Introduction

The discharge of nitrogen containing wastewater into natural waters has become a global concern, resulting in problems such as acidification and eutrophication (Canfield et al. 2010; Kuypers et al. 2018; Bailes et al. 2020; Lan et al. 2011). Thus, optimizing the performance of nitrogen removal in WWTPs is essential in order to control water pollution caused by the release of anthropogenic waste. Biological nitrification and denitrification procedures have been widely applied to ammonium-rich WWTPs (Lan et al. 2011; Ali and Okabe 2015) in which ammonia is firstly transformed to nitrate via nitrification, with nitrate then converted to nitrogen gas by anoxic biotransformation using organic carbon as an electron source (Yang et al. 2020a, b). However, biological nitrification and denitrification is not the most effective process in nitrogen removal owing to the high oxygen demand of complete nitrification, the heterotrophic denitrification requires an external carbon source, accompanied by the production of sludge and nitrous oxide (N2O) (Shalini et al. 2012; Wang et al. 2018; ** a mathematical modeling method for determining the potential rates of microbial ammonia oxidation and nitrite oxidation in environmental samples. Int Biodeterior Biodegrad 133:116–123" href="/article/10.1186/s13568-021-01321-6#ref-CR24" id="ref-link-section-d48317407e761">2018). Briefly, the 250 mL sterile wide-mouth bottles were used to incubate samples (HDPE, Thermo Fisher Scientific, US) containing 1 g activated sludge slurry, which were then incubated at 28 ℃ with continual agitation at 75 rpm to maintain aerated conditions. To decrease dissolved inorganic nitrogen (DIN) assimilation by phytoplankton, samples were cultured in the dark. The incubation supernatant was sampled every 6 h for measurement of ammonia, nitrite, and nitrate concentrations. The previous research has suggested that mathematical modeling methods are more suitable for the analysis of nitrification rates because nitrification was a non-linear dynamic process (Hong et al. 2018). Large errors will introduce if nitrification rates are calculated via using only two or three data points to establish linear regression. Therefore, a mathematical modeling method was adopted in the present study (Fig. 1). Firstly, the corresponding mathematical equation was obtained by fitting a smoothed time series of DIN concentration using the Boltzmann distribution pattern. The fitted NOx− curves were used to calculate the average rates of AO (Va, Eq. (1)) and NO (Vn, Eq. (2)). Then, instantaneous Va and Vn equations and curves were calculated according to the first derivative of the fitting curves (Fig. 1). The average and instantaneous data parameters (including maximum instantaneous data points) were suggested in Fig. 2. The three indices (Va, Vn and their maximum value) described the average nitrification potential and maximum nitrification potential of a sample. The dynamic process of nitrification in samples was depicted in Fig. 1. The index of nitrogen balance (INB) was used to make an estimation with the total amount of variability of DIN in the incubation system (Eq. (3)).

$$\overline{{V_{a} }} = \frac{{\left[ {NO_{{_{3} }}^{ - } + NO_{2}^{ - } } \right]_{final} \_\left[ {NO_{3}^{ - } + NO_{2}^{ - } } \right]_{initial} }}{\Delta T}$$
(1)
$$\overline{{V_{n} }} = \frac{{\left[ {NO_{3}^{ - } } \right]_{final} - \left[ {NO_{3}^{ - } } \right]_{initial} }}{\Delta T}$$
(2)
$$INB = \frac{{DIN_{final} }}{{DIN_{initial} }} = \frac{{\left[ {NH_{4}^{ + } } \right]_{final} + \left[ {NO_{2}^{ - } } \right]_{final} + \left[ {NO_{3}^{ - } } \right]_{final} }}{{\left[ {NH_{4}^{ + } } \right]_{initial} + \left[ {NO_{2}^{ - } } \right]_{initial} + \left[ {NO_{3}^{ - } } \right]_{initial} }}$$
(3)
Fig. 1
figure 1

Boltzmann fitted curves (a, c) and differential curves (b, d) from Boltzmann fitting equations of the DIN time series for instantaneous rates of ammonia oxidation (Va) and nitrite oxidation (Vn) of the water samples

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Fig. 2
figure 2

Average and maximum rates of ammonia oxidation and nitrite oxidation

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DNA extraction and PCR amplification

Total DNA was extracted 0.3 g from each sample using a PowerSoil DNA isolation kit (Mo Bio, CA, US), then stored it in the refrigerator with temperature of − 80 °C for further analysis. The NanoDrop Lite spectrophotometer (Thermo Fisher Scientific, DE, US) was used to determine the concentration of extracted DNA. Using extracted DNA as a template, the respective primer pairs (Arch-amoAF/Arch-amoAR, amoA-1F/amoA-2R, Nxr-f27/Nxr-r617) were used to participate in the PCR amplification targeting archaeal amoA, bacterial amoA, and nxrB genes separately (Reddy et al. 2014; Rotthauwe et al. 1997; Hong et al. 2020). Optimized PCR was run using mixtures with a total amount of 25 μL, composed of 12.5 μL of GoTaq Green Master Mix (Promega, US), 1 μL of forward and reverse primers (15 μM), 1 μL of template DNA, and 9.5 μL of ddH2O. The thermocycler program for archaeal amoA and bacterial amoA was shown below: 95 °C for 3 min, followed by 35 cycles of 95 °C for 45 s, 53 °C for 45 s, and 72 °C for 45 s, finally followed by elongation at 72 °C for 10 min. The thermocycler program for nxrB was shown below: 95 °C for 2 min, followed by 32 cycles of 95 °C for 45 s, 62 °C for 45 s, and 72 °C for 45 s, finally followed by elongation at 72 °C for 5 min. The NanoDrop spectrophotometer (NanoDrop Technologies, DE, US) and MiniBEST agarose gel DNA extraction kit (TaKaRa, Bei**g, China) were used to check the concentration of PCR products and purify the products respectively. Then, stored it in a single tube for high-throughput sequencing. Finally, the PCR products through purification were sequenced on the Illumina MiSeq platform.

High-throughput sequencing analysis

High-throughput sequencing was performed according to the standard protocol described in previously studies using Mothur software v.1.40.5 (Schloss et al. 2009; Wu et al. 2018; He et al. 2016). Tags and primers were firstly removed from the obtained sequences. Then, the quality-trimmed sequences were aligned to the gene database including sequences for amoA-AOA (He 2018), amoA-AOB (The 16S rRNA gene sequences of the identified strains and uncultured sequences from environmental samples as seed sequences in database were obtained from NCBI and Fungene), and nxrB (Jiao et al. 2018). The seqs. command was applied to filter and remove badly aligned sequences, while the commands pre.cluster, chimera.uchime and remove.seqs were used to reduce errors and remove chimeric sequences. Operational taxonomic units (OTUs) were identified using 0.06, 0.13, and 0.06 cut off values for OTU clustering of AOA, AOB, and Nitrospira, respectively.

Real-time quantitative PCR

The abundance of nitrifier prokaryotes was determined by real-time PCR (Q-PCR) using an Eco Real-Time PCR System (Ilumia, CA, US) with SYBR Green PCR Master Mix (Promega, US). Q-PCR was run with a 15 μL reaction mixture containing 1.0 μL template DNA, 0.1 μL of forward and reverse primers respectively, 6.3 μL ddH2O and 7.5 μL SYBR Green PCR Master Mix. The primers Arch-amoAF/Arch-amoAR were used for the quantification of archaeal amoA gene, with a PCR condition starting at 95 °C for 3 min, followed by 45 cycles of 30 s at 95 °C, 45 s at 53 °C and 1 min at 72 °C. The primers Nxr-f27/Nxr-r617 were used for Nitrospira nxrB gene quantification, with a PCR condition starting at 95 °C for 2 min, followed by 40 cycles of 45 s at 95 °C, 45 s at 62 °C and 45 s at 72 °C. The primers amoA-1F/amoA-2R were used for bacterial amoA gene quantification, with a PCR condition starting at 95 °C for 3 min, followed by 40 cycles of 15 s at 95 °C, 1 min at 55 °C, and 1 min at 72 °C. The Plasmid Mini Preparation Kit (Tiagen, China) was used to extract plasmids containing the targeted gene fragments from E.coli hosts. The extracted plasmid DNA with tenfold serial dilutions was used to obtain the standard curves. Samples and standards were determined in triplicate and average values were calculated. Melt curves generating in each assay were applied to check the specificity of the amplified products.

Nucleotide data of high throughput deposition

The nucleic acid sequences of high throughput used in this study was deposited in the National Omics Data Encyclopedia (NODE). The accession number of archaeal amoA gene, bacterial amoA gene and nxrB gene were OEP002783, OEP002784, OEP002785, respectively.

Results

Time series of DIN during incubations

Time series of ammonia, nitrite, and nitrate concentrations during the incubations of sample A (SA) and sample B (SB) were displayed in Fig. 3.

Fig. 3
figure 3

Time series for the concentrations of ammonium, nitrite and nitrate in the activated sludge of the aeration tank in ** a mathematical modeling method for determining the potential rates of microbial ammonia oxidation and nitrite oxidation in environmental samples. Int Biodeterior Biodegrad 133:116–123" href="/article/10.1186/s13568-021-01321-6#ref-CR24" id="ref-link-section-d48317407e1529">2018). The INB value of sample A firstly decreased from 1.0 to 0.8 at the initial stage of incubation and then began to increase from 0.8 to 1.37, suggesting that assimilation and mineralization co-occurred in the period. Also, the change was closely related to the accumulation of nitrite.

The trend of DIN variation in sample B was inclined to be different from that of sample A. The ammonia concentration gradually decreased at a relatively stable rate, reaching a minimum concentration (6.34 µmol L−1) at 8 h. No remarkable nitrite accumulation was observed in the incubation period, suggesting that the rate of NO was equal to or stronger than the rate of AO. The nitrite concentration increased slowly, reaching a maximum level (8.66 µmol L−1) at 4 h and then reducing to very low level at 8 h. Due to the absence of nitrite accumulation, the time series of nitrate variation followed a similar trend to that of the sum of nitrate and nitrite. In the incubation of sample B, the INB value remained stable at about 1.0.

Ammonia oxidation and nitrite oxidation rates

For samples A and B, the Boltzmann fitted curves for both Va and Vn exhibited similar patterns with an initial lag phase, an exponential growth phase, and a plateau phase. Based on the fitted curve data, the average Va and Vn values in sample A were calculated as 6.51 µmol L−1 h−1 and 6.52 µmol L−1 h−1 respectively, while the average Va and Vn values in sample B were 14.48 µmol L−1 h−1 and 14.59 µmol L−1 h−1, respectively (Fig. 2).

The equations and curves of instantaneous Va and Vn values (Fig. 1b and d) were established based on the first derivative of the fitted curves (Fig. 1a and c). The dynamic characteristics of Va and Vn was clearly exhibited in the instantaneous rate curves in the incubations of sludge samples. In the incubation of sample A, both instantaneous Va and Vn were inclined to present the characteristics of first increasing and then decreasing. After a slow period of growth lasting for about 20 h, Va and Vn began to increase at a faster rate and reached their maximum levels of 11.11 µmol L−1 h−1 and 13.68 µmol L−1 h−1 by 50 h, then gradually declined until the end of the incubation period (Fig. 1). However, the instantaneous Va and Vn curves of sample B were significantly different from those in sample A. Both instantaneous Va and Vn maintained a declining trend in the incubation period, resulting in the initial Va and Vn values their maximum values were 7.6 µmol L−1 h−1 and 11.11 µmol L−1 h−1, respectively.

The abundance of microbial communities in nitrification

The abundances of AOA, AOB and NOB communities were estimated via the genes amoA and nxrB using the qPCR method. The abundance of AOA was the lowest in the microbial communities, ranging from 1.2 × 105 to 1.5 × 105 copies/g sludge (Fig. 4). In comparison, the abundance of AOB was 4 orders of magnitude higher than AOA, ranging from 1.0 × 109–9.7 × 109 copies/g sludge. However, the abundance of NOB Nitrospira was found to be 1–2 orders of magnitude higher than that of AOB.

Fig. 4
figure 4

Abundances of AOA, AOB and NOB in the aeration tank of **nfeng WWTPs

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OTU-level composition of nitrification microbial communities

All the raw sequences obtained via high-throughput sequencing were subjected to quality control processing, trimming, filtering, and removing low-quality sequences to obtain a total of 489, 24,524, and 14,283 high-quality AOA, AOB and NOB functional gene sequences, respectively.

The AOA, AOB and NOB community composition was shown in Fig. 5. Based on the 0.06 distance level, a total of 12 dominant OTUs were obtained for the AOA amoA gene. About half of these sequences were attributed to three known genera, including Nitrosotenuis (33.33%), Nitrososphaera (8.33%), and Nitrosocosmicus (8.33%) (Fig. 5a). Similarly, 154 dominant OTUs were obtained for the AOB amoA gene at the 0.13 distance level, which were assigned to four species. Nitrosomonas sp. was the most dominant (79.75%), followed by Nitrosococcus sp. (0.91%), with only a small portion classified as specific species, including Nitrosomonas europaea (0.61%) and Nitrosomonas oligotropha (1.96%) (Fig. 5b). Among the NOB community, 1556 dominant OTUs were obtained at the 0.06 distance level, the majority of which were not affiliated with any known genera (74.29%), while the remaining sequences were classified as Candidatus Nitrospira defluvii (17.74%), Nitrospira inopinata (5.01%), Candidatus Nitrospira Ecomare2 (2.63%), and others (0.32%) including Nitrospira japonica and Nitrospira marina (Fig. 5c).

Fig. 5
figure 5

Community composition of AOA, AOB and NOB based on the dominated OTUs

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Discussion

Despite sample A and B were collected at different times from the anammox-inoculated wastewater treatment tank, the variation of time series of DIN in two samples exhibited similar trends. Sample A and sample B were collected in different seasons with similar temperature. Therefore, sampling times may be a major element affecting the nitrification activity of activated sludge in this study. The nitrification activity of sample A was weaker than that of sample B, which may be related to the various sampling times of activated sludge. In the incubation period of sample A, nitrite was accumulated that associated with substrates provided by the AO process. However, NOB had the highest abundance among the nitrification community which represented that the oxidation of nitrite may be delayed while NO activity may need induction with sufficient nitrite concentrations. In the incubation of sample B, almost no nitrite accumulation was observed, indicating a high NO capability and highlighting the different active function states in sample B compared to sample A.

Results showed that the instantaneous rates of Va were initially higher than Vn for both sample A and B, then the instantaneous rates of Vn gradually exceeded that of Va in the incubation (Fig. 1). The instantaneous rates of both Va and Vn were higher in sample B than sample A, with the maximum rate of Va in sample B being fivefold higher than sample A, while the maximum rate of Vn in sample B was more than threefold higher than that of sample A. Furthermore, after the maximum rates were achieved, the reduction in rates observed in sample B were faster than that of sample A, which were consistent with the observed rates of NO, which tended to be higher than that of AO in sample B (Fig. 3).

The abundance of AOB performed higher than that of AOA in sludge, indicating that AOB played a dominant role in AO (Fig. 4). This result was similar to the majority of consequences in former researches that suggested the AOB amoA gene abundance was typically 2–4 orders of magnitude higher than that of AOA (** et al. 2010; Limpiyakorn et al. 2011; Wells et al. 2009; Yapsakli 2010). The abundance of AOB has been reported to be higher than AOA in NH3+ rich environments (Yang et al. 2016), with Nitrosomonas AOB usually detected in this circumstance (Ke et al. 2012; Wang et al. 2013). However, some previous studies have suggested that the dominance of AOMs may not directly be related to their contribution to ammonia oxidation, they showed the process was mediated mainly by AOA though AOB was more abundant (Pan et al. 2018a, b; Niu et al. 2013). In the current study, the copy numbers of NOB in this study have exceeded many previous studies from various environmental habitats such as Chinese WWTPs (Pan et al. 2018a, b), Drinking Water Treatment Plants sludge (DWTPs) (Flower et al. 2018), freshwater recirculating aquaculture systems (Bartelme et al. 2017), and soils (Hu et al. 2017).

The abundances of all three nitrification microbial communities in sample A were lower than that of sample B as well as the abundances of AOB and NOB in sample B were tenfold and fivefold higher than sample A, respectively (Fig. 4). The most previous studies focused on ammonia-oxidizing microbes, neglecting the important role of nitrite-oxidizing microbes (Gao et al. 2018; Zhang et al. 2018; Meng et al. 2017; Ali et al. 2015). The results of the present study show that the activated sludge had strong nitrite oxidation activity besides ammonia oxidation activity.

Except the cultured three known genera of AOA, the rest of its community were composed of uncultured or unclassified genera because of the limited size of 16S reads and various databases via ambiguous classification. Some new AOA species existing in the system was possible which was similar to the study of nitrifying archaeal in the north Pacific (Semedo et al. 2021). As we known, the Nitrosomonas was dominant species in AOB. Among the Nitrosomonas bacterial community, all species were unclassified species except for Nitrosomonas europaea and Nitrosomonas oligotropha. Nitrosomonas was one of the most common genera found to be responsible for AO in WWTPs (Coskuner et al. 2002; Siripong et al. 2007; Wang et al. 2012). These findings were consistent with previous results for **nfeng WWTPs (Yang et al. 2020a, b), suggesting that Nitrosomonas was the only AOB genus exhibiting significant levels of enrichment in the incubation. NOB Nitrospira was found to be the main contributor of NO in this study. However, there are many bacterial genera known to be capable of NO, with the known nitrite oxidizing genera including Nitrospira, Nitrobacter, Nitrotoga, Nitrococcus, Nitrospina, and Nitrolancea (STéPHANIE et al. 1994; Alawi et al. 2009; Daims et al. 2016). In particular, previous studies have suggested that Nitrobacter was a driver of NO in biological WWTPs (Gieseke et al. 2003). Then, the later researches found both Nitrobacter and Nitrospira existed in municipal WWTPs (Siripong et al. 2007). Nitrospira played a key role in the NOB community in activated sludge and nitrifying fluidized bed reactors especially in WWTPs of nitrite limitation due to the competitive advantage provided by the periplasmic nitrite oxidoreductase (NXR) (Yang et al. 2020a, b; Hovanec et al. 1996; Hovanec et al. 1998; Juretschko et al. 1998; Schramm et al. 1998; Wagner et al. 1996; Yang et al. 2021; Daims et al. 2016). But Nitrobacter sp. were found to be dominant rather than Nitrospira sp. in the nitrifying systems of coke WWTPs and in bioreactors treating high strength wastewater (Cho et al. 2014; Kim et al. 2011a, b; Figuerola et al. 2010; Kim et al. 2011a, b). It was also suggested that NOB Nitrotoga was generally detected in WWTPs at low temperatures (Alawi et al. 2009). Therefore, the actual abundance of NOB in **nfeng WWTPs may be much higher than the level reported in the present study owing to the similar temperatures of sampling times and the same sampling location.

Implication for regulating the nitrification process

Anammox has been identified in recent decades as an efficient and cost-effective pathway for N removal using NH4+ and NO2− as substrates to generate N2 gas without the production of N2O in anaerobic environments (Li et al. 2020; Yang et al. 2020a, b; Meng et al. 2021). Although anammox plays an important part in the **nfeng anammox-inoculated WWTPs, it couldn’t replace the effect of the combined network composed of nitrification, denitrification and anammox pathways in N removal. In ammonium-rich wastewater treatment, microbial nitrification is an indispensable process, which was a significant limiting-rate step in the nitrogen removal process, connecting mineralization and nitrogen loss processes (Kuypers et al. 2018; Zhang et al. 2018; Zhou et al. 2015; Yang et al. 2021). Through the nitrification process, microorganisms can transform ammonia into nitrite and nitrate (Yang et al. 2020a, b; Yang et al. 2018; Wang et al. 2021), providing effective substrates for both the anammox and denitrification processes, ensuring effective nitrogen removal by the whole treatment system (Additional file 1).

Notably, a certain concentration of nitrite helps to maintain efficient anammox processes in nitrification. However, high of NO was observed, with the activity exceeding that of AO, inhibiting nitrite accumulation (Fig. 2). Subsequently, the anammox process would be weakened. Therefore, appropriate inhibition of NOB activity in the treatment system would be an alternative method for improving the anammox activity in anammox-inoculated WWTPs, avoiding an increase in operational costs and a reduction in nitrogen removal efficiency caused by the lack of nitrite accumulation. Overall, establishing methods to regulate the nitrification process is an important issue and the composition and activity of NOB community in anammox-inoculated WWTPs should be pay more attention in future studies.