Effluent Ammonia Concentration Research Articles

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.

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.

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.

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.

Estimating ammonium changes in pilot and full-scale constructed wetlands using kinetic model, linear regression, and machine learning

Constructed wetlands (CWs) are a widely utilized nature-based wastewater treatment method for various effluents. However, their application has been more focused on pilot and full-scale CWs with substantial surface areas and extended operation times, which hold greater relevance in practical scenarios. This study used kinetics, linear regression (LR), and machine learning (ML) models to estimate effluent ammonium in pilot and full-scale CWs. From screening 1476 papers, 24 pilot and full-scale CW studies were selected to extract data containing 15 features and 975 data points. Nine models were fit to this data, revealing that linear models were less effective in capturing CW effluent compared to nonlinear ML algorithms. For training data, the Monod kinetic model predicted the poorest performance with an RMSE of 41.84 mg/L and R2 of 0.34, followed by simple LR (RMSE 24.29 mg/L and R2 0.77) and multiple LR (RMSE 22.63 mg/L and R2 0.80). In contrast, Cubist and Random Forest achieved high performances, with an average RMSE of 12.01 ± 5.38 and an average R2 of 0.93 ± 0.07 for Cubist, and an average RMSE of 15.94 ± 10.69 and an average R2 of 0.91 ± 0.08 for RF. The trained Random Forest performed the best for new data, with an R2 of 0.93 and RMSE of 13.48 mg/L. This ML-based model is a valuable tool for efficiently estimating effluent ammonium concentration in pilot and full-scale CWs, thereby facilitating the design of systems.

Achieving partial nitrification by harnessing basic hydrolysis of sulphide salts amid high dissolved oxygen conditions

Attempts are going on to incorporate sulphur into the carbon and nitrogen removal processes at a biological wastewater treatment plant by external dosing or its intrinsic presence. A possible form of dosing sulphide at the field scale is the external dosing as the sodium salt of sulphide which under basic hydrolysis increases the pH. We demonstrated that the sodium salt dosage of sulphide can also be useful in achieving partial nitrification (PN). The results showed that 15 mgS/L of the sodium salt of sulphide was sufficient to establish PN in the low-strength synthetic and real wastewater under adequate dissolved oxygen concentrations (1.3 ± 0.4 mg/L during discharge phase of SBR cycle to 5.3 ± 0.2 mg/L during aeration phase of SBR cycle). PN was achieved only during the operational phases when the pH rise due to the basic hydrolysis of sodium sulphide was not controlled. The dosage of sulphide as its sodium salt has dual advantages as an inhibitor to nitrite oxidising bacteria (NOB). The activity of NOBs was 10–20 mgN/gVSS/d during the operational phases when PN was effective. The long-term operation showed a nitrite accumulation ratio of 70 ± 19% with an effluent ammonia concentration of 19 ± 4 mgN/L and nitrite concentrations of 18 ± 4 mgN/L. At the genus level, the NOB communities Nitrospira and Nitrobacter diminished in the long-term phases of stable PN with real wastewater. NOB communities evolved again when sulphide dosage was stopped indicating this strategy needs to be adopted continuously to achieve PN not time-based. The study demonstrated a strategy to achieve the feasibility of sulfide-driven PN systems and has a potential application in nitrogen removal from domestic wastewater.

Biofilm Reactor with Permeable Materials as Carriers Archives Better and More Stable Performance in Treatment of Slightly Polluted Water during Long-Term Operation

Biofilms in reactors usually grow on impermeable surfaces, and the mass transfer of nutrients in biofilms is mainly driven by diffusion, which is inefficient especially for thick biofilms. In this study, permeable materials (i.e., nylon meshes) were used as biocarriers in a biofilm reactor, and their performance was evaluated and compared with the commercial biocarriers (PE08 and PE10) used for treating slightly polluted water. The results indicate that the mesh-based bioreactor achieved complete nitrification faster than the commercial biocarriers, with a more stable and better effluent quality during long-term operation. At a two-hour hydraulic retention time, the average effluent ammonia (NH4+-N) and nitrite (NO2−-N) concentrations during the stabilized phase were 0.97 ± 0.79 and 0.61 ± 0.32 mg-N, respectively, which are significantly lower than those with commercial carriers. The estimated specific surface area activities for the mesh, PE08, and PE10 carriers were 1620, 769, and 1300 mg-N/(m2·d)), respectively. The biofilms formed on the nylon mesh were porous, while they were compact and nonporous on the PE carriers. Water with substrates might pass through the porous biofilms formed on the meshes, which could enhance mass transfer and result in a better and more stable treatment performance.

Flow rate impact on the performance of immobilized nanoFeCu for sewage treatment and its reusability

AbstractAmmonia pollution is a global issue, and it endangers human and aquatic life. The role of nanoFeCu in oxidizing ammonia has been identified, but its practicality and performance on the pilot scale are still unclear. This study was conducted to investigate immobilized nanoFeCu's performance in terms of ammonia, nitrate, nitrite, biological oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solid (TSS) by using sewage as feed. The effect of sewage flow rate was studied to determine the optimum operating condition of a pilot‐scale reactor. The reusability test was conducted to address sustainability concerns. The nanoFeCu was synthesized, immobilized in polymer clay, and placed into the reactor filled with sewage at a varied flow rate of from 210 to 1200 mL/min. Results showed that a higher sewage flow rate increased the ammonia removal rate within a shorter time but exhausted the immobilized nanoFeCu at a higher rate. Nitrate, nitrite, and TSS have a similar removal trend as ammonia. 800 mL/min was identified as the optimum flow rate with effluent ammonia concentration below 10 ppm in 9 h and maintained for 12 h. A reusability study showed that immobilized nanoFeCu could be reused for at least 10 successive cycles. The stability and performance of immobilized nanoFeCu suggested that it could be an alternative to treat sewage wastewater for real‐life applications.

Study of Ammonia Removal Efficiency and Process Regulation Strategy of Biological Aerated Filter in Treating Low-Temperature Micropolluted Urban Landscape Water

To explore the treatment performance of a biological aerated filter (BAF) for exogenous pollutants in low-temperature landscape water, a laboratory and pilot system were constructed in urban rivers in the alpine region of northern China, and the key influencing factors such as pH, hydraulic load, air–water ratio, and temperature were studied. The results showed that the operation parameters of the BAF need to be controlled to achieve good nitrogen removal effect. Based on the hazard identification of ammonia nitrogen in landscape water effluent, the critical control points of the BAF process were determined, and corresponding control strategies were formulated. By adjusting operation parameters and other measures, hazards can be identified and overcome with reasonable accuracy. Therefore, the effluent ammonia nitrogen and concentration can be kept within the limits of the law. Double-layer aerated organisms can operate stably at normal temperature, in a transition period, and at low temperature period. The average removal rate of ammonia nitrogen in the three periods can reach 83.29%, 68.53%, and 56.55%, respectively, and the effluent quality can meet the requirements of landscape water during operation.

Achieving deep autotrophic nitrogen removal from low strength ammonia nitrogen wastewater in aeration sponge iron biofilter: Simultaneous nitrification, Feammox, NDFO and Anammox

Partial nitrification and anammox process is considered to be a good choice for nitrogen removal. Nevertheless, it is difficult to achieve in low ammonia wastewater because of the need to control further oxidation of nitrite. Nitrification combined with Feammox (anaerobic ammonia oxidation coupled to Fe (III) reduction) and NDFO (nitrate/nitrite-dependent Fe (II) oxidation) can achieve autotrophic removal of low ammonia. And sponge iron can avoid the disadvantage of continuously adding iron sources in this process. Therefore, a sponge iron biofilter (SIBF) treating low ammonia wastewater under aeration was explored in this study. The optimal operating conditions for SIBF were hydraulic retention time (HRT) of 9 h and gas–water ratio (R) of 9:1, at which the total inorganic nitrogen removal efficiency was up to 77.2 %, and effluent ammonia and total inorganic nitrogen concentrations were less than 5 mg/L and 15 mg/L, respectively. This met the 1A discharge standard of municipal wastewater in China. Stable ratio isotope results indicated that Feammox, NDFO and Anammox resulted in high nitrogen loss in SIBF. And different nitrogen transformation contribution tests indicated NDFO had the dominant contribution, which accounted for 55.7 %. Besides, the production of N2O was minimum under HRT of 9 h and R of 9:1, in which the N2O emission factor was 0.7 %. Stable isotope tracing analysis showed that 92.8 % of N2O was derived from ammonia and the dominant production pathway was nitrifier denitrification. The results of microbial community showed that the nitrification bacteria-Nitrosomonas, Nitrotoga, Nitrospira, Feammox bacteria-Dechloromonas, NDFO bacteria-Thiobacillus and anammox bacteria-Brocadia played major roles in SIBF. Therefore, SIBF shows superior performance and application potential in autotrophic nitrogen removal from low-ammonia nitrogen wastewater under aeration.

Integrated Modeling and Lab-Scale Investigations Demonstrate the Impact of Sulfide on a Membrane Aerated Biofilm Reactor

An emerging innovation in the wastewater treatment field is the application of membrane aerated biofilm reactors (MABRs) as low-energy and small-footprint nitrogen removal technologies. MABRs use membranes supplied with oxygen to grow mixed-redox counter-diffusional biofilms that are advantageous for studies of coupled sulfur and nitrogen cycling. We have previously shown that sulfide can induce dissimilatory nitrite reduction to ammonia (DNRA) in an MABR. However, the implications of sulfide-induced DNRA on reactor performance have not been quantified, and the overall performance of a MABR treating sulfide-ladened wastewater has not been evaluated. Here, we assessed the impact of sulfide on nitrogen removal in a MABR using modeling and experimental approaches. Experimentally, influent sulfide was increased stepwise into a methane-fed nitrifying MABR over 420 days, and microsensor profiling was used to quantify sulfide, nitrous oxide, and oxygen fluxes. Moderate increases in sulfide caused effluent ammonium concentrations to double and nitrous oxide emissions to increase a hundred-fold. Potential rates of DNRA were determined by calibrating a biofilm model to experimental data and were found to exceed nitrification rates at even moderate sulfide concentrations. The results described here highlight the importance of considering DNRA in engineered systems, especially when treating moderately sulfide-laden wastewaters.

Design methodologies to determine optimal staging of membrane-aerated biofilm reactors for mainstream treatment with anammox.

Partial nitritation anammox (PNA) membrane-aerated biofilm reactors (MABRs) can be used in mainstream nitrogen removal to help facilities reduce their energy consumption. Previous PNA MABR research has not investigated the impacts of staging, i.e. arraying MABRs in series, on their nitrogen removal performance, operation, and ability to suppress nitrite oxidizing bacteria. In this paper, a mathematical model simulated PNA MABR performance at different influent total ammonia concentrations and loadings. A design methodology for staging PNA MABRs was created and found that the amount of membrane surface area is dependent upon the total ammonia-nitrogen concentration and loading, and the air loading to the membrane must be proportional to the total ammonia-nitrogen loading to maximize the total inorganic nitrogen (TIN) removal rate. This led to approximately equal-sized stages that each had a TIN removal percentage of 71% of the influent total ammonia nitrogen. Staging a treatment train resulted in 9.8% larger total ammonia and 9.3% larger total nitrogen removal rates when compared with an un-staged reactor. The un-staged reactor also was not able to produce an effluent total ammonia concentration below 5 mg N/L which would be necessary for many facilities' permits.

Improving wastewater treatment plant performance based on effluent quality, operational costs, and reliability using control strategies for water and sludge lines

As a result of urbanization and population growth, wastewater treatment plants (WWTPs) are experiencing increased influent flow rates, pollutant loads, and stricter standards for effluent quality. Therefore, implementing a control strategy is critical for improving the performance of WWTPs in response to highly variable effluent quality. In this study, different strategies for both water and sludge lines are deployed in WWTP model to control dissolved oxygen and biomass concentration in activated sludge system by manipulating aeration intensity and sludge flow rates. The efficiency of suggested control strategies is evaluated using different performance criteria, including effluent quality indicators, operational costs, and system reliability. The proposed method is applied in a real WWTP located in south of Tehran, the capital of Iran. According to the findings, controlling aeration system and sludge flow rate improve effluent quality and system reliability despite increasing operational costs. In the case of simultaneous control of water and sludge lines, the effluent quality index has improved by 9.3%, while the operational costs have increased by 1%. Moreover, the system reliability based on effluent ammonia, TSS, and COD concentration have increased by 33.4%, 9%, and 12.2%, respectively.

Simultaneous nitrification and denitrification in membrane bioreactor: Effect of dissolved oxygen

Membrane bioreactor with the floc activated sludge (mixed liquor suspended solids (MLSS) = 7500 mg/L) was constructed in this work for simultaneously nitrification and denitrification (SND). The effect of dissolved oxygen (DO) on SND process and the nitrogen pathways were investigated. The average TN removal efficiencies were 63.05%, 91.17%, 87.04% and 70.02% for DO 0.5, 1, 2 and 3 mg/L systems, respectively. The effluent ammonia concentration was continuously lower than 5.0 mg/L when the DO was higher than 1 mg/L. Nitrogen in DO 1 and DO 2 mg/L systems was mainly removed via the SND process. The rise of DO concentration increased the abundance of nitrite oxidizing bacteria (NOB) and Nitrospira was the predominant NOB in all the four MBRs. Dechloromonas and Azoarcus were the dominant denitrifying bacteria (DNB) in DO 1 systems responsible for nitrite denitrification. The dominant aerobic DNB Pseudomonas also contributed SND via nitrate denitrification and was little affected by DO changes. Nitrate reductase was the main enzyme for the reduction of NO3−-N to NO2−-N, and narG was the main responsible gene. Nitrite oxidoreductase was the main enzyme for the oxidation of NO2−-N to NO3−-N, and nxrA was the main responsible gene in all the four MBR systems.

Achieving PN through the selective recovery of AOB activity in inactivated nitrifying bacteria: Combined aerobic starvation and FA

A novel strategy combining aerobic starvation and free ammonia (FA) was proposed to achieve partial nitrification (PN). The impact of the combined strategy on nitrifying bacteria was explored in a 200-day experiment. The effluent concentration of ammonia was below the detection limits (0.1 mg/L), and the effluent concentration of nitrite and nitrate was 68.12 mg/L and 3.46 mg/L without adding carbon source to the artificial wastewater. The nitrite accumulation rate (NAR) was maintained at 90.15% even when the dissolved oxygen (DO) concentration was 1.50 mg/L. Further analysis showed that PN was achieved by selectively restoring the activity of ammonia-oxidizing bacteria (AOB) in nitrifying bacteria that had lost their activity after starvation. The specific ammonia oxidation rate (SAOR) was 46.25 mg N/g MLVSS/h, and the specific nitrate product rate (SNPR) was only 0.73 mg N/g MLVSS/h in the stable operation stage. The increase in AOB abundance (from 2.79% to 7.13%) and the decrease in nitrite-oxidizing bacteria (NOB) abundance (from 8.75% to 1.44%) explained this phenomenon. Finally, the analyses on the secretion of extracellular polymer substance (EPS), strategies to resist harsh environments, and physical properties of sludge explored the potential mechanism and provided references for applying the combined strategy.

Study on the preparation of manganese oxide filter media for catalytic oxidation removal of ammonium and manganese in high alkalinity groundwater: The effect of copper and cobalt doping

Owing to the low efficiency of catalytic oxidation of ammonium and manganese in high alkalinity groundwater by manganese oxide filter column, the effect of doping copper (Cu) and cobalt (Co) on catalytic activity was investigated. A pilot-scale filter column system was used in this experiment. The catalytic oxidation activities of manganese oxide filter media prepared at different Cu and Co concentrations (0, 0.1, 1.0 mg/L) in raw water were compared. The results showed that the removal efficiency of ammonium by the filter column increased from 65% to 100% due to low Cu doping, while the removal efficiency of manganese decreased from 95% to 60% due to high Cu doping. Low Co doping was beneficial to the start-up period of the filter column and the effluent ammonium concentration reached the standard on the 8th day. In addition, X-ray diffraction (XRD) and Scanning electron microscopy coupled energy dispersive spectroscopy (SEM-EDS) results showed that the high alkalinity of water led to the formation of a large amount of calcium carbonate on the surface of the filter media. Combined with X-ray photoelectron spectroscopy (XPS) analysis, it was found that the conversion of different valence manganese species caused by Cu and Co doping and the Cu and Co elements with different valence were the key factors determining the catalytic activity of manganese oxide filter media. Cu2+/Cu+ and Co3+/Co2+ electron cycles also participated in the electron transfer process of catalytic reaction. This study provides a research idea for solving the pollution problem of ammonium and manganese in high alkalinity water.

Performance of integrated vertical-flow constructed wetland-microbial fuel cells during long-term operation: The contribution of substrate type and vegetation

An integrated vertical-flow constructed wetland-microbial fuel cell (CW-MFC) system, consisting of an upflow chamber and a downflow chamber, was designed to treat synthetic sewage wastewater. The potential influence of vegetation (with and without the planting of Canna indica) and substrate type (ceramsite, quartz and zeolite granules filled in CM-A, CM-B and CM-C, respectively) on the performance of wastewater treatment and electricity generation in the CW-MFCs was evaluated during long-term operation. A two-way analysis of variance (ANOVA) indicated that both vegetation and substrate type significantly affected the effluent ammonium (NH4+-N) and orthophosphate (PO43--P) concentrations (p < 0.05), and the interaction term (i.e., vegetation × substrate type) was significant for both NH4+-N and PO43--P removal (p < 0.05). The efficient N removal in the planted CM-A and CM-C could be partly attributed to the enhanced cathodic denitrification in association with the high-power outputs. After plant removal, the deterioration of wastewater treatment and electricity generation performance was observed in all of the CW-MFCs except for CM-A. Using ceramsite as the substrate of a CW-MFC would contribute to a promising performance. However, the increased nirK/nirS and nosZ/16 S rDNA ratios indicated a risk of N2O emission in the unplanted CM-A, while the gradual decrease in the PO43--P removal efficiency indicated the saturation of adsorption sites on the ceramsite granules after two years of operation.

Hospital sewage treatment facilities witness the fighting against the COVID-19 pandemic

Real-time evaluation of the fighting activities during a sudden unknown disaster like the COVID-19 pandemic is a critical challenge for control. This study demonstrates that the temporal variations of effluents from hospital sewage treatment facilities can be used as an effective indicator for such evaluation. Taking a typical infection-suffering city in China as an example, we found that there was an obvious decrease in effluent ammonia and COD concentrations in line with the start of city lockdown, and its temporal variations well indicated the major events happened during the pandemic control. Notably, the lagging period between the change point of effluent residual chlorine and the change points of COD and ammonia concentration coincided with a period in which there was a deficiency in local medical resources. In addition, the diurnal behavior of effluents from designated hospitals has varied significantly at different stages of the pandemic development. The effluent ammonia peaks shifted from daytime to nighttime after the outbreak of the COVID-19 pandemic, suggesting a high workload of the designated hospitals in fighting the rapidly emerging pandemic. This work well demonstrates the necessary for data integration at the wastewater–medical service nexus and highlights an unusual role of the effluents from hospital sewage treatment facilities in revealing the status of fighting the pandemic, which helps to control the pandemic.