Effluent Ammonia Research Articles

Simultaneous nitrification and denitrification framework for decentralized systems: Long-term study utilizing rope-type biofilm media under field conditions

This research introduces a novel approach to achieve simultaneous nitrification-denitrification (SND) under dynamic load conditions using a cost-effective rope-type biofilm technology. The approach represents a significant advancement in wastewater treatment, particularly beneficial for remote and decentralized communities. The biofilm-based SND process was developed using a pilot-scale flow-through reactor by implementing upstream carbon management with constant-timer-based aeration control versus dynamic-sensor-based aeration control strategies. The findings indicate that adding an upstream anaerobic pretreatment process to handle excess carbon plays a substantial role in achieving a sustainable SND process under a dynamic load environment using simple aeration on-off control. The most optimal nitrification performance of 0.32 g NH3-N/m2/d (89 % removal) was achieved under a 1-hour ON/30-minute OFF aeration. The process sustained an average bulk liquid DO of 5.16 mg/L and 3.80 mg/L during the aeration ON and OFF periods, respectively, facilitating a 0.13 g N/m2/d (41 %) total inorganic nitrogen (TIN) removal, notably, implementing advanced aeration strategies driven by DO, NH3, and NO3 sensors enhanced TIN removal efficiency to 72 %. The nitrification performance remained comparable (89 % removal), resulting in 3 and 10 mg N/L effluent ammonia and TIN concentration, respectively. Additionally, utilizing two multivariate approaches accounting for 82 % and 64 % of the variance, this study discerned patterns in monitored variables and performance. Additionally, the analysis underscored the difference of bulk liquid DO levels in the biofilm versus suspended systems inhibiting the SND process. Distinct bacterial communities were established in biofilms under aerobic, anaerobic, and SND conditions, with the SND reactor showing a hierarchy of functional group and enzymes, enriched sequentially from heterotrophs to denitrifiers, nitrifiers, and anammox bacteria. These innovations underline the potential of tailored control strategies to enhance a passive biofilm-based SND process efficiency under dynamic conditions, providing scalable solutions for diverse target water quality demands in remote communities and decentralized systems.

Investigation of Nitrogen Removal in Flue Gas Desulfurization and Denitrification Wastewater Utilizing Halophilic Activated Sludge.

In the process of flue gas desulfurization and denitrification, the generation of high-sulfate wastewater containing nitrogen is a significant challenge for biological wastewater treatment. In this study, halophilic activated sludge was inoculated in a Sequencing Batch Reactor to remove nitrogen from wastewater with a high sulfate concentration (60 g/L). With the influent concentration of 180 mg/L, the removal rate of total nitrogen was more than 96.7%. The effluent ammonium nitrogen concentration was lower than 1.94 mg/L, and the effluent nitrate nitrogen and nitrite nitrogen concentrations were even lower than 0.77 mg/L. The salt tolerance of activated sludge is mainly related to the increase in the content of ectoine in microbial cells. The Specific Nitrite Oxidation Rate is quite low, while the Specific Nitrite Reduction Rate and Specific Nitrate Reduction Rate are relatively strong. In the system, there are various nitrogen metabolic processes, including aerobic nitrification, anaerobic denitrification, and simultaneous nitrification-denitrification processes. By analyzing the nitrogen metabolic mechanisms and microbial community structure of the reaction system, dominate bacteria can be identified, such as Azoarcus, Thauera, and Halomonas, which have significant nitrogen removal capabilities.

High-performance Multi-stage Baffled A2O Treatment Process for Domestic Sewage on Plateaus

At high altitudes, the low air pressure, low atmospheric oxygen content, and cryogenic environment during the cold season greatly limit the treatment efficiency of wastewater treatment plants (WWTPs). A novel pilot-plant configuration of the multi-stage baffled A2O wastewater treatment process was proposed and tested in Xizang. Different operational conditions involving at different influent loads and at low temperatures (10.0–11.0°C) were tested. When the influent flowrate increased to 4 m3∙d−1, the hydraulic retention time (HRT), internal and external reflux ratio, dissolved oxygen (DO), and aeration demands (gas to water ratio) all decreased to 34.2 h, 3.5/7, a stable 2.0–2.5 mg∙L−1, and 17.5, respectively. The effluent chemical oxygen demand (COD), total nitrogen (TN), ammonia nitrogen (NH+4−N), and total phosphorus (TP) all met the requirements of Class 1 Grade A of the China National Municipal Wastewater Discharge Standards (GB 18918-2002). The contribution of denitrifying phosphorus removal (DPR) to the removal of both nitrogen and phosphorus was over 50%. The alpha diversity and abundance of the top genera in the microbial community structure were both higher than the plateau WWTP. The reaction activity of the DPR process was significantly enhanced via the increased abundance of key functional genes within the metabolism pathway of ammonia-oxidizing bacteria (AOB) and nitrogen-oxidizing bacteria (NOB). The special multi-stage baffled structure featured a strategy of high sludge storage that improved the system tolerance for low temperatures and ensured favorable and stable performance for nitrogen and phosphorus removal at low temperatures. A short, periodic, and cyclically intermittent operation mode, with each cycle lasting only 20 minutes, effectively inhibited filamentous bacteria sludge bulking, resulting in a sludge volume index (SVI) that decreased to within 120 mL∙g−1 during the first 15 days of system start-up. A long sludge retention time (SRT) with no sludge discharging over 169 days and reduced aeration demands contributed to lower operation costs. The investigation revealed that the system had a high capacity for storing sludge phosphorus, possessing a TP content within a range of 23.45–28.99 mg∙g−1. This study provides a feasible solution for efficiently and economically treating wastewater in high-altitude areas.

Continuous self-circulating up-flow granular sludge fluidized bed process treating low-strength real municipal wastewater at high hydraulic loads

Conditions conducive to aerobic granular sludge (AGS) growth and maintenance are very difficult to realize in continuous-flow biological treatment processes. This study conducted a continuous-flow self-circulating up-flow granular sludge fluidized bed (Zier process) treating real urban wastewater approximately one year. The substantial self-circulating multiple times (RSCMT, 8–15 times) and up-flow velocity (8–15 m/h) generated by aeration, the only power equipment in Zier process, facilitated pollutant removal, particle granulation and stabilization. With hydraulic retention time of 5 h, RSCMT of 9.3–14.4 times and chemical oxygen demand (COD)/total nitrogen (TN) ratio of 5.9 ± 1.0, the effluent COD, ammonia nitrogen and TN were 28.6 ± 7.7, 1.1 ± 1.2, and 13.3 ± 1.7 mg/L, respectively. The median particle size was 150–250 μm and effluent suspended solids concentration was 33.4 ± 14.5 mg/L. It is unnecessary to set up sludge reflux which simplifies the subsequent mud-water separation facilities. The Zier process provides a new process structure for implementation of continuous-flow AGS process.

Robust nitrogen removal via nitrification-partial denitrification / anammox to co-treat acrylic fiber wastewater and sewage without external carbon

Acrylic fiber (AF) wastewater is characterized by high levels of ammonia nitrogen, refractory organic compounds, and toxic substances, making biological nutrient removal challenging. Conventional biological treatment technologies are often ineffective due to the complex composition, high toxicity, and low C/N ratios of AF wastewater. In this study, a two-stage nitrification-partial denitrification/anammox (N-PDA) process was firstly developed for the combined treatment of AF wastewater and domestic sewage, which integrated nitrification-SBR(N-SBR) and partial denitrification/anammox-UASB (PDA-UASB). The N-SBR achieved stable nitrification (ARE=100 %, HRT=4.2 h) with increasing ratios of AF wastewater, while the PDA-UASB utilized organic carbon from domestic sewage to simultaneously remove nitrate (91.7 mg/L) and ammonium (66.3 mg/L) through the PDA pathway (HRT=11.3 h). After 200 days, the relative abundance of Candidatus Brocadia increased from 0.6 to 5.4 %, ensuring robust nitrogen removal performance. The system achieved effluent ammonium and total nitrogen concentrations below 1.0 mg/L and 15.0 mg/L, respectively, with a total nitrogen removal rate of 87 %. This study demonstrates the N-PDA process as an efficient, energy-saving, and cost-effective solution for treating AF wastewater without the additional organic carbon sources, enhancing nitrogen removal efficiency and operational stability.

Modular stochastic configuration network with attention mechanism for soft measurement of water quality parameters in wastewater treatment processes

Due to the complex and multi-distributed nature of wastewater treatment process data, stochastic configuration networks (SCNs) face difficulties such as large network structure and complex construction process when dealing with data modeling tasks. To address these challenges, this paper proposes a modular stochastic configuration network with attention mechanism, which integrates the advantages of modular partitioning, attention mechanism, and SCN. The model employs a fuzzy mean clustering strategy to simplify and decompose the complex multi-case and multi-distribution nonlinear modeling task, thereby reducing the modeling complexity of a single model. Furthermore, the model utilizes a stochastic configuration algorithm to learn the subtasks and construct corresponding sub-networks. The output weights are then assigned to each sub-network based on the attention mechanism to obtain the model’s final output. The validity of the proposed method is validated through two benchmark experiments. Then the model is applied to measure effluent ammonia nitrogen. The results indicate that compared with the classical randomized and modularized network models, the proposed model exhibits certain advantages in terms of learning accuracy and generalization performance.

Biochar as ammonia exchange biofilm carrier for enhanced aerobic nitrification in activated sludge

The effects of biochar on aerobic nitrification in activated sludge were investigated in sequencing batch reactors. Biochar amended reactors exhibited 87–94 % lower ammonia in effluent and 16–71 % greater removal of total Kjeldahl nitrogen compared to control reactors. Quantitative qPCR analyses revealed that the relative abundance of ammonia oxidizing bacteria (AOB, amoA/16S rRNA genes) was greater in biochar than in control reactors. AOB were enriched on biochar surfaces, with biochar particles having up to 12.1 times greater relative abundance of AOB compared to suspended biomass. Biochar’s maximum ammonia sorption capacity of 4.4 mg N/g at pH 7 decreased with decreasing pH, however a pH-sensitive fluorescent probe was used to show that biofilms growing on biochar surfaces maintain a median pH of > 6.7 despite reactor acidification by nitrification. Microbial colonization of biochar in activated sludge creates a pH-sheltered environment that sustains biochar’s ammonia sorption capacity, resulting in enrichment of AOB on biochar particles and improved nitrification.

Adaptive type-2 fuzzy-neural switching control for wastewater treatment process under several operating conditions

The effluent ammonia and total nitrogen variation lead to multiple operating conditions in wastewater treatment process (WWTP), which challenges the control strategies based on a single control model. Therefore, it is essential to design suitable control strategies for WWTP to enhance the control performance under different operating conditions. In this paper, an adaptive type-2 fuzzy-neural switching control (AT2FSC) strategy is proposed to solve this problem. First, a soft-sensing model is developed based on fuzzy neural network and influent data in WWTP to estimate the effluent ammonia nitrogen and total nitrogen status. Then, the online operating conditions are determined by the critical values of effluent ammonia nitrogen and total nitrogen. Second, the corresponding fuzzy rules are devised for different operating conditions and a type 2 fuzzy switching model (AT2FSM) is constructed to capture the operating status changes of WWTP. Then, the proposed AT2FSC can design the switching control strategy according to AT2FSM to adapt to the current operating condition. Third, the Lyapunov theorem ensures the stability of the proposed AT2FSC method. Then, it facilitates the further application of AT2FSC in WWTP. Finally, the performance of AT2FSC under various operating conditions is validated on the benchmark simulation model No.1 (BSM1). Simulation results under four different operating conditions demonstrate that the proposed AT2FSC strategy can reduce the excess peak of effluent ammonia and total nitrogen while maintaining the operating performance.

Biological treatment of N-methylpyrrolidone, cyclopentanone, and diethylene glycol monobutyl ether distilled residues and their effects on nitrogen removal in a full-scale wastewater treatment plant

Manufacturing processes in semiconductor and photonics industries involve the use of a significant amount of organic solvents. Recycle and reuse of these solvents produce distillate residues and require treatment before being discharged. This study aimed to evaluate the performance of the biological treatment system in a full-scale wastewater treatment plant that treats wastewater containing distillate residues from the recycling of electronic chemicals. Batch experiments were conducted to investigate the optimal operational conditions for the full-scale wastewater treatment plant. To achieve good nitrogen removal efficiency with effluent ammonia and nitrate concentrations below 20 mg N/L and 50 mg N/L, respectively, it was suggested to control the ammonia concentration and pH of the influent below 500 mg N/L and 8.0, respectively. In addition, the biodegradability of N-methylpyrrolidone, diethylene glycol monobutyl ether, and cyclopentanone distillate residues from the electronic chemicals manufacturing process were evaluated under aerobic, anoxic, and anaerobic conditions. N-methylpyrrolidone and cyclopentanone distillate residues were suggested to be treated under anoxic condition. However, substrate inhibition occurred when using cyclopentanone distillate residue as a carbon source with chemical oxygen demand (COD) levels higher than 866 mg/L and nitrate levels higher than 415 mg N/L. Under aerobic condition, the COD from both N-methylpyrrolidone and cyclopentanone distillate residues could be easily degraded. Nevertheless, a negative effect on nitrification was observed, with a prolonged lag time for ammonia oxidation as the initial COD concentration increased. The specific ammonia oxidation rate and nitrate production rate decreased under high COD concentration contributed by N-methylpyrrolidone and cyclopentanone distillate residues. Furthermore, the biodegradability of diethylene glycol monobutyl ether distillate residue was found to be low under aerobic, anoxic, and anaerobic conditions. With respect to the abundance of nitrogen removal microorganisms in the wastewater treatment plant, results showed that Comammox may have an advantage over ammonia oxidizing bacteria under high pH conditions. In addition, Comammox may have higher resistance to environmental changes. Dominance of Comammox over ammonia oxidizing bacteria under high ammonia condition was first reported in this study.

Optimizing wastewater treatment plant operational efficiency through integrating machine learning predictive models and advanced control strategies

This research optimizes wastewater treatment plant (WWTP) operational performance by integrating advanced control strategies and predictive modeling. Emphasizing the significance of machine learning (ML), feature extraction techniques (filter, wrapper, and embedded methods) were employed to develop robust prediction models. The random forest (RF) model was applied to predict target variables, effluent ammonia, and nitrogen concentrations. Integrating these predictive models into the WWTP’s control system is necessary for enhanced efficiency and pollution regulation. Benchmark Simulation Model 1 (BSM1) was used as the WWTP model. The two tested control strategies included a hybrid approach, combining feedforward and feedback control, resulting in an improved effluent quality index (EQI), a marginal increase in aeration energy (AE) and the operational cost index (OCI), and a significant decrease in effluent ammonia concentration. The second strategy utilized self-organizing fuzzy inference system (SOFIS) control, resulting in promising outcomes with improvements in EQI, ammonia, and nitrogen concentrations, with negligible increases in AE and OCI. The findings highlight the pivotal role of predicting effluent quality parameters and integrating the prediction into WWTP control systems. This integrated approach proves effective in optimizing pollutant regulation and overall system performance. The research provides insights into the practical implementation of ML-based control strategies in wastewater treatment. It offers future scope for exploring advanced ML algorithms and their real-time application in operational WWTPs. This research introduces a novel approach by integrating machine learning with the BSM1 weather dataset and sensor data for feature selection to predict effluent concentrations in a WWTP. Through the comparative analysis with the default proportional-integral (PI) control configuration, the research highlights the importance of integrating machine learning techniques into WWTP control systems.

An attempt to augment performance of machine learning models in a pilot-scale urban wastewater treatment system

The challenge we face is striking a balance between ensuring the standards for wastewater treatment processes and minimizing energy consumption. As such, accurate predictive models become critical. With machine learning algorithms having the ability to discern relationships between inputs and outputs, broad interest has been shown in their applications in wastewater treatment. A key research focus is on effectively adjusting these generic machine learning algorithms to adapt to frequent fluctuations of water quality, inflow volume, and environmental variations. This study developed a Nonlinear Autoregressive neural network (NARX) model having an adaptive real-time updating module to predict the ammonium concentration in the treated effluent. The model has exhibited high predictive accuracy on the testing data, achieving an R2 value of 0.997. By incorporating an adaptive real-time updating module, the model's capability to predict effluent ammonium concentration on novel data sets has been significantly improved, with the R2 value for these predictions increasing from an initial 0.918 to a more refined range of 0.966 to 0.984. Our findings confirm that integrating real-time updating capabilities with machine learning algorithms substantially improves the stability and predictive performance of the models. These outcomes will prompt a broader integration of artificial intelligence in the domain of environmental engineering, which shall foster the implementation of more accurate and eco-friendly wastewater treatment strategies.

Bio-degumming of ramie fiber using an integrated spiral symmetry stream anaerobic bioreactor

The bio-degumming method focused research interest on green degumming technology in the textile sector, addressing environmental pollution, water usage and energy consumption caused by chemical degumming methods while enhancing fiber quality. The application of recent developments in the chemical degumming method of fiber remains a challenge for environmental protection. The novel spiral symmetry stream anaerobic bioreactor was employed in a system that integrates the ramie degumming process and reduces wastewater generation. This study analyzed the effect of liquor ratios on the water quality indicators, fiber quality and microbial community composition. After this study, the effluent chemical oxygen demand concentration, pH and ammonia nitrogen were 264.2 ± 114.2, 7.2 ± 0.1 and 7.4 ± 4.8 mg/L, respectively. This shows a mean chemical oxygen demand removal rate of 55.7 ± 11.8%, which is over 79% higher than previous bio-degumming methods. The fiber quality met the ramie fine dry hemp standard requirements. The high-throughput sequencing results revealed that the microbial community adapted to this process. The relative abundance of dominant microbes ( Bacteroidetes and Methanosaeta), which perform biodegradation and methane production, was present. This study demonstrates a promising green degumming technology and eco-friendly concept for the textile industry.

Soft Measurement of Effluent Ammonia Nitrogen Based on IABC-MFFNN

In wastewater treatment processes (WWTPs), it is difficult to accurately measure effluent ammonia nitrogen. a soft measurement method of effluent ammonia nitrogen is proposed based on improved artificial bee colony of multi-feedback fuzzy neural network (IABC-MFFNN). Firstly, multiple feedback links are added to the fuzzy neural network (FNN). Due to existence of internal feedback and external feedback, the system can perform self-adaptive adjustments based on previous status information and output signals, which can fully reflect the dynamic information. Secondly, an improved artificial bee colony (IABC) is proposed as the parameter optimization method of the network. The multi-strategy selection mechanism was used to design different search strategies for each subgroup, and the double index dynamic subgroup mechanism was used to adjust the number of food sources for each ordinary subgroup, which improved the accuracy of the soft measurement method. Simulation experiment results show that the proposed method has higher prediction accuracy compared with other methods.

Thermal hydrolysis pretreatment of wastewater biosolids: Modelling the impact of the aerobic sludge age

This study investigates the impact of thermal hydrolysis pretreatment (THP) on the characteristics of wastewater biosolids, methane production, anaerobic biodegradability, and recycled sidestream nitrogen loads, as a function of the sludge age of the activated sludge (AS) system through a series of BioWin simulations of 3 different process configurations at sludge ages of 5, 10, 15, 20, 30, and 60 d, with and without thermal hydrolysis. BioWin's anaerobic digester and thermal hydrolysis units were calibrated by adopting and identifying various kinetic and operational parameters from the available literature and through series of full-scale plant simulations. Thermal hydrolysis improved the hydrolysis rates of the biosolids by 181%. Improvements in anaerobic biodegradability, mainly due to the conversion of endogenous products, ranged from 14% to 67% at sludge ages of 5–15 d. At sludge ages of 20–60 d, anaerobic biodegradability improved by 59% to 186% depending on the process configuration. At sludge ages of 15–60 d, methane production was relatively stable and insensitive to the biosolids composition. Despite the increase in the recycled nitrogen load by 42% to 127%, it only constituted 13% of the influent nitrogen load. Effluent ammonia concentrations remained unchanged and effluent nitrates concentrations only increased by 1.4 to 2 mg N/L, maintaining effluent standards without additional sidestream treatment.

Novel three-sludge municipal wastewater treatment process coupling denitrifying phosphorus removal with anaerobic ammonium oxidation

The two-sludge anoxic dephosphation (DEPHANOX) process frequently encounters the challenge of elevated effluent ammonia levels in practical applications. In this study, the anaerobic ammonium oxidation (anammox) biofilm was introduced into the DEPHANOX system, transforming it into a three-sludge system, enabling synchronous nitrogen and phosphorus elimination, particularly targeting ammonia. Despite a chemical oxygen demand/total nitrogen ratio of 4.3 ± 0.8 in the actual municipal wastewater and 4.5 h of aeration, the effluent total nitrogen was 13.7 mg/L, lower than the parallel wastewater treatment plant. Additionally, the effluent ammonia reduced to 5.1 ± 2.5 mg/L. Notably, denitrifying phosphorus removal and anammox were coupled in the anoxic zone, yielding 74.5 % nitrogen and 87.8 % phosphorus removal. 16S rRNA gene sequencing identified denitrifying phosphorus-accumulating organisms primarily in floc sludge (Saprospiraceae 7.07 %, Anaerolineaceae 1.95 %, Tetrasphaera 1.57 %), while anammox bacteria inhabited the biofilm (Candidatus Brocadia 4.00 %). This study presents a novel process for efficiently treating municipal wastewater.

Absorption of Ammonium by Three Substrates Materials in Constructed Wetland System

The adsorption characteristics of ammonia nitrogen for constructed wetland were studied with ceramsite, quartz sand, and gravel. The material was characterized using scanning electron microscopy and a BET-specific surface area analyzer. It was found that the surface of ceramide was coarser than that of quartz sand and gravel, and the internal pores were more developed. The specific surface area of ceramide (18.97 m2·g-1) was higher than that of quartz sand and gravel. In the pure ammonia nitrogen solution and Grade I B standard for the wastewater treatment plant effluent ammonia nitrogen solution of the effluent from the simulated sewage plant, the adsorption capacity of the three substrates was as follows:ceramsite > gravel > quartz sand. The saturated adsorption capacity (63.55 m2·g-1) of ceramides was the highest in the mixed solution. The adsorption process of ammonia nitrogen by ceramides accorded with the pseudo-second-order kinetic model (R2 of 0.99 in the pure ammonia nitrogen solution and 0.98 in the mixed solution). The Freundlich and Langmuir models were used to fit the isothermal adsorption results in a pure ammonia nitrogen solution. It was found that the Freundlich model described the adsorption characteristics of the ceramics more accurately than the Langmuir model (R2=0.93), indicating that the adsorption of ammonia nitrogen by the ceramics was multilayer adsorption. In conclusion, the adsorption capacity of ceramide was strong, and the adsorption capacity of ceramide in the mixed solution was 31% higher than that in the pure ammonia nitrogen solution, which was suitable to be used as the matrix filler of constructed wetland.

Total ammonia aeration control (TAAC) theory – An innovative ammonia-based aeration controller

Abstract Water resource recovery facilities (WRRFs) need optimized and robust solutions to ensure efficient and reliable operation for this critical environmental service. Secondary treatment aeration control is a prime example as the activated sludge treatment process consumes the largest amount of energy for WRRFs, which require oxygen to biologically remove the ammonia content through nitrification. The selected control strategy will directly impact system efficiency and ability to maintain discharge permit compliance levels. The use of an ammonia-based aeration controller has two major benefits for these systems: (1) cost savings, through minimization of energy usage, and (2) enhanced performance from a steady effluent ammonia concentration. These benefits come from an increase in the system biological kinetics. The process control improvements result in a higher rate of total nitrogen removal, via simultaneous nitrification/denitrification, through delivery of the minimum instantaneous oxygen necessary over time. The thesis contained herein is a novel controller algorithm, which leverages the relationship between primary input and output variables of this complex treatment process. The approach provides continuous output stability and a substantial reduction of the overall system costs, through decreased wear of large-budget equipment and by requiring fewer algorithm input data sources than any other possible solution.

Characteristics of Greenhouse Gas Emissions from Constructed Wetlands Vegetated with Myriophyllum aquatic: The Effects of Influent C/N Ratio and Microbial Responses

This study designed surface flow constructed wetlands (SFCWs) with Myriophyllum aquaticum (M. aquaticum) to evaluate how different influent C/N ratios (0:1 (C0N), 5:1 (C5N), 10:1 (C10N), and 15:1 (C15N)) affect pollutant removal, greenhouse gas (GHG) emissions, and microbial communities. The results showed that effluent ammonia nitrogen (NH4+-N), nitrate nitrogen (NO3−-N), and total nitrogen (TN) concentrations decreased, but effluent chemical oxygen demand (COD) concentration increased with increasing influent C/N ratios. The highest removal rates of TN (73.17%) and COD (74.56%) were observed with C5N. Regarding GHG emissions, a few changes in CO2 fluxes were caused by the influent C/N ratio, whereas CH4 fluxes obviously increased with the increasing influent C/N ratio. The highest N2O emission occurred with C0N (211.03 ± 44.38 mg-N·m−2·h−1), decreasing significantly with higher C/N ratios. High-throughput sequencing revealed that different influent C/N ratios directly influenced the microbial distribution and composition related to CH4 and N2O metabolism in SFCWs. The highest abundance (46.24%) of denitrifying bacteria (DNB) was observed with C5N, which helped to achieve efficient nitrogen removal with a simultaneous reduction in N2O emissions. Methanogen abundance rose with higher C/N ratios, whereas methanotrophs peaked under C5N and C10N conditions. Additionally, the random forest model identified influent C/N ratio and Rhodopseudomonas as primary factors influencing CH4 and N2O emissions, respectively. This highlights the importance of the influent C/N ratio in regulating both pollutant removal and GHG emissions in constructed wetlands.

Study on the enhancement of low carbon-to-nitrogen ratio urban wastewater pollutant removal efficiency by adding sulfur electron acceptors.

The effective elimination of nitrogen and phosphorus in urban sewage treatment was always hindered by the deficiency of organic carbon in the low C/N ratio wastewater. To overcome this organic-dependent barrier and investigate community changes after sulfur electron addition. In this study, we conducted a simulated urban wastewater treatment plant (WWTP) bioreactor by using sodium sulfate as an electron acceptor to explore the removal efficiency of characteristic pollutants before and after the addition of sulfur electron acceptor. In the actual operation of 90 days, the removal rate of sulfur electrons' chemical oxygen demand (COD), ammonia nitrogen, and total phosphorus (TP) with sulfur electrons increased to 94.0%, 92.1% and 74%, respectively, compared with before the addition of sulfur electron acceptor. Compared with no added sulfur(phase I), the reactor after adding sulfur electron acceptor(phase II) was demonstrated more robust in nitrogen removal in the case of low C/N influent. the effluent ammonia nitrogen concentration of the aerobic reactor in Pahse II was kept lower than 1.844 mg N / L after day 40 and the overall concentration of total phosphorus in phase II (0.35 mg P/L) was lower than that of phase I(0.76 mg P/L). The microbial community analysis indicates that Rhodanobacter, Bacteroidetes, and Thiobacillus, which were the predominant bacteria in the reactor, may play a crucial role in inorganic nitrogen removal, complex organic degradation, and autotrophic denitrification under the stress of low carbon and nitrogen ratios. This leads to the formation of a distinctive microbial community structure influenced by the sulfur electron receptor and its composition. This study contributes to further development of urban low-carbon-nitrogen ratio wastewater efficient and low-cost wastewater treatment technology.

Recurrent multi-stressor floc treatments with sulphide and free ammonia enabled mainstream partial nitritation/anammox

Selective suppression of nitrite-oxidising bacteria (NOB) over aerobic and anoxic ammonium-oxidising bacteria (AerAOB and AnAOB) remains a major challenge for mainstream partial nitritation/anammox implementation, a resource-efficient nitrogen removal pathway. A unique multi-stressor floc treatment was therefore designed and validated for the first time under lab-scale conditions while staying true to full-scale design principles. Two hybrid (suspended + biofilm growth) reactors were operated continuously at 20.2 ± 0.6 °C. Recurrent multi-stressor floc treatments were applied, consisting of a sulphide-spiked deoxygenated starvation followed by a free ammonia shock. A good microbial activity balance with high AnAOB (71 ± 21 mg N L−1 d−1) and low NOB (4 ± 17 % of AerAOB) activity was achieved by combining multiple operational strategies: recurrent multi-stressor floc treatments, hybrid sludge (flocs & biofilm), short floc age control, intermittent aeration, and residual ammonium control. The multi-stressor treatment was shown to be the most important control tool and should be continuously applied to maintain this balance. Excessive NOB growth on the biofilm was avoided despite only treating the flocs to safeguard the AnAOB activity on the biofilm. Additionally, no signs of NOB adaptation were observed over 142 days. Elevated effluent ammonium concentrations (25 ± 6 mg N L−1) limited the TN removal efficiency to 39 ± 9 %, complicating a future full-scale implementation. Operating at higher sludge concentrations or reducing the volumetric loading rate could overcome this issue. The obtained results ease the implementation of mainstream PN/A by providing and additional control tool to steer the microbial activity with the multi-stressor treatment, thus advancing the concept of energy neutrality in sewage treatment plants.