) and Acidovorax (4.5%). Therefore, microbial community could differ significantly under the long-term acclimation with different electron acceptors, and functional gene abundances would be extensively altered even for the same denitrifier. In the previous studies, it was found that the enriched Comamonas and type II methanotrophic archaea could efficiently convert NO2−-N into N2O (Gao et al., 2017). In our study, the relative abundance of Comamonas was 2.2% in SBRA, while 1.5% in SBRI. The relative abundance of Azoarcus in SBRA and SBRI was 0.1% and 1.0%. Azoarcus
Conflicts of interest The authors declare no conflict of interest. The authors claim that no financial and personal relationships with other people or organizations that could inappropriately influence (bias) our work. Acknowledgements This work was supported by the National Key Research and Development Program of China for International Science and Innovation Cooperation Major Project between Governments (No. 2016YFE0118800) and the Development and Reform Commission of Shenzhen Municipality (urban water recycling and environment safety program). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ibiod.2019.01.007. References Altschul, S., Madden, T., Schäffer, A., Zhang, J., Zhang, Z., Miller, W., Lipman, D., 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25 (17), 3389–3402. Anthonisen, A., Loehr, R., Prakasam, T., Srinath, E., 1976. Inhibition of nitrification by ammonia and nitrous acid. J. Water Pollut. Control Fed. 48 (5), 835–852. APHA, 1999. Standard Methods for the Examination of Water and Wastewater, twentieth ed. American Public Health Association, Washington, DC. Baek, S., Hartsock, A., Shapleign, J., 2005. Agrobacterium tumefaciens C58 uses ActR and FnrN to control nirK and nor expression. J. Bacteriol. 190 (1), 78–86. Coyotzi, S., Doxey, A., Clark, I., Lapen, D., Cappellen, P., Neufeld, J., 2017. Agricultural soil denitrifiers possess extensive nitrite reductase gene diversity. Environ. Microbiol. 19, 1189–1208. Drewer, J., Yamulki, S., Leeson, S.R., Anderson, M., Perks, M.P., Skiba, U.M., McNamara, N.P., 2017. Difference in soil methane (CH4) and nitrous oxide (N2O) fluxes from bioenergy crops SRC willow and SRF Scots pine compared with adjacent arable and fallow in a temperate climate. Bioenergy Res. 10 (2), 575–582. Du, R., Peng, Y., Cao, S., Wang, S., Niu, M., 2016. Characteristic of nitrous oxide production in partial denitrification process with high nitrite accumulation. Bioresour. Technol. 203, 341–347. Frison, N., Chiumenti, A., Katsou, E., Malamis, S., Fatone, F., 2015. Mitigating off-gas emissions in the biological nitrogen removal via nitrite process treating anaerobic effluents. J. Clean. Prod. 93, 126–133. Fu, L., Niu, B., Zhu, Z., Wu, S., Li, W., 2012. CD-HIT: accelerated for clustering the nextgeneration sequencing data. Bioinformatics 28 (23), 3150–3152.
97
International Biodeterioration & Biodegradation 138 (2019) 92–98
Y. Zhao et al.
the anaerobic metabolism of the biological phosphorus removal process: stoichiometry and pH influence. Biotechnol. Bioeng. 43 (6), 461–470. Spinelli, M., Eusebi, A.L., Vasilaki, V., Katsou, E., Frison, N., Cingolani, D., Fatone, F., 2018. Critical analyses of nitrous oxide emissions in a full scale activated sludge system treating low carbon-to-nitrogen ratio wastewater. J. Clean. Prod. 190, 517–524. Van Doan, T., Lee, T.K., Shukla, S.K., Tiedje, J.M., Park, J., 2013. Increased nitrous oxide accumulation by bioelectrochemical denitrification under autotrophic conditions: kinetics and expression of denitrification pathway genes. Water Res. 47 (19), 7087–7097. Wang, Y., Li, P., Zuo, J., Gong, Y., Wang, S., Shi, X., Zhang, M., 2018. Inhibition by free nitrous acid (FNA) and the electron competition of nitrite in nitrous oxide (N2O) reduction during hydrogenotrophic denitrification. Chemosphere 213, 1–10. Wood, N., Alizadeh, T., Bennett, S., Pearce, J., Ferguson, S., Richardson, D., Moir, J., 2001. Maximal expression of membrane-bound nitrate reductase in Paracoccus is induced by nitrate via a third FNR-like regulator named NarR. J. Bacteriol. 183, 3606–3613. Wunderlin, P., Mohn, J., Joss, A., Emmenegger, L., Siegrist, H., 2012. Mechanisms of N2O production in biological wastewater treatment under nitrifying and denitrifying conditions. Water Res. 46 (4), 1027–1037. Yamamoto, Y., Tachibana, T., 2018. Burning velocities of dimethyl ether (DME)–nitrous oxide (N2O) mixtures. Fuel 217, 160–165. Yin, Q., Miao, J., Li, B., Wu, G., 2017. Enhancing electron transfer by ferroferric oxide during the anaerobic treatment of synthetic wastewater with mixed organic carbon. Int. Biodeterior. Biodegrad. 119, 104–110. Zeng, D., Miao, J., Wu, G., Zhan, X., 2018. Nitrogen removal, microbial community and electron transport in an integrated nitrification and denitrification system for ammonium-rich wastewater treatment. Int. Biodeterior. Biodegrad. 133, 202–209. Zeng, R.J., Yuan, Z., Keller, J., 2003. Enrichment of denitrifying glycogen-accumulating organisms in anaerobic/anoxic activated sludge system. Biotechnol. Bioeng. 81 (4), 397–404. Zhang, M., Lawlor, P.G., Li, J., Zhan, X., 2012. Characteristics of nitrous oxide (N2O) emissions from intermittently-aerated sequencing batch reactors treating the separated liquid fraction of anaerobically digested pig manure. Water Air Soil Pollut. 223 (5), 1973–1981. Zhang, M., Lawlor, P.G., Wu, G., Lynch, B., Zhan, X., 2011. Partial nitrification and nutrient removal in intermittently aerated sequencing batch reactors treating separated digestate liquid after anaerobic digestion of pig manure. Bioproc. Biosyst. Eng. 34 (9), 1049–1056. Zhang, Y., Ji, G., Wang, R., 2017. Quantitative responses of nitrous oxide accumulation to genetic associations across a temperature gradient within denitrification biofilters. Ecol. Eng. 102, 145–151. Zhao, Y., Miao, J., Ren, X., Wu, G., 2018. Effect of organic carbon on the production of biofuel nitrous oxide during the denitrification process. Int. J. Environ. Sci. Technol. 15 (2), 461–470. Zumft, W., 1997. Cell biology and molecular basis of denitrification. Microbiol. Mol. Biol. Rev. 61, 533–616.
Gao, H., Liu, M., Griffin, J., Xu, L., Xiang, D., Scherson, Y., Liu, W., Wells, G., 2017. Complete nutrient removal coupled to nitrous oxide production as a bioenergy source by denitrifying polyphosphate-accumulating organisms. Environ. Sci. Technol. 51 (8), 4531–4540. Gao, S., Fan, L., Peng, L., Guo, J., Agulló-Barceló, M., Yuan, Z., 2016. Determining multiple responses of Pseudomonas aeruginosa PAO1 to an antimicrobial agent, free nitrous acid. Environ. Sci. Technol. 50 (10), 5305–5312. Jha, N., Saggar, S., Giltrap, D., Tillman, R., Deslippe, J., 2017. Soil properties impacting denitrifier community size, structure, and activity in New Zealand dairy-grazed pasture. Biogeosciences 14, 4243–4253. Kondaveeti, S., Lee, S., Park, H., Min, B., 2014. Bacterial communities in a bioelectrochemical denitrification system: the effects of supplemental electron acceptors. Water Res. 51, 25–36. Li, R., Li, Y., Kristiansen, K., Wang, J., 2008. SOAP: short oligonucleotide alignment program. Bioinformatics 24 (5), 713–714. Lin, Z., Sun, D., Dang, Y., Holmes, D.E., 2018. Significant enhancement of nitrous oxide energy yields from wastewater achieved by bioaugmentation with a recombinant strain of Pseudomonas aeruginosa. Sci. Rep. 8, 11916. Liu, S., Chen, Q., Ma, T., Wang, M., Ni, J., 2018. Genomic insights into metabolic potentials of two simultaneous aerobic denitrification and phosphorus removal bacteria Achromobacter sp. GAD3 and Agrobacterium sp. LAD9. FEMS Microbiol. Ecol. 94 (4), 20. Mannina, G., Ekama, G.A., Capodici, M., Cosenza, A., Van Loosdrecht, M.C.M., 2018. Influence of carbon to nitrogen ratio on nitrous oxide emission in an integrated fixed film activated sludge membrane bioreactor plant. J. Clean. Prod. 176, 1078–1090. Miao, J., Zhao, Y., Wu, G., 2017. Effect of organic carbons on microbial activity and structure in denitrifying systems acclimated to nitrite as the electron acceptor. Int. Biodeterior. Biodegrad. 118, 66–72. Myung, J., Wang, Z., Yuan, T., Zhang, P., Nostrand, J., Zhou, J., Criddle, C.S., 2015. Production of nitrous oxide from nitrite in stable type II Methanotrophic enrichments. Environ. Sci. Technol. 49 (18), 10969–10975. Noguchi, H., Park, J., Takagi, T., 2006. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Res. 34 (19), 5623–5630. Pan, Y., Ni, B.J., Bond, P.L., Ye, L., Yuan, Z., 2013. Electron competition among nitrogen oxides reduction during methanol-utilizing denitrification in wastewater treatment. Water Res. 47 (10), 3273–3281. Philippot, L., 2002. Denitrifying genes in bacterial and Archaeal genomes. BBA-Gene Struct. Expr. 1577 (3), 355–376. Pomowski, A., Zumft, W., Kroneck, P., Einsle, O., 2011. N2O binding at a [4Cu:2S] copper–sulphur cluster in nitrous oxide reductase. Nature 477, 234–237. Reinhold-Hurek, B., Hurek, T., 2006. The genera Azoarcus, Azovibrio, Azospira and Azonexus. In: Rosenberg, E., DeLong, E.F., Lory, S., Stackebrandt, E., Thompson, F. (Eds.), The Prokaryotes, fourth ed. Springer-Verlag, New York, pp. 873–891. Scherson, Y., Woo, S.G., Criddle, C.S., 2014. Production of nitrous oxide from anaerobic digester centrate and its use as a co-oxidant of biogas to enhance energy recovery. Environ. Sci. Technol. 48 (10), 5612–5619. Smolders, G.J.F., Van der Meij, J., Van Loosdrecht, M.C.M., Heijnen, J.J., 1994. Model of
98
Contents lists available at ScienceDirect
International Biodeterioration & Biodegradation journal homepage: www.elsevier.com/locate/ibiod
Efficient nitrous oxide production and metagenomics-based analysis of microbial communities in denitrifying systems acclimated with different electron acceptors
T
Yingfen Zhao1, Danfei Zeng1, Guangxue Wu∗ Guangdong Province Engineering Research Center for Urban Water Recycling and Environmental Safety, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Nitrous oxide Free nitrous acid Electron acceptor Metagenomics analysis
Effects of electron acceptors and free nitrous acid (FNA) concentrations on the denitrification performance and nitrous oxide (N2O) generation were investigated in denitrifying systems acclimated with nitrate (SBRA) and nitrite (SBRI). Denitrifying genes and functional microbial communities were also analyzed. Under the same influent organic carbon to nitrogen ratio, the nitrate removal percentage in SBRA was 75.2%, while that of nitrite was 99.8% in SBRI. The highest N2O conversion ratio of 89.9% was obtained when nitrite was applied to SBRA due to the insufficient electrons for N2O reductase (Nos). Higher N2O generation was attributed to the severe FNA inhibition on Nos when NO2eN was used as the electron acceptor, rather than the relative abundance of gene nos. The dominant denitrifier was Acidovorax with the abundance of 21.9% in SBRA, while Thauera of 11.3% in SBRI.
1. Introduction Nitrous oxide (N2O) is produced during biological nitrogen removal, and its greenhouse effect is about 265–298 times that of CO2; on the other hand, N2O is a kind of oxidant which can be used in propulsion or automotive applications (Drewer et al., 2017; Yamamoto and Tachibana, 2018). N2O emission and mitigation have long been the focus in wastewater treatment (Frison et al., 2015; Mannina et al., 2018). However, the production of N2O during wastewater treatment is an important strategy for energy recovery in the form of nitrogen, especially from ammonia-rich wastewater, such as livestock wastewater. The combustion of 1 mole methane (CH4) and N2O produces about 30% more heat than 1 mole CH4 and oxygen (O2) (Lin et al., 2018). Scherson et al. (2014) found that the energy production capacity increased by 5.7–7.3% by adding N2O and O2 to the biogas combustion reactor, with the similar production rate of N2O during practical wastewater treatment. Therefore, it is of great significance to intensify the generation of N2O during wastewater treatment to increase the energy utilization potential. Autotrophic nitrification and heterotrophic denitrification are two main pathways responsible for N2O generation during biological nitrogen removal (Wunderlin et al., 2012). During nitrification, N2O can
be produced as a byproduct via nitrifier denitrification and/or hydroxylamine oxidation. In addition, N2O is an obligatory intermediate of denitrification. Denitrification involves sequential reduction from nitrate (NO3−-N) to nitrite (NO2−-N), nitric oxide (NO), N2O and nitrogen gas (N2), usually with organic carbon as the electron donor. However, denitrification performance varies when NO3−-N or NO2−-N is served as the electron acceptor, and the regulation of the electron acceptor is important to promote N2O production. Zeng et al. (2003) successfully acclimated denitrifying glycogen accumulating organisms in an anaerobic-anoxic system, and N2O rather than N2 was the main denitrification product. According to Zhao et al. (2018), the conversion ratio of NO3−-N to N2OeN was 56% during denitrification with glucose as the electron donor. Gao et al. (2017) proposed that using acetate/ propionate as the organic carbon and NO2−-N as the electron acceptor to acclimate denitrifying polyphosphate-accumulating organisms (DNPAOs), the NO2−-N to N2OeN conversion ratio reached to 70%–80%. A similar NO2−-N to N2OeN conversion ratio was also obtained by Myung et al. (2015) with polyhydroxybutyrate (generated by methanotrophs from CH4) as the electron donor. Therefore, effects of electron acceptors during denitrification on N2O production should be further investigated so as to maximize the N2O generation. Free nitrous acid (FNA) is an important factor affecting N2O
∗
Corresponding author. E-mail address: [email protected] (G. Wu). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.ibiod.2019.01.007 Received 30 October 2018; Received in revised form 6 January 2019; Accepted 12 January 2019 0964-8305/ © 2019 Elsevier Ltd. All rights reserved.
International Biodeterioration & Biodegradation 138 (2019) 92–98
Y. Zhao et al.
2.2. SBR cyclic experiments
generation through the regulation of microbial activity and metabolic pathways. During biological nitrogen removal, N2O generation increased with the accumulation of NO2−-N (Du et al., 2016), which was mainly caused by the FNA inhibition on N2O reduction. The possible reason was that FNA could combine with the active site of the Cucontained N2O reductase (Nos), resulting in competitive inhibition of N2O reduction. Wang et al. (2018) illustrated that the inhibition of N2O reduction was reversible when the FNA concentration was below 0.0828 mg/L. In a denitrifying system with NO2−-N as the electron acceptor and starch as the carbon source, Miao et al. (2017) obtained that the maximum N2OeN conversion ratio (62.7%) occurred at the FNA concentration of 0.1875 mg/L. Gao et al. (2016) investigated the inhibition of FNA on Pseudomonas aeruginosa PAO1, and found that at the FNA concentration of 0.1 mg/L, the NO reductase (Nor) activity increased while the transcription level of N2O reduction gene decreased, providing a reasonable explanation for N2O accumulation. Based on above results, further exploration of the influence mechanism of FNA on the microbial activity and N2O generation is necessary for the realization of energy recovery during denitrification. Types of denitrifiers and their functional gene abundances are important factors determining the efficiency of biological nitrogen removal and N2O production. Therefore, a comprehensive understanding of the microbes (especially functional microbes) is beneficial for performance improvement of wastewater treatment processes. The metagenomics techniques based on high-throughput sequencing analysis, such as 454 pyrosequencing and Illumina, can provide information of microbial taxonomy, functional genes and metabolic pathways. For instance, Liu et al. (2018) deduced the metabolic models for different phylogenetic clades of Achromobacter and Agrobacterium, and found the varied denitrifying potentials based on the comparative genome analysis. Metagenomics analysis helps provide a deeper understanding of the mechanisms for microbial interactions in wastewater treatment processes, which may contribute to the explanation of N2O generation during denitrification subjected to different electron acceptors and FNA concentrations. This study aims to investigate the effects of electron acceptors and FNA concentrations on denitrification and N2O generation, in combination with the metagenomics analysis of microbial community and functional genes. The study not only clarified denitrification mechanisms from aspects of biological kinetics and microbial interactions, but also provided strategies for biofuel N2O generation in the denitrification process.
The cyclic experiments were carried out in a conical bottle with an effective volume of 2 L. The applied condition was the same as the parent SBR. Glucose was also used as the carbon source with the corresponding COD concentration of 800 mg/L; NaNO3 and NaNO2 were applied as the electron acceptors, respectively, with the nitrogen concentration of 200 mg/L. The sealed silicone tube and microelectrode were connected to the rubber stopper for liquor sampling and aqueous N2O detection, respectively. The bottle was placed in 25 °C water bath and magnetic stirrer was adopted to ensure complete mixing. Each test lasted for 170 min. Samples were taken for the detection of NO3−-N, NO2−-N and gaseous N2OeN. 2.3. Denitrification under different electron acceptors and FNA concentrations Batch tests were carried out under different electron acceptors and FNA concentrations. Glucose was the carbon source, and the initial COD concentration was 400 mg/L under all conditions. For activated sludges taken from SBRA and SBRI, NO3−-N or NO2−-N was used as the electron acceptor, respectively, with the initial concentrations of 100, 200 and 400 mg/L. Experiments were conducted using 1 L silk mouth bottles in 25 °C water bath. Each test lasted for 60 min, with gas and liquid samples taken at the interval of 10 min. pH and aqueous N2O were measured online. Before each reaction, 20 mL mixed liquor was withdrawn for the test of volatile suspended solids (VSS) concentrations. 2.4. Analytical methods VSS, NO3−-N and NO2−-N were measured according to standard methods (APHA, 1999). The NO3−-N, NO2−-N and N2OeN reduction rates were calculated through linear regression of measured profiles. The specific NO3−-N, NO2−-N and N2OeN reduction rates (rNO3-N, rNO2-N, rN2OeN) were determined by division of the corresponding VSS. FNA was calculated by the following equation (Anthonisen et al., 1976):
FNA (mg HNO2 /L) =
47 CNO2 −N × 14 [exp( −2300/(273 + T )) × 10 pH ] + 1
where T is the temperature (°C), and CNO2eN is the concentration of NO2−-N (mg/L). The N2O conversion ratio during denitrification was calculated as follows:
2. Materials and methods
N2 O conversion ratio (%) = 2.1. System setup and operation
rN2O−N r or N2O−N rNO2 −N rNO3 −N
where rN2OeN is the production rate of N2OeN (mg N/g VSS·h), rNO2-N is the reduction rate of NO2−-N when NO2−-N is the electron acceptor (mg N/g VSS·h), and rNO3-N is the reduction rate of NO3−-N when NO3−-N is the electron acceptor (mg N/g VSS·h). Nitrogen balance was calculated using a modified equation (Zhang et al., 2011, 2012):
Two sequencing batch reactors (SBRs), with the effective volume of 6 L, were adopted to acclimate denitrifiers at 25 °C. The seed sludge was taken from a wastewater treatment plant in Shenzhen, Guangdong, China. Each SBR cycle consisted of 4 h, including 10 min of filling, 160 min of anoxic mixing, 20 min of aeration, 35 min of settling and 15 min of discharging. 600 mL mixed liquor was discharged at the end of the aerobic phase to maintain the sludge retention time (SRT) at 10 d. The influent and effluent were manipulated by timer-controlled peristaltic pumps. Microporous aerators were used for aeration during the aerobic phase. The carbon source was glucose, with the chemical oxygen demand (COD) concentration of 800 mg/L. NaNO3 and NaNO2 were used to provide electron acceptors for each reactor (noted as SBRA and SBRI) with the nitrogen concentration of 200 mg/L. The COD/N ratio of the influent wastewater was 4. Other components of both reactors were the same of 200 mg/L NaHCO3, 250 mg/L NH4Cl, 25 mg/L Na2HPO4, 45 mg/L CaCl2, 100 mg/L MgSO4, 15 mg/L yeast extract, and 0.2 mL/L trace elements (Smolders et al., 1994).
TNIn = TNE + TND (N2, NO) + TND (N2 Og ) + TND (N2 Oaq) where TNIn is the influent nitrogen concentration (mg N/L), TNE is the effluent nitrogen concentration (mg N/L), TND (N2, NO) is the nitrogen removed via N2 and NO gases during denitrification (mg N/L), TND (N2Og) is the nitrogen removed via gaseous N2O (mg N/L), and TND (N2Oaq) is the nitrogen in the form of dissolved N2O (mg N/L). The specific electron consumption rates (mmol e−/g VSS·h) of nitrate reductase (Nar), nitrite reductase (Nir), Nor and Nos, and the electron distribution among different reductases were calculated according to Pan et al. (2013) and Zhao et al. (2018). The gaseous N2O was analyzed by the Agilent 6280 Gas Chromatograph (Agilent Technologies, USA) with an electron capture 93
International Biodeterioration & Biodegradation 138 (2019) 92–98
Y. Zhao et al.
detector and a HP-PLOT/Q column (J&W GC Columns, Agilent Technologies, USA). Temperatures during testing were 50 °C for the injection port, 50 °C for the oven, and 300 °C for the detector. Nitrogen gas was used as the carrier gas at the flow rate of 15 mL/min. Pure N2O gas was used as the standard for calibration. The aqueous N2O was measured online using microelectrode (N2O-500-9707, Unisense, Denmark). All the experiments were conducted with duplicates. 2.5. 16S rRNA and metagenomic sequencing Total DNA in sludge samples was extracted by TruSeq DNA Sample Prep Kit (Illumina Inc, USA). The 16S rRNA gene was amplified with universal primers 515F (GTGCCAGCMGCCGCGGTAA) and 806R (GGACTACHVGGGTWTCTAAT), and then high-throughput 16S rRNA gene sequencing was performed to analyze microbial community (Yin et al., 2017). The raw data of 16S rRNA genes was deposited in the NCBI SRA database (https://www.ncbi.nlm.nih.gov/sra) with the project ID of PRJNA503920. The metagenomic sequencing was performed on the Illumina Hiseq platform. Raw DNA reads were quality filtered to get the clean reads, which were further assembled into contigs using the SOAPdenovo software (Li et al., 2008). Then open reading frames (ORF) were predicted based on the assembled contigs using the MetaGene software (Noguchi et al., 2006). All the ORFs were clustered using the CD-HIT software (95% identity, 90% coverage) (Fu et al., 2012) to establish the non-redundancy gene set. Using the SOAPaligner software (Li et al., 2008), the high quality reads of each sample were compared with the non-redundancy gene set (95% identity) so as to count the gene abundance in the samples. BLASTP (Altschul et al., 1997) was applied to compare the gene set with the NR database (non-redundancy amino acid sequence database) to gain the species annotation and the taxonomic classification. The Evolutionary Genealogy of Genes_Nonsupervised Orthologous Groups database and the Kyoto Encyclopedia of Genes and Genomes database were used to annotate the function and metabolic pathways of the genes by the BLASTP program at the E value of 10−5. The metagenomic raw data was deposited in NCBI with the project ID of PRJNA504741. 3. Results and discussion 3.1. Long-term system performance and cyclic experiments Fig. S1 illustrates the effluent NO3−-N and NO2−-N concentrations in SBRA and the effluent NO2−-N concentrations in SBRI during the long-term operation. Both SBRs gradually reached to steady state after 3 SRTs, and VSS concentrations of SBRA and SBRI under steady state were 6.3 and 5.9 g/L, respectively. Compared with SBRA, SBRI achieved complete denitrification, with the average effluent NO2−-N concentration of 0.2 mg/L and the removal percentage of 99.8%. The average effluent NO3−-N and NO2−-N concentrations of SBRA were 24.8 and 22.3 mg/L, and the NO3−-N removal percentage was 75.2%. Miao et al. (2017) also found that the denitrification efficiency was high when NO2−-N was used as the electron acceptor and easily biodegradable organic carbon as the electron donor. Fig. 1 and Fig. S2 show dynamics of nitrogen concentrations, COD concentrations and nitrogen balance within typical cycles of SBRA and SBRI. During the initial 10 min of the reaction in SBRA, NO3−-N concentrations dropped rapidly, while NO2−-N and dissolved N2OeN concentrations swiftly increased. The electron consumption rates of Nar, Nir, Nor and Nos in the initial 10 min were 5.0, 1.6, 1.6 and 0.1 mmol e−/g VSS·h, respectively. The corresponding electron distribution ratio was 59.4%, 19.5%, 19.5% and 1.5%, respectively. At this stage, organic carbon was relatively sufficient to generate reducing power, which could provide adequate electrons for denitrification. According to the electron consumption rate of four denitrification reductases, Nar possessed the competitive advantage, and the electron
Fig. 1. Denitrification performance, N2O generations (a–b) and nitrogen balance (c–d) in typical SBR reaction cycles of SBRA and SBRI.
consumption rate of Nar was 3 times that of Nir and 38 times that of Nos. Therefore, NO3−-N was degraded rapidly, while NO2−-N and N2OeN accumulated in the initial 10 min. After 30 min of the reaction in SBRA, nitrogen concentrations showed a linear reaction trend, with the corresponding rNO3-N, rNO2-N and rN2OeN of −3.5, −0.5 and 2.5 mg N/g VSS·h, respectively. The electron consumption rates of Nar, Nir, Nor and Nos were 0.5, 0.3, 0.3 and 0.1 mmol e−/g VSS·h, and the electron distribution ratio was 42.3%, 24.2%, 24.2% and 9.3%, respectively. The organic carbon was gradually consumed and the ability to provide reducing power for denitrification was deteriorated significantly. As a result, the electron consumption rate decreased for all denitrification reductases. Notably, the electron consumption rate of Nar decreased remarkably, indicating that Nar was more susceptible to carbon availability than other three denitrification reductases. The electron consumption rate of Nos maintained at a low level during the whole process, suggesting the lowest electron competition ability of Nos. Therefore, N2O was continuously accumulated until it reached to the maximum concentration of 49.3 mg/L at the end of the reaction. The N2OeN to NO3−-N ratio
94
International Biodeterioration & Biodegradation 138 (2019) 92–98
Y. Zhao et al.
was 65.8%, and the NO3−-N concentration was 29.9 mg/L, with the denitrification efficiency of 71.4%. The NO2−-N concentration was 15.5 mg/L. The gaseous N2OeN production was 1.6 mg/L and was generated at a low rate. At the end of the reaction in SBRI, the NO2−-N concentration was 0 mg/L, dissolved N2OeN concentration was 15.1 mg/L, and the conversion ratio from NO2−-N to N2OeN was 15.4%. Similar to SBRA, NO2−-N degraded rapidly in the initial 10 min, and the dissolved N2OeN accumulated to 24.9 mg/L. Nir consumed electrons at a higher rate than Nos, which was manifested by the fact that the electron consumption ratio of Nir to Nos was 5. This could explain the N2OeN accumulation. During 10–120 min, the degradation rate of NO2−-N was 6.9 mg N/g VSS·h, and the N2OeN accumulation rate decreased to 1.1 mg N/g VSS·h. After that, the dissolved N2OeN concentration gradually decreased, which was possibly due to the low NO2−-N concentration (4.9 mg/L). The availability of organic carbon is an important factor affecting nitrogen removal, including N2O emission (Spinelli et al., 2018). Based on the above analysis, in SBRA and SBRI, the nitrogen concentrations decreased rapidly at the initial stage. Ample carbon sources provided adequate reducing power for denitrification. As a result, even though the electron capture capacity was different among Nar, Nir, Nor and Nos, adequate electrons could supply them independently. The threestep denitrification in SBRI afforded more electrons to Nos than the four-step denitrification in SBRA, resulting in less N2O generations in SBRI. Similarly, when the organic carbon was limited, the electron competition was more severe among the four denitrification reductases in SBRA than in SBRI. The weak electron competition ability of Nos further contributed to more N2OeN generation in SBRA (Pan et al., 2013). In SBRI, however, the NO2−-N concentration was too low to accept numerous electrons, so N2O was served as the major electron acceptor and the dissolved N2OeN concentration decreased to a low level.
Fig. 2. Aqueous N2O production rates with different electron acceptors in SBRA and SBRI. Maximal rate refers to the rapid production rate at the initial stage of denitrification (0–10 min). Average rate refers to the average rate in the whole process of denitrification (0–60 min).
production rate in SBRI was 1.6 times as much as in SBRA in the initial stage. While the average N2O production rate in SBRI was lower than in SBRA. It demonstrated that SBRI had been adapted to high NO2−-N concentrations. In the initial stage, owing to the long-term acclimation in SBRI, the activity of Nir could be stimulated and high competitive existed in SBRI. Then fewer electrons were obtained by Nos in SBRI, which caused rapid generations of N2O. However, with decreasing concentrations of NO2−-N, Nir demanded fewer electrons so that Nos received more. Compared with SBRA, Nos in SBRI had been adapted to abundant NO2−-N in the long-term operation, so as to readily obtain the electrons and reduce N2OeN. Thus, less N2O production on average was observed in SBRI.
3.2. Effects of electron acceptors on N2O generation during denitrification As shown in Fig. S3, when NO2−-N was the electron acceptor, a similar trend in SBRA and SBRI was observed that N2O production firstly increased, and then slowed down or declined under different oxidized nitrogen (NOX-N) concentrations. By contrast, N2O was increasingly generated with NO3−-N as the electron acceptor. Fig. 2 demonstrates the NOX-N degradation rates in SBRA and SBRI with NO3−-N and NO2−-N (100 mg/L) as the electron acceptor, respectively. In the initial stage with NO2−-N as the electron acceptor, N2O was rapidly produced at the rate of 18.0 mg N/g VSS·h in SBRA, whereas 28.9 mg N/ g VSS·h in SBRI. The average N2O production rates in SBRA and SBRI were 12.4 and 6.4 mg N/g VSS·h, respectively. In contrast, N2O production rates with NO3−-N as the electron acceptor in SBRA and SBRI were 7.4 and 0.1 mg N/g VSS·h, respectively. Obviously, the production of N2O utilizing NO2−-N as the electron acceptor was much higher than utilizing NO3−-N. A previous study reported that denitrification via NO2−-N as the electron acceptor produced more N2O than via NO3−-N (Du et al., 2016). This can be explained by a modified model of electron transfer chain during denitrification (Zeng et al., 2018). Electrons from organics metabolism are transported by two pathways, one in priority supplying the electrons for Nar, and the other one supplying the electrons shared by Nir, Nor and Nos. Electron competition exists among Nir, Nor and Nos. In this study, when NO3−-N was the electron acceptor, NO2−-N was gradually produced, resulting in less electron consumption of Nir. Hence, enough electrons for Nos ensured the reduction of N2O. However, when NO2−N was the electron acceptor, high concentrations of NO2−-N required more electrons for Nir and accordingly fewer electrons were reserved for Nos. As a result, N2O production increased when NO2−-N was the electron acceptor. In addition, when NO2−-N was the electron acceptor, the N2O
3.3. Effect of FNA on N2O generation during denitrification The relationship between the maximal FNA concentration and the N2O production rate is shown in Fig. 3 under different initial NO3−-N concentrations. For SBRA, the FNA concentration ranges were 0.0004–0.0078, 0.0003–0.0044 and 0.0004–0.0053 mg/L under initial NO3−-N concentrations of 100, 200 and 400 mg/L, respectively. The corresponding final concentrations of N2OeN were 18.0, 27.1 and 26.1 mg/L. However, the N2OeN generation was significantly enhanced with NO2−-N as the electron acceptor. At the end of the reaction, N2OeN productions were 37.2, 45.2 and 30.4 mg/L under initial NO2−-N concentrations of 100, 200 and 400 mg/L, with FNA concentrations of 0.0022–0.0319, 0.0038–0.0459 and 0.0061–0.1527 mg/ L, respectively. Especially, the N2OeN conversion ratios were 89.9% and 84.7% at initial NO2−-N concentrations of 100 and 200 mg/L, which were notably higher than those with NO3−-N as the electron acceptor. By comparison, when NO2−-N was used as the electron acceptor, the maximal FNA concentrations were 4–28 times as much as NO3−-N, which had a stronger inhibition on Nos and significantly increased the N2OeN generation. For SBRI, when NO2−-N was used as the electron acceptor, the dissolved N2O concentration increased rapidly in the initial 10 min, and then subsequently slowed down and reached to the maximum concentrations of 17.1, 31.1 and 27.6 mg/L, respectively, under the initial NO2−-N concentrations of 100, 200 and 400 mg/L. The corresponding N2OeN conversion ratios were 38.3%, 52.0% and 42.7%. The FNA concentrations were 0.0023–0.0071, 0.0029–0.0448 and 95
International Biodeterioration & Biodegradation 138 (2019) 92–98
Y. Zhao et al.
Fig. 4. Relative gene abundances encoding denitrification enzymes in SBRA and SBRI. Fig. 3. Aqueous N2O production rates under different FNA concentrations in SBRA and SBRI.
abundance of nar α, encoding the active catalytic subunit of Nar, it was 0.07% in SBRA while 0.03% in SBRI. Therefore, the relative gene abundances in SBRI were all lower whether in terms of the Nar encoding gene or the active catalytic site nar α encoding gene than those in SBRA. This provided a possible explanation for the lower ability of microbes acclimated to NO2−-N to utilize NO3−-N. On the other hand, the expression of the denitrification genes required suitable conditions, such as the existence of NO3−-N or other denitrification intermediates. For example, NO3−-N, NO2−-N and N2O were found to be the triggers for the expressions of their respective denitrification genes (Baek et al., 2005). The gene abundance encoding Nir was 4232, accounting for 24.2% of the denitrification genes, and 0.06% of the total genes in the sequenced sample of SBRA. The percentages of nirK and nirS genes were 0.03% and 0.02%, respectively. In SBRI, the Nir encoding gene abundance was 1278, which made up 25.3% of the denitrification genes and 0.04% of the total genes. Accordingly, the ratios of nirK and nirS genes were 0.01% and 0.03%, respectively. These two types of Nir possess the same function, albeit different distributions among diverse microbial species. Therefore, different types of Nir might be complementary for microbes, especially in varied environments so as to ensure the efficient reduction of NO2−-N (Zhang et al., 2017). By analysis of the Nir encoding gene, the gene abundances did not differ much in SBRA and SBRI. In addition, the Nir encoding gene tended to be highly expressed to ensure denitrification since NO2−-N was accumulated in SBRA. Therefore, microbes in SBRA had relatively strong ability to reduce NO2−-N when it was the electron acceptor. Nos is encoded by the gene nos, which generally consists of three transcriptional subunits, i.e. nosZ, nosR and nosDFYL. Among them, the nosZ gene encodes the catalytic subunit NosZ, which has two conservative regions of CuA (as the electron entrance) and CuZ (as the catalytic site) (Pomowski et al., 2011). Based on the metagenomics analysis, the relative gene abundances encoding nosZ were similar, i.e. 0.029% in SBRA and 0.022% in SBRI. Zhang et al. (2017) pointed out that the ratio of (nirK + nirS)/nosZ might influence N2O accumulation. In this study, the ratios in SBRA and SBRI were 1.98 and 1.93, respectively. Therefore, N2O generations during denitrification were not directly relevant to the gene abundance of nos.
0.0136–0.0245 mg/L. In contrast, under different NO3−-N concentrations, the N2OeN generation in SBRI was much lower. The final N2OeN concentrations were 0.2, 0.2 and 0.5 mg/L, with the N2OeN conversion ratios of 1.1%, 0.9% and 2.2%, respectively. The lowest FNA concentrations were observed, i.e. 0.0002–0.0004, 0.0006–0.0009 and 0.0009–0.0014 mg/L, respectively, which might be the main reason for the low N2OeN generation. Therefore, relatively weak inhibition of FNA with NO3−-N as the electron acceptor resulted in a low N2OeN generation in SBRI. Generally, higher concentrations of FNA induced more N2O production. Nonetheless, although with less FNA in SBRI, SBRI tended to produce more N2O than SBRA at the initial stage of denitrification. This was attributed to the high activity of Nir in SBRI owing to the long-term acclimation to NO2−-N. High concentrations of NO2−-N further stimulated Nir and caused inadequate electrons for Nos. Thus, more N2OeN was generated in SBRI than SBRA at the initial stage of denitrification. In this study, the highest N2O conversion ratio was obtained at 89.9% when NO2−-N was applied to SBRA. In previous studies, Van Doan et al. (2013) proposed that approximately 70% of NO3−-N was converted into N2OeN in a bioelectrochemical denitrification system, and Myung et al. (2015) obtained the conversion ratio from NO2−-N to N2O was around 70% with the accumulated polyhydroxybutyrate as the electron donor. Therefore, the present study provided a valuable method for N2O generation with high efficiency. 3.4. Metagenomics analysis of denitrification genes Two types of dissimilated nitrate reductases existed in microbes, categorized as membrane-bound nitrate reductase (Nar) and cytoplasmic nitrate reductase (Nap) (Philippot, 2002). The abundance of the gene encoding Nar (EC: 1.7.99.4) was 8442 in SBRA, accounting for 48.2% of total denitrification genes and 0.1% of total genes of the sequenced sample. In SBRI, the Nar (EC: 1.7.99.4) coding gene abundance was 2076, and the corresponding percentages were 41.1% of denitrification genes and 0.1% of total genes. The relative subunit gene abundances of Nar were higher in SBRA than those in SBRI (Fig. 4). Some denitrifiers, such as Paracoccus denitrificans, reduced NO3−-N mainly by Nar under anoxic conditions, while Nap was responsible for the catalytic reduction of NO3−-N during denitrification under oxic conditions. In SBRA, the Nar/(Nar + Nap) ratio was 0.11%, higher than that in SBRI of 0.04%. In addition, in perspective of the relative
3.5. Microbial community and relevant functions Proteobacteria and Bacteroidetes were the dominant microbes at the phylum level in SBRA and SBRI (Fig. S4), which was consistent with results of Kondaveeti et al. (2014). The relative abundance of Proteobacteria in SBRA was 61.6%, and it was 62.1% in SBRI. For Bacteroidetes, 96
International Biodeterioration & Biodegradation 138 (2019) 92–98
Y. Zhao et al.
could use NO3−-N as the electron acceptor for anaerobic metabolism (Reinhold-Hurek and Hurek, 2006). Paracoccus was capable of complete denitrification, which could express genes encoding four types of denitrification reductases (Zumft, 1997). The relative abundance of Paracoccus in SBRI (4.3%) was higher than SBRA (0.9%). Paracoccus had three known paralogs, which regulated the transcription in denitrification such as the NarR sensitive protein, and could participate in the NO3−-N reduction (Wood et al., 2001). 4. Conclusions Complete nitrogen removal was achieved when NO2−-N was the electron acceptor in the long-term acclimation. The N2O production was higher with NO2−-N as the electron acceptor, and it was generally improved with increasing FNA concentrations. The highest N2O conversion ratio of 89.9% was obtained when NO2−-N was applied to SBRA, because the abrupt electron demand from Nir caused insufficient electrons for Nos. The disparate abilities for microbes to degrade NO3−N and NO2−-N in SBRA and SBRI were mainly caused by different gene abundances encoding Nar and Nir. FNA concentrations, rather than the gene abundance of nos, impacted on the N2O generation. The dominant denitrifier in SBRA was Acidovorax, while Thauera was the most abundant one in SBRI.
Fig. 5. Relative abundances of denitrifiers at the genus level in SBRA and SBRI.
the relative abundances in SBRA and SBRI were 18.9% and 18.3%, respectively. At the genus level, Acidovorax was the dominant microbes in SBRA, with the percentage of 21.9%. Other microbes of relative high abundances were Rubrivivax (5.7%), a genus in Bacteroidetes (8.0%) and Dechloromonas (2.3%). In SBRI, the most abundant three species were Thauera (11.3%), Acidovorax (4.7%), and Candidatus_Accumulibacter (4.1%). Thauera was capable of complete denitrification, and the relative abundance was 2.5% in SBRA. Acidovorax belongs to denitrifier, and the abundance of Nar encoding gene in Acidovorax accounted for 49.6%. This might prove that Acidovorax was the main species in SBRA to reduce NO3−-N. Regarding to the genes encoding Nir, Nor and Nos in Acidovorax, the respective abundances took up 63.8%, 32.4% and 32.4% of the total Nir, Nor and Nos encoding genes. It indicated that Acidovorax was a complete denitrifier and played a major role in denitrification in SBRA. In SBRI, however, the relative abundance of Acidovorax was 4.7%, and the Nar encoding gene abundance was 138, accounting 5.5% of the total Nar encoding gene. Accordingly, the abundances of Nir, Nor and Nos encoding genes were 74, 58 and 30, with the respective percentage of 22.4%, 5.6% and 4.5%. Fig. 5 illustrates the relative abundances of different denitrifiers in the two reactors. The total relative abundance of all denitrifiers in SBRA was 32.6%, with the most abundant species of Acidovorax. In SBRI, the percentage of all denitrifiers was 26.9%, and Thauera was the dominant species. It was worth mentioning that not all denitrifiers had the ability of complete denitrification. Some microbes, lacking of N2O reductases, only conduct the incomplete denitrification and emit N2O as the final product (Jha et al., 2017). Some microorganisms in the phylum of Proteobacteria were acclimated during denitrification, such as Burkholderia, Ralstonia, Azoarcus and Thauera. All of them had the typical nosZ gene and were complete denitrifiers (Coyotzi et al., 2017). Acidovorax had the most Nos encoding gene, accounting for 35.1% of the total Nos encoding gene in SBRA, followed by Thauera (8.4%) and Dechloromonas (6.9%). In SBRI, however, most Nos encoding genes were detected in Thauera, with the relative gene abundance of 35.7%, followed by Rhodobacter (4.8%) and Acidovorax (4.5%). Therefore, microbial community could differ significantly under the long-term acclimation with different electron acceptors, and functional gene abundances would be extensively altered even for the same denitrifier. In the previous studies, it was found that the enriched Comamonas and type II methanotrophic archaea could efficiently convert NO2−-N into N2O (Gao et al., 2017). In our study, the relative abundance of Comamonas was 2.2% in SBRA, while 1.5% in SBRI. The relative abundance of Azoarcus in SBRA and SBRI was 0.1% and 1.0%. Azoarcus
Conflicts of interest The authors declare no conflict of interest. The authors claim that no financial and personal relationships with other people or organizations that could inappropriately influence (bias) our work. Acknowledgements This work was supported by the National Key Research and Development Program of China for International Science and Innovation Cooperation Major Project between Governments (No. 2016YFE0118800) and the Development and Reform Commission of Shenzhen Municipality (urban water recycling and environment safety program). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ibiod.2019.01.007. References Altschul, S., Madden, T., Schäffer, A., Zhang, J., Zhang, Z., Miller, W., Lipman, D., 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25 (17), 3389–3402. Anthonisen, A., Loehr, R., Prakasam, T., Srinath, E., 1976. Inhibition of nitrification by ammonia and nitrous acid. J. Water Pollut. Control Fed. 48 (5), 835–852. APHA, 1999. Standard Methods for the Examination of Water and Wastewater, twentieth ed. American Public Health Association, Washington, DC. Baek, S., Hartsock, A., Shapleign, J., 2005. Agrobacterium tumefaciens C58 uses ActR and FnrN to control nirK and nor expression. J. Bacteriol. 190 (1), 78–86. Coyotzi, S., Doxey, A., Clark, I., Lapen, D., Cappellen, P., Neufeld, J., 2017. Agricultural soil denitrifiers possess extensive nitrite reductase gene diversity. Environ. Microbiol. 19, 1189–1208. Drewer, J., Yamulki, S., Leeson, S.R., Anderson, M., Perks, M.P., Skiba, U.M., McNamara, N.P., 2017. Difference in soil methane (CH4) and nitrous oxide (N2O) fluxes from bioenergy crops SRC willow and SRF Scots pine compared with adjacent arable and fallow in a temperate climate. Bioenergy Res. 10 (2), 575–582. Du, R., Peng, Y., Cao, S., Wang, S., Niu, M., 2016. Characteristic of nitrous oxide production in partial denitrification process with high nitrite accumulation. Bioresour. Technol. 203, 341–347. Frison, N., Chiumenti, A., Katsou, E., Malamis, S., Fatone, F., 2015. Mitigating off-gas emissions in the biological nitrogen removal via nitrite process treating anaerobic effluents. J. Clean. Prod. 93, 126–133. Fu, L., Niu, B., Zhu, Z., Wu, S., Li, W., 2012. CD-HIT: accelerated for clustering the nextgeneration sequencing data. Bioinformatics 28 (23), 3150–3152.
97
International Biodeterioration & Biodegradation 138 (2019) 92–98
Y. Zhao et al.
the anaerobic metabolism of the biological phosphorus removal process: stoichiometry and pH influence. Biotechnol. Bioeng. 43 (6), 461–470. Spinelli, M., Eusebi, A.L., Vasilaki, V., Katsou, E., Frison, N., Cingolani, D., Fatone, F., 2018. Critical analyses of nitrous oxide emissions in a full scale activated sludge system treating low carbon-to-nitrogen ratio wastewater. J. Clean. Prod. 190, 517–524. Van Doan, T., Lee, T.K., Shukla, S.K., Tiedje, J.M., Park, J., 2013. Increased nitrous oxide accumulation by bioelectrochemical denitrification under autotrophic conditions: kinetics and expression of denitrification pathway genes. Water Res. 47 (19), 7087–7097. Wang, Y., Li, P., Zuo, J., Gong, Y., Wang, S., Shi, X., Zhang, M., 2018. Inhibition by free nitrous acid (FNA) and the electron competition of nitrite in nitrous oxide (N2O) reduction during hydrogenotrophic denitrification. Chemosphere 213, 1–10. Wood, N., Alizadeh, T., Bennett, S., Pearce, J., Ferguson, S., Richardson, D., Moir, J., 2001. Maximal expression of membrane-bound nitrate reductase in Paracoccus is induced by nitrate via a third FNR-like regulator named NarR. J. Bacteriol. 183, 3606–3613. Wunderlin, P., Mohn, J., Joss, A., Emmenegger, L., Siegrist, H., 2012. Mechanisms of N2O production in biological wastewater treatment under nitrifying and denitrifying conditions. Water Res. 46 (4), 1027–1037. Yamamoto, Y., Tachibana, T., 2018. Burning velocities of dimethyl ether (DME)–nitrous oxide (N2O) mixtures. Fuel 217, 160–165. Yin, Q., Miao, J., Li, B., Wu, G., 2017. Enhancing electron transfer by ferroferric oxide during the anaerobic treatment of synthetic wastewater with mixed organic carbon. Int. Biodeterior. Biodegrad. 119, 104–110. Zeng, D., Miao, J., Wu, G., Zhan, X., 2018. Nitrogen removal, microbial community and electron transport in an integrated nitrification and denitrification system for ammonium-rich wastewater treatment. Int. Biodeterior. Biodegrad. 133, 202–209. Zeng, R.J., Yuan, Z., Keller, J., 2003. Enrichment of denitrifying glycogen-accumulating organisms in anaerobic/anoxic activated sludge system. Biotechnol. Bioeng. 81 (4), 397–404. Zhang, M., Lawlor, P.G., Li, J., Zhan, X., 2012. Characteristics of nitrous oxide (N2O) emissions from intermittently-aerated sequencing batch reactors treating the separated liquid fraction of anaerobically digested pig manure. Water Air Soil Pollut. 223 (5), 1973–1981. Zhang, M., Lawlor, P.G., Wu, G., Lynch, B., Zhan, X., 2011. Partial nitrification and nutrient removal in intermittently aerated sequencing batch reactors treating separated digestate liquid after anaerobic digestion of pig manure. Bioproc. Biosyst. Eng. 34 (9), 1049–1056. Zhang, Y., Ji, G., Wang, R., 2017. Quantitative responses of nitrous oxide accumulation to genetic associations across a temperature gradient within denitrification biofilters. Ecol. Eng. 102, 145–151. Zhao, Y., Miao, J., Ren, X., Wu, G., 2018. Effect of organic carbon on the production of biofuel nitrous oxide during the denitrification process. Int. J. Environ. Sci. Technol. 15 (2), 461–470. Zumft, W., 1997. Cell biology and molecular basis of denitrification. Microbiol. Mol. Biol. Rev. 61, 533–616.
Gao, H., Liu, M., Griffin, J., Xu, L., Xiang, D., Scherson, Y., Liu, W., Wells, G., 2017. Complete nutrient removal coupled to nitrous oxide production as a bioenergy source by denitrifying polyphosphate-accumulating organisms. Environ. Sci. Technol. 51 (8), 4531–4540. Gao, S., Fan, L., Peng, L., Guo, J., Agulló-Barceló, M., Yuan, Z., 2016. Determining multiple responses of Pseudomonas aeruginosa PAO1 to an antimicrobial agent, free nitrous acid. Environ. Sci. Technol. 50 (10), 5305–5312. Jha, N., Saggar, S., Giltrap, D., Tillman, R., Deslippe, J., 2017. Soil properties impacting denitrifier community size, structure, and activity in New Zealand dairy-grazed pasture. Biogeosciences 14, 4243–4253. Kondaveeti, S., Lee, S., Park, H., Min, B., 2014. Bacterial communities in a bioelectrochemical denitrification system: the effects of supplemental electron acceptors. Water Res. 51, 25–36. Li, R., Li, Y., Kristiansen, K., Wang, J., 2008. SOAP: short oligonucleotide alignment program. Bioinformatics 24 (5), 713–714. Lin, Z., Sun, D., Dang, Y., Holmes, D.E., 2018. Significant enhancement of nitrous oxide energy yields from wastewater achieved by bioaugmentation with a recombinant strain of Pseudomonas aeruginosa. Sci. Rep. 8, 11916. Liu, S., Chen, Q., Ma, T., Wang, M., Ni, J., 2018. Genomic insights into metabolic potentials of two simultaneous aerobic denitrification and phosphorus removal bacteria Achromobacter sp. GAD3 and Agrobacterium sp. LAD9. FEMS Microbiol. Ecol. 94 (4), 20. Mannina, G., Ekama, G.A., Capodici, M., Cosenza, A., Van Loosdrecht, M.C.M., 2018. Influence of carbon to nitrogen ratio on nitrous oxide emission in an integrated fixed film activated sludge membrane bioreactor plant. J. Clean. Prod. 176, 1078–1090. Miao, J., Zhao, Y., Wu, G., 2017. Effect of organic carbons on microbial activity and structure in denitrifying systems acclimated to nitrite as the electron acceptor. Int. Biodeterior. Biodegrad. 118, 66–72. Myung, J., Wang, Z., Yuan, T., Zhang, P., Nostrand, J., Zhou, J., Criddle, C.S., 2015. Production of nitrous oxide from nitrite in stable type II Methanotrophic enrichments. Environ. Sci. Technol. 49 (18), 10969–10975. Noguchi, H., Park, J., Takagi, T., 2006. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Res. 34 (19), 5623–5630. Pan, Y., Ni, B.J., Bond, P.L., Ye, L., Yuan, Z., 2013. Electron competition among nitrogen oxides reduction during methanol-utilizing denitrification in wastewater treatment. Water Res. 47 (10), 3273–3281. Philippot, L., 2002. Denitrifying genes in bacterial and Archaeal genomes. BBA-Gene Struct. Expr. 1577 (3), 355–376. Pomowski, A., Zumft, W., Kroneck, P., Einsle, O., 2011. N2O binding at a [4Cu:2S] copper–sulphur cluster in nitrous oxide reductase. Nature 477, 234–237. Reinhold-Hurek, B., Hurek, T., 2006. The genera Azoarcus, Azovibrio, Azospira and Azonexus. In: Rosenberg, E., DeLong, E.F., Lory, S., Stackebrandt, E., Thompson, F. (Eds.), The Prokaryotes, fourth ed. Springer-Verlag, New York, pp. 873–891. Scherson, Y., Woo, S.G., Criddle, C.S., 2014. Production of nitrous oxide from anaerobic digester centrate and its use as a co-oxidant of biogas to enhance energy recovery. Environ. Sci. Technol. 48 (10), 5612–5619. Smolders, G.J.F., Van der Meij, J., Van Loosdrecht, M.C.M., Heijnen, J.J., 1994. Model of
98