Water-energy-carbon nexus and de-carbonation pathways in integrated urban water system for a megacity study.

Climate change, growing urban pollution, and increasing water scarcity are forcing cities to adopt strategies that enhance resilience to both climatic and anthropogenic pressures. The Barcelona Superblock model is a novel urban planning strategy that highly restricts vehicle traffic, converts streets into pedestrian-priority corridors, and promotes green spaces. Within this framework, green infrastructures, also called sustainable urban drainage systems (SUDS), are implemented as local, site-specific measures to capture (i.e., flood control), treat, and infiltrate treated stormwater (i.e., aquifer recharge). To evaluate the Superblock model impact on urban water quality, we conducted seven sampling campaigns across three Barcelona districts, targeting rainfall, stormwater "first-flush" from roads and pedestrianized streets, as well as SUDS influent and effluent within and in the vicinity to Superblocks, with a focus on dissolved trace metals and dissolved organic matter (DOM). Results showed that Superblocks reduced pollutant loads and mitigated ecotoxicological risks. Trace metal and DOM concentrations followed the trend: Rain < SUDS effluent < Pedestrian Street runoff < Road runoff, highlighting traffic-related impacts and SUDS treatment capacity (23-70% in best case scenario). Risk assessment indicated episodic ecotoxicological risk in stormwater, especially in road runoff due to elevated concentrations of Cu and Zn, while SUDS consistently remained below risk thresholds. SUDS also transform DOM into more stable, humic-like forms. Trace metals and DOM emerged as biogeochemical proxies for stormwater quality, enabling more effective and sustainable urban water management. These findings support the integration of Superblock-like strategies into urban planning to control and reduce contaminant urban discharges.

Development and validation of an online SPE-HPLC-MS/MS for tire-derived pollutants of environmental concern (6PPD and 6PPD-Q): Detection and rainfall-driven dynamics in an urban river-estuary continuum.

Green roofs play a vital role in urban climate adaptation by reducing heat stress and stormwater runoff. However, their effectiveness is highly dependent on substrate moisture. During dry periods, extensive green roofs rapidly dry out, significantly diminishing their cooling capacity. This restricts plant selection to drought-tolerant species such as succulents, which offer only limited evaporative cooling potential and biodiversity benefits. Supplemental irrigation can enhance green roof cooling performance and may allow the cultivation of more diverse and transpiring vegetation. However, excessive or poorly timed irrigation may reduce the system's capacity to retain stormwater. In this study, a novel smart irrigation system was developed, which is based on the assimilation of weather forecast data into a hydrological model to allow for demand-driven water supply. The aim of the smart irrigation management is to avoid water stress for plants and provide cooling only on warm days, while targeting to maximize the retention capacity before rainfall events to achieve the effective interaction of multiple ecosystem services. The performance of the smart irrigation system is compared to conventional irrigation approaches relying on fixed intervals or simple soil-moisture threshold controls. Irrigation substantially enhanced daytime surface cooling, reducing surface temperatures by up to 6.85K compared to the non-irrigated roof. While irrigated green roofs offered thermal regulation on warm days, the non-irrigated green roof tended to exhibit even higher surface temperatures than a conventional gravel roof. Hydrologically, the smart irrigation system required lower amounts of irrigation compared to timer and sensor-based irrigation regimes (reduction by 46.3% and 23.5%, respectively) without negatively affecting plant vitality and showed notably better average runoff reduction performance for heavy precipitation events (86.3% vs. 68.4% and 79.5%) This study demonstrates that novel smart irrigation routines for extensive green roofs have the potential to enhance the contribution to urban microclimate regulation and sustainable water management.

• Comprehensive feasibility studies offer an in-depth analysis of any potential barriers to technology deployment and integration. • Successful deployment of VIV-EH enhances real-time water system monitoring by providing reliable power in remote areas. • High correlation between environmental impacts and energy generation output determined by the fluid velocity. • A solution like VIV-EH offers a low-impact renewable energy generation solution suitable for harnessing energy at low velocity. With today’s focus on the transition towards a cleaner energy future, interest is growing in exploring ways to utilise 10 TWh of hidden energy potential in EU water infrastructure. Vortex-Induced Vibration Energy Harvesters (VIV-EHs) offer an innovative approach to harnessing untapped hydropower potential in urban and rural water systems, thereby supporting the transition to green energy. These devices enable decentralised energy generation, support monitoring systems in critical infrastructure, and reduce reliance on fossil fuel-powered backups. However, their economic and environmental feasibility must be carefully assessed to ensure viable deployment and integration. This study introduces a Feasibility Assessment Framework to evaluate the technical, economic, and environmental aspects of VIV-EHs, with a particular focus on do-it-yourself (DIY) design. A case study in the Mestna Gradaščica River channel in Ljubljana, Slovenia, was assessed utilizing experimental data, computational modelling, and life cycle analysis. Two configurations of the DIY VIV-EH, one with a 49 mm cylinder diameter and another with a 61 mm diameter, were evaluated for energy output, costs, and emissions. The results demonstrate that the 49 mm configuration achieved a capacity factor of 94%, generating between 131.30 kWh and 406.8 kWh over a 12-year lifespan. In comparison, the 61 mm configuration produced between 164.7 kWh and 311.0 kWh, with greater stability across a variety of velocities. The Levelized Cost of Energy (LCOE) remains high, averaging 6.5 €/kWh, indicating potential for cost reduction through optimisation. Environmental impacts were moderate, with lifecycle emissions ranging from 0.071 to 0.221 kgCO2eq/kWh, depending on velocity and configuration. These findings demonstrate that overall VIV-EHs have promise in powering remote monitoring sensors, enhancing the resilience of water and energy systems, and reducing dependence on diesel generators. Future research should focus on enhancing device efficiency and minimizing manufacturing impacts to facilitate their wider adoption as a sustainable energy solution

Combined sewer overflows (CSO) are a significant source of urban water pollution, with sediments originating from sewers contributing substantial loads of organic matter, nutrients, and particulate-associated contaminants to receiving waters. In Germany, constructed wetlands for CSO treatment (CSOCWs) are well-established, yet their large surface area limits their implementation in urban areas. The present study investigates the potential of compact CSOCWs, operated at doubled flow rate, with tailored filter substrates and the addition of biopolymers to enhance treatment performance. A series of flocculation experiments was first conducted to identify a suitable biopolymer for CSO treatment. Subsequently, small-scale pilot column tests were performed to evaluate compact CSOCW configurations. Here, different filter substrates were investigated, including sand, sand-gravel, gravel, shells, sand-activated carbon, and sand-zeolite, with and without the selected biopolymer introduced into the inflow to assess its effect on treatment performance. Results show that compact CSOCWs with suitable substrates and increased flow rates achieved removal efficiencies comparable to the reference CSOCW in standard operation, exhibiting only minor deviations. Sorptive and fine-grained substrates (activated carbon, zeolite) demonstrated high performance for particulates, COD, DOC, and NH4N, respectively. Biopolymer dosing significantly enhanced particle and COD retention in coarse filter substrates (gravel, shells), thereby reducing performance differences across configurations. In contrast, phosphorus removal remained limited across all configurations, independent of biopolymer dosing. These findings demonstrate that compact CSOCWs can provide a robust treatment option for CSOs and urban runoff under increased hydraulic loading, particularly in densely populated areas.

Spatiotemporal variation in radiocesium concentrations under low-flow conditions in urban water bodies in Fukushima Prefecture, Japan.

Urban rivers receiving multiple wastewater inputs require reliable diagnostic tools to disentangle overlapping nitrogen sources. Here, we investigate wastewater source attribution in a densely urbanized basin using isotopic tracers together with diagnostic indicators based on contaminants of emerging concern (CECs), rather than CEC occurrence alone. Monthly water sampling over 2yearsat five stations in an urban river system included nitrate isotopes, conventional hydrochemistry, and a broad suite of CECs. Nitrate concentrations reached up to 18.3mgL-1 as NO3--N, and δ15N-NO3- values ranged from +6.1 ‰ to +24.2 ‰, consistent with variations in total nitrogen loads along the river continuum. Although isotope signals indicated increasing wastewater influence downstream, isotopic information alone was insufficient to distinguish between human and animal derived wastewater sources. The integration of nitrate isotopes with CEC-derived diagnostic indices, including the Human Wastewater Index (HWI) and the Hospital Index (HI), within a Bayesian mixing framework based on characterized source signatures revealed that human wastewater contributions exceeded 60 % at all monitoring stations. Elevated HI values further indicated hospital wastewater inputs that were not detectable using isotopic variables alone. An ecological risk assessment indicated that, in 70 % of the samples, the combined risk from detected CECs was high, with over half of the individual compounds present at concentrations exceeding their respective acute toxicity thresholds. These results demonstrate that CEC-based diagnostic indicators provide complementary information to isotopic tracers and substantially improve wastewater source attribution in complex urban river systems, with direct implications for monitoring and management of urban water quality.

Influence of customer satisfaction and perceived water service quality on urban water supply management: A systematic review

Green AI architectures: Navigating the security-sustainability paradox in critical infrastructure protection.

System dynamics-based dynamic evaluation of value synergy and effectiveness verification of governance strategies for urban water conservancy projects

Metagenomic analysis of urban water systems uncovers the interplay between antibiotic resistance genes and microbial communities in response to PFAS contamination.

The total availability of water resources is currently under stress due to climatic changes, and continuous increase in water demand linked to the global population increase. A Smart Water management is a two-way real time network with sensors and devices that continuously and remotely monitor the water distribution system. Smart water meters can monitor many different parameters such as pressure, quality, flow rates, temperature, and others. Existing situation of water supply system of new north zone of Surat city is studied by collecting secondary data and analysis of it. Infrastructure leakage index was found for study area using benchleak software and SWOT analysis is performed for deriving strength weakness opportunity and strength of system.

Response of urban lake water quality to monthly hydro-meteorological drivers at the catchment scale.

Urban cities have been intensely prone to floods during extreme rainfall events and water scarcity issues during dry periods in recent years. In this context, identifying rainwater harvesting potential (RWHP) regions in urban environments provides a sustainable approach to mitigate both urban flooding and water security, thereby improving urban stormwater management. Geospatial mapping of RWHP has tried to consider various hydrometeorological, topographical and other geospatial datasets, but integrating socio-economic factors over urban environments has not been explored much. The present study integrated remote sensing and hydrological-based information, such as slope, soil type, drainage density, geomorphology, topographic wetness index (TWI), land use land cover (LULC), rainfall, runoff coefficient, proximity to roads, and proximity to settlements for geospatial mapping of RWH potential zones for Hyderabad city using multi-criteria decision analysis (MCDA) and weighted overlay analysis (WOA). The resulting RWH potential map indicates that 80.20% of the area falls within the “low” potential category, 17.53% as “moderate”, 2.0% as “very low”, and only 0.25% as “high” potential, mainly in the southeastern portion near the Hussain Sagar outlet. These categories are spatially verified using Sentinel-2 LULC and Google Earth imagery to assess the qualitative plausibility of the mapped RWH potential zones. Northwestern areas, with loamy soils and mild slopes, demonstrate suitability for rooftop collection and percolation structures, highlighting the effectiveness of the proposed modelling framework for sustainable stormwater management for urban environments.

Toward Autonomous Planning and Management of Urban Water Systems

Abstract Constantine Province, located in the semi-arid region of north-eastern Algeria, is experiencing increasing water resource stress driven by rapid urban expansion, population growth and intensifying climate change impacts. By 2025, the population is projected to reach approaximately 1.2 million inhabitants, while the average daily production of drinking water production capacity is estimated at 355,000 m 3 . The annual population growth rate is estimated at 1.5%, whereas urban water demand is projected to increase by approaximately 2% annually over the coming decades. Despite major infrastructural investments, particularly the mobilisation of water from the Béni Haroun Dam, persistent spatial disparities in water distribution, high leakage rates and management inefficiencies continue to undermine water security across municipalities. Projections indicate that, by 2050, daily water demand could reach approaximately 440,000 m 3 to supply a population of nearly 1.76 million, thereby intensifying pressure on existing infrastructure and available resources. Without targeted interventions, daily losses could exceed 100,000 m 3 , which would seriously affect the region’s water balance and resilience. Using an integrated analytical approach to Water Resources Management (IWRM), climate adaptation and urban sustainability, this study evaluates the key challenges facing Constantine’s urban water system and proposes strategic pathways to ensure long-term water security.

The safe and stable operation of urban water supply networks is critical to ensure urban functionality and sustainable economic development. However, the frequent occurrence of pipeline damage incidents highlights the limitations of traditional monitoring methods based on water quality, flow rate, and pressure. This study employs a wireless monitoring system that overcomes the constraints of traditional monitoring approaches by collecting real-time multi-source data on pipeline structure and operating environments. Support vector regression, feedforward neural networks, and physics-informed machine learning model (PIML) were used to quantitatively analyze the impact of various environmental factors on pipeline structural deflection angles and to train a high-accuracy machine learning prediction model. The results reveal significant variations in earth pressure, soil structure, temperature at the pipe crown and invert, and pore water pressure during pipeline operation, reflecting characteristics of backfilling, foundation settlement, and groundwater dynamics. Machine learning models trained on the monitoring data exhibited outstanding predictive accuracy, with PIML achieving the highest performance—showing an R 2 of 0.985 and a 96.9% overlap between predicted and actual distributions. Furthermore, interpretative analyses identified soil structure variation and burial depth as the primary driving factors influencing pipeline structural deflection angles. Building on this, monitoring strategies can be optimized to provide robust support for improving the safety and operational efficiency of urban water supply pipeline systems.

The pervasive occurrence of microplastics (MPs) in aquatic environments presents growing challenges for environmental monitoring. Conventional MP detection methods often require extensive pretreatment and struggle to differentiate mixed polymer compositions in complex matrices. Here, we present a pretreatment-free analytical method that integrates an electrostatically functionalized surface-enhanced Raman scattering (SERS) substrate with an interpretable deep learning framework. A hierarchically porous gold sponge modified with poly(diallyldimethylammonium chloride) facilitates efficient electrostatic enrichment and size-selective capture of negatively charged MPs, while embedded gold nanoparticles generate plasmonic hotspots for enhanced Raman signal amplification. A modular binary convolutional neural network framework (CNN) employing a one-vs-rest architecture enables accurate and interpretable classification of five representative MPs, i.e., polytetrafluoroethylene, polypropylene, polystyrene, polyvinyl chloride, and polyethylene terephthalate, achieving a precision of 0.9896 within the evaluated Raman data set. Gradient-weighted class activation mapping (Grad-CAM) analysis highlights key Raman bands characteristic of each polymer type, e.g., the C-C vibrations in the benzene ring of PET, supporting the chemical interpretability of the CNN model. The system was validated in urban tap water and natural surface waters, which represent both low-interference and heavy-metal-impacted complex matrices. This integrated platform provides a sensitive and adaptable approach for pretreatment-free identification of MPs in complex water matrices, demonstrating its potential for practical environmental analysis.

Multi-objective Optimization of Low Impact Development (LID) Facilities Spatial Layout for Urban Water Management