Search Results (149)

Rapid urban population growth has intensified traffic congestion in smart cities. This has resulted in longer travel times, higher fuel consumption, increased environmental pollution, greater operational costs, and slower emergency response services. Existing traffic congestion prediction models primarily rely on traffic-flow and temporal features; the effects of road accidents and peak-hour conditions are not adequately addressed. This limitation is particularly significant in smart cities where both recurrent congestion (peak-hour demand) and non-recurrent congestion (road accidents) influence traffic conditions they have a significant impact on the performance of the road network. This study introduces a novel Historical Accident-Aware Peak-Hour GAN-GRU (APG-GRU) framework. The proposed framework employs a data processing pipeline integrating traffic data with historical accident-related features to predict traffic congestion using these features. Extensive experiments are conducted on a novel integrated dataset consist on Automatic Number Plate Recognition (ANPR) traffic data and ANPR traffic observations with historical accident features. The results demonstrate that the APG-GRU framework achieved superior performance on the integrated features dataset, attaining an accuracy of 97.50%, a congested precision of 91.86%, a congested recall of 97.31%, and a congested F1-score of 94.51%, outperforming both the ANPR traffic-only dataset and all baseline models. The APG-GRU framework significantly outperforms a suite of benchmark models, including XGBoost, Long Short-Term Memory (LSTM), and Random Forest as baselines, which achieved accuracies between 84% and 95.5% with correspondingly lower precision, recall, and F1-scores. External validation using a traffic dataset collected from Lahore, Pakistan, further demonstrated the robustness and generalizability of the proposed APG-GRU framework. A web-based interface developed for the APG-GRU framework to visualize accident hotspots and route-level traffic conditions. Routes with smooth traffic flow are highlighted in green, whereas congested routes are highlighted in red, demonstrating the practical applicability of the proposed framework for smart city traffic management systems. Full article

Heavy metal contamination in urban street dust is often highly heterogeneous, limiting the effectiveness of conventional geostatistical mapping approaches. In Mexico City, previous studies have reported very low spatial autocorrelation for key elements, making interpolation-based methods unsuitable for representing contamination patterns. This study proposes a multiscale cartographic framework to analyze and visualize heavy metal contamination in street dust from 482 sampling sites using the contamination factor (CF) and pollution load index (PLI) at three levels of spatial analysis: (i) city-scale patterns identified through hierarchical clustering of districts based on median CF values, (ii) district-scale variability assessed through statistical comparisons of PLI distributions, and (iii) site-scale identification of contamination hotspots using observed PLI values. Results revealed five contamination clusters and significant differences in pollution load among districts (Kruskal–Wallis, p < 0.05); PLI values in Xochimilco and Tláhuac are significantly lower than in Cuauhtémoc, Gustavo A. Madero, and Magdalena Contreras. Higher contamination levels were concentrated in northern and central districts, whereas lower levels predominated in the south. Site-scale analysis identified localized hotspots associated with transportation infrastructure, industrial areas, and commercial corridors, reflecting the influence of local emission sources. The results demonstrate that contamination patterns operate simultaneously at city, district, and site scales and cannot be adequately represented through interpolation alone. The proposed framework provides a practical approach for visualizing heterogeneous contamination datasets, supporting environmental decision-making, and may apply to other metropolitan regions characterized by weak spatial autocorrelation and localized pollution processes. Full article

This study aims to build a spatial model for selecting the optimal site for a radio astronomy observatory in southern Jordan. Geographic Information Systems (GISs) and Multi-Criteria Decision Analysis (MCDA)-based methodology were used in this study to develop a spatial model for choosing the best location for a radio astronomy observatory in southern Jordan. The criteria were weighted using a hybrid framework that combined the Analytic Hierarchy Process (AHP) and the entropy method to account for the actual spatial diversity of the data, in addition to expert judgment. The study assesses site suitability by considering several environmental and logistical factors that mitigate radio frequency interference (RFI), including elevation, cloud cover, artificial light pollution, and accessibility. A final map highlighting the optimal areas for radio astronomy observatories in southern Jordan has been created. The study methodology started with MCDA, and was followed by several stages, including visual evaluation, overlay analysis, establishment of 500 m buffer zones, extraction of the “Very High Suitability” class, and conversion to a transparent vector layer that is free from urban overlap and electromagnetic interference. The results show that the majority of large observatories (10 km²; equivalent to ≥10,000,000 m²) are located in Aqaba and Ma’an, which offer natural isolation and wide expanses ideal for global projects. Medium observatories (0.5–10 km²; equivalent to 500,000–10,000,000 m²) were generally identified at a reasonable cost in Ma’an and Aqaba, with the possibility of radio surveillance and infrastructure expansion. Many small observatories (0.01–0.5 km²; equivalent to 10,000–500,000 m²) were constructed near academic institutions, providing viable, easily accessible places for university research with little regulatory restraints. This research contributes to national astronomy infrastructure planning and serves as a model for other countries experiencing dry or semi-arid climates. It also offers decision-makers a useful spatial database. Full article

Microplastic (MP) pollution in highly urbanized rivers poses increasing ecological risks; however, the ingestion characteristics of wild fish across urbanization gradients remain insufficiently understood. In this study, gastrointestinal suspected microplastic ingestion was investigated in a diverse fish assemblage (n = 310, 10 species) collected from core urban, intermediate urban, and suburban reaches of the Suzhou River, Shanghai. A total of 496 visually identified suspected microplastics were detected, with an overall detection rate of 71.29% and a mean abundance of 1.60 ± 1.67 items/individual. Statistical analyses revealed significant spatial variation in suspected microplastic abundance (p < 0.05), with fish from the core urban area (2.08 ± 1.77 items/individual) and intermediate urban area (1.87 ± 1.80 items/individual) exhibiting significantly higher levels than those from suburban reaches (1.27 ± 1.50 items/individual). In contrast, differences associated with feeding habits and habitat layers were not statistically significant (p > 0.05). In the indicator species Coilia nasus, gastrointestinal suspected microplastic abundance showed a significant positive correlation with both body length (R² = 0.205, p < 0.001) and body weight (R² = 0.153, p < 0.005). Morphological characterization indicated that small-sized (≤1 mm, 79.47%), transparent fibres predominated among the detected particles. Overall, these findings suggest an association between urbanization and fish suspected microplastic exposure, although downstream accumulation likely co-drives this spatial pattern. Furthermore, differences associated with specific ecological traits were not statistically significant and require further targeted investigation. This study provides baseline ecological evidence for understanding microplastic exposure in urban river ecosystems and highlights the importance of integrated long-term monitoring for ecological risk assessment and management of emerging pollutants. Full article

Accurate simulation modeling of the danger zone and real-time visualization of the toxic cloud spread during a fire and explosion at an industrial facility in a nearby urban area are in demand by rescue services conducting evacuation. Using a cellular automaton method allows us to create an optimal predictive model of the danger zone spread, combine modeling accuracy with computational speed, and consider multiple input variables and the cascading nature of an accident during visualization. The objective of this study was to develop a mobile application for calculating the parameters of the danger zone during an accident at an industrial facility caused by a toxic cloud spreading into an urban area, based on the selection of a cellular automaton algorithm. The primary objective of the study was a highly detailed visualization of the danger zone with several predicted values of toxic substance concentrations in the air. The authors developed a cellular automaton-based model, which forms the basis of the mobile application. It takes into account several variables characterizing chemicals in the explosion and fire zone, climate factors, occupancy, building parameters, and the availability of respiratory protection. The FireSoft Mobile app was developed using the Visual Studio 2022 development environment, C# 10.0, and .NET MAUI, adapted for Android 8.0 and higher. The mobile app was tested to visualize a cloud of toxic pollutants forming a hazardous zone in an urban agglomeration for cases involving an ammonia tank explosion and a large fire involving a large amount of polyvinyl chloride. The results demonstrate the app’s feasibility and effectiveness in predicting, planning, and managing evacuation measures during accidents at an industrial facility. Full article

In equatorial Africa, rapid urbanization has increased city populations and particulate matter emissions. Street dust is a visual indicator that can be used to track urban pollution. In the present study, the total concentration and speciation of 10 potentially toxic elements (PTEs; As, Cd, Cu, Cr, Ni, Mn, Fe, Pb, Co, and Zn) in dust (n = 36) sampled from three streets of Mbarara City, Uganda, were determined using Energy Dispersive X-ray Fluorescence and Inductively Coupled Plasma-Optical Emission Spectrometry. The concentration of PTEs (0.27–36,401.50 mg/kg) geostatistically indicated moderate to extremely high enrichment of Cd, Cu, and Co in street dust. According to principal component and hierarchical cluster analyses, As, Pb, Cu, Zn, and Cd originated mainly from anthropogenic inputs, Fe and Mn came from geogenic sources, while Cr, Ni, and Co were from both natural and anthropogenic contributions. The mobility of the PTEs followed a general trend, Zn > Co > Cd > Ni > Cr, with Zn and Co being more environmentally mobile. Human health risk assessments indicated that discernible non-carcinogenic health risks may result from ingestion of dust by both children and adults. Children could also experience cancer health effects through the same exposure pathway. Full article

The impact of PM_2.5 pollution on the health inequality of vulnerable groups is a core issue in environmental justice research. However, existing studies in China mostly focus on severely polluted areas in northern China. They lack comparative cases in economically developed eastern regions. They also rarely consider changes in the impact of air pollution on residents’ health amid rapid urbanization. Based on multi-source data, this study employed spatial visualization, spatial autocorrelation analysis and spatial regression models. It investigated the impact of PM_2.5 pollution on the health inequality of vulnerable elderly groups in 92 districts and counties of Jiangsu Province from 2010 to 2020. The results show that: first, the regional pattern of health inequality between PM_2.5 pollution and vulnerable elderly groups in Jiangsu has continuously evolved, with a “lower in the south and higher in the north” pollution pattern and high overlap between high-pollution areas and high elderly health risk areas in northern Jiangsu. Second, the spatial coupling between PM_2.5 and elderly health risks has gradually strengthened, showing significant positive spatial agglomeration in 2020, confirming obvious spatial agglomeration characteristics of air pollution’s health impact. Third, the adverse health impact of PM_2.5 on vulnerable elderly groups became significant in 2020, exhibiting cumulative and lagged characteristics; urbanization and regional coordinated development have played a positive role in alleviating regional health inequality, while a lagging energy structure further exacerbates the health vulnerability of the elderly. This study fills the gap of insufficient research on economically developed eastern regions and provides targeted empirical references for urban refined governance and precise prevention and control of environmental health inequality. Full article

The durability of building façades is critical to sustainable construction because it affects maintenance demand, safety, and long-term service performance. As building stocks age, especially in rapidly urbanizing countries such as China, reliable prediction of façade degradation becomes increasingly important for service-life planning and maintenance decision-making. However, conventional service-life prediction methods are commonly based on ordinary least squares (OLS) regression, which mainly estimates the conditional mean and may therefore fail to represent the heterogeneity of degradation processes. Using visual inspection data from 375 painted façade samples in Ningbo, China, this study applies quantile regression (QR) to model façade degradation and predict service life. Degradation was quantified using an overall degradation level (ODL) index that integrates defects related to aesthetic deterioration, loss of integrity, and loss of adhesion. The results show that façade degradation follows heterogeneous rather than uniform trajectories, and that the effects of key variables vary across degradation levels. In particular, pollution exposure and water ingress become markedly more influential at higher quantiles, while the effect of routine maintenance weakens in severely degraded façades. After 5-fold cross-validation, the median quantile model reduced MAE by approximately 5.3% relative to the OLS benchmark (0.0537 vs. 0.0567), and the fitted quantiles showed good calibration, with empirical coverage deviations not exceeding 0.007. The QR framework predicted a service-life range of 4.3–31.8 years, substantially wider than the 8.8–20.2 years obtained from the MLR model, indicating a stronger ability to represent uncertainty and high-risk degradation paths. These results show that QR provides a more informative basis for risk-based inspection planning and façade service-life assessment in existing buildings. Full article

This paper addresses the issue of lighting in historic urban spaces, using Tarnow (Poland) as a case study. The aim is to assess the impact of artificial light sources on visual comfort within the area, with particular consideration given to light pollution. A comprehensive inventory of active street lighting in the Old Town was conducted. Measurements taken at ground and eye level revealed strong inconsistencies: some areas were under-lit (<1 lx), while others showed façade illuminance above 100 lx, far exceeding recommended thresholds. The highest environmental impact was shown by decorative and globe-type fixtures, with Sky Glow Contribution Index (SGCI) values of up to 0.62. Only suspended street luminaires met CIE requirements (ULR ≤ 15%). The findings reveal that several lighting installations do not meet recommended standards, adversely affecting both human comfort and ecological balance. The study proposes strategies to optimise urban lighting, such as replacing inefficient fixtures with full cut-off LED luminaires and implementing intelligent lighting control systems which could reduce energy consumption by 50-67% while preserving the architectural character of the historic centre. The results provide evidence-based strategies for sustainable lighting modernisation in heritage cities across Europe. Full article

Driven by the goal of carbon neutrality, low-carbon development in township spaces is essential for sustainable urban–rural growth. This paper employs a carbon accounting methodology, taking Fuqiushan Town in the Dongting Lake Ecological Economic Zone as a case study to develop a detailed carbon measurement inventory at the township scale. Using spatial analysis techniques, it synthesizes multi-source data—including land use, agricultural inputs, and population—to estimate emissions from key sources such as crop cultivation, livestock and poultry breeding, industrial production, and residential activities. The study also evaluates the carbon sequestration capacity of sinks such as woodlands and water bodies, enabling the spatial visualization of both carbon emissions and carbon sinks. Key findings include: (1) Fuqiushan Town exhibits a carbon emission profile characterized by “industrial activities as the primary source, supplemented by agriculture, with additional contributions from residential and transportation sectors,” while forested areas and water bodies serve as core carbon sink zones. (2) An innovative multidimensional indicator system for low-carbon development efficiency was established, consisting of the Low-Carbon Development Efficiency Index in Production, the Daily Life Carbon Responsibility Efficiency Index, and the Ecological Carbon Sink Efficiency Index, which together form a Comprehensive Efficiency Index for Low-Carbon Development. (3) Analysis reveals significant spatial coupling relationships and efficiency differentiation patterns among carbon emissions, industrial structure, energy dependence, and ecological background. Based on dominant carbon emission types, low-carbon efficiency thresholds, and spatial factor interactions, the 17 villages and one forest farm in the township are classified into five zones: “Industrial High-Carbon Transition Zone,” “Agricultural Pollution Reduction and Carbon Emission Reduction Synergy Zone,” “Ecological Low-Carbon Conservation Zone,” “Human Settlements Balanced Development Zone,” and “Ecological Core Zone.” Tailored low-carbon spatial planning strategies for material resources are proposed for each zone. These results offer quantitative support and spatially targeted insights for low-carbon spatial planning in ecologically sensitive townships, contributing to the achievement of objectives such as “carbon reduction and sink increase” and “rural revitalization.” Full article

Visual pollution in urban environments has a significant impact on aesthetic quality, level of environmental complexity, coherence, and emotional well-being. Due to that, it needs to be analysed considering not only physical environment features and indicators but also aspects of environmental psychology and human emotional needs towards the urban environment. Taking into account this approach, in this research, it is studied applying a genotype-based framework using space syntax analysis and emotional mapping. Spatial analysis tools, such as space syntax and visibility graph analysis (VGA) provide reliable tools for statistically analysing this phenomenon. This method evaluates visual exposure and connectedness to polluting components across the map, resulting in locations with the most obvious pollution (The research examines spatial metrics such as integration, connectivity, and visibility, as well as emotional responses, to reveal significant links between urban spatial configurations and the visual pollution index (VPI). Zones with great accessibility and reachable by people, such as parks and public spaces, have positive emotional responses and low VPI scores, suggesting accessibility and visual harmony. On the contrary, low-integrated and fragmented areas have high VPI ratings, suggesting visual clutter, poor maintenance, and user dissatisfaction. Visual pollution affects the quality of urban surroundings by filling the visual space with contrasting and varied elements, resulting in visual dissonance. Common sources of visual pollution include architectural forms, billboards, advertising boards, signage, and poorly maintained building façades, particularly in modernist neighbourhoods. The Dainava neighbourhood in Kaunas city is used as a case study to apply this integrated methodology, revealing spatial and emotional aspects of the neighbourhood relevant to the VPI assessment. The findings highlight the relevance of a complex methodological approach that integrates spatial and emotional qualities of the environment and the importance of targeted actions, such as improving visibility, creating visual relations, and reducing visual clutter, in establishing inclusive, legible, and visually harmonious urban spaces. This methodological framework provides urban planners with a practical tool for the evaluation of visual pollution that integrates egzogenous (physical) and endogenous (emotional) factors and has predictive capacities to indicate the environment that is the most sensitive to visual pollution. Full article

Understanding the behavior of light non-aqueous phase liquids (LNAPLs) in urban subsurface environments is essential to developing effective pollution control strategies, designing remediation systems, and managing waste and resources sustainably. Oil leakage from urban industrial facilities, underground pipelines, and fueling systems often leads to contamination that is challenging to characterize due to complex soil structures, limited access beneath densely built infrastructure, and dynamic groundwater conditions. In this study, we integrate a large-scale soil tank experiment with multiphase flow simulations to elucidate LNAPL distribution mechanisms under fluctuating groundwater conditions. A 2.4-m-by-2.4-m-by-0.6-m soil tank was used to visualize oil movement with high-resolution multispectral imaging, enabling a quantitative evaluation of saturation distribution over time. The results showed that a rapid rise in groundwater can trap 60–70% of the high-saturation LNAPL below the water table. In contrast, a subsequent slow rise leaves 10–20% residual saturation within pore spaces. These results suggest that vertical redistribution caused by groundwater oscillation significantly increases residual contamination, which cannot be evaluated using static groundwater assumptions. Comparisons with a commonly used NAPL simulator revealed that conventional models overestimate lateral spreading and underestimate trapped residual oil, thus highlighting the need for improved constitutive models and numerical schemes that can capture sharp saturation fronts. These results emphasize that an accurate assessment of LNAPL contamination in urban settings requires an explicit consideration of groundwater fluctuation and dynamic multiphase interactions. Insights from this study support rational monitoring network design, reduce uncertainty in remediation planning, and contribute to sustainable urban environmental management by improving risk evaluation and preventing the long-term spread of pollution. Full article

Water quality monitoring faces increasing challenges due to accelerating industrialization and urbanization, demanding accurate, real-time, and reliable prediction technologies. This study presents a novel ensemble learning framework integrating Gaussian Process Regression, Support Vector Regression, and Random Forest algorithms for high-precision water quality pH prediction. The research utilized a comprehensive spatiotemporal dataset, comprising 11 water quality parameters from 37 monitoring stations across Georgia, USA, spanning 705 days from January 2016 to January 2018. The ensemble model employed a dynamic weight allocation strategy based on cross-validation error performance, assigning optimal weights of 34.27% to Random Forest, 33.26% to Support Vector Regression, and 32.47% to Gaussian Process Regression. The integrated approach achieved superior predictive performance, with a mean absolute error of 0.0062 and coefficient of determination of 0.8533, outperforming individual base learners across multiple evaluation metrics. Statistical significance testing using Wilcoxon signed-rank tests with a Bonferroni correction confirmed that the ensemble significantly outperforms all individual models (p < 0.001). Comparison with state-of-the-art models (LightGBM, XGBoost, TabNet) demonstrated competitive or superior ensemble performance. Comprehensive ablation experiments revealed that Random Forest removal causes the largest performance degradation (+4.43% MAE increase). Feature importance analysis revealed the dissolved oxygen maximum and conductance mean as the most influential predictors, contributing 22.1% and 17.5%, respectively. Cross-validation results demonstrated robust model stability with a mean absolute error of 0.0053 ± 0.0002, while bootstrap confidence intervals confirmed narrow uncertainty bounds of 0.0060 to 0.0066. Spatiotemporal analysis identified station-specific performance variations ranging from 0.0036 to 0.0150 MAE. High-error stations (12, 29, 33) were analyzed to distinguish characteristics, including higher pH variability and potential upstream pollution influences. An integrated software platform was developed featuring intuitive interface, real-time prediction, and comprehensive visualization tools for environmental monitoring applications. Full article

Soil heavy metal contamination poses significant risks to ecological safety and human health, particularly in rapidly industrializing cities. Effectively identifying pollution sources is crucial for risk management and remediation. GIS coupled with data mining techniques, provide a powerful tool for quantifying and visualizing these sources. This study investigates the concentration, spatial distribution, and sources of heavy metals in urban soils of Bengbu City, an industrial and transportation hub in eastern China. A total of 139 surface soil samples from the urban core were analyzed for nine heavy metals. Using integrated GIS and PCA-APCS-MLR data mining techniques, we systematically determined their contamination characteristics and apportioned sources. The results identified widespread Hg enrichment, with concentrations exceeding background levels at all sampling sites, and a Cd exceedance rate of 28.06%, leading to a moderate ecological risk level overall. Spatial patterns revealed significant heterogeneity. Quantitative source apportionment identified four primary sources: industrial source (37.1%), which was the dominant origin of Cr, Cu, and Ni, primarily associated with precision manufacturing and metallurgical activities; mixed source (26.7%) governing the distribution of Mn, As, and Hg, mainly from coal combustion and the natural geological background; traffic source (22.3%) significantly contributing to Pb and Zn; and a specific cadmium source (13.9%) potentially originating from non-ferrous metal smelting, electroplating, and agricultural activities. These findings provide a critical scientific basis for targeted pollution control and sustainable land-use management in analogous industrial cities. Full article

Urban tree canopy is increasingly recognized as a health-protective form of green infrastructure, yet its distribution remains uneven across socioeconomically stratified neighborhoods. This study quantifies fine-scale tree-canopy inequity across Census Block Groups (CBGs) in Baltimore and examines associations with socioeconomic deprivation and modeled pollution-related cancer risk. We integrated (i) 2023 US Forest Service canopy estimates aggregated to CBGs, (ii) Area Deprivation Index (ADI) national and state ranks, (iii) American Community Survey 5-year population counts, and (iv) EPA NATA/HAPs cancer-risk estimates aggregated to CBGs using population-weighted means. Associations were assessed using Spearman correlations and visualized with LOESS smoothers. Canopy was negatively associated with ADI national and state ranks (ρ = −0.509 and −0.503), explaining 29–31% of canopy variation. Population-weighted canopy declined from 47–51% in the least deprived decile to 13–15% in the most deprived (3.4–4.1× disparity). Beyond socioeconomic gradients, overall distributional inequity was quantified using a population-weighted Tree Canopy Inequality Index (TCI; weighted Gini), yielding TCI = 0.312, indicating substantial inequality. The population-weighted Atkinson index rose sharply under increasing inequality aversion (A_0.5 = 0.084; A₂ = 0.402), revealing extreme canopy deficits concentrated among the most disadvantaged neighborhoods. Canopy was also negatively associated with modeled cancer risk (ρ = −0.363). We constructed a Triple Burden Index integrating canopy deficit, deprivation, and cancer risk, identifying spatially clustered high-burden neighborhoods that collectively house over 86,000 residents. These findings demonstrate that canopy inequity in Baltimore is structurally concentrated and support equity-targeted greening and sustained maintenance strategies guided by distributional justice metrics. Full article