Search Results (15,462)

Sludge is a common engineering byproduct that poses environmental and land-use challenges when disposed of directly. Converting sludge into high-quality subgrade filling material through solidification is therefore of both engineering and ecological significance. In this study, dynamic triaxial tests were conducted on sludge soils stabilized with mineral-based cementitious binders to investigate the effects of binder content, loading frequency, and curing age on the backbone curve, dynamic shear modulus, maximum shear modulus, ultimate stress amplitude, shear modulus ratio, and damping ratio. Scanning electron microscopy (SEM) was further employed to examine the microstructural evolution of the stabilized soils. The results indicate that increasing binder content and curing age significantly enhance the dynamic shear modulus while reducing the damping ratio, and the modulus exhibits a frequency-dependent behavior within the tested loading range. The modified Hardin-Drnevich constitutive model was successfully applied to fit the experimental data, accurately characterizing the dynamic response of stabilized sludge soils and enabling the development of a normalized model for the dynamic shear modulus ratio. SEM observations confirm that hydration reactions between the binder and soil produce gel products that fill interparticle pores, leading to a denser structure and explaining the observed macroscopic improvements in mechanical behavior. Overall, this work elucidates the dynamic response mechanisms of sludge stabilized with mineral-based cementitious materials and provides theoretical and experimental support for its resource utilization in road engineering applications. Full article

Oases in arid regions are crucial for sustaining agricultural production and ecological stability, yet few studies have simultaneously examined the coupled dynamics of land use/cover change (LUCC), carbon emissions, and ecosystem service value (ESV) at the oasis–agricultural scale. This gap limits our understanding of how different land use trajectories shape trade-offs between carbon processes and ecosystem services in fragile arid ecosystems. This study examines the spatiotemporal interactions between land use carbon emissions and ESV from 1990 to 2020 in the Wensu Oasis, Northwest China, and predicts their future trajectories under four development scenarios. Multi-period remote sensing data, combined with the carbon emission coefficient method, modified equivalent factor method, spatial autocorrelation analysis, the coupling coordination degree model, and the PLUS model, were employed to quantify LUCC patterns, carbon emission intensity, ESV, and its coupling relationships. The results indicated that (1) cultivated land, construction land, and unused land expanded continuously (by 974.56, 66.77, and 1899.36 km²), while grassland, forests, and water bodies declined (by 1363.93, 77.92, and 1498.83 km²), with the most pronounced changes occurring between 2000 and 2010; (2) carbon emission intensity increased steadily—from 23.90 × 10⁴ t in 1990 to 169.17 × 10⁴ t in 2020—primarily driven by construction land expansion—whereas total ESV declined by 46.37%, with water and grassland losses contributing substantially; (3) carbon emission intensity and ESV exhibited a significant negative spatial correlation, and the coupling coordination degree remained low, following a “high in the north, low in the south” distribution; and (4) scenario simulations for 2030–2050 suggested that this negative correlation and low coordination will persist, with only the ecological protection scenario (EPS) showing potential to enhance both carbon sequestration and ESV. Based on spatial clustering patterns and scenario outcomes, we recommend spatially differentiated land use regulation and prioritizing EPS measures, including glacier and wetland conservation, adoption of water-saving irrigation technologies, development of agroforestry systems, and renewable energy utilization on unused land. By explicitly linking LUCC-driven carbon–ESV interactions with scenario-based prediction and evaluation, this study provides new insights into oasis sustainability, offers a scientific basis for balancing agricultural production with ecological protection in the oasis of the arid region, and informs China’s dual-carbon strategy, as well as the Sustainable Development Goals. Full article

Coasts are critical ecological, economic and social interfaces between terrestrial and marine systems. The current upsurge in the acquisition and availability of remote sensing datasets, such as Landsat remote sensing data series, provides new opportunities for analyzing multi-decadal coastal changes and other components of coastal risk. The emergence of machine learning-based techniques represents a new trend that can support large-scale coastal monitoring and modeling using remote sensing big data. This study presents a comprehensive multi-decadal analysis of coastal changes for the period from 1990 to 2024 using Landsat remote sensing data series along the eastern and southern coasts of Peninsular Malaysia. These coastal regions include the states of Kelantan, Terengganu, Pahang, and Johor. An innovative approach combining deep learning-based shoreline extraction with the Digital Shoreline Analysis System (DSAS) was meticulously applied to the Landsat datasets. Two semantic segmentation models, U-Net and DeepLabV3+, were evaluated for automated shoreline delineation from the Landsat imagery, with U-Net demonstrating superior boundary precision and generalizability. The DSAS framework quantified shoreline change metrics—including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), and Linear Regression Rate (LRR)—across the states of Kelantan, Terengganu, Pahang, and Johor. The results reveal distinct spatial–temporal patterns: Kelantan exhibited the highest rates of shoreline change with erosion of −64.9 m/year and accretion of up to +47.6 m/year; Terengganu showed a moderated change partly due to recent coastal protection structures; Pahang displayed both significant erosion, particularly south of the Pahang River with rates of over −50 m/year, and accretion near river mouths; Johor’s coastline predominantly exhibited accretion, with NSM values of over +1900 m, linked to extensive land reclamation activities and natural sediment deposition, although local erosion was observed along the west coast. This research highlights emerging erosion hotspots and, in some regions, the impact of engineered coastal interventions, providing critical insights for sustainable coastal zone management in Malaysia’s monsoon-influenced tropical coastal environment. The integrated deep learning and DSAS approach applied to Landsat remote sensing data series provides a scalable and reproducible framework for long-term coastal monitoring and climate adaptation planning around the world. Full article

Human activities have accelerated the extinction of species, driving biodiversity loss and ecosystem degradation. Establishing protected areas (PAs) that encompass habitats of endangered species is essential for achieving biodiversity conservation and ecosystem protection goals. This study aimed to identify and prioritize critical conservation areas for the endangered long-tailed goral (Naemorhedus caudatus) in five regions of Gangwon and Gyeongbuk Provinces, South Korea. The MaxEnt model was applied to predict the potential habitat of the species, considering key environmental factors such as topographic, distance-related, vegetation, and land cover variables. The InVEST Habitat Risk Assessment (HRA) model was used to quantitatively assess cumulative risks within the habitat from the impacts of forest development and anthropogenic pressures. Subsequently, the Zonation software was employed for spatial prioritization by integrating the outputs of the models, and core conservation areas (CCAs) with high ecological value were identified through overlap analysis with 1st-grade areas from the Ecological and Nature Map (ENM). Results indicated that suitable habitats for the long-tailed goral were mainly located in forested regions, and areas subjected to multiple stressors faced elevated habitat risk. High-priority areas (HPAs) were primarily forested zones with high habitat suitability. The overlap analysis emphasized the need to implement conservation measures targeting CCAs while also managing additional HPAs outside CCAs, which are not designated as ENM. This study provides a methodological framework and baseline data to support systematic conservation planning for the long-tailed goral, offering practical guidance for future research and policy development. Full article

Nitrogen (N) is a key nutrient for sustaining ecosystem productivity and agricultural sustainability; however, achieving high-precision monitoring in wetlands with highly heterogeneous surface types remains challenging. This study focuses on Caohai, a representative karst plateau wetland in China, and integrates Sentinel-2 multispectral and Zhuhai-1 hyperspectral remote sensing data to develop a soil nitrogen inversion model based on spectral indices, texture features, and their integrated combinations. A comparison of four machine learning models (RF, SVM, PLSR, and BPNN) demonstrates that the SVM model, incorporating Zhuhai-1 hyperspectral data with combined spectral and texture features, yields the highest inversion accuracy. Incorporating land-use type as an auxiliary variable further enhanced the stability and generalization capability of the model. The study reveals the spatial enrichment of soil nitrogen content along the wetland margins of Caohai, where remote sensing inversion results show significantly higher nitrogen levels compared to surrounding areas, highlighting the distinctive role of wetland ecosystems in nutrient accumulation. Using Caohai Wetland on the Chinese karst plateau as a case study, this research validates the applicability of integrating spectral and texture features in complex wetland environments and provides a valuable reference for soil nutrient monitoring in similar ecosystems. Full article

This study investigates the environmental stability of ferronickel slag (FNS) and primary lead slags (GCS and FCS) from historical metallurgical complexes in Greece, in rainwater and seawater media. Leaching experiments revealed that nickel is the most mobile element from FNS (43.5 μg·g⁻¹ in seawater after 90 days). Chromium release, on the other hand, is very limited, not exceeding 0.04 μg·g⁻¹. In lead slags, zinc and lead exhibit significant leaching (up to 650 and 230 μg·g⁻¹, respectively), while arsenic release reaches 22.6 μg·g⁻¹. GCS contains pores primarily in the range of 50–90 Å. The majority of pore volume in FCS is centered around 30 Å. The porosity appears to have a significant effect on the element’s leachability. Pb, Zn, As, Sb, and Cd are released in significantly higher amounts from the finely porous FCS compared to GCS. Thermodynamic modeling was used to identify the pollutant speciation in water media in relation to the oxygen concentration. The release of toxic elements such as Cr from FNS and As from lead slags is enhanced under oxic (open-air) conditions. Therefore, their land disposal poses a greater environmental threat compared to sea disposal, where anoxic conditions prevail. Full article

The study presents a spectral assessment and analysis of various data and methods for snow cover analysis in different ranges of the electromagnetic spectrum through a differentiated approach applied to the territory of Bansko, Pirin Mountain. The aim of the presented research is to assess the effectiveness and accuracy of satellite observations together with field (in situ) measurements and to create a model of an integrated methodology. To achieve this goal, several indices, such as land surface temperature (LST), optical indices, Tasseled Cap Transformation (TCT) with wetness component (TCW), High-Resolution (HR) imagery, and Synthetic Aperture Radar (SAR) measurements, were analyzed. The results of the analysis proved that combining satellite and field data through a mobile thermal camera provides an accurate and comprehensive picture of snow conditions in high mountain regions for powder, hard-packed and wet snow. As the most important, there is the verification and validation of the results through the so-called regression analysis of the different data types, through which multiple correlations (over 10) were established, both in data from Sentinel 1SAR, Sentinel 2MSI, Sentinel 3 SLSTR, and PlanetScope. The results showed the effectiveness of optical indices for hard and fresh snow and radar and LST data for wet snow. The results can be used to improve snow surveys, event prediction (e.g., avalanches), and the interpretation of spectral analysis of snow. The study does not aim to perform a temporal analysis; all satellite data is from the temporal period 30 December 2024–5 January 2025. Full article

Microplastics released from synthetic garments pose a complex challenge to society and the environment. Textiles contribute to microplastic pollution throughout their entire lifecycle—from design and production to washing and use to their disposal—and can enter the environment through wastewater, soil, and air. The detachment of fibre fragments and their fate in the environment has received attention in the recent literature but lacks a harmonised research methodology and a holistic approach to the topic. This work presents a model to estimate the flows of microplastic fibres and synthetic garments in geographical Europe, expressed in tonnes per year. It was developed through a search of the literature to provide an estimate of synthetic fibres entering the environment and to identify the connections between the stakeholders involved. A first-level multicriteria decision analysis was conducted to recognise relevant pollution flows: the study revealed significant but poorly understood pathways, such as the flow of microplastics in the indoor and outdoor air during garment wear. Also, the flow of microplastics from the combined sewer overflow of untreated water during heavy precipitation and the flow to the agricultural land from the application of sewage sludge result in relevant pathways to water and soil, respectively. By fostering collaboration across multiple actors, the transition toward sustainable textile practices can significantly reduce fibre pollution. Full article

Effective station–city integration is crucial for sustainable development around high-speed rail stations. However, research assessing public preferences regarding the aspects of this integration remains limited. We constructed a performance evaluation model for station–city integration in high-speed rail station areas. By considering the high-speed rail station area in the Yangtze River Delta region as a research object, which is located in the metropolitan cities centered on Shanghai, China, we dissected the five dimensions of population, industry, land use, function, and environment into 15 indicators that flow into the three value objectives of attraction–retention–integration (NPI). Subsequently, we systematically analyzed the performance differentiation characteristics of station–city integration in the Yangtze River Delta region’s high-speed rail station areas by employing a multiple regression model to delve into the influence mechanisms affecting the performance differentiation patterns of station–city integration. Our findings indicate the following. (1) Regarding station–city integration performance grade differentiation, a few high-speed rail station areas in the Yangtze River Delta region exhibit a high-efficiency integration level, whereas more areas fall within the higher and general integration levels. (2) Spatially, the station–city integration performance in high-speed rail station areas within the Yangtze River Delta region exhibits a distinct distribution characterized by “high-grade point-block dependence and low-grade concentrated contiguous patches.” (3) The spatial distribution of the five dimensions of station–city integration performance exhibits significant disparities. (4) Regarding the development types of station–city integration performance advantages, efficient integration of stations and cities represents a multidimensional advantageous development type and higher integration falls into the same category. (5) Station–city integration performance results from the comprehensive effects of four factors: government policy inducement, station energy level attraction, station–city relationship adhesion, and urban energy level promotion. This study advances a systematic framework—encompassing performance measurement, mechanistic inquiry, and strategy formulation—for examining station–city integration in HSR station areas. By integrating the perspective of cyclical cumulative development into the node–place model from urban planning and geographical viewpoints, we articulate a new performance model that clarifies critical influencing factors and mechanisms, thus broadening the theoretical scope of HSR station area research. We believe that the NPI evaluation model can provide valuable insights for guiding the integrated development of high-speed rail station areas and enhancing the quality of urban development. Full article

Spatial planning deals with the organization and regulation of space with the goal to improve the quality of life of its inhabitants. Spatial planning plays a vital role in land administration, encompassing land development, management, land use assessment, resource allocation, and environmental protection. The significance of integrating spatial-planning information into the ISO 19152 Land Administration Domain Model (LADM) framework has been recognized in the LADM second edition, Part 5, where a part for spatial plan information is introduced. The aim of this paper is to analyze the applicability of the LADM Part 5: Spatial Plan Information draft international standard to the Serbian spatial and urban planning system and to develop a country profile for Serbia in alignment with Serbian laws and regulations. An analysis of spatial and urban planning in Serbia will be performed, determining the hierarchy of spatial and urban plans based on an analysis of laws on spatial planning. The created conceptual model for spatial planning for Serbia based on the LADM Part 5: Spatial Plan Information will be harmonized with the previously created LADM country profile for Serbia. Full article

Exercise-induced fatigue can degrade athletic performance and increase injury risk, yet traditional fatigue assessments often rely on subjective measures. This study proposes an objective fatigue recognition approach using high-fidelity motion capture data and deep learning. This study induced both cognitive and physical fatigue in 50 male participants through a dual task (mental challenge followed by intense exercise) and collected three-dimensional lower-limb joint kinematics and kinetics during vertical jumps. A bidirectional Gate Recurrent Unit (GRU) with an attention mechanism (BiGRU + Attention) was trained to classify pre- vs. post-fatigue states. Five-fold cross-validation was employed for within-sample evaluation, and attention weight analysis provided insight into key fatigue-related movement phases. The BiGRU + Attention model achieved superior performance with 92% classification accuracy and an Area Under Curve (AUC) of 96%, significantly outperforming the single-layer GRU baseline (85% accuracy, AUC 92%). It also exhibited higher recall and fewer missed detections of fatigue. The attention mechanism highlighted critical moments (end of countermovement and landing) associated with fatigue-induced biomechanical changes, enhancing model interpretability. This study collects spatial data and biomechanical data during movement, and uses a bidirectional Gate Recurrent Unit (GRU) model with an attention mechanism to distinguish between non-fatigue states and fatigue states involving both physical and psychological aspects, which holds certain pioneering significance in the field of fatigue state identification. This study lays the foundation for real-time fatigue monitoring systems in sports and rehabilitation, enabling timely interventions to prevent performance decline and injury. Full article

Rapid urbanization threatens water ecosystem services (WESs) in China’s Greater Bay Area. This study employs a Markov-FLUS land-use simulation coupled with the InVEST model to project land-use patterns for 2035 under four scenarios—Natural Development (ND), Farmland Protection (FP), Economic Priority (EP), and Ecological Protection (EcoP)—and evaluates their impacts on water yield, soil retention, and total phosphorus (TP) export. Under ND and FP scenarios, modest gains in water yield (+32.25% and +32.13%) and soil retention (+46.16% and +45.91%) are achieved, but TP control remains limited (−0.05% and +4.82%). In contrast, the EP scenario drives severe declines in water yield (−13.39%) and soil retention (−2.11%) alongside a TP surge (+5.87%), evidencing ecological degradation under high-intensity development. Conversely, the EcoP scenario yields substantial improvements, water yield +50.67%, soil retention +70.94%, and TP export −8.17%, reflecting the synergistic “multiplier effect” of combined woodland and water-body restoration. Spatially, urban cores and agricultural margins exhibit divergent service responses, underscoring the need for differentiated management. We developed a spatial priority map by integrating the predicted WES changes under the Ecological Protection scenario with indicators of urban proximity and pollution risk. This map identifies critical intervention zones. We propose targeted spatial optimization—strict protection of sensitive ecological zones, green transformation in urban expansion areas, and diffuse pollution controls in agricultural peripheries—to reconcile development with ecosystem resilience. Full article

The Yellow River Basin (YRB), a pivotal ecoregion in China, has long been plagued by a range of ecological problems, including water loss, soil erosion, and ecological degradation. Despite previous reports on the ecological environment of YRB, systematic studies on the multi-factor driving mechanism and the coupling between the ecological and hydrological systems remain scarce. In this study, with multi-source remote-sensing imagery and measured hydrological data, the random forest (RF) model and the geographical detector (GD) technique were employed to quantify the dynamic spatiotemporal changes in the ecological environment of YRB in 2000–2024 and identify the driving factors. The variables analyzed in this study included gross primary productivity (GPP), fractional vegetation cover (FVC), land use and cover change (LUCC), meteorological statistics, as well as runoff and sediment data measured at hydrological stations in YRB. The main findings are as follows: first, the GPP and FVC increased significantly by 37.9% and 18.0%, respectively, in YRB in 2000–2024; second, LUCC was the strongest driver of spatiotemporal changes in the ecological environment of YRB; third, precipitation and runoff contributed positively to vegetation growth, whereas the sediment played a contrary role, and the response of ecological variables to the hydrological processes exhibited a time lag of 1–2 years. This study is expected to provide scientific insights into ecological conservation and water resources management in YRB, and offer a decision-making basis for the design of sustainability policies and eco-restoration initiatives. Full article

The Athabasca River flows through the Lower Athabasca Region (LAR) in Alberta, Canada, which is characterized by variable inter-annual weather, long winters and short summers. LAR is important for the extraction of energy resources and industrial activities that lead to environmental concerns, including river pollution and exploitation. This study attempts to forecast the Athabasca River at Fort McMurray and understand the suitability of HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) in cold weather regions, characterized by poorly gauged streams. Daily temperature and precipitation records (1971–2014) were employed in two calibration–validation schemes: (1) a temporally dependent partition (1971–2000 for calibration; 2001–2014 for validation) and (2) a temporally independent partition (alternating years assigned to calibration and validation). The temporally independent approach achieved superior performance, with a Nash–Sutcliffe efficiency of 0.88, outperforming previously developed regional models. HEC-HMS successfully reproduced hydrologic dynamics and peak discharge events under conditions of sparse hydroclimatic data and limited computational inputs, underscoring its robustness for operational forecasting in data-scarce, cold-climate catchments. However, long-term projections may be subject to uncertainty due to the exclusion of anticipated changes in land use and climate forcing. These results substantiate the applicability of HEC-HMS as a cost-effective and reliable tool for hydrological modeling and flow forecasting in support of water resource management, particularly in regions subject to industrial pressures and associated environmental impacts. Full article

The scientific site selection for pallet pooling systems is pivotal to enhancing logistics efficiency and environmental performance. However, previous studies mainly adopt single-objective optimization approaches, which fail to simultaneously account for economic, environmental, and operational performance factors. The contribution of this paper lies in proposing an integrated decision-making method based on BWM-GIS-DEA to address the site selection problem for pallet pooling service centers. First, the Best-Worst Method (BWM) determines the weights of 13 criteria across 5 dimensions: economic, transportation, geographical location, technological, and service coverage. These criteria include factors such as the distribution density of pallet manufacturers and potential customers. Then, suitability maps are generated using Geographic Information System (GIS) spatial overlay technology to identify 6 alternative cities. Finally, a two-layer Data Envelopment Analysis (DEA) model is applied to measure the efficiency of the alternative sites. This method is applied in Inner Mongolia, China, and Ejin Horo Banner is identified as the optimal site with an efficiency score of 1.156, demonstrating superior resource allocation characterized by lower land costs and higher pallet turnover rates. The proposed framework not only fills a methodological gap in sustainable facility location research but also provides a replicable and policy-ready tool to guide practical decision-making. Full article