The Fourth-Revolution in the Water Sector Encounters the Digital Revolution

How to Provide Nitrite Robustly for Anaerobic Ammonium Oxidation in Mainstream Nitrogen Removal

Innovation in decarbonizing wastewater treatment is urgent in response to global climate change. The practical implementation of anaerobic ammonium oxidation (anammox) treating domestic wastewater is the key to reconciling carbon-neutral management of wastewater treatment with sustainable development. Nitrite availability is the prerequisite of the anammox reaction, but how to achieve robust nitrite supply and accumulation for mainstream systems remains elusive. This work presents a state-of-the-art review on the recent advances in nitrite supply for mainstream anammox, paying special attention to available pathways (forward-going (from ammonium to nitrite) and backward-going (from nitrate to nitrite)), key controlling strategies, and physiological and ecological characteristics of functional microorganisms involved in nitrite supply. First, we comprehensively assessed the mainstream nitrite-oxidizing bacteria control methods, outlining that these technologies are transitioning to technologies possessing multiple selective pressures (such as intermittent aeration and membrane-aerated biological reactor), integrating side stream treatment (such as free ammonia/free nitrous acid suppression in recirculated sludge treatment), and maintaining high activity of ammonia-oxidizing bacteria and anammox bacteria for competing oxygen and nitrite with nitrite-oxidizing bacteria. We then highlight emerging strategies of nitrite supply, including the nitrite production driven by novel ammonia-oxidizing microbes (ammonia-oxidizing archaea and complete ammonia oxidation bacteria) and nitrate reduction pathways (partial denitrification and nitrate-dependent anaerobic methane oxidation). The resources requirement of different mainstream nitrite supply pathways is analyzed, and a hybrid nitrite supply pathway by combining partial nitrification and nitrate reduction is encouraged. Moreover, data-driven modeling of a mainstream nitrite supply process as well as proactive microbiome management is proposed in the hope of achieving mainstream nitrite supply in practical application. Finally, the existing challenges and further perspectives are highlighted, i.e., investigation of nitrite-supplying bacteria, the scaling-up of hybrid nitrite supply technologies from laboratory to practical implementation under real conditions, and the data-driven management for the stable performance of mainstream nitrite supply. The fundamental insights in this review aim to inspire and advance our understanding about how to provide nitrite robustly for mainstream anammox and shed light on important obstacles warranting further settlement.

Environmental and economic evaluation of implementing membrane technologies and struvite crystallisation to recover nutrients from anaerobic digestion supernatant

The present study investigates the environmental and economic feasibility of implementing membrane technologies and struvite crystallisation (SC) for nutrient recovery from the anaerobic digestion supernatant. To this end, one scenario combining partial-nitritation/Anammox and SC was compared with three scenarios combining membrane technologies and SC. The combination of ultrafiltration, SC and liquid-liquid membrane contactor (LLMC) was the less environmentally impactful scenario. SC and LLMC were the most important environmental and economic contributors in those scenarios using membrane technologies. The economic evaluation illustrated that combining ultrafiltration, SC and LLMC (with or without reverse osmosis pre-concentration) featured the lowest net cost. The sensitivity analysis highlighted that the consumption of chemicals for nutrient recovery and the ammonium sulphate recovered had a large impact on environmental and economic balances. Overall, these results demonstrate that implementing membrane technologies and SC for nutrient recovery can improve the economic and environmental implications of future municipal wastewater treatment plants.

Digitalization and real-time control to mitigate environmental impacts along rivers: Focus on artificial barriers, hydropower systems and European priorities

Hydropower globally represents the main source of renewable energy, and provides several benefits, e.g., water storage and flexibility; on the other hand, it may cause significant impacts on the environment. Hence sustainable hydropower needs to achieve a balance between electricity generation, impacts on ecosystems and benefits on society, supporting the achievement of the Green Deal targets. The implementation of digital, information, communication and control (DICC) technologies is emerging as an effective strategy to support such a trade-off, especially in the European Union (EU), fostering both the green and the digital transitions. In this study, we show how DICC can foster the environmental integration of hydropower into the Earth spheres, with focus on the hydrosphere (e.g., on water quality and quantity, hydropeaking mitigation, environmental flow control), biosphere (e.g., improvement of riparian vegetation, fish habitat and migration), atmosphere (reduction of methane emissions and evaporation from reservoirs), lithosphere (better sediment management, reduction of seepages), and on the anthroposphere (e.g., reduction of pollution associated to combined sewer overflows, chemicals, plastics and microplastics). With reference to the abovementioned Earth spheres, the main DICC applications, case studies, challenges, Technology Readiness Level (TRL), benefits and limitations, and transversal benefits for energy generation and predictive Operation and Maintenance (O&M), are discussed. The priorities for the European Union are highlighted. Although the paper focuses primarly on hydropower, analogous considerations are valid for any artificial barrier, water reservoir and civil structure which interferes with freshwater systems.

Chances and barriers of building information modelling in wastewater management

The advancing digitalisation is one of the great challenges of our times. Related activities also concern the wastewater sector. In the field of building construction, one emerging approach is building information modelling (BIM). The presented work investigates to which extent BIM practices have already found their way to wastewater management, and what kind of benefits and constraints are incorporated. Information is collected by means of a literature review and international expert surveys. Results indicate that several BIM-related key elements are already well established in the sector, but not necessarily in the intended manner. Consequently, the digital transition in the wastewater sector is not about replacing existing procedures and techniques but to rethink and optimise them. This primarily concerns data and information management in combination with the application of digital tools. Furthermore, wastewater management requires more integrated approaches, involving interdisciplinary/collaborative concepts and life cycle perspectives. Appropriate change management is necessary to give support and guidance to employees during the transition process. Furthermore, also from the political side, a clear definition and communication of the pursued digital vision is important. This article aims at stimulating discussion and research to optimise wastewater management from the digital perspective.

Increasing resilience through nudges in the urban water cycle: An integrative conceptual framework to support policy decision-making

Relevant challenges associated with the urban water cycle must be overcome to meet the United Nations Sustainable Development Goals (SDGs) and improve resilience. Unlike previous studies that focused only on the provision of drinking water, we propose a framework that extends the use of the theory of nudges to all stages of the overall urban water cycle (drinking water and wastewater services), and to agents of influence (citizens, organizations, and governments) at different levels of decision making. The framework integrates four main drivers (the fourth water revolution, digitalization, decentralization, and climate change), which influence how customers, water utilities and regulators approach the challenges posed by the urban water cycle. The proposed framework, based on the theory of nudges first advanced by the Nobel Prize in behavioral economics Richard H. Thaler and Cass R. Sunstein (Thaler and Sunstein, 2009), serves as a reference for policymakers to define medium- and long-term strategies and policies for improving the sustainability and resilience of the urban water cycle. Finally, we provide new insights for further research on resilience approaches to the management of the urban water cycle as an element to support the more efficient formulation of policies.

Water and wastewater digital surveillance for monitoring and early detection of the COVID-19 hotspot: industry 4.0

There are a high number of COVID-19 cases per capita in the world that goes undetected including clinical diseases compatible with COVID-19. While the presence of the COVID-19 in untreated drinking water is possible, it is yet to be detected in the drinking-water supplies. COVID-19 viral fragments have been found in excrete, this call for wastewater monitoring and analysis (wastewater surveillance) of the potential health risk. This raises concern about the potential of the SARS-CoV-2 transmission via the water systems. The economic limits on the medical screening for the SARS-CoV-2 or COVID-19 worldwide are turning to wastewater-based epidemiology as great potential tools for assessing and management of the COVID-19 pandemic. Surveillance and tracking of the pathogens in the wastewater are key to the early warning system and public health strategy monitoring of the COVID-19. Currently, RT-qPCR assays is been developed for SARS-CoV-2 RNA specimen clinical testing and detection in the water system. Convectional wastewater treatment methods and disinfection are expected to eradicate the SAR-CoV-2. Chlorine, UV radiation, ozone, chloramine is been used to inactivate and disinfect the water treatment system against the SARS-CoV-2. Water management and design of the water infrastructure require major changes to accommodate climate change, water cycle, reimaging of digitalization, infrastructure and privacy protection. The water digital revolution, biosensors and nanoscale, contact tracing, knowledge management can accelerate with disruption of the COVID-19 outbreak (water-health-digital nexus).

Costs and benefits of combined sewer overflow management strategies at the European scale

Combined sewer overflows (CSOs) may represent a significant source of pollution, but they are difficult to quantify at a large scale (e.g. regional or national), due to a lack of accessible data. In the present study, we use a large scale, 6-parameter, lumped hydrological model to perform a screening level assessment of different CSO management scenarios for the European Union and United Kingdom, considering prevention and treatment strategies. For each scenario we quantify the potential reduction of CSO volumes and duration, and estimate costs and benefits. A comparison of scenarios shows that treating CSOs before discharge in the receiving water body (e.g. by constructed wetlands) is more cost-effective than preventing CSOs. Among prevention strategies, urban greening has a benefit/cost ratio one order of magnitude higher than grey solutions, due to the several additional benefits it entails. We also estimate that real time control may bring on average a CSO volume reduction of just above 20%. In general, the design of appropriate CSO management strategies requires consideration of context-specific conditions, and is best made in the context of an integrated urban water management plan taking into account factors such as other ongoing initiatives in urban greening, the possibility to disconnect impervious surfaces from combined drainage systems, and the availability of space for grey or nature-based solutions.

Economic, technical, and environmental evaluation of retrofitting scenarios in a full-scale industrial wastewater treatment system

The use of mathematical models is a well-established procedure in the field of (waste) water engineering to "virtually" evaluate the feasibility of novel process modifications. In this way, only options with the highest chance of success are further developed to be implemented at full-scale, while less interesting proposals can be disregarded at an early stage. Nevertheless, there is still lack of studies, where different plant-wide model predictions (effluent quality, process economics, and technical aspects) are comprehensibly verified in the field with full-scale data. In this work, a set of analysis/evaluation tools are used to assess alternative retrofitting options in the largest industrial wastewater treatment plant in Northern Europe. A mechanistic mathematical model is simulated to reproduce process behavior (deviation < 11%). Multiple criteria are defined and verified with plant data (deviation < 5%). The feasibility of three types of scenarios is tested: (1) stream refluxing, (2) change of operational conditions and (3) the implementation of new technologies. Experimental measurements and computer simulations show that the current plant´s main revenues are obtained from the electricity produced by the biogas engine (54%) and sales of the inactivated bio-solids for off-site biogas production (33%). The main expenditures are the discharge fee (39%), and transportation and handling of bio-solids (30%). Selective treatment of bio-solid streams strongly modifies the fate of COD and N compounds within the plant. In addition, it increases revenues (+3%), reduces cost (-9%) and liberates capacity in both activated sludge (+25%) and inactivation reactors (+50%). Better management of the buffer tank promotes heterotrophic denitrification instead of dissimilatory nitrate conversion to ammonia. In this way, 11% of the incoming nitrogen is removed within the anaerobic water line and does not overload the activated sludge reactors. Only a marginal increase in process performance is achieved when the anaerobic granular sludge reactor operates at full capacity. The latter reveals that influent biodegradability is the main limiting factor rather than volume. Usage of either NaOH or heat (instead of CaO) as inactivation agents allows anaerobic treatment of the reject water, which substantially benefits revenues derived from higher electricity recovery (+44%). However, there is a high toll paid on chemicals (+73%) or heat recovery (-19%) depending on the inactivation technology. In addition, partial nitration/Anammox and a better poly-aluminum chloride (PAC) dosage strategy is necessary to achieve acceptable (< 2%) N and P levels in the effluent. The scenarios are evaluated from a sustainability angle by using life cycle impact assessment (LCIA) in form of damage stressors grouped into three categories: human health, ecosystems quality, and resource scarcity. The presented decision support tool has been used by the biotech company involved in the study to support decision-making on how to handle future expansions.

The role of deep learning in urban water management: A critical review

Deep learning techniques and algorithms are emerging as a disruptive technology with the potential to transform global economies, environments and societies. They have been applied to planning and management problems of urban water systems in general, however, there is lack of a systematic review of the current state of deep learning applications and an examination of potential directions where deep learning can contribute to solving urban water challenges. Here we provide such a review, covering water demand forecasting, leakage and contamination detection, sewer defect assessment, wastewater system state prediction, asset monitoring and urban flooding. We find that the application of deep learning techniques is still at an early stage as most studies used benchmark networks, synthetic data, laboratory or pilot systems to test the performance of deep learning methods with no practical adoption reported. Leakage detection is perhaps at the forefront of receiving practical implementation into day-to-day operation and management of urban water systems, compared with other problems reviewed. Five research challenges, i.e., data privacy, algorithmic development, explainability and trustworthiness, multi-agent systems and digital twins, are identified as key areas to advance the application and implementation of deep learning in urban water management. Future research and application of deep learning systems are expected to drive urban water systems towards high intelligence and autonomy. We hope this review will inspire research and development that can harness the power of deep learning to help achieve sustainable water management and digitalise the water sector across the world.

Developing stacking ensemble models for multivariate contamination detection in water distribution systems

This study presents a new stacking ensemble model for contamination event detection using multiple water quality parameters. The stacking model consists of a number of machine learning base predictors and a meta-predictor, and it is trained using cross-validation to capture different features in multiple water quality parameters and then used for water quality predictions. For each water quality parameter, the residuals between predicted and measured data are classified to identify anomalies with thresholds derived from the sequential model-based optimization method and detection probabilities updated using Bayesian analysis. Alarms derived from individual water quality parameters are fused to enhance the anomaly signals and improve the detection accuracy. The proposed stacking-based method is evaluated using a data set of six water quality parameters from a real water distribution system with randomly simulated events. The stacking-based method could detect 2496 events out of a total 2500 events without a false alarm. The results show that the stacking method outperforms an artificial neural network (ANN) benchmark method in contamination event detection. The stacking method has a higher true positive rate, lower false positive rate and higher F1 score than the ANN method. This implies that the stacking method has great promise of detecting contamination events in the water distribution system.

A review of the application of machine learning in water quality evaluation

With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments.

The Digital Revolution in the Urban Water Cycle and Its Ethical–Political Implications: A Critical Perspective

The development and application of new forms of automation and monitoring, data mining, and the use of AI data sources and knowledge management tools in the water sector has been compared to a ‘digital revolution’. The state-of-the-art literature has analysed this transformation from predominantly technical and positive perspectives, emphasising the benefits of digitalisation in the water sector. Meanwhile, there is a conspicuous lack of critical literature on this topic. To bridge this gap, the paper advances a critical overview of the state-of-the art scholarship on water digitalisation, looking at the sociopolitical and ethical concerns these technologies generate. We did this by analysing relevant AI applications at each of the three levels of the UWC: technical, operational, and sociopolitical. By drawing on the precepts of urban political ecology, we propose a hydrosocial approach to the so-called ‘digital water ‘, which aims to overcome the one-sidedness of the technocratic and/or positive approaches to this issue. Thus, the contribution of this article is a new theoretical framework which can be operationalised in order to analyse the ethical–political implications of the deployment of AI in urban water management. From the overview of opportunities and concerns presented in this paper, it emerges that a hydrosocial approach to digital water management is timely and necessary. The proposed framework envisions AI as a force in the service of the human right to water, the implementation of which needs to be (1) critical, in that it takes into consideration gender, race, class, and other sources of discrimination and orients algorithms according to key principles and values; (2) democratic and participatory, i.e., it combines a concern for efficiency with sensitivity to issues of fairness or justice; and (3) interdisciplinary, meaning that it integrates social sciences and natural sciences from the outset in all applications.

Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook

A viral outbreak is a global challenge that affects public health and safety. The coronavirus disease 2019 (COVID-19) has been spreading globally, affecting millions of people worldwide, and led to significant loss of lives and deterioration of the global economy. The current adverse effects caused by the COVID-19 pandemic demands finding new detection methods for future viral outbreaks. The environment's transmission pathways include and are not limited to air, surface water, and wastewater environments. The wastewater surveillance, known as wastewater-based epidemiology (WBE), can potentially monitor viral outbreaks and provide a complementary clinical testing method. Another investigated outbreak surveillance technique that has not been yet implemented in a sufficient number of studies is the surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in the air. Artificial intelligence (AI) and its related machine learning (ML) and deep learning (DL) technologies are currently emerging techniques for detecting viral outbreaks using global data. To date, there are no reports that illustrate the potential of using WBE with AI to detect viral outbreaks. This study investigates the transmission pathways of SARS-CoV-2 in the environment and provides current updates on the surveillance of viral outbreaks using WBE, viral air sampling, and AI. It also proposes a novel framework based on an ensemble of ML and DL algorithms to provide a beneficial supportive tool for decision-makers. The framework exploits available data from reliable sources to discover meaningful insights and knowledge that allows researchers and practitioners to build efficient methods and protocols that accurately monitor and detect viral outbreaks. The proposed framework could provide early detection of viruses, forecast risk maps and vulnerable areas, and estimate the number of infected citizens.

The value of human data annotation for machine learning based anomaly detection in environmental systems

Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, which do not require labels. While academic research has produced a vast array of tools and machine learning models for automated anomaly detection, the research community focused on environmental systems still lacks a comparative analysis that is simultaneously comprehensive, objective, and systematic. This knowledge gap is addressed for the first time in this study, where 15 different supervised and unsupervised anomaly detection models are evaluated on 5 different environmental datasets from engineered and natural aquatic systems. To this end, anomaly detection performance, labelling efforts, as well as the impact of model and algorithm tuning are taken into account. As a result, our analysis reveals the relative strengths and weaknesses of the different approaches in an objective manner without bias for any particular paradigm in machine learning. Most importantly, our results show that expert-based data annotation is extremely valuable for anomaly detection based on machine learning.

Digital solutions for continued operation of WRRFs during pandemics and other interruptions

This paper includes survey results from 17 full-scale water resource recovery facilities (WRRFs) to explore their technical, operational, maintenance, and management-related challenges during COVID-19. Based on the survey results, limited monitoring and maintenance of instrumentation and sensors are among the critical factors during the pandemic which resulted in poor data quality in several WRRFs. Due to lockdown of cities and countries, most of the facilities observed interruptions of chemical supply frequency which impacted the treatment process involving chemical additions. Some plants observed influent flow reduction and illicit discharges from industrial wastewater which eventually affected the biological treatment processes. Delays in equipment maintenance also increased the operational and maintenance cost. Most of the plants reported that new set of personnel management rules during pandemic created difficulties in scheduling operator's shifts which directly hampered the plant operations. All the plant operators mentioned that automation, instrumentation, and sensor applications could help plant operations more efficiently while working remotely during pandemic. To handle emergency circumstances including pandemic, this paper also highlights resources and critical factors for emergency responses, preparedness, resiliency, and mitigation that can be adopted by WRRFs.

Data Analytics for Environmental Science and Engineering Research

The advent of new data acquisition and handling techniques has opened the door to alternative and more comprehensive approaches to environmental monitoring that will improve our capacity to understand and manage environmental systems. Researchers have recently begun using machine learning (ML) techniques to analyze complex environmental systems and their associated data. Herein, we provide an overview of data analytics frameworks suitable for various Environmental Science and Engineering (ESE) research applications. We present current applications of ML algorithms within the ESE domain using three representative case studies: (1) Metagenomic data analysis for characterizing and tracking antimicrobial resistance in the environment; (2) Nontarget analysis for environmental pollutant profiling; and (3) Detection of anomalies in continuous data generated by engineered water systems. We conclude by proposing a path to advance incorporation of data analytics approaches in ESE research and application.

Implications of Social Distancing Policies on Drinking Water Infrastructure: An Overview of the Challenges to and Responses of U.S. Utilities during the COVID-19 Pandemic

Social distancing policies (SDPs) implemented throughout the United States in response to COVID-19 have led to spatial and temporal shifts in drinking water demand and, for water utilities, created sociotechnical challenges. During this unique period, many water utilities have been forced to operate outside of design conditions with reduced workforce and financial capacities. Few studies have examined how water utilities respond to a pandemic; such methods are even absent from many emergency response plans. Here, we documented how utilities have been impacted by the COVID-19 pandemic. We conducted a qualitative analysis of 30 interviews with 53 practitioners spanning 28 U.S. water utilities. Our aim was to, first, understand the challenges experienced by utilities and changes to operations (e.g., demand and deficit accounts) and, second, to document utilities' responses. Results showed that to maintain service continuity and implement SDPs, utilities had to overcome various challenges. These include supply chain issues, spatiotemporal changes in demand, and financial losses, and these challenges were largely dependent on the type of customers served (e.g., commercial or residential). Examples of utilities' responses include proactively ordering extra supplies and postponing capital projects. Although utilities' adaptations ensured the immediate provision of water services, their responses might have negative repercussions in the future (e.g., delayed projects contributing to aging infrastructure).

A critical review of the data pipeline: how wastewater system operation flows from data to intelligence

Faced with an unprecedented amount of data coming from evermore ubiquitous sensors, the wastewater treatment community has been hard at work to develop new monitoring systems, models and controllers to bridge the gap between current practice and data-driven, smart water systems. For additional sensor data and models to have an appreciable impact, however, they must be relevant enough to be looked at by busy water professionals; be clear enough to be understood; be reliable enough to be believed and be convincing enough to be acted upon. Failure to attain any one of those aspects can be a fatal blow to the adoption of even the most promising new measurement technology. This review paper examines the state-of-the-art in the transformation of raw data into actionable insight, specifically for water resource recovery facility (WRRF) operation. Sources of difficulties found along the way are pinpointed, while also exploring possible paths towards improving the value of collected data for all stakeholders, i.e., all personnel that have a stake in the good and efficient operation of a WRRF.

When the fourth water and digital revolution encountered COVID-19

The ongoing COVID-19 pandemic is, undeniably, a substantial shock to our civilization which has revealed the value of public services that relate to public health. Ensuring a safe and reliable water supply and maintaining water sanitation has become ever more critical during the pandemic. For this reason, researchers and practitioners have promptly investigated the impact associated with the spread of SARS-CoV-2 on water treatment processes, focusing specifically on water disinfection. However, the COVID-19 pandemic impacts multiple aspects of the urban water sector besides those related to the engineering processes, including sanitary, economic, and social consequences which can have significant effects in the near future. Furthermore, this outbreak appears at a time when the water sector was already experiencing a fourth revolution, transitioning toward the digitalisation of the sector, which redefines the Water-Human-Data Nexus. In this contribution, a product of collaboration between academics and practitioners from water utilities, we delve into the multiple impacts that the pandemic is currently causing and their possible consequences in the future. We show how the digitalisation of the water sector can provide useful approaches and tools to help address the impact of the pandemic. We expect this discussion to contribute not only to current challenges, but also to the conceptualization of new projects and the broader task of ameliorating climate change.