Search Results (211)

Historic cities face a dual challenge of managing waterlogging risks while adhering to strict preservation constraints. Traditional drainage upgrades often require extensive excavation, threatening cultural heritage. This study establishes a quantitative assessment framework for the historic urban district of City B using a 1D-2D-coupled hydrodynamic model (InfoWorks ICM). The model was calibrated using continuous monitoring data, achieving a Nash–Sutcliffe Efficiency (NSE) of 0.91. Its spatial accuracy was subsequently validated against historical waterlogging records, showing a strong consistency between simulated flood-prone areas and observed flood locations. We simulated waterlogging distribution under rainfall events with return periods of 0.5 to 5 years. Results reveal two key deficiencies in the current drainage system under a 0.5-year return period storm event. Firstly, 75.3% of the pipe segments are hydraulically overloaded, failing to meet the design standard. Secondly, this widespread network overload contributes to surface waterlogging, with 9.58 ha (1.80% of the total area) being waterlogged. We evaluated three strategies: Low Impact Development (LID), underground storage tanks, and intercepting sewers. A hybrid grey-green infrastructure (HGGI) system was proposed, integrating source reduction and terminal storage. The HGGI system reduced waterlogged areas by 83.58% (0.5-year event) and 64.87% (5-year event), outperforming single measures. Crucially, this hybrid system achieves minimal intervention in historic street patterns through trenchless construction for intercepting sewers, decentralized LID layout and underground storage tanks, avoiding large-scale road excavation while enhancing flood resilience. This study demonstrates that hybrid strategies can effectively balance flood resilience with environmental and cultural preservation in high-density historic districts. Full article

Despite the importance of wastewater environmental monitoring in disease prevention and response strategies, its use remains poorly documented in Senegal. In addition, there is more onsite sanitation than sewer networks in Dakar, and open drains channel for rainwater are also used as clandestine wastewater discharge into the sea. This study aimed to assess the presence of specific pathogens in wastewater, faecal sludge, and bathing water (the sea). Samples were taken at treatment plants, an open drain, and in the receiving environment (the sea) from June to December 2023. Total nucleic acid was subjected to multiplex qualitative qPCR using SeeGene Allplex™ kits targeting 34 gastrointestinal pathogens. Descriptive statistics, multiple correspondence analysis (MCA) and logistic regression were performed. Considering all matrices, across 51 analysed samples, the results revealed strong bacterial (96.08%, n = 49), parasitic (84.31%, n = 43), and viral (68.63%, n = 35) presence. These results showed high levels of Aeromonas spp. (96.08%), Blastocystis hominis (80.39%), Enterocytozoon (58.82%), and Norovirus GII (74.51%) among bacteria, protozoa, helminths, and viruses, respectively. Moreover, faecal sludge and pumping station samples show more identified pathogen than wastewater treatment plant and seawater samples. The MCA revealed that the dry season is spatially associated with a greater number of pathogens than the rainy season, but the latter showed a greater species diversity. Logistic regression showed that certain physicochemical parameters, including BOD5, turbidity, pH, and suspended solids, influence pathogen detection. However, qualitative detection and sampling period may constitute limitations. These results reveal that wastewater and bathing water can serve as sources of information on the circulation of pathogens of interest with epidemic potential. Therefore, this valuable epidemiological tool could serve as an adjunct to clinical surveillance in order to prevent future epidemics. Full article

Wastewater-based epidemiology (WBE) allows for early surveillance of viral pathogens, including SARS-CoV-2. Simplified low-cost approaches are needed to deploy WBE surveillance in resource-limited small-island settings, where high sensitivity must be maintained. In this study, we optimized key upstream steps in an electronegative membrane virus adsorption–elution (VIRADEL) workflow, including sample acidification, composite sampling duration, and RT-qPCR inhibition mitigation. Wastewater influent was sampled at a pump station in Grenada using 12 h and 24 h time-weighted composite samples, concentrated using electronegative membrane VIRADEL with and without sample acidification (pH 3.5), and used Phi 6 (enveloped virus) and MS2 (non-enveloped virus) bacteriophages as process controls and PMMoV as a fecal-derived normalization target. Targets for SARS-CoV-2 N1 and a non-enveloped virus surrogate were measured by RT-qPCR. Quantitative wastewater data were compared to reported clinical cases in the community. Sample acidification significantly increased recovery of the enveloped process control, Phi 6 (p < 0.01) indicating improved efficiency in capturing enveloped viral targets during filtration. Twelve-hour composite samples had a false-negative percentage of 88%, while 24 h samples had only 6% false negatives and were able to mirror clinical case trends. Wastewater viral signals were detected 3–5 days prior to an increase in clinical cases. Hydraulic travel time within the contributing sewer network was not directly measured; therefore, the reported 3–5 day lead time reflects the combined effect of shedding dynamics, sampling integration, and sewer transport. This optimized workflow was deployed for nine months showing sustained analytical performance and operational feasibility. Full article

Predictive pest management in urban sewer networks represents a sustainable alternative to reactive, biocide-based methods. Using data collected through an IoT architecture and validated with manual inspections across eight manholes over 113 days, we implemented a rigorous comparative framework evaluating eleven data mining algorithms, including classical methods (KNN, SVM, decision trees) and advanced ensemble techniques (XGBoost, LightGBM, CatBoost) optimized for unbalanced datasets. Gradient boosting models with explicit handling of class imbalance—where the absence of pests exceeds 77% of observations—showed exceptional performance, achieving a Macro-F1 score above 0.92 and high precision in identifying the minority high-risk class. Explainability analysis using SHAP consistently revealed that elevated CO₂ concentrations are the primary predictor of infestation, enabling early identification of critical zones. This study demonstrates that carbon dioxide (CO₂) acts as the most robust bioindicator for predicting severe infestations of Periplaneta americana, significantly outperforming conventional environmental variables such as temperature and humidity. The implementation of the model in a real-time monitoring platform generates interpretable heat maps that support proactive and localized interventions, optimizing resource use and reducing dependence on biocides. This study presents a scalable, operationally viable predictive system designed for direct integration into municipal asset management workflows, offering a concrete, industry-ready solution to transform pest control from a reactive, labor-intensive process into a data-driven, proactive operational paradigm. This approach not only transforms pest management from reactive to predictive but also aligns with the Sustainable Development Goals, offering a scalable, interpretable, and operationally viable system for smart cities. Full article

Ground subsidence in urban areas often reflects hidden failures within water and sewer infrastructure, amplified by hydrologic and seasonal conditions. This study analyzes 303 documented subsidence incidents in Gyeonggi Province, South Korea, from 2018 to 2024, focusing on infrastructure-related causes and their spatial and seasonal patterns. Incident records were reviewed to identify root causes, geographic distribution, seasonal trends, and impacts, including human injury and vehicle damage. Descriptive analysis showed that sewer pipe damage (39.3%) was the leading cause, followed by poor compaction or backfilling (22.8%) and excavation-related damage (14.2%). Subsidence linked to sewer systems occurred disproportionately during the summer monsoon, highlighting interactions between rainfall, pipe deterioration, and soil erosion. Statistical analysis using the Extended Fisher’s Exact Test revealed significant associations between subsidence causes and seasonality, vehicle damage, and regional location, but no significant link with human injury. Defective pipe construction contributed to elevated regional vulnerability, particularly in eastern municipalities, while excavation-related incidents were more common in spring. These results underscore the need for seasonally adaptive inspections, targeted rehabilitation of aging water and sewer networks, and region-specific asset management. By connecting subsurface failures with hydrologic conditions and infrastructure performance, this study offers data-driven insights to enhance proactive water infrastructure management and urban resilience. Full article

To manage sewer networks, reliable data is needed, which is often challenging. This study proposes a novel methodology to reconstruct the sewer network topology using graph theory. Two core procedures—flow adjustment and edge addition—re-establish hydraulically consistent flow paths and restore connectivity in disconnected portions of the network by reversing and adding links. The proposed approach operates at the pipe level, repairing directional reachability. It leverages only the existing network topology to reconstruct connectivity, guided by the principle that every node must have a downstream path to an outlet. The methodology is first applied to reconstruct the sewer network of Montpellier Metropolis in the South of France. Then it is validated by deliberately removing and reversing edges and applying the algorithms to test the methodology’s capability in recovering the correct topology. Both methods performed well individually, especially at lower percentages of reversal (1%) and removal (1%), with a correctness of 0.99 for flow adjustment and 0.8 for edge addition. Although the results were poorer when combining the methods and increasing data degradation—particularly at 10% reversal and 10% removal (correctness of 0.64)—the methodology continued to produce a functionally consistent and logically coherent network, highlighting its robustness given the absence of supporting attribute data. Full article

Understanding the seasonal behaviour of sewer failures is essential for infrastructure reliability and sustainable asset management. This study presents a seasonality-centred, data-driven analysis of monthly sewer failures over a 15-year period (2010–2024) in a major city in south-eastern Poland. The assessment is based exclusively on operational failure records, allowing intrinsic temporal regularities to be extracted without the use of external meteorological covariates. Seasonal Decomposition of Time Series by LOESS (STL), Autocorrelation Function (ACF), Seasonal Index (SI) and the Winter–Summer Index (WSI) were applied to quantify periodicity, seasonal amplitude and long-term variability. The results confirm a pronounced annual cycle, with failures peaking around March and reaching minima in September, supported by a strong autocorrelation at a 12-month lag (r ≈ 0.45). The mean WSI value (1.05) indicates a nearly balanced but still winter-sensitive pattern, while annual WSI values ranged from 0.71 to 1.51. The STL seasonal amplitude remained structurally stable at ≈61 failures throughout the study period, while annual values showed a modest but statistically significant increasing tendency. Trend analysis showed no significant monotonic trend in the deseasonalized series (Z ≈ 0.89, p = 0.37), whereas the raw series exhibited a weak but significant upward trend (τ ≈ 0.33, p < 0.001), largely attributable to short-term operational variability rather than to changes in intrinsic failure rate. The study demonstrates that long-term operational data alone are sufficient to capture seasonal and long-term dynamics in sewer failures. The presented framework supports utilities in integrating seasonality diagnostics into preventive maintenance, resource allocation and resilience planning, even in the absence of detailed climatic datasets. Full article

The long-distance transport of wastewater in sewers inevitably leads to the formation of biofilms on the inner wall of sewers. Numerous studies have focused on analyzing the hydrogen sulfide, methane production, and emission patterns associated with sewer biofilms in sewer systems. This study employed bibliometric methods to analyze the research progress in the field of sewer biofilms from 1995 to 2025, and revealed the associated development trend, international cooperation network, and research hotspots. The results demonstrate a substantial increase in the number of annual publications over the past decade, with China and Australia as the primary contributors. The journal Water Research has been found to exert a significant influence. The research hotspots concentrate on the generation and control of hydrogen sulfide and methane, sewer corrosion mechanisms, and microbial community dynamics, with chemical dosing, sulfate-reducing bacteria, and biofilm metabolism as the key directions. The evolution of keywords demonstrates that early research focused on organic matter transformation, and in recent years, there has been a shift towards microbial ecology and wastewater epidemiology, along with other emerging areas. Recent years have seen China as well as China’s institution and authors emerge as the primary contributors in the sewer biofilm field, a development attributable to the country’s policy support, which has precipitated the development of green technologies and smart monitoring systems. This study demonstrates the necessity of international cooperation and provides theoretical references and technological directions for future sewer biofilms research. Full article

The water quality characteristics of urban stormwater in a small town (La Almunia, 8000 inhabitants) in Northeast Spain with a combined sewer system have been studied. A specific device was designed to collect stormwater just before it enters the drainage network at five different points in the urban area, thus obtaining an approximate calculation of the mean event concentration values for the surface runoff generated during eight rainfall episodes. The results obtained demonstrated a high variability in the average concentrations of the events. The highest measured values corresponded mainly to the periods of the greatest road traffic from agricultural machinery within the town (harvest and manure seasons), resulting in peaks mainly in electrical conductivity and dissolved oxygen demand. This finding has been confirmed by the spatial study of the results, since the maximum values of these parameters were located in those areas of preferential transit of agricultural machinery; in addition, a possible relationship has also been observed between the maximum values of nitrogen and phosphorus in stormwater and older urban areas, due to the washing of bird droppings accumulated on the roofs. In general, all obtained results indicate that the stormwater samples generated in La Almunia present a low contaminant load, with the mean concentration event values calculated for half of the events falling within the discharge limit values established by the European Union. This fact, combined with the spatial and temporal location of the highest levels of stormwater pollution, helps evaluate urban cleanup operations and the operational capacity of both the urban drainage network and the wastewater treatment plant. Full article

Municipal sewer networks span across large areas in cities around the world and require regular inspection to identify structural failures, blockages, and other issues that pose public health risks. Traditional inspection methods rely on remote-controlled robotic cameras or CCTV surveys performed by skilled inspectors. These processes are time-consuming, expensive, and often inconsistent; for example, the United States alone has more than 1.2 million miles of underground sewer pipes, and up to 75,000 failures are reported annually. Manual CCTV inspections can only cover a small fraction of the network each year, resulting in delayed discovery of defects and costly repairs. To address these limitations, this paper proposes a scalable and low-power fault detection system that integrates embedded machine vision and Tiny Machine Learning (TinyML) on resource-constrained microcontrollers. The system uses transfer learning to train a lightweight TinyML model for defect classification using a dataset of sewer pipe images and deploys the model on battery-powered devices. Each device captures images inside the pipe, performs on-device inference to detect cracks, intrusions, debris, and other anomalies, and communicates inference results over a long-range LoRa radio link. The experimental results demonstrate that the proposed system achieves 94% detection accuracy with sub-hundred-millisecond inference time and operates for extended periods on battery power. The research contributes a template for autonomous, scalable, and cost-effective sewer condition assessment that can help municipalities prioritize maintenance and prevent catastrophic failures. Full article

Over the past five decades, the Tallet Alsauda district of Aleppo (Syria) has experienced multiple catastrophic collapses, attributed to a network of subsurface chalk cavities formed through historic quarrying and possible natural karstification. Yet, no comprehensive investigation has previously been conducted to characterise the cavities or clarify the governing failure mechanisms. Such assessments are particularly difficult in historic urban environments, where void geometries are irregular, subsurface data scarce, and underground access limited. This study addresses these challenges through an integrated programme of fourteen boreholes, laboratory testing, and inverse-distance interpolation to reconstruct subsurface geometry and overburden thickness. These data-informed three-dimensional finite element simulations are designed to test the hypothesis that chalk deterioration, driven by both natural and anthropogenic processes, controls the instability of cavity roofs. Rock mass parameters, particularly the Geological Strength Index (GSI), were progressively reduced and evaluated against the site’s documented collapse history. The simulations revealed that a modest decline in GSI from ~53 to 47 precipitated abrupt displacements (>300 mm) and upward-propagating plastic zones, consistent with field evidence of past collapses. These results confirm that instability is governed by threshold reductions in material strength, with sewer leakage identified as a principal trigger accelerating chalk softening and roof destabilisation. Full article

The inspection and maintenance of urban sewer infrastructure remain critical challenges for megacities, where conventional manual inspection approaches are labor-intensive, time-consuming, and prone to human error. Although deep learning has been increasingly applied to sewer inspection, the field lacks both a publicly available large-scale dataset and a systematic evaluation of CNN and transformer-based models on real sewer footage. The primary aim of this study is to systematically evaluate and compare state-of-the-art deep learning architectures for automated sewer defect detection using a newly introduced dataset. We present the Istanbul Sewer Defect Dataset (ISWDS), comprising 13,491 expert-annotated images collected from Istanbul’s wastewater network and covering eight defect categories that account for approximately 90% of reported failures. The scientific novelty of this work lies in both the introduction of the ISWDS and the first systematic benchmarking of YOLO (v8/11/12) and RT-DETR (v1/v2) architectures under identical protocols on real sewer inspection footage. Experimental results demonstrate that RT-DETR v2 achieves the best performance (F1: 79.03%, Recall: 81.10%), significantly outperforming the best YOLO variant. While transformer-based architectures excel in detecting partially occluded defects and complex operational conditions, YOLO models provide computational efficiency advantages for resource-constrained deployments. Furthermore, a QGIS-based inspection tool integrating the best-performing models was developed to enable real-time video analysis and automated reporting. Overall, this study highlights the trade-offs between accuracy and efficiency, demonstrating that RT-DETR v2 is most suitable for server-based processing. In contrast, compact YOLO variants are more appropriate for edge deployment. Full article

In the context of global carbon neutrality, municipal wastewater treatment plants (WWTPs), as key sources of greenhouse gas emissions, urgently require quantification of carbon emissions and implementation of mitigation strategies. This study establishes a life-cycle carbon footprint model encompassing the stages of pretreatment, biological treatment (AAO process), and sludge treatment, with integrated consideration of municipal sewer networks. Key findings reveal the following: The biological treatment stage contributes 68.14% of total carbon emissions. N₂O (nitrous oxide), due to its high global warming potential (GWP), is the primary source of direct emissions (0.333 kg CO₂eq/m³). In the pretreatment stage, 80.4% of carbon emissions originate from the electricity consumption of sewage lifting pump stations (0.030 kg CO₂eq/m³). During the sludge treatment stage, carbon emissions are concentrated in residual sludge lifting (0.0086 kg CO₂eq/m³) and sludge dewatering/pressing (0.0088 kg CO₂eq/m³). Accordingly, this study proposes the following mitigation strategies: novel nitrogen removal processes should be implemented to optimize aeration control and enhance methane (CH₄) recovery during the biological period, and variable frequency drive (VFD) pumps and IoT (Internet of Things) technologies should be employed to reduce energy consumption during the pretreatment period, and during the sludge treatment period, low-carbon dewatering technologies should be adopted. This work provides a theoretical foundation for process-specific carbon management in WWTPs and facilitates the synergistic advancement of environmental stewardship and dual-carbon objectives through technology–system integration. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

This study aims to analyse the effect of partially clogged inlets on the behaviour of urban drainage systems at the city scale, particularly regarding intercepted volumes and flood depths. The main challenges were to represent the inlet network in detail at a rather large scale and to avoid the effect of sewer network surcharging on the draining capacity of inlets. This goal has been achieved through a 1D/2D coupled hydraulic model of the whole urban drainage system in La Almunia de Doña Godina (Zaragoza, Spain). The model focuses on the interaction between grated drain inlets and the sewer network under partial clogging conditions. The model is fed with data obtained on field surveys. These surveys identified 948 inlets, classified into 43 types based on geometry and grouped into 7 categories for modelling purposes. Clogging patterns were derived from field observations or estimated using progressive clogging trends. The hydrological model combines a semi-distributed approach for micro-catchments (buildings and courtyards) and a distributed “rain-on-grid” approach for public spaces (streets, squares). The model assesses the impact of inlet clogging on network performance and surface flooding during four rainfall scenarios. Results include inlet interception volumes, flooded surface areas, and flow hydrographs intercepted by single inlets. Specifically, the reduction in intercepted volume ranged from approximately 7% under a mild inlet clogging condition to nearly 50% under severe clogging conditions. Also, the model results show the significant influence of the 2D mesh detail on flood depths. For instance, a mesh with high resolution and break lines representing streets curbs showed a 38% increase in urban areas with flood depths above 1 cm compared to a scenario with a lower-resolution 2D mesh and no curbs. The findings highlight how inlet clogging significantly affects the efficiency of urban drainage systems and increases the surface flood hazard. Further novelties of this work are the extent of the analysis (city scale) and the approach to improve the 2D mesh to assess flood depth. Full article