Search Results (300)

Land subsidence in soft-soil urban transport hubs arises from the complex coupling of natural geology and intensive anthropogenic activities, yet its spatial differentiation mechanisms and seasonal drivers remain poorly understood in high-development core areas. This study develops a progressive analytical framework integrating Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR), GeoDetector, Gaussian Mixture Model (GMM), and Singular Spectrum Analysis (SSA) to investigate spatiotemporal deformation patterns and driving mechanisms in the Shanghai Hongqiao Transport Hub Core Area from 2015 to 2024 using 209 Sentinel-1A images. Validation against official subsidence contours yields a Pearson correlation coefficient of 0.697 (p < 0.001) and an RMSE of 4.23 mm, confirming good spatial pattern agreement. Urban functional zones and construction stages are identified as the dominant influencing factors, with their interaction exhibiting notable bi-factor enhancement. Six distinct deformation response types are delineated via GMM, and two opposing seasonal signals are distinguished: near-instantaneous precipitation-driven surface loading on shallow soft soil and temperature-driven thermoelastic expansion of built structures. These findings may inform differentiated subsidence management and offer a transferable workflow for analogous soft-soil urban areas. Full article

The Upper Jinsha River region on the southeastern Tibetan Plateau is characterized by intense tectonic activity, extreme topographic relief, and widespread large-scale landslides, which pose significant threats to communities, transportation networks and major infrastructure. However, accurate landslide susceptibility assessment in high-relief mountainous terrain remains challenging. Conventional methods rely on point-based sampling and static environmental factors, which fail to capture the spatial heterogeneity of large landslides and adequately represent the influence of continuous surface deformation on landslide evolution, resulting in considerable uncertainty and limited predictive accuracy. To address these issues, this study established a polygon-based landslide inventory comprising 3831 landslides and developed an improved susceptibility assessment framework integrating multi-scale polygon-based sampling with InSAR-derived surface deformation constraints. By coupling the Random Forest (RF) and Optimized Frequency Ratio (OFR) models, quantitative susceptibility assessment and model validation were conducted. The results show that the integrated RF-OFR model achieved the best predictive performance, with an Area Under the Curve (AUC) of 0.906, representing improvements of 6.0%, 4.2%, and 2.7% over the conventional FR, RF-FR, and OFR models, respectively. Fluvial incision, terrain relief, and precipitation were identified as the dominant conditioning factors controlling regional landslide occurrence. High-susceptibility zones are primarily distributed along the Jinsha River and deeply incised tributary valleys, showing strong spatial agreement with active fault zones and persistent surface deformation. Compared with conventional single-scale point sampling, the proposed multi-scale polygon sampling strategy more effectively represents the spatial characteristics of large landslides in high-relief mountain regions, reduces sampling bias and incorporating InSAR-derived deformation information further enhances the identification of actively deforming slopes, demonstrating the value of integrating dynamic surface deformation with conventional environmental factors in landslide susceptibility modeling. The proposed framework provides an effective approach for regional landslide susceptibility assessment and can support hazard identification and risk-informed infrastructure and land use planning in high-relief mountainous regions. Full article

The National Transport Safety Board found that Wheels-Up Landing was the second highest defining event for aircraft accidents from 2008 to 2022. The increasing push for sustainable propulsion and accompanying novel airframe architectures present new integration and safety challenges for aircraft design and development, among which is the structural integrity and crashworthiness of the aircraft under such extreme events. This paper examines the regulations and design requirements governing aircraft emergency Wheels-Up Landing scenarios, emphasising their implications for aircraft safety and structural integrity. It consolidates standards from aviation authorities, such as the FAA and EASA, which identify and define key requirements relating to occupant safety and fire prevention and protection during such events. The paper then considers the Wheels-Up Landing scenario and its design requirements within the context of future novel aircraft employing sustainable propulsion systems, from higher bypass turbofan to electric- and hydrogen-based technologies. The unique characteristics and challenges of these emerging propulsion technologies are described, highlighting how alternative structural configurations, weight distributions and powerplant architectures may influence the aircraft response under a Wheels-Up Landing event. Finally, an exploration of predictive modelling strategies and methods currently used in Wheels-Up Landing analysis was conducted. While reviewing the breadth of accurate, high-fidelity modelling methods targeting fuselage impact, it also highlighted the gap in both considering the increasingly relevant and frequent powerplant impact scenarios, and the provision of lightweight modelling approaches necessary to rapidly and adequately address the emergency Wheels-Up Landing response early in the aircraft design process. Full article

Urban–rural governance and planning systems, as viewed through the lens of online public attention, are reshaping approaches to achieving coordinated development of cultural heritage conservation and urban space. Taking Nanchang as a case study, this paper investigates the application mechanism of online public attention in the evaluation of heritage corridor nodes and the formulation of optimization strategies. Employing multi-source data collection, we extracted textual content from online travelogues. We quantified the online attention level of each cultural heritage unit and analyzed its spatial distribution using kernel density estimation and standard deviational ellipse methods. We then applied a minimum resistance model, incorporating geographic data on land use and transportation networks, to identify potential heritage corridor routes. Based on attention classification, we propose a three-tier optimization strategy: core-leading, marginal-activating, and gradient-linking. Specifically, high-attention heritage units are positioned as central nodes that reinforce corridor connectivity; low-attention nodes are targeted for activation through corridor links to overcome spatial isolation; and attention gradients serve as the basis for hierarchical integration across the network. Our results reveal a pronounced spatial pattern of “core agglomeration and peripheral dispersion” in Nanchang’s heritage corridors. High-attention resources are heavily concentrated in the historic urban core, anchoring the main corridor axes, whereas low-attention resources are scattered across peripheral counties, exhibiting a clear urban–rural attention gradient. Importantly, attention levels show a positive correlation with corridor hierarchy. These findings provide a novel perspective for heritage corridor spatial planning and offer practical insights for sustainable cultural heritage governance and culture–tourism integration in an era of pervasive internet and urban expansion. Full article

Assessing climate performance through emission-centric metrics provides an incomplete picture of countries’ progress toward integrated climate objectives. This study proposes the Climate Integrated Performance Index (CIPI), a multidimensional and explainable composite indicator for 27 European countries during 2015–2023. CIPI integrates six thematic dimensions: emissions, energy systems, mitigation capacity, transport, agriculture, and waste–land-use interactions, using robust normalization, a policy-informed weighting framework, and formal statistical validation. Based on 243 country–year observations, the results indicate that CIPI is non-redundant. Pearson correlations reveal strong positive associations with the Energy Index (r = 0.899) and Mitigation Index (r = 0.894), alongside a significant negative association with the Agriculture Index (r = −0.659), highlighting sectoral trade-offs. Variance decomposition further shows that energy and mitigation dimensions jointly account for approximately 87% of explained variance, whereas agriculture exerts a systematic counterbalancing influence. To support structural interpretation, an explainable machine learning framework combining XGBoost and SHAP was implemented as a diagnostic layer. Renewable-energy capacity emerged as the dominant structural driver of integrated climate performance, and SHAP-based analyses revealed a nonlinear threshold effect, with positive contributions accelerating beyond a normalized renewable-capacity level of approximately 0.58 (95% bootstrap confidence interval: 0.54–0.62), particularly under low fossil-fuel dependency conditions. Because the machine learning models use indicators that also contribute to index construction, the results are interpreted as evidence of structural consistency and diagnostic interpretability rather than independent predictive discovery. To address this limitation, repeated cross-validation, subsample validation, benchmark comparisons, and weighting-sensitivity analyses were conducted. Ranking robustness remained high under alternative weighting schemes (Spearman ρ > 0.96), while comparison with an emission-centric benchmark demonstrated substantial rank reversals, indicating that broader sectoral and policy dimensions influence climate-performance assessment. Overall, CIPI functions not only as a benchmarking tool but also as a transparent diagnostic framework for identifying structural trade-offs, nonlinear relationships, and policy-relevant climate-transition dynamics. Full article

This study aims to spatially analyze the potential soil loss rates of the Özkonak Watershed, located within Nevşehir Province, using the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information Systems (GIS) and Remote Sensing (RS) technologies. In the 149.9 km² watershed, the main parameters triggering erosion—rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), land cover and management (C), and support practices (P)—were modeled in a GIS environment. According to the spatial analysis results, 85.8% of the watershed area falls within the “very low” and “low” erosion susceptibility classes. Nevertheless, erosion increases markedly in the northern areas with high slope gradients and in areas where agricultural activities are concentrated. The mean soil loss across the watershed was calculated as 2.75 t ha⁻¹ yr⁻¹. The eroded and transported material was determined to constitute a threat to the dam. The findings indicate that conservation plans, including afforestation in the upper watershed, adjustment of land use to natural land capability, and construction of check dams, should be implemented to ensure sustainable management of the watershed. From a soil and sediment remediation perspective, the identification of erosion-source areas and sediment-transport pathways provides a scientific basis for source-control measures aimed at reducing sediment delivery and associated water-quality deterioration in the Bayramhacılı Dam reservoir. Full article

Assessing ecosystem services, identifying their driving factors, and optimizing their spatial patterns are essential for coordinating ecological conservation and socioeconomic development in mountainous regions. Taking the Anning River Basin in southwestern China as the study area, this study quantitatively assessed ecosystem services from 2010 to 2024, integrated trade-off intensity into the Integrated Ecosystem Services Index, applied a Bayesian network model to identify the driving factors of ecosystem services, and proposed strategies for spatial pattern optimization. The results showed that: (1) the mean values of water conservation and soil conservation in the Anning River Basin were 94.45 mm and 1179.64 t ha⁻¹, respectively, both showing substantial interannual fluctuations. The habitat quality index was 0.83, and the mean carbon storage was 132.68 t ha⁻¹; both habitat quality and carbon storage declined slightly. The mean food production was 0.46 t ha⁻¹, indicating an improvement in the food production function. (2) The trade-off intensity among ecosystem services was 0.33, and the Integrated Ecosystem Services Index was 0.51, remaining generally stable overall. Ecosystem services were mainly influenced by land use, precipitation, population count, and fractional vegetation cover. Their spatial heterogeneity was pronounced, with relatively low values in the Anning River Plain, the Jinsha River dry-hot valley, and the Yanyuan Basin. (3) The identified priority conservation areas for ecosystem services covered 8307 km² and were mainly distributed within ecological conservation redline areas and regulated zones. The general functional areas for ecosystem services covered 149 km² and were concentrated in urban construction areas and along major transportation corridors in the basin. Targeted strategies were further proposed to enhance the synergistic improvement of ecosystem service supply. This study provides theoretical and practical support for ecosystem management in the Anning River Basin and other mountainous watersheds. Full article

As urban rail transit systems enter a stage of high-quality development, accurate short-term passenger flow forecasting has become essential for efficient operation and management. To improve the accuracy and robustness of multi-step short-term passenger flow forecasting under fluctuating demand conditions, this study develops a feature-enhanced Informer framework that embeds Complex Network Representation (CNR) into the Informer architecture to jointly capture both the topological characteristics of the subway network and spatiotemporal passenger flow dynamics. External factors, including subway schedules and land use around stations, are further integrated to enrich the input features. In addition, the ProbSparse self-attention mechanism is adopted to improve long-sequence dependency modeling, thereby enabling efficient multi-step passenger flow forecasting. Experiments were conducted on the Beijing metro passenger flow dataset from January to October 2024 to evaluate the proposed model. The dataset covered 264 stations and was aggregated at 15 min intervals. Based on historical passenger flow and multi-source features, the model predicts passenger flow over multiple future time steps. The overall evaluation metrics were calculated on the test set and averaged over all test samples and observed stations. The experimental results show that, compared with the standard Transformer model, the proposed model reduces the average prediction error by 16.59% on weekdays and 20.48% on weekends while maintaining stable predictive performance during peak hours. Sensitivity analysis and ablation studies are further conducted to evaluate the model performance across different station types and forecasting horizons. The results demonstrate that the proposed model can provide reliable decision support for intelligent urban rail transit operations, including transport capacity scheduling, passenger service improvement, and operating cost reduction. Full article

Rapid urbanization along transportation corridors is a key driver of land transformation and landscape homogenization in developing mega-cities. This study analyzes land use and land cover (LULC) change within a 5 km buffer, from Gulberg to T-Chowk, along the Islamabad Expressway, Pakistan, from 2010 to 2024, using multi-temporal Landsat imagery (2010, 2015, 2020, 2024) and landscape metrics. Built-up area increased by 190.8% (51.02 to 148.34 km²) between 2010 and 2024. The expansion is accompanied by sharp declines in vegetation (−36.0%; 89.17 to 57.06 km²), barren land (−93.8%; 63.88 to 3.94 km²), and water bodies (−64.2%; 8.20 to 2.93 km²). All class-area estimates were derived from the R landscapemetricspipeline and independently verified against visual inspection of the classified rasters, confirming correct class labelling throughout. Landscape structure shifted towards homogenization, with Shannon’s Diversity Index decreasing from 1.19 to 0.74, Patch Density from 74.39 to 21.08 patches per 100 ha, and Edge Density from 219.65 to 99.78 m/ha. LULC maps achieved an accuracy greater than 96% ($\kappa >0.96$) for each classification year, and metric estimates showed cross-platform agreement between R and FRAGSTATS within ±5%. A distance-zone analysis across four successive 1.25 km bands from the Expressway showed that this expansion was pervasive rather than confined to the roadside, with the urban share of classified land rising by 43.6–47.7 percentage points in every band. A complementary 300 m × 300 m fishnet-grid multivariable logistic regression ($n=2162$ cells classified as non-urban in 2010) identified proximity to existing 2010 built-up land and location within a formal housing scheme as the strongest independent correlates of conversion to urban land (AOR = 1.88 for housing-scheme proximity; 95% CI: 1.38–2.56), with acceptable model discrimination (AUC = 0.739). These findings indicate rapid corridor-scale consolidation associated with infrastructure-led growth, reducing landscape heterogeneity and potentially weakening ecological resilience. Unlike previous city-scale studies in the Islamabad–Rawalpindi region, this study adopts a transportation-corridor perspective, combining descriptive landscape ecology with spatially explicit statistical modelling to quantify both the pattern and the spatial drivers of infrastructure-led land transformation. The study underscores the necessity for integrated, corridor-scale land-use governance—including transit-oriented development and landscape-metric-based ecological zoning—to balance urban expansion with ecosystem sustainability. Full article

This study evaluated the economic viability and risk of a eucalyptus forestry project by integrating deterministic and probabilistic economic analyses. Operational costs for motor–manual harvesting, manual extraction and loading, and forest transportation were estimated using standard cost models, while silvicultural, road maintenance, and transportation costs were obtained from operational records. Economic viability was assessed using Net Present Value (NPV), Internal Rate of Return (IRR), Equivalent Annual Value (EAV), and Average Production Cost (APC). Economic risk was evaluated through Monte Carlo simulation (10,000 iterations), considering ±10% variation in interest rate, transportation distance, harvesting productivity, labor, fuel, lubricant, and road maintenance costs, followed by sensitivity and correlation analyses. The project was economically viable, presenting an NPV of USD 110.11 ha⁻¹, an IRR of 8.0%, an EAV of USD 20.43 ha⁻¹ year⁻¹, and an APC of USD 29.11 m⁻³. Transportation distance showed a limited operational slack of only 10 km when land cost was considered, indicating that the project operates close to its economic limit. Monte Carlo simulation indicated an 11.5% probability of economic infeasibility, while interest rate, transportation distance, and manual loading cost exerted the greatest negative influence on NPV. The integrated deterministic and probabilistic approach enabled the identification of critical operational thresholds for transportation distance, timber price, wood productivity, and land cost under the evaluated conditions, providing practical decision-support parameters for transportation planning, cost management, and economic risk assessment in similar forestry projects. Full article

Rural spatial restructuring in rapidly urbanizing regions is jointly shaped by road expansion and ecological constraints, yet existing studies often examine transportation and ecological systems in isolation. This study develops a coupled “road–ecology” network framework to investigate the evolution of county spatial structure in Huangchuan County, China, from 2013 to 2023. Using administrative village, transportation, and land-use data, we construct and analyze road and ecological networks with complex network metrics, multilayer network analysis, and Exponential Random Graph Models (ERGM). The results show that the road network evolved from a single-center hierarchical structure toward a balanced multi-center configuration, while the ecological network maintained structural stability through strengthened regional ecological clustering. The coupled network underwent a transition from spatial exclusion to limited integration, and its persistently negative interlayer assortativity values are consistent with ecological land configuration acting as a spatial constraint on road expansion. Multilayer network metrics further indicate a trend toward increased local coordination in the road-ecology coupled system, indicating a gradual evolution toward a more spatially coordinated configuration. This study advances the application of multilayer network approaches in rural spatial research and provides a new perspective for understanding sustainable county spatial restructuring under driving-constraint balance. Full article

The changing climate raises the level of hydroclimatic non-stationarity and export of pollutants at the event scale in agricultural, mixed-land-use, and urbanizing watersheds. In this review, there is an emphasis on nitrogen, phosphorus, and sediment; however, selective references are made to pesticides, pathogens, microplastics, and wet-weather mixed-source processes when characteristics similar to event-driven transport, threshold exceedance, and adaptive control are identified. Drawing on a structured literature search of studies published from 2000 to December 2025, this narrative review synthesizes evidence from 138 selected references on how extreme rainfall, drought–rewetting, warming, and freeze–thaw processes alter source activation, hydrological connectivity, biogeochemical processing, and receiving-water hazards. Our resilience assessment is based on resistance, recovery, robustness, and persistence, which we interpret using exposure, sensitivity, and adaptive capacity. It is shown that standard average-load and fixed-baseline measurements may not detect short pollution pulses, cross-scenario failure, and long-term drift; operational measurement must thus involve event thresholds, recovery trajectories, tail-risk measures, and propagation of uncertainty. Extrapolation, interpretability, data demand, and applicability for data-sparse basins are used to compare process-based, data-driven, and hybrid models. Adaptation options are associated with measurable triggers as part of a monitoring–trigger–action cycle with location-specific instructions for monsoon-agricultural, cold-region, semi-arid and urban systems. The novel aspect of this framework is the integration of mechanism-based evidence, quantitative resilience indicators, model uncertainty, and adaptive governance into one decision-focused workflow. This sustainability-oriented framework advances long-term watershed management by linking water-quality protection and resilient development. Full article

The rapid development of airport economic zones has significantly reshaped regional land-use structures and industrial spatial organization. Taking the Nanjing Airport Economic Zone as the study area, this study integrates multi-source geospatial data, including land-use data, enterprise registration records, Points of Interest (POIs), transportation networks, nighttime light intensity, population, topography, and ecological-environmental variables for 2013, 2018, and 2023. Land-use transition matrices, spatial autocorrelation analysis, standard deviation ellipse analysis, Geodetector, and Multiscale Geographically Weighted Regression (MGWR) models were employed to examine land-use transition, industrial spatial restructuring, and their influencing factors from 2013 to 2023. The results show that: (1) Land-use change in the study area was mainly characterized by the decline of cropland, the expansion of impervious surfaces, and the shrinkage of water bodies. From 2013 to 2023, cropland decreased from 81.07 km² to 70.12 km², impervious surfaces increased from 10.98 km² to 25.65 km², and water bodies decreased from 5.50 km² to 1.79 km². The conversion from cropland to impervious surfaces was the dominant transition pathway, covering 14.67 km². (2) Industrial space exhibited significant spatial clustering, with a Moran’s I value of 0.9639 in 2023. The standard deviation ellipse results indicate that industrial space expanded during 2013–2018 and contracted during 2018–2023, suggesting a shift from extensive outward expansion to relative agglomeration around the core area and major transport corridors. (3) Nighttime light intensity and distance to major transport access points were important explanatory factors for industrial spatial distribution, with q-values of 0.396 and 0.310, respectively. The interaction between slope and metro accessibility showed the strongest explanatory power, with a q-value of 0.6967. The MGWR results further revealed the spatial heterogeneity of the effects of transportation, economic activity, population concentration, and ecological constraints. Overall, land-use transition and industrial spatial restructuring in the Nanjing Airport Economic Zone were jointly shaped by transportation accessibility, economic vitality, population agglomeration, and ecological constraints. These findings provide a reference for land-use optimization and industrial spatial governance in airport economic zones. Full article

Identifying where nature-based solutions should be prioritized has become a critical task for climate-adaptive urban stormwater management under the combined pressures of climate change and urban expansion. Taking the central urban area of Beijing as a case study, this study develops a dynamic prediction framework that incorporates the Source–Flow–Sink (SFS) process of urban waterlogging. The framework integrates a future land use simulation model (FLUS), the Soil Conservation Service (SCS) hydrological model, and the Maximum Entropy (MaxEnt) model and incorporates both climate change (RCP8.5) and urban expansion to simulate the spatial configuration of waterlogging risk in 2031. High-risk areas were then overlaid with land-cover data and open-space distribution to identify potential NbS opportunity spaces, which were further examined through field investigation. The results show that future waterlogging risk in Beijing exhibits a clear corridor-oriented pattern closely associated with transportation infrastructure. Transportation-related variables account for more than 80% of total model contribution, suggesting a strong statistical association between future waterlogging occurrence and transportation-related spatial features. Field investigation further reveals that many roadside green spaces are elevated above adjacent roads, limiting their ability to receive and retain runoff. Thus, the key adaptation challenge lies not simply in the amount of green space, but in the weak hydrological connection between runoff pathways and adjacent open spaces. While Beijing’s priority areas are mainly corridor-based, other cities may be shaped by different processes and spaces. More broadly, this study demonstrates how hydrological risk simulation can be translated into spatially explicit planning priorities and more locally grounded adaptation decisions. Full article

Landfill leachate represents a long-term source of contamination that may significantly affect groundwater and receiving water bodies through the migration of organic, inorganic, and toxic pollutants. This study evaluated the long-term migration of landfill leachate and its potential environmental impacts using the LandSim Release 2 probabilistic software model applied to two municipal waste landfills in the Republic of Serbia: the regional sanitary landfill “Gigoš” in Jagodina and the sanitary landfill “Meteris” in Vranje. The modelling framework integrated laboratory leachate analyses, hydrogeological conditions, engineered barrier system characteristics, and receptor-oriented contaminant transport assessment. Model validation was performed through comparison of simulated and laboratory-measured concentrations. Two scenarios were analyzed for each site: an engineered sanitary landfill scenario with a functional containment system and a conservative barrier-failure scenario representing complete loss of engineered barrier functionality. Ten representative leachate parameters were included, covering nitrogen compounds, inorganic ions, toxic substances, and heavy metals/metalloids. The results showed that engineered protection systems significantly delay contaminant migration and reduce receptor concentrations, while barrier-failure conditions lead to earlier pollutant breakthrough and higher environmental risk. The simulations demonstrated that under the engineered sanitary landfill scenario, receptor concentrations of all analyzed contaminants remained below the corresponding maximum allowable concentrations, with contaminant migration occurring only after several centuries. In contrast, the conservative barrier-failure scenario resulted in substantially earlier contaminant breakthrough, with nitrogen compounds and phenols representing the greatest environmental concern due to their rapid migration and exceedance of regulatory thresholds, while the “Meteris” landfill generally exhibited higher receptor concentrations than the “Gigoš” landfill. These findings highlight the importance of predictive modelling and long-term monitoring for sustainable landfill management and groundwater protection. Full article