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Article

Effects of Hydraulic Retention Time on the Performance and Microbial Communities of a High-Load Partial Nitrification Reactor

by
Yuhan Liu
1,2,3,
Shuyan He
4,
Hangyi Li
1,2,3,
Yue Jin
5,
Chunfang Zhang
6 and
Wenjie Zhang
1,2,3,*
1
Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin 541006, China
2
University Engineering Research Center, Watershed Protection and Green Development Guilin University of Technology, Guilin 541006, China
3
Key Laboratory of Carbon Emission and Pollutant Collaborative Control, Education Department of Guangxi Zhuang Autonomous Region, Guilin University of Technology, Guilin 541006, China
4
Hebei Blue Environmental Protection Engineering Co., Ltd., Shijiazhuang, China
5
College of Tourism & Landscape Architecture, Guilin University of Technology, Guilin 541006, China
6
Institute of Marine Biology and Pharmacology, Ocean College, Zhejiang University, Zhoushan 316021, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(21), 3130; https://doi.org/10.3390/w17213130
Submission received: 26 September 2025 / Revised: 22 October 2025 / Accepted: 27 October 2025 / Published: 31 October 2025
(This article belongs to the Section Wastewater Treatment and Reuse)
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Abstract

A high-load partial nitrification reactor (HLPNR) was operated to treat high-ammonia wastewater by varying the hydraulic retention time (HRT). The associated shifts in the microbial community were analyzed using PCR-DGGE and high-throughput sequencing. The results indicated that the reactor achieved a maximum nitrogen loading rate (NLR) of 10.14 kg·N/(m3·d) at an HRT of 1.5 h, with a nitrite accumulation rate (NAR) of 86%. PCR-DGGE analysis revealed Proteobacteria and Nitrosomonas as the dominant phylum and genus, respectively, whose relative abundances varied significantly with HRT. Specifically, the relative abundance of Nitrosomonas sp. G1 increased from 15% to 40%, indicating that the abundances of Proteobacteria and Nitrosomonas were directly related to the load of the HLPNR. High-throughput sequencing revealed a marked decline in both the diversity and abundance of the HLPNR’s microbial community under conditions of reducing load. The dominant genus changed; however, the stability of the HLPNR was not destroyed. It can be inferred that the stability of the HLPNR primarily depended on the enrichment of key functional bacteria rather than on the overall microbial community composition.

1. Introduction

Ammonia nitrogen is an essential nutrient for aquatic plants and algae; however, it is also a major pollutant in water bodies. Elevated concentrations can lead to eutrophication, water quality deterioration, toxicity, and even the death of sensitive aquatic organisms and severe disruption of aquatic ecosystem balance. Therefore, in recent years, environmental regulations have had increasingly stringent standards for the discharge of ammonia nitrogen in wastewater. High ammonia nitrogen concentration wastewaters (HANCWs) such as pig farm wastewater and coking wastewater have a comprehensive source, complex composition, and poor biodegradability [1,2,3]. Traditional nitrification and denitrification processes need to increase aeration and organic carbon sources, which leads to increased operating costs. The HLPNR controls the nitrification process to terminate at the nitrite stage, which can reduce aeration volume by 60%. This reduction in aeration volume not only decreases the energy consumption associated with operating aeration equipment but also lowers the overall operating costs of the treatment system. In addition, the HLPNR can operate without the addition of external organic carbon sources, simplifying the treatment process and eliminating costs related to the purchase, storage, and addition of organic carbon sources, thus reducing material costs. How to treat HANCW efficiently with low energy consumption has been a global challenge in wastewater treatment engineering. In this paper, a high-load partial nitrification reactor (HLPNR) was used to treat HANCW. The main purpose was to enrich and cultivate ammonia oxidation bacteria (AOB) with a high nitrification rate and high load in the HLPNR. Compared with the traditional nitrification and denitrification process, the HLPNR could reduce the amount of aeration by 60% without an external organic carbon source. It has been proven to be an effective treatment method for HANCW treatment [4,5,6].
The fundamental principle of the HLPNR is to exploit the inherent differences in microbial kinetics between ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB), thereby controlling the nitrification process to terminate at the nitrite (NO2−-N) stage. A certain proportion (approximately 56%) of NH4+-N in wastewater was oxidized to NO2−-N [7,8], which could be achieved by inhibiting NOB and promoting AOB reproduction [9]. Increasing the NO2−-N concentration by partial nitrification provided substrate for the anaerobic ammonia oxidation (anammox) reaction [10]. A review of the literature shows that the loads of the partial nitrification and anammox process are usually below 1 kg-N/(m3-d) [11,12]. In comparison, the anammox process of two-stage mode can reach a load of more than 10 kg-N/(m3-d) [13]. The stringent conditions of the partial nitrification process, however, hinder the stable accumulation of nitrite (NO2−-N). Therefore, the partial nitrification can only operate under lower loads during practical applications [14]. In summary, increasing the load of the partial nitrification with stable operation is one of the urgent problems in practical application.
The hydraulic retention time (HRT) is an essential factor affecting the operation of partial nitrification reactors. HRT is defined as the average time wastewater spends in the reactor and is calculated using the formula:
HRT = V/Q
where V is the effective volume of the reactor (e.g., the aerobic tank in HLPNR, in m3) and Q is the influent flow rate (in m3/h).
Short HRT causes the loss of the sludge. On the other hand, the ammonia nitrogen in the wastewater has insufficient time to react with the microorganisms due to the limited time, which will affect the treatment efficiency of the reactor. Conversely, while long HRT mitigates sludge washout and ensures sufficient ammonia nitrogen degradation, it poses new challenges. Operationally, extended retention time reduces reactor throughput (i.e., wastewater volume treated per unit time), lowering productivity and applicability in large-scale scenarios. Economically, longer HRT usually requires larger reactor volume to maintain capacity, increasing both capital costs (e.g., materials, equipment for larger reactors) and long-term operational costs (e.g., higher aeration/mixing energy, sludge treatment expenses). This trade-off between short and long HRT emphasizes optimizing this key parameter for specific goals. For example, in HLPNR systems treating HANCW, subsequent studies should focus on balancing HRT to minimize sludge loss and maximize ammonia removal while controlling capital and operational costs. Such optimization will enhance reactor stability and economic feasibility for industrial use.
The novelty of this study lies in several aspects. First, the application of the HLPNR represents a novel approach to treating HANCW. By reducing the amount of aeration by 60% without the need for an external organic carbon source, it overcomes the major drawbacks of traditional nitrification and denitrification processes, such as high energy consumption and high cost due to additional carbon-source requirements. Second, compared to the common partial nitrification and anammox processes, the HLPNR shows the potential to reach a higher load. Third, this study focuses on the effect of HRT on the stability and microbial community of the high-load partial nitrification reactor, which has been rarely explored in previous research. These innovative aspects not only contribute to the solution of the global challenge of HANCW treatment but also provide new insights into the field of wastewater treatment engineering. Therefore, an HLPNR was designed and used to achieve stable operation of the partial nitrification reactor treating HANCW with changing HRTs. Using Polymerase Chain Reaction–Denaturing Gradient Gel Electrophoresis (PCR-DGGE) and high-throughput sequencing techniques, sludges during different HRTs were analyzed. The sludge microbial community was investigated across a range of operational parameters.

2. Materials and Methods

2.1. Reactor

The operation of the HLPNR system, depicted in Figure 1, is as follows:
First, wastewater is held in the distribution bucket and then transferred to the aerobic tank via the feeding pump. In the aerobic tank, a blower supplies air to create an aerobic environment, which is essential for aerobic microorganisms to decompose organic matter in the wastewater. The agitator in the tank ensures that the wastewater and microorganisms are well mixed, enhancing the treatment efficiency.
During the process, the on-line monitoring device keeps track of parameters like pH. If adjustments are needed, the acid (alkali)-adding pump injects acid or alkali to maintain the optimal pH for microbial activity.
After treatment in the aerobic tank, the wastewater flows into the sedimentation tank. Here, solids (sludge) settle at the bottom. The clarified water can be discharged, while the Sludge return pump sends a portion of the settled sludge back to the aerobic tank. This sludge contains a high concentration of microorganisms, which helps to maintain the microbial population in the aerobic tank and ensures continuous and efficient treatment.
The HLPNR system, depicted in Figure 1, was composed of a 9 L aerobic tank coupled with a 5.5 L sedimentation tank. Operational parameters were strictly controlled: the pH in the aerobic tank was kept at 7.5–7.6 through the addition of a diluted 1 mol/L Na2CO3 solution, the dissolved oxygen (DO) level was maintained at around 2 mg/L, and the temperature was held at 30 ± 1 °C by means of a heater.

2.2. Inoculated Sludge and Wastewater Compositions

The influent was artificially simulated wastewater with the compositions shown in Table 1. The inoculated sludge was taken from the Wayao Wastewater Treatment Plant in Guilin, China [15,16]. The volume of inoculated sludge was 1000 mL, which was filtered through a screen and poured into the reactor. The initial value of sludge Mixed Liquor Suspended Solids (MLSS) was 3073 mg/L, and the initial value of Mixed Liquor Volatile Suspended Solids (MLVSS) was 970 mg/L.

2.3. Analytical Methods

During the study, water samples were collected and stored in a refrigerator at 4 °C daily. NH4+-N and NO2−-N were measured according to the Standard Methods protocol. The Total Nitrogen (TN) was determined using the persulfate method, and NO3−-N was calculated as the difference between the TN and the sum of NH4+-N and NO2−-N. The MLSS and MLVSS of the sludge samples were measured in accordance with the Standard Methods protocol. The pH was measured using a pH meter (9010; Jenco Instruments, San Diego, CA, USA), and the DO was measured using a DO meter (9010; Jenco Instruments, San Diego, CA, USA). The water temperature was measured by online monitoring DEC digital pH meter (DPH10AC; Tianjian Innovation Monitoring, Beijing, China). Person correlation analysis was done based on IBM SPSS Statistics 23.0, IBM Corporation, Armonk, NY, USA; the nitrogen loading rate (NLR) and the nitrogen removal rate (NRR) were obtained by referring to the formulae in the literature [17,18,19].

2.4. Determination of Microbial Communities

Over the course of the study, a total of seven sludge samples were obtained. The initial sample (Sample 1) represented the inoculated sludge used to seed the reactor. Sample 2 was collected following successful start-up, under conditions of an influent ammonia concentration of 450 mg/L and a 21 h HRT. Subsequent samples (Sample 3 through 7) were taken at progressively reduced HRTs of 15, 6, 3, 2, and 1.5 h. The samples were collected and stored at −20 °C. Total DNA was extracted from the sludges using the Powersoil® DNA isolation kit (MoBio 12888-50, Carlsbad, CA, USA) according to the operating instructions, and the extracted nucleic acids were tested for compliance using a micro UV spectrophotometer (Q5000, Quawell Technology, Inc., San Jose, CA, USA) [20].
Amplification targeted the bacterial 16S rRNA V6 region, using primers whose structural compositions are provided in Table 2.

2.5. High-Throughput Sequencing

Microbial diversity dynamics in response to varying NLR were profiled by high-throughput sequencing. Two critical sludge samples, designated H and L, were collected at NLRs of 22.6 and 2.1 kg·N/(m3·d), respectively.

3. Results and Discussions

3.1. Start-Up Period

The HLPNR was operated for 10 days during the start-up period, and the treatment performance is shown in Figure 2. As shown in Figure 2a, during the start-up period, the influent NH4+-N concentration (INC) was 160–470 mg/L, the HRT was 24–21 h, and the NH4+-N load rate (NLR) was 0.26–0.98 kg·N/(m3·d). The NH4+-N conversion rate (NCR) increased from 51.51% to 97.62%, increasing NO2−-N accumulation rate (NAR) from 66% to 99.8%, and the effluent NO3−- N decreased from 70 mg/L to 1 mg/L. Successful start-up of the HLPNR was achieved on day 10. The process was marked by a transient decline in NCR and NAR in response to elevated INC and NLR, which was quickly reversed, underscoring the system’s robustness for NO2−-N accumulation.

3.2. Effects of HRTs

After the start-up period, the HRTs were set at 18, 15, 12, 6, 3, 2, 1.8, and 1.5 h to observe the effects of HLPNR performance (Figure 2). In stage II, the effluent NH4+-N concentration decreased continuously, and the lowest effluent NH4+-N concentration reached 37 mg/L. In stages III and IV, the NLR was 1.58–3.83 kg·N/(m3·d), the effluent NH4+-N was around 75 mg/L, and the effluent NO2−-N was around 850 mg/L. The HLPNR could still operate relatively stably at the HRT of 6 h in stage IV, when the highest NLR was 3.83 kg·N/(m3·d). In stage V, as the HRT was shortened to 3 h, the effluent NH4+-N concentration increased to 312 mg/L, and the effluent NO2−-N concentration decreased to 539 mg/L. Subsequently, in stages VI-VIII, the HRT was continuously shortened to 1.5 h and the NLR was increased to 10.14 kg·N/(m3·d). The effluent NH4+-N concentration increased continuously to a maximum of 648 mg/L. The effluent NO2−-N concentration decreased continuously to a minimum of 370 mg/L. Following the continuous shortening of HRTs, the effluent NO2−-N concentration increased continuously in stages I–IV, which might be due to the increase in DO concentration in the wastewater. As the HRTs decreased and the influent flow rate increased, the activity of AOB was promoted [21]. With the continuous shortening of the HRTs during stages V–VIII, the amount of NH4+-N per unit time decreased as the influent flow increased, and NH4+-N did not have sufficient time to contact and react with the microorganisms in the reactor, thus causing the increase in effluent NH4+-N concentration and the decrease in NO2−-N accumulation rate. Meanwhile, it could be seen from Figure 3 that the effluent NO3−-N concentration did not increase significantly, which showed that the reactor still operated in the partial nitrification stage.
Figure 2b shows the NCR and NAR versus ammonia nitrogen loading at different HRTs. The reactor maintained a high value of effluent NO2−-N concentration at HRTs of 18 h, 15 h, 12 h, and 6 h. The NAR could reach about 95%, and the highest NLR was 3.83 kg·N/(m3·d) at HRT of 6 h. However, as the HRTs further decreased, the effluent NO2−-N concentration in the reactor decreased, and the NAR was around 80–90%. At the HRT of 1.5 h, the HLPNR reached the highest NLR of 10.14 kg·N/(m3·d), at which the NCR and NO2−-N accumulation rate were at a low level. It could be seen that the high NLR of the HLPNR would cause incomplete oxidation of NH4+-N and an increase in free ammonia (FA), which inhibited the partial nitrification reaction, leading to a decrease in NCR and NO2−-N accumulation rate. However, in reference [22], the inhibitory concentrations of free ammonia (FA) were established at 10–150 mg/L for AOB and 0.1–1.0 mg/L for NOB, which showed that the effect of high FA on AOB was limited, so the partial nitrification was still maintained in the reactor. Even under high ammonia and NLRs, the NAR was still at a high level. However, the high FA suppressed the instability of NO2−-N accumulation [22,23,24].

3.3. PCR-DGGE

Figure 3 is a DGGE electrophoresis image of the sampling sludges. As shown in Figure 3, the microbial community in each stage of the reactor had changed significantly. The microbial community in the initial sludge (sample 1) was vibrant, and Bands 1, 2, 3, and 4 in the initial stage of partial nitrification (sample 2) had been eliminated and disappeared. With the decrease in HRTs, the microbial community in the system was continuously domesticated, and the dominant bacteria Bands 5, 6, 7, 9, and 10 had been further screened.
The samples were subjected to cloning and sequencing by Shanghai Biotech Corporation, Shanghai, China, and the resulting sequences were compared with those in the National Center for Biotechnology Information (NCBI) database. The phylogenetic relationships, constructed using MEGA 7 software and the Neighbor-joining method (Figure 4), along with the similarity data presented in Table 3, revealed that the sequence similarities between Bands 1–13 and known bacteria ranged from 89% to 100%.
Band 3 and Band 4 were Myxobacterium AT3-01 [24] and Myxococcales bacterium Gsoil 473 [25], respectively, belonging to Proteobacteria. Band 5 was Brasilonema sp. CR6-5A [26], belonging to Pseudobranchidae. Band 6 was Nitrosomonas sp. G1 [27], belonging to Nitrosomonas. Band9 was Myxococcales bacterium Gsoil 473, belonging to Myxococcus. Band 10 was Curvibacter sp. [28], Band 11 was Limnohabitans sp. PRE28D2 [29], and Band 12 was Paracoccusalkenifer [30], belonging to Bacteria, Proteobacteria, α-Proteobacteria, and Paracoccus. Band 13 is Curvibacterlanceolatus [31], belonging to Bacteria, Proteobacteria, β-Proteobacteria, and Burkholderia.
Among them, Band 6, Nitrosomonas sp. G1, belongs to Nitrosomonas, a genus of AOB. AOB is responsible for oxidizing ammonia nitrogen to nitrite. Nitrosomonas sp. G1 was the functional bacterium in the HLPNR. According to the system microbial evolution diagram, Bands 3, 4, 9, 10, 11, 12, 13, and Band 6 belonged to Proteobacteria.
Microbial population relative concentrations during the study are shown in Figure 5. After the start-up period, strain 6 appeared, accounting for about 15%. In response to elevated NLR, Nitrosomonas sp. G1 emerged as the dominant strain, with its 40% relative abundance indicating successful partial nitrification. The community composition further shifted at HRTs of 15 h and 6 h, showing a high abundance of Myxococcales bacterium Gsoil 473. Further NLR increase led to the dominance of bacterium strain RM6 [32], a putative coexisting bacterium with Nitrosomonas sp. G1, concomitant with the gradual disappearance of the original Strains 1, 2, and 4. The microbial species in the reactor have been significantly reduced, and the community structure tends to be simplified. Therefore, it is speculated that the change in HRTs had a significant impact on the structure of the microbial community.

3.4. High-Throughput Sequencing Analysis

The Venn diagram in Figure 6, comparing samples H and L, revealed a significant shift in microbial diversity between the two conditions. There were 2030 OTUs in sample H, while the number of OTUs in sample L was only 569. The two samples exhibited remarkably low biodiversity similarity, as evidenced by the presence of only 216 shared taxa. Therefore, microbial communities decreased significantly during the reactor’s operation. The diversity and abundance of microbial community structure in the system decreased continuously.
As shown in Figure 7, which depicts the phylum-level distribution across samples H and L, 19 bacterial phyla were detected. The community in sample H was characterized by a predominance of Proteobacteria (74.4%), alongside significant contributions from Deinococcus-Thermus (21.7%) and Bacteroidetes (1.85%), which collectively accounted for over 97% of the relative abundance, confirming the dominance of Proteobacteria under the tested HRT conditions. In sample L, Proteobacteria accounted for 87.54%, Firmicutes accounted for 5.81%, Bacteroidetes accounted for 2.00%, Deinococcus thermos accounted for 1.82%, and Actinobacteria accounted for 1.59%. Proteobacteria was the primary strain. With the decrease in NLR, the proportion of Proteobacteria increased from 74.4% to 87.54%, and Deinococcus thermos decreased from 21.7% to 1.82%. The proportion of bacteria in H and L samples changed little.
Figure 8 displays the community composition of samples H and L. Analysis indicated that Nitrosomonas was the most prevalent bacterial genus in sample H, with Truepera [33], Comamonas [34], and Thermomonas [35] following in abundance. Thus, Nitrosomonas emerged as the dominant genus in the HRT experiment. Acinetobacter [36] in sample L accounted for the most significant proportion. Other strains such as Exiguobacterium [37] and Pseudomonas [38] also accounted for a certain proportion in the reactor. The dominant genus, Nitrosomonas, was eliminated with the reduction in NLR of the reactor. Finally, Acinetobacter became the dominant genus under low NLR.
The analysis of high-throughput sequencing data demonstrated an inverse relationship between NLR and microbial community metrics: as NLR diminished, both the diversity and abundance of the microbiota declined steadily. Under high NLR, the dominant phyla and genera were Proteobacteria and Nitrosomonas, respectively. Under low NLR, the dominant phyla and genus were Proteobacteria and Acinetobacter, respectively.
The NCR and NAR of the system under high NLR were 56.8% and 96.5%, respectively, and the NCR and NAR of the system under low NLR were 99% and 99.1%, respectively. The high NO2−-N accumulation rate observed under elevated NLR can be attributed to the stable microbial community structure that developed within the reactor. The dominant bacteria under high NLR were Nitrosomonas.
The activated sludge system in the experimental reactor was also a tiny ecosystem. With the gradual reduction in the NLR of the reactor, the overall microbial diversity in the system was decreasing. Also, the stability of the system was not destroyed. At the same time, a progressive domestication and enrichment of core functional bacteria was observed. Consequently, the reactor’s stability became primarily a function of the development of these specific populations, rather than of general diversity fluctuations. Changing the NLR of the reactor could affect the evolution of microbial community structure in the reactor, therefore affecting the function of the partial nitrification reactor.

4. Conclusions

The reactor achieved a nitrite–nitrogen (NO2−-N) accumulation rate (NAR) of approximately 95% at hydraulic retention times (HRTs) of 18 h, 15 h, 12 h, and 6 h. When the HRT was reduced to 1.5 h, the HLPNR reached its maximum nitrogen loading rate (NLR) of 10.14 kg N/(m3 d), while the NAR and nitrogen conversion rate (NCR) decreased from 98% to 73% and from 98% to 38%, respectively. These results indicate that shorter HRTs are unfavorable for stable nitrite accumulation. This study recommends an optimal HRT range of 6–18 h for the HLPNR to achieve stable and efficient nitrite accumulation (≥95% NAR), which balances treatment performance and operational efficiency, providing a practical reference for the design and operation of similar nitrification systems.
At the microbial level, the relative abundance of Nitrosomonas sp. G1 increased progressively from 12% to 38% throughout the experiment, and its presence was critical for maintaining a high NAR even under increasing NLRs. Notably, Proteobacteria (phylum) and Nitrosomonas (genus) remained the dominant taxa, accounting for 65–72% and 28–38% of the total community, respectively, even under low NLR conditions.
HLPNR shows great promise as an innovative solution for HANCW treatment, but key questions remain. A major gap is the lack of a detailed cost-reduction analysis: without quantified cost savings, wastewater treatment plant operators and decision-makers cannot make informed decisions on adopting HLPNR. Additionally, while HRT’s effects on reactor performance and microbial communities have been studied, HLPNR’s long-term stability under different conditions (e.g., long-term operational performance, stability, or shifts in microbial communities affecting efficiency) still needs exploration.
Future research should focus on three areas: (1) conduct in-depth cost–benefit analyses, quantifying savings from reduced aeration and no external carbon sources, along with factors like equipment lifespan, maintenance needs, and sludge treatment cost changes (life-cycle and sensitivity analyses are applicable here); (2) deepen HLPNR microbial ecology studies to clarify microbe interactions (especially under variable conditions), such as how coexisting bacteria interact with AOB to influence partial nitrification; (3) explore how environmental factors (temperature fluctuations, pH changes, wastewater contaminants) affect HLPNR performance and microbial structure.
In conclusion, HLPNR has high potential for HANCW treatment but requires further research. Addressing these gaps will optimize HLPNR for wider application, supporting global sustainable wastewater treatment and environmental protection.

Author Contributions

Conceptualization, Y.L. and W.Z.; methodology, Y.L., S.H., H.L., C.Z., Y.J. and W.Z.; software, S.H., H.L. and Y.L.; validation, Y.L., S.H., H.L., C.Z., Y.J. and W.Z.; formal analysis, Y.L. and W.Z.; investigation, Y.L., S.H., H.L., C.Z., Y.J. and W.Z.; data curation, Y.L. and W.Z.; writing—original draft preparation, Y.L.; writing—review and editing, Y.L., S.H., H.L., C.Z., Y.J. and W.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by National Natural Science Foundation of China [grant numbers 52360004]; Guilin Agricultural Water and Soil Resources and Environment Observation and Research Station of Guangxi, Guilin University of Technology, Guilin, 541006, China; Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area, Guilin University of Technology, Guilin, 541006, China; and Guilin Lijiang River Ecology and Environment Observation and Research Station of Guangxi, Guilin University of Technology, Guilin, 541006, China.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

During the preparation of this manuscript/study, the authors used [DeepSeek, V3.1] for the purposes of [Refine the English translation]. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

Author Shuyan He was employed by the company Hebei Blue Environmental Protection Engineering Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Water 17 03130 g001
Figure 1. Schematic diagram of HLPNR.
Figure 1. Schematic diagram of HLPNR.
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Figure 2. Effects of HRTs on HLPNR treatment performance.
Figure 2. Effects of HRTs on HLPNR treatment performance.
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Figure 3. Denaturing gradient gel electrophoresis (DGGE) gel imaging. From right to left, Sample 1, initial sludge; Sample 2, (Ca2+) = 25 mg/L; Sample 3, (Ca2+) = 50 mg/L; Sample 4, (Ca2+) = 75 mg/L; Sample 5, (Ca2+) = 80 mg/L; Sample 6, (Ca2+) = 100 mg/L; and Sample 7, (Ca2+) = 125 mg/L.
Figure 3. Denaturing gradient gel electrophoresis (DGGE) gel imaging. From right to left, Sample 1, initial sludge; Sample 2, (Ca2+) = 25 mg/L; Sample 3, (Ca2+) = 50 mg/L; Sample 4, (Ca2+) = 75 mg/L; Sample 5, (Ca2+) = 80 mg/L; Sample 6, (Ca2+) = 100 mg/L; and Sample 7, (Ca2+) = 125 mg/L.
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Figure 4. Microbial phylogenetic tree (the ruler length represents 10% divergence. The number of nodes represents confidence).
Figure 4. Microbial phylogenetic tree (the ruler length represents 10% divergence. The number of nodes represents confidence).
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Figure 5. Microbial population relative concentrations during the study.
Figure 5. Microbial population relative concentrations during the study.
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Figure 6. Venn graph of under H and L.
Figure 6. Venn graph of under H and L.
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Figure 7. The phylum-level distribution of Sample H and Sample L.
Figure 7. The phylum-level distribution of Sample H and Sample L.
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Figure 8. The genus-level distribution of Sample H and Sample L.
Figure 8. The genus-level distribution of Sample H and Sample L.
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Table 1. Inlet water composition.
Table 1. Inlet water composition.
Components (mg/L)Start-Up PeriodThe Period of Continuous HRT Shortening
Stages IStages IIStages IStages IIStages IIIStages IVStages VStages VIStages VIIStages VIII
NH4HCO3159~178431~4691000
HRT (h)24211815126321.81.5
Time (d)4634714451013
KH2PO4 (mg/L)50
NaHCO3 (mg/L)1000
CaCl2·2H2O (mg/L)100
MgSO4·7H2O (mg/L)100
Table 2. DNA sequences of primers.
Table 2. DNA sequences of primers.
PrimerSequences
968FAACGCGAAGAACCTTAC
GC-968FCGCCCGGGGCGCGCCCCGGGCGGGGCGGGGGCACGGGGGGAACGCGAAGAACCTTAC
1401RCGGTGTGTACAAGACCC
Table 3. Homology search results for 16S rRNA gene sequences of the main bacterial members in the sludge community.
Table 3. Homology search results for 16S rRNA gene sequences of the main bacterial members in the sludge community.
NumberSimilarityGene Bank NumberBacterial Species
1100%KF771448.1uncultured bacterium
299%JN366634.1bacterium enrichment culture clone 3_83
398%AB246772.1myxobacterium AT3-01
489%AB245340.1Myxococcales bacterium Gsoil 473
5100%KY365505.1Brasilonema sp. CR6-5A G1
698%AF353155.1Nitrosomonas sp.
796%AB021341.1bacterium rM6
899%KC238409.1uncultured bacterium
989%AB245340.1Myxococcales bacterium Gsoil 473
1099%MF689018.1Curvibacter sp.
1199%KM187617.1Limnohabitans sp. PRE28D2
1294%LT221244.1Paracoccus alkenifer
1399%KX815269.1Curvibacter lanceolatus
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MDPI and ACS Style

Liu, Y.; He, S.; Li, H.; Jin, Y.; Zhang, C.; Zhang, W. Effects of Hydraulic Retention Time on the Performance and Microbial Communities of a High-Load Partial Nitrification Reactor. Water 2025, 17, 3130. https://doi.org/10.3390/w17213130

AMA Style

Liu Y, He S, Li H, Jin Y, Zhang C, Zhang W. Effects of Hydraulic Retention Time on the Performance and Microbial Communities of a High-Load Partial Nitrification Reactor. Water. 2025; 17(21):3130. https://doi.org/10.3390/w17213130

Chicago/Turabian Style

Liu, Yuhan, Shuyan He, Hangyi Li, Yue Jin, Chunfang Zhang, and Wenjie Zhang. 2025. "Effects of Hydraulic Retention Time on the Performance and Microbial Communities of a High-Load Partial Nitrification Reactor" Water 17, no. 21: 3130. https://doi.org/10.3390/w17213130

APA Style

Liu, Y., He, S., Li, H., Jin, Y., Zhang, C., & Zhang, W. (2025). Effects of Hydraulic Retention Time on the Performance and Microbial Communities of a High-Load Partial Nitrification Reactor. Water, 17(21), 3130. https://doi.org/10.3390/w17213130

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