Biological Nutrient Removal Processes Research Articles

Distinguished denitrifying phosphorus removal in the high-rate anoxic/microaerobic system for sewage treatment

This study re-evaluated the role of anoxic and anaerobic zones during the enhanced biological phosphorus (P) removal process by investigating the potential effect of introducing an anoxic zone into a high-rate microaerobic activated sludge (MAS) system (1.60–1.70 kg chemical oxygen demand (COD) m−3 d−1), i.e., a high-rate anoxic/microaerobic (A/M) system for sewage treatment. In the absence of a pre-anaerobic zone, introducing an anoxic zone considerably reduced effluent NOx−-N concentrations (7.2 vs. 1.5 mg L−1) and remarkably enhanced total nitrogen (75% vs. 89%) and total P (18% vs. 60%) removal and sludge P content (1.48% vs. 1.77% (dry weight)) due to further anoxic denitrifying P removal in the anoxic zone (besides simultaneous nitrification and denitrification in the microaerobic zone). High-throughput pyrosequencing demonstrated the niche differentiation of different polyphosphate accumulating organism (PAO) clades (including denitrifying PAO [DPAO] and non-DPAO) in both systems. Introducing an anoxic zone considerably reduced the total PAO abundance in sludge samples by 42% and modified the PAO community structure, including 17–19 detected genera. The change was solely confined to non-DPAOs, as no obvious change in total abundance or community structure of DPAOs including 7 detected genera was observed. Additionally, introducing an anoxic zone increased the abundance of ammonia-oxidizing bacteria by 39%. The high-rate A/M process provided less aeration, higher treatment capacity, a lower COD requirement, and a 75% decrease in the production of waste sludge than the conventional biological nutrient removal process.

Impact of dissolved oxygen levels on N2O emissions and metabolically active bacterial community in biological nitrogen removal

Biological nutrient removal (BNR) is an effective process for removing nitrogen from wastewater using microorganisms under different environmental conditions. However, these processes can result in nitrous oxide (N2O) emission. Dissolved oxygen (DO) levels shape bacterial communities and affect N2O emissions in BNR. Ammonia-oxidizing bacteria (AOB), the main N2O producers in the BNR process, are significantly affected by DO. The bacterial communities responding to DO at the DNA level in BNR have been elucidated. However, the metabolically active microbes are truly participant in the biodegradation process. Thus, it is worth exploring how metabolically active bulk and AOB microbial communities and their N2O production respond to the DO levels. In this study, N2O emission were measured in lab-scale sequencing batch reactors at varying DO levels. High-throughput sequencing of the metabolically active bacterial 16S rRNA and amoA genes was performed. The results indicate that increasing DO levels positively influence metabolically active bacterial biodiversity and AOB relative abundance without increasing N2O emissions. The carbon source significantly affected denitrification and nitrification in activated sludge. Optimal DO levels and adequate carbon sources can effectively reduce N2O accumulation. However, excessive aeration reduces the abundance of Zoogloea, leading to compromised sludge settling. This study revealed the effect of DO on N2O producing microorganisms based on cDNA levels, which provided an important basis for mitigating N2O emission in BNR.

Combined Phototrophic Simultaneous Nitrification-Endogenous Denitrification with Phosphorus Removal (P-SNDPR) System Treating Low Carbon to Nitrogen Ratio Wastewater for Potential Carbon Neutrality.

Conventional biological nutrient removal processes rely on external aeration and produce significant carbon dioxide (CO2) emissions. This study constructed a phototrophic simultaneous nitrification-denitrification phosphorus removal (P-SNDPR) system to treat low carbon to nitrogen (C/N) ratios wastewater and investigated the impact of sludge retention time (SRT) on nutrient removal performance, nitrogen conversion pathway, and microbial structure. Results showed that the P-SNDPR system at SRT of 15 days had the highest nutrient removal capacity, achieving over 85% and 98% removal of nitrogen and phosphorus, respectively, meanwhile maintaining minimal CO2 emissions. Nitrogen removal was mainly through assimilation at SRTs of 5 and 10 days, and nitrification-denitrification at SRTs of 15 and 20 days. Stable partial nitrification was facilitated by photoinhibition and low DO levels. Flow cytometry sorting technique results revealed SRT drove community structural changes in translational activity (BONCAT+) microbes, where BONCAT+ microbes were mainly simultaneous nitrogen and phosphorus removal bacteria (Candidatus Accumulibacter), denitrifying bacteria (Candidatus Competibacter and Plasticicumulans), ammonia-oxidizing bacteria (Nitrosomonas), and microalgae (Chlorella and Dictyosphaerium). The P-SNDPR system represents a novel, carbon-neutral process for efficient nutrient removal from low C/N ratio wastewater without aeration and external carbon source additions.

Comammox and unknown ammonia oxidizers contribute to nitrite accumulation in an integrated A-B stage process that incorporates side-stream EBPR (S2EBPR)

A novel integrated pilot-scale A-stage high rate activated sludge, B-stage short-cut biological nitrogen removal and side-stream enhanced biological phosphorus removal (A/B-shortcut N-S2EBPR) process for treating municipal wastewater was demonstrated with the aim to achieve simultaneous and carbon- and energy-efficient N and P removal. In this studied period, an average of 7.62 ± 2.17 mg-N/L nitrite accumulation was achieved through atypical partial nitrification without canonical known NOB out-selection. Network analysis confirms the central hub of microbial community as Nitrospira, which was one to two orders of magnitude higher than canonical aerobic oxidizing bacteria (AOB) in a B-stage nitrification tank. The contribution of comammox Nitrospira as AOB was evidenced by the increased amoB/nxr ratio and higher ammonia oxidation activity. Furthermore, oligotyping analysis of Nitrospira revealed two dominant sub-clusters (microdiveristy) within the Nitrospira. The relative abundance of oligotype II, which is phylogenetically close to Nitrospira_midas_s_31566, exhibited a positive correlation with nitrite accumulation in the same operational period, suggesting its role as comammox Nitrospira. Additionally, the phylogenetic investigation suggested that heterotrophic organisms from the family Comamonadacea and the order Rhodocyclaceae embedding ammonia monooxygenase and hydroxylamine oxidase may function as heterotrophic nitrifiers. This is the first study that elucidated the impact of integrating the S2EBPR on nitrifying populations with implications on short-cut N removal. The unique conditions in the side-stream reactor, such as low ORP, favorable VFA concentrations and composition, seemed to exert different selective forces on nitrifying populations from those in conventional biological nutrient removal processes. The results provide new insights for integrating EBPR with short-cut N removal process for mainstream wastewater treatment.

Biological performance and membrane fouling of a microalgal-bacterial membrane photobioreactor for wastewater treatment without external aeration and carbonation

Biological nutrient removal processes involving the use of activated sludge (AS) to treat municipal wastewater normally result in high aeration energy consumption and significant greenhouse gas (GHG) emissions. Therefore, developing cost-efficient and environmentally friendly processes for wastewater treatment is vital. In this work, a novel non-aerated microalgal-bacterial membrane photobioreactor (MB-MPBR) was proposed, and its feasibility for organic contaminant and nutrient removals was evaluated, for the first time. The effects of inoculation ratio (microalgae to bacteria (M/B)) on the biological performance and membrane fouling were systematically investigated. The results showed that 95.9% of the chemical oxygen demand (COD), 74.5% of total nitrogen (TN), 98.5% of NH4+-N and 42.0% of total phosphorus (TP) were removed at an inoculation M/B ratio of 3:2 at steady state, representing a significant improvement compared to the M/B inoculation ratio of 1:3. Additionally, the higher inoculation M/B ratio (3:2) significantly promoted the biomass production owing to the favorable mutual exchange of oxygen and carbon dioxide between microalgae and bacteria. Cake layer formation was the primary fouling mechanism owing to the absence of aeration scouring on the membrane surface. The membrane fouling rate was slightly higher at the higher inoculation ratio (M/B = 3:2) owing to the increased biomass and extracellular polymeric substances (EPS) productions, despite the larger particle size. These results demonstrated that the non-aerated MB-MPBR could achieve superior biological performance, of which the inoculation M/B ratio was of critical importance for the initiation and maintenance of microalgal-bacterial symbiotic system, yet possibly caused severer membrane fouling in the absence of external aeration and carbonation. This study provides a new perspective for further optimizing and applying non-aerated MB-MPBR to enhance municipal wastewater treatment.

Simultaneous nitrogen and phosphorus removal from black water: Effects of dissolved oxygen level and sludge concentration on full-scale performance and bacterial community dynamics

Removal of nutrients from pretreated black water is difficult during the biological nutrient removal process, owing to the lack of carbon sources. To improve nitrogen and phosphorus removal, this study explored optimal operating strategies in a plant-scale multistage activated sludge-membrane bioreactor system for black water treatment. The results showed that the nutrient removal efficiency was highly dependent on the regulation of dissolved oxygen (DO) levels and sludge concentrations. The decreased DO levels (0.6–0.7 mg O2/L) in aerobic zones O3–O5 and the increased sludge concentrations (10.5 g/L) enhanced simultaneous nitrification and denitrification (SND), thereby improving the nitrogen removal efficiency from 82.4 % to 94.3 %. Additionally, when the nitrates were mostly reduced in the aerobic zones, phosphorus removal significantly increased from 29.6 % to 75.7 %. High-throughput sequencing analyses revealed a continuous increase in bacterial diversity during the entire operation period to adapt to the black water properties. Although the proportion of nitrogen- and phosphorus-removing bacteria decreased under oxygen-limited conditions, nitrogen removal efficiency was guaranteed via the SND pathway. The bacterial relative abundances could be partially or fully recovered by appropriately adjusting the DO levels and sludge concentrations.

Effluent parameters prediction of a biological nutrient removal (BNR) process using different machine learning methods: A case study

This paper proposes a novel targeted blend of machine learning (ML) based approaches for controlling wastewater treatment plant (WWTP) operation by predicting distributions of key effluent parameters of a biological nutrient removal (BNR) process. Two years of data were collected from Plajyolu wastewater treatment plant in Kocaeli, Türkiye and the effluent parameters were predicted using six machine learning algorithms to compare their performances. Based on mean absolute percentage error (MAPE) metric only, support vector regression machine (SVRM) with linear kernel method showed a good agreement for COD and BOD5, with the MAPE values of about 9% and 0.9%, respectively. Random Forest (RF) and EXtreme Gradient Boosting (XGBoost) regression were found to be the best algorithms for TN and TP effluent parameters, with the MAPE values of about 34% and 27%, respectively. Further, when the results were evaluated together according to all the performance metrics, RF, SVRM (with both linear kernel and RBF kernel), and Hybrid Regression algorithms generally made more successful predictions than Light GBM and XGBoost algorithms for all the parameters. Through this case study we demonstrated selective application of ML algorithms can be used to predict different effluent parameters more effectively. Wider implementation of this approach can potentially reduce the resource demands for active monitoring the environmental performance of WWTPs.

Full dynamic control of dairy wastewater treatment by aerobic granular sludge using electric conductivity and oxygen uptake rate

Abstract The objective of the current study was to determine the applicability of a sensor-based dynamic control strategy for the treatment of real variable dairy wastewater by aerobic granular sludge (AGS) performing enhanced biological phosphorus removal (EBPR). Two parallel sequencing batch reactors (SBRs) were set up that used only an anaerobic feast/aerobic famine microbial selection strategy to successfully obtain sludge granulation. SBR-STA used a fixed cycle length, while the duration of the reaction steps in SBR-DYN was variable. The control strategy was based solely on (derived) signals from low-cost and common sensors. The profile of the electric conductivity during the anaerobic reaction step was related to the microbial release of phosphate (PO4-P) and the associated uptake of dissolved organic carbon (DOC) by polyphosphate-accumulating organisms (PAOs). Control of the aerobic reaction step was based on the oxygen uptake rate (OUR). This resulted in a dynamic reactor operation with significant efficiency gains, such as 32% shorter cycle times and 42% higher sludge loading rates without impairing the effluent quality. These results extend the existing potential of indirect control strategies to full biological nutrient removal processes, which may be of great assistance to the operators and designers of industrial installations.

WITHDRAWN: Distinguished denitrifying phosphorus removal in the high-rate anoxic/microaerobic system for sewage treatment

This study re-evaluated the role of anoxic and anaerobic zones during the enhanced biological phosphorus (P) removal process by investigating the potential effect of introducing an anoxic zone into a high-rate microaerobic activated sludge (MAS) system (1.60-1.70 kg chemical oxygen demand (COD) m-3 d-1), i.e., a high-rate anoxic/microaerobic (A/M) system for sewage treatment. In the absence of a pre-anaerobic zone, introducing an anoxic zone considerably reduced effluent NOx--N concentrations (7.2 vs. 1.5 mg L-1) and remarkably enhanced total nitrogen (75% vs. 89%) and total P (18% vs. 60%) removal and sludge P content (1.48% vs. 1.77% (dry weight)) due to further anoxic denitrifying P removal denitrification in the anoxic zone (besides simultaneous nitrification and denitrification in the microaerobic zone). High-throughput pyrosequencing demonstrated the niche differentiation of different polyphosphate accumulating organism (PAO) clades (including denitrifying PAO [DPAO] and non-DPAO) in both systems. Introducing an anoxic zone considerably reduced the total PAO abundance in sludge samples by 42% and modified the PAO community structure, including 17-19 detected genera. The change was solely confined to non-DPAOs, as no significant change in total abundance or community structure of DPAOs including seven detected genera was observed. Additionally, introducing an anoxic zone increased the abundance of ammonia-oxidizing bacteria by 39%. The high-rate A/M process provided less aeration, higher treatment capacity, a lower COD requirement, and a 75% decrease in the production of waste sludge than the conventional biological nutrient removal process.

Integrating Bio-Electrochemical Sensors and Machine Learning to Predict the Efficacy of Biological Nutrient Removal Processes at Water Resource Recovery Facilities.

Monitoring biological nutrient removal (BNR) processes at water resource recovery facilities (WRRFs) with data-driven models is currently limited by the data limitations associated with the variability of bioavailable carbon (C) in wastewater. This study focuses on leveraging the amperometric response of a bio-electrochemical sensor (BES) to wastewater C variability, to predict influent shock loading events and NO3- removal in the first-stage anoxic zone (ANX1) of a five-stage Bardenpho BNR process using machine learning (ML) methods. Shock loading prediction with BES signal processing successfully detected 86.9% of the influent industrial slug and rain events of the plant during the study period. Extreme gradient boosting (XGBoost) and artificial neural network (ANN) models developed using the BES signal and other recorded variables provided a good prediction performance for NO3- removal in the ANX1, particularly within the normal operating range of WRRFs. A sensitivity analysis of the XGBoost model using SHapley Additive exPlanations indicated that the BES signal had the strongest impact on the model output and current approaches to methanol dosing that neglect C availability can negatively impact nitrogen (N) removal due to cascading impacts of overdosing on nitrification efficacy.

Reduction of SARS-CoV-2 by biological nutrient removal and disinfection processes in full-scale wastewater treatment plants

Detection of SARS-CoV-2 RNA in wastewater poses people's concerns regarding the potential risk in water bodies receiving wastewater treatment effluent, despite the infectious risk of SARS-CoV-2 in wastewater being speculated to be low. Unlike well-studied nonenveloped viruses, SARS-CoV-2 in wastewater is present abundantly in both solid and liquid fractions of wastewater. Reduction of SARS-CoV-2 in past studies were likely underestimated, as SARS-CoV-2 in influent wastewater were quantified in either solid or liquid fraction only. The objectives of this study were (i) to clarify the reduction in SARS-CoV-2 RNA during biological nutrient removal and disinfection processes in full-scale WWTPs, considering the SARS-CoV-2 present in both solid and liquid fractions of wastewater, and (ii) to evaluate applicability of pepper mild mottle virus (PMMoV) as a performance indicator for reduction of SARS-CoV-2 in WWTPs. Accordingly, large amount of SARS-CoV-2 RNA were partitioned in the solid fraction of influent wastewater for composite sampling than grab sampling. When SARS-CoV-2 RNA in the both solid and liquid fractions were considered, log reduction values (LRVs) of SARS-CoV-2 during step-feed multistage biological nitrogen removal (SM-BNR) and enhanced biological phosphorus removal (EBPR) processes ranged between>2.1–4.4 log and did not differ significantly from those in conventional activated sludge (CAS). The LRVs of SARS-CoV-2 RNA in disinfection processes by ozonation and chlorination did not differ significantly. PMMoV is a promising performance indicator to secure reduction of SARS-CoV-2 in WWTPs, because of its higher persistence in wastewater treatment processes and abundance at a detectable concentration even in the final effluent after disinfection.

Volatile fatty acid production from primary and secondary sludges to support efficient nutrient management

Enhanced hydrolysis of sludges during fermentation is an important factor to achieve solubilization of complex carbon sources and increase the amount of soluble COD that microorganisms could use as food during biological nutrient removal processes. This research shows that a combination of mixing, bioaugmentation, and co-fermentation can be used to increase the hydrolysis of sludges and enhanced the production of volatile fatty acids (VFA). Mixing of primary sludge (PS) at 350 revolutions per minute (RPM) during fermentation increased the hydrolysis of the sludge and increased the soluble chemical oxygen demand (sCOD) by 72% compared to no mixing. Mixing also increased the production of VFA by 60% compared to no mixing conditions. PS hydrolysis was also evaluated using bioaugmentation with the bacteria Bacillus amyloliquefacients, a known producer of the biosurfactant surfactin. Results showed that bioaugmentation enhanced the hydrolysis of the PS by increasing the amount of soluble carbohydrates and soluble proteins present in the form of sCOD. Methanogenesis experiments performed with co-fermentation of decanted primary sludge (PS) and raw waste-activated sludge (WAS) at 75:25 and 50:50 ratios displayed a decreased in production of total biogas by 25.58% and 20.95% and a reduction on methane production by 20.00% and 28.76% respectively, compared to co-fermentation of raw sludges. Compared to fermentation of the sludges separately, co-fermentation of PS and WAS increased the production of VFA and it was determined that 50:50 was the optimum co-fermentation ratio for production of VFA while reducing the reintroduction of nutrients produced during the fermentation process to BNR processes.

The ASM2d model with two-step nitrification can better simulate biological nutrient removal systems enriched with complete ammonia oxidizing bacteria (comammox Nitrospira)

The discovery of comammox Nitrospira, a complete ammonia-oxidizing microorganism belonging to the genus Nitrospira, has brought new insights into the nitrification process in wastewater treatment plants (WWTPs). The applicability of Activated Sludge Model No. 2 d with one-step nitrification (ASM2d-OSN) or two-step nitrification (ASM2d-TSN) for the simulation of the biological nutrient removal (BNR) processes of a full-scale WWTP in the presence of comammox Nitrospira was studied. Microbial analysis and kinetic parameter measurements showed comammox Nitrospira was enriched in the BNR system operated under low dissolved oxygen (DO) and long sludge retention time (SRT). The relative abundance of Nitrospira under the conditions of stage I (DO = 0.5 mg/L, SRT = 60 d) was about twice of that under stage II conditions (DO = 4.0 mg/L, SRT = 26 d), and the copy number of the comammox amoA gene for stage I was 33 times higher than that for stage II. Compared to the ASM2d-OSN model, the ASM2d-TSN model simulated the performance of the WWTP under stage I conditions better, and the Theil inequality coefficient values of all the tested water quality parameters were lower than using ASM2d-OSN. These results indicate that an ASM2d model with two-step nitrification is a better choice for the simulation of WWTPs with the presence of comammox.

Effect of thermal hydrolyzed sludge filtrate as an external carbon source on biological nutrient removal performance of A2/O system

Thermal hydrolyzed sludge filtrate (THSF) rich in biodegradable organics could be a promising external carbon source for biological nutrient removal (BNR). The use of THSF can effectively reduce wastewater treatment plants operating costs and recover bioresources and bioenergy from the waste activated sludge. In this study, the effect of THSF on the BNR process was investigated using a lab-scale anaerobic/anoxic/oxic (A2/O) system. Total nitrogen (TN) and total phosphorus (TP) removal efficiencies of 74.26 ± 3.36% and 92.20 ± 3.13% at a 0.3% dosing ratio were achieved, respectively. Moreover, 20.42% of the chemical oxygen demand (COD) contained in THSF contributed to denitrification, enhancing nitrogen removal efficiency from 55.30 to 74.26%. However, the effluent COD increased by approximately 36.80%, due to 18.39% of the COD contained in THSF discharged with effluent. In addition, the maximum denitrification rate was approximately 16.01 mg N g VSS-1 h-1, while the nitrification rate was not significantly affected by THSF. Nitrosomonas, a common chemoautotrophic nitrifier, was not detected after the introduction of THSF. The aerobic denitrifier Rubellimicrobium was stimulated, and its relative abundance increased from 0.16 to 3.03%. Moreover, the relative abundance of Dechloromonas was 3.93%, indicating that the denitrifying phosphorus removal process was enhanced. This study proposes an engineering application route of THSF, and the chemical phosphate removal pretreatment might be a means to suppress the phosphate recirculation.

Variations in antibiotic resistance genes and microbial community in sludges passing through biological nutrient removal and anaerobic digestion processes in municipal wastewater treatment plants

Antimicrobial resistance (AMR) represents a relentless, silent pandemic. Contributing to this are wastewater treatment plants (WWTPs), a potential source of antibiotic resistance genes' (ARGs) transmission to the environment, threatening public health. The presence of ARGs in pathogenic bacteria and their release into the environment by WWTPs threatens the public health. The current study investigated changes in ARGs' abundance in biological nutrient removal (BNR) processes and anaerobic digestion (AD) reactors of two WWTPs. Also, microbial community structure, which is known to shape the distribution and abundance of ARGs, was also analyzed. The relative abundance of eight ARGs (tetX, tetA, tetM, TEM, sul1, sul2, ermB and qnrD) was quantified as ARGs’ copies/16 S rRNA gene copies using quantitative polymerase chain reaction (qPCR). Microbial community composition was assessed by 16 S rRNA microbiome sequencing analysis. TetX was prevalent among the eight ARGs, followed by TEM and sul1. However, its abundance was decreased in the AD sludges compared to BNR sludges. Proteobacteria was the major bacterial phylum found in all the sludge samples, while Arcobacter, 12up and Acidovorax were the predominant genera. Acinetobacter and Flavobacterium were significantly more abundant in the BNR sludges, while 12up and Aeromonas were predominant in AD sludges. Principal component analysis (PCA) revealed a clear difference in dominant ARGs and bacteria between the sludges in the processes of BNR and AD of the two WWTPs. Clinically relevant bacterial genera, Klebsiella and Enterococcus, found in both the BNR and AD sludges, were significantly correlated with the tetX gene. Throughout this study, the relationship between microbial communities and specific ARGs was revealed, illustrating that the composition of the microbial community could play a vital role in the abundance of ARGs. These results will better inform future studies aimed at controlling the spread of ARGs and their potential hosts from WWTPs.

Performance and mechanism of modified biological nutrient removal process in treating low carbon-to-nitrogen ratio wastewater

For solving the challenge of difficult nutrient removal, high running cost and CO2 emission at low carbon-to-nitrogen (C:N) ratio, Bi-Bio-Selector for nitrogen and phosphorus removal (BBSNP) process was developed. Under parallel operation conditions, full-scale BBSNP was less influence by low C:N ratio (3.5–2) than Anaerobic-anoxic-aerobic (AAO) and achieved better nitrogen removal performance. The mechanism of performance advantage in BBSNP was analyzed by mass balance and high throughout sequencing. It demonstrated BBSNP developed unique microbial community at C:N ratio of 2. Higher abundance of Saccharibacteria, Ferruginibacter, Ottowia, Dokdonella, Candidatus_Nitrotoga and Nitrospira in BBSNP was responsible for better chemical oxygen demand (COD) utilization efficiency, denitrification, denitrifying phosphorus removal and nitrification. Meanwhile, under low C:N ratio, BBSNP could save 10% organic carbon and 15% oxygen requirement, reduce 53% running cost and 21% CO2 emission, which had practical value in relieving energy crisis and carbon emission of wastewater treatment plants (WWTPs).

Performance Prediction of the Full-Scale KNR Biological Nutrient Removal Process Using Multivariate Dynamic Process Model with the Grammar-Based Genetic Programming (GBGP) System

Biological wastewater treatment systems are characterized by large temporal variability of inflow, variable concentrations of components in the incoming wastewater to the plant, and highly variable biological reactions within the process. This paper proposes an automatic process model induction system using an evolutionary computional intelligence, called grammar-based genetic programming (GBGP), that is specially designed to automatically discover multivariate dynamic process models that best fit observed process data. This automatic process model induction system combines an evolutionary self-organizing system of genetic programming paradigm with various mathematical functions for a multivariate nonlinear model evolution using a grammar system via the mechanism of genetics and natural selection. In the GBGP, a four-step modelling procedure was performed: (1) initialization: generatr an initial population of P models using contex-free grammar. generation K=0; (2) optimize each model (variables and constants) in the population; (3) execute and evaluate the fitness of each model in the population; (4) genetic loop: repeat until termination criterion is met (maximum generation Kmax). The results demonstrate that the multivariate dynamic process modelling technique presented using a grammar-based genetic programming (GBGP) provides a valuable tool for predicting the out puts with high levels of accuracy and identifying key operating variables for the full-scale KNR biological nutrient removal process. These modells are derived automatically in the form of understandable mathematical formulas that enable engineers to extract important knowledge hidden in the data and develop better operation and control strategies.

Long-term exposure to nanoplastics reshapes the microbial interaction network of activated sludge

Wastewater treatment plants have been identified as an important gathering spot for nanoplastics, possibly having unintended impacts on important biological nutrient removal processes. The underlying effects of long-term exposure of activated sludge to nanoplastics on nutrient removal and the mechanisms involved remain unclear. This study investigated the effect of polystyrene nanoplastics (Nano-PS) on the treatment performance and microbial community structure, and network in activated sludge. The results indicate that 1000 μg/L Nano-PS had chronic negative effects on the treatment performance in a continuous test over 140 days. Nano-PS had no significant impact in the earlier stages (0–50 days). However, as exposure time increased, the removal efficiencies of chemical oxygen demand, total phosphorous, and total nitrogen (TN) decreased by 2.7, 33.2, and 23.5%, respectively, in the later stages (87–132 days). These adverse impacts further manifested as a change in the topological characteristics, forming a smaller scale, lower complexity, and weaker transfer efficiency of the microbial network. Moreover, the scale and complexity of subnetwork-nitrogen removal bacteria and subnetwork-nitrifier were inhibited, leading to an increase in the effluent TN and NH4+-N. The decreased modules and connectors (keystone taxa) likely caused the deterioration of treatment performance and functional diversity, which was consistent with the change in PICRUSt results. Less competition, denser nodes, and more complex module structures were induced as a strategy to mediate the long-term stress of nano-PS. To our knowledge, this is the first attempt to explore the long-term effects of nano-PS on the microbial interaction network of activated sludge, laying an experimental foundation for reducing the risks associated with nanoplastics.

Interrogating nitritation at a molecular level: Understanding the potential influence of Nitrobacter spp.

Water resource recovery facilities (WRRFs) increasingly must maximize nitrogen and phosphorus removal, but concurrently face challenges to reduce their energy usage and environmental footprint. In particular, biological nutrient removal (BNR), which targets removal of phosphorus and nitrogen, exhibits a large energy demand. However, a BNR process achieving partial oxidation of NH3 to NO2 (nitritation) could reduce energy demands, with secondary environmental emission benefits. Research was conducted on bench-scale systems performing nitritation and nitrification to better understand how mixed microbial consortia, cultured on real wastewater, can sustain nitritation. BNR configurations achieved nitrite accumulation ratios of 64–82%, with excellent overall effluent quality. Applying phylogenetic, transcriptomic, and metabolomic methods, coupled with process monitoring, results indicate that partial nitritation may be induced through a combination of: (1) Employing ammonia-based aeration control, with an ammonia setpoint of 2, 3 mgN/L; (2) Maintaining an aerobic period DO of 1.0–2.0 mg/L; and (3) Operating BNR post-anoxically, integrated within enhanced biological phosphorus removal (EBPR). Significant nitritation was achieved despite the presence Nitrobacter spp., but nitrite oxidoreductase must be functionally impaired or structurally incomplete. Overall, this research demonstrated the value of interrogating a mixed microbial consortia at a macro and molecular level to explore unique metabolic responses such as nitritation.

Modeling and simulation of the biological process for treating wastewater with a low COD:N ratio under different dissolved oxygen conditions using an extended ASM2d model

Studies are currently lacking on the reaction mechanism of bacterial biopolymers (extracellular polymeric substances (EPS) and soluble microbial products (SMP)) for biological nutrient removal (BNR) in the activated sludge model No. 2d (ASM2d). Meanwhile, the relationship between biopolymers and BNR under different dissolved oxygen (DO) conditions is not completely clear. Thus, this work presented a modified ASM2d model that combines EPS and SMP (E-S-ASM2d), described in detail the interaction between the different bacteria and biopolymer dynamics in an anaerobic, anoxic, microaerobic and oxic (AAMO) reactor, and simulated the influences of DO on BNR process in low COD:N wastewater and on bacterial biopolymers. In addition, 16S rRNA gene analysis was used to characterize the microbial community dynamics in the AAMO reactor under different DO conditions, thus verifying the necessity of introducing microbial interactions to the E-S-ASM2d model. According to the measurements, the average total nitrogen removal rate of effluent increased 19.4 %, and the average EPS content increased 15.5 % under low DO. According to sensitivity analysis, high sensitivity parameters vary under different DO concentrations (namely 3 mg L−1 and 0.5 mg L−1 DO), which demonstrates the need to determine and separately calibrate these parameters according to DO. Finally, compared with traditional ASM2d model, the correlation likelihood values of E-S-ASM2d model for COD, ammonia nitrogen, total nitrogen and total phosphorus of AAMO effluent increased from 0.622, 0.704, 0.639 and 0.329 to 0.850, 0.894, 0.909 and 0.897, respectively, indicating E-S-ASM2d can significantly improve the simulation of nutrient removal under low influent COD:N.