The illicit connections between sewage and stormwater pipes result in the discharge of untreated sewage into receiving rivers, posing significant odor and health hazards. While thioethers are recognized as key odorants in sewage systems, their distribution in stormwater systems remain poorly characterized. This study analyzed 12 types of thioethers in stormwater pipes sampled at 21 sites in China. Advanced analytical techniques, including Mantel analysis and Structural Equation Modeling, were employed to examine the relationships between overlying water properties, sediment microbial characteristics, and thioether concentrations. Results showed that sediment thioether loads (36.77 ± 50.14 μg S/m; range: 7.24–99.96 μg S/m) were substantially higher than those in the overlying water (12.02 ± 42.52 μg S/m; range: 0.03–92.76 μg S/m), highlighting sediment as a critical pollution reservoir. Dissolved oxygen, NH₃-N, and terrestrial-derived dissolved organic nitrogen were identified as key factors shaping sediment microbiome composition, particularly fermentative, sulfate-reducing, and denitrifying bacteria, which in turn drives thioether formation. Specifically, dominant compounds like dimethyl disulfide and dimethyl trisulfide were found to be produced through the anaerobic fermentation of methionine and redox conversion of methanethiol, as well as the anaerobic fermentation of cysteine and methylation of polysulfides. Humic substances could facilitate methanethiol redox conversion and polysulfide methylation by serving as methyl donors and enhancing electron transfer efficiency. Additionally, NH₃-N may promote microbial metabolism by providing amino groups essential for the synthesis of metabolic precursors. Therefore, effective mitigation of odorous thioethers in stormwater systems necessitates integrated strategies targeting both sulfur-containing organic precursors and nitrogen-rich pollutants.

As cities expand and land becomes built over, more rainwater will run off rather than infiltrate or evapo(trans)pirate, increasing the likelihood of urban pluvial flooding. Stormwater management and planning is essential to ensure that urban areas are well adapted to climate change, involving cooperation between diverse actors with their own objectives. Current tools to support decision-making have a narrow technical focus and do not incorporate the multi-actor context. In this paper, we present a serious game called Urban dRain, developed with the aim to integrate technical assessment of blue, green and grey solutions and actor negotiation. In the game, participants are challenged to develop a stormwater management strategy for a Dutch neighbourhood in multiple rounds, first within their own separate groups, and then collectively. We present results from validation and play-testing the final game prototype with 70 students and researchers. Results show that the game supports socio-technical learning by encouraging players to come up with a range of stormwater management plans and negotiate for their individual goals while achieving a collective goal. The game demonstrates potential to bring actors with varying perspectives together and co-develop solutions to pluvial flooding, overcoming limitations of existing technology-focused tools.

Urban drainage network models (UDNMs) have been widely used to facilitate flood management. Typically, a UDNM is developed using data from Geographic Information Systems (GIS), and hence it consists of many short pipes and connection nodes or manholes. To improve modeling efficiency, a GIS-based model is generally skeletonized by removing many elements. However, there has been surprisingly a lack of knowledge on to what extent such skeletonization can affect the model's simulation accuracy, resulting in uncertainty in flood risk estimation. This paper makes the first attempt to quantitatively evaluate multidimensional impacts of different skeletonization levels on hydraulic properties of UDNMs. This goal is achieved by a new evaluation framework comprising of eight existing and new metrics that make use of hydrographs, main pipe hydraulics and flood distribution properties. A real-life UDNM is used to illustrate the new framework under various rainfall conditions and different skeletonization levels. The new framework is also used to compare the performance of two compensation methods in mitigating impacts caused by model skeletonization. Results obtained show that: (a) model skeletonization can significantly affect the magnitude of peak flow at the outfall, with a maximum overestimation of up to 20%, (b) hydraulics in main pipes can also be affected by model skeletonization with the maximum flow increasing up to 35%, and (c) model skeletonization may significantly alter the flood distribution properties which has been largely ignored in past studies. These findings provide guidance for UDNM skeletonization, where their associated impacts should be aware in engineering practice.

Storm-driven runoff scours accumulated sediments within stormwater drainage systems, transporting multi-source pollutants (including pathogens) into surface water through stormwater overflows, thereby elevating contamination risks in the recipient. Chlorine-based disinfection of overflowed stormwater applied in related storage tanks mitigates these risks before release. This study reveals that chlorophenylacetonitriles (CPANs), which are formed during the disinfection process, exhibit toxicity levels higher than conventional trihalomethanes and haloacetonitriles. Laboratory analyses conducted in this work demonstrated that sewer sediments — not runoff or stormwater — are the dominant precursor source for CPAN formation during overflow disinfection. Source apportionment further identified a robust linear correlation (R² = 0.95) between sediment indole concentrations (0.093–0.91 μg/g) and CPAN formation, experimentally confirming for the first time that indole is a critical precursor. Laboratory experiments also uncovered the presence of monochloroindoles in indole chlorination, a novel class of aromatic nitrogenous disinfection byproducts (DBPs). In addition, density functional theory calculations demonstrated that monochloroindole formation has lower activation energy barriers compared to CPAN pathways, resulting in new molecular-level insights into their preferential transformation. Given that indole serves as a shared precursor for both highly toxic CPANs and even more ecotoxic monochloroindoles, this study emphasizes the urgent need for sewer sediment management to mitigate the ecological and human health risks associated with these highly toxic nitrogenous DBPs.

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.

This review explores recent advancements in modeling the flow behavior of Herschel-Bulkley (HB) fluids in pipes, discussing theoretical, semi-empirical, computational, and experimental methods. While the laminar flow of non-Newtonian HB fluids can be effectively modeled using first-principle physics, significant challenges remain in turbulent and transitional flow regimes. Existing turbulence models, though widely used, may not always fully align with experimental data, often requiring further validation or complex mathematical tuning, leading to higher computational costs. Further, the transition to turbulence in HB fluids is influenced by shear-thinning and yield stress, yet current models often fail to account for this delayed transition. Consequently, stability and Reynolds number-based transition models can exhibit inconsistencies, limiting their broader applicability. Progress is further hindered by limited experimental studies, constrained by resolution, attenuation, cost, and material combinations. Inaccuracies in rheological modeling—due to inappropriate shear rate ranges, curve-fitting techniques, or simplifying assumptions such as homogeneity and non-elasticity—further complicate flow predictions. Through this review, we delve deeper into the state-of-the-art modeling of HB fluids, highlighting progress and these challenges. Addressing these limitations requires advanced experimental and numerical studies, particularly for near-wall measurements, to better capture flow complexities and improve model predictions. This could also facilitate the development of data-driven approaches and operational envelopes that define their validity thresholds. Future research should also prioritize the independent effects of yield stress and shear-thinning properties while considering material attributes and settling phenomena in non-Newtonian suspensions. Ultimately, these advancements will enable more accurate flow predictions and practical solutions for industrial applications.

There is a trend towards decentralized source separation (DSS) for wastewater treatment and resource recovery. An assessment framework is required to assess whether implementing a DSS treatment over a conventional centralized one is advantageous. This framework needs to account for the performance of the wastewater treatment plant (WWTP) and the effect that resource recovery has on closely-linked sectors such as food and energy production. A framework is lacking that covers the economic dimension, the circularity, the nature reciprocity of resource recovery and that can be applied to real-life cases. A novel WFE framework has been developed here to compare a conventional centralized and a DSS-based WWTP. This novel WFE framework contains assessment methods that are reproducible, and applicable to real-life cases. It also accounts for the local climatic conditions that determine irrigation water requirements. The comparison results revealed that the need to construct new DSS infrastructure leads to a lower economic efficiency of water treatment. Further, chemical-intensive treatment reduces the DSS's material resource circularity and efficiency. Using heat pumps increases the energy use of the DSS WWTP, causing a reduction in water treatment energy efficiency. However, the advantages of DSS show up in the freshwater and nutrient efficiency of food production as well as in the energy self-sufficiency of the WWTP. The novel WFE framework contains indicators specific to water treatment and the food production sectors to improve inter-sectoral communication. Also, including the nature reciprocity assessment can help demonstrate the issue with treated wastewater discharge, especially in arid regions with low stream flows. It can potentially help improve the acceptance of treated wastewater-based reuse. To conclude, the novel framework helps to assess real-life case studies in a more integrated and holistic way. It can help make decisions related to decentralization and source separation by simultaneously considering the water treatment, energy production, and food production sectors.

This chapter presents a detailed description of the circularity and efficiency assessment methods to be used in the context of resource recovery solutions in the water sector. The resource recovery solutions are meant to produce multiple benefits, such as increased resource efficiency and decreased negative environmental impact, among others. The solutions need to be assessed and compared using a comprehensive set of criteria and indicators. This report discusses key concepts, explains methods, and briefly presents a spreadsheet tool developed for conducting the assessment. Some key concepts, such as circular bioeconomy and efficiency, are first introduced. Several resources may be recovered from the water sector, and thus, commonly recovered resources are classified. This is followed by an expansion of the scope of the material circularity indicator, a prevalent assessment method. This is done to apply the MCI to the resources relevant to the water sector. Equations for the resource categories are developed and presented. Efficiency is then defined as a ratio between the benefits and costs of a resource recovery solution. Two categories of cost indicators are introduced, followed by three categories of benefit indicators. By combining different cost and benefit indicators, several indicators such as simple material efficiency, service unit-based energy efficiency, and service unit ecoefficiency can be created. Circularity and efficiency assessment methods are explained with simple examples, and screenshots from the spreadsheet assessment tool are presented. Lastly, the assessment method is demonstrated on a real-life case study located in Italy. The case study involves reuse of treated wastewater (TW) for irrigation. The newly developed circularity and efficiency methods demonstrate the improvements in the circularity and efficiency resulting from TW irrigation reuse, along with pointing to the most crucial factors to be considered for such cases.

The odor nuisance of urban surface water after rainfall events has aroused public concerns and threaten the aquatic organisms. Herein, the first study to investigate 150 odorants in storm sewer discharge was performed in humid regions of China. During rainfall events, the total concentrations of odorants at storm sewer outlet increased by 1.3–2.1 fold from 1.7–9.4 μg/L to 2.1–20.0 μg/L with 37 odorants having detection frequencies above 50 % on rainy days, and the concentrations of total odorants in air also significantly increased resulting in worse odor nuisance. The accumulation of odorants in sewer sediment and the remobilization of sewer sediment were factors resulting in more intensified emission of odorants from storm sewer on rainy days. More than half of odorants discharged during rainfall were contributed by sewer sediment. Thioethers, indoles, 2-isopropyl-3‑methoxy pyrazine, acetophenone and coumarin exhibited high sediment-accumulation. Quantitative structure-property relationship models revealed that enhanced sediment-accumulation of chained aliphatic and aromatic odorants can be explained by the electrostatic attraction and topological characteristic, respectively. The multicriteria analysis was further introduced for relative odorants ranking by considering the variations in hazard criteria of environmental occurrence, ecotoxicity, persistence, odor nuisance and sediment accumulation. Among priority odorants, thioethers and indoles were attributed by their distinct sediment-accumulation and odor nuisance potential, while chlorinated anisole and pinenes prioritized due to their higher ecotoxicity. These findings provide novel insights into the odorants from storm sewer discharges and explore the environmental behaviors of odorants in sewer sediment.

The inflow and infiltration (I&I) is an issue for many urban sewer networks (USNs), which can significantly affect system functioning. Placing sensors within the USNs is a typical approach to detect large I&I event, but deploying a limited number of sensors while achieving maximum detection reliability is challenging. While some methods are available for sensor placement, they are generally heuristic search-based methods (HSBMs) and hence the resultant sensor placement strategies (SPSs) are variable over different algorithm runs or parameterizations. This paper develops a new deterministic two-stage clustering method for SPS optimization based on information entropy. Within the first stage, the Spectral Clustering method is applied to assign USN nodes to different clusters according to their joint entropy. In the second stage, the topology structure property is considered to enable further clustering for improving detection reliability. Average I&I detection reliability is used to select clusters and the optimal SPS is identified by maximizing joint entropy of all possible solutions where a single sensor is assigned to each selected cluster. The proposed method and two existing HSBMs are applied to a real USN and their performance is compared. The results obtained show that: (i) a strong correlation coefficient R (R > 0.95) is observed between joint entropy and SPS's detection reliability, which has not been revealed before, (ii) the proposed method consistently outperforms the other two approaches in efficiently offering SPSs with about 7–15 % higher detection reliability, and (iii) the proposed method provides the optimal SPS in a deterministic manner, which makes it attractive for engineering applications.

The analysis of the spatial distribution of drinking water temperature (DWT) in the drinking water distribution system (DWDS) can allow for the detection of hotspots and the identification of suitable mitigation interventions to enhance the climate resilience. For this purpose, a water temperature model is implemented in EPANET-MSX and coupled with the hydraulic model of the DWDS in the town of Almere (the Netherlands). This model is then used to assess the effectiveness of a range of interventions against the unwanted water warming under a climate scenario of an extreme air temperature increase in a Dutch summer. Finally, a solution scenario is suggested to comply with the Dutch legislative limit of 25 °C on DWT at the tap.

Sewage pipe defects can significantly affect the urban water environment, like leakage of pollutants through pipe cracks to groundwater. Currently, sewage pipe defects are detected mainly through closed-circuit television inspection, which is conducted manually and is time-consuming. This study proposes an integrated deep-learning-based algorithm to detect and quantify pipe cracks from images, namely the Crack Detection and Characterization (CDC) method. The method is based on models created in two steps (i) crack detection by semantic segmentation, and (ii) crack length quantification using an innovative algorithm. The CDC algorithm is verified by images both artificially created in the laboratory and from actual inspection. For both laboratory and field cases, the CDC method is verified precisely. From the results, the CDC method exhibited a higher accuracy in crack identification and length quantification than other existing models. The results also show that the deblurring process can greatly improve accuracy. This study can contribute to decision-making in sewage pipe maintenance and water environment management by providing an innovative way of more efficient and accurate pipe defect assessment compared with traditional labor-intensive work.

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.

A new type of bio-composite material is being produced from water-recovered resources such as cellulose fibres from wastewater, calcite from the drinking water softening process, and grass and reed from waterboard sites. These raw materials may be contaminated with pathogens and chemicals such as Escherichia coli, heavy metals, and resin compounds. A novel risk assessment framework is proposed here, addressing human health risks during the production of new bio-composite materials. The developed framework consists of a combination of existing risk assessment methods and is based on three main steps: hazard identification, qualitative risk mapping, and quantitative risk assessment. The HAZOP and Event Tree Analysis methodologies were used for hazard identification and risk mapping stages. Then, human health risks were quantitatively assessed using quantitative chemical risk assessment, evaluating cancer and non-cancer risk, and quantitative microbial risk assessment. The deterministic and the stochastic approaches were performed for this purpose. The contamination of raw materials may pose human health concerns, resulting in cancer risk above the threshold. Microbial risk is also above the safety threshold. Additional analysis would be significant as future research to better assess the microbial risk in biocomposite production. The framework has been effectively used for chemical and microbial risk assessment.

The increasing focus on sustainability and circularity is driving the global production of environmentally friendly products. The Netherlands started producing new bio-composite materials which are created by reclaiming resources from various sectors of the water industry. These materials can be used for a variety of applications including façade elements in buildings. However, their potential environmental impact, particularly with regard to leaching of potentially harmful substances into surface water, necessitates further evaluation. To address this issue, a systematic environmental risk assessment methodology combined with novel experimental data is presented here. To collect this data, façade panels made of two different bio-composite materials were first subjected to a series of laboratory tests, including analysis in both new and weathered forms, the latter subject to a cyclic UV radiation and high humidity, in order to simulate the effects of aging. Leaching tests were then conducted to determine the potential release of specific chemical substances such as heavy metals and resin compounds, under two different rainfall conditions (every day and more extreme). The data generated this way was used to perform the risk assessment using the existing European ERA framework. The results obtained reveal different leaching behaviour of the new and weathered samples, as well as between the two analysed bio-composite materials, depending on the rain intensity. To overcome the uncertainties caused by the limited input data, a sensitivity analysis was carried out whereby leaching concentrations and rainfall intensities were varied and their influence on the environmental risk was assessed. The results obtained demonstrated that, despite some variability, both materials appear safe to use, i.e., with estimated risks below the established safety threshold. While these findings provide a preliminary indication, they are based on laboratory conditions and assumptions hence further field studies are recommended to obtain more definitive conclusions.

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.

Graph Neural Networks (GNNs) have been applied to network data such as traffic flow and water distribution systems, yet their use in predicting the state of urban stormwater drainage systems remains rare. This study investigates the application of Graph-WaveNet (GWN), a type of GNN, in forecasting the states of stormwater systems in Kowloon, Hong Kong. Data was sourced from the Storm Water Management Model (SWMM) spanning 43 rainfall events from 2020 to 2023. Based on the preceding 30 to 60 min of network states and rainfall data, GWN predicted junction inflows, pipe flow rates, and relative water depths (fraction of full area filled by flow) for lead times up to 20, 20, and 30 min, with an R2 greater than 0.6, respectively. Prediction accuracy declines with longer forecast horizons. GWN predicts more time steps ahead for pipes’ flow rates and junctions’ inflows, but fewer for relative water depths during peak versus non-peak periods. It is also more effective at predicting states of large pipes and connected junctions downstream, compared to smaller upstream components. GWN's accuracy improves significantly with precise rainfall nowcasting inputs. This study establishes a significant baseline for GWN's performance in predicting urban stormwater systems during rainfall events.

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.

Sectorization of a water distribution network (WDN) into district meter areas (DMAs) is considered a key strategy for an efficient management of WDNs. Typically, it involves a two-stage procedure: a clustering stage, in which the division of the WDN into clusters is performed, and a dividing stage, which involves the placement of valves and flow meters on the cluster’s boundary pipes to define the DMAs. While recently published methods attempt to enhance both the clustering and dividing stages, they fail to provide decision-making flexibility. They also neglect to consider the presence of existing valves in the WDN, which can significantly affect the evaluated implementation cost, often considered the primary decision-driving factor. This paper presents improvements to the previously introduced DeNSE method for sectorization of WDNs, aiming to address these deficiencies. The methodology consists of a clustering stage, based on the network uniformity index, and a dividing stage, in which the originally used heuristic procedure is replaced with Genetic Algorithm (GA) optimization, minimizing implementation cost. Consideration of existing valves in WDN and criteria for water supply security are also included in the dividing stage to offer a better estimate of implementation costs and post-sectorized operational efficiency of the WDN. Finally, GIS visualization is implemented, and a hydraulic model of the sectorized WDN (EPANET file) is generated, providing practitioners with valuable insights and decision-making flexibility. The methodology is tested on a part of the Amsterdam WDN in the Netherlands, serving as a pilot for methodology evaluation. A range of feasible sectorization solutions is generated and compared based on implementation cost and three performance indicators (PIs). The paper provides an in-depth discussion on the selection of preferable sectorization solution. The reported results demonstrate the method’s efficiency in optimizing sectorization solutions with minimum implementation cost whilst preserving the WDN operational efficiency and meeting the local design criteria.