Search Results (721)

Urban wildlife conservation is emerging as a critical component of sustainable city ecosystems. Rather than simply increasing tree abundance or species richness, conservation management should focus on key species. In this research, Xishan Forest Park in Beijing was chosen as a case study. Our aim was to identify keystone taxa critical for avian foraging and nesting during the breeding season. We performed a network analysis linking bird species, their diets, and nest plants. Dietary components were detected using DNA metabarcoding conducted with avian fecal samples. Nest plants were identified via transect surveys. Two indices of the network, degree and weighted mean degree, were calculated to evaluate the importance of the dietary and nest plant species. We identified 13 bird host species from 107 fecal samples and 14 bird species from 107 nest observations. Based on the degree indices, fruit trees Morus and Prunus were detected as key food sources, exhibiting both the highest degree (degree = 9, 9) and weighted mean degree (lnwMD = 5.21, 4.63). Robinia pseudoacacia provided predominant nesting sites, with a predominant degree of 7. A few taxa, such as Styphnolobium japonicum and Rhamnus parvifolia, served dual ecological significance as both essential food sources and nesting substrates. Scrublands, as a unique habitat type, provided nesting sites and food for small-bodied birds. Therefore, targeted management interventions are recommended to sustain or enhance these keystone resource species and to maintain the multi-layered vertical vegetation structure to preserve the diverse habitats of birds. Full article

To study the generation and dissipation process of agglomerate fog in mountainous expressways and deeply understand the hazard mechanisms of agglomerate fog sections in mountainous expressways, based on the analysis of the geographical location characteristics of mountainous expressways and the spatial and temporal distribution characteristics of agglomerate fog, the airspace constitutive model of agglomerate fog in mountainous expressways was constructed based on Newton constitutive theory. Firstly, the properties of the Newtonian fluid and cluster fog were compared and analyzed, and the influence mechanism of environmental factors such as the altitude difference, topography, water system, valley effect, and vegetation on the generation and dissipation of agglomerate fog in mountainous expressways was analyzed. Based on Newton’s constitutive theory, the constitutive model of temperature, humidity, wind speed, and agglomerate fog points in the foggy airspace of the mountainous expressway was established. Then, the time and spatial distribution of fog in Chongqing and Guizhou from 2021 to 2023 were analyzed. Finally, the model was verified by using the meteorological data and fog warning data of Liupanshui City, Guizhou Province in 2023. The results show that the foggy airspace of mountainous expressways can be defined as “the space occupied by the agglomerate fog that occurs above the mountain expressway”; The temporal and spatial distribution of foggy airspace on expressways in mountainous areas is closely related to the topography, water system, vegetation distribution, and local microclimate formed by thermal radiation. The horizontal and vertical movements of the atmosphere have little influence on the foggy airspace on expressways in mountainous areas. The specific manifestation of time distribution is that the occurrence of agglomerate fog is concentrated from November to April of the following year, and the daily occurrence time is mainly concentrated between 4:00–8:00 and 18:00–22:00. The calculation results of the foggy airspace constitutive model of the expressway in the mountainous area show that when there is low surface radiation or no surface radiation, the fogging value range is [90, 100], and the fogging value range is [50, 70] when there is high surface radiation (>200), and there is generally no fog in other intervals. The research results can provide a theoretical basis for traffic safety management and control of mountainous expressway fog sections. Full article

Background/Objectives: Vertical jump is considered a reliable and valid method of assessing the level of muscular power and coordination across one’s lifespan. The main aim of the present study was to establish sex- and age-normative data for vertical jump outcomes in pre-school children. Methods: We recruited 411 boys and girls aged 3−6 years from four major cities in Croatia and Slovenia. Vertical jump was assessed with two tests: countermovement jump (CMJ) without and with arm swing using a reliable and valid Optojump measuring platform. Data were presented for the 5th, 15th, 25th, 50th (median), 75th, 90th, and 95th percentile. Results: No significant differences were observed in multiple vertical jump outcomes between boys and girls. The mean values for CMJ without and with arm swing between boys and girls were as follows: contact time (1.4 vs. 1.4 s/1.8 vs. 1.7 s), flight time (0.32 vs. 0.31 s/0.33 vs. 0.32), height (12.3 vs. 12.2 cm/13.0 vs. 12.5 cm), power (9.4 vs. 9.5 W/kg/9.3 vs. 9.1 W/kg), pace (0.7 vs. 0.7 steps/s/0.6 vs. 0.6 steps/s), reactive strength index (RSI; 0.10 vs. 0.09 m/s/0.08 vs. 0.08 m/s), and verticality (2.5 vs. 2.3/1.9 vs. 1.9). A gradual increase in all measures according to ‘age’ was observed (p for trend < 0.05). No significant ‘sex*age’ interaction was observed (p > 0.05). Conclusions: This is one of the first studies to provide sex- and age-normative data for complete vertical jump outcomes in pre-school children. These data will serve as an avenue for monitoring and tracking motor development in this sensitive period. Full article

The Xiong’an New Area, a newly established national-level zone in China, faces the threat of land subsidence and ground fissure due to groundwater overexploitation and geothermal extraction, threatening urban safety. This study integrates time-series InSAR and GNSS monitoring to analyze spatiotemporal deformation patterns from 2017/05 to 2025/03. The key results show: (1) Three subsidence hotspots, namely northern Xiongxian (max. cumulative subsidence: 591 mm; 70 mm/yr), Luzhuang, and Liulizhuang, strongly correlate with geothermal wells and F4/F5 fault zones; (2) GNSS baseline analysis (e.g., XA01-XA02) reveals fissure-induced differential deformation (max. horizontal/vertical rates: 40.04 mm/yr and 19.8 mm/yr); and (3) InSAR–GNSS cross-validation confirms the high consistency of the results (Pearson’s correlation coefficient = 0.86). Subsidence in Xiongxian is driven by geothermal/industrial groundwater use, without any seasonal variations, while Anxin exhibits agricultural pumping-linked seasonal fluctuations. The use of rooftop GNSS stations reduces multipath effects and improves urban monitoring accuracy. The spatiotemporal heterogeneity stems from coupled resource exploitation and tectonic activity. We propose prioritizing rooftop GNSS deployments to enhance east–west deformation monitoring. This framework balances regional and local-scale precision, offering a replicable solution for geological risk assessments in emerging cities. Full article

This study investigates ground deformations in the southeastern Po Plain (northern Italy), focusing on the Bologna area—a densely populated region affected by natural and anthropogenic subsidence. Ground deformations in the area result from geological processes (e.g., sediment compaction and tectonic activity) and human activities (e.g., ground water production and underground gas storage—UGS). We apply a multidisciplinary approach integrating subsurface geology, ground water production, advanced differential interferometry synthetic aperture radar—DInSAR, gas storage data, and land use information to characterize and analyze the spatial and temporal variations in vertical ground deformations. Seasonal and trend decomposition using loess (STL) and cluster analysis techniques are applied to historical DInSAR vertical time series, targeting three representatives areas close to the city of Bologna. The main contribution of the study is the attempt to correlate the lateral extension of ground water bodies with seasonal ground deformations and water production data; the results are validated via knowledge of the geological characteristics of the uppermost part of the Po Plain area. Distinct seasonal patterns are identified and correlated with ground water production withdrawal and UGS operations. The results highlight the influence of superficial aquifer characteristics—particularly the geometry, lateral extent, and hydraulic properties of sedimentary bodies—on the ground movements behavior. This case study outlines an effective multidisciplinary approach for subsidence characterization providing critical insights for risk assessment and mitigation strategies, relevant for the future development of CO₂ and hydrogen storage in depleted reservoirs and saline aquifers. Full article

Urban environments present substantial obstacles to GPS positioning accuracy, primarily due to multipath interference and limited satellite visibility. To address these challenges, we propose a novel weighting approach, referred to as the HK model, that enhances real-time GPS positioning performance by leveraging the variability of the signal-to-noise ratio (SNR), without requiring auxiliary sensors. Analysis of 24 h observational datasets collected across diverse environments, including open-sky (OS), city streets (CS), and urban canyons (UC), demonstrates that multipath-affected non-line-of-sight (NLOS) signals exhibit significantly greater SNR variability than direct line-of-sight (LOS) signals. The HK model classifies received signals based on the standard deviation of their SNR and assigns corresponding weights during position estimation. Comparative performance evaluation indicates that relative to existing weighting models, the HK model improves 3D positioning accuracy by up to 22.4 m in urban canyon scenarios, reducing horizontal RMSE from 13.0 m to 4.7 m and vertical RMSE from 19.5 m to 6.9 m. In city street environments, horizontal RMSE is reduced from 11.6 m to 3.8 m. Furthermore, a time-sequential analysis at the TEHE site confirms consistent improvements in vertical positioning accuracy across all 24-hourly datasets, and in terms of horizontal accuracy, in 22 out of 24 cases. These results demonstrate that the HK model substantially surpasses conventional SNR- or elevation-based weighting techniques, particularly under severe multipath conditions frequently encountered in dense urban settings. Full article

On 22 May 2012, a magnitude Mw 5.6 earthquake struck the Pernik region of western Bulgaria, causing structural damage in nearby cities, including Sofia. This study assesses the seismic response of a six-story reinforced concrete building in central Sofia, utilizing real accelerogram data recorded at the basement (SGL1) and sixth floor (SGL2) levels during the earthquake. Using the Kanai–Yoshizawa (KY) model, the study estimates inter-story motion and assesses amplification effects across the structure. Analysis of peak ground acceleration (PGA), velocity (PGV), displacement (PGD), and spectral ratios reveals significant dynamic amplification of peak ground acceleration and displacement on the sixth floor, indicating flexible and dynamic behavior, as well as potential resonance effects. The analysis combines three spectral techniques—Horizontal-to-Vertical Spectral Ratio (H/V), Floor Spectral Ratio (FSR), and the Random Decrement Method (RDM)—to determine the building’s dynamic characteristics, including natural frequency and damping ratio. The results indicate a dominant vibration frequency of approximately 2.2 Hz and damping ratios ranging from 3.6% to 6.5%, which is consistent with the typical damping ratios of mid-rise concrete buildings. The findings underscore the significance of soil–structure interaction (SSI), particularly in sedimentary basins like the Sofia Graben, where localized geological effects influence seismic amplification. By integrating accelerometric data with advanced spectral techniques, this research can enhance ongoing site-specific monitoring and seismic design practices, contributing to the refinement of earthquake engineering methodologies for mitigating seismic risk in earthquake-prone urban areas. Full article

Ensuring a sustainable and healthy human environment in university dormitories is essential for students’ learning, living, and overall health and well-being. To address this need, we carried out a series of systematic field measurements of the noise levels at 30 dormitories in three representative major urban universities in a major provincial capital city in China and designed and implemented a comprehensive questionnaire and surveyed 1005 students about their perceptions of their acoustic environment. We proposed and applied a sustainability–health-oriented, multidimensional assessment framework to assess the acoustic environment of the dormitories and student responses to natural sound, technological sounds, and human-made sounds. Using the Structural Equation Modeling (SEM) approach combined with the field measurements and student surveys, we identified three categories and six factors on student health and well-being for assessing the acoustic environment of university dormitories. The field data indicated that noise levels at most of the measurement points exceeded the recommended or regulatory thresholds. Higher noise impacts were observed in early mornings and evenings, primarily due to traffic noise and indoor activities. Natural sounds (e.g., wind, birdsong, water flow) were highly valued by students for their positive effect on the students’ pleasantness and satisfaction. Conversely, human and technological sounds (traffic noise, construction noise, and indoor noise from student activities) were deemed highly disturbing. Gender differences were evident in the assessment of the acoustic environment, with male students generally reporting higher levels of the pleasantness and preference for natural sounds compared to female students. Educational backgrounds showed no significant influence on sound perceptions. The findings highlight the need for providing actionable guidelines for dormitory ecological design, such as integrating vertical greening in dormitory design, water features, and biodiversity planting to introduce natural soundscapes, in parallel with developing campus activity standards and lifestyle during noise-sensitive periods. The multidimensional assessment framework will drive a sustainable human–ecology–sound symbiosis in university dormitories, and the category and factor scales to be employed and actions to improve the level of student health and well-being, thus, providing a reference for both research and practice for sustainable cities and communities. Full article

In recent years, China has experienced growing impacts from extreme weather events, emphasizing the importance of understanding regional atmospheric moisture dynamics, particularly Precipitable Water Vapor (PWV), to support sustainable environmental and urban planning. This study utilizes ten years (2013–2022) of Global Navigation Satellite System (GNSS) observations in typical cities in eastern China and proposes a comprehensive multiscale frequency-domain analysis framework that integrates the Fourier transform, Bayesian spectral estimation, and wavelet decomposition to extract the dominant PWV periodicities. Time-series analysis reveals an overall increasing trend in PWV across most regions, with notably declining trends in Beijing, Wuhan, and southern Taiwan, primarily attributed to groundwater depletion, rapid urban expansion, and ENSO-related anomalies, respectively. Frequency-domain results indicate distinct latitudinal and coastal–inland differences in the PWV periodicities. Inland stations (Beijing, Changchun, and Wuhan) display annual signals alongside weaker semi-annual components, while coastal stations (Shanghai, Kinmen County, Hong Kong, and Taiwan) mainly exhibit annual cycles. High-latitude stations show stronger seasonal and monthly fluctuations, mid-latitude stations present moderate-scale changes, and low-latitude regions display more diverse medium- and short-term fluctuations. In the short-term frequency domain, GNSS stations in most regions demonstrate significant PWV periodic variations over 0.5 days, 1 day, or both timescales, except for Changchun, where weak diurnal patterns are attributed to local topography and reduced solar radiation. Furthermore, ERA5-derived vertical temperature profiles are incorporated to reveal the thermodynamic mechanisms driving these variations, underscoring region-specific controls on surface evaporation and atmospheric moisture capacity. These findings offer novel insights into how human-induced environmental changes modulate the behavior of atmospheric water vapor. Full article

Buildings are a significant component of urban space and are essential to smart cities, catastrophe monitoring, and land use planning. However, precisely extracting building polygons from remote sensing images remains difficult because of the variety of building designs and intricate backgrounds. This paper proposes an end-to-end polygon dynamic adjustment algorithm (PDAA) to improve the accuracy and geometric consistency of building contour extraction by dynamically generating and optimizing polygon vertices. The method first locates building instances through the region of interest (RoI) to generate initial polygons, and then uses four core modules for collaborative optimization: (1) the feature enhancement module captures local detail features to improve the robustness of vertex positioning; (2) the contour vertex tuning module fine-tunes vertex coordinates through displacement prediction to enhance geometric accuracy; (3) the learnable redundant vertex removal module screens key vertices based on a classification mechanism to eliminate redundancy; and (4) the missing vertex completion module iteratively restores missed vertices to ensure the integrity of complex contours. PDAA dynamically adjusts the number of vertices to adapt to the geometric characteristics of different buildings, while simplifying the prediction process and reducing computational complexity. Experiments on public datasets such as WHU, Vaihingen, and Inria show that PDAA significantly outperforms existing methods in terms of average precision (AP) and polygon similarity (PolySim). It is at least 2% higher than existing methods in terms of average precision (AP), and the generated polygonal contours are closer to the real building geometry. Values of 75.4% AP and 84.9% PolySim were achieved on the WHU dataset, effectively solving the problems of redundant vertices and contour smoothing, and providing high-precision building vector data support for scenarios such as smart cities and emergency response. Full article

Orientation entropy serves as a critical metric for assessing the directional disorder of urban street networks. However, conventional two-dimensional (2D) approaches neglect vertical variations, limiting their applicability in cities with complex terrains. This study proposes a three-dimensional (3D) orientation entropy framework by integrating elevation data, providing a more comprehensive assessment of urban spatial complexity. We developed a computational workflow combining ArcGIS 10.8 for spatial data extraction and Python 3.10.10 for entropy calculation. A case study in Chongqing, China, explores the relationship between 3D orientation entropy and residents’ perceptions of spatial disorder through a small-scale survey. Although no statistically significant correlation was observed, the findings suggest emerging patterns and underscore the necessity of multidimensional frameworks in evaluating urban spatial experience. This research contributes a novel metric to urban design assessment, particularly in topographically diverse environments, and offers a foundation for future empirical studies. Full article

Urbanization has profoundly transformed land surface morphology and amplified thermal environmental modifications, culminating in intensified urban heat island (UHI) phenomena. Local climate zones (LCZs) provide a robust methodological framework for quantifying thermal heterogeneity and dynamics at local scales. Our study investigated the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXA) as a case study and systematically examined spatiotemporal patterns of LCZs and land surface temperature (LST) from 2002 to 2019, while elucidating mechanisms influencing urban thermal environments and proposing optimized cooling strategies. Key findings demonstrated that through multi-source remote sensing data integration, long-term LCZ classification was achieved with 1,592 training samples, maintaining an overall accuracy exceeding 70%. Landscape pattern analysis revealed that increased fragmentation, configurational complexity, and diversity indices coupled with diminished spatial connectivity significantly elevate LST. Rapid development of the city in the vertical direction also led to an increase in LST. Among seven urban morphological parameters, impervious surface fraction (ISF) and pervious surface fraction (PSF) demonstrated the strongest correlations with LST, showing Pearson coefficients of 0.82 and −0.82, respectively. Pearson coefficients of mean building height (BH), building surface fraction (BSF), and mean street width (SW) also reached 0.50, 0.55, and 0.66. Redundancy analysis (RDA) results revealed that the connectivity and fragmentation degree of LCZ_8 (COHESION8) was the most critical parameter affecting urban thermal environment, explaining 58.5% of LST. Based on these findings and materiality assessment, the regional cooling model of “cooling resistance surface–cooling source–cooling corridor–cooling node” of CZXA was constructed. In the future, particular attention should be paid to the shape and distribution of buildings, especially large, openly arranged buildings with one to three stories, as well as to controlling building height and density. Moreover, tailored protection strategies should be formulated and implemented for cooling sources, corridors, and nodes based on their hierarchical significance within urban thermal regulation systems. These research outcomes offer a robust scientific foundation for evidence-based decision-making in mitigating UHI effects and promoting sustainable urban ecosystem development across urban agglomerations. Full article

(1) Background: Urban parks play a critical role in conserving biodiversity within city landscapes, yet the effects of fine-scale microhabitat heterogeneity remain poorly understood. This study examines how land cover and vegetation unit type within parks influence butterfly diversity. (2) Methods: From July to September 2019 and June to September 2020, adult butterflies were surveyed in 27 urban parks across Beijing. We classified vegetation into units based on vertical structure and management intensity, and then applied the patch–matrix framework and landscape metrics to quantify fine-scale heterogeneity in vegetation unit composition and configuration. Generalized linear models (GLM), generalized additive models (GAM), and random forest (RF) models were applied to identify factors influencing butterfly richness (Chao1 index) and abundance. (3) Results: In total, 10,462 individuals representing 37 species, 28 genera, and five families were recorded. Model results revealed that the proportion of park area covered by spontaneous herbaceous areas (SHA), wooded spontaneous meadows (WSM), and the Shannon diversity index (SHDI) of vegetation units were positively associated with butterfly species richness. In contrast, butterfly abundance was primarily influenced by the proportion of park area covered by cultivated meadows (CM) and overall green-space coverage. (4) Conclusions: Fine-scale vegetation patch composition within urban parks significantly influences butterfly diversity. Our findings support applying the patch–matrix framework at intra-park scales and suggest that integrating spontaneous herbaceous zones—especially wooded spontaneous meadows—with managed flower-rich meadows will enhance butterfly diversity in urban parks. Full article

Walking-friendly cities not only promote health and environmental benefits but also play crucial roles in urban development and local economic revitalization. Typically, pedestrian interviews and surveys are used to evaluate walkability. However, these methods can be costly to implement at scale, as they demand considerable time and resources. To address the limitations in current methods for evaluating pedestrian pathways, we propose a novel approach utilizing wearable sensors and deep learning. This new method provides benefits in terms of efficiency and cost-effectiveness while ensuring a more objective and consistent evaluation of sidewalk surfaces. In the proposed method, we used wearable accelerometers to capture participants’ acceleration along the vertical (V), anterior-posterior ($AP$), and medio-lateral ($ML$) axes. This data is then transformed into the frequency domain using Fast Fourier Transform (FFT), a Kalman filter, a low-pass filter, and a moving average filter. A deep learning model is subsequently utilized to classify the conditions of the sidewalk surfaces using this transformed data. The experimental results indicate that the proposed model achieves a notable accuracy rate of 95.17%. Full article

Due to rising temperatures, energy use, and thermal discomfort, urban heat islands (UHIs) pose a serious environmental threat to urban sustainability. This systematic review synthesizes peer-reviewed literature on various forms of green infrastructure and their mechanisms for mitigating UHI effects, and the function of urban green spaces (UGSs) in reducing the impact of UHI. In connection with urban parks, green roofs, street trees, vertical greenery systems, and community gardens, important mechanisms, including shade, evapotranspiration, albedo change, and ventilation, are investigated. This study emphasizes how well these strategies work to lower city temperatures, enhance air quality, and encourage thermal comfort. For instance, the findings show that green areas, including parks, green roofs, and street trees, can lower air and surface temperatures by as much as 5 °C. However, the efficiency of cooling varies depending on plant density and spatial distribution. While green roofs and vertical greenery systems offer localized cooling in high-density urban settings, urban forests and green corridors offer thermal benefits on a larger scale. To maximize their cooling capacity and improve urban resilience to climate change, the assessment emphasizes the necessity of integrating UGS solutions into urban planning. To improve the implementation and efficacy of green spaces, future research should concentrate on policy frameworks and cutting-edge technology such as remote sensing. Full article