Search Results (914)

Wind–Rain Bridge architecture is an important type of ancient bridge architecture. To reveal the factors contributing to the decline in the spatial performance of Wind-Rain Bridges in Hunan under the influence of urbanization and to assess their spatial legibility across different urbanization gradients, this study addresses the limitations of traditional research, which has largely relied on static observations and lacked quantitative analysis. The findings provide a scientific basis for the refined conservation and adaptive revitalization of traditional architecture. This study examines 535 Wind–Rain Bridges in Hunan. Using Space Syntax, core indicators such as Integration and Choice were quantitatively calculated for both Wind–Rain Bridges and modern bridges. A controlled variable experiment was further employed to analyze the disturbance effects of modern road networks. In addition, an XGBoost multi-classification model was constructed to assess spatial legibility levels and identify key spatial-topological predictors. The results indicate that the decline in the spatial performance of Wind–Rain Bridges in Hunan is not linearly correlated with urbanization. Urban-type Wind–Rain Bridges are most significantly affected by the replacement effect of modern road networks, whereas Semi-Village-type Wind–Rain Bridges exhibit the strongest resilience. Among the variables included in this study, Integration and Mean Depth are the strongest predictors of spatial legibility. This study establishes an analytical framework of “type classification–factor identification–differentiated strategy formulation” and confirms that the primary driver of the spatial performance decline of Wind–Rain Bridges in Hunan is the fragmentation of traditional pedestrian road networks caused by modern transportation systems under urbanization, rather than the aging of the bridge structures themselves. Full article

Ice–snow tourism in Northeast China is developing rapidly. Against this backdrop, revealing the spatial network structure of ice–snow tourism cities and assessing their disturbance resistance capacity is of great significance for achieving high-quality development of regional ice–snow tourism. This study takes 25 cities across the three northeastern provinces as network nodes, using data covering the period from January 2024 to March 2025. Integrating a complex network analysis framework, this paper comprehensively employs an accessibility model, tourism symbiotic linkage intensity model, and core–periphery model to distinguish core and peripheral cities within the network, analyze its structural characteristics and spatial patterns, and evaluate network vulnerability by simulating two scenarios: random attacks and deliberate attacks. The results indicate that: (1) Accessibility presents a concentric zonal pattern that attenuates gradually from the center to the periphery, accompanied by pronounced north–south disparities. Urban symbiosis intensity is strongly influenced by transportation distance, exhibiting a distinct proximity symbiosis pattern. (2) An ice–snow tourism symbiotic network has initially taken shape among northeastern cities. The network displays small-world properties; however, urban development is unbalanced, with marked hierarchical differentiation. Based on geographic location and resource endowments, the network can be divided into four cohesive subgroups. (3) The symbiotic network proves robust under random attacks, whereas connectivity declines sharply under deliberate attacks, embodying typical “robust-yet-vulnerable” structural characteristics. Both expanding the scale of core nodes and optimizing inter-node connection weights can significantly enhance network robustness. The static identification and dynamic dependency evaluation framework constructed in this study can effectively identify key nodes and vulnerable links within ice–snow city networks, and can serve as a reference for the coordinated development and structural optimization of ice-snow tourism in Northeast China. Full article

Dissolved organic matter (DOM) plays a key role in shaping lake microbiomes and water quality, yet its spatial variability and regional links to microbial activity remain unclear. Using three-dimensional excitation–emission matrix and self-organizing map analysis on 38 samples from a human-impacted lake network in Hubei (affected by tourism, agriculture, and urban areas), this study clarifies DOM heterogeneity and its environmental connections. Microbial metabolic activity represented by total bacterial content (BC) and Escherichia coli (E. coli) activity was rapidly and automatically measured with a ColiMinder device. Random forest (RF) modeling and principal component analysis (PCA) were applied to identify key drivers of microbial activity and to clarify correlations between DOM characteristics and microbial activitiy. Results indicated that although DOM in all three sectors primarily originated from microbial activities during the flat-water period, Tuanhu (TH) exhibited a higher degree of DOM humification and a larger average relative molecular mass, reflecting stronger terrestrial source characteristics. RF analysis identified NH₄⁺ as the main predictor of both BC and E. coli levels, while total organic carbon (TOC) and total nitrogen (TN) were also important predictors. PCA further revealed clear differences in DOM composition across the lakes. DOM in TH was predominantly autochthonous, whereas DOM in Miaohu (MH) and Guozheng (GZ) was mainly of humic origin. This study adopts an integrated method combining rapid microbial detection, EEM and DOM–microbe correlation analysis to analyze human disturbances across segmented connected lakes in Hubei. It provides scientific support for targeted water quality management of human-influenced freshwater. Full article

Against the backdrop of global climate change and carbon neutrality strategies, land use carbon emissions have become a prominent topic amid regional efforts toward low-carbon transformation. However, existing studies on land-use carbon emissions have predominantly focused on humid and economically developed regions, while the unique carbon metabolism pathways of arid oasis–desert ecosystems, which are characterized by extremely low environmental carrying capacity and high sensitivity to land-use disturbance, remain largely unexplored. This study takes the oasis urban cluster in the Tarim Basin in southern Xinjiang Uygur Autonomous Region as the research object. This region belongs to a typical oasis–desert composite ecosystem, with a simple structure and low environmental carrying capacity (reflected by sparse vegetation cover < 20%, annual precipitation < 100 mm, extremely limited water resources, and high sensitivity to land disturbance). Its carbon metabolism pathway (i.e., the dynamic balance between carbon sources and sinks induced by land-use change) is fundamentally different from that in humid areas, and thus merits dedicated investigation. This study selects the period from 2000 to 2020 as the research period, which completely covers the acceleration period of urbanization and agricultural expansion in the Tarim Basin oasis urban cluster since the advancement of China’s Western Development Initiative. The data have a temporal resolution of 5 years (samples in 2000, 2005, 2010, 2015, 2020) and a spatial resolution of 30 m for land use and prefecture level for socio-economic indicators. Based on this, to fill the above-mentioned research gap, a research framework integrating the carbon emission coefficient accounting method, landscape pattern index, spatial autocorrelation analysis and geographic detector is adopted. Specifically, this study aims to systematically quantify the spatio-temporal evolution of land use carbon emissions and identify the most robust driving factors in the Tarim Basin oasis urban cluster by integrating multiple models, an approach that has not been previously applied to arid oasis regions. The research results show: (1) Based on the carbon emission coefficient method, total carbon emissions increased from 1.4455 million tons to 22.364 million tons, following a ‘slow-then-fast’ trajectory. In terms of temporal evolution, the study period can be further divided into three sub-stages: 2000–2005 (slow diffusion, with emission center skewed toward the northern energy-intensive zone), 2005–2015 (rapid restructuring, characterized by a ‘unipolar surge’ in Aksu and spread to the central oasis belt), and 2015–2020 (high-intensity stabilization, forming a cross-regional emission belt). Meanwhile, the land use structure has undergone a significant transformation. Construction land and cultivated land have continued to expand, while ecological land has significantly shrunk, resulting in a complex transformation pattern of oasis–desert ecotone. (2) The overall landscape became increasingly fragmented and diversified, the integrity of ecological space was damaged, and the regional carbon sink function was weakened. (3) The spatial autocorrelation analysis indicates that the spatial distribution of carbon emissions shows a heterogeneous pattern, forming a high-emission concentration area centered around Aksu-Bayingol. However, the global Moran’s I index is negative (such as −0.171 in 2020, p > 0.05), suggesting that carbon emissions have not formed a significant spatial clustering. (4) Carbon emissions are dominated by human and economic factors, and the interaction of factors is significant. The geographic detector identifies population density (average q value 0.904) and the proportion of construction land (average q value 0.858) as the key determinants of spatial variation in carbon emissions, reflecting the sensitive response of the human-nature system of arid zones to the urbanization process. These findings not only clarify the spatio-temporal features and driving forces of land use carbon emissions in the Tarim Basin oasis urban cluster, but also provide a replicable analytical framework for carbon-emission research in other arid and semi-arid regions worldwide. Based on these findings, we discuss the unique driving mechanisms of carbon emissions in arid regions, conclude that construction land expansion and population density are the dominant factors, and recommend a three-tier zoning governance system (carbon source control zone, carbon sink enhancement zone, coordinated development zone) for low-carbon spatial planning in arid areas. Full article

As demand for non-motorized travel continues to rise, the underdevelopment of non-motorized lane infrastructure in high-density cities has become increasingly evident, affecting cyclists’ travel experience and safety. Existing cycling environment assessment methods have developed relatively comprehensive frameworks, but they still have difficulty capturing the various disturbances encountered during actual cycling and identifying segment-level problems for targeted interventions. To address these limitations, this study proposes a cycling-data-based framework for non-motorized lane information extraction and rideability assessment. The framework integrates cycling trajectories, first-person cycling videos, urban road networks, and points of interest (POIs) to extract information on road space, facility attributes, pavement conditions, visual environment, and static and dynamic disturbances, and further transforms this information into segment-level rideability assessment indicators. On this basis, an assessment system covering safety, comfort, attractiveness, and accessibility is constructed, and Wuhan is used as an empirical case study. Fuzzy C-means (FCM) clustering is then applied to identify six typical lane types and support differentiated governance strategies. The findings provide practical references for non-motorized lane planning, slow-traffic space improvement, and the management of motorized–non-motorized traffic conflicts. Full article

Rapid urbanization and the urban-rural dualism are subjecting traditional villages to various slow-onset disturbances. The resilience of traditional villages (RTV) has become essential for their sustainable development. By measuring, classifying, and zoning RTV, this study aims to reveal its actual state and heterogeneous characteristics, thereby offering clear guidance for differentiated sustainable development strategies in traditional villages. From an integrated perspective of the tripartite attributes of traditional villages, this study develops an RTV assessment framework comprising three dimensions: structural persistability (SP) as vernacular heritage, functional adaptability (FA) as rural communities, and industrial transformability (IT) as tourism resources. Using hierarchical clustering, the obstacle degree model, the optimal parameters-based geographical detector, and spatially weighted hierarchical clustering, this study identifies distinct RTV types, along with their statistical distributions, key constraints, and spatial patterns. The main conclusions are as follows. (1) Most traditional villages in Jincheng exhibit low or medium-low levels of resilience. Moreover, the three dimensions of RTV are unevenly developed, with the IT dimension lagging markedly behind the others. (2) The key obstacles to enhancing RTV are the scarcity of high-value heritage resources, insufficient public services, low regional socioeconomic vitality, low public visibility, a scarcity of high-quality tourism assets, inadequate tourism support facilities, and a limited local tourism supply market. (3) Jincheng’s traditional villages cluster into four resilience-based zones, enabling a regional approach to their conservation. Full article

With the continuing advancement of global environmental change and rapid urbanization, rural human settlements are facing multiple pressures, including ecological degradation, spatial decline, population outflow, and functional weakening. Based on the vulnerability analysis framework, studies on rural vulnerability provide an important perspective for assessing villages’ risk exposure, disturbance response, and functional degradation when coping with internal and external disturbances. However, existing studies often rely on single-dimensional or linearly weighted evaluations, making it difficult to comprehensively reveal the coupling relationships among multiple discriminative variables and the spatial differentiation patterns of vulnerability. Taking rural areas in Hancheng City, Shaanxi Province, as the research object, this study selects 12 indicators from three dimensions—natural ecological constraints, settlement spatial organization, and public service support—to provide proxy representations of conditions related to potential rural vulnerability. K-means clustering was used to identify potential vulnerability configuration types under multidimensional indicator combinations. A Python-based XGBoost model was then employed as an interpretable surrogate model to assist in characterizing the clustering boundaries, while SHAP analysis was used to explain the key discriminative variables associated with type membership. The results show that the potential rural vulnerability configurations in Hancheng City present a significant west–central–east spatial differentiation pattern. Elevation, village core density, topographic wetness index, distance to town centers, accessibility of daily service facilities, distance to major roads, and normalized difference vegetation index are the main discriminative variables distinguishing different potential vulnerability configuration types. Among them, village core density shows a particularly strong explanatory role. Different key discriminative variables also exhibit evident nonlinear response characteristics across different potential types. Under the indicator system and the $K$ = 4 clustering scheme adopted in this study, the potential rural vulnerability configurations in Hancheng City can be summarized into four types: service-concentrated settlement type, complex terrain-constrained type, human–land coupling transitional type, and natural ecological isolation type. The findings reveal the spatial differentiation characteristics, variable combination relationships, and typological discriminative features of potential rural vulnerability configurations in Hancheng City. They can provide a case-based reference for identifying potential vulnerability, conducting spatial zoning diagnosis, and supporting classified governance in similar county-level rural areas within the loess hilly and gully region. In practical terms, this framework can serve as a diagnostic tool for local governments and planners in classified rural governance. It can be used to identify priority areas for public service and infrastructure investment, review key risk-control areas in complex terrain zones, delineate low-intensity use and protection boundaries in ecologically isolated areas, and guide differentiated resource allocation for different types of villages. Full article

Aging and deterioration of urban underground drainage pipelines frequently trigger road collapses, urban waterlogging and groundwater contamination, posing critical challenges to the operation, maintenance and disaster prevention of urban underground infrastructure. Conventional rehabilitation solutions, including cement-based linings and traditional polymer liners, suffer from inherent limitations such as reduced effective flow cross-sections caused by excessive lining thickness, unsatisfactory corrosion resistance and durability, and high construction disturbance. In this study, a modified polyurea (MPU) material was applied to the trenchless rehabilitation of drainage pipelines via spray-applied pipe lining technology. The mechanical properties and interfacial bonding performance of MPU were systematically characterized at the material scale; full-scale external pressure tests were conducted to investigate the effects of 3–8 mm thick MPU linings on the bearing capacity and failure characteristics of structurally damaged concrete pipes; and the anti-seepage repair performance for local perforation defects was evaluated through void-crossing testing. The results demonstrate that MPU lining can meet the engineering performance requirements for pipeline rehabilitation when applied with matched interfacial primer following standard construction procedures. Even the baseline bond strength tested without primer remains sufficient to ensure stable cooperative load bearing between the lining and the host concrete pipe. The 3–8 mm thick linings increase the cracking load of damaged pipes by 61.7–145.7% and the ultimate load by up to 162.2%, while transforming the failure mode from brittle fracture to ductile failure. For local perforation repair, the 3 mm thick MPU lining achieves a critical hydrostatic failure pressure of 1.23 MPa, maintaining favorable structural integrity and interfacial bonding stability under the test conditions. With a well-balanced combination of thin lining thickness, rapid curing and high structural strengthening efficiency, as well as favorable inherent corrosion resistance, the MPU lining provides novel material alternatives and fundamental experimental evidence for the green trenchless rehabilitation of aged underground pipelines and offers technical support for the safe operation and maintenance of urban underground infrastructure. Full article

Archaeological feature detection at complex, mixed-environment sites requires accurate, efficient methods for identifying subtle morphological signatures. This study presents a semi-automated remote sensing pipeline for the detection and delineation of archaeological earthworks at Cahokia Mounds (Illinois, USA), a major Mississippian urban center and UNESCO World Heritage Site. Three LiDAR datasets, two collected via UAV-mounted sensors and one from a piloted aircraft survey, were processed into Digital Terrain Models and transformed into Local Relief Models (LRM). K-means clustering was applied to segment the LRMs into feature classes, followed by contour bounding using the OpenCV library to outline mounds and borrow pits. Additionally, SAR-derived Local Incidence Angle (LIA) rasters from PALSAR-3 and Sentinel-1 were processed through angular deviation mapping to identify slope anomalies associated with archaeological features. Results across all five datasets demonstrate the complementary strengths of LiDAR and SAR: LiDAR excels at resolving elevation-defined features such as mound footprints, while LIA captures directional slope behavior that highlights mound edges, borrow pit rims, and linear features such as causeways. Comparative analysis of LiDAR acquisition frequencies reveals minimal differences in archaeological feature recovery between pulse settings, suggesting that sensor platform choice matters more than power-density tradeoffs for this application. Despite the need for human review to filter modern disturbances and natural false positives, the integrated workflow meaningfully accelerates prospection and reduces interpretive subjectivity. The methods are scalable, site-invariant, and work with open-access data, making them applicable to archaeological landscapes worldwide. Full article

Conventional static approaches to cultivated land quality (CLQ) assessment often fail to capture the rapid spatial restructuring of cultivated land in urbanizing regions. This study takes Chengdu, China, as the study area and employs multi-source and multi-indicator datasets from 2010, 2017, and 2023 to assess CLQ using an integrated AHP-CRITIC weighting approach, combined with obstacle degree and constraint factor analyses. The results show that the mean CLQ score increased from 0.520 in 2010 to 0.695 in 2023, reflecting the continuous improvement in stable cultivated land quality. Constraint factors also shifted from natural endowment limitations to engineering- and management-related disturbances: converted land was mainly constrained by climatic and topographic conditions, newly added land by soil moisture and fertility after land-use conversion, and stable land by compound soil-water and terrain constraints. These findings provide scientific evidence and practical references for high-standard farmland construction and refined cultivated land governance in rapidly urbanizing grain-producing regions. Full article

A methodology for the development of nonlinear model predictive control for thermal management of a 40-ton fuel-cell heavy-duty truck is presented, using the medium-temperature cooling circuit as a case study. The approach integrates control-oriented modeling, parameter estimation, and experimental validation based on drivetrain test bench measurements under controlled high-temperature ambient conditions. A lumped-parameter model of the medium-temperature circuit, including coolant, oil, electric motors, and power-electronics auxiliaries, is derived and implemented in a Simulink environment, with heat-transfer parameters calibrated from test bench data and radiator air-side resistance and fan characteristics derived from CFD simulations and manufacturer specifications, respectively. Model parameters are identified using a systematic estimation procedure and the resulting model is validated against long-duration roller test measurements, achieving coefficients of determination above R² = 0.9 and normalized RMSE values below 10% for all key temperatures. The validated model is then used as the prediction model in a model predictive controller that manipulates radiator fan and coolant-pump speeds, while treating component heat losses, vehicle speed and ambient temperature as measured disturbances. The controller is evaluated in a model-in-the-loop environment for representative long-haul and urban driving cycles and different ambient temperatures, and its performance is benchmarked against conventional rule-based and PI-based control strategies. Depending on the driving cycle and ambient conditions, the proposed NMPC reduces cooling system energy consumption by up to 39.6% compared to a PI controller (VECTO Urban Delivery cycle, 35 °C ambient), with an average reduction of 16.6% across all investigated driving cycles and ambient conditions, without a significant increase in average or maximum coolant temperature. The proposed methodology provides a transferable workflow for developing predictive thermal management control in fuel-cell heavy-duty vehicles and other complex automotive cooling systems. Full article

Myrmecochory is a form of seed dispersal mediated by ants. Although this mechanism of dispersal has received less research attention than other dispersal processes, the wide distribution and high biomass of ants mean that it can strongly influence plant dispersal patterns. In particular, the underlying mechanisms and key agents of myrmecochory remain poorly understood in the context of anthropogenic perturbations; furthermore, such research is especially scarce in East Asia. This review aims to elucidate the ecological mechanisms underlying myrmecochory, to explore how this interaction may be affected by urbanization and climate change, and to determine its potential ecological role in disturbed ecosystems. We first review past research on the three major hypotheses proposed for the emergence of ant-mediated seed dispersal—directed dispersal, distance dispersal, and predator avoidance. We then compile taxonomic information on myrmecochorous plants and ants from global databases and regional literature, expanding the checklist of Korean myrmecochorous plants to 130 species and reclassifying them as endangered, rare, or endemic. Our synthesis suggests that invasive ants could threaten myrmecochory by displacing native myrmecochorous ants, increasing seed predation, and facilitating the dispersal of invasive plants. Moreover, the urban heat island effect and habitat fragmentation could disturb the dispersal, germination, and growth of myrmecochorous plants, threats that may be further intensified by climate-driven phenological mismatches. Consequently, in temperate East Asian countries experiencing anthropogenically generated environmental changes, myrmecochory emerges as a pivotal ecological process that underscores ecosystem vulnerability and resilience. Ultimately, incorporating these plant–ant interactions into biodiversity monitoring is essential for predicting ecosystem shifts and designing robust, proactive conservation strategies in changing environments. Full article

In order to improve the assessment of the dynamic risk of urban waterlogging, this study addresses the limitations of existing methods in capturing the responses of surface roads and subway systems to inundation, as well as the resulting spatiotemporal risks. Using the Hanyang District in Wuhan as a case study, the research proposes a framework for assessing urban waterlogging risks based on the dynamic inundation responses of surface and underground transport systems under various rainfall scenarios. The waterlogging process is simulated using seven representative rainfall scenarios with a hydrodynamic model that integrates a one-dimensional pipe network, a two-dimensional surface overland flow model, and a generalized underground space model. A coupled road–subway transportation network is developed to analyze traffic capacity degradation, path redistribution, and cascading failures caused by waterlogging disturbances. Quantified dynamic response indicators are integrated into the H-E-V-C framework to assess dynamic urban waterlogging risk. The results indicate that direct failure caused by water accumulation is typically the primary catalyst for extensive degradation of the transportation network, while the expansion of congestion and localized overload failures further exacerbate cascading effects. Different rainfall patterns influence not only peak risk but also the duration and spatial development of high-risk areas. Incorporating the dynamic response of the transport system enables a more accurate assessment of the degradation of emergency accessibility and the ongoing accumulation of localized high-risk areas. These findings highlight the importance of dynamic risk assessment in identifying time-varying urban vulnerabilities and supporting the planning of sustainable urban drainage, traffic management, and phased early warning systems. Full article

Multi-temporal ecosystem-type maps for 1945, 1996, and 2022 were developed to examine how long-term ecosystem-type change influences regulating ecosystem services (ESs) across five Natura 2000 sites in Greece. We quantified three regulating ESs: climate regulation, hydrological regulation, and soil erosion regulation, using InVEST, and assessed multifunctionality using the combined Comprehensive Ecosystem Services Index (CESI). ES dynamics were assessed through a multi-metric framework of change indices comprising the Ecosystem Services Change Index (ESCI) and the Ecosystem Service Status Index (ESSI). In addition, we explored ES synergies and trade-offs and identified ES bundles using Self-Organizing Maps. The results showed pronounced spatial and temporal heterogeneity. Sites characterized by gradual woody expansion generally exhibited stable ES structures and modest improvements in regulating service status. In contrast, sites affected by disturbances and anthropogenic pressures (notably wildfire and urban expansion), showed persistent declines and an expansion of low-performing zones. Hydrologically dynamic systems characterized by land–water shifts exhibited persistent trade-offs between hydrological regulation and the other regulating services. Overall, ecosystem-type change analysis, combined with ES metrics quantification and spatial bundling, provided valuable insights for the assessment of the spatio-temporal dynamics of ESs. Study findings can also facilitate the preliminary translation of ES patterns into functional zones, serving as decision-support indicators for spatially targeted and adaptive Natura 2000 management measures and actions. Full article

Under the “dual carbon” goals, Taiyuan, a prefecture-level administrative unit and energy-intensive region in Shanxi Province, China, has experienced changes in soil carbon storage and soil carbon flux under rapid urbanization and industrialization. To clarify the spatial patterns of soil carbon storage and flux, 26 field sampling sites, including 78 soil samples, were analyzed using laboratory measurements and an optimized tunable diode laser absorption spectroscopy–geographic information system (TDLAS–GIS) integrated monitoring approach. This study investigated the spatial patterns of soil carbon storage and flux and discussed their potentially associated factors, providing an exploratory workflow for regional carbon monitoring. The results showed clear spatial heterogeneity, with an average soil organic carbon (SOC) content of 10.86 g/kg. High-SOC areas were mainly located in the southern and southwestern plains, while lower SOC levels occurred in urban expansion zones and highly disturbed surfaces. The western mountainous areas were important ecological barriers but were not the highest measured SOC zones. At the site level, arable land and forestland showed higher mean SOC values than grassland, with average SOC contents of 12.47, 12.07, and 8.27 g/kg, respectively, although these land-use-related differences were not statistically significant. Soil carbon flux was relatively higher in some mountainous regions and industrial–ecological transition areas but lower in several urban expansion areas. The results suggest that urbanization and industrial activity may be associated with changes in SOC and soil-atmosphere CO₂ exchange. This study describes the spatial variation characteristics of soil carbon storage and flux, establishes a reproducible TDLAS–GIS workflow for regional carbon monitoring, and provides exploratory support for ecological sustainability, sustainable land management, and the “dual carbon” strategy in northern resource-based cities. Full article