Search Results (421)

In Ecuador, the transmission of dengue has steadily increased in recent decades, particularly in coastal cities like Manta, where the conditions are favorable for the proliferation of the Aedes aegypti mosquito. The objective of this study was to model the spatial distribution of dengue transmission risk in Manta, a coastal city in Ecuador with consistently high incidence rates. A total of 148 georeferenced dengue cases from 2018 to 2021 were collected, and environmental and socioeconomic variables were incorporated into a maximum entropy model (MaxEnt). Additionally, climate and social zoning were performed using a multi-criteria model in TerrSet. The MaxEnt model demonstrated excellent predictive ability (training AUC = 0.916; test AUC = 0.876) and identified population density, sewer system access, and distance to rivers as the primary predictors. Three high-risk clusters were identified in the southern, northwestern, and northeastern parts of the city, while the coastal strip showed lower suitability due to low rainfall and vegetation. These findings reveal the strong spatial heterogeneity of dengue risk at the neighborhood level and provide operational information for targeted interventions. This approach can support more efficient surveillance, resource allocation, and community action in coastal urban areas affected by vector-borne diseases. Full article

One of the limiting factors in the implementation of water resource management is the absence of tools that help water programs evaluate processes and progress. This is because, until now, the indicators that have been developed have not addressed specific local characteristics and issues. Therefore, in this research, a set of indicators has been proposed, with the purpose of developing a management index for urban public water supply, which will consider the Drinking Water and Sewer System of León (SAPAL), in the Mexican state of Guanajuato, as case study. This index will be useful to measure progress toward sustainable development, monitor the impact of public policies, and foster citizen participation. In order to propose a methodology that aligns with the changing environments, where proper decision-making is key to the current water management requirements, the combination of the Analytic Hierarchy Process (AHP) and Fuzzy Logic (FL) methodologies will be helpful for proper decision-making. All this will foster a paradigm shift towards appropriate water management actions that allow for the conditions and availability of human and natural resources, which the municipality has control of, for a long-term improvement that guarantees the well-being of the population. 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

Sustainable management of aquatic ecosystems requires effective strategies to monitor and mitigate microplastic pollution, particularly in vulnerable tidal river systems. Microplastic accumulation in these environments poses significant environmental risks, threatening biodiversity, ecosystem health, and long-term water quality. This study employs a three-dimensional hydrodynamic model (TELEMAC-3D—v8p5) coupled with a Lagrangian particle tracking model (CaMPSim-3D—v1.2.1) to simulate microplastic transport dynamics in the lower Fraser River, British Columbia, Canada. The model incorporates tidal forcing, riverine hydrodynamics, and mixing processes, and was validated with good agreement against observed water levels. This model provides a high-resolution representation of microplastic dispersion under varying release scenarios, including emissions from combined sewer overflows (CSOs) and wastewater treatment plants (WWTPs). A novel approach is proposed to identify microplastic accumulation zones using the OPTICS (Ordering Points to Identify the Clustering Structure) clustering algorithm. Accumulation zone locations remain spatially consistent despite variations in release volume. Persistent clusters occurred near channel constrictions and shoreline segments associated with flow deceleration. These findings demonstrate the robustness of the method and provide a systematic framework for prioritizing high-risk areas, supporting targeted monitoring and informing sustainable estuarine management. Full article

Pressure on fresh water resources has been aggravated in recent decades, basically due to population growth, rapid urbanization, and global warming. Integrated engineering solutions and the circular economy, considering the urban water cycle as a whole, are becoming fundamental, particularly in arid and semi-arid regions under permanent or recurrent hydric deficit. This study aims to develop and present an integrated engineering solution for water supply, wastewater collection, and treated wastewater reuse for landscape irrigation in a large, topographically complex, and arid to semi-arid coastal urban region at the south of Santiago Island, Cape Verde. The region is one of the driest and most arid of the Island, with a current average annual precipitation between about 100 and 200 mm, and has very limited underground water resources. The main study area, with about 600 ha, has altitudes ranging from values close to sea level up to about 115 m and has several topographic difficulties, including several relatively rugged zones. The devised water supply system considers four altimetric distribution levels, three main reservoirs connected to each other by a serial system of pipelines with successive pumping, a fourth downstream reservoir for pressure balance in one of the levels, and desalinated water as the source. The sanitary sewer pipes of the urbanizations drain to an interceptor system that operates predominantly in open channel flow in a closed pipe. The long interceptor crosses laterally along the coast several very dug valleys in the path to the Praia Wastewater Treatment Plant in the east, and requires several conduits working under pressure for the crossings, either lifting or governed by gravity. The under-pressure pipeline system of recycled water is partially forced and partially ruled by gravity and transports the treated wastewater from the plant in the opposite direction of the interceptor to a natural reservoir or lake located in the region of urbanizations and the main green spaces to be irrigated. The conceived design of the interceptor and recycled water pipeline minimizes the construction and operation costs, maximizing their hydraulic performance. 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

Urban flooding and the management of stormwater present significant challenges that necessitate innovative and sustainable solutions. This research examines the effectiveness of green stormwater infrastructure (GSI) for resilient storm sewer systems using the Storm Water Management Model (SWMM), based on customized local climate scenarios. Daily climate data downscaled by four CMIP6 models—CESM2, GFDL-CM4, GFDL-ESM4, and NorESM2-MM—was used. The daily data was disaggregated into 15 min temporal resolution using the HyetosMinute R-package. Two GSI types—bio-retention and rain gardens—were evaluated with a maximum coverage of 30%. The analysis focuses on two future climate scenarios, SSP2-4.5 and SSP5-8.5, predicted under the Shared Socioeconomic Pathways (SSPs) framework. The performance of the stormwater network was assessed for mid-century (2041–2060) and late century (2081–2100), both before and after integration of GSI. Three performance metrics were applied: node flooding volume, number of nodes flooded, and pipe surcharging duration. The simulation results showed an average reduction in flooding volumes ranging between 86 and 98% over the area after integration of GSI. Similarly, reductions ranging between 78 and 89% and between 75 and 90% were observed in pipe surcharging duration and number of nodes vulnerable to flooding, respectively, following GSI. These findings underscore the potential of GSI in fostering sustainable urban water management and enhancement of sustainable development goals (SDGs). Full article

Urban sewer conditions assessment is important for the proper conveyance of sanitary water to wastewater treatment plants prior to environmental discharge. An effective approach to address this important process needs to be developed. This paper presents a data-driven methodology for sewer condition assessment with gridding-based chemical markers measurement in combination with a Bayesian chemical mass balance (CMB) model. A field study was performed in an urban sewer in Nanjing, China, to test the robustness of the developed methodology. In this site, data library of chemical markers (total nitrogen, phosphate, chloride, and total hardness) for source flows, including domestic wastewater, commercial wastewater and groundwater, was established. Meanwhile, a gridding-based measurement of these chemical markers in sewer flows was performed along the assessed sewer. Then, the CMB model with Bayesian inference and parallel Markov Chain Monte Carlo simulations was developed to quantify source contributions in sewer flows based on the chemical markers data of source and sewer flows. Accordingly, the proportion of clean water infiltration into the sewer and associated sewer defect level can be assessed. The Bayesian CMB model presented that groundwater contributed 11~14% of the sewer flow, indicating a neglectable sewer defect condition. The sewer assessment result was further verified by on-site physical inspection with distributed temperature sensing of in-sewer flows, proving the reliability of the developed methodology. Using this data-driven approach, a preliminary screening of the high-risk sub-catchments with severe sewer defect levels can be made for the following targeted sewer defects locations, optimizing the labor-intensive, system-wide physical inspections. Therefore, the proposed approach offers a cost-effective solution for system-wide sewer inspections. Full article

Urban estuarine environments face increasing water safety risks due to microbial contamination from combined sewer overflows (CSOs), particularly during heavy rainfall events. In megacities like Tokyo, where waterfronts are widely used for recreation, such contamination poses significant public health risks. The challenge is compounded by the variability in both intensity and spatial distribution of rainfall across the catchment, combined with complex tidal dynamics making effective water quality management difficult. To address this challenge, we conducted a series of hydrodynamic–microbial fate simulations to examine the spatial and vertical behavior of Escherichia coli (E. coli) under different rainfall–tide conditions. Focusing on the Sumida River estuary, rainfall data from eight drainage areas were classified into six event types using cluster analysis. Two contrasting events were selected for detailed analysis: a light rainfall (G2, 15 mm over 13 h) and an intense event (G6, 272 mm over 34 h). Vertical water quality profiling was performed along an 8.5 km transect from the Kanda–Sumida River confluence to the Tokyo Bay Tunnel, illustrating E. coli and salinity. The results showed that the rainfall intensity and tidal phase at the event onset are critical in shaping both the magnitude and vertical distribution of microbial contamination. The intense event (G6) led to deep microbial intrusion (up to 6–7 m) and major salinity disruption, while the lighter event (G2) showed surface-layer confinement. Salinity gradients were more strongly affected during G6, indicating freshwater intrusion. Tidal phase also influenced transport: the flood-high condition retained E. coli, whereas ebb-low tides facilitated downstream flushing. These findings highlight the influence of rainfall intensity and tidal timing on microbial distribution and support the use of vertical profiling in estuarine water quality management. They also support the development of dynamic, event-based water quality risk assessment tools. With appropriate local calibration, the modeling framework is transferable to other urban estuarine systems to support proactive and adaptive water quality management. Full article

Wastewater can serve as both a source of pathogens that pose risks to human health and a valuable resource for tracking and predicting disease prevalence through wastewater-based surveillance (WBS). In WBS for SARS-CoV-2, both nucleocapsid-specific (N1 and N2) and the envelope (E) genes are common targets for primer design, but ambiguity remains regarding differences in results depending on the gene target chosen. This study investigated how and why two SARS-CoV-2 gene targets (N2 and E) varied when analyzed in a multiplex RT-ddPCR assay for a COVID-19 wastewater monitoring study. From December 2021 to June 2022, over 700 raw wastewater samples were collected from thirteen manholes in the University of California, Irvine sewer system. Murine hepatitis virus (MHV) was used as a matrix recovery and process control in the triplex RT-ddPCR assay. Water quality tests (TSS, COD, pH, turbidity and NH₃-N) were performed on all samples. Analyses showed that in over 10% of samples, the E gene concentration exceeded N2 by more than one order of magnitude. To evaluate matrix effects on amplification efficiency for N2 and E genes, multiple regression analysis was performed to explore whether water quality variables and MHV recovery efficiency could predict variance in gene concentrations, but no clear relationship was identified. However, viral recovery, as indicated by MHV recovery efficiency, was negatively impacted in samples with higher TSS and COD, suggesting PCR inhibition. These findings contribute to methodological standardization efforts in WBS and emphasize the importance of primer selection for large-scale monitoring. Full article

This study investigates the capability of Gaussian process regression (GPR) models in the probabilistic forecasting of water flow and depth in a combined sewer system. Traditionally, deterministic methods have been implemented in sewer flow forecasting and anomaly detection, two crucial techniques for a good wastewater network and treatment plant management. However, with the uncertain nature of the factors impacting on sewer flow and depth, a probabilistic approach which takes uncertainties into account is preferred. This research introduces a novel use of GPR in sewer systems for real-time control and forecasting. To this end, a composite kernel is designed to capture flow and depth patterns in dry- and wet-weather periods by considering the underlying physical characteristics of the system. The multi-input, single-output GPR model is evaluated using root mean square error (RMSE), coverage, and differential entropy. The model demonstrates high predictive accuracy for both treatment plant inflow and manhole water levels across various training durations, with coverage values ranging from 87.5% to 99.4%. Finally, the model is used for anomaly detection by identifying deviations from expected ranges, enabling the estimation of surcharge and overflow probabilities under various conditions. Full article

Over the past 30 years, the City of Portland, Oregon, USA, has emerged as a national leader in green stormwater infrastructure (GSI). The initial impetus for implementing sustainable stormwater infrastructure in Portland stemmed from concerns about flooding and water quality in the city’s two major rivers, the Columbia and the Willamette. Heavy rainfall often led to combined sewer overflows, significantly polluting these waterways. A partial solution was the construction of “The Big Pipe” project, a large-scale stormwater containment system designed to filter and regulate overflow. However, Portland has taken a more comprehensive and long-term approach by integrating sustainable stormwater management into urban planning. Over the past three decades, the city has successfully implemented GSI to mitigate these challenges. Low-impact development strategies, such as bioswales, green streets, and permeable surfaces, have been widely adopted in streetscapes, pathways, and parking areas, enhancing both environmental resilience and urban livability. This perspective highlights the history of the implementation of Portland’s GSI programs, current design and performance standards, and challenges and lessons learned throughout Portland’s recent history. Innovative approaches to managing runoff have not only improved stormwater control but also enhanced green spaces and contributed to the city’s overall climate resilience while addressing economic well-being and social equity. Portland’s success is a result of strong policy support, effective integration of green and gray infrastructure, and active community involvement. As climate change intensifies, cities need holistic, adaptive, and community-centered approaches to urban stormwater management. Portland’s experience offers valuable insights for cities seeking to expand their GSI amid growing concerns about climate resilience, equity, and aging infrastructure. Full article

Antimicrobial resistance (AMR) is considered a global healthcare emergency in the 21st century. Although the evolution of microorganisms through Darwinian mechanisms and antibiotic misuse are established drivers, the structural socioeconomic factors of AMR remain insufficiently explored. This review takes on an analytical perspective, drawing upon a wide spectrum of evidence to examine the extent to which socioeconomic factors contribute to the global proliferation of AMR, with an emphasis on low- and middle-income countries (LMICs). The analytical review at hand was carried out through a search for relevant articles and reviews on PubMed, Google Scholar, the Centers for Disease Control and Prevention, and the World Health Organization database using combinations of the keywords “antimicrobial resistance,” “socioeconomic factors,” “low- and middle-income countries,” “surveillance,” “healthcare access,” and “agriculture.” Preference was given to systematic reviews, high-impact primary studies, and policy documents published in peer-reviewed journals or by reputable global health organizations. Our analysis identifies a complex interplay of systemic vulnerabilities that accelerate AMR in resource-limited settings. A lack of regulatory frameworks regarding non-prescription antibiotic use enables the proliferation of multi-drug-resistant microorganisms. Low sewer connectivity facilitates the environmental dissemination of resistance genes. Proper antibiotic selection is hindered by subpar healthcare systems and limited diagnostic capabilities to deliver appropriate treatment. Additionally, gender disparities, forced migration, and climate-driven zoonotic transmission compound the burden. During the COVID-19 pandemic, antimicrobial misuse surged, further amplifying resistance trends. AMR is not solely a biological phenomenon, but a manifestation of global inequity. Mitigation requires a transformation of policy directed toward a “One Health” strategy that incorporates socioeconomic, environmental, and health system reforms. Strengthening surveillance, investing in infrastructure, regulating pharmaceutical practices, and promoting health equity are essential to curb the rising tide of resistance. Full article

Microbially induced concrete deterioration (MID) poses a significant and urgent challenge to urban sewerage systems globally, particularly in tropical coastal regions. Despite the acknowledged importance of biofilms in MICC, limited research on sewer pipe biofilms has hindered a comprehensive understanding of their deterioration mechanisms. To overcome this limitation, our research employed multiple staining techniques and digital volume correlation (DVC) technology, creating a new method to analyze the microstructure of biofilms, precisely identify the components of EPSs, and quantitatively examine MID mechanisms from a microscopic viewpoint. Our results revealed that the biofilm on concrete surfaces regulates the types of amino acids, thereby creating an environment conducive to microbial aggregate survival. Additionally, salinity significantly influences biofilm component distribution, while proteins play a pivotal role in biofilm mechanical stability. Notably, a high salinity fosters microbial migration within the biofilm, exacerbating deterioration. Through this multidimensional inquiry, our study established an advanced echelon of comprehension concerning the intricate mechanisms underpinning MICC. Meanwhile, by peering into the biofilms and elucidating their interplay with concrete, our findings offer profound insights, which can aid in devising strategies to counter urban sewer system deterioration. Full article