Investigation of Nitrogen Conversion Efficiency in Hydrogen-based Autotrophic Nitrate Reduction Reactor

Investigation of Nitrogen Conversion Efficiency in Hydrogen-based Autotrophic Nitrate Reduction Reactor | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigation of Nitrogen Conversion Efficiency in Hydrogen-based Autotrophic Nitrate Reduction Reactor Yu-Fei Zhao, Chun-Yu Lai, He-Ping Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5965337/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the performance of a hydrogen-based membrane biofilm reactor (MBfR) for nitrogen transformation over an extended operational period exceeding 200 days. During operational monitoring, the reactor sustained highly alkaline conditions with pH consistently exceeding 11.20, while achieving an ammonia conversion efficiency above 60% and maintaining a nitrite accumulation rate below 1%. Integrated mass balance calculations and microbial community profiling revealed the coexistence of denitrification and dissimilatory nitrate reduction to ammonium (DNRA) pathways within the nitrogen transformation network. A factorial experimental design was implemented with hydraulic retention time (HRT) and influent nitrate concentration as independent variables, generating nine distinct operational regimes through cross-variable permutations. Subsequent analysis of extensive experimental datasets enabled the development of a Response Surface Methodology (RSM) model to simulate nitrogen conversion dynamics. Model validation confirmed the statistical reliability and predictive accuracy of the RSM framework. Critical analysis demonstrated that ammonia conversion efficiency in the hydrogen autotrophic nitrate reduction system exhibited significant correlation with individual variables themselves but showed negligible dependence on their interactive effects. These findings provide novel insights into process optimization strategies and theoretical understanding of nitrogen convention. RSM Nitrogen Conversion MBfR Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The nitrogen cycle is a vital biogeochemical process within ecosystems, profoundly inflecting global climate change and environmental health(Fang et al., 2024 ). In nature, the reduction of nitrate plays an essential role in nitrogen transformation processes(Grzyb et al., 2021 ). This reduction primarily occurs through a series of microbially driven processes, with the two most important being denitrification and dissimilatory nitrate reduction to ammonium (DNRA)(Liao et al., 2025 ). These processes are widely present in soils(Friedl et al., 2018 ), water bodies, and other environments(Zhao et al., 2022a ), and they have substantial implications for the fate of nitrogen and the availability of nitrogen sources(C. Wang et al., 2024 ). Denitrification is a microbial process in which nitrate (NO₃⁻) is sequentially reduced to gaseous nitrogen forms(Arumugham et al., 2024 ), primarily dinitrogen (N₂) and nitrous oxide (N₂O), through the metabolic activity of denitrifying bacteria(Li et al., 2024 ). This process not only effectively reduces excess nitrate in water bodies and soils but also plays a critical role in the production and release of the greenhouse gas N₂O(Hassan et al., 2022 ). The efficiency of denitrification is strongly influenced by multiple environmental factors, including oxygen availability(Van Huynh et al., 2023 ), temperature(Qu et al., 2022 ), pH(Shi et al., 2022 ), and the composition of microbial communities(Ayiti and Babalola, 2022 ). Given the dual role of denitrification in nitrogen removal and greenhouse gas production, current research emphasizes the optimization of this process to maximize nitrate elimination while minimizing N₂O emissions, thereby addressing both water quality concerns and climate change mitigation(Kumar and Bordoloi, 2024 ). Dissimilatory Nitrate Reduction to Ammonium (DNRA) represents a distinct microbial pathway in the nitrogen cycle(Zhao et al., 2022a ), wherein nitrate (NO₃⁻) is reduced to ammonium (NH₄⁺)(C. Wang et al., 2024 ) rather than gaseous nitrogen compounds(Trinh et al., 2025 ). Unlike denitrification, DNRA conserves nitrogen within the ecosystem by converting nitrate into bioavailable ammonium(Y. Wan et al., 2023 ), which serves as a preferred nitrogen source for plant uptake(Yuan et al., 2022 ). This process plays a crucial role in nitrogen retention and recycling within both terrestrial and aquatic environments(Jiang et al., 2022 ). The dynamics of DNRA are governed by a complex interplay of environmental parameters, including redox conditions, organic carbon availability(H. Wang et al., 2024 ), and the functional composition of microbial communities(Z. Wang et al., 2024 ), as well as the relative abundance of competing nitrate-reducing microorganisms(Bryson et al., 2022 ). Recent studies have revealed that DNRA and denitrification can coexist and interact within specific environmental niches(D. Zhang et al., 2023 ), creating complex nitrogen transformation dynamics(Chen et al., 2023 ). Particularly in eutrophic aquatic systems, the prevalence of DNRA has been shown to significantly elevate ammonium concentrations, which can fundamentally alter nitrogen cycling patterns(Lo et al., 2022 ). This shift in nitrogen speciation not only affects nutrient availability but also potentially exacerbates eutrophication processes by providing readily bioavailable nitrogen that stimulates algal blooms(Jiang et al., 2023 ) and influences aquatic ecosystem structure(L. Wan et al., 2023 ). Although denitrification and DNRA represent distinct pathways with fundamentally different nitrogen transformation mechanisms(Pan et al., 2020 ) and end products, these processes frequently coexist within the same ecological matrix(Zhao et al., 2022b ), creating a dynamic interplay governed by multiple environmental variables. The relative predominance of these competing pathways is regulated by a suite of interconnected factors(Zhu et al., 2024 ), including nitrate and organic carbon availability, redox potential, pH, temperature, and the functional composition of microbial communities(Z. Zhang et al., 2024 ). This competitive interaction between denitrification and DNRA significantly influences their respective nitrogen conversion efficiencies, as microbial populations can strategically modulate their metabolic activities in response to fluctuating substrate concentrations and environmental conditions(M. Zhang et al., 2023 ), potentially shifting between these pathways to optimize energy acquisition and growth(Ahmad et al., 2023 ). Current research efforts have been predominantly directed toward optimizing and regulating denitrification processes, while DNRA has received comparatively limited scientific attention. The fundamental mechanisms governing DNRA efficiency and its regulation within nitrogen transformation networks remain poorly characterized(Du et al., 2024 ). To address this knowledge gap and advance sustainable nitrogen management, emerging research focuses on elucidating the control mechanisms of nitrogen transformation efficiency, particularly within hydrogen-based autotrophic nitrate reduction systems. This research direction holds significant promise for enhancing nitrogen removal capacity, mitigating greenhouse gas emissions, and optimizing nitrogen flux partitioning in engineered and natural ecosystems. The Membrane Biofilm Reactor (MBfR) represents an advanced treatment technology that synergistically integrates gaseous electron donors with membrane-supported biofilm systems(Martin and Nerenberg, 2012 ), offering versatile applications in wastewater treatment(L.-D. Zhang et al., 2024 ) and resource recovery(Nerenberg, 2016 ). In contemporary research, hydrogen has emerged as a particularly promising electron donor due to its exceptional energy density, clean oxidation byproducts, and thermodynamic advantages. The hydrogen-based MBfR capitalizes on these benefits by enabling efficient autotrophic denitrification through hydrogenotrophic microbial communities, while maintaining operational safety and process controllability through its unique membrane-mediated gas delivery system(Gao et al., 2024 ). However, current investigations into hydrogen-based autotrophic nitrate reduction have primarily concentrated on elucidating fundamental reaction mechanisms(Ye et al., 2024 ), characterizing microbial community dynamics, and analyzing metabolic byproducts. A significant research gap persists in understanding the systematic regulation of nitrogen transformation efficiency, including the development of strategies for enhancing nitrogen conversion kinetics and the optimization of operational parameters. To advance the practical application and performance of this technology, comprehensive studies are critically needed to unravel the complex interplay between process variables and nitrogen transformation efficiency, ultimately enabling more effective process control and optimization. This study aims to systematically investigate the regulatory mechanisms governing nitrogen transformation efficiency in hydrogen-based autotrophic nitrate reduction systems. The research objectives are threefold: (1) to establish and maintain long-term stable operation of a hydrogenotrophic denitrification reactor, generating comprehensive experimental data and theoretical insights at the laboratory scale; (2) to develop and validate a robust kinetic model through experimental data fitting, ensuring its accuracy and reliability for process prediction and optimization; (3) to elucidate the intricate relationships between microbial community structure, functional gene expression, and reactor performance, thereby providing a scientific basis for optimizing operational parameters and developing effective control strategies. 2. Methods and materials 2.1 Reactor configuration A MBfR was utilized in this study, the schematic representation of which is depicted in Figure S1 . The reactor employed nonporous polypropylene (PP) membrane fibers, characterized by an outer diameter (OD) of 200 µm, an inner diameter (ID) of 90 µm, and a wall thickness of 55 µm. These fibers, manufactured from Teijin, Ltd. (Tokyo, Japan), facilitate gas diffusion through their surface pores in a bubbleless manner, allowing gas to dissolve directly into the water. The main reactor column contained 35 fibers, while the length of each fiber was 38 cm. The total volume of this H 2 -MBfR reactor was 60 mL, and the total membrane surface area was 120 cm 2 . The liquid was completely mixed through a recirculation pump (Longer Pump, model 1515X, Longer Precision Pump Co, Ltd., China). The reactor was equipped with an additional layer of heating resistor wire and sensors to maintain a constant temperature at 28–29 degrees Celsius. 2.2 Inoculum sludge and synthetic wastewater The inoculum sludge was collected from the secondary sedimentation tank of the Shibei Municipal Wastewater Treatment Plant in Huzhou, China. Hydrogen was delivered from a hydrogen generator to the MBfR via hollow fibers to feed the biofilm. The contents per liter of synthetic wastewater are shown in Table stage 1, and trace element concentrates were added to the simulated culture medium, the composition of which are shown in Table S2. NaHCO 3 is a source of inorganic carbon for microorganisms in the reactor, which concentration is shown in Figure S1 with other contents. 2.3 H 2 -MBfR starts up The provided search results do not contain relevant information regarding the installation and operation of membrane biofilm reactors, or the specific procedures mentioned in your query. Therefore, I will summarize the content based on existing knowledge. In a membrane biofilm reactor (MBfR), the process begins with the sterile injection of a mixed solution into the reactor's bottom inlet using a sterile syringe. Following this, simulated wastewater is rapidly introduced to elevate the internal water level to a standard height. The initiation of an internal circulation system ensures thorough mixing of the reactor contents. During this phase, microorganisms from the sludge gradually colonize the hollow fiber membranes, leading to biofilm formation. As this closed system continuously stirs the mixed solution, it is anticipated that quantifiable biomass will adhere to the membrane surface after this preparatory stage. Following the colonization phase, a visible biofilm is expected to develop on the membrane fibers. After 48 hours, the system transitions to a continuous effluent supply mode controlled by an influent pump. This process is critical for enhancing microbial activity and optimizing wastewater treatment efficiency within the MBfR framework. 2.4 Chemical analyses of sample processing We used a 5 ml sealed syringe to sample the effluent from the reactor, and immediately after collecting the liquid, we filtered the samples through 0.22 µm membrane filters. The filtered liquid samples were stored in a refrigerator (4 ℃). The concentrations of key ions, nitrate and nitrite, in the study were detected and quantified using an ion chromatography (ICS 2000, Dionex, USA)(Guo et al., 2024 ). The ammonia concentration was determined through a colorimetric dish test, with the specific experiment conducted according to the description by Han et al., ( 2023 ). 2.5 DNA extraction and quantification Biofilm samples were taken from the reactor when the reactor was stabilized at different stages of operation, and three parallel sets of samples were retained for each sample. For each biofilm sample, about 10 cm sections from the middle and both ends of the MBfR hollow fibers were cut and combined for DNA extraction. The biofilm was collected by vertexing the fibers in the influent medium, then washing and centrifuging the solids. In particular, the outer layer of biofilm was obtained by increasing the internal circulation flow rate to 100 ml/min and the influent flow rate to 10 ml/min, collecting the microbial flocs flushed out with the water flow(Zhang et al., 2025 ). Microbial DNA was extracted using the DNeasy PowerSoil Kit (QIAGEN, USA), following the manufacturer's protocol. DNA concentrations were measured by a Nano-300 micro-spectrophotometer (YOONING, China) 2.6 16S rRNA gene sequencing of biological samples The collected biofilm samples were immediately subjected to DNA extraction using the DNeasy PowerSoil Kit(Qiagen, USA), following the manufacturer’s protocol. Subsequently, DNA concentrations and qualities were determined using a Nano-300 microspectrophotometer (YOONING, China). The DNA samples were sent to Novogene (Beijing, China) for amplification using primers 341F(5’-CCTAYGGGRBGCASCAG-3’) and 806R(5’-GGACTACNNGGGTATCTAAT-3’), targeting the bacterial 16S rRNA gene. The amplicons were sequenced using Illumina MiSeq Sequencing, and the data were analyzed using QIIME 2(version 2021.11), following the protocol described by Liu et al. (2021). 2.7 Construction of Response Surface Models In most experiments, continuous variation of operational parameters often leads to corresponding changes in experimental results. Response Surface Methodology (RSM) is based on this premise and employs a rational experimental design along with sufficient experimental data to fit the functional relationship between influencing variables and response variables using multiple quadratic regression equations(Han et al., 2025 ). This methodology analyzes the regression equations to seek optimal parameters and utilizes graphical tools to visualize the functional relationships of the regression equations, providing a more intuitive format for optimizing operational parameters. The experimental design in RSM includes two main approaches: Central Composite Design (CCD) and Box-Behnken Design (BBD). CCD is typically used when continuous experimentation is required, as it demands more comprehensive experimental data, allowing for a more accurate reflection of the effects caused by variations in influencing variables. In contrast, BBD generally requires fewer experimental groups, making it easier to illustrate the functional relationships between influencing and response variables. In this experiment, Design-Expert software was utilized to input the data from various experimental conditions of reactor operation along with performance indicators such as nitrite accumulation rate (NAR)、total nitrogen removal (TNR) and ammonia conversion rate (ACR). A quadratic regression equation was fitted to these data, and the resulting algebraic expression of the functional relationship was imported into OriginLab software for visualization, thereby completing the construction of the response surface model. 3. Results and discussion 3.1 Performance of reactor in different operational status The reactor was operated continuously for over 200 days, during which the influent nitrate concentration and hydraulic retention time (HRT) were adjusted in phases. Each phase ensured stable operation for at least 14 days, with biological samples collected on the final day of each phase. Throughout the entire operation of the reactor, the pH value was maintained above 11.20. The average pH during the reactor's operation was 11.65, indicating a relatively stable pH variation. Over the course of more than 200 days of stable operation, the average concentration of nitrite detected in the effluent was only 0.23 ppm, with no accumulation of nitrite observed at any time. After obtaining the physicochemical data from the effluent, quantitative calculations and organization of related parameters such as nitrite accumulation rate (NAR), total nitrogen removal rate (TNR), and ammonia conversion rate (ACR) were performed, as shown in Fig. 1 . Throughout the more than 200 days of reactor operation, the ammonia conversion rate consistently remained above 60%. The average nitrite accumulation rate was only 1.45%, with a maximum not exceeding 3%. These results indicate that there was no accumulation of nitrite within the reactor. Furthermore, it can be observed that the ammonia conversion rate exhibited a negative correlation with the overall nitrogen removal rate. 3.1.1 The impact of HRT on nitrogen conversion efficiency When analyzing the reactor data with hydraulic retention time as a single variable, as shown in Fig. 2 (a), it was observed that at an influent nitrate concentration of 20 ppm, HRT had minimal impact on the nitrogen transformation efficiency of the reactor. Under the three different HRT conditions set in the experiment, the nitrogen transformation efficiency remained relatively consistent. The nitrite concentration within the reactor consistently remained low and did not vary with changes in HRT, confirming that there was no accumulation of nitrite throughout the experiment. However, under influent nitrate concentrations of 30 ppm and 40 ppm, an increase in ammonia conversion rate (ACR) was observed as HRT decreased. Nonetheless, for the two conditions with HRT of 2.73 hours and 1.75 hours, there were no significant changes in nitrogen transformation efficiency. This may be attributed to the DNRA process within the reactor nearing saturation at these conditions, preventing further increases in efficiency with reduced HRT. 3.1.2 The impact of influent nitrate concentration on nitrogen conversion efficiency Like the previous analysis method, the reactor's physicochemical data was organized with influent nitrate concentration as a single variable, as illustrated in Fig. 2 (b). Overall, under consistent hydraulic retention time conditions, the nitrogen transformation efficiency within the reactor exhibited a positive correlation with influent nitrate concentration. Specifically, as the influent nitrate concentration increased, the levels of ammonia nitrogen detected in the effluent also rose. Additionally, consistent with the effects of hydraulic retention time, the nitrite concentration within the reactor remained low across different influent nitrate concentration conditions. This observation aligns with the finding that there was no accumulation of nitrite within the system at any point during the experiment. 3.2 Establishment and Analysis of the RSM The experimental data from each phase were consolidated to determine nine stable operating phases under different variable conditions, as shown in Table 1 . In Design-Expert software, the relationships between two influencing variables, a (hydraulic retention time) and b (influent nitrate concentration), and the response variables y1 (ammonia conversion rate), y2 (nitrite accumulation rate), y3 ( total nitrogen removal rate), and y4 (pH) were determined. These relationships were simulated and the corresponding surface expressions for each were calculated through multiple regression analysis: Table 1 The RSM experiment design of the reactor a Inf. NO 3 − ppm b HRT h y 1 ACR % y 2 NAR % y 3 TNR % y 4 pH 1 20.00 5.50 63.66 0.89 27.97 11.46 2 30.00 5.50 63.29 1.53 30.60 11.49 3 40.00 5.50 60.68 3.83 32.25 11.83 4 20.00 2.73 73.00 7.34 10.19 11.40 5 30.00 2.73 83.24 3.28 12.95 11.42 6 40.00 2.73 85.09 1.40 8.62 11.58 7 20.00 1.75 81.32 7.77 9.57 11.39 8 30.00 1.75 84.07 4.15 10.34 11.40 9 40.00 1.75 84.34 2.87 12.06 11.47 $$\:{\text{y}}_{\text{1}}\text{=}\text{131.71}\text{-}\text{3}\text{8.97}\text{a}\text{-1.60}\text{b}\text{+}\text{1.35}\text{ab}\text{+}\text{4.96}{\text{a}}^{\text{2}}\text{-}\text{0.19}{\text{a}}^{\text{2}}b$$ 3.1 $$\:{\text{y}}_{\text{2}}\text{=}\text{45.19}\text{-}\text{7.51}\text{a}\text{-}\text{1.93}\text{b}\text{+}\text{0.24}\text{ab}\text{+}\text{0.02}{\text{b}}^{\text{2}}\text{-}\text{0.01}{\text{a}\text{b}}^{\text{2}}\text{+}{\text{0.01}\text{a}}^{\text{2}}b$$ 3.2 $$\:{\text{y}}_{\text{4}}\text{=}\text{-12.01+}\text{1}\text{7.07}\text{a}\text{+}\text{1.}\text{12}\text{b}\text{-1.03}\text{ab}\text{-1.71}{\text{a}}^{\text{2}}\text{+0.01}\text{a}{\text{b}}^{\text{2}}$$ 3.3 $$\:{\text{y}}_{\text{3}}\text{=}\text{11.35+0.11}\text{a}\text{+}\text{0.01}\text{b}\text{-0.01}\text{ab}\text{+}\text{0.02}{\text{a}}^{\text{2}}\text{-}\text{0.01}{\text{b}}^{\text{2}}\text{-}{\text{0.01}\text{a}}^{\text{2}}b\text{-}\text{0.01}{\text{a}\text{b}}^{\text{2}}$$ 3.4 The results of the fitted model are shown in Fig. 3 . To verify the significance of the model established through response surface analysis, variance analysis and significance testing were conducted on the model equations and data in Design-Expert software, with the results presented in Table 2 . The P-values for all four response variables were below 0.05, while the F-values were all greater than 1, indicating that the model possesses a high level of statistical significance and reliability. The determination. coefficient R 2 and the adjusted determination coefficient Adj R 2 were both greater than 0.75, further reflecting the model's high significance and accuracy. The AP value represents the signal-to-noise ratio of the model; an AP value greater than 4 indicates good signal strength. In this study, the AP values for all four response variables were significantly greater than 4. The C.V.% (coefficient of variation) reflects the variability of the model; all four response variables exhibited a low C.V.%, suggesting that the values of this model had a small degree of dispersion, and no outliers were present in the data. Table 2 The analysis of RSM model parameters Parameter p-value F-value R 2 Adj R 2 AP C.V. % y 1 0.0068 36.66 0.9779 0.9839 14.6519 2.86 y 2 0.0455 21.30 0.9846 0.9384 13.4153 3.61 y 3 0.0308 31.85 0.9896 0.9586 15.1273 6.42 y 4 0.0243 1003.08 0.9999 0.9989 98.8554 0.04 As shown in Table 3 , in the ammonia nitrogen conversion rate (ACR) model, the P-values for the linear terms indicate that a<b<0.05, while the P-value for the interaction term ab is greater than 0.05. This analysis reveals that the factors significantly affecting the ACR in the reactor are a (hydraulic retention time) and b (influent nitrate concentration) itself. This suggests that the ammonia nitrogen conversion rate is significantly correlated with a single variable, either hydraulic retention time or influent nitrate concentration, while the interaction between these two factors does not have a significant effect. Table 3 ANOVA quadratic analysis of ACR Source Sum of Squares Mean Square F-value P-value ACR Model 850.43 170.09 36.66 0.0068 a 651.04 651.04 140.32 0.0013 b 49.32 49.32 10.63 0.0471 ab 10.24 10.24 2.21 0.2341 a 2 13.00 13.00 2.80 0.1928 a 2 b 35.75 35.75 7.71 0.0692 Residual 13.92 4.64 Cor Total 864.34 In addition, a relevant statistical analysis based on mathematical principles was conducted on the established RSM model to assess its scientific validity and practical applicability. Figure S2 illustrates the linear correlation between the model's predicted values and the actual observed values, where (a), (b), (c), and (d) correspond to the response variables y1 (ammonia conversion rate), y2 (nitrite accumulation rate), y3 (total nitrogen removal rate), and y4 (pH), respectively. From the figure S3, it can be observed that the model's predicted values closely align with the straight line and are evenly distributed on both sides, indicating a strong correlation between the predicted and actual values. The analysis of the studentized residuals of the model is shown in Figure S3, which presents the probability distribution. From the figure S3, it is evident that the studentized residuals for y1 (ammonia conversion rate), y2 (nitrite accumulation rate), y3 (total nitrogen removal rate), and y4 (pH) can be well fitted to a straight line, with most of the data points lying on this line. This suggests that the model fits the data well and adheres to the normal distribution assumption. 3.3 Microbial community analysis 3.3.1 Dominance analysis at the Phylum level On the last day of operation for each phase, biofilm samples from the reactor were collected and subjected to high-throughput sequencing. The obtained data are illustrated in Fig. 4 . At the phylum level, the dominant phyla identified in the system were Deinococcota , Proteobacteria , and F irmicutes . Throughout the long-term operation, the abundance of Deinococcota was initially low but gradually increased over time. According to existing literature, the phylum Deinococcota is known for its strong resistance to environmental hazards and is often referred to as extremophiles due to their thick cell walls(Huang et al., 2024 ). Additionally, Proteobacteria consistently accounted for approximately 30% of the entire community. This phylum includes many bacteria associated with nitrogen metabolism(Huang et al., 2022 ), capable of completing the DNRA process(Fang et al., 2022 ), and primarily consists of denitrifying functional bacteria that facilitate nitrogen cycling. Studies have confirmed that increasing the abundance of Proteobacteria can accelerate nitrogen cycling and enhance denitrification rates(Liu et al., 2024 ). The abundance of Firmicutes increased steadily during the operation, reaching 14.8%. Their optimal temperature range is between 25–30°C(Dan et al., 2023 ), which aligns with the maintained constant temperature of 28–29°C within the reactor. Although Actinobacteria has also been reported to possess DNRA potential and may be a major functional microorganism in wetland ecosystems(Fang et al., 2022 ), its abundance in this study exhibited instability(Li et al., 2022 ), fluctuating within a certain range across different phases. This instability may be attributed to the lower resistance of Actinobacteria to extreme environments; while capable of completing DNRA processes, their survival in the alkaline conditions of this reactor may be compromised(Yaradoddi, 2022 ). 3.3.2 Dominance analysis at the Genus level At the genus level, Meiothermus and Thauera were the dominant genera in the system, together accounting for nearly 80% of the community. Both genera have been reported to possess nitrogen metabolism functions(Wang et al., 2023 ). At the genus level, Meiothermus had a relative abundance of only 1.7% in the initial inoculum, but became the dominant genus over the long-term operation, reaching 72.4% at the end. Meiothermus is a Gram-negative, aerobic bacterium that can use nitrate as an electron acceptor(Lukina et al., 2023 ). It is known for its thermophilic nature, thriving in moderate temperatures and alkaline environments(Wilson and King, 2022 ), with an optimal pH range of 5.9–8.7(Jiao et al., 2022 ). Previous research has suggested that the enrichment of thermophilic bacteria with specific functional capabilities in mixed cultures can confer greater tolerance to high pH(Aliyu et al., 2024 ), although the underlying mechanisms remain unclear. This hypothesis aligns with the observations in the reactor. The relative abundance of Thauera in the system increased from 7.9–10.1%, making it another important microbial group. Existing reports have confirmed that Thauera possesses the genomic traits for denitrification processes and is capable of dominating hydrogenotrophic denitrification(Shi et al., 2023 ). In addition, Dethiobacter , an alkaliphilic bacterium, was also detected in the system with an abundance of approximately 5%. According to relevant literature, Dethiobacter has been previously identified in soda lakes characterized by high salinity and elevated pH levels. Despite exhibiting multiple extreme parameters, soda lakes are recognized for their high productivity and harbor functionally complete, diverse haloalkaliphilic microbial communities that drive biogeochemical cycling of carbon, nitrogen, and sulfur. Notably, Dethiobacter strains isolated from mixed anaerobic sediments in soda lakes of northeastern Mongolia have been documented to utilize H 2 as an energy source(Zavarzina et al., 2023 ) while demonstrating tolerance to high alkalinity - physiological characteristics that align with the environmental conditions present in the reactor system. Similarly, Ahniella was detected in the system with a comparable abundance of approximately 5%. This genus has been reported to correlate with total nitrogen removal in engineered systems and is capable of mediating the denitrification process. Specifically, Ahniella demonstrates metabolic functionality aligned with nitrogen cycle regulation(Tang et al., 2023 ). Its presence further underscores the system's intrinsic capacity for biogeochemical cycling of nitrogen species through specialized microbial consortia(Pang et al., 2024 ). 3.3.3 Microbial community diversity analysis To investigate species with significant differences between groups, species abundance data at different taxonomic levels were analyzed using the MetagenomeSeq method to perform hypothesis testing, yielding p-values(Ham and Park, 2022 ). Species exhibiting significant differences between groups were selected based on these p-values, and box plots illustrating the abundance distribution of these differential species across groups were generated. The analysis of the microbial communities at the phylum and genus levels for the nine phases is presented in Fig. 5 (a) and 5(b). As shown in Fig. 5 (a), there were no significant differences among the interspecific abundances of several dominant phyla, indicating that the main dominant phyla consistently exerted their influence throughout the overall operation. At the genus level, as illustrated in Fig. 5 (b), significant differences were observed among other genera, except for the primary dominant genera Meiothermus and Thauera. This suggests that the presence and contribution of other genera varied across different stages, while Meiothermus and Thauera remained non-significant among all groups, confirming their role as dominant genera continuously exerting a major influence within the system(Islam et al., 2024 ). Additionally, a phylogenetic analysis was conducted, as shown in Fig. 5 (c). Based on the phylogenetic tree of the top 100 genera, microorganisms within the phylum Deinococcota were highly concentrated, almost entirely composed of Meiothermus . This explains the high similarity in abundance changes observed at both phylum and genus classification levels. In contrast, Proteobacteria , another dominant phylum, encompasses a diverse array of microbial species. The abundance distribution among genera within Proteobacteria was relatively uniform, including various microorganisms involved in nitrogen metabolism without significant differences. This finding aligns with the significance results obtained from the MetagenomeSeq analysis, mutually validating the reliability and scientific rigor of the biological sample data. 3.4 Functional gene predicted through PICRUSt 2 As shown in Fig. 6 , the abundance of functional genes in each biofilm sample was predicted using PICRUSt 2 based on the 16s rRNA gene sequencing results. We focus on the changes of functional genes related to nitrogen cycling, including: (1) Denitrification: membrane-bound nitrate reductase ( narG ), periplasmic nitrate reductase ( napA) , nitrite reductase ( nirK / nirS ); (2) DNRA: nitrite reductase (cytochrome C-552) ( nrfA ), nitrite reductase (NADH) large subunit ( nirB ), nitrite reductase (NADH) small subunit ( nirD ); Normalization was performed using the origin samples as a baseline to more visually explore the trends in the abundance of each functional gene at each stage. It was observed that nirS and nirK (encoding nitrite reductase) were highly enriched in the system, suggesting that denitrification proceeded throughout the entire process. Furthermore, the abundance variations of functional genes associated with the DNRA process, including nrfA, nirB , and nirD (Zhao et al., 2022a ), exhibited a positive correlation with the ammonia conversion rate results at each stage. This finding reveals the occurrence of functional genes related to the DNRA process within the system and aligns with the changes in the abundance of the Meiothermus microbial community present in the system. 4. Conclusion In this study, a hydrogen-based membrane biofilm reactor was operated stably over an extended period, yielding over 200 days of physicochemical data. Based on this data, a novel response surface model was established through regression fitting. Rigorous statistical analyses confirmed the scientific validity and reliability of the model. Preliminary analysis indicated that in the hydrogen autotrophic nitrate reduction reactor, the ammonia nitrogen conversion rate was significantly correlated only with the individual variables of hydraulic retention time or influent nitrate concentration, while the interaction between these two variables did not have a significant effect. Additionally, analysis of biological samples from the reactor further revealed the impact of microbial communities on nitrogen transformation efficiency within the hydrogen-based autotrophic nitrate reduction reactor. This research focuses on the nitrogen transformation efficiency of the reactor, providing new perspectives and insights for related processes and theoretical studies. Declarations Author Contribution Yu-Fei Zhao: Investigation, Methodology, Data Curation, Writing–Original Draft, Writing–Reviewing and Editing. Chun-Yu Lai: Investigation, Methodology, Data Curation, Writing–Original Draft. He-Ping Zhao: Supervision, Funding acquisition, Writing–Reviewing and Editing. Acknowledgments The authors greatly thank the “National Natural Science Foundation of China (Grant Nos. 22325604)”, and the “National Key Technology R&D Program (Grant Nos. 2023YFE0198800)” for their financial support. References Ahmad, H.A., Ahmad, S., Gao, L., Wang, Z., El-Baz, A., Ni, S.-Q., 2023. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5965337","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":411984540,"identity":"cc565d4b-c7f5-41ce-a3c8-94fcace423c9","order_by":0,"name":"Yu-Fei Zhao","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Yu-Fei","middleName":"","lastName":"Zhao","suffix":""},{"id":411984541,"identity":"5d5eee7d-56de-447a-824c-0da11408d49c","order_by":1,"name":"Chun-Yu Lai","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Chun-Yu","middleName":"","lastName":"Lai","suffix":""},{"id":411984543,"identity":"2175f476-a526-445e-9057-7296229a5802","order_by":2,"name":"He-Ping Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYDACCQYGZgYGGwbGBiCHhwQtaaRrOQzhEKVFfnbzw8eFO87bM89IYHzwto1B3pyQFsY5x4yNZ565zcw4I4HZcG4bg+HOBgJamCUSzKR5226zAbWwARkMCQYHCGhhk0j/BlR5jgeohf03UVp4JHJAthyQANnCTJQWCYmcYmPeM8kGjD0PmyXnnJMw3EBIi/yM9I2PeXfY2Ru2Jx/88KbMRp6gLWAAikbDBnBkShCjHqpFnki1o2AUjIJRMAIBAPD1OIIv99lwAAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"He-Ping","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-02-05 11:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5965337/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5965337/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75885895,"identity":"1108080f-f872-4775-ad3c-550c7122e8f7","added_by":"auto","created_at":"2025-02-10 09:09:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":322819,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)The diagram of reactor operation monitoring. (b)The diagram of conversion rate calculation NAR：nitrite accumulation rate TNR：total nitrogen removal ACR：ammonia conversion rate\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5965337/v1/cbf06bfa010ca951badd4b5f.png"},{"id":75885896,"identity":"4c0208c3-1537-42e2-87cd-4f26991a1082","added_by":"auto","created_at":"2025-02-10 09:09:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":388522,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe graph of changes of the concentrations of nitrogen ions in the reactor with (a) influent nitrate concentration and (b) hydraulic retention time\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5965337/v1/551ba726a39bcf8ebcad3be6.png"},{"id":75885894,"identity":"5b5d5653-be40-4daf-886a-d007e60bff9b","added_by":"auto","created_at":"2025-02-10 09:09:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1282819,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe plot of RSM of the effects of the Inf. NO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e and HRT on the (a) ACR, (b) NAR, (c) pH and (d) TNR\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5965337/v1/73603df50a16f169c77b963b.png"},{"id":75886581,"identity":"d5da49e6-755e-4544-a5f8-db0e9a96913a","added_by":"auto","created_at":"2025-02-10 09:17:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":193837,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial community composition in the biofilm at (a) phylum and (b) genus level (Relative abundance lower than 1% were classified into group “Others”)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5965337/v1/d0a477f1c7a8e5d798aed6d4.png"},{"id":75886579,"identity":"8d19978a-1c55-4eca-ae77-c2ec1fa07597","added_by":"auto","created_at":"2025-02-10 09:17:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":533092,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHypothesis Testing Plot of the MetagenomeSeq Method at (a) phylum level, (b) genus level. (c) Genus-Level Top 100 Phylogenetic Tree Circular Diagram Group A :HRT=5.5 h; Group B :HRT=2.73h; Group C :HRT=1.75h.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5965337/v1/7e4485ce0c2b5190a258cd4a.png"},{"id":75886578,"identity":"92fd8195-c8b4-4601-ab87-ed7fe91ded0d","added_by":"auto","created_at":"2025-02-10 09:17:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":77380,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional gene prediction through PICRUSt 2 K00370:\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e nar G\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e; K02567: \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003enap A\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e; K15864: \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003enir S;\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e K00368:\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e nir K\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e; K00385: \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003enrfA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e; K00362: \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003enir B\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e; K00363: \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003enir D.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5965337/v1/29048fe5c05f01976adde1d5.png"},{"id":76049269,"identity":"0846c3ea-e064-4ac6-8dd5-7d144ca0da98","added_by":"auto","created_at":"2025-02-11 20:17:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3939952,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5965337/v1/a80f25a4-f545-4674-8e5e-c17f4ca5a814.pdf"},{"id":75885910,"identity":"55dd6b81-ca57-48d1-91b2-6e92884df929","added_by":"auto","created_at":"2025-02-10 09:09:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":409147,"visible":true,"origin":"","legend":"","description":"","filename":"SIZYF.docx","url":"https://assets-eu.researchsquare.com/files/rs-5965337/v1/428e3092b3b7c4d58c5562fb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigation of Nitrogen Conversion Efficiency in Hydrogen-based Autotrophic Nitrate Reduction Reactor","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe nitrogen cycle is a vital biogeochemical process within ecosystems, profoundly inflecting global climate change and environmental health(Fang et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In nature, the reduction of nitrate plays an essential role in nitrogen transformation processes(Grzyb et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This reduction primarily occurs through a series of microbially driven processes, with the two most important being denitrification and dissimilatory nitrate reduction to ammonium (DNRA)(Liao et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These processes are widely present in soils(Friedl et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), water bodies, and other environments(Zhao et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e), and they have substantial implications for the fate of nitrogen and the availability of nitrogen sources(C. Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDenitrification is a microbial process in which nitrate (NO₃⁻) is sequentially reduced to gaseous nitrogen forms(Arumugham et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), primarily dinitrogen (N₂) and nitrous oxide (N₂O), through the metabolic activity of denitrifying bacteria(Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This process not only effectively reduces excess nitrate in water bodies and soils but also plays a critical role in the production and release of the greenhouse gas N₂O(Hassan et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The efficiency of denitrification is strongly influenced by multiple environmental factors, including oxygen availability(Van Huynh et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), temperature(Qu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), pH(Shi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and the composition of microbial communities(Ayiti and Babalola, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Given the dual role of denitrification in nitrogen removal and greenhouse gas production, current research emphasizes the optimization of this process to maximize nitrate elimination while minimizing N₂O emissions, thereby addressing both water quality concerns and climate change mitigation(Kumar and Bordoloi, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDissimilatory Nitrate Reduction to Ammonium (DNRA) represents a distinct microbial pathway in the nitrogen cycle(Zhao et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e), wherein nitrate (NO₃⁻) is reduced to ammonium (NH₄⁺)(C. Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) rather than gaseous nitrogen compounds(Trinh et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Unlike denitrification, DNRA conserves nitrogen within the ecosystem by converting nitrate into bioavailable ammonium(Y. Wan et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which serves as a preferred nitrogen source for plant uptake(Yuan et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This process plays a crucial role in nitrogen retention and recycling within both terrestrial and aquatic environments(Jiang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The dynamics of DNRA are governed by a complex interplay of environmental parameters, including redox conditions, organic carbon availability(H. Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the functional composition of microbial communities(Z. Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), as well as the relative abundance of competing nitrate-reducing microorganisms(Bryson et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies have revealed that DNRA and denitrification can coexist and interact within specific environmental niches(D. Zhang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), creating complex nitrogen transformation dynamics(Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Particularly in eutrophic aquatic systems, the prevalence of DNRA has been shown to significantly elevate ammonium concentrations, which can fundamentally alter nitrogen cycling patterns(Lo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This shift in nitrogen speciation not only affects nutrient availability but also potentially exacerbates eutrophication processes by providing readily bioavailable nitrogen that stimulates algal blooms(Jiang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and influences aquatic ecosystem structure(L. Wan et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough denitrification and DNRA represent distinct pathways with fundamentally different nitrogen transformation mechanisms(Pan et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and end products, these processes frequently coexist within the same ecological matrix(Zhao et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e), creating a dynamic interplay governed by multiple environmental variables. The relative predominance of these competing pathways is regulated by a suite of interconnected factors(Zhu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), including nitrate and organic carbon availability, redox potential, pH, temperature, and the functional composition of microbial communities(Z. Zhang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This competitive interaction between denitrification and DNRA significantly influences their respective nitrogen conversion efficiencies, as microbial populations can strategically modulate their metabolic activities in response to fluctuating substrate concentrations and environmental conditions(M. Zhang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), potentially shifting between these pathways to optimize energy acquisition and growth(Ahmad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrent research efforts have been predominantly directed toward optimizing and regulating denitrification processes, while DNRA has received comparatively limited scientific attention. The fundamental mechanisms governing DNRA efficiency and its regulation within nitrogen transformation networks remain poorly characterized(Du et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To address this knowledge gap and advance sustainable nitrogen management, emerging research focuses on elucidating the control mechanisms of nitrogen transformation efficiency, particularly within hydrogen-based autotrophic nitrate reduction systems. This research direction holds significant promise for enhancing nitrogen removal capacity, mitigating greenhouse gas emissions, and optimizing nitrogen flux partitioning in engineered and natural ecosystems.\u003c/p\u003e \u003cp\u003eThe Membrane Biofilm Reactor (MBfR) represents an advanced treatment technology that synergistically integrates gaseous electron donors with membrane-supported biofilm systems(Martin and Nerenberg, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), offering versatile applications in wastewater treatment(L.-D. Zhang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and resource recovery(Nerenberg, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In contemporary research, hydrogen has emerged as a particularly promising electron donor due to its exceptional energy density, clean oxidation byproducts, and thermodynamic advantages. The hydrogen-based MBfR capitalizes on these benefits by enabling efficient autotrophic denitrification through hydrogenotrophic microbial communities, while maintaining operational safety and process controllability through its unique membrane-mediated gas delivery system(Gao et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, current investigations into hydrogen-based autotrophic nitrate reduction have primarily concentrated on elucidating fundamental reaction mechanisms(Ye et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), characterizing microbial community dynamics, and analyzing metabolic byproducts. A significant research gap persists in understanding the systematic regulation of nitrogen transformation efficiency, including the development of strategies for enhancing nitrogen conversion kinetics and the optimization of operational parameters. To advance the practical application and performance of this technology, comprehensive studies are critically needed to unravel the complex interplay between process variables and nitrogen transformation efficiency, ultimately enabling more effective process control and optimization.\u003c/p\u003e \u003cp\u003eThis study aims to systematically investigate the regulatory mechanisms governing nitrogen transformation efficiency in hydrogen-based autotrophic nitrate reduction systems. The research objectives are threefold: (1) to establish and maintain long-term stable operation of a hydrogenotrophic denitrification reactor, generating comprehensive experimental data and theoretical insights at the laboratory scale; (2) to develop and validate a robust kinetic model through experimental data fitting, ensuring its accuracy and reliability for process prediction and optimization; (3) to elucidate the intricate relationships between microbial community structure, functional gene expression, and reactor performance, thereby providing a scientific basis for optimizing operational parameters and developing effective control strategies.\u003c/p\u003e"},{"header":"2. Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Reactor configuration\u003c/h2\u003e \u003cp\u003eA MBfR was utilized in this study, the schematic representation of which is depicted in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The reactor employed nonporous polypropylene (PP) membrane fibers, characterized by an outer diameter (OD) of 200 \u0026micro;m, an inner diameter (ID) of 90 \u0026micro;m, and a wall thickness of 55 \u0026micro;m. These fibers, manufactured from Teijin, Ltd. (Tokyo, Japan), facilitate gas diffusion through their surface pores in a bubbleless manner, allowing gas to dissolve directly into the water. The main reactor column contained 35 fibers, while the length of each fiber was 38 cm. The total volume of this H\u003csub\u003e2\u003c/sub\u003e-MBfR reactor was 60 mL, and the total membrane surface area was 120 cm\u003csup\u003e2\u003c/sup\u003e. The liquid was completely mixed through a recirculation pump (Longer Pump, model 1515X, Longer Precision Pump Co, Ltd., China). The reactor was equipped with an additional layer of heating resistor wire and sensors to maintain a constant temperature at 28\u0026ndash;29 degrees Celsius.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Inoculum sludge and synthetic wastewater\u003c/h2\u003e \u003cp\u003eThe inoculum sludge was collected from the secondary sedimentation tank of the Shibei Municipal Wastewater Treatment Plant in Huzhou, China. Hydrogen was delivered from a hydrogen generator to the MBfR via hollow fibers to feed the biofilm. The contents per liter of synthetic wastewater are shown in Table stage 1, and trace element concentrates were added to the simulated culture medium, the composition of which are shown in Table S2. NaHCO\u003csub\u003e3\u003c/sub\u003e is a source of inorganic carbon for microorganisms in the reactor, which concentration is shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e with other contents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 H\u003csub\u003e2\u003c/sub\u003e-MBfR starts up\u003c/h2\u003e \u003cp\u003eThe provided search results do not contain relevant information regarding the installation and operation of membrane biofilm reactors, or the specific procedures mentioned in your query. Therefore, I will summarize the content based on existing knowledge.\u003c/p\u003e \u003cp\u003eIn a membrane biofilm reactor (MBfR), the process begins with the sterile injection of a mixed solution into the reactor's bottom inlet using a sterile syringe. Following this, simulated wastewater is rapidly introduced to elevate the internal water level to a standard height. The initiation of an internal circulation system ensures thorough mixing of the reactor contents. During this phase, microorganisms from the sludge gradually colonize the hollow fiber membranes, leading to biofilm formation.\u003c/p\u003e \u003cp\u003eAs this closed system continuously stirs the mixed solution, it is anticipated that quantifiable biomass will adhere to the membrane surface after this preparatory stage. Following the colonization phase, a visible biofilm is expected to develop on the membrane fibers. After 48 hours, the system transitions to a continuous effluent supply mode controlled by an influent pump.\u003c/p\u003e \u003cp\u003eThis process is critical for enhancing microbial activity and optimizing wastewater treatment efficiency within the MBfR framework.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Chemical analyses of sample processing\u003c/h2\u003e \u003cp\u003eWe used a 5 ml sealed syringe to sample the effluent from the reactor, and immediately after collecting the liquid, we filtered the samples through 0.22 \u0026micro;m membrane filters. The filtered liquid samples were stored in a refrigerator (4 ℃). The concentrations of key ions, nitrate and nitrite, in the study were detected and quantified using an ion chromatography (ICS 2000, Dionex, USA)(Guo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The ammonia concentration was determined through a colorimetric dish test, with the specific experiment conducted according to the description by Han et al., (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 DNA extraction and quantification\u003c/h2\u003e \u003cp\u003eBiofilm samples were taken from the reactor when the reactor was stabilized at different stages of operation, and three parallel sets of samples were retained for each sample. For each biofilm sample, about 10 cm sections from the middle and both ends of the MBfR hollow fibers were cut and combined for DNA extraction. The biofilm was collected by vertexing the fibers in the influent medium, then washing and centrifuging the solids. In particular, the outer layer of biofilm was obtained by increasing the internal circulation flow rate to 100 ml/min and the influent flow rate to 10 ml/min, collecting the microbial flocs flushed out with the water flow(Zhang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Microbial DNA was extracted using the DNeasy PowerSoil Kit (QIAGEN, USA), following the manufacturer's protocol. DNA concentrations were measured by a Nano-300 micro-spectrophotometer (YOONING, China)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 16S rRNA gene sequencing of biological samples\u003c/h2\u003e \u003cp\u003eThe collected biofilm samples were immediately subjected to DNA extraction using the DNeasy PowerSoil Kit(Qiagen, USA), following the manufacturer\u0026rsquo;s protocol. Subsequently, DNA concentrations and qualities were determined using a Nano-300 microspectrophotometer (YOONING, China). The DNA samples were sent to Novogene (Beijing, China) for amplification using primers 341F(5\u0026rsquo;-CCTAYGGGRBGCASCAG-3\u0026rsquo;) and 806R(5\u0026rsquo;-GGACTACNNGGGTATCTAAT-3\u0026rsquo;), targeting the bacterial 16S rRNA gene. The amplicons were sequenced using Illumina MiSeq Sequencing, and the data were analyzed using QIIME 2(version 2021.11), following the protocol described by Liu et al. (2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Construction of Response Surface Models\u003c/h2\u003e \u003cp\u003eIn most experiments, continuous variation of operational parameters often leads to corresponding changes in experimental results. Response Surface Methodology (RSM) is based on this premise and employs a rational experimental design along with sufficient experimental data to fit the functional relationship between influencing variables and response variables using multiple quadratic regression equations(Han et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This methodology analyzes the regression equations to seek optimal parameters and utilizes graphical tools to visualize the functional relationships of the regression equations, providing a more intuitive format for optimizing operational parameters.\u003c/p\u003e \u003cp\u003eThe experimental design in RSM includes two main approaches: Central Composite Design (CCD) and Box-Behnken Design (BBD). CCD is typically used when continuous experimentation is required, as it demands more comprehensive experimental data, allowing for a more accurate reflection of the effects caused by variations in influencing variables. In contrast, BBD generally requires fewer experimental groups, making it easier to illustrate the functional relationships between influencing and response variables.\u003c/p\u003e \u003cp\u003eIn this experiment, Design-Expert software was utilized to input the data from various experimental conditions of reactor operation along with performance indicators such as nitrite accumulation rate (NAR)、total nitrogen removal (TNR) and ammonia conversion rate (ACR). A quadratic regression equation was fitted to these data, and the resulting algebraic expression of the functional relationship was imported into OriginLab software for visualization, thereby completing the construction of the response surface model.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Performance of reactor in different operational status\u003c/h2\u003e \u003cp\u003eThe reactor was operated continuously for over 200 days, during which the influent nitrate concentration and hydraulic retention time (HRT) were adjusted in phases. Each phase ensured stable operation for at least 14 days, with biological samples collected on the final day of each phase. Throughout the entire operation of the reactor, the pH value was maintained above 11.20.\u003c/p\u003e \u003cp\u003eThe average pH during the reactor's operation was 11.65, indicating a relatively stable pH variation. Over the course of more than 200 days of stable operation, the average concentration of nitrite detected in the effluent was only 0.23 ppm, with no accumulation of nitrite observed at any time. After obtaining the physicochemical data from the effluent, quantitative calculations and organization of related parameters such as nitrite accumulation rate (NAR), total nitrogen removal rate (TNR), and ammonia conversion rate (ACR) were performed, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Throughout the more than 200 days of reactor operation, the ammonia conversion rate consistently remained above 60%. The average nitrite accumulation rate was only 1.45%, with a maximum not exceeding 3%. These results indicate that there was no accumulation of nitrite within the reactor. Furthermore, it can be observed that the ammonia conversion rate exhibited a negative correlation with the overall nitrogen removal rate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 The impact of HRT on nitrogen conversion efficiency\u003c/h2\u003e \u003cp\u003eWhen analyzing the reactor data with hydraulic retention time as a single variable, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a), it was observed that at an influent nitrate concentration of 20 ppm, HRT had minimal impact on the nitrogen transformation efficiency of the reactor. Under the three different HRT conditions set in the experiment, the nitrogen transformation efficiency remained relatively consistent. The nitrite concentration within the reactor consistently remained low and did not vary with changes in HRT, confirming that there was no accumulation of nitrite throughout the experiment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, under influent nitrate concentrations of 30 ppm and 40 ppm, an increase in ammonia conversion rate (ACR) was observed as HRT decreased. Nonetheless, for the two conditions with HRT of 2.73 hours and 1.75 hours, there were no significant changes in nitrogen transformation efficiency. This may be attributed to the DNRA process within the reactor nearing saturation at these conditions, preventing further increases in efficiency with reduced HRT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 The impact of influent nitrate concentration on nitrogen conversion efficiency\u003c/h2\u003e \u003cp\u003eLike the previous analysis method, the reactor's physicochemical data was organized with influent nitrate concentration as a single variable, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(b). Overall, under consistent hydraulic retention time conditions, the nitrogen transformation efficiency within the reactor exhibited a positive correlation with influent nitrate concentration. Specifically, as the influent nitrate concentration increased, the levels of ammonia nitrogen detected in the effluent also rose.\u003c/p\u003e \u003cp\u003eAdditionally, consistent with the effects of hydraulic retention time, the nitrite concentration within the reactor remained low across different influent nitrate concentration conditions. This observation aligns with the finding that there was no accumulation of nitrite within the system at any point during the experiment.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Establishment and Analysis of the RSM\u003c/h2\u003e \u003cp\u003eThe experimental data from each phase were consolidated to determine nine stable operating phases under different variable conditions, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In Design-Expert software, the relationships between two influencing variables, \u003cem\u003ea\u003c/em\u003e (hydraulic retention time) and \u003cem\u003eb\u003c/em\u003e (influent nitrate concentration), and the response variables \u003cem\u003ey1\u003c/em\u003e (ammonia conversion rate), \u003cem\u003ey2\u003c/em\u003e (nitrite accumulation rate), \u003cem\u003ey3 (\u003c/em\u003etotal nitrogen removal rate), and \u003cem\u003ey4\u003c/em\u003e (pH) were determined. These relationships were simulated and the corresponding surface expressions for each were calculated through multiple regression analysis:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe RSM experiment design of the reactor\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ea\u003c/em\u003e\u003c/p\u003e \u003cp\u003eInf. NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eppm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e \u003cp\u003eHRT\u003c/p\u003e \u003cp\u003eh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eACR\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eNAR\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eTNR\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{\\text{y}}_{\\text{1}}\\text{=}\\text{131.71}\\text{-}\\text{3}\\text{8.97}\\text{a}\\text{-1.60}\\text{b}\\text{+}\\text{1.35}\\text{ab}\\text{+}\\text{4.96}{\\text{a}}^{\\text{2}}\\text{-}\\text{0.19}{\\text{a}}^{\\text{2}}b$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e3.1\u003c/div\u003e\u003c/div\u003e \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{\\text{y}}_{\\text{2}}\\text{=}\\text{45.19}\\text{-}\\text{7.51}\\text{a}\\text{-}\\text{1.93}\\text{b}\\text{+}\\text{0.24}\\text{ab}\\text{+}\\text{0.02}{\\text{b}}^{\\text{2}}\\text{-}\\text{0.01}{\\text{a}\\text{b}}^{\\text{2}}\\text{+}{\\text{0.01}\\text{a}}^{\\text{2}}b$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e3.2\u003c/div\u003e\u003c/div\u003e \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{\\text{y}}_{\\text{4}}\\text{=}\\text{-12.01+}\\text{1}\\text{7.07}\\text{a}\\text{+}\\text{1.}\\text{12}\\text{b}\\text{-1.03}\\text{ab}\\text{-1.71}{\\text{a}}^{\\text{2}}\\text{+0.01}\\text{a}{\\text{b}}^{\\text{2}}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e3.3\u003c/div\u003e\u003c/div\u003e \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{\\text{y}}_{\\text{3}}\\text{=}\\text{11.35+0.11}\\text{a}\\text{+}\\text{0.01}\\text{b}\\text{-0.01}\\text{ab}\\text{+}\\text{0.02}{\\text{a}}^{\\text{2}}\\text{-}\\text{0.01}{\\text{b}}^{\\text{2}}\\text{-}{\\text{0.01}\\text{a}}^{\\text{2}}b\\text{-}\\text{0.01}{\\text{a}\\text{b}}^{\\text{2}}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e3.4\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the fitted model are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. To verify the significance of the model established through response surface analysis, variance analysis and significance testing were conducted on the model equations and data in Design-Expert software, with the results presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The P-values for all four response variables were below 0.05, while the F-values were all greater than 1, indicating that the model possesses a high level of statistical significance and reliability. The determination. coefficient R\u003csup\u003e2\u003c/sup\u003e and the adjusted determination coefficient \u003cem\u003eAdj R\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e were both greater than 0.75, further reflecting the model's high significance and accuracy. The AP value represents the signal-to-noise ratio of the model; an AP value greater than 4 indicates good signal strength. In this study, the AP values for all four response variables were significantly greater than 4. The C.V.% (coefficient of variation) reflects the variability of the model; all four response variables exhibited a low C.V.%, suggesting that the values of this model had a small degree of dispersion, and no outliers were present in the data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe analysis of RSM model parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eF-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAdj R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eAP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eC.V.\u003c/em\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.6519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.4153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.1273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1003.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.8554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, in the ammonia nitrogen conversion rate (ACR) model, the P-values for the linear terms indicate that a\u0026lt;b\u0026lt;0.05, while the P-value for the interaction term \u003cem\u003eab\u003c/em\u003e is greater than 0.05. This analysis reveals that the factors significantly affecting the ACR in the reactor are \u003cem\u003ea\u003c/em\u003e (hydraulic retention time) and \u003cem\u003eb\u003c/em\u003e(influent nitrate concentration) itself. This suggests that the ammonia nitrogen conversion rate is significantly correlated with a single variable, either hydraulic retention time or influent nitrate concentration, while the interaction between these two factors does not have a significant effect.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA quadratic analysis of ACR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eF-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACR Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e850.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e170.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e651.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e651.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e140.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0471\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ea\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ea\u003csup\u003e2\u003c/sup\u003eb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCor Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e864.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition, a relevant statistical analysis based on mathematical principles was conducted on the established RSM model to assess its scientific validity and practical applicability. Figure S2 illustrates the linear correlation between the model's predicted values and the actual observed values, where (a), (b), (c), and (d) correspond to the response variables \u003cem\u003ey1\u003c/em\u003e (ammonia conversion rate), \u003cem\u003ey2\u003c/em\u003e (nitrite accumulation rate), \u003cem\u003ey3\u003c/em\u003e (total nitrogen removal rate), and \u003cem\u003ey4\u003c/em\u003e (pH), respectively. From the figure S3, it can be observed that the model's predicted values closely align with the straight line and are evenly distributed on both sides, indicating a strong correlation between the predicted and actual values.\u003c/p\u003e \u003cp\u003eThe analysis of the studentized residuals of the model is shown in Figure S3, which presents the probability distribution. From the figure S3, it is evident that the studentized residuals for \u003cem\u003ey1\u003c/em\u003e (ammonia conversion rate), \u003cem\u003ey2\u003c/em\u003e (nitrite accumulation rate), \u003cem\u003ey3\u003c/em\u003e (total nitrogen removal rate), and \u003cem\u003ey4\u003c/em\u003e (pH) can be well fitted to a straight line, with most of the data points lying on this line. This suggests that the model fits the data well and adheres to the normal distribution assumption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Microbial community analysis\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Dominance analysis at the Phylum level\u003c/h2\u003e \u003cp\u003eOn the last day of operation for each phase, biofilm samples from the reactor were collected and subjected to high-throughput sequencing. The obtained data are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. At the phylum level, the dominant phyla identified in the system were \u003cem\u003eDeinococcota\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, and F\u003cem\u003eirmicutes\u003c/em\u003e. Throughout the long-term operation, the abundance of \u003cem\u003eDeinococcota\u003c/em\u003e was initially low but gradually increased over time. According to existing literature, the phylum \u003cem\u003eDeinococcota\u003c/em\u003e is known for its strong resistance to environmental hazards and is often referred to as extremophiles due to their thick cell walls(Huang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, \u003cem\u003eProteobacteria\u003c/em\u003e consistently accounted for approximately 30% of the entire community. This phylum includes many bacteria associated with nitrogen metabolism(Huang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), capable of completing the DNRA process(Fang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and primarily consists of denitrifying functional bacteria that facilitate nitrogen cycling. Studies have confirmed that increasing the abundance of \u003cem\u003eProteobacteria\u003c/em\u003e can accelerate nitrogen cycling and enhance denitrification rates(Liu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The abundance of \u003cem\u003eFirmicutes\u003c/em\u003e increased steadily during the operation, reaching 14.8%. Their optimal temperature range is between 25\u0026ndash;30\u0026deg;C(Dan et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which aligns with the maintained constant temperature of 28\u0026ndash;29\u0026deg;C within the reactor. Although \u003cem\u003eActinobacteria\u003c/em\u003e has also been reported to possess DNRA potential and may be a major functional microorganism in wetland ecosystems(Fang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), its abundance in this study exhibited instability(Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), fluctuating within a certain range across different phases. This instability may be attributed to the lower resistance of \u003cem\u003eActinobacteria\u003c/em\u003e to extreme environments; while capable of completing DNRA processes, their survival in the alkaline conditions of this reactor may be compromised(Yaradoddi, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Dominance analysis at the Genus level\u003c/h2\u003e \u003cp\u003eAt the genus level, \u003cem\u003eMeiothermus\u003c/em\u003e and \u003cem\u003eThauera\u003c/em\u003e were the dominant genera in the system, together accounting for nearly 80% of the community. Both genera have been reported to possess nitrogen metabolism functions(Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the genus level, \u003cem\u003eMeiothermus\u003c/em\u003e had a relative abundance of only 1.7% in the initial inoculum, but became the dominant genus over the long-term operation, reaching 72.4% at the end. \u003cem\u003eMeiothermus\u003c/em\u003e is a Gram-negative, aerobic bacterium that can use nitrate as an electron acceptor(Lukina et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is known for its thermophilic nature, thriving in moderate temperatures and alkaline environments(Wilson and King, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with an optimal pH range of 5.9\u0026ndash;8.7(Jiao et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous research has suggested that the enrichment of thermophilic bacteria with specific functional capabilities in mixed cultures can confer greater tolerance to high pH(Aliyu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), although the underlying mechanisms remain unclear. This hypothesis aligns with the observations in the reactor. The relative abundance of \u003cem\u003eThauera\u003c/em\u003e in the system increased from 7.9\u0026ndash;10.1%, making it another important microbial group. Existing reports have confirmed that \u003cem\u003eThauera\u003c/em\u003e possesses the genomic traits for denitrification processes and is capable of dominating hydrogenotrophic denitrification(Shi et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, \u003cem\u003eDethiobacter\u003c/em\u003e, an alkaliphilic bacterium, was also detected in the system with an abundance of approximately 5%. According to relevant literature, \u003cem\u003eDethiobacter\u003c/em\u003e has been previously identified in soda lakes characterized by high salinity and elevated pH levels. Despite exhibiting multiple extreme parameters, soda lakes are recognized for their high productivity and harbor functionally complete, diverse haloalkaliphilic microbial communities that drive biogeochemical cycling of carbon, nitrogen, and sulfur. Notably, \u003cem\u003eDethiobacter\u003c/em\u003e strains isolated from mixed anaerobic sediments in soda lakes of northeastern Mongolia have been documented to utilize H\u003csub\u003e2\u003c/sub\u003e as an energy source(Zavarzina et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) while demonstrating tolerance to high alkalinity - physiological characteristics that align with the environmental conditions present in the reactor system. Similarly, \u003cem\u003eAhniella\u003c/em\u003e was detected in the system with a comparable abundance of approximately 5%. This genus has been reported to correlate with total nitrogen removal in engineered systems and is capable of mediating the denitrification process. Specifically, \u003cem\u003eAhniella\u003c/em\u003e demonstrates metabolic functionality aligned with nitrogen cycle regulation(Tang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Its presence further underscores the system's intrinsic capacity for biogeochemical cycling of nitrogen species through specialized microbial consortia(Pang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Microbial community diversity analysis\u003c/h2\u003e \u003cp\u003eTo investigate species with significant differences between groups, species abundance data at different taxonomic levels were analyzed using the MetagenomeSeq method to perform hypothesis testing, yielding p-values(Ham and Park, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Species exhibiting significant differences between groups were selected based on these p-values, and box plots illustrating the abundance distribution of these differential species across groups were generated. The analysis of the microbial communities at the phylum and genus levels for the nine phases is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(a) and 5(b). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(a), there were no significant differences among the interspecific abundances of several dominant phyla, indicating that the main dominant phyla consistently exerted their influence throughout the overall operation. At the genus level, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(b), significant differences were observed among other genera, except for the primary dominant genera \u003cem\u003eMeiothermus\u003c/em\u003e and \u003cem\u003eThauera.\u003c/em\u003e This suggests that the presence and contribution of other genera varied across different stages, while \u003cem\u003eMeiothermus\u003c/em\u003e and \u003cem\u003eThauera\u003c/em\u003e remained non-significant among all groups, confirming their role as dominant genera continuously exerting a major influence within the system(Islam et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, a phylogenetic analysis was conducted, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(c). Based on the phylogenetic tree of the top 100 genera, microorganisms within the phylum \u003cem\u003eDeinococcota\u003c/em\u003e were highly concentrated, almost entirely composed of \u003cem\u003eMeiothermus\u003c/em\u003e. This explains the high similarity in abundance changes observed at both phylum and genus classification levels. In contrast, \u003cem\u003eProteobacteria\u003c/em\u003e, another dominant phylum, encompasses a diverse array of microbial species. The abundance distribution among genera within \u003cem\u003eProteobacteria\u003c/em\u003e was relatively uniform, including various microorganisms involved in nitrogen metabolism without significant differences. This finding aligns with the significance results obtained from the MetagenomeSeq analysis, mutually validating the reliability and scientific rigor of the biological sample data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Functional gene predicted through PICRUSt 2\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the abundance of functional genes in each biofilm sample was predicted using PICRUSt 2 based on the 16s rRNA gene sequencing results. We focus on the changes of functional genes related to nitrogen cycling, including:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(1) Denitrification: membrane-bound nitrate reductase (\u003cem\u003enarG\u003c/em\u003e), periplasmic nitrate reductase (\u003cem\u003enapA)\u003c/em\u003e, nitrite reductase (\u003cem\u003enirK / nirS\u003c/em\u003e);\u003c/p\u003e \u003cp\u003e(2) DNRA: nitrite reductase (cytochrome C-552) (\u003cem\u003enrfA\u003c/em\u003e), nitrite reductase (NADH) large subunit (\u003cem\u003enirB\u003c/em\u003e), nitrite reductase (NADH) small subunit (\u003cem\u003enirD\u003c/em\u003e);\u003c/p\u003e \u003cp\u003eNormalization was performed using the origin samples as a baseline to more visually explore the trends in the abundance of each functional gene at each stage. It was observed that \u003cem\u003enirS\u003c/em\u003e and \u003cem\u003enirK\u003c/em\u003e (encoding nitrite reductase) were highly enriched in the system, suggesting that denitrification proceeded throughout the entire process.\u003c/p\u003e \u003cp\u003eFurthermore, the abundance variations of functional genes associated with the DNRA process, including \u003cem\u003enrfA, nirB\u003c/em\u003e, and \u003cem\u003enirD\u003c/em\u003e(Zhao et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e), exhibited a positive correlation with the ammonia conversion rate results at each stage. This finding reveals the occurrence of functional genes related to the DNRA process within the system and aligns with the changes in the abundance of the \u003cem\u003eMeiothermus\u003c/em\u003e microbial community present in the system.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eIn this study, a hydrogen-based membrane biofilm reactor was operated stably over an extended period, yielding over 200 days of physicochemical data. Based on this data, a novel response surface model was established through regression fitting. Rigorous statistical analyses confirmed the scientific validity and reliability of the model. Preliminary analysis indicated that in the hydrogen autotrophic nitrate reduction reactor, the ammonia nitrogen conversion rate was significantly correlated only with the individual variables of hydraulic retention time or influent nitrate concentration, while the interaction between these two variables did not have a significant effect. Additionally, analysis of biological samples from the reactor further revealed the impact of microbial communities on nitrogen transformation efficiency within the hydrogen-based autotrophic nitrate reduction reactor. This research focuses on the nitrogen transformation efficiency of the reactor, providing new perspectives and insights for related processes and theoretical studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYu-Fei Zhao: Investigation, Methodology, Data Curation, Writing\u0026ndash;Original Draft, Writing\u0026ndash;Reviewing and Editing. Chun-Yu Lai: Investigation, Methodology, Data Curation, Writing\u0026ndash;Original Draft. He-Ping Zhao: Supervision, Funding acquisition, Writing\u0026ndash;Reviewing and Editing.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors greatly thank the \u0026ldquo;National Natural Science Foundation of China (Grant Nos. 22325604)\u0026rdquo;, and the \u0026ldquo;National Key Technology R\u0026amp;D Program (Grant Nos. 2023YFE0198800)\u0026rdquo; for their financial support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad, H.A., Ahmad, S., Gao, L., Wang, Z., El-Baz, A., Ni, S.-Q., 2023. Energy-efficient and carbon neutral anammox-based nitrogen removal by coupling with nitrate reduction pathways: A review. 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Science of The Total Environment 912, 169389. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2023.169389\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2023.169389\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"RSM, Nitrogen Conversion, MBfR","lastPublishedDoi":"10.21203/rs.3.rs-5965337/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5965337/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the performance of a hydrogen-based membrane biofilm reactor (MBfR) for nitrogen transformation over an extended operational period exceeding 200 days. During operational monitoring, the reactor sustained highly alkaline conditions with pH consistently exceeding 11.20, while achieving an ammonia conversion efficiency above 60% and maintaining a nitrite accumulation rate below 1%. Integrated mass balance calculations and microbial community profiling revealed the coexistence of denitrification and dissimilatory nitrate reduction to ammonium (DNRA) pathways within the nitrogen transformation network. A factorial experimental design was implemented with hydraulic retention time (HRT) and influent nitrate concentration as independent variables, generating nine distinct operational regimes through cross-variable permutations. Subsequent analysis of extensive experimental datasets enabled the development of a Response Surface Methodology (RSM) model to simulate nitrogen conversion dynamics. Model validation confirmed the statistical reliability and predictive accuracy of the RSM framework. Critical analysis demonstrated that ammonia conversion efficiency in the hydrogen autotrophic nitrate reduction system exhibited significant correlation with individual variables themselves but showed negligible dependence on their interactive effects. These findings provide novel insights into process optimization strategies and theoretical understanding of nitrogen convention.\u003c/p\u003e","manuscriptTitle":"Investigation of Nitrogen Conversion Efficiency in Hydrogen-based Autotrophic Nitrate Reduction Reactor","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-10 09:09:11","doi":"10.21203/rs.3.rs-5965337/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"33135487-2950-4b18-9ce4-0a831d0de071","owner":[],"postedDate":"February 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-11T20:08:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-10 09:09:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5965337","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5965337","identity":"rs-5965337","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}