Search Results (130)

Wildfires are increasingly recognized as a climatological hazard, able to threaten industrial and critical infrastructure safety and operations and lead to Natech disasters. Future projections of exacerbated fire regimes increase the likelihood of Natech disasters, therefore increasing expected direct damage costs, clean-up costs, and long-term economic losses due to business interruption and environmental remediation. While large industrial complexes, such as oil, gas, and chemical facilities have sufficient resources for the implementation of effective prevention and mitigation plans, small-to-medium-sized industrial hubs are particularly vulnerable due to their scattered distribution and limited resources for investing in comprehensive fire prevention systems. This study targets the vulnerability of these communities by proposing the deployment of Wireless Sensor Networks (WSNs) as cost-effective Early Wildfire Detection Systems (EWDSs) to safeguard wildland and industrial domains. The proposed approach leverages wildland–industrial interface (WII) geospatial data, simulated wildfire dynamics data, and mathematical optimization to maximize detection efficiency at minimal cost. The WII delimits the boundary where the presence of wildland fires impacts industrial activity, thus representing a proxy for potential Natech disasters. The methodology is tested in Cocentaina, Spain, a municipality characterized by a highly flammable Mediterranean landscape and medium-scale industrial parks. Results reveal the complex trade-offs between detection characteristics and the degree of protection in the combined wildland and WII areas, enabling stakeholders to make informed decisions. This methodology is easily replicable for any municipality and industrial installation, or for generic wildland–human interface (WHI) scenarios, provided there is access to wildfire dynamics data and geospatial boundaries delimiting the areas to protect. Full article

This study addresses the issue of water losses in drinking water distribution networks, a problem exacerbated by climate change, drought, and aging infrastructure. The research was conducted in the operational area of Frýdek-Místek, managed by Severomoravské vodovody a kanalizace Ostrava a.s., covering 59 municipalities, 1024.4 km of pipeline, and more than 32,594 service connections. The objective was to evaluate the impact of implementing the “Leakage monitor” software system (ver. 19-11-2024), which focuses on continuous monitoring of minimum night flows (Q_min), on the reduction in Non-Revenue Water (NRW). The system, deployed since 2019, enables automated data collection, remote transmission, and analysis for timely leak detection and localization using acoustic and correlator methods within district metered areas. The results confirmed a reduction in NRW from 14.6% in 2019 to 11.5% in 2024. The implementation of a “Leak monitor” has proven to be an effective tool for improving operational efficiency and ensuring both economic and environmental sustainability of water supply systems. Full article

Arid and semi-arid regions of Mexico, such as the Bajío of Guanajuato, face a huge challenge in water resource management. The municipality of León, located in the State of Guanajuato, persistently lacks access to water resources despite having high coverage in urban areas by the León Water Utility System (SAPAL, the abbreviation in Spanish of “Sistema de Agua Potable y Alcantarillado de León”), particularly in peri-urban and rural areas. In this context, this study compares water distribution network expansion with rainwater harvesting (RWH) systems in four rural communities of León. A cost–benefit analysis (CBA) with a 20-year horizon and a 10% social discount rate (SDR) was applied. Results indicate that network expansion is financially unfeasible, whereas RWH emerges as a technically and economically viable alternative, providing household savings and strengthening community resilience. Full article

Farmland abandonment is becoming a growing land use challenge in the Brazilian Cerrado, yet its extent, spatial distribution, and underlying drivers remain poorly understood. This study addresses the following question: Can deep learning methods reliably identify abandoned farmlands in tropical savanna environments using multispectral satellite images? To answer this question, we used a Fully Connected Neural Network (FCNN) classifier to map abandoned farmlands in the municipality of Buritizeiro, Minas Gerais State, Brazil, using Sentinel-2 images acquired in 2018 and 2022. Seven land use and land cover (LULC) classes were mapped using visible and near-infrared bands, spectral indices, spectral mixture components, and principal components as input parameters for the CNN. The LULC map for 2022 achieved high classification performance (overall accuracy = 94.7%; Kappa coefficient = 0.93). Agricultural areas classified in 2018 as annual croplands, cultivated pastures, eucalyptus plantations, or harvested eucalyptus that transitioned to grasslands or shrublands in 2022 were considered abandoned. Based on this definition, we identified 13,147 hectares of abandoned land in 2022, representing 4.7% of the municipality’s agricultural area in 2018. Most abandoned areas corresponded to eucalyptus plantations established for charcoal production. This study provides the first deep learning-based assessment of farmland abandonment in the Cerrado. Our findings demonstrated the potential of FCNN classifiers for detecting abandoned farmlands in this biome and provide important contribution for public policies focused on ecological restoration, carbon sequestration, and sustainable agricultural planning. Full article

Effective municipal waste management is fundamental to environmental sustainability and the circular economy. This case study assesses the operational effectiveness of the Recycling/Civic Amenity Site (CAS) network in Białostocki county, Poland, during the 2014–2018 national waste management transition. A multi-criteria assessment was employed, integrating compliance audits, infrastructure checks, and spatial analysis of waste type distributions to evaluate CAS operations. The findings reveal a socio-economic divergence between more urbanised (town-and-village) and purely rural (village) municipalities, which is directly reflected in their distinct waste composition patterns. The town-and-village areas produced homogeneous, high-quality packaging waste streams that support recycling goals. Conversely, the village municipalities generated more commingled, heterogeneous streams that challenge recycling efforts. An optimised CAS model was proposed for the county to enhance sustainability by adaptively differentiating CAS services to local needs. However, a direct stock-take of all 16 CASs revealed significant infrastructural disparities, limiting the model’s potential. The study concludes that overcoming both the qualitative waste stream divergence and quantitative infrastructure disparities through tailored strategies is essential for meeting national recycling targets and achieving long-term sustainability. The methodology provides a replicable framework for pinpointing the root causes of inefficient operations, offering local authorities evidence-based tools to optimise CAS design and ensure infrastructure investments directly support overarching sustainability goals. Full article

As critical national energy arteries, long-distance large-scale cross-regional gas transmission networks are characterized by high operating pressures, extensive spatial coverage, and complex topological structures. Thus, the multi-hazard profiles threatening its safety and reliability operation differ significantly from those of local urban gas distribution networks. This research develops a resilience assessment framework capable of quantifying resistance, adaptation, and recovery capacities of such energy systems. The framework establishes performance indicator systems based on design parameters, installation environments, and construction methods for long-distance trunk pipelines and key facilities such as storage facilities. Furthermore, based on complex network theory, the size of the largest connected component and global efficiency of the transmission network are selected as core topological metrics to characterize functional scale retention and transmission efficiency under disturbances, respectively, with corresponding quantification methods proposed. A cross-regional pipeline transmission network within a representative municipal-level administrative region in China is used as a case for empirical analysis. The quantitative assessment results of pipeline and network resilience are analyzed. The research indicates that trunk pipeline resilience is significantly affected by characteristic parameters, the laying environment, and installation methods. It is notably observed that installation methods like jacking and directional drilling, used for road or river crossings, offer greater resistance than direct burial but considerably lower restoration capacity due to the complexity of both the environment and the repair processes, which increases time and cost. Moreover, simulation-based comparison of recovery strategies demonstrates that, in this case, a repair-time-prioritized strategy more effectively enhances overall adaptive capacity and restoration efficiency than a node-degree-prioritized strategy. The findings provide quantitative analytical tools and decision-support references for resilience assessment and optimization of cross-regional energy transmission networks. Full article

This article presents the accumulated technical and scientific knowledge from energy performance upgrade work in emblematic and essential municipal and public buildings in Crete and the Greek islands, such as the Venetian historical building Loggia, which is used as the Heraklion City Hall, the Natural History Museum of Crete, Pancretan Stadium, the municipal swimming pool of the municipality of Minoa Pediadas, the indoor sports hall in Leros, primary schools, high schools and a cultural center. Each one of the aforementioned buildings has a distinct use, thus covering almost all different categories of municipal or public buildings and facilities. The applied energy performance upgrade process in general terms is: (1) Mapping of the current situation, regarding the existing infrastructure and final energy consumption. (2) Formulation and sizing of the proposed passive measures and calculation of the new indoor heating and cooling loads. (3) Selection, sizing and siting of the proposed active measures and calculation of the new expecting energy sources consumption. (4) Sizing and siting of power and heat production systems from renewable energy sources (RES). Through the work accomplished and presented in this article, practically all the most technically and economically feasible passive and active measures were studied: insulation of opaque surfaces, opening overhangs, natural ventilation, replacement of openings, daylighting solar tubes, open-loop geo-exchange plants, refrigerant or water distribution networks, air-to-water heat pumps, solar thermal collectors, lighting systems, automation systems, photovoltaics etc. The main results of the research showed energy savings through passive and active systems that can exceed 70%, depending mainly on the existing energy performance of the facility. By introducing photovoltaic plants operating under the net-metering mode, energy performance upgrades up to zero-energy facilities can be achieved. The payback periods range from 12 to 45 years. The setup budgets of the presented projects range from a few hundred thousand euros to 7 million euros. Full article

Health professionals’ safety is directly linked to organizational culture, and during the COVID-19 pandemic, weaknesses at organizational, emotional, professional, and structural levels became evident. This study aimed to assess health professionals’ perceptions of their safety in facing the COVID-19 pandemic. A cross-sectional, descriptive, and analytical study was conducted with professionals from the public health network of a Brazilian municipality who had taken medical leave due to COVID-19. Data were collected between October and December 2022 using the validated Questionnaire on Health Professional Safety in the COVID-19 Pandemic (QSP COVID-19), which consists of 30 items distributed across four domains: organizational, emotional, professional, and structural. A cut-off score ≥75 was considered a positive perception. Associations were tested using the chi-square test followed by the Bonferroni post hoc test, and multivariate logistic regression was applied to identify associated factors. Significance level: 5%. A total of 235 professionals participated, mostly women (81.7%), working in primary health care (68.1%), and employed under statutory contracts (74.5%). Only 50.6% reported an overall positive perception of safety. The emotional domain showed the highest score (85.5%), followed by the professional domain (74.0%). Organizational (50.6%) and structural (33.6%) domains had the lowest scores, reflecting shortcomings in infrastructure, human resources, and material supplies. In the multivariate analysis, temporary contracts, a 20 h workweek, and specific training were protective factors, whereas belonging to a risk group and being female increased the likelihood of negative perception in the structural dimension. Health professionals’ safety is supported by continuous education, emotional support, adequate infrastructure, professional recognition, participatory leadership, and strengthening of the safety culture. Full article

In order to enhance leakage detection accuracy in water distribution networks (WDNs) while reducing sensor deployment costs, an intelligent algorithm for the optimal deployment of water network monitoring sensors based on the automatic labelling and graph neural network (ALGN) was proposed for the optimal deployment of WDN monitoring sensors. The research aims to develop a data-driven, topology-aware sensor deployment strategy that achieves high leakage detection performance with minimal hardware requirements. The methodology consisted of three main steps: first, the dung beetle optimization algorithm (DBO) was employed to automatically determine optimal parameters for the DBSCAN clustering algorithm, which generated initial cluster labels; second, a customized graph neural network architecture was used to perform topology-aware node clustering, integrating network structure information; finally, optimal pressure sensor locations were selected based on minimum distance criteria within identified clusters. The key innovation lies in the integration of metaheuristic optimization with graph-based learning to fully automate the sensor placement process while explicitly incorporating the hydraulic network topology. The proposed approach was validated on real-world WDN infrastructure, demonstrating superior performance with 93% node coverage and 99.77% leakage detection accuracy, surpassing state-of-the-art methods by 2% and 0.7%, respectively. These results indicate that the ALGN framework provides municipal water utilities with a robust, automated solution for designing efficient pressure monitoring systems that balance detection performance with implementation cost. Full article

The increasing complexity of Water Distribution Systems (WDSs), driven by urbanization, climate change, and aging infrastructure, necessitates robust methods for risk assessment and visualization. This study presents a practical methodology for mapping the risk of water supply disruption or reduction using five parameters: Probability (P), Consequences (C), Water Pipe category (WP), Inhabitants exposed (I), and response Efficiency (E). The approach enables comprehensive analysis of the risk associated with specific pipeline segments within an Analyzed Supply Area (ASA). The method integrates statistical and operational data, allowing utilities to evaluate vulnerability, identify Critical Infrastructure (CI), and prioritize maintenance. The investigation conducted during the study revealed that cast iron and steel pipes with large diameters (e.g., 400 mm) show the highest failure probability and impact. Despite a calculated risk value (r_LW = 80), effective response measures—including specialized repair teams and equipment—kept the risk acceptable. The results demonstrate that historical failure and response data enhance risk identification and management. The generated risk maps facilitate spatial visualization of high-risk areas, supporting decision-making processes, renovation planning, and emergency preparedness. Integration with GIS tools, including GeoMedia and Google Earth programmes, enables dynamic map creation and simulation of response scenarios. The methodology is scalable and adaptable to any WDS, and potentially to other municipal systems such as wastewater and heating networks. By accounting for both technical and social dimensions of risk, the method supports improved water safety planning and infrastructure resilience. Future development should include real-time data integration and climate-related risk scenarios to increase predictive accuracy and system adaptability. Full article

Water supply systems (WSSs) are energy-intensive infrastructure that present significant opportunities for decarbonization through the integration of renewable energy (RE). This study evaluated the RE generation potential within the WSSs of Incheon Metropolitan City (IMC), Republic of Korea, using a site-specific, data-driven approach. Three RE technologies were considered: solar photovoltaic (PV) systems installed in water-treatment plants (WTPs), micro-hydropower (MHP) utilizing the residual head at the inlet chamber of a WTP, and in-pipe MHP recovery using the discharge from water supply tanks in water distribution networks. Actual facility data, hydraulic simulations, and spatial analyses were used to estimate an annual RE generation potential of 32,811 MWh in the WSSs of IMC, including 18,830 MWh from solar PV in WTPs, 4938 MWh from MHP in WTPs, and 9043 MWh from in-pipe MHP. This corresponds to an energy self-sufficiency rate of approximately 22.3%, relative to the IMC WSS total annual electricity consumption of 147,293 MWh in 2022. The results demonstrated that decentralized RE deployment within existing WSSs can significantly reduce grid dependency and carbon emissions. This study provides a rare empirical benchmark for RE integration in large-scale WSSs and offers practical insights for municipalities seeking energy-resilient and climate-aligned infrastructure transitions. Full article

When desalinated water enters the existing drinking water distribution systems (DWDSs), the balance between water and scale will be destroyed, resulting in the release of iron and water quality problems, causing “yellow water”. This study investigated the inhibitory effects of pH, alkalinity, and phosphate on iron release and the optimal control condition using pipe section reactors with a response surface. For steel pipe, the optimal condition for iron release control was pH = 8.5, alkalinity = 250 mg/L CaCO₃, and phosphate = 0.1 mg/L. For cast iron pipe, the optimal condition was pH = 8.0, alkalinity = 250 mg/L CaCO₃, and phosphate = 0.1 mg/L. This study can provide theoretical support for subsequent water supply safety and lay a foundation for the water supply safety of the municipal pipe network. Full article

Background: Access to healthy food in emerging-economy cities is challenged by last-mile constraints and poor infrastructure. Aligned with the UN SDGs on Zero Hunger and Sustainable Cities, this study examines how a strategically located nanostores network can help close these gaps while fostering local resilience. Focusing on Colombia’s Sabana Centro region, we designed a nanostore network that maximizes spatial coverage, proximity, and affordability. Methods: A competitive facility-location model combined with a discrete choice model captures consumer heterogeneity in price and location preferences. Results: Results show that locating nanostores in peripheral rather than central areas improves equity: the proposed network meets about 65,400 kg of weekly demand—51% fruit, 36% vegetables, 13% tubers—representing 16% of total regional demand and reaching underserved municipalities. This is notable given that existing nanostores already satisfy roughly 37% of household needs. Conclusions: By linking consumer behavior with sustainable spatial planning, the research offers both theoretical insight and practical tools for equitable distribution. Future work should evaluate supportive policies and supply chain innovations to secure nanostores’ long-term viability and community impact. Full article

Accurate prediction of effluent quality indicators is essential for ensuring stable operation and regulatory compliance in wastewater treatment plants. However, the inherent spatial distribution and temporal fluctuations of wastewater processes present significant challenges for modeling. In this study, we propose a dynamic multi-scale spatio-temporal Transformer (DMST-Transformer) with a symmetric architecture to enhance prediction accuracy in complex wastewater systems. Unlike conventional asymmetric designs, the DMST-Transformer extracts spatial and temporal features in parallel using a spatial graph convolutional network and a multi-scale self-attention mechanism coupled with a dynamic self-tuning module. The model is evaluated on a full-process dataset collected from a municipal wastewater treatment plant, with biochemical oxygen demand selected as the target indicator. Experimental results on test data show that the DMST-Transformer achieves a coefficient of determination of 0.93, root mean square error of 1.40 mg/L, and mean absolute percentage error of 6.61%, outperforming classical models such as linear regression, partial least squares, and graph convolutional networks, as well as advanced deep learning baselines including Transformer and ST-Transformer. Ablation studies confirm the complementary effectiveness of the spatial and temporal modules, and computational time comparisons demonstrate the model’s suitability for real-time applications. These results validate the practical potential of the DMST-Transformer for robust effluent quality monitoring in wastewater treatment plants. Future research will focus on scaling the model to larger and more diverse datasets, extending it to predict additional water quality indicators, and deploying it in real-time environmental monitoring systems to support intelligent water resource management. Full article

Unbalanced accelerations occurring during tram travel have a significant impact on passenger comfort and safety, as well as on the rate of wear and tear on infrastructure and rolling stock. Ideally, these dynamic forces should be monitored continuously in real-time; however, traditional systems require high-precision accelerometers and proprietary software—investments often beyond the reach of municipally funded tram operators. To this end, as part of the research project “Accelerometer Measurements in Rail Passenger Transport Vehicles”, pilot measurement campaigns were conducted in Poland on tram lines in Gdańsk, Toruń, Bydgoszcz, and Olsztyn. Off-the-shelf smartphones equipped with MEMS accelerometers and GPS modules, running the Physics Toolbox Sensor Suite Pro app, were used. Although the research employs widely known methods, this paper addresses part of the gap in affordable real-time monitoring by demonstrating that, in the future, equipment equipped solely with consumer-grade MEMS accelerometers can deliver sufficiently accurate data in applications where high precision is not critical. This paper presents an analysis of a subset of results from the Gdańsk tram network. Lateral (x) and vertical (z) accelerations were recorded at three fixed points inside two tram models (Pesa 128NG Jazz Duo and Düwag N8C), while longitudinal accelerations were deliberately omitted at this stage due to their strong dependence on driver behavior. Raw data were exported as CSV files, processed and analyzed in R version 4.2.2, and then mapped spatially using ArcGIS cartograms. Vehicle speed was calculated both via the haversine formula—accounting for Earth’s curvature—and via a Cartesian approximation. Over the ~7 km route, both methods yielded virtually identical results, validating the simpler approach for short distances. Acceleration histograms approximated Gaussian distributions, with most values between 0.05 and 0.15 m/s², and extreme values approaching 1 m/s². The results demonstrate that low-cost mobile devices, after future calibration against certified accelerometers, can provide sufficiently rich data for ride-comfort assessment and show promise for cost-effective condition monitoring of both track and rolling stock. Future work will focus on optimizing the app’s data collection pipeline, refining standard-based analysis algorithms, and validating smartphone measurements against benchmark sensors. Full article