Abstract

The purpose of this study is to explore the nitrogen removal efficiency of the moving bed biofilm reactor (MBBR) under different ammonia nitrogen concentrations (states P1–P5), especially the composition of various forms of nitrogen, related genes and microbial community structure and succession law in the effluent of the reactor. The results show that the average efficiency of MBBR effluent denitrification is 63.63%. The concentration dynamics of NO3-N and NO2-N in the effluent indicated a relatively short-range nitrification and denitrification reaction in the MBBR. The results of 16SrDNA sequencing of P1–P5 microbial samples found that changes in the concentration of ammonia nitrogen in the influent produced significant changes in the composition of the microbial community in the MBBR. The genera Ottowia and Flavobacterium played an important role in the nitrogen removal of the MBBR system.

HIGHLIGHTS

  • Changing the concentration of ammonia nitrogen in the influent to explore the succession rule of microbial community in the MBBR reactor.

  • The pathway of nitrogen metabolism in the MBBR reactor was constructed to explore the functional genes and their characteristics involved in nitrogen metabolism in the reactor.

INTRODUCTION

Most of the urban sewage in southern China has the characteristics of high ammonia nitrogen concentration and low chemical oxygen demand (COD). These problems may be attributed to poor capacity to remove nitrogen and phosphorus and abnormal operation in the sewage plants in cities and towns. Thus, it is increasingly important to treat the sewage water (Wei & Song 2008). Although the traditional biological denitrification process has a good denitrification effect, the investment and operating costs are generally high, energy consumption is large, and the amount of remaining sludge is also large. The moving bed biofilm reactor (MBBR) based on suspended packing has many advantages such as strong load resistance, less floor space and low operating cost (Wang & Luo 2015). In recent years, there have been many studies on the effects of denitrification in MBBR biofilms (Torkaman et al. 2015; Gani et al. 2016) and there have been few reports on the analysis of microbial communities using various molecular biological sequencing methods (Wang et al. 2008; Raudkivi et al. 2017; Cai et al. 2018). Research on its metabolic pathways is still insufficient. Therefore, the study on the microbial community structure in the MBBR biofilm in this experiment can clarify the relationship between the microbial community structure in the reactor and the wastewater treatment efficiency, and the construction of nitrogen metabolism pathway is of great significance for explaining how to improve the wastewater treatment efficiency and optimize the denitrification effect from the gene level.

In this experiment, the MBBR reactor was used to treat simulated domestic sewage, control the concentration of ammonia nitrogen in the influent, and determine the quality of the effluent separately. The nitrification reaction principle is as follows: nitrification is a biochemical reaction process in which nitrifying bacteria convert ammonia nitrogen to nitrate nitrogen and nitrite nitrogen using oxygen as electron acceptor under aerobic conditions. The reaction is divided into two steps: first, transforming NH4+-N into NO2-N by ammonia oxidizing bacteria (AOB) (Equation (1)), second, converting NO2-N into NO3-N by nitrite oxidizing bacteria (Equation (2)).
formula
(1)
formula
(2)
Denitrification is a process in which heterotrophic microorganism denitrifying bacteria convert nitrite nitrogen and nitrate nitrogen into nitrogen and finally discharge them into the atmosphere under the hypoxia condition of extremely low molecular dissolved oxygen and use organic matter as electron donor.
formula
(3)
formula
(4)

Experiments were carried out on simulated domestic sewage with ammonia concentrations of 20 mg/L, 30 mg/L, 50 mg/L, 100 mg/L and 200 mg/L. The stable effluent water quality of each reactor with ammonia concentration was tested. Meanwhile, 16SrDNA sequencing was conducted on microbial samples of biofilm in the reactor to obtain the microbial community under different ammonia concentrations. Microbe samples in P1 state were sequenced using the Illumina Hiseq2500 platform, and the mechanism of biological denitrification in the MBBR reactor was studied from the perspective of microbial functional genes.

MATERIALS AND METHODS

Experimental device

As shown in Figure 1, the MBBR test device uses Plexiglas material. The main body of the device is a cylinder with a diameter of 20 cm and a height of 108 cm. The effective volume at the bottom is 30 L. It is hemispherical. The top is equipped with a mixer tube. The carriers are a polypropylene material consisting of a hollow sphere containing 24 spherical petal-shaped sheets (see also Table 1). The carriers' filling ratio used is about 40%, which makes them have better hydraulic conditions (Wang et al. 2019).

Figure 1

Schema of experimental plant.

Figure 1

Schema of experimental plant.

Experimental water quality and protocol

In this experiment, artificial simulated domestic sewage was used, and the water quality indexes were determined according to the water quality of typical urban sewage in south China. The simulated wastewater was prepared with glucose as carbon source, ammonium chloride as nitrogen source, potassium dihydrogen phosphate as phosphorus source and other trace elements required for microbial growth. The water quality of the formulated sewage: COD concentration is 300 mg/L, NH4+-N concentration is 20 mg/L, total phosphorus (TP) concentration is about 4 mg/L, dissolved oxygen is about 2.5 mg/L, pH is about 7.0. The membrane hanging method is fast hanging method, the inoculated sludge comes from the aerobic tank of Shenzhen Nanshan Wastewater Treatment Plant and is domesticated for about 25 d. The biofilm on the carriers is obviously thickened, and the water quality index is stable, indicating that the device is successful.

After the successful hanging membrane, according to the difference of ammonia and nitrogen concentration in the incoming water, the test running process was divided into five reaction stages (P1–P5), other incoming water conditions remained unchanged, and the hydraulic retention time (HRT) was 12 hours for continuous operation for 100 d, with 20 d as a reaction stage. Each stage running conditions and numbers are shown in Table 2. The water quality condition of the discharge water was regularly tested until the discharge water indicators were stable. After each stage was stable for 4 d continuously tested each discharge water quality indicators, and on the last day of each stage, biofilm samples were taken from the medial and lateral of the carriers, DNA was extracted and 16S rDNA sequencing was carried out; then 1 group of biofilm samples with the best treatment effect and high diversity was selected for metagenomic sequencing.

Table 1

Scientific details about the carriers

Diameter × height × wall thickness (mm)Void fraction (%)Specific area (m2·m3)Density (kg/m3)Packing density (kg/m3)
25 × 25 × 1.0 90 460 0.91 ∼ 0.94 96 
Diameter × height × wall thickness (mm)Void fraction (%)Specific area (m2·m3)Density (kg/m3)Packing density (kg/m3)
25 × 25 × 1.0 90 460 0.91 ∼ 0.94 96 

Under aerobic conditions, the concentration of ammonia nitrogen in the MBBR influent was changed, the indicators of NH4+-N, COD, TP, NO3-N, and NO2-N were regularly checked, and the treatment efficiency of the MBBR reactor for sewage with different ammonia-nitrogen concentrations was investigated.

Based on the second-generation high-throughput sequencing technology, 16SrDNA gene sequencing was performed on MBBR biofilm samples under different influent ammonia nitrogen concentrations to analyse and compare the changes of microbial community structure and diversity, and explore the succession laws of MBBR microbial community.

According to the water quality test results and 16SrDNA sequencing results, the P1 stage biofilm samples for sequencing of the metagenomics were selected. Statistical analysis was performed on functional genes involved in various metabolic activities, the nitrogen metabolic pathways were analysed, and the internal mechanism of microbial denitrification in MBBR biofilms was explored.

Water quality analyses

For the water quality indicators to be tested in this test and the specific testing procedures of the testing methods used refer to Wei (2002). In the experiment, NH4+-N and total nitrogen (TN) were measured by Nessler reagent spectrophotometry (UV-7504 (A), ultraviolet and visible spectrophotometer, Xinmao, Shanghai); pH was measured by portable pH meter (pH 3210, WTW, Germany); COD was measured by Hash fast determination method (DR3900, HACH, USA); DO was measured by portable dissolved oxygen meter (Oxi 3210, WTW, Germany); NO3-N, NO2-N were measured by ICS-1500 ion chromatograph.

Microbial and molecular biology analysis

DNA extraction

The PowerSoil® DNA Isolation Kit DNA reagent extraction kit from MOBIO was used. After the extraction of genomic DNA was completed, the integrity and purity of the DNA were tested by 1% agarose gel electrophoresis, and the concentration and purity of DNA were also detected by NanoDrop One.

PCR amplification

Using the Premix Taq instrument, the 16S V4 region primers (806R and 515F) were selected to complete the polymerase chain reaction (PCR) amplification. The full primer information used for the reaction is shown in Table 3. Then the GeneTools Analysis Software (Version 4.03.05.0, SynGene) was selected for the PCR product concentration comparison, according to the quality principle. The volume required for each sample was obtained and each PCR product was mixed. The E.Z.N.A.® Gel Extraction Kit was used to recover the PCR mixed product. The TE buffer eluted and recovered the target DNA fragments.

Table 2

Operating conditions of each reaction stage

Stage numberNH4+-N (mg·L−1)HRT/hFilling rate (%)DO (mg·L−1)T(°C)Operating days
P1 20 12 40 2.5 ∼ 3.0 25 ± 5 20 
P2 30 12 40 2.5 ∼ 3.0 25 ± 5 20 
P3 50 12 40 2.5 ∼ 3.0 25 ± 5 20 
P4 100 12 40 2.5 ∼ 3.0 25 ± 5 20 
P5 200 12 40 2.5 ∼ 3.0 25 ± 5 20 
Stage numberNH4+-N (mg·L−1)HRT/hFilling rate (%)DO (mg·L−1)T(°C)Operating days
P1 20 12 40 2.5 ∼ 3.0 25 ± 5 20 
P2 30 12 40 2.5 ∼ 3.0 25 ± 5 20 
P3 50 12 40 2.5 ∼ 3.0 25 ± 5 20 
P4 100 12 40 2.5 ∼ 3.0 25 ± 5 20 
P5 200 12 40 2.5 ∼ 3.0 25 ± 5 20 

Reaction conditions: (1) denaturation for 30 s at 94 °C; (2) 52 °C annealing for 30 s; (3) extension for 30 s at 72 °C. It was circulated 30 times and stored at 4 °C.

Library construction and sequencing

The library was completed according to the NEBNext® Ultra™ DNA Library Prep Kit for Illumina® standard process. PE250 sequencing of the amplicon was then performed using the sequencing platform Illumina Hiseq2500.

Data analysis

All clean tags from each sample were clustered using usearch software (V8.0.1517, http://www.drive5.com/usearch/), which clusters sequences at 97% similarity to operational taxonomic units (OTUs), while discarding chimeras and singleton sequences. The representative sequences of OTUs were compared with the Greengenes (http://greengenes.secondgenome.com/) and Unite (ITS, http://unite.ut.ee/index.php) databases using Qiime to obtain annotation information for the species.

Sequencing quality control

Direct high-throughput sequencing of total sample DNA using the Illumina Hiseq2500 platform. Fastqc software was used to evaluate the quality of raw reads from both ends, and Flash and Mothur were used to splice the two ends of the reads into a complete destination fragment, while the quality of the reads and the effect of the merge were examined and filtered to obtain the final high-quality, clean data.

Gene sequence assembly

Metagenomic assembly of the samples was based on the effective data after quality control. Second-generation data sequence splicing software MEGAHIT was used to perform sequence de novo splicing to obtain high-quality Scaffold fragments. After the chimera was filtered, the assembled Scaffold was disconnected at one or more consecutive N points to obtain the sequence fragment from which N was removed, namely Scaftigs. Then all the fragments less than 500 bp were removed in Scaftigs and then mixed with the unused reads in the sample to facilitate the detection of low-abundance species information.

Gene prediction

According to the screened original samples and the mixed assembled Scaftigs sequence, MetaGeneMark software was used to complete the open reading frame) gene prediction, and the predicted genes were subjected to CD-HIT de-redundancy operation.

Functional notes

On the basis of non-redundant genes (unigenes), functional gene analysis was performed, the functional gene annotation of KEGG (Kyoto Encyclopedia of Genes and Genomes) completed, and the main nitrogen metabolism pathway drawn in the sample according to the annotation results.

RESULTS AND DISCUSSION

MBBR reactor pollutant removal efficiency

Suspension of the biofilm was successful and continuous input of water gradually increased the inflow load. After a period of debugging, the quality of the effluent was stable. At this time, the average concentration of COD and NH4+-N and the average removal rate of the effluent are shown in Table 4. The concentration of COD, NH4+-N, TN, NO3-N, NO2-N and other indicators of the effluent in each stage was detected to know the composition of various forms of nitrogen in the effluent of the reactor. The specific detected data are shown in Figures 2 and 3.

Figure 2

(a) Change graph of COD removal effect under different influent ammonia nitrogen concentrations; (b) change graph of ammonia nitrogen removal effect under different influent ammonia nitrogen concentration.

Figure 2

(a) Change graph of COD removal effect under different influent ammonia nitrogen concentrations; (b) change graph of ammonia nitrogen removal effect under different influent ammonia nitrogen concentration.

Figure 3

(a) Variation of TN removal effect under different influent ammonia nitrogen concentrations; (b) changes of NO3-N and NO2-N in the effluent under different ammonia nitrogen concentrations.

Figure 3

(a) Variation of TN removal effect under different influent ammonia nitrogen concentrations; (b) changes of NO3-N and NO2-N in the effluent under different ammonia nitrogen concentrations.

Based on the analysis of these water quality indicators, changes in various forms of nitrogen do not conform to the traditional ammonia nitrogen nitrification process theory. In the traditional nitrification process, ammonia nitrogen is first oxidized to NO2-N, and this reaction is usually slower, while NO2-N can be quickly converted to NO3-N, so the general situation will not cause the accumulation of NO2-N. However, short-range nitrification and denitrification reaction means that the nitrification reaction is retained at the nitrite stage, without the steps of conversion of nitrite nitrogen to nitrate nitrogen. The purpose is to complete denitrification directly using NO2 as the electron acceptor. The reaction equation is as follows:
formula
(5)
formula
(6)
From the NO2-N and NO3-N concentration change curves of the effluent quality detection in this reactor, we found that the two produced a certain amount of accumulation, and under the condition that the TN consumption is basically the same, with the effluent NO2-N concentration decreased, but the NO3-N concentration did not. This showed that some NO2-N may not be converted to NO3-N and directly converted into N2 to be removed, and a short-range nitrification and denitrification reaction occurred (Ding et al. 2014).

The aerobic bacteria outside the carriers in MBBR and the aerobic or anaerobic bacteria inside can exist in the same reactor, which provides an optimal condition for the simultaneous nitrification and denitrification (SND) of the reactor. It can be obtained from the calculation equation of simultaneous nitrification and denitrification efficiency. In the P1–P5 stages, the values are 85.8%, 93.5%, 89.3%, 69.3%, 75.9%, which is similar to MBBR reactors such as Ding et al. (2014). The synchronous nitrification and denitrification efficiency of the experiment is basically the same. At the same time, we found that the high concentration of ammonia nitrogen in the influent will affect the efficiency of SND, so the SND rate was reduced.

16S rDNA high-throughput sequencing of MBBR biofilm samples

16S rDNA high-throughput sequencing was performed on the biological samples of 5 reaction stages (P1-P5) to analyse the composition and relative abundance of microbial communities in MBBR biofilms.

The bacterial population was divided into 35 different phyla, and the abundance of which is greater than 1% is listed in Figure 4.

Figure 4

Distribution of bacteria at the phylum classification level.

Figure 4

Distribution of bacteria at the phylum classification level.

It can be seen from Figure 4 that there are five main categories, namely Proteobacteria, Bacteroidetes, Verrucomicrobia, Planctomycetes and Firmicutes.

In the P1–P5 reaction stage, the most dominant phylum was Proteobacteria with relative abundances of 82.65%, 56.31%, 72.43%, 51.82% and 77.27%, respectively. Proteobacteria are gram-negative bacteria, most of which are facultative or obligate anaerobic bacteria. Most of them are heterotrophic bacteria and are important contributors to the COD and nitrogen removal processes. The most important class contained in the Proteobacteria is β-proteobacteria, which contains more aerobic or facultative bacteria and is considered to be closely related to sludge denitrification (Thomsen et al. 2007).

The second dominant phylum is Bacteroidetes, which are denitrifying alkali-producing bacteria. It belongs to chemical energy organic nutrition type and can degrade some complex solid organic substances (cellulose, lipids, proteins, etc.) (Vavourakis et al. 2016). There are also some nitrogen-fixing bacteria (Sphingobacterium spp., etc.) that can play a role in denitrification (Hill et al. 2007).

The third dominant phylum is Verrucomicrobia, which is a newly classified bacterial phylum and contains a large number of genes involved in carbohydrate degradation (i.e. glycoside hydrolase, sulfate esterase, carbohydrate esterase and polysaccharide lyase) (Martinez-Garcia et al. 2011). With the increase of ammonia nitrogen concentration in the influent, the predominant flora remains unchanged, and the relative abundance difference is small. Hao's study showed that the relative abundance of Proteobacteria was less affected by the nutrient content in the influent and more affected by the dissolved oxygen content in the inlet water (Hao et al. 2019). Further analysis, a total of 521 genera were detected, and the bacterial genera whose abundance greater than 1% are shown in Figure 5.

Figure 5

Distribution of bacteria at the genus classification level.

Figure 5

Distribution of bacteria at the genus classification level.

It can be seen from Figure 5 that there are a total of 25 major bacterial genera. In the P1 reaction stage, the Ottowia genus (66.76%) of the β-proteobacteria Comamonadaceae was the most dominant genus, followed by the β-proteobacteria (Hyphomonadaceae) UKL13-1 genus (4.27%) and Flavobacterium (2.66%) of Bacteroidetes. Among them, three species in the Ottowia have been isolated successfully: Ottowia thiooxydans is facultative anaerobic bacteria, isolated from the activated sludge system, can perform denitrification and produce N2O; Ottowia beijingensis AN3T is an aerobic bacterium, isolated from coking wastewater activated sludge system, which can degrade phenols; Ottowia pentelensis is also separated from the coking wastewater activated sludge system and can form flocculent bacteria (Spring et al. 2004; Cao et al. 2011; Felföldi et al. 2011). Flavobacterium is a gram-negative bacterium, strictly aerobic, organic energy nutrition, can use some organic nitrogen compounds as nitrogen source, decompose organic matter to maintain its own growth and reproduction, and can secrete extracellular polymers to form bacterial micelle to enhance the adhesion of biofilms (Liu et al. 2016).

In the P2 reaction stage, Arcobacter (6.80%) of the ɛ-proteobacteria (Campylobacteraceae) (6.80%) became the most dominant genus, followed by the γ-proteobacteria (Moraxellaceae) Acinetobacter (6.07%) and Beta-proteobacteria (Comamonadaceae) Hydrogenophaga (6.06%). Among them, the genus Arcobacter are gram-negative bacteria belonging to Campylobacteraceae. It uses amino acids and organic acids as carbon sources to reduce nitrate to nitrite. Hydrogenophaga is a gram-negative bacterium that can produce an insoluble yellow pigment; part of the species can use nitrate anaerobic respiration, denitrification, but less use of carbohydrates (Choi et al. 2020).

In the P3 reaction stage, the genus Ottowia (34.40%) was reverted to the most dominant genus, followed by Haliangium genus (25.13%).

In the P4 reaction stage, the genus Emticicia (19.33%) of the Sphingobacteria class Flexibacteraceae is the first dominant genus, and its role in the biofilm system needs further study, followed by the genus Hydrogenophaga (101.90%).

In the P5 reaction stage, the Ottowia genus (53.88%) once again occupied the absolute advantage, becoming the first dominant genus, followed by the Flavobacterium genus (8.98%).

From the above results, it can be seen that the composition of the microbial community at each stage has significantly changed at the genus classification level due to the concentration of ammonia nitrogen in the influent. Among them, the genus Ottowia related to denitrification outcompeted in the P1, P3, P5 reaction stage, while in the P2 stage, the relative abundance of the genus Ottowia becomes 2.19%, less than Arcobacter, Acinetobacter spp. (6.07%), Hydrogenophaga spp. (6.06%); at the P4 stage, the relative abundance of Ottowia spp. becomes 3.20%, and the dominant position is Emticicia spp. (19.33%), Hydrogenophaga spp. (11.90) %) replaced. Flavobacterium genus is one of the dominant genera at all stages. This indicates that Ottowia and Flavobacterium may play an important role in the removal of nitrogen in the MBBR system, and the genus Ottowia could be more sensitive to changes in the concentration of ammonia nitrogen. As the concentration of ammonia nitrogen in the influent increases, its relative abundance fluctuates first, then increases, and decreases and finally increases; the fluctuating changes in the microbial community structure may be related to the competition for the dominant position of various denitrifying fungi and Ottowia fungi such as Arcobacter and Hydrogenophaga.

Metagene sequencing

The results of DNA quality detection of biofilm samples using NanoDrop One to perform quality inspection on the extracted sample DNA are shown in Table 5.

Table 3

Primer pairs used in the experiment

Amplification areaName of the primerPrimer sequence (5, → 3,)Fragment length/bp
V4 515F GTGCCAGCMGCCGCGGTAA 310 
806R GGACTACHVGGGTWTCTAAT 310 
Amplification areaName of the primerPrimer sequence (5, → 3,)Fragment length/bp
V4 515F GTGCCAGCMGCCGCGGTAA 310 
806R GGACTACHVGGGTWTCTAAT 310 

It can be seen from Table 5 that the sample DNA concentration is ≥20 ng/μL, 1.7 ≤ A260/280 ≤ 2.0, and the volume is ≥15 μL, indicating that the extracted DNA has high purity and subsequent operations can be performed.

  • Data preprocessing and sequence assembly

The results of using Fastqc, Flash and Mothur software for quality filtering are shown in Table 6. The results of using the second-generation data sequence splicing software MEGAHIT to perform sequence de novo splicing and assembly are shown in Table 7.

  • Annotation analysis of gene function

Table 4

Leachate average quality indexes of the inlet and outlet of MBBR

Water quality indexInfluent (mg·L−1)Effluent (mg·L−1)Removal rate (%)
COD 316.50 27.25 91.46 
NH4+-N 21.38 7.73 63.63 
Water quality indexInfluent (mg·L−1)Effluent (mg·L−1)Removal rate (%)
COD 316.50 27.25 91.46 
NH4+-N 21.38 7.73 63.63 
Table 5

NanoDrop One test results

Sample serial numberConcentration (ng/μL)A260/280Volume (μL)
801.722 1.879 35 
Sample serial numberConcentration (ng/μL)A260/280Volume (μL)
801.722 1.879 35 
Table 6

Statistics of sample data pretreatment results

SampleRaw readsRaw basesClean readsClean basesClean Q30 (%)Clean GC (%)Effective (%)
46,526,720 6,979,008,000 38,106,206 5,614,559,227 96.34 51.00 80.45 
SampleRaw readsRaw basesClean readsClean basesClean Q30 (%)Clean GC (%)Effective (%)
46,526,720 6,979,008,000 38,106,206 5,614,559,227 96.34 51.00 80.45 
Table 7

Statistical analysis of sample gene sequence assembly results

SampleTotal numberTotal length (bp)Average length (bp)Max length (bp)N50 length (bp)N90 length (bp)GC (%)
172,271 204,121,575 1184.89 284,455 1267 581 46.35 
SampleTotal numberTotal length (bp)Average length (bp)Max length (bp)N50 length (bp)N90 length (bp)GC (%)
172,271 204,121,575 1184.89 284,455 1267 581 46.35 

On the basis of non-redundant genes, functional gene analysis and KEGG functional annotation are performed. The first and second-level gene function classification statistics are shown in Figures 6 and 7, respectively.

Figure 6

Classification and statistics of KEGG functional genes in samples (level 1).

Figure 6

Classification and statistics of KEGG functional genes in samples (level 1).

Figure 7

Classification and statistics of KEGG functional genes in samples (level 2).

Figure 7

Classification and statistics of KEGG functional genes in samples (level 2).

It can be seen from Figure 6 that the number of functional genes measured in MBBR samples is 81962, which are divided into 6 major categories. Among them, the number of functional genes related to metabolism (49047, 59.84%) is greatest, with rich metabolic diversity. The second abundant group are functional genes related to genetic information processing (8559, 10.44%) and environmental information processing (7880, 9.61%).

It can be seen from Figure 7 that the functional genes in the MBBR sample are divided into 43 subcategories. The number of functional genes related to conventional metabolism overview (including carbon metabolism, secondary metabolite biosynthesis, amino acid biosynthesis, etc. (8094, 9.88%) is the most, followed by carbohydrate metabolism (7802, 9.52%) and amino acid metabolism (7683, 9.37%), which confirmed that most of the microorganisms in MBBR samples have the function of degrading COD; this is consistent with the study on the microorganism metabolism of activated sludge in sewage plants (Guo et al. 2015, 2017). The highly expressed genes are energy metabolism (6321, 7.70%), metabolism of cofactors and vitamins (4704, 5.74%), and signal transduction (4096), 4.99%), membrane transport (3783, 4.62%) and translation (3736, 4.56%). These functional genes are inseparable from the growth and metabolism of MBBR biofilms and the degradation of pollutants.

Functional genes and metabolic pathways related to nitrogen metabolism

The KEGG database was compared with the functional genes related to nitrogen metabolism. A total of 759 functional genes were involved in nitrogen metabolism, accounting for 0.93% of all functional genes. At the same time, based on the KEGG database comparison results, the brief nitrogen metabolism pathway is shown in Figure 8.

Figure 8

Major nitrogen metabolic pathways in MBBR.

Figure 8

Major nitrogen metabolic pathways in MBBR.

The metabolism of nitrogen in microorganisms mainly includes reaction processes such as nitrogen fixation, denitrification, nitrification, reduction of dissimilatory nitrate to ammonia, and reduction of assimilated nitric acid to ammonia (Albertsen et al. 2012; Yang et al. 2014). It can be seen from Figure 8 that in the five reaction processes plotted, there are mainly 14 functional genes including NarGHI, NapAB, NarB, NasAB, NifDKH, AmoCAB, NirK, NirS, NirBD, NrfAH, Hao, NorBC, NxrAB and NosZ. The functional genes related to denitrification (NarGHI, NapAB, NirK, NorBC, NosZ, NirS) have the highest relative abundance, reaching 188, followed by the functional genes related to nitrification (AmoCAB, Hao, NxrAB, 75), dissimilatory nitrate reduction to ammonia-related functional genes (NirBD, NrfAH, 42), and assimilation nitrate reduction to ammonia-related functional genes (NarB, NasAB, 13).

In the five reaction processes of denitrification, nitrification and denitrification dominate and play a key role in MBBR denitrification. At the same time, there are short-term nitrification and denitrification in the reactor. Among them, responsible for encoding EC: 1.7.99.4 denitrification enzyme, the NarGHI gene content concerning the nitrate to nitrite conversion process is the most abundant; responsible for encoding EC: 1.14.18.3 1.14.99.39 nitrification enzyme, EC: 1.7.2.6 nitrification enzyme, AmoCAB and Hao genes involved in the conversion of ammonia nitrogen to nitrite nitrogen and nitrate nitrogen are relatively low, indicating a low AOB abundance. Judging from the quality of the effluent, ammonia nitrogen is the main nitrogen element, which also confirms the result. This may be one of the reasons why the MBBR reactor failed to achieve higher ammonia removal rate.

CONCLUSION

  • The average efficiency of MBBR effluent denitrification in each stage of P1–P5 reached 63.63%; analysis of the NO2-N and NO3-N concentration change curves in the water revealed that there was a short-range nitrification and denitrification reaction in the MBBR denitrification reaction.

  • The results of 16SrDNA of P1–P5 microbial samples found that changes in the concentration of ammonia nitrogen in the influent produced significant changes in the composition of the microbial community in MBBR, and the genera Ottowia and Flavobacterium played an important role in the nitrogen removal of the MBBR system.

  • Metabolic pathways using redundant analysis of functional genes show that MBBR biofilms have the largest number of functional genes related to metabolism, rich metabolic diversity, and the highest relative abundance of functional genes related to denitrification (NarGHI, NapAB, NirK, NorBC, NosZ, NirS, 188), followed by nitrification-related functional genes (AmoCAB, Hao, NxrAB, 75). Nitrification and denitrification dominate the denitrification reaction, of which, involved in the conversion of nitrate to nitrite the NarGHI gene content is the most abundant in the salt reaction process, while the AmoCAB and Hao gene content involved in the conversion process of ammonia nitrogen to nitrite nitrogen and nitrate nitrogen is less.

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

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