Decision support systems (DSS) for wastewater treatment plants - A review of the state of the art

Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives

Membrane technologies have become proficient alternatives for advanced wastewater treatment, ensuring high contaminant removal and sustainable resource recovery. Despite significant progress, ongoing research efforts aim to further optimize treatment performance. Among the challenges faced, membrane fouling persists as a relevant obstacle in membrane technologies, necessitating the development of more effective mitigation strategies. Mathematical models, widely employed for predicting treatment performance, generally exhibit low accuracy and suffer from uncertainties due to the complex and variable nature of wastewater. To overcome these limitations, numerous studies have proposed artificial intelligence (AI) modeling to accurately predict membrane technologies' performance and fouling mechanisms. This approach aims to provide advanced simulations and predictions, thereby enhancing process control, optimization, and intensification. This literature review explores recent advancements in modeling membrane-based wastewater treatment processes through AI models. The analysis highlights the enormous potential of this research field in enhancing the efficiency of membrane technologies. The role of AI modeling in defining optimal operating conditions, developing effective strategies for membrane fouling mitigation, enhancing the performance of novel membrane-based technologies, and improving membrane fabrication techniques is discussed. These enhanced process optimization and control strategies driven by AI modeling ensure improved effluent quality, optimized resource consumption, and minimized operating costs. The potential contribution of this cutting-edge approach to a paradigm shift toward sustainable wastewater treatment is examined. Finally, this review outlines future perspectives, emphasizing the research challenges that require attention to overcome the current limitations hindering the integration of AI modeling in wastewater treatment plants.

Response surface methodology for process optimization in livestock wastewater treatment: A review

With increasing demand for meat and dairy products, the volume of wastewater generated from the livestock industry has become a significant environmental concern. The treatment of livestock wastewater (LWW) is a challenging process that involves removing nutrients, organic matter, pathogens, and other pollutants from livestock manure and urine. In response to this challenge, researchers have developed and investigated different biological, physical, and chemical treatment technologies that perform better upon optimization. Optimization of LWW handling processes can help improve the efficacy and sustainability of treatment systems as well as minimize environmental impacts and associated costs. Response surface methodology (RSM) as an optimization approach can effectively optimize operational parameters that affect process performance. This review article summarizes the main steps of RSM, recent applications of RSM in LWW treatment, highlights the advantages and limitations of this technique, and provides recommendations for future research and practice, including its cost-effectiveness, accuracy, and ability to improve treatment efficiency.

A decision support system for localized planning of reclaimed water around wastewater treatment plants

Exploding population, industrialization, and an increase in water pollution has led to acute shrinkage in freshwater availability. Numerous countries have started exploring municipal wastewater as a new potential source of water to bring a paradigm shift from linearity to obtaining circularity in human water cycle management. This study aims to develop a decision support system for integrated water and wastewater management (DSS_IWWM), targeted towards reuse-focused selection of appropriate wastewater treatment technology, and localized planning around STPs in terms of reclaimed water demand identification, estimation, allocation, and sustainable pricing. The developed DSS_IWWM comprises of a repository of fourteen reuse purposes, reuse quality criteria, and 25 wastewater treatment technologies (WWTTs) in 360 combinations. It is sensitive to local resource scenarios and applies a socioeconomic and technology-focused methodology for addressing the interests of the community and investing agencies and viably. To validate the application of the DSS_IWWM, it is first tested with data from three cities in the state of Uttar Pradesh (India)-Lucknow, Prayagraj, and Agra-and then extended to nine more Indian cities with varying influent quality characteristics, resource inputs, existing STP technologies, and same target quality and decision criteria prioritization, to present a comparison of appropriate WWTTs and associated average prices obtained in different scenarios. It is concluded that influent quality, existing technology, and target quality criteria play significant role in selection of appropriate WWTTs. The traditional technologies such as UASB and ASP are required to be augmented and supplemented with high-performing WWTTs, such as BIOFOR-F with (C + F + RSF) and SBT + WP to obtain desired effluent quality. High-performing advanced oxidation process (AOP)-based systems such as A2O, SBR, and BIOFOR-F require WWTTs with relatively lower average costs (such as SBT and OP). The developed DSS_IWWM may prove to be very useful and beneficial for policymakers, government officials, engineers, and scientific community as it will facilitate rational decision-making for efficient investment planning in reuse focused wastewater treatment towards achieving circular economy in sustainable water resource management.

Indicator based multi-criteria decision support systems for wastewater treatment plants

Wastewater treatment plant decision makers face stricter regulations regarding human health protection, environmental preservation, and emissions reduction, meaning they must improve process sustainability and circularity, whilst maintaining economic performance. This creates complex multi-objective problems when operating and selecting technologies to meet these demands, resulting in the development of many decision support systems for the water sector. European Commission publications highlight their ambition for greater levels of sustainability, circularity, and environmental and human health protection, which decision support system implementation should align with to be successful in this region. Following the review of 57 wastewater treatment plant decision support systems, the main function of multi-criteria decision-making tools are technology selection and the optimisation of process operation. A large contrast regarding their aims is found, as process optimisation tools clearly define their goals and indicators used, whilst technology selection procedures often use vague language making it difficult for decision makers to connect selected indicators and resultant outcomes. Several recommendations are made to improve decision support system usage, such as more rigorous indicator selection protocols including participatory selection approaches and expansion of indicators sets, as well as more structured investigation of results including the use of sensitivity or uncertainty analysis, and error quantification.

A Review of Computational Modeling in Wastewater Treatment Processes

Wastewater treatment companies are facing several challenges related to the optimization of energy efficiency, meeting more restricted water quality standards, and resource recovery potential. Over the past decades, computational models have gained recognition as effective tools for addressing some of these challenges, contributing to the economic and operational efficiencies of wastewater treatment plants (WWTPs). To predict the performance of WWTPs, numerous deterministic, stochastic, and time series-based models have been developed. Mechanistic models, incorporating physical and empirical knowledge, are dominant as predictive models. However, these models represent a simplification of reality, resulting in model structure uncertainty and a constant need for calibration. With the increasing amount of available data, data-driven models are becoming more attractive. The implementation of predictive models can revolutionize the way companies manage WWTPs by permitting the development of digital twins for process simulation in (near) real-time. In data-driven models, the structure is not explicitly specified but is instead determined by searching for relationships in the available data. Thus, the main objective of the present review is to discuss the implementation of machine learning models for the prediction of WWTP effluent characteristics and wastewater inflows as well as anomaly detection studies and energy consumption optimization in WWTPs. Furthermore, an overview considering the merging of both mechanistic and machine learning models resulting in hybrid models is presented as a promising approach. A critical assessment of the main gaps and future directions on the implementation of mathematical modeling in wastewater treatment processes is also presented, focusing on topics such as the explainability of data-driven models and the use of Transfer Learning processes.

Centralization of wastewater treatment in a tourist area: A comparative LCA considering the impact of seasonal changes

Nowadays, environmental protection has become a topic of primary importance, and the interest in wastewater treatment plants (WWTPs) has increased due to the need for a paradigm shift from linear to circular economy. The centralization level of wastewater infrastructure is the basis for a successful system. The aim of this study was to investigate the environmental impacts generated from the centralized treatment of wastewater in a tourist area in central Italy. The combined use of BioWin 6.2 simulation software and life cycle assessment (LCA) methodology was implemented to evaluate the potential connection of a small decentralized WWTP to a medium-size centralized facility. Two different scenarios (decentralized system, corresponding to the current situation, and centralized) were evaluated in two separate periods: high season (HS), corresponding to the main tourist season, and low season (LS), which is the period before the main tourist season. Two sensitivity analyses were conducted, assuming different N2O emission factors, and considering the period at the end of tourist season, respectively. Although with modest advantages (up to -6 % in pollutant emissions), WWTP connection was the best management option in 10 out of 11 indicators in HS, and 6 out of 11 categories in LS. The study showed that wastewater centralization was promoted by scale factors in HS, as the most impactful consumptions decreased as the degree of centralization increased; on the other hand, the decentralized system was less penalized in LS, as small WWTP was less stressed and energy consuming in this period. Sensitivity analysis confirmed the results obtained. Site-specific conditions can lead to conflicting circumstances, as key parameters may have different behaviors depending on seasonal variations, and the degree of centralization in tourist areas should be addressed by distinguishing separate periods, based on changes in tourist flows and pollution loads.

A review of how decision support tools address resource recovery in sanitation systems

Globally, there is increasing interest in recovering resources from sanitation systems. However, the process of planning and implementing circular sanitation is complex and can necessitate software-based tools to support decision-making. In this paper, we review 24 decision support software tools used for sanitation planning, to generate insights into how they address resource recovery across the sanitation chain. The findings reveal that the tools can address many planning issues around resource recovery in sanitation including analysis of material flows, integrating resource recovery technologies and products in the design of sanitation systems, and assessing the sustainability implications of resource recovery. The results and recommendations presented here can guide users in the choice of different tools depending on, for example, what kind of tool features and functions the user is interested in as well as the elements of the planning process and the sanitation service chain that are in focus. However, some issues are not adequately covered and need improvements in the available tools including quantifying the demand for and value of resource recovery products, addressing retrofitting of existing sanitation infrastructure for resource recovery and assessing social impacts of resource recovery from a life cycle perspective. While there is scope to develop new tools or to modify existing ones to cover these gaps, communication efforts are needed to create awareness about existing tools, their functions and how they address resource recovery. It is also important to further integrate the available tools into infrastructure planning and programming processes by e.g. customizing to relevant planning regimes and procedures, to move them beyond research and pilots into practice, and hopefully contribute towards more circular sanitation systems.

Effect of organic loading rate on the production of Polyhydroxyalkanoates from sewage sludge

The aim of this work was to study the effect of organic loading rate on the production of Polyhydroxyalkanoates (PHA) from sewage sludge. Synthesis of PHA using sewage sludge as platform was achieved in this work. Three pilot-scale selection-sequencing batch reactors (S-SBR) were used for obtaining a culture able to accumulate PHA following a strategy of aerobic dynamic feeding (ADF) at different volumetric organic-loading-rate (vOLR): 1.3, 1.8 and 0.8 g COD L^-1 d^-1 for S-SBR 1, S-SBR 2 and S-SBR 3, respectively. Decreasing the vOLR enhanced the general performance of the process as for organic matter removal (from 99.2% ± 0.3% in S-SBR-3 to 92 ± 2 in S-SBR-2) while the opposite trend was recorded for PHA production (6.0 PHA % w/w in S-SBR-3 vs 13.7 PHA % w/w in S-SBR-2 at the end of the feast phase). Furthermore, indirect and direct emissions, as N2O, were evaluated during the process for the first time. Finally, three accumulation tests were performed achieving 24% w/w.

Analysis of wastewater reuse options using a multicriteria decision tool for Phuket, Thailand

The act of balancing between water demand and water supply in Phuket Island is facing challenges, suggesting water reuse options in various activities on the island should be properly promoted considering the potential benefits in a variety of dimensions. This research presented options to reuse effluent water from wastewater treatment plants for Phuket Municipality in 3 main activity groups, namely, domestic reuse, agricultural reuse, and raw water for water treatment plants (WTP). Water demand, additional water treatment trains, and the length of the major water distribution pipeline for each water reuse option were designed, and its cost and expenses were calculated. Multi-criteria decision analysis (MCDA) was used by 1000Minds internet-based software to prioritize the suitability of each water reuse option based on a four-dimensional scorecard, including economic, social, health, and environmental aspects. The decision algorithm for the trade-off scenario based on the government's budget allocation was proposed to obtain weighing without subjective expert opinions. The results revealed that recycling effluent water as raw water for the existing WTP was the first priority, followed by agriculture reuse for planting coconut, Phuket's economic crops, and domestic reuse. There was a significant difference in the total scores of economic and health indicators between the first- and second-priority options because of the difference in the additional treatment system in which the first-priority option applied the microfiltration and reverse osmosis system, which could effectively eliminate viruses and chemical micropollutants. In addition, the first priority option required a much smaller piping system than other water reuse options because it relied on the existing plumbing system of WTP, lowering the investment cost, which was a very important indicator for decision-making.

Recent advances in the biological treatment of wastewater rich in emerging pollutants produced by pharmaceutical industrial discharges

Pharmaceuticals and personal care products present potential risks to human health and the environment. In particular, wastewater treatment plants often detect emerging pollutants that disrupt biological treatment. The activated sludge process is a traditional biological method with a lower capital cost and limited operating requirements than more advanced treatment methods. In addition, the membrane bioreactor combines a membrane module and a bioreactor, widely used as an advanced method for treating pharmaceutical wastewater with good pollution performance. Indeed, the fouling of the membrane remains a major problem in this process. In addition, anaerobic membrane bioreactors can treat complex pharmaceutical waste while recovering energy and producing nutrient-rich wastewater for irrigation. Wastewater characterizations have shown that wastewater's high organic matter content facilitates the selection of low-cost, low-nutrient, low-surface-area, and effective anaerobic methods for drug degradation and reduces pollution. However, to improve the biological treatment, researchers have turned to hybrid processes in which all physical, chemical, and biological treatment methods are integrated to remove various emerging contaminants effectively. Hybrid systems can generate bioenergy, which helps reduce the operating costs of the pharmaceutical waste treatment system. To find the most effective treatment technique for our research, this work lists the different biological treatment techniques cited in the literature, such as activated sludge, membrane bioreactor, anaerobic treatment, and hybrid treatment, combining physicochemical and biological techniques.

A comparative LCA of three WWTPs in a tourist area: Effects of seasonal loading rate variations

Although the wastewater treatment is a fundamental utility for the protection of human health and the environment, non-evident drawbacks are associated with it. Wastewater treatment plants (WWTPs) located in tourist areas generally suffer from the seasonal increase in wastewater flow-rate and associated pollution loads. In this study, a Life Cycle Assessment (LCA) of three medium-size urban WWTPs, located in a tourist area in central Italy, was carried out. The study compared the environmental impacts generated by 1 m³ of treated wastewater in low season (LS) and high season (HS). All the material and energy flows involved in the operational phase of wastewater treatment were considered within the system boundaries, including the disposal or recovery treatment of the waste streams generated by the WWTPs, namely screenings, waste from grit removal and wastewater sludge. The analysis was conducted using almost only real data from full-scale plants. In each WWTP, the environmental impacts produced in HS were higher than those generated in LS; therefore, the environmental impacts increased as the loading rate increased. Furthermore, a correlation between WWTP size and environmental performance was observed. Indeed, in 8 out of 11 environmental indicators, the percentage increase in pollutant emissions due to wastewater treatment in HS decreased as the WWTP size increased. Results revealed that larger WWTPs suffered less from seasonal loading rate variations, showing greater flexibility. The existence of a scale factor suggests that the centralization of WWTPs in tourist areas can be an option to enable better treatment performance in terms of environmental impacts. A sensitivity analysis was performed, increasing N₂O emission factors from wastewater treatment in LS: considering a 75 % increase, the outcomes found in default LCA were not confirmed. Future research should investigate the operational factors and biological mechanisms that most affect wastewater treatment when significant seasonal variations are present.

A review of serious games for urban water management decisions: current gaps and future research directions

Urban water management (UWM) is a complex problem characterized by multiple alternatives, conflicting objectives, and multiple uncertainties about key drivers like climate change, population growth, and increasing urbanization. Serious games are becoming a popular means to support decision-makers who are responsible for the planning and management of urban water systems. This is evident in the increasing number of articles about serious games in recent years. However, the effectiveness of these games in improving decision-making and the quality of their design and evaluation approaches remains unclear. To understand this better, in this paper, we identified 41 serious games covering the urban water cycle. Of these games, 15 were shortlisted for a detailed review. By using common rational decision-making and game design phases from literature, we evaluated and mapped how the shortlisted games contribute to these phases. Our research shows that current serious game applications have multiple limitations: lack of focus on executing the initial phases of decision-making, limited use of storytelling and adaptive game elements, use of low-quality evaluation design and explicit indicators to measure game outcomes, and lastly, lack of attention to cognitive processes of players playing the game. Addressing these limitations is critical for advancing purposeful game design supporting UWM.

Intelligent Tools to Monitor, Control and Predict Wastewater Reclamation and Reuse

Contemporary wastewater reclamation units entail several diverse treatment and extraction processes, with a multitude of monitored quality characteristics, controlled by a variety of key operational parameters directly affecting the efficiency of treatment. The conventional optimization of this highly complex system is time- and energy- consuming, frequently relying on intuitive decision making by operators, and does not predict or forecast efficiency changes and system maintenance. In this paper, we introduce intelligent solutions to enhance the operational control of the unit with minimal human intervention and to develop an AI-powered DSS that is installed atop the sensors of a water treatment module. The DSS uses an expert model, both to assess the quality of water and to offer suggestions based on current values and future trends. More specifically, the quality of the produced water was successfully visualized, assessed and rated, based on a set of input operational variables (pH, TOC for this case), while future values of monitored sensors were forecasted. Additionally, monitoring services of the DSS were able to identify unexpected events and to generate alerts in the case of observed violation of operational limits, as well as to implement changes (automatic responses) to operational parameters so as to reestablish normal operating conditions and to avoid such events in the future. Up to now, the DSS suggestion and forecasting services have proven to be adequately accurate. Though data are still being collected from early adopters, the solution is expected to provide a complete water treatment solution that can be adopted by a vast range of parties.

Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks

Technical, economic, regulatory, environmental, and social and political interests make the process of selecting an appropriate wastewater treatment technology complex. Although this problem has already been addressed from the dimensioning approach, our proposal in this research, a model of decision making for conventional secondary treatment of municipal wastewater through continuous-discrete, non-parametric Bayesian networks was developed. The most suitable network was structured in unit processes, independent of each other. Validation, with data in a mostly Mexican context, provided a positive predictive power of 83.5%, an excellent kappa (0.77 > 0.75), and the criterion line was surpassed with the location of the model in a receiver operating characteristic (ROC) graph, so the model can be implemented in this region. The final configuration of the Bayesian network allows the methodology to be easily extended to other types of treatments, wastewater, and to other regions.

A conceptual framework for a multi-criteria decision support tool to select technologies for resource recovery from urban wastewater

In the context of circular economy, wastewater can be used to address some of the 21st century's challenges regarding the transition to renewable resources for water, energy, and nutrients. Despite all the research, development, and experience with resource recovery from urban wastewater, its implementation is still limited. The transition from treatment to resource recovery is complex due to the difficulty of selecting unit processes from a large number of candidate processes considering the operational limitations of each process, and sustainability objectives. Presently, a multi-criteria decision support tool that deals with the difficulty of unit process selection for resource recovery from wastewater has not been developed. Therefore, this paper presents the conceptual framework of a decision support tool to find the optimum treatment train consisting of compatible unit processes which can recover water, energy and/or nutrients from a specified influent composition. The framework presents the relationship between the user input, the knowledge library of technologies and a weighted multi-objective nonlinear programming model to aid process selection. The model presented here shows, not only how the processes are selected, but also the four-dimensional sustainability impact of the generated treatment train while considering the weight provided by the user. Thus, this study presents a reproducible framework which can support private and public decision-makers in transparent evidence-based decision making and eventually the systematic implementation of resource recovery from urban wastewater.

Uptake and Dissemination of Multi-Criteria Decision Support Methods in Civil Engineering—Lessons from the Literature

The SCOPUS and Wed of Science bibliometric databases were searched for papers related to the use of multi-criteria methods in civil engineering related disciplines. The results were analyzed for information on the reported geographical distribution of usage, the methods used, the application areas with most usage and the software tools used. There was a wide geographical distribution of usage with all northern hemisphere continents well represented. However, of the very many methods available, a small number seemed to dominate usage, with the Analytic Hierarchy Process being the most frequently used. The application areas represented in the documents found was not widely spread and mainly seemed to be focused on issues such as sustainability, environment, risk, safety and to some extent project management, with less usage on other areas. This may be due to individual engineer’s choices in relation to if and how to disseminate the results of their work and to their choice of keywords and titles that determine if their publications are selected in bibliographic searches and thus more visible to a wider readership. A comparison with more topic focused searches, relating to Bridge Design, Earthquake Engineering, Cladding, Sewage Treatment, Foundation design, Truss design, Water Supply, Building Energy, Route selection and Transport mode showed very different results. Analysis of the papers in this area indicated that the full range of supporting software available for multi-criteria decision analysis (many listed in this paper) may not be fully appreciated by potential users.

Multi-period evaluation and selection of rural wastewater treatment technologies: a case study

Rapid population growth and agricultural development are generating a considerable amount of effluents, which poses threats to the quality of rural water resources as well as sanitary conditions. However, with a range of rural wastewater treatment (WT) technologies available, one major problem facing the practitioners is which to choose as the most favorable option suited to specific areas. In this study, a novel decision-making framework is proposed to evaluate and select the optimal alternative in rural areas of Xi'an within multiple consecutive time periods. Firstly, an evaluation index system is constructed and picture fuzzy numbers (PFNs) are used to represent both evaluation levels and experts' refusal due to limitation of knowledge. Secondly, fuzzy analytical hierarchy process (FAHP) is applied to derive weights of criteria, which enables experts to assign fuzzy numbers to express their preferences for comparison judgments. Thirdly, evidence theory is utilized to obtain the aggregated values from multiple time periods. Finally, based on the belief intervals obtained, sequencing batch reactor (A₄) is determined as the optimal rural WT technology in Xi'an from 2006 to 2020, whereas the membrane bio-reactor (A₂) is the last option. The effectiveness of the proposed framework is further validated by comparative analysis. This research can hopefully serve as useful guidance for the assessment of rural WT technologies in various regions.

Development of a decision support system for the selection of wastewater treatment technologies

Multiple factors including technical, social, economic, regulatory, governmental, and environmental add complexity in the process of selecting a suitable wastewater treatment technology. To overcome this issue, this paper aims to propose a decision support system (DSS) for the selection of wastewater treatment technologies. The proposed system has been developed using a detailed review of the state-of-the-art in wastewater treatment, implemented using Microsoft Visual Studio 2010 and validated through real-time case studies. The system is categorized into four treatment levels based on wastewater complexity and the required degree of treatment. These include preliminary, primary, secondary, and tertiary treatment. Based on the identified treatment levels, the proposed system suggests using any physical, biological, chemical, or hybrid treatment process. The developed DSS will aid the selection of suitable wastewater treatment technology from a set of alternatives while keeping user constraints, conflicting requirements, and prevailing conditions under consideration. Moreover, the system is capable to customize the treatment assembly at the planning stage with minimized costs, eliminate mistakes at the planning and design stage, facilitate decision making by narrowing down the alternative solution as per user requirements and prevailing conditions, incorporate customer demand, and promote sustainable development.

Real-time model predictive control of a wastewater treatment plant based on machine learning

Two separate goals should be jointly pursued in wastewater treatment: nutrient removal and energy conservation. An efficient controller performance should cope with process uncertainties, seasonal variations and process nonlinearities. This paper describes the design and testing of a model predictive controller (MPC) based on neuro-fuzzy techniques that is capable of estimating the main process variables and providing the right amount of aeration to achieve an efficient and economical operation. This algorithm has been field tested on a large-scale municipal wastewater treatment plant of about 500,000 PE, with encouraging results in terms of better effluent quality and energy savings.

Implementation of an environmental decision support system for controlling the pre-oxidation step at a full-scale drinking water treatment plant

Drinking water treatment plants (DWTPs) face changes in raw water quality, and treatment needs to be adjusted to produce the best water quality at the minimum environmental cost. An environmental decision support system (EDSS) was developed for aiding DWTP operators in choosing the adequate permanganate dosing rate in the pre-oxidation step. To this end, multiple linear regression (MLR) and multi-layer perceptron (MLP) models are compared for choosing the best predictive model. Besides, a case-based reasoning (CBR) model was approached to provide the user with a distribution of solutions given similar operating conditions in the past. The predictive model consisted of an MLP and has been validated against historical data with sufficient good accuracy for the utility needs (R² = 0.76 and RSE = 0.13 mg·L^-1). The integration of the predictive and the CBR models in an EDSS gives the user an augmented decision-making capacity of the process and has great potential for both assisting experienced users and for training new personnel in deciding the operational set-point of the process.

New perspectives on microbial communities and biological nitrogen removal processes in wastewater treatment systems

Biological nitrogen removal (BNR) is a critical process in wastewater treatment. Recently, there have new microbial communities been discovered to be capable of performing BNR with novel metabolic pathways. This review presents the up-to-date status on these microorganisms, including ammonia oxidizing archaea (AOA), complete ammonia oxidation (COMAMMOX) bacteria, anaerobic ammonium oxidation coupled to iron reduction (FEAMMOX) bacteria, anaerobic ammonium oxidation (ANAMMOX) bacteria and denitrifying anaerobic methane oxidation (DAMO) microorganism. Their metabolic pathways and enzymatic reactions in nitrogen cycle are demonstrated. Generally, these novel microbial communities have advantages over canonical nitrifiers or denitrifiers, such as higher substrate affinities, better physicochemical tolerances and/or less greenhouse gas emission. Also, their recent development and/or implementation in BNR is discussed and outlook. Finally, the key implications of coupling these microbial communities for BNR are identified. Overall, this review illustrates novel microbial communities that could provide new possibilities for high-performance and energy-saving nitrogen removal from wastewater.