Search Results (422)

Physicochemical and microbiological parameters are important indicators of drinking water quality. This study assessed the quality of groundwater used for drinking in four regions of Kosovo at 16 locations using an integrated assessment framework that combined physicochemical, microbiological, and Water Quality Index (WQI) approaches. The results reveal substantial spatial variability in water quality. While most physicochemical parameters remained within recommended limits, elevated values of total dissolved solids (up to 2792.5 mg/L), electrical conductivity (up to 2768.5 µS/cm), nitrate (up to 60.75 mg/L), and phosphate (up to 0.875 mg/L) were observed at several locations, indicating localized hydrogeochemical and anthropogenic influences. Dissolved oxygen levels were generally low (0.68–5.49 mg/L), reflecting limited aeration conditions in groundwater systems. Microbiological analysis revealed critical contamination, with Escherichia coli concentrations up to 299.9 CFU/100 mL, and all sampling sites exceeded permissible limits, indicating widespread fecal pollution and rendering the groundwater unsafe for direct consumption. WQI assessment further confirmed this condition, where 93.75% of locations were classified as medium quality using the NSF-WQI method, whereas the WA-WQI method categorized 68.75% of samples as poor and 6.25% as very poor. The novelty of this study lies in the integrated evaluation of hydrogeochemical processes and microbiological contamination using dual WQI methods and multivariate statistical analysis, providing a comprehensive understanding of groundwater degradation pathways. The findings are significant for policymakers, environmental managers, and public health authorities, highlighting the urgent need for groundwater treatment, improved sanitation infrastructure, and sustainable water resource management strategies in vulnerable regions. Full article

Surface water systems are increasingly exposed to multiple pressures generated by climate variability, intensified water resource exploitation, and evolving geopolitical dynamics. This study provides a novel contribution by identifying critical threshold effects and non-linear interactions that influence nitrate concentrations through an integrated information systems framework. It develops an integrated information-system-based analytical framework that combines hydrological, climatic, geopolitical, and strategic indicators to shape the broader contextual framework within which hydrological and climatic pressures operate, rather than serving as direct predictors. Considering the nitrate concentration in rivers as a key parameter of water quality, the paper goes beyond univariate analysis of nitrite concentration, examining its relationship with four explanatory variables: the Water Exploitation Index Plus (WEI+), the number of heat stress days (Heat_Stress), the Geopolitical Risk Index (GPR), and a proxy variable representing the presence of strategic infrastructure (Nuclear_State) using a Reduced Error Pruning Tree (REPTree) decision tree algorithm with 10-fold cross-validation. The results indicate that climatic stress emerges as the primary predictor, with a critical threshold of approximately 7.83 heat stress days, beyond which nitrate concentrations increase significantly. Under conditions of high climatic stress and intensive water exploitation (WEI+ ≥ 67.39), predicted nitrate levels exceed 20 mg/L and can reach extreme values of up to 58.82 mg/L. In contrast, low hydrological pressure (WEI+ < 0.39) combined with moderate climatic stress is associated with very low nitrate concentrations, around 2.75 mg/L. The model demonstrates strong predictive performance, with a correlation coefficient of 0.976, a Mean Absolute Error (MAE) of 0.593, a Root Mean Squared Error (RMSE) of 2.046, and a Receiver Operating Characteristic (ROC) area exceeding 0.94 for classification tasks. While geopolitical and strategic variables do not act as direct predictors, they contribute to shaping the contextual framework influencing water resource management and environmental vulnerability. Overall, the study highlights the non-linear and systemic nature of water quality dynamics and demonstrates the effectiveness of decision tree-based models within integrated information systems for supporting environmental monitoring and decision-making under conditions of climate stress and geopolitical uncertainty. Full article

Background/Objectives: Brain death determination in children is clinically challenging. When standard clinical examination cannot be completed or reliably interpreted, ancillary testing is required—yet many established methods depend on infrastructure or patient transport that may not be feasible in critically ill pediatric patients. Orbital ultrasonography is bedside-applicable and non-invasive, but remains poorly characterized in children. Methods: We conducted a single-center retrospective study of 28 pediatric patients evaluated for suspected brain death between January 2021 and February 2025. Patients were classified as brain death-positive [BD(+), n = 20] or brain death-negative [BD(−), n = 8] based on clinical criteria independent of imaging findings. Orbital color Doppler parameters (ophthalmic artery, central retinal artery, posterior ciliary artery) and optic nerve sheath diameter (ONSD) were measured under a standardized protocol by a single experienced operator. Ophthalmic artery resistive index (OA-RI) was defined a priori as the primary outcome; ONSD was the secondary outcome. Group comparisons used the Mann–Whitney U test with Cliff’s delta effect sizes; false discovery rate correction was applied to secondary and exploratory comparisons. ROC analyses were performed to assess discriminative performance. The study was reported in accordance with the STARD 2015 guidelines for diagnostic accuracy research. Results: OA-RI was markedly higher in BD(+) patients (0.84 [IQR 0.80–0.90] vs. 0.65 [0.58–0.69]; p < 0.001; δ = 0.975). ROC analysis yielded an AUC of 0.99 (95% CI: 0.96–1.00); at a cut-off of ≥0.77, sensitivity was 95.0% and specificity 100.0%. ONSD also differed significantly between groups (4.75 [4.15–5.08] mm vs. 3.90 [3.40–4.15] mm; p = 0.012; δ = 0.619; AUC = 0.81, 95% CI: 0.62–1.00; cut-off ≥ 4.2 mm; sensitivity and specificity both 75.0%). Across all three orbital vessels, end-diastolic velocity was consistently reduced and resistive indices elevated in BD(+) patients. Systolic velocities did not differ meaningfully between groups. Cut-off values represent cohort-specific statistical optima and should be interpreted as exploratory. Conclusions: Orbital Doppler ultrasonography demonstrates a coherent high-resistance hemodynamic pattern in pediatric brain death. OA-RI showed strong discriminative performance and may serve as a useful bedside adjunct in selected cases where ancillary testing is indicated. ONSD provides complementary anatomical evidence. These findings are exploratory and require prospective validation in larger, multicenter pediatric cohorts. Full article

Oxidative aging drives asphalt pavement degradation, causing critical structural failures. This study evaluated hazelnut (HS) and walnut shell (WS) powders (0–3% w/w; 10–12 μm) as sustainable antioxidants, from valued residues, to mitigate thermo-oxidative aging in CA-24 binders. After evaluating the antioxidant potential (ORAC; Oxygen radical absorbance capacity, and TPC; Total phenolic content), modified binders underwent RTFO (Rolling thin film oven) and PAV (Pressure aging vessel) aging, evaluated by Fraass fragility, Relative Aging Index (RAI), dynamic shear rheometry (G*/sin δ), and Multiple Stress Creep Recovery (MSCR). WS exhibited significantly higher antioxidant capacity (6000 μmol TE g DW⁻¹) and TPC than HS. The 3% treatments demonstrated optimal antioxidant efficacy, reducing long-term RAI by 14% and improving low-temperature flexibility by 3.8 °C (Fraass point −12.3 °C). However, MSCR revealed initial plasticizing effects decreasing elastic recovery (70%) and increasing non-recoverable compliance (Jnr) compromising unaged rutting resistance. Principal component analysis confirmed progressive diversification of aging-induced properties, evidencing complex multivariate trajectories. Ultimately, while nutshell derived phenolic modifiers provide effective concentration-dependent antioxidant protection, practical application requires optimization through targeted phenolic extraction, particle engineering, or elastomeric co-modification. Balancing aging resistance with high temperature stability remains essential for advancing these sustainable biomodification strategies in road infrastructure. Full article

Transport infrastructure serves as a critical physical carrier for constructing a unified national market and promoting coordinated regional economic development. Addressing the practical contradiction between rapid transport network expansion and persistent regional development imbalances, this paper constructs a comprehensive transport infrastructure service efficiency index using panel data from 297 prefecture-level cities in China from 2010 to 2023. We systematically investigate the nonlinear impact and underlying mechanisms of transport infrastructure on inter-city economic disparities. The findings reveal a significant inverted U-shaped relationship between transport infrastructure construction and regional economic disparity. Specifically, in the early stages of transport development, the dominance of the agglomeration effect leads to widening regional gaps; once a specific threshold is crossed (an index value of approximately 0.274), the diffusion effect emerges, facilitating convergence. This nonlinear relationship exhibits significant regional heterogeneity: the eastern region has largely crossed the inflection point into the convergence phase, while the western region remains in the “climbing” period dominated by polarization effects. Mechanism testing indicates that labor factor allocation is the core driver of this inverted U-shaped evolution. This study not only clarifies the dynamic boundaries of transport infrastructure’s impact on regional economic patterns but also provides empirical evidence for formulating differentiated transport and regional coordination policies for regions at different developmental stages. Full article

Rail trespassing remains a persistent safety challenge at the system level in the United States, with a 24% increase in incidents within the last decade (2016–2025). Identifying hotspots proactively is difficult due to limited incident data and strong spatial dependencies within the built environment. This study thus creates a ZIP-code–level geospatial analytics framework to identify current and emerging trespassing hotspots across North Carolina by combining land-use composition, rail exposure metrics, and historical Federal Railroad Administration (FRA) trespassing records. Geospatial layers were integrated within a GIS workflow to derive attributes such as rail miles, grade crossings, population density, and land-use types. Exploratory spatial analysis showed significant clustering of trespassing incidents, with Global Moran’s I indicating positive spatial autocorrelation across multiple neighborhood sizes. Permutation z-scores confirmed non-random hotspot formation along major rail corridors. A k-means clustering method also identified four structural risk environments, and a Composite Risk Index (CRI) was developed from weighted, standardized exposure and land-use variables to quantify latent risk, independent of raw casualty counts. Results indicate that clusters characterized by higher rail infrastructure exposure and mixed land-use environments exhibit the highest CRI values and elevated hotspot probabilities. In contrast, clusters with limited rail infrastructure, including predominantly commercial and rural ZIP codes, show substantially lower risk levels. The findings highlight that trespassing risk is more strongly associated with structural exposure conditions than with isolated historical incident counts. The resulting risk surfaces and hotspots provide an interpretable and scalable framework for statewide safety planning, early hotspot detection, and targeted interventions by transportation agencies. Full article

This study developed and tested a neighborhood-scale framework that integrates unmanned aerial vehicle (UAV)-based multispectral mapping and georeferenced socioeconomic data to identify inequities in urban green infrastructure and translate them into an operational prioritization tool for inclusive planning. Using object-based image analysis, impervious surfaces, low vegetation, and woody vegetation (trees and shrubs) were mapped across 33 Neighborhood Units in Temuco, Chile, and landscape metrics describing dominance, edge, isolation/connectivity, and diversity were derived. Socioeconomic conditions were summarized through Principal Component Analysis, and their relationships with vegetation metrics were evaluated using Generalized Additive Models. The results revealed strongly nonlinear and metric-specific associations, with the most robust relationships observed for woody-structure metrics, particularly total woody edge and built-environment isolation, whereas landscape diversity showed weaker but still significant dependence on resource-access gradients. To support inclusive planning, a dimensionless Green Infrastructure Prioritization Index (GIPI) was computed by combining standardized green deficit and standardized social vulnerability with equal weights. GIPI values ranged from 0.318 to 0.740 (median = 0.528), identifying 11 high-priority units characterized by higher social vulnerability and less favorable woody structure, including lower largest-patch dominance and greater isolation. Sensitivity analyses varying the deficit weight from 0.30 to 0.70 showed that 10 of the 11 high-priority units remained in the same class in at least 80% of weighting scenarios, indicating a stable priority set. Further classification of high-priority units according to dominant deficit type supported a staged intervention strategy, in which woody canopy is first increased in deficit nodes and subsequently reinforced through corridor-oriented greening to improve structural connectivity. These findings demonstrate the value of coupling fine-scale vegetation mapping with socioeconomic gradients to support more equitable urban green infrastructure planning. Full article

Uneven urbanization can lead to significant quantities of vacant and abandoned land while exacerbating urban heat island (UHI) effects and simultaneously adversely affecting socioeconomically disadvantaged communities. This study examines the correlation between land surface temperature (LST) and urban vacant and abandoned land in socially vulnerable neighborhoods in Houston, TX, USA, where extreme heat can present significant environmental and public health challenges. Six critical study locations exhibiting a social vulnerability index (SVI) over 0.7 and average land surface temperature (LST) values surpassing 82 °F (27.8 °C) are analyzed through spatial analytics and drone footage. Findings indicate that vegetated vacant spaces help mitigate urban heat by decreasing land surface temperature, but abandoned structures exacerbate temperatures due to heat retention from non-permeable surfaces. Findings suggest that elevated socioeconomic vulnerability correlates with increased land surface temperature, exacerbating heat-related hazards in at-risk communities. In this six-site sample, the abandonment rate exhibited a positive correlation with the site mean land surface temperature (exploratory linear fit: +2.42 °F [0.74, 4.11]/+1.35 °C [0.41, 2.28] per +1% increase in abandonment; to be interpreted as exploratory and potentially confounded). Results provide critical insights for climate resilience planning and urban heat reduction through high-resolution thermal and geographical analysis, highlighting the impact of vacant and abandoned land on LST. Such findings endorse certain urban cooling techniques, including land reutilization and green infrastructure, to enhance environmental equality and adaptation. Full article

Urban green infrastructure plays a crucial role in enhancing environmental resilience in cities, particularly in arid regions characterized by water scarcity, soil salinity, and high climatic stress. However, arid coastal cities remain insufficiently studied with regard to spatially explicit assessments of the structure and dynamics of green infrastructure. This study evaluates the state and spatial organization of urban green infrastructure in Aktau, Kazakhstan, over the period 2015–2025, with the most recent satellite observations obtained in June 2025. Sentinel-2 satellite imagery was used to calculate seasonal Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) values, and zonal statistics were applied to assess intra-urban differentiation across functional zones. In addition, inventory-based indicators—Green Planting Density (GPD), Structural Composition of Greenery (SCG), and Protective Green Infrastructure (PGI)—were integrated to complement the remote sensing analysis. The results indicate a moderate overall increase in mean NDVI values (from 0.21 to 0.28), with the most significant growth observed in central and coastal areas (ΔNDVI = +0.12; ΔSAVI = +0.21), while industrial and newly developed zones exhibit only limited changes. Despite these localized improvements, the spatial configuration of green infrastructure remains fragmented, reflecting a persistent center–periphery asymmetry in urban greening. These results underline the importance of irrigation practices and spatially targeted greening strategies for improving vegetation conditions in arid urban environments. The proposed integrated approach combining satellite-derived vegetation indices and inventory-based indicators can serve as a useful tool for monitoring urban green infrastructure and supporting evidence-based planning in arid coastal cities. Full article

Disaster resilience has become a key focus of risk reduction efforts, but measuring it remains complex due to differences in hazards, development paths, and data systems. This study modifies the Baseline Resilience Indicators for Communities (BRIC) approach, based on the Disaster Resilience of Place (DROP) framework, to evaluate community resilience in Serbia and highlight regional differences. An initial list of 186 indicators was created from international BRIC studies and resilience research, then tailored to Serbian conditions through contextual review and data checks. Indicators were normalized using min–max scaling (0–1), and indicators with negative orientation were inverted to ensure that higher values indicate greater resilience. Scores for each dimension were calculated as equally weighted averages across six areas: social, economic, social capital, institutional, infrastructural, and environmental. The overall BRIC index was derived as the average of these dimension scores. Z-scores facilitated the classification of resilience levels and the comparison between regions. The results show clear regional disparities: in the complete model, Belgrade has the highest resilience (BRIC = 0.557), while Southern and Eastern Serbia have the lowest (BRIC = 0.414). Patterns across dimensions show that Belgrade excels in social and economic capacity but lags in environmental indicators; Vojvodina has the strongest institutional and infrastructural capacity; and Šumadija and Western Serbia perform best in environmental indicators. Correlation analysis revealed multicollinearity, leading to the removal of 14 redundant indicators and the refinement to a set of 57. After this reduction, regional rankings change, with Vojvodina (BRIC = 0.530) and Šumadija and Western Serbia (BRIC = 0.522) emerging as higher-resilience regions, while Southern and Eastern Serbia remain the least resilient (BRIC = 0.456). The adapted BRIC-DROP model offers a clear, locally relevant tool for mapping resilience and guiding targeted policies in Serbia, enabling region-specific efforts to address structural resilience gaps. Full article

The adoption of circular economy (CE) practices and artificial intelligence (AI) in the supply chain (SC) has become extremely significant in manufacturing organizations. The CE seeks to facilitate sustainable growth by managing the flow of materials and energy within closed-loop systems. The CE has resulted in the development of sustainable business models. AI capabilities transform work activities, data flows, and organizational processes. Therefore, the present study aims to develop a framework to improve circular supply chain (CSC) adoption in the automobile manufacturing sector by identifying and analyzing CE practices and AI-enabled digital strategies. The proposed framework was analyzed by employing a hybrid approach of Prioritized Weighted Average–Criteria Importance Through Intercriteria Correlation–Preference Ranking Organization Method for Enrichment Evaluations-II (PWA-CRITIC-PROMETHEE-II) under an Interval-Valued Fermatean Fuzzy (IVFF) environment. IVFF-CRITIC was employed to determine the CE practices’ weights, while IVFF-PROMETHEE-II was utilized to establish the relative index of AI-enabled digital strategies to enhance the CSC adoption. The key findings of the current study indicate that “AI-enabled infrastructure configuration for circular economy adoption in the supply chain”, “AI-integrated equipment to facilitate adaptability and mass personalization”, and “Robotics and AI-driven manufacturing and material reclamation” are the most significant AI-based digital strategies that support CE practices to enhance the adoption of a CSC and encourage case example manufacturing organizations to align their operations with AI and CE. Moreover, the outcomes of the study will deliver a comprehensive evaluation of CE practices and AI-enabled digital strategies for SC managers, based on the relative indexing obtained through the implementation of the hybrid approach. Full article

Riverine bridges are critical infrastructure that are increasingly exposed to severe hydrological hazards. This study proposes and validates a synergistic methodology for the assessment of riverine bridge resilience, integrating the conceptual 4R framework (robustness, rapidity, resourcefulness, and redundancy) with field inspections, hydrological and hydraulic modeling, including scour evaluation, within a multicriteria analysis scheme. The methodology comprises: (i) a systematic review of literature and regulations to construct a 30-parameter matrix across five dimensions (technical, economic, social, organizational, and environmental); (ii) data acquisition through field inspections, detailed topography, and technical studies; and (iii) one-dimensional hydraulic modeling in HEC-RAS under extreme scenarios (return periods of 100 to 750 years and a critical 500 m³/s scenario representing a potential overflow of the Aguada Blanca reservoir). The Bridge Resilience Index (BRI) is computed through a weighted additive model and a sensitivity analysis. Application to the San Martín Bridge (Arequipa, Peru), a structure with more than 60 years of service and recurrent preventive closures during flood events, revealed critical conditions: minimum freeboard of 0.26 m, absence of hydraulic protections, and limited institutional capacity. The resulting BRI value (1.898) indicates a low resilience level. The proposed framework provides a useful tool for risk-informed decision-making, the prioritization of interventions, and the strengthening of resilience in critical infrastructure. Full article

Air quality improvement represents a critical challenge for the European Union, with particulate matter (PM) being the most harmful pollutant in urban areas. Urban Green Infrastructures (UGIs) provide essential ecosystem services that mitigate air pollution, notably through PM₁₀ removal via deposition on leaf surfaces, reducing health risks associated with poor air quality. This study quantifies the PM₁₀ removal supplied by urban forests in the Bydgoszcz–Toruń area (Poland) using a spatially explicit modeling framework. Remotely sensed Leaf Area Index, vegetation cover, and PM₁₀ concentration data were integrated within a GIS environment, with all analyses conducted on a seasonal basis to capture temporal variability in vegetation phenology and pollutant levels. Resulting maps of mean seasonal PM₁₀ removal efficiency (kg/ha) reveal distinct functional group patterns: deciduous broadleaves reach peak efficiency in summer, whereas conifers provide a more consistent year-round contribution, resulting in the highest annual removal. Monetary valuation was performed using externality costs from the European Environmental Agency. Overall, urban forests remove 3360.40 Mg of PM₁₀ annually, corresponding to an estimated value of 255.69 M€. Integrating biophysical and economic perspectives supports urban planning and highlights UGIs as nature-based solutions to enhance air quality, protect public health and promote ecosystem biodiversity and resilience. Full article

This research examines the structural evolution and functional performance of urban spatial expansion in Changchun, Northeast China. Utilizing an integrated framework of the Adjusted Sprawl Index, Gaussian two-step floating catchment area (Gaussian 2SFCA) accessibility modeling, and XGBoost-SHAP machine learning, the study identifies a decoupled growth pattern where land development and infrastructure construction proceed without a corresponding increase in population density, reflecting a structural-demographic divergence. Empirical results demonstrate that land expansion reached a significant peak between 2015 and 2020, followed by a transition toward morphological equalization and stabilization after 2020. This process manifests as asynchronous urbanism, where the strategic deployment of physical infrastructure frameworks systematically precedes the functional integration of essential social services. The analysis reveals the emergence of localized service-value misalignment clusters in peripheral zones. The phenomenon represents a deviation from the traditional monocentric paradigm toward McCann’s framework of modern urban economics, as high residential valuations are sustained by social capital and institutional expectations despite physical service gaps. Within these clusters, the club realm and private enclosure function as critical forward-looking mechanisms, where the presence of influential groups signals future social and infrastructural investment. A negative interaction effect between property management levels and regional accessibility confirms that these private governance structures effectively substitute for maturing public resources. These findings suggest that future development should prioritize the functional integration of social systems over mere material expansion. Full article

This study aimed to analyze changes in vegetation, built-up areas, and population growth in Lahore city from 1990 to 2023. The data was acquired from Google Earth Engine, and the spectral bands were retrieved from Landsat 5 and Landsat 8. The decadal analysis of the landscape was conducted from 1993 to 2001, 2001 to 2012, and from 2013 to 2023. Further analysis was conducted in ArcGIS version 10.3 to evaluate the Normalized Difference Vegetation Index and the Normalized Difference Built-up Index to assess vegetation and built-up areas, respectively. To analyze the urban population of Lahore, data were obtained from the Global Human Settlement Layer for 1990, 2000, 2010, and 2020. Results revealed that the total vegetated area of Lahore city decreased from 1453.0 km² in 1993–2001 to 788.2 km² in 2013–2023. Moreover, the urban built-up area expanded from 319.6 km² in 1993–2001 to 966.8 km² in 2013–2023. Sub-district-level analysis indicated that Model Town and Raiwind areas of Lahore depicted better vegetation recovery in this decade. The population of Lahore has been increasing steadily, with the 2010s being a particularly rapid period of growth. The projections for 2030 also depict a continuous growth pattern. This study was further developed by integrating multi-decadal averaging coupled with selected-year analysis to distinguish gradual land transformation from relatively accelerated phases of urban expansion of Lahore. Also, by combining NDVI and NDBI values on both Lahore and its tehsil level, the research provides a collective sub-district- and district-level perspective into the spatial heterogeneity of peri-urban transformations. The findings of the study explain how major infrastructural projects shape the urban growth patterns of cities like Lahore and cause a decline in the green areas of fast-growing cities in South Asia. This study further highlights the consequences of unplanned urban expansion in regions where high population growth has compromised green infrastructure and threatened ecological balance. In addition, it supports several Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land) by providing spatial evidence of urban expansion of the city and losses of its green spaces. The findings offer empirical insights to support climate-resilient developments. The study also demonstrates the necessity of integrating green infrastructure and providing robust strategies for forthcoming urban planning projects and policy development regarding urban expansion. Full article