The effective environmental management of combined sewer systems requires reliable estimation of discharge and pollutant loads conveyed at the outlet during rainstorms. This study investigates how, with a lumped modelling approach, it is possible to reproduce the quality characteristics of discharged water, provided that high temporal resolution experimental data of pollutant concentrations are available. The methodology is applied to the combined sewer of a real urban drainage network where a continuous high resolution monitoring campaign of water quality and quantity has been carried out at an overflow structure location near the outlet of the drainage system. The lumped modelling approach has been implemented in the Storm Water Management Model (SWMM) with hydrological parameters estimated from cartographic information, based on recently proposed methodology that allows reliably simulating the storm hydrographs without model calibration. A semi-distributed model has been also developed using the SWMM with hydrologic parameters randomly sampled to fit the measured hydrographs of different training and validation data. The results obtained show that the uncalibrated lumped model simulates the observed hydrographs with similar performance as with the semi-distributed model (i.e., the normalized Nash-Sutcliff efficiency index of the validation set is 0.753 for the uncalibrated lumped model and 0.765 for the best-performing sampled parameter set of the semi-distributed model). The water quality parameters describing the build-up and wash-off of total dissolved solids (TDS) in a lumped model have been calibrated too, as well as those describing the mixing and consumption of dissolved oxygen (DO). The results show that a lumped modelling approach can reproduce the water quality dynamics in a combined sewer system, representing a promising tool for effective environmental management. However, event-specific calibrated parameter values have been obtained in some cases, which require further investigation and still limit the general applicability of the obtained results, thus confirming that setting up a reliable model requires water quality measurements.

Blue green infrastructure (BGI) is widely implemented as an adaptive stormwater management measure at the household level to reduce flood risk. However, more greenery also raises water demand during droughts due to higher evapotranspiration. This study examines the impact of 14 commonly used BGI types on household water balance under climate projections in the Netherlands. Several scenarios were modeled, from a ‘Grey’ setup with no BGI to a ‘Greenest’ option with an intensive green roof, facade, and orchard. Intermediate configurations were also analyzed, representing more common household configurations. On a typical 100 m2 household plot, the ‘Greenest’ option results in an extra demand of 154.3 L/day. This exceeds the current average daily indoor water use of a typical household of 129 L/day. In contrast, intermediate setups with a native plant garden or fully grassed garden and a gray roof require 8.4 and 9.9 L/day, respectively. To meet 80% of the projected additional external water demand from intensified greenery, intermediate setups need up to 2.3 m3 of rainwater tank. The ‘Greenest’ option requires 14.9 m3 of water storage to achieve the same coverage, underscoring the challenge of balancing space for water harvesting systems and intensified greenery within a limited household plot.

Co-located infrastructure networks such as road, water, and sewer in theory offer the possibility for integrated multi-infrastructure interventions. However, how closely these networks are aligned in space and time determines the practical extent to which such coordinated interventions can be realized. This study quantifies the spatial alignment of the aforementioned infrastructure networks and demonstrates its application for integrated interventions and potential cost savings. It proposes two metrics, namely 1) shared surface area and, 2) shared trench volume, to quantify the spatial relationship (i.e., degree of co-location) of infrastructures. Furthermore, the study demonstrates how the degree of co-location can be used as a proxy for cost-saving potential of integrated interventions compared to silo-based, single-infrastructure, interventions. Through six case studies conducted in Norwegian municipalities, the research reveals that implementing integrated interventions across road, water, and sewer networks can result in potential average cost savings of 24% in urban areas and 11% in rural areas. Utility-specific savings under different cost-sharing scenarios were also analysed. To identify the yearly potential of integrated multi-infrastructure interventions, future work should add the temporal alignment of rehabilitation of infrastructures (i.e., time of intervention need for the infrastructures).

Computational models for water resources often experience slow execution times, limiting their application. Metamodels, especially those based on machine learning, offer a promising alternative. Our research extends a prior Graph Neural Network (GNN) metamodel for the Storm Water Management Model (SWMM), which efficiently learns with less data and generalizes to new UDS sections via transfer learning. We extend the metamodel’s functioning by adding flowrate prediction, crucial for assessing water quality and flooding risks. Using an Encoder–Processor–Decoder architecture, the metamodel displays high accuracy on the simulated time series. Future work is aimed at incorporating more physical principles and testing further transferability.

This chapter introduces asset management for urban drainage systems with a focus on operation and maintenance. Good asset management ensures the performance of assets is in line with the owner and operator's requirements.

Storm water systems (SWSs) are essential infrastructure providing multiple services including environmental protection and flood prevention. Typically, utility companies rely on computer simulators to properly design, operate, and manage SWSs. However, multiple applications in SWSs are highly time-consuming. Researchers have resorted to cheaper-to-run models, i.e. metamodels, as alternatives of computationally expensive models. With the recent surge in artificial intelligence applications, machine learning has become a key approach for metamodelling urban water networks. Specifically, deep learning methods, such as feed-forward neural networks, have gained importance in this context. However, these methods require generating a sufficiently large database of examples and training their internal parameters. Both processes defeat the purpose of using a metamodel, i.e., saving time. To overcome this issue, this research focuses on the application of inductive biases and transfer learning for creating SWS metamodels which require less data and retain high performance when used elsewhere. In particular, this study proposes an auto-regressive graph neural network metamodel of the Storm Water Management Model (SWMM) from the Environmental Protection Agency (EPA) for estimating hydraulic heads. The results indicate that the proposed metamodel requires a smaller number of examples to reach high accuracy and speed-up, in comparison to fully connected neural networks. Furthermore, the metamodel shows transferability as it can be used to predict hydraulic heads with high accuracy on unseen parts of the network. This work presents a novel approach that benefits both urban drainage practitioners and water network modeling researchers. The proposed metamodel can help practitioners on the planning, operation, and maintenance of their systems by offering an efficient metamodel of SWMM for computationally intensive tasks like optimization and Monte Carlo analyses. Researchers can leverage the current metamodel’s structure for developing new surrogate model architectures tailored to their specific needs or start paving the way for more general foundation metamodels of urban drainage systems.

The lack of flexibility in current drainage infrastructure combined with uncertainty, changing societal needs and demands affect the robustness and resilience of current drainage systems to future pressures. This chapter looks at how new thinking and alternative, more innovative ways to deliver a sustainable, affordable operating model for drainage services is required, including taking advantage of the opportunities from using a landscape-based approach to stormwater management. It examines skills and capacity planning, and the need to futureproof the workforce of the sector. In considering the pressures on drainage infrastructure, advances in digitalisation and technological innovation are considered as a means to unlock the potential of data-driven decision making and artificial intelligence, thus transforming sector efficiency.

Enhancing sediment accumulation monitoring techniques in sewers will enable a better understanding of the build-up processes to develop improved cleaning strategies. Thermal sensors provide a solution to sediment depth estimation by passively monitoring temperature fluctuations in the wastewater and sediment beds, which allows evaluation of the heat-transfer processes in sewer pipes. This study analyses the influence of the flow conditions on heat-transfer processes at the water-sediment interface during dry weather flow conditions. For this purpose, an experimental campaign was performed by establishing different flow, temperature patterns, and sediment depth conditions in an annular flume, which ensured steady flow and room-temperature conditions. Numerical simulations were also performed to assess the impact of flow conditions on the relationships between sediment depth and harmonic parameters derived from wastewater and sediment-bed temperature patterns. Results show that heat transfer between water and sediment occurred instantaneously for velocities greater than 0.1 m/s, and that sediment depth estimations using temperature-based systems were barely sensitive to velocities between 0.1 and 0.4 m/s. A depth estimation accuracy of ±7 mm was achieved. This confirms the ability of using temperature sensors to monitor sediment build-up in sewers under dry weather conditions, without the need for flow monitoring.

Background
Wastewater surveillance may support early and comprehensive detection of infectious diseases’ community transmission, particularly in settings where other health surveillance systems provide biased or limited information. Amid the SARS-CoV-2 pandemic, deploying passive samplers to monitor targeted populations gained importance. Evaluation of the added public health value of this approach in the field can support its broader adoption.

Aim
We aimed to assess the feasibility and utility of on-demand wastewater surveillance, employing passive samplers, for SARS-CoV-2 and monkeypox virus (MPXV) in small/targeted populations, also considering ethical aspects.

Methods
Pilot case studies in the Rotterdam-Rijnmond region were used for a systematic assessment of the feasibility and utility of wastewater monitoring of SARS-CoV-2 (variants) and MPXV using passive sampling. Each case study was instigated by actual questions from the Public Health Service about disease transmission.

Results
Case study results demonstrated the feasibility and utility of on-demand wastewater surveillance with successful identification of a local peak in SARS-CoV-2 transmission, early detection of wider Omicron variant transmission after the first case was reported, as well as indication of no emerging local MPXV transmission. Ethical considerations led to the abandonment of one case study involving a displaced population.

Conclusions
The study confirms the feasibility and utility of passive sampling for real-time infectious disease surveillance, at desired spatiotemporal resolution. Ethical concerns and operational challenges were identified, highlighting the need for early stakeholder engagement and ethical guideline adherence. The method could be used to study under-surveyed populations and be extended beyond SARS-CoV-2 and MPXV to other pathogens.

Sediments in urban drainage systems (UDS) significantly impact their operation, so effective strategies are required to reduce their negative effects. Monitoring sediment accumulation provides valuable insights into sediment characteristics, sediment transport dynamics, and system performance. However, the effectiveness of monitoring systems is limited due to cost constraints and installation challenges. This study describes the development and application of a new system based on temperature dynamics to measure sediment depths in sewer systems. The methodology involves the analysis of temperature time series under dry weather flow conditions to identify harmonic patterns between wastewater and sediment-bed temperatures. These patterns are increasingly attenuated by increasing sediment depth. This study combines a system called MONitoring Temperatures in SEdiments (MONTSE), which integrates a dual-probe heat-pulse (DPHP) method to characterize sediment thermal properties, and a surrogate model, which includes temperature pattern analysis, to estimate sediment depths. Likewise, laboratory-scale experiments were performed to validate the temperature monitoring system and the surrogate model performance. The maximum absolute errors in measured sediment depths were less than 22 mm, and the uncertainty of the system was estimated at ±7.3 mm. Groundbreaking measurements of thermal properties of UDS sediments were also reported. Reliable information on sediment depths and properties was provided, so the system could significantly optimize sewer system operation and cleaning strategies.

Small utilities often lack the required amount of data to train machine learning-based models to predict pipe failures, and hence are unable to harness the possibilities and predictive power of machine learning. This study evaluates the generalizability and transferability of a machine learning model to see if small utilities can benefit from the data and models of other utilities. Using nine Norwegian utilities’ datasets, we trained nine global models (by merging multiple datasets) and nine local models (by utilizing each utility's dataset) using random survival forest. Several pre-processing techniques including addressing left-truncated break data and break data scarcity are also presented. The global models and three of the local models were tested to predict the pipe failure of the utilities which were not included in their training datasets. The results indicate that the global models can predict other utilities with sufficient accuracy while local models have some limitations. However, if a representative utility with a sufficiently large (and information rich) dataset is selected, its model can predict the other utility's pipe breaks as accurate as the global models. Furthermore, survival curves for defined cohorts as proxies for uncertainty, and variable importance show that pipes with and without previous breaks behave extremely different. With the understanding of models’ generalizability and transferability, small utilities can benefit from the data and models of other utilities.

Urban drainage systems are composed of subsystems. The ratio of the storage and discharge capacities of the subsystems determines the performance. The performance of the urban water system may deteriorate as a result of the change in the ratio of storage to discharge capacity due to aging, urbanisation and climate change. We developed the graph-based weakest link method (GBWLM) to analyse urban drainage systems. Flow path analysis from graph theory is applied instead of hydrodynamic model simulations to reduce the computational effort. This makes it practically feasible to analyse urban drainage systems with multi-decade rainfall series. We used the GBWLM to analyse the effect of urban water system aging and/or climate scenarios on flood extent and frequency. The case study shows that the results of the hydrodynamic models and the GBWLM are similar. The rainfall intensities of storm events are expected to increase by approximately 20% in the Netherlands due to climate change. For the case study, such an increase in load has little impact on the flood frequency and extent caused by gully pots and surface water. However, it could lead to a 50% increase in the storm sewer flood frequency and an increase in the extent of flooding.

Illicit connections of wastewater to stormwater systems are the main drawback of separate sewer systems, as they lead to a direct discharge of untreated wastewater to the aquatic environment. Consequently, several inspection methods have been developed for detecting illicit connections. This study simultaneously applied several low- and high-tech methods for the detection of illicit connections in the same catchment (De Heuvel, the Netherlands). The methods included mesh wire screens for capturing coarse contamination, measurements of electroconductivity and temperature, sampling and quantification of Escherichia coli and extended-spectrum ß-lactamase-producing E. coli (ESBL-EC), DNA analysis via quantitative polymerase chain reaction for human-, dog-, and bird-specific fecal indicators, and distributed temperature sensing. Significant illicit connections could be identified using all methods. Nonetheless, hydraulic conditions and, predominantly, the sewage volume determine whether a misconnection can be detected by especially the low-tech methods. Using these results, the identified misconnections were repaired and biological and DNA analyses were repeated. Our results demonstrate that there were no changes in E. coli or ESBL-EC before and after mitigation, suggesting that these common markers of fecal contamination are not specific enough to evaluate the performance of mitigation efforts. However, a marked decrease in human wastewater markers (HF183) was observed.

Urban water systems worldwide need integrated, cross-sectoral innovations to anticipate developments like climate change and population growth. Development and implementation of such innovations is challenging due to the operational and sectoral mindset of organizations in which these innovations take place. This study uses the concept of ambidexterity to get a better understanding of how organizations responsible for urban water management deal with the tension between operation and the need for innovation. We focused on Amsterdam and Rotterdam, two Dutch cities that are global frontrunners in urban water management. Combining a desk study with 25 semi-structured interviews, we found four mechanisms to manage innovation and operation tensions: network, hierarchical, process and human-resource mechnanisms. Different from the literature on ambidexterity, our empirical findings show that the connection between operation and innovation is dominated by networks rather than by executives. Hierarchical mechanisms could be used to complement this, catalyzing innovation or formalizing it.

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.

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.

Despite high vaccination rates in the Netherlands, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to circulate. Longitudinal sewage surveillance was implemented along with the notification of cases as two parts of the surveillance pyramid to validate the use of sewage for surveillance, as an early warning tool, and to measure the effect of interventions. Sewage samples were collected from nine neighborhoods between September 2020 and November 2021. Comparative analysis and modeling were performed to understand the correlation between wastewater and case trends. Using high resolution sampling, normalization of wastewater SARS-CoV-2 concentrations, and ‘normalization’ of reported positive tests for testing delay and intensity, the incidence of reported positive tests could be modeled based on sewage data, and trends in both surveillance systems coincided. The high collinearity implied that high levels of viral shedding around the onset of disease largely determined SARS-CoV-2 levels in wastewater, and that the observed relationship was independent of variants of concern and vaccination levels. Sewage surveillance alongside a large-scale testing effort where 58 % of a municipality was tested, indicated a five-fold difference in the number of SARS-CoV-2-positive individuals and reported cases through standard testing. Where trends in reported positive cases were biased due to testing delay and testing behavior, wastewater surveillance can objectively display SARS-CoV-2 dynamics for both small and large locations and is sensitive enough to measure small variations in the number of infected individuals within or between neighborhoods. With the transition to a post-acute phase of the pandemic, sewage surveillance can help to keep track of re-emergence, but continued validation studies are needed to assess the predictive value of sewage surveillance with new variants. Our findings and model aid in interpreting SARS-CoV-2 surveillance data for public health decision-making and show its potential as one of the pillars of future surveillance of (re)emerging viruses.

Urban water is a crucial element of cities for the purpose of events and recreation, raising concern over the water quality and related hygienic safety. In this study, a near real-time monitoring system of the β-d-glucuronidase activity, the BACTcontrol, was tested in the canals of Breda city in the Netherlands in order to gain insight in its suitability to constitute part of an effective warning system for urban surface waters. Additionally, the qPCR method was also evaluated as a complementary method aiming at determining the E. coli or Bacteroides bacteria, while conventional culture-based measurements aiming at E. coli served as a reference. Analysis of the results obtained via monitoring and sampling during three consecutive bathing seasons revealed that the BACTcontrol demonstrated a timely but short response, implying that it was capable of detecting contamination peaks but not indicating when the water was hygienically safe again. This gap could be filled with qPCR measurements, which proved to provide reliable and fast results. Therefore, the combination of the BACTcontrol with qPCR measurements offers the opportunity to build an effective strategy concerning the use of urban surface waters for recreational purposes, based on timely information on the emergence and duration of contamination events.

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.