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Resources recovery can improve the economic efficiency and reduce the negative environmental impacts of municipal wastewater treatment plants (MWWTP). The recovered resources can also actively benefit the natural environment enabling a reciprocal relationship between human society and nature. Focusing on these benefits can reveal new resources recovery opportunities. Moreover, for certain environmental impact categories such as emissions of reactive nitrogen, mere damage reduction is insufficient because these emissions are already beyond planetary limits. However, quantitative methods to assess nature benefits are lacking. A new method is developed to calculate the potential nature benefits in three categories: Freshwater restoration, biomass assimilation of nutrients, and soil organic matter sequestration and it is demonstrated on a real-life MWWTP. Focusing on resources recovery helps to purify the wastewater sufficiently for discharge and to benefit the natural environment. Treated wastewater discharge into a river can support freshwater restoration depending on the effluent quality. High quality is achieved by the sufficient removal of the nutrients and organic matter and discharging into a high-flow stream. The recovery of nutrients helps to close the nutrient cycle through biomass assimilation. To maximize this benefit, the nutrient recovery efficiency from the MWWTP must be maximized. But, increasing the nutrient uptake efficiency in agriculture is also crucial, especially for nitrogen. The wastewater sludge products can be applied to soil to sequester organic matter and the products with low volatile solids should be preferred. The development of the new method is a start to recognizing and assessing the potentially positive role of humans in nature., Sanitary Engineering

The paper proposes a novel methodology to locate and quantify entrapped air pockets created during pipe-filling events often found in intermittent water supply systems. Different filling conditions were tested in an experimental pipe with a high point. Measurements were taken and video recordings were carried out to assess air pocket volumes for different air release conditions at the downstream end of the pipe. The stochastic nature of air pocket creation resulted in varying air volumes. A new numerical model capable of simulating the air pocket creation, dragging and entrainment has been proposed. The new model, AirSWMM, was implemented as an extension of the Stormwater Management Model (SWMM) with stochasticity of air pocket formation reproduced by simulations with different air entrainment rates. The obtained numerical results show that the proposed model, even though based on a single-phase one-dimensional flow, can accurately locate and approximately quantify the entrapped air pocket volumes., Sanitary Engineering

The concept of circular economy, aiming at increasing the sustainability of products and services in the water and other sectors, is gaining momentum worldwide. Driven by this concept, novel bio-composite materials produced by recovering resources from different parts of the water cycle are now manufactured in The Netherlands. The new materials are used for different products such as canal bank protection elements, as an alternative to similar elements made of hardwood. As much as these new materials are appealing from the sustainability point of view, they may leach toxic substances into the aquatic environment given some of their ingredients, e.g., cellulose recovered from wastewater treatment. Therefore, a methodology for the assessment of related environmental risks is needed and it does not exist currently. This paper addresses this knowledge gap by presenting a framework for this. The framework is based on European environmental risk assessment guidelines, and it includes four key steps: (i) hazard identification, (ii) dose–response modelling, (iii) exposure assessment and (iv) risk characterisation (i.e. assessment). As part of the first step, laboratory leaching tests were carried out to evaluate the potential release of specific chemical substances such as heavy metals and resin compounds into the aquatic environment. Laboratory test results were then used as input data to evaluate the risk of potential leaching from canal bank protection elements into surface water. A deterministic model was used first to identify the chemicals exceeding the guideline threshold. Subsequently, a stochastic model was applied to evaluate the environmental risks across a range of leachate concentrations and water velocities in the canal, thereby simulating a broader spectrum of possible situations. The risk analyses were conducted for four alternative bio-composite materials made of different ingredients, two different flow conditions (stagnant water and advective flow) in two, Sanitary Engineering

Modeling fully developed turbulent flow for Herschel–Bulkley (HB) fluids in pipes is a long-standing challenge. Existing semi-empirical, theoretical, and numerical methods are either inconsistent with experimental data or are validated for low Reynolds numbers. This study focuses on validating a novel approach using rheology-based wall functions within Reynolds-averaged Navier–Stokes solvers. Simulations of wall shear stress and velocity profiles were conducted across a wide range of Reynolds numbers using a single-phase HB fluid, with measurements taken both upstream and downstream of a 90 pipe bend. Two turbulence closure models, the k–e model and the Reynolds stress model, were employed with the wall function implemented as a specified shear boundary condition. Results demonstrate significant improvements over the Newtonian-based models, such as standard wall function by Launder–Spalding or with available semi-empirical models, achieving strong statistical correlations and minimal deviation (from the experimental findings) at high Reynolds numbers. The study also examines the utility of the wall viscosity Reynolds number and assesses the reliability of semi-empirical models for HB fluids. These findings offer valuable insights for enhancing modeling accuracy in complex fluid flow scenarios, with potential applications spanning industries like mining, chemical processing, petroleum transportation, and sanitation systems, providing practical alternatives to costly experimental procedures in pipe systems., Sanitary Engineering

A biokinetic model based on BioWin's Activated Sludge Digestion Model (ASDM) coupled with a nitrous oxide (N2O) model was setup and calibrated for a full-scale wastewater treatment plant (WWTP) Amsterdam West, in the Netherlands. The model was calibrated using one year of continuous data to predict the seasonal variations of N2O emissions in the gaseous phase. This, according to our best knowledge, is the most complete full-scale data set used to date for this purpose. The results obtained suggest that the currently available biokinetic model predicted the winter, summer, and autumn N2O emissions well but failed to satisfactorily simulate the spring peak. During the calibration process, it was found that the nitrifier denitrification pathway could explain the observed emissions during all seasons while a combination of the nitrifier denitrification and incomplete heterotrophic denitrification pathways seemed to be dominant during the emissions peak observed during the spring season. Specifically, kinetic parameters related to free nitrous acid (FNA) displayed significant sensitivity leading to increased N2O production. The obtained values of two kinetic parameters, i.e., the FNA half-saturation during ammonia oxidising bacteria (AOB) denitrification and the FNA inhibition concentration related to heterotrophic denitrification, suggested a strong influence of the FNA bulk concentration on the N2O emissions and the observed seasonal variations. Based on the suboptimal performance and limitations of the biokinetic model, further research is needed to better understand the biochemical processes behind the seasonal peak and the influence of FNA., Sanitary Engineering

Intermittent water supply systems are prone to air entrapments during the pipe filling phase. This work aims to analyse and discuss the numerical results obtained by applying the recently developed AirSWMM model, an extension of SWMM incorporating air phase, to a laboratory network. Experimental data consisting of pressure-head at multiple locations and video recordings of air entrapments are collected in a single loop network with a high point, for different pipe-filling conditions, system layouts and node elevations. Experimental tests have shown that the air entrapment occurred not only at the high point but also throughout the pipe network, creating air pockets with elongated shapes and larger volumes than for single pipes. AirSWWM model with air-entrapment formation, growth and transport is tested in the pipe network, and results are compared with measurements. AirSWWM model can correctly locate large air pockets but underestimates their volume., Education AE, Sanitary Engineering

Under global climate change, urban flooding occurs frequently, leading to huge economic losses and human casualties. Extreme rainfall is one of the direct and key causes of urban flooding, and accurate rainfall estimates at high spatiotemporal resolution are of great significance for real-time urban flood forecasting. Using existing rainfall intensity measurement technologies, including ground rainfall gauges, ground-based radar, and satellite remote sensing, it is challenging to obtain estimates of the desired quality and resolution. However, an approach based on processing distributed surveillance camera network imagery through machine learning algorithms to estimate rainfall intensities shows considerable promise. Here, we present a novel approach that first extracts raindrop information from the surveillance camera images (rather than using the raw imagery directly), followed by the use of convolutional neural networks to estimate rainfall intensity from the resulting raindrop information. Evaluation of the approach on 12 rainfall events under both daytime and nighttime conditions shows that generalization ability, and especially nighttime predictive performance, is significantly improved. This represents an important step toward achieving real-time, high spatiotemporal resolution, measurement of urban rainfall at relatively low cost., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

To alleviate the geographical mismatch between supply and demand of water resources, virtual water trade had attracted extensive attention. Many studies had estimated the virtual water flow and measured the virtual water inequality using Environmental Input-Output (EIO) model. However, EIO model ignores the feedback effect in the trade, which may lead an overestimation or underestimation of virtual water transfer. Moreover, while considering the relation between economic benefits and environmental costs, the studies of virtual water inequality are still limited in both number and methodology. Here, to address these gaps, we recalibrated the virtual water and value-added transfer in China's 30 provinces in 2017 using a new Environmental Spillover-Feedback Effects (ESFEs) model, and then measured the inequality between virtual water transfer and the resource endowments taking the value-added into account. Our results show that the virtual water transfer of half of provinces changed exceeding 50 %, with a maximum of 428 %. The ratio of net virtual water outflow to one-way virtual water inflow (which is called virtual water plunder index in this study) in Xinjiang is up to 935 %, which directly contributing to the inequality among regions. Moreover, the virtual water transfer in different regions is not compensated equally from the perspective of economy. As a result, some regions are getting both water resources and economic benefits, while others are getting the opposite. Our study highlights the importance of considering both the pressure on water resources and economic benefits when measuring the virtual water inequality. Our findings support policymakers in developing adequate responses, i.e., clarifying regional responsibilities of virtual water trade, building a whole industrial chain, and balancing the transfer of value-added and virtual water., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

Supervised Deep Learning (DL) methods have shown promise in monitoring the floating litter in rivers and urban canals but further advancements are hard to obtain due to the limited availability of relevant labeled data. To address this challenge, researchers often utilize techniques such as transfer learning (TL) and data augmentation (DA). However, there is no study currently reporting a rigorous evaluation of the effectiveness of these approaches for floating litter detection and their effects on the models' generalization capability. To overcome the problem of limited data availability, this work introduces the “TU Delft—Green Village” dataset, a novel labeled dataset of 9,473 camera and phone images of floating macroplastic litter and other litter items, captured using experiments in a drainage canal of TU Delft. We use the new dataset to conduct a thorough evaluation of the detection performance of five DL architectures for multi-class image classification. We focus the analysis on a systematic evaluation of the benefits of TL and DA on model performances. Moreover, we evaluate the generalization capability of these models for unseen litter items and new device settings, such as increasing the cameras' height and tilting them to 45°. The results obtained show that, for the specific problem of floating litter detection, fine-tuning all layers is more effective than the common approach of fine-tuning the classifier alone. Among the tested DA techniques, we find that simple image flipping boosts model accuracy the most, while other methods have little impact on the performance. The SqueezeNet and DenseNet121 architectures perform the best, achieving an overall accuracy of 89.6 and 91.7%, respectively. We also observe that both models retain good generalization capability which drops significantly only for the most complex scenario tested, but the overall accuracy raises significantly to around 75% when adding a limited amount of images to training data, combined w, Sanitary Engineering

Water distribution networks (WDNs) are a vital component of urban water infrastructure. They transport water from production sites (sources) to spatially distributed consumers (sinks). Multiobjective optimization procedures are often used to minimize construction costs and at the same time maximize the resilience of such systems, which is usually a very computationally expensive task. Recently, highly efficient approaches based on complex network analysis (CNA) have been developed to solve this task more computationally efficiently. With CNA, very large WDNs can be optimized, considering network topology and demand distribution (using, e.g., demand edge betweenness centrality). However, existing CNA approaches do not consider network topography (i.e., height differences between sources and sinks). Comparing design solutions based on CNA with those found by evolutionary algorithms shows that the least-cost CNA design cannot compete with the latter. In this work, a hybrid approach is developed, where low-cost design CNA solutions are evaluated with a hydraulic solver (Epanet2), and subsequently the demand edge betweenness centrality distribution is iteratively altered for nodes with pressure deficits. This enhanced CNA-based optimization is tested on two different large case studies from the literature and shows promising results (2% cost increase). These solutions were obtained using significantly less computational effort (at least factor 1,000 faster), enabling solving very large WDN optimization problems (>150,000 decision variables)., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

This study presents a novel approach for urban flood forecasting in drainage systems using a dynamic ensemble-based data mining model which has yet to be utilised properly in this context. The proposed method incorporates an event identification technique and rainfall feature extraction to develop weak learner data mining models. These models are then stacked to create a time-series ensemble model using a decision tree algorithm and confusion matrix-based blending method. The proposed model was compared to other commonly used ensemble models in a real-world urban drainage system in the UK. The results show that the proposed model achieves a higher hit rate compared to other benchmark models, with a hit rate of around 85% vs 70 % for the next 3 h of forecasting. Additionally, the proposed smart model can accurately classify various timesteps of flood or non-flood events without significant lag times, resulting in fewer false alarms, reduced unnecessary risk management actions, and lower costs in real-time early warning applications. The findings also demonstrate that two features, “antecedent precipitation history” and “seasonal time occurrence of rainfall,” significantly enhance the accuracy of flood forecasting with a hit rate accuracy ranging from 60 % to 10 % for a lead time of 15 min to 3 h., Sanitary Engineering

Researchers and engineers employ machine learning (ML) tools to detect pipe bursts and prevent significant non-revenue water losses in water distribution systems (WDS). Nonetheless, many approaches developed so far consider a fixed number of sensors, which requires the ML model redevelopment and collection of sufficient data with the new sensor configuration for training. To overcome these issues, this study presents a novel approach based on Long Short-Term Memory neural networks (NNs) that leverages transfer learning to manage a varying number of sensors and retain good detection performance with limited training data. The proposed detection model first learns to reproduce the normal behavior of the system on a dataset obtained in burst-free conditions. The training process involves predicting flow and pressure one-time step ahead using historical data and time-related features as inputs. During testing, a post-prediction step flags potential bursts based on the comparison between the observations and model predictions using a time-varied error threshold. When adding new sensors, we implement transfer learning by replicating the weights of existing channels and then fine-tune the augmented NN. We evaluate the robustness of the methodology on simulated fire hydrant bursts and real-bursts in 10 district metered areas (DMAs) of the UK. For real bursts, we perform a sensitivity analysis to understand the impact of data resolution and error threshold on burst detection performance. The results obtained demonstrate that this ML-based methodology can achieve Precision of up to 98.1% in real-life settings and can identify bursts, even in data scarce conditions., Sanitary Engineering

Cutting-edge digital visualisation tools (CDVT) are playing an increasingly important role in improving urban flood risk management. However, there is a paucity of comprehensive research examining their role across all stages of urban flood risk management. To address, this study conducts an integrated critical review to identify the application of CDVT and assess their contribution to the prevention, mitigation, preparation, response, and recovery stages of flood risk management. The results show that virtual reality, augmented reality, and digital twin technologies are the primary CDVT used in urban flood visualisation, with virtual reality being the most frequently used. The focus of urban flood visualisation studies has been primarily on preparation and mitigation stages. However, there is a need to investigate the application of these technologies in the entire urban water cycle. Furthermore, there is potential for greater adoption of digital twin, especially in simulating urban flood inundation and flood evacuation routes. Integrating real-time data, data-driven modeling, and CDVT can significantly improve real-time flood forecasting. This benefits stakeholders and the public by enhancing early warning systems, preparedness, and flood resilience, leading to more effective flood risk management and reduced impacts on communities., Sanitary Engineering

Water distribution networks (WDNs) require large capital investment and ongoing operational costs, resulting in their optimisation being a highly researched field. Despite the benefits tanks bring to networks, most optimisation models omit them as decision variables due to the complexity they can introduce to heuristic approaches. This paper addresses the least-cost optimisation of WDN design and operation through the development and application of the Small-network Configurator for Optimising Pump Energy consumption (SCOPE). The SCOPE algorithm incorporates pipes, pumps and tanks as decision variables and solves the optimisation problem through an iterative approach that pairs EPANET simulation results with subsequent hydraulic calculations to converge on the pumping and storage configuration which yields the lowest energy consumption. The SCOPE output can then be used to seed further optimisation techniques. This approach has been tested on the synthetic Nametown and real-life Dili networks and demonstrated annual cost savings of 10% and 25%, respectively., Sanitary Engineering

Urban areas are constantly developing and thereby affect the local water cycle. Real-time control (RTC) strategies are used to operate urban drainage systems optimally during these transitions. This paper aims to develop a methodology to study the impacts of common gradual changes occurring in the urban environment (densification of the urban area and implementation of blue-green infrastructure), forming cumulative transitions, on the functioning of real-time optimization procedures. A new generic methodology, relying on a comprehensive evaluation strategy based on three indicators assessing the continued optimal performance of RTC was proposed. This methodology was applied to two urban drainage catchments in Eindhoven and Rotterdam using both probabilistic and projected transitional paths. Based on the results obtained it can be noted that the performances of the RTC procedures were not strongly affected by the modeled transitions although the relative performance compared to the maximum performance potential decreases significantly with the large-scale implementation of blue-green infrastructure, indicating that the revision of RTC procedures could improve the sewer system functioning further. The relative performance loss associated with the modeled transitions was higher for model predictive control compared to heuristic RTC procedures for one case study and vice versa for the other. Continuous re-evaluation of the RTC strategy is, therefore, an important but overlooked part of the implementation of RTC procedures., Sanitary Engineering

Resource recovery solutions can reduce the water sector's resource use intensity. With many such solutions being proposed, an assessment method for effective decision-making is needed. The water sector predominantly deals with biogeochemical resources (e.g., nitrogen) that are different from technical resources (e.g., industrial coagulants) in three ways: (1) they move through the environment in natural cycles; (2) they fulfil different human and environmental functions; and (3) they are subject to substantial environmental losses. Whilst several circularity assessment methods exist for technical resources, biogeochemical resources have received less attention. To address this, a well-established material circularity indicator (MCI) method is modified. This is done by redefining the terms: restoration, regeneration, and linear flows to create a new circularity assessment approach. The new approach is demonstrated in a real-life case study involving treated wastewater (TW) fertigation. The new approach reveals that using the original MCI method underestimates the circularity of resource recovery solutions involving biogeochemical resources. This is because, in the original MCI method, only the flows that are reused/recycled for human functions can be considered circular, whereas, in the new approach, one also considers flows such as N2 emission and groundwater infiltration as circular flows. Even though these may not be reuse/recycle type flows, they still contribute towards future resource availability and, thus, towards sustainability. The modified assessment method shows that TW fertigation can significantly improve nitrogen and water circularity. However, careful planning of the fertigation schedule is essential since increasing fertigation frequency leads to lower water but higher nitrogen circularity. Additionally, collecting drainage water for reuse can improve nitrogen circularity. In conclusion, using the modified MCI approach, circularity can, Sanitary Engineering

Stormwater management model (SWMM) software has recently become a modeling tool for the simulation of intermittent water supply systems. However, SWMM is not capable of accurately simulating the air behavior in the pipe-filling phase, missing therefore a relevant factor during pipe pressurization. This work proposes the integration of a conventional accumulator model in the existing SWMM hydraulic model to overcome this gap. SWMM source code was modified to calculate the air piezometric head inside the pipe based on the system boundary conditions, and the air piezometric head was incorporated in the SWMM flow rate pressure component. Experimental data were collected during the rapid filling of a pipe system for three possible configurations that are likely to occur in intermittent water supply pipe systems: no air release, small air release, and large air release. Results show that the improved SWMM better describes the effect of the air behavior using the extended transport (EXTRAN) surcharge method when compared to the original SWMM. Results also show that the SLOT method with predefined slot width is not suitable for this purpose; thus, further research is needed to assess if an adjusted slot width could provide better results., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

Model Predictive Control (MPC) of Urban Drainage Systems (UDS) has been established as a cost-effective method to reduce pollution. However, the operation of large UDS (containing over 20 actuators) can only be optimized by oversimplifying the UDS dynamics, potentially leading to a decrease in performance and reduction in users' trust, thus inhibiting widespread implementation of MPC procedures. A Heuristic And Predictive Policy (HAPPy) was set up, relying on the dynamic selection of the actuators with the highest impact on the UDS functioning and optimizing those in real-time. The remaining actuators follow a pre-set heuristic procedure. The HAPPy procedure was applied to two separate UDS in Rotterdam with the control objective being the minimization of overflow volume in each of the two cases. Results obtained show that the level of impact of the actuators on the UDS functioning changes during an event and can be predicted using a Random Forest algorithm. These predictions can be used to provide near-global optimal actuator settings resulting in the performance of the HAPPy procedure that is comparable to a full-MPC control and outperforming heuristic control procedures. The number of actuators selected to obtain near-global optimal settings depends on the UDS and rainfall characteristics showing an asymptotic real-time control (RTC) performance as the number of actuators increases. The HAPPy procedure showed different RTC dynamics for medium and large rainfall events, with the former showing a higher level of controllability than the latter. For medium events, a relatively small number of actuators suffices to achieve the potential performance improvement., Sanitary Engineering

Urban flooding is a major problem for cities around the world, with significant socio-economic consequences. Conventional real-time flood forecasting models rely on continuous time-series data and often have limited accuracy, especially for longer lead times than 2 hrs. This study proposes a novel event-based decision support algorithm for real-time flood forecasting using event-based data identification, event-based dataset generation, and a real-time decision tree flowchart using machine learning models. The results of applying the framework to a real-world case study demonstrate higher accuracy in forecasting water level rise, especially for longer lead times (e.g., 2–3 hrs), compared to traditional models. The proposed framework reduces root mean square error by 50%, increases accuracy of flood forecasting by 50%, and improves normalised Nash–Sutcliffe error by 20%. The proposed event-based dataset framework can significantly enhance the accuracy of flood forecasting, reducing the occurrences of both false alarms and flood missing and improving emergency response systems., Sanitary Engineering

Urban Drainage Systems can cause ecological and public health issues by releasing untreated contaminated water into the environment. Real-time control (RTC), augmented with rainfall nowcast, can effectively reduce these pollution loads. This research aims to identify key dynamics in the nowcast accuracies and relate those to the performance of nowcast-informed rule-based (RB)-RTC procedures. The developed procedures are tested in the case study of Rotterdam, the Netherlands. Using perfect nowcast data, all developed procedures showed a reduction in combined sewer overflow volumes of up to 14.6%. Considering real nowcast data, it showed a strong ability to predict if no more rain was expected, whilst performing poorly in quantifying rainfall depths. No relation was found in the nowcast accuracy and the consistency of the predicted rainfall using a moving horizon. Using the real nowcast data, all procedures, with the exception of the one predicting the end of the rainfall event, showed a significant risk of operative deterioration (performing worse than the baseline RB-RTC), linked to the relative performance of the nowcast algorithm. Understanding the strengths of a nowcast algorithm can ensure the reliability of the RB-RTC procedure and can negate the need for detailed modelling studies by inferring risks from nowcast data., Sanitary Engineering

Plastic pollution in water bodies is an unresolved environmental issue that damages all aquatic environments, and causes economic and health problems. Accurate detection of macroplastic litter (plastic items >5 mm) in water is essential to estimate the quantities, compositions and sources, identify emerging trends, and design preventive measures or mitigation strategies. In recent years, researchers have demonstrated the potential of computer vision (CV) techniques based on deep learning (DL) for automated detection of macroplastic litter in water bodies. However, a systematic review to describe the state-of-the-art of the field is lacking. Here we provide such a review, and we highlight current knowledge gaps and suggest promising future research directions. The review compares 34 papers with respect to their application and modeling related criteria. The results show that the researchers have employed a variety of DL architectures implementing different CV techniques to detect macroplastic litter in various aquatic environments. However, key knowledge gaps must be addressed to overcome the lack of: (i) DL-based macroplastic litter detection models with sufficient generalization capability, (ii) DL-based quantification of macroplastic (mass) fluxes and hotspots and (iii) scalable macroplastic litter monitoring strategies based on robust DL-based quantification. We advocate for the exploration of data-centric artificial intelligence approaches and semi-supervised learning to develop models with improved generalization capabilities. These models can boost the development of new methods for the quantification of macroplastic (mass) fluxes and hotspots, and allow for structural monitoring strategies that leverage robust DL-based quantification. While the identified gaps concern all bodies of water, we recommend increased efforts with respect to riverine ecosystems, considering their major role in transport and storage of litter., Sanitary Engineering

Model Predictive Control (MPC) of combined sewer systems can reduce environmental degradation caused by uncontrolled overflows. However, practical uncertainties are often neglected when assessing the potential of MPC strategies. This paper aims to understand the risks associated with using a non-perfect internal MPC-model, real precipitation forecast, and realistic dynamic system capacity fluctuations. An MPC with the objective to reduce the total combined sewer overflow (CSO) volume was implemented in the case study of Eindhoven in the Netherlands where highly sensitive waterways receive the sewer overflows. Two types of risks were identified: relative system performance loss and operative deterioration. The former entails a practical decrease in efficacy of controlling CSO spills compared to the theoretical situation, whereas the latter describes the aggravation of environmental pollution compared to a static form of system operation. The results obtained demonstrate that precipitation forecast uncertainty is associated with a small relative system performance loss. Opposite to this, significant performance loss was observed as a consequence of uncertainties in the internal MPC model and the actual sewer system capacity available. The latter caused additional combined sewer overflows compared to a statically optimised control for smaller precipitation events., Sanitary Engineering

Limiting the introduction of excess nitrogen to natural water sources is a growing priority for water security and environmental health. This poses particular difficulties in urban environments where available land for potential solutions is limited. A promising option is the integrated fixed-film activated sludge (IFAS) process that requires only a small footprint and is capable of high total nitrogen (TN) removal through multiple pathways. In light of the sustainable development goals set out by the United Nations, the present work has sought to compare the sustainability of two TN removal pathways by comparing the technical, economic and environmental performance of their optimum configurations. Through modelling, a single-stage configuration demonstrated the capacity to achieve an effluent TN concentration of 8.7 mg/L by the simultaneous nitrification denitrification pathway when a dissolved oxygen concentration of 3.5 mg/L was provided. Addition of a post-anoxic stage at equal volume to the aerobic stage (1:1 aerobic to anoxic ratio) to target conventional nitrification denitrification could realise an effluent TN concentration of 4.2 mg/L when DO was increased to 4.5 mg/L, although 5.8 mg/L of effluent TN could be achieved with only a 5:1 ratio. In terms of environmental burden and economic costs, analysis of the system's life-cycle under these different configurations indicated considerable asymmetry of the two pathways during the operational phase due mainly to the increased aeration. However in spite of this, the two conventional configurations were ultimately both shown to be more sustainable than that of the simultaneous pathway due to the greater TN removal capacity afforded., Sanitary Engineering

Blockages are a major issue for wastewater utilities around the world, causing loss of service, environmental pollution, and significant cleanup costs. Increasing telemetry in combined sewer overflows (CSOs) provides the opportunity for near real-time data-driven modelling of wastewater networks. This paper presents a novel methodology, designed to detect blockages and other unusual events in the proximity of CSO chambers in near real-time. The methodology utilises an evolutionary artificial neural network (EANN) model for short-term CSO level predictions and statistical process control (SPC) techniques to analyse unusual level behaviour. The methodology was evaluated on historic blockage events from several CSOs in the UK and was demonstrated to detect blockage events quickly and reliably, with a low number of false alarms., Sanitary Engineering

Resource recovery solutions are an essential part of a sustainable water sector. Sustainability of these solutions needs to be analysed to assess, compare and optimize them. Life Cycle Sustainability Assessment (LCSA) is the most commonly used framework for sustainability assessment. This review paper discusses three critical characteristics of water sector resource recovery solutions: (i) their potential to actively benefit natural processes through reciprocal services, (ii) their dependence upon natural resources and processes, and (iii) their goal to avoid transgression of environmental thresholds. We analyse these three characteristics in the context of the following features of LCSA: (i) it being a damage assessment-based framework, (ii) its treatment of economic and natural capital as substitutable and (iii) the absence of environmental thresholds and past emissions in its environmental assessment methodology. We use a real-life resource recovery case study from the Netherlands to evaluate and demonstrate the mentioned features of the existing LCSA framework. Our review indicates that, LCSA can be modified for application to resource recovery solutions if it includes reciprocity towards nature as an essential component, limits compensations between economic welfare and environmental damage, and incorporates environmental thresholds and past emissions., Sanitary Engineering

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., Sanitary Engineering, Policy Analysis

Emerging contaminants (ECs) continue to threaten our fragile ecosystem, yet their mitigation remains limited by economic factors. Meanwhile, a relatively expensive material, Graphene Oxide (GO), has shown promise as a solution for EC removal following further development into three graphene-based materials (GBMs): Porous graphene adsorbent (PGa), Graphene-oxide foam adsorbent (GOFa), and the hybrid filter. Due to the nuances of each synthesis process, financial costs will differ throughout the GBMs’ life cycle which have been quantified and compared in the present work at a range of possible breakthrough times. Finally, economic and environmental costs have been combined for each technology to compare eco-efficiency. Results demonstrated a substantial economic advantage of the GBMs when compared to alternative technologies, most notably the GOFa filter that incurred the lowest life-cycle costs at $1.73 ± 0.09/m3. This was mainly attributed to the lower demand of GOFa on the most expensive material required for material synthesis, hydra-zine. In addition, the material demands of GOFa were more evenly distributed which suggest a higher resilience of the overall costs to price hikes of individual materials required for synthesis. In terms of eco-efficiency the GOFa filter also demonstrated the greatest improvement when compared to the reference technology These results have provided robust total investment costs for several technologies that can now offer contrast to other EC-removal solutions., Sanitary Engineering

Effective functioning of sewer systems is critical for the everyday life of people in the urban environment. This is achieved, among other things, by the means of regular, planned maintenance of these systems. A large water utility would normally have several maintenance teams (or crews) at their disposal who can perform related jobs at different locations in the company area and with different levels of priority. This paper presents a new methodology for the optimisation of related maintenance schedules resulting in clear prioritisation of the ordering of maintenance tasks for crews. The scheduling problem is formulated as a multi-objective optimisation problem with the following three objectives, namely the minimisation of the total maintenance cost, the minimisation of travel times of maintenance teams and the maximisation of the job's priority score, all over a pre-defined scheduling horizon. The optimisation problem is solved using the Nondominated Sorting Genetic Algorithm-II (NSGA-II) optimisation method. The results obtained from a real-life UK case study demonstrate that the new methodology can determine optimal, low-cost maintenance schedules in a computationally efficient manner when compared to the corresponding existing company schedules. Daily productivity of maintenance teams in terms of number of jobs completed improved by 26% when the methodology was applied to scheduling in the case study. Given this, the method has the potential to be applied within water utilities and the water utility Welsh Water (Dwr Cymru Welsh Water (DCWW)) is currently implementing it into their systems., Sanitary Engineering

Surrogate models replace computationally expensive simulations of physically-based models to obtain accurate results at a fraction of the time. These surrogate models, also known as metamodels, have been employed for analysis, control, and optimization of water distribution and urban drainage systems. With the advent of machine learning (ML), water engineers have increasingly resorted to these data-driven techniques to develop metamodels of urban water networks (UWNs). In this article, we review 31 recent articles on ML-based metamodeling of UWNs to outline the state-of-the-art of the field, identify outstanding gaps, and propose future research directions. For each article, we critically examined the purpose of the metamodel, the metamodel characteristics, and the applied case study. The review shows that current metamodels suffer several drawbacks, including (a) the curse of dimensionality, hindering implementation for large case studies; (b) black-box deterministic nature, limiting explainability and applicability; and (c) rigid architecture, preventing generalization across multiple case studies. We argue that researchers should tackle these issues by resorting to recent advancements in ML concerning inductive biases, robustness, and transferability. Recently developed neural network architectures, which extend deep learning methods to graph data structures, are preferred candidates for advancing surrogate modeling in UWNs. Furthermore, we foresee increasing efforts for complex applications where metamodels may play a fundamental role, such as uncertainty analysis and multi-objective optimization. Lastly, the development and comparison of ML-based metamodels can benefit from the availability of new benchmark datasets for urban drainage systems and realistic complex networks., Sanitary Engineering

IFAS systems are inherently complex due to the hybrid use of both suspended and attached bacterial colonies for the purpose of pollutant degradation as part of wastewater treatment. This poses challenges when attempting to represent these systems mathematically due to the vast number of parameters involved. Besides becoming convoluted, large effort will be incurred during model calibration. This paper demonstrates a systematic approach to calibration of an IFAS process model that incorporates two sensitivity analyses to identify influential parameters and detect collinearity from a subset of 68 kinetic and stoichiometric parameters, and the use of the Nelder–Mead optimization algorithm to estimate the required values of these parameters. The model considers the removal of three critical pollutants including biochemical oxygen demand (BOD), total nitrogen (TN) and total suspended solids (TSS). Results from the sensitivity analyses identified four parameters that were the primary influence on the model. The model was found to be most sensitive to the two stoichiometric parameters including aerobic heterotrophic yield on soluble substrate whose total effects were responsible for 92.4% of the model’s BOD output sensitivity and 92.8% of the model’s TSS output sensitivity. The anoxic heterotrophic yield on soluble substrate was observed to be responsible for 54.3% of the model’s TN output sensitivity. To a lesser extent the two kinetic parameters, aerobic heterotrophic decay rate and reduction factor for denitrification on nitrite, were responsible for only 8.0% and 13.1% of the model’s BOD and TN output sensitivities respectively. Parameter estimation identified the need for only minor adjustments to default values in order to achieve sufficient accuracy of simulation with deviation from observed data to be only ± 3.6 mg/L, ± 1.3 mg/L, and ± 9.5 mg/L for BOD, TN and TSS respectively. Validation showed the model was limited in its capacity to predict system behaviour u, Sanitary Engineering

An early contamination warning system with deployed water quality sensors is often used to enhance the safety of a water distribution system (WDS). While algorithms have been developed to select an optimal water quality sensor placement strategy (WQSPS) for WDSs, many of them do not account for the influences caused by future uncertainties, such as sensor failures and system changes (e.g., demand variations and configuration/expansion changes in the WDS). To this end, this paper proposes a comprehensive framework to evaluate the robustness of WQSPSs to these possible uncertainties. This is achieved by considering five different performance objectives of WQSPSs as well as possible future demand and typology variations of WDSs under a wide range of sensor failure scenarios. More specifically, an optimization problem is formulated to evaluate the robustness of the WQSPSs, in which an evolutionary-based optimization approach coupled with an efficient data-archive method is used to solve this optimization problem. The framework is demonstrated on two real-world WDSs in China. The results show that: (1) the WQSPS's robustness can be highly dependent on the performance objectives considered, implying that an appropriate objective needs to be carefully selected for each case driven by practical needs, (2) the WDS's demand and configuration changes can have a significant influence on the WQSPS's robustness, in which the solution with more sensors in or close to the affected area is likely to better cope with these system changes, and (3) the proposed framework enables critical sensors to be identified, which can then be targeted for prioritizing maintenance actions., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

Most of the contamination source localization methods for water distribution systems (WDSs) assume the availability of accurate water quality models and multi-parameter online sensors, which are often out of reach of many water utilities. To address this, a novel manual grab-sampling method (MGSM) is developed to effectively and efficiently locate continuous contamination sources in a WDS using a dynamic and cyclical sampling strategy. The grab samples are collected at a pre-specified number of hydrants by the corresponding teams followed by laboratory tests. The MGSM optimizes the sampling plan at each cycle by making the probability of contamination source(s) in each sub-network as equal as possible, where sub-networks are determined by the selected hydrants and current flow pipe directions. The CS's size is reduced at each cycle by exploiting sample testing results obtained in the previous cycle until there are no further hydrants to sample from. Two real-world WDSs are used to demonstrate the effectiveness of the proposed MGSM. The results obtained show that the MGSM can significantly reduce the spatial range of the CS (to about 5% of the entire WDS) for a range of scenarios including multiple contamination sources and pipe flow direction changes. We found that an optimal number of sampling teams exists for a given WDS, representing a balanced trade-off between detection efficiency and sampling/testing budgets. Due to its relative simplicity, the proposed MGSM can be used in engineering practice straightaway and it represents a viable alternative to the methods associated with water quality models and sensors., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

Urban drainage models (UDMs) are often used to manage urban flooding. However, these models generally involve many parameters to represent the underlying complex hydrodynamic processes. This results in significant challenges to achieving effective and robust model calibration especially with frequently limited observations, leading to unreliable model predictions. This paper makes the first attempt at UDM calibration using the Bayesian-based Ensemble Smoother (ES) method. Three ES variants are considered, that is, the primary ES, the versions with multiple data assimilation (ES-MDA) and iterative local update (ES-ILU). Two synthetic cases and one real-world application with up to 5,236 calibration parameters are tested. Results obtained show that: (a) both ES-MDA and ES-ILU can produce effective model calibration with ES-ILU outperforming ES-MDA in terms of both accuracy and uncertainty while ES exhibits limited performance; (b) for the real-world case, both the ES-MDA and ES-ILU methods provide better calibration results than the best-known solution manually obtained, (c) a minimum number of observations are required to enable an overall accurate model calibration (e.g., four and ten more monitoring sites are needed in the two synthetic cases); and (d) the model calibrated using an intense rainfall event is generally robust to make reliable predictions across different rainfall events while the model calibrated using less intense rainfall event does not perform well for more intense rainfall events. It was also found that ubiquitous parameter equifinality significantly hinders unique parameter identification even when overall accurate state estimates are obtained. This should be clearly understood in practical applications., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

This paper proposes a new method to identify the near-optimal response to failures in water distribution networks in near real time. The response method is formulated as a two-objective optimization problem with objectives being the minimization of failure impacts and related operational costs. The new heuristics-based method is developed and used to solve this optimization problem. The method comprises three steps. In the first step, the initial list of available interventions is identified offline. In the second step (online), the narrowed-down list of interventions considered in the optimization is identified. Finally, in the last step (online), a novel heuristic algorithm is applied to identify near-optimal solutions in near real time. The new optimization method was validated and demonstrated in two case studies, a semireal case study based on a C-Town network and an assumed failure event (pipe burst), and a real UK case study involving a complex real pipe network and event caused by shutting down the Water Treatment Works. The Pareto front of response interventions identified by the new heuristics method approximates well the non-dominated sorting genetic algorithm II Pareto front in both cases with the largest differences measured in terms of end-impacts (between relevant solutions for the same cost) being 4% and 9%, respectively. In addition, the new heuristics method is able to identify near-optimal response solutions in a computationally fast manner (15 min and 1 h for the two cases). Therefore, the heuristics method can be used in near real time in real-life situations., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

A key challenge in designing algorithms for leakage detection and isolation in drinking water distribution systems is the performance evaluation and comparison between methodologies using benchmarks. For this purpose, the Battle of the Leakage Detection and Isolation Methods (BattLeDIM) competition was organized in 2020 with the aim to objectively compare the performance of methods for the detection and localization of leakage events, relying on supervisory control and data acquisition (SCADA) measurements of flow and pressure sensors installed within a virtual water distribution system. Several teams from academia and the industry submitted their solutions using various techniques including time series analysis, statistical methods, machine learning, mathematical programming, met-heuristics, and engineering judgment, and were evaluated using realistic economic criteria. This paper summarizes the results of the competition and conducts an analysis of the different leakage detection and isolation methods used by the teams. The competition results highlight the need for further development of methods for leakage detection and isolation, and also the need to develop additional open benchmark problems for this purpose., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

This paper presents a multi-criteria risk-based approach for managing urban flood hazards by using a combination of conventional measures and contemporary Sustainable Drainage Systems (SuDS). A multi-objective optimisation model coupled with a simulation model of UDS in the SWMM software is developed with the three objectives of minimising total costs, the risk of flooding and pollution discharged into receiving waters. K-means clustering technique is used to group the optimal solutions. A few optimal solutions and individual SuDS solutions are then ranked together by using the compromise programming (CP) method. The methodology is demonstrated by its application on a case study of the Golestan city UDS in Iran. The results obtained show there are indirect correlations between non-dominated solutions that minimise the risk of either flooding or pollution. The results also show the selected optimal solutions can provide cost-effective strategies that reduce both flood and pollution risks by at least 27% and 50%, respectively., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

Urban flooding is a major issue worldwide, causing huge economic losses and serious threats to public safety. One promising way to mitigate its impacts is to develop a real-time flood risk management system; however, building such a system is often challenging due to the lack of high spatiotemporal rainfall data. While some approaches (i.e., ground rainfall stations or radar and satellite techniques) are available to measure and/or predict rainfall intensity, it is difficult to obtain accurate rainfall data with a desirable spatiotemporal resolution using these methods. This paper proposes an image-based deep learning model to estimate urban rainfall intensity with high spatial and temporal resolution. More specifically, a convolutional neural network (CNN) model called the image-based rainfall CNN (irCNN) model is developed using rainfall images collected from existing dense sensors (i.e., smart phones or transportation cameras) and their corresponding measured rainfall intensity values. The trained irCNN model is subsequently employed to efficiently estimate rainfall intensity based on the sensors’ rainfall images. Synthetic rainfall data and real rainfall images are respectively utilized to explore the irCNN's accuracy in theoretically and practically simulating rainfall intensity. The results show that the irCNN model provides rainfall estimates with a mean absolute percentage error ranging between 13.5% and 21.9%, which exceeds the performance of other state-of-the-art modeling techniques in the literature. More importantly, the main feature of the proposed irCNN is its low cost in efficiently acquiring high spatiotemporal urban rainfall data. The irCNN model provides a promising alternative for estimating urban rainfall intensity, which can greatly facilitate the development of urban flood risk management in a real-time manner., Sanitary Engineering

Intermittent water supply (IWS) is widely used around the world, and with the increase in population and predicted future water scarcity, IWS applications seem to continue. While most of the existing studies on water supply concentrate on continuous water supply (CWS), the research focused on the IWS is now becoming mainstream. Hydraulic modelling is an effective tool for the process of planning, design, rehabilitation, and operation of water distribution systems. It helps significantly in engineers’ decision-making processes. The necessity of modelling IWS systems arises from the complexity and variety of problems caused by intermittency. This paper offers a review of the state-of-the-art IWS modelling and identifies the key strengths and limitations of the available approaches, and points at potential research directions. Currently, neither computer software nor a practically used approach is available for modelling IWS. For a rigorous simulation of IWS, system characteristics first need to be understood, i.e., the user behaviour under pressure-deficient conditions, water losses, and filling and emptying processes. Each of them requires further attention and improvement. Additionally, the necessity of real data from IWSs is stressed. Accurate modelling will lead to the development of improved measures for the problems caused by intermittency., Sanitary Engineering

Bio-composite materials made from resources recovered from the water cycle are the future of the holistic approach towards sustainable wastewater treatment. The raw ingredients for these materials are coming from contaminated sources such as wastewater resources, water plants from surface water etc.. Thus, different risks like human health, environmental and product quality risks need to be assessed. Existing literature was analysed regarding these risks, especially methods concerning the risk assessment in wastewater and drinking water treatment and water/wastewater-based resource recovery for reuse. The reviewed literature identified several risk assessment methods such as FMEA, FMECA, FTA, QMRA and QCRA as frequently used ones for these purposes. However, no dedicated methods were identified for the corresponding risk assessments related to bio-composite materials representing key knowledge gaps. The literature review also showed that the above identified risk assessment methods cannot be directly applied for bio-composite materials as many required input data are missing. To overcome above gaps, future research directions have been identified. These include use of qualitative risk assessment methods such as HAZOP and ETA to first identify hazards and map the risks. Once this is done, QMRA and QCRA could be used in combination with Monte Carlo analysis to assess the actual risks. However, before this can be done, additional work should be carried out to collect the missing data required for the use of these methods in the context of bio-composite materials. In addition, additional experimental work such as column leaching tests should be carried out to assess the environmental risks, in particular, looking at the release of toxic chemical compounds such as heavy metals in the aquatic environment. Finally, a list of quality requirements for bio-composite material and related products (e.g. requirements for mechanical properties, purity of raw materials, etc.) shou, Sanitary Engineering

In the plight for sustainable development and to support net zero ambitions for climate change mitigation, a broad range of aeration strategies have been developed with the hope of improving efficiency to minimize environmental and economic costs associated with the wastewater treatment processes. However, a balance is levied between reducing oxygen availability and hindering aerobic processes thus compromising performance. In the present work, we evaluate and compare the sustainability of a range of investigated strategies including continuous aeration (CA) at different dissolved oxygen (DO) setpoints (0.5 mg/L, 2.5 mg/L, 4.5 mg/L) and intermittent aeration (IA) at different oxic-anoxic portions (2.5 h on/0.5 h off, 2.0 h on/1.0 h off, 1.5 h on/1.0 h off). To achieve this, an eco-efficiency assessment is performed based on the results of previous life cycle impact and costing analyses for each strategy, while also incorporating a third factor to account for their respective treatment performance. The results demonstrate a clear pattern of increased sustainability for the IA strategies (0.54–0.56 Pt/m3), compared to the CA strategies (0.76–0.77 Pt/m3). While only negligible difference was observed within each aeration type, the trade-off between environmental and economic efficiency and treatment performance was distinct in CA strategies. At the individual pollutant level, IA strategies demonstrated decreasing sustainability for total phosphorous (TP) removal as the anoxic cycle portion increased, while CA at 0.5 mg/L was shown to be the most sustainable strategy for the removal of this pollutant (0.61 Pt/m3). Further work is suggested to incorporate the relative N2O emissions generated by each strategy and to investigate other strategies based on automated control., Sanitary Engineering

Emerging contaminants (ECs) continue to threaten our fragile ecosystem, yet their mitigation remains limited by economic factors. Meanwhile, a relatively expensive material, Graphene Oxide (GO), has shown promise as a solution for EC removal following further development into three graphene-based materials (GBMs): Porous graphene adsorbent (PGa), Graphene-oxide foam adsorbent (GOFa), and the hybrid filter. Due to the nuances of each synthesis process, financial costs will differ throughout the GBMs’ life cycle which have been quantified and compared in the present work at a range of possible breakthrough times. Finally, economic and environmental costs have been combined for each technology to compare eco-efficiency. Results demonstrated a substantial economic advantage of the GBMs when compared to alternative technologies, most notably the GOFa filter that incurred the lowest life-cycle costs at $1.73 ± 0.09/m3. This was mainly attributed to the lower demand of GOFa on the most expensive material required for material synthesis, hydra-zine. In addition, the material demands of GOFa were more evenly distributed which suggest a higher resilience of the overall costs to price hikes of individual materials required for synthesis. In terms of eco-efficiency the GOFa filter also demonstrated the greatest improvement when compared to the reference technology These results have provided robust total investment costs for several technologies that can now offer contrast to other EC-removal solutions., Sanitary Engineering

Effective functioning of sewer systems is critical for the everyday life of people in the urban environment. This is achieved, among other things, by the means of regular, planned maintenance of these systems. A large water utility would normally have several maintenance teams (or crews) at their disposal who can perform related jobs at different locations in the company area and with different levels of priority. This paper presents a new methodology for the optimisation of related maintenance schedules resulting in clear prioritisation of the ordering of maintenance tasks for crews. The scheduling problem is formulated as a multi-objective optimisation problem with the following three objectives, namely the minimisation of the total maintenance cost, the minimisation of travel times of maintenance teams and the maximisation of the job's priority score, all over a pre-defined scheduling horizon. The optimisation problem is solved using the Nondominated Sorting Genetic Algorithm-II (NSGA-II) optimisation method. The results obtained from a real-life UK case study demonstrate that the new methodology can determine optimal, low-cost maintenance schedules in a computationally efficient manner when compared to the corresponding existing company schedules. Daily productivity of maintenance teams in terms of number of jobs completed improved by 26% when the methodology was applied to scheduling in the case study. Given this, the method has the potential to be applied within water utilities and the water utility Welsh Water (Dwr Cymru Welsh Water (DCWW)) is currently implementing it into their systems., Sanitary Engineering

IFAS systems are inherently complex due to the hybrid use of both suspended and attached bacterial colonies for the purpose of pollutant degradation as part of wastewater treatment. This poses challenges when attempting to represent these systems mathematically due to the vast number of parameters involved. Besides becoming convoluted, large effort will be incurred during model calibration. This paper demonstrates a systematic approach to calibration of an IFAS process model that incorporates two sensitivity analyses to identify influential parameters and detect collinearity from a subset of 68 kinetic and stoichiometric parameters, and the use of the Nelder–Mead optimization algorithm to estimate the required values of these parameters. The model considers the removal of three critical pollutants including biochemical oxygen demand (BOD), total nitrogen (TN) and total suspended solids (TSS). Results from the sensitivity analyses identified four parameters that were the primary influence on the model. The model was found to be most sensitive to the two stoichiometric parameters including aerobic heterotrophic yield on soluble substrate whose total effects were responsible for 92.4% of the model’s BOD output sensitivity and 92.8% of the model’s TSS output sensitivity. The anoxic heterotrophic yield on soluble substrate was observed to be responsible for 54.3% of the model’s TN output sensitivity. To a lesser extent the two kinetic parameters, aerobic heterotrophic decay rate and reduction factor for denitrification on nitrite, were responsible for only 8.0% and 13.1% of the model’s BOD and TN output sensitivities respectively. Parameter estimation identified the need for only minor adjustments to default values in order to achieve sufficient accuracy of simulation with deviation from observed data to be only ± 3.6 mg/L, ± 1.3 mg/L, and ± 9.5 mg/L for BOD, TN and TSS respectively. Validation showed the model was limited in its capacity to predict system behaviour u, Sanitary Engineering

Surrogate models replace computationally expensive simulations of physically-based models to obtain accurate results at a fraction of the time. These surrogate models, also known as metamodels, have been employed for analysis, control, and optimization of water distribution and urban drainage systems. With the advent of machine learning (ML), water engineers have increasingly resorted to these data-driven techniques to develop metamodels of urban water networks (UWNs). In this article, we review 31 recent articles on ML-based metamodeling of UWNs to outline the state-of-the-art of the field, identify outstanding gaps, and propose future research directions. For each article, we critically examined the purpose of the metamodel, the metamodel characteristics, and the applied case study. The review shows that current metamodels suffer several drawbacks, including (a) the curse of dimensionality, hindering implementation for large case studies; (b) black-box deterministic nature, limiting explainability and applicability; and (c) rigid architecture, preventing generalization across multiple case studies. We argue that researchers should tackle these issues by resorting to recent advancements in ML concerning inductive biases, robustness, and transferability. Recently developed neural network architectures, which extend deep learning methods to graph data structures, are preferred candidates for advancing surrogate modeling in UWNs. Furthermore, we foresee increasing efforts for complex applications where metamodels may play a fundamental role, such as uncertainty analysis and multi-objective optimization. Lastly, the development and comparison of ML-based metamodels can benefit from the availability of new benchmark datasets for urban drainage systems and realistic complex networks., Sanitary Engineering

Blockages are a major issue for wastewater utilities around the world, causing loss of service, environmental pollution, and significant cleanup costs. Increasing telemetry in combined sewer overflows (CSOs) provides the opportunity for near real-time data-driven modelling of wastewater networks. This paper presents a novel methodology, designed to detect blockages and other unusual events in the proximity of CSO chambers in near real-time. The methodology utilises an evolutionary artificial neural network (EANN) model for short-term CSO level predictions and statistical process control (SPC) techniques to analyse unusual level behaviour. The methodology was evaluated on historic blockage events from several CSOs in the UK and was demonstrated to detect blockage events quickly and reliably, with a low number of false alarms., Sanitary Engineering

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., Sanitary Engineering, Policy Analysis

Resource recovery solutions are an essential part of a sustainable water sector. Sustainability of these solutions needs to be analysed to assess, compare and optimize them. Life Cycle Sustainability Assessment (LCSA) is the most commonly used framework for sustainability assessment. This review paper discusses three critical characteristics of water sector resource recovery solutions: (i) their potential to actively benefit natural processes through reciprocal services, (ii) their dependence upon natural resources and processes, and (iii) their goal to avoid transgression of environmental thresholds. We analyse these three characteristics in the context of the following features of LCSA: (i) it being a damage assessment-based framework, (ii) its treatment of economic and natural capital as substitutable and (iii) the absence of environmental thresholds and past emissions in its environmental assessment methodology. We use a real-life resource recovery case study from the Netherlands to evaluate and demonstrate the mentioned features of the existing LCSA framework. Our review indicates that, LCSA can be modified for application to resource recovery solutions if it includes reciprocity towards nature as an essential component, limits compensations between economic welfare and environmental damage, and incorporates environmental thresholds and past emissions., Sanitary Engineering

Real Time Control (RTC) is widely accepted as a cost-effective way to operate urban drainage systems (UDS) effectively. However, what factors influence RTC efficacy and how this might change in the long term remains largely unknown. This paper reviews the literature to understand what these factors likely are, and how they can be assessed in the future. Despite decades of research, inconsistent definitions of the performance of RTC are used, hindering an objective and quantitative examination of the benefits and drawbacks of different control strategies with regard to their performance and robustness. Furthermore, a discussion on the changes occurring and projected to occur to UDS reveals that the potential impact of these changes on the functioning of RTC systems can be significant and should be considered in the design stage of the RTC strategy. Understanding this 'best-before' characteristic of an RTC strategy is the key step to ensure long term optimal functioning of the UDS. Additionally, unexplored potential for RTC systems might exist in the transitions, rehabilitation and construction of drainage systems. The research gaps highlighted here could guide the way for further development of RTC strategies, and enabling more optimal, long term implementation of RTC for urban drainage systems., Sanitary Engineering

Resilience-informed water quality management embraces the growing environmental challenges and provides greater accuracy by unpacking the systems' characteristics in response to failure conditions in order to identify more effective opportunities for intervention. Assessing the resilience of water quality requires complex analysis of influential parameters which can be challenging, time consuming and costly to compute. It may also require building detailed conceptual and/or physically process-based models that are difficult to build, calibrate and validate. This study utilises Artificial Neural Network (ANN) to develop a novel application to predict water quality resilience to simplify resilience evaluation. The Fuzzy Analytic Hierarchy Process method is used to rank water basins based on their level of resilience and to identify the ones that demand prompt restoration strategies. The commonly used ‘magnitude * duration of being in failure state’ quantification method has been used to formulate and evaluate resilience. A 17-years long water quality dataset from the 22 water basins in the State of São Paulo, Brazil, was used to train and test the ANN model. The overall agreement between the measured and simulated WQI resilience values is satisfactory and hence, can be used by planners and decision makers for improved water management. Moreover, comparative analyses show similarities and differences between the ‘level of criticalities’ reported in each zone by Environment Agency of the state of São Paulo (CETESB) and by the resilience model in this study., Accepted Author Manuscript, Sanitary Engineering

With the aim of reducing the environmental burden of decentralized wastewater treatment plants in India, this project investigated five primary materials (stainless steel (SS), mild steel (MS), glass fibre reinforced polymer (GFRP), high density polyethylene (HDPE), and reinforced concrete cement (RCC)) in terms of the relative environmental impact that each would incur across 13 midpoint and 4 endpoint impact categories during the early life stages. The results showed that SS demonstrated substantially higher impact in total (5.47 Pt) and across each of the endpoint categories, most notably human health (3.12 Pt). Further investigations demonstrated that this was largely fed by the respiratory inorganics midpoint category that accounted for 50 % of the total impact (2.75 Pt), while global warming (0.93 Pt), non-renewable energy (0.70 Pt) and terrestrial ecotoxicity (0.62 Pt) were the only other considerable impacts. GFRP incurred the second greatest impact overall (2.32 Pt), while MS, RCC and HDPE followed with 1.82 Pt, 0.78 Pt, and 0.39 Pt respectively. HDPE afforded the greatest efficiency in all midpoint categories except carcinogens where RCC incurred the least environmental cost. Results were then compared with previous work and likely causal factors highlighted. Further study is recommended to investigate the longevity of the alternative materials in a wastewater containment role to support these results., Sanitary Engineering