Search Results (1,208)

The selection of tree species for afforestation in Mediterranean environments involves challenges related to adaptability, impact on soil properties, and overall environmental quality. Cupressus lusitanica has been recognized for its rapid growth, environmental resilience, and versatile applications, positioning it as a promising candidate for these regions. Although it has been used for afforestation in Northeast Portugal since the 1990s, no comprehensive studies have evaluated its performance under local conditions. To address this knowledge gap, this study assessed a 14-year-old C. lusitanica stand in Northeast Portugal. The wood’s anatomical, physical, chemical, and mechanical properties, as well as biomass production, were evaluated. The species showed superior radial growth and adaptability compared with other species under similar environmental conditions. Despite exhibiting lower fiber length (1.6 mm) and basic wood density (404 kg/m³), shrinkage values fell within the typical range for softwoods. Nevertheless, a marked tendency for warping was observed. The extractive content was relatively high (5.1%), with the ethanol-soluble fraction being predominant (3.6%). Mechanical tests revealed low values for both Modulus of Elasticity (MOE) (3592.5–3617.1 MPa) and Modulus of Rupture (MOR) (57.7–68.9 MPa), with both properties significantly influenced by knot presence. Given the results obtained, the species C. lusitanica, despite its low wood density and potential limitations in use, exhibits remarkable growth and adaptability, which confer a high potential for biomass production and carbon sequestration, as well as potential applications of its wood in reconstituted panels and fiber- or particle-based boards. Full article

Despite advances in precision irrigation, no systematic review has yet integrated the roles of remote sensing, agro-meteorological data, and wireless sensor networks in high-value, water-sensitive crops such as mango, avocado, and vineyards. Existing research often isolates technologies or crop types, overlooking their convergence and joint performance in the field. This review fills that gap by examining how these tools estimate crop water demand and support sustainable, site-specific irrigation under variable climate conditions. A structured search across major databases yielded 365 articles, of which 92 met the inclusion criteria. Studies were grouped into four categories: remote sensing, agro-meteorology, wireless sensor networks, and integrated approaches. Remote sensing techniques, including multispectral and thermal imaging, enable the spatial monitoring of vegetation indices and stress indicators, such as the Crop Water Stress Index. Agro-meteorological data feed evapotranspiration models using temperature, humidity, wind, and radiation inputs. Wireless sensor networks provide continuous, localized data on soil moisture and canopy temperature. Integrated approaches combine these sources to improve irrigation recommendations. Findings suggest that combining remote sensing, wireless sensor networks, and agro-meteorological inputs can reduce water use by up to 30% without yield loss. Challenges include sensor calibration, data integration complexity, and limited scalability. This review also compares methodologies and highlights future directions, including artificial intelligence systems, digital twins, and affordable Internet of Things platforms for irrigation optimization. Full article

The anomalous enrichment of arsenic (As) and fluoride (F) in groundwater in the oasis area at the southern margin of the Tarim Basin has become a critical environmental and sustainability challenge. It poses not only potential health risks but also profound socio-economic impacts on local communities, threatening the long-term security of water resources in arid regions. Therefore, an in-depth investigation of the hydrochemical characteristics of groundwater and the co-enrichment mechanism of As and F is essential for advancing sustainable groundwater management. In this study, 110 phreatic water samples and 50 confined water samples were collected, and mathematical and statistical methods were applied to analyze the hydrochemical characteristics, sources, and co-enrichment mechanisms of As and F. The results show that (1) the groundwater chemistry types are mainly Cl·SO₄-Na, SO₄·Cl-Na·Mg, Cl·SO₄-Na·Mg, and Cl-Na, and the chemistry is primarily controlled by evaporation and concentration processes, with additional influence from human activities and cation exchange; (2) As and F mainly originate from soils and minerals, and are released through dissolution; (3) As and F enrichment is positively correlated with pH, Na⁺, and HCO₃⁻, but negatively correlated with Ca²⁺, Mg²⁺, and SO₄²⁻, indicating that a weakly alkaline hydrochemical environment with high HCO₃⁻ and Na⁺, and low Ca²⁺ promotes their enrichment; (4) strong evaporative concentration in retention zones, combined with artificial groundwater extraction, further intensifies As and F accumulation. This study not only provides an innovative theoretical and methodological framework for exploring trace element enrichment mechanisms in groundwater under arid conditions but also delivers critical scientific evidence for developing sustainable water resource management strategies, mitigating water-related health risks, and supporting regional socio-economic resilience under global climate change. Full article

On 6 February 2023, two devastating earthquakes struck the Kahramanmaraş region in southeastern Türkiye, causing widespread destruction across multiple provinces. Among the most severely affected areas was Hatay, where this study conducted a comprehensive post-earthquake field investigation. The research integrates tectonic, geological, and seismic analyses with structural performance assessments of reinforced concrete and masonry buildings. Particular attention is given to the influence of local soil conditions and geomorphological features on damage distribution. Ground motion records are evaluated alongside observed structural failures to identify key vulnerability factors. The findings highlight critical deficiencies in construction practices and regulatory compliance, and the study concludes with recommendations aimed at enhancing seismic resilience through improved code enforcement, site-specific design strategies, and rigorous quality control during construction to reduce future loss of life and property. Full article

Sustainable pavement design requires a balanced consideration of economic, environmental, and social impacts. In line with Federal Highway Administration (FHWA) guidelines for sustainable roadway infrastructure, incorporating recycled materials such as reclaimed asphalt pavement (RAP), recycled pavement material (RPM), recycled asphalt shingles (RASs), and warm-mix asphalt (WMA) has been shown to reduce natural resource depletion while promoting circular construction practices. This study investigates the structural performance of Portland cement concrete (PCC) pavements constructed on RAP and RPM base layers. A series of design scenarios was modeled using site-specific laboratory and field data—particularly subgrade soil properties and climatic conditions—from El Paso and San Antonio, Texas. The analysis incorporates unsaturated soil parameters and follows the performance thresholds set by the Mechanistic-Empirical Pavement Design Guide (MEPDG). Findings indicate that concrete mixture design, pavement structure, and local weather conditions are the primary drivers of distress in jointed plain concrete pavements (JPCPs). However, subsoil characteristics have a significant impact on joint faulting in JPCP and punchout occurrences in continuously reinforced concrete pavements (CRCPs), especially in thinner sections. Notably, the use of up to 50% recycled material in the base layer had minimal adverse effects on pavement performance, underscoring its viability as a sustainable design strategy for rigid pavements. Full article

A crucial initial step for the automatic extraction of plant traits from imagery is the segmentation of individual plants. This is typically performed using supervised deep learning (DL) models, which require the creation of an annotated dataset for training, a time-consuming and labor-intensive process. In addition, the models are often only applicable to the conditions represented in the training data. In this study, we propose a pipeline for the automatic extraction of plant traits from high-resolution unmanned aerial vehicle (UAV)-based RGB imagery, applying Segment Anything Model 2.1 (SAM 2.1) for label-free segmentation. To prevent the segmentation of irrelevant objects such as soil or weeds, the model is guided using point prompts, which correspond to local maxima in the canopy height model (CHM). The pipeline was used to measure the crown diameter of approximately 15000 ball-shaped chrysanthemums (Chrysanthemum morifolium (Ramat)) in a 6158 ${\mathrm{m}}^2$ field on two dates. Nearly all plants were successfully segmented, resulting in a recall of 96.86%, a precision of 99.96%, and an ${\mathrm{F}}_1$ score of 98.38%. The estimated diameters showed strong agreement with manual measurements. The results demonstrate the potential of the proposed pipeline for accurate plant trait extraction across varying field conditions without the need for model training or data annotation. Full article

The growing concerns over nuclear power plant safety in the wake of extreme impact events have highlighted the need for containment structures with superior resistance to large commercial aircraft strikes. Conventional reinforced concrete containment has shown limitations in withstanding high-mass and high-velocity impacts, posing potential risks to structural integrity and operational safety. Addressing this challenge, this study focuses on the dynamic impact resistance and vibration behavior of steel–ultra-high-performance concrete (S-UHPC) composite containment, aiming to enhance nuclear facility resilience under beyond-design-basis aircraft impact scenarios. Validated finite element models in LS-DYNA were developed to simulate impacts from four representative large commercial aircraft types, considering variations in wall and steel plate thicknesses, UHPC grades, and soil–structure interaction conditions. Unlike existing studies that often focus on isolated parameters, this work conducts a systematic parametric analysis integrating multiple aircraft types, structural configurations, and foundation conditions, providing comprehensive insights into both global deformation and high-frequency vibration behavior. Comparative analyses with conventional reinforced concrete containment were performed, and floor response spectra were evaluated to quantify high-frequency vibration characteristics under different site conditions. The results show that S-UHPC containment reduces peak displacement by up to ~24% compared to reinforced concrete of the same thickness while effectively localizing core damage without through-thickness failure. In addition, aircraft impacts predominantly excite 90–125 Hz vibrations, with soft soil conditions amplifying acceleration responses by more than four times, underscoring the necessity of site-specific dynamic analysis in nuclear containment and equipment design. Full article

Arid and semi-arid regions show distinctive bacterial groups important for the sustainability of ecosystems and soil health. This study aims to investigate how environmental factors across five Qatari soils influence the taxonomic composition of bacterial communities and their predicted functional roles using 16S rRNA amplicon sequencing and soil chemical analysis. Soil samples from five different locations in Qatar (three coastal and two inland) identified 26 bacterial phyla, which were dominated by Actinomycetota (35–43%), Pseudomonadota (12–16%), and Acidobacteriota (4–13%). Species-level analysis discovered taxa such as Rubrobacter tropicus, Longimicrobium terrae, Gaiella occulta, Kallotenue papyrolyticum, and Sphingomonas jaspsi, suggesting the presence of possible novel microbial families. The functional predictions showed development in pathways related to amino acid metabolism, carbohydrate metabolism, and stress tolerance. In addition, heavy-metal-related taxa, which are known to harbor genes for metal resistance mechanisms including efflux pumps, metal chelation, and oxidative stress tolerance. The presence of Streptomyces, Pseudomonas, and Bacillus highlights their roles in stress tolerance, biodegradation, and metabolite production. These findings improve the understanding of microbial roles in dry soils, especially in nutrient cycling and ecosystem resilience. They highlight the importance of local bacteria for sustaining desert soil functions. Further research is needed to validate these relationships, using metabolomic approaches while monitoring microbial-community-changing aspects under fluctuating environmental conditions. Full article

Cotinus coggygria is a widespread medicinal and aromatic species known for its ecological plasticity, pharmacological potential, and cultivation prospects. Despite its broad distribution across heterogeneous habitats, little is known about how local ecological and pedochemical factors influence its physiological traits and secondary metabolite production. This study addresses this knowledge gap by analyzing the eco-physiological and phytochemical variability of C. coggygria across six natural populations differing in substrate type and geochemical conditions. The research reveals significant inter-population variability in element accumulation, oxidative stress markers, morphometric traits, and the qualitative and quantitative composition of essential oils and phenolic compounds. Soil analyses demonstrated notable differences in element concentrations (e.g., Ca, Fe, Co, Zn) across localities, correlating with geochemical conditions. Morphological traits, such as leaf size and petiole length, varied significantly, with pronounced differences observed in plants from thermophilous and metalliferous habitats. Oxidative stress, indicated by malondialdehyde (MDA) levels, was highest in populations from thermophilous habitats. Phenolic compound analysis revealed locality-specific differences, with plants from thermophilous habitats exhibiting the highest concentrations of gallic acid, catechin, and rutin. Essential oil yield and composition also varied: leaves from metalliferous habitats had the highest monoterpene hydrocarbon content, while bark samples from thermophilous habitats showed elevated sesquiterpene levels. This comprehensive analysis underscores the interplay between habitat-specific conditions and the physiological and biochemical processes of C. coggygria. The findings provide valuable insights for optimizing substrate conditions and ecological management, with implications for the cultivation of the species to enhance the synthesis of bioactive compounds. These results support sustainable land use practices and the development of high-value plant-based products, offering significant implications for agriculture, pharmacology, and ecosystem restoration. Future studies should further explore the genetic and biochemical mechanisms underlying this species’ adaptability and resource optimization in heterogeneous environments. Full article

In many scientific and engineering fields, such as hydrogeology, petroleum engineering, geotechnical research, and developing renewable energy solutions, fluid flow modeling in porous media is essential. In these areas, optimizing extraction techniques, forecasting environmental effects, and guaranteeing structural safety all depend on an understanding of the behavior of single-phase flows—fluids passing through connected pore spaces in rocks or soils. Darcy’s law, which results in an elliptic partial differential equation controlling the pressure field, is usually the mathematical basis for such modeling. Analytical solutions to these partial differential equations are seldom accessible due to the complexity and variability in natural porous formations, which makes the employment of numerical techniques necessary. To approximate subsurface flow solutions, traditional methods like the finite difference method, two-point flux approximation, and multi-point flux approximation have been employed extensively. Accuracy, stability, and computing economy are trade-offs for each, though. Deep learning techniques, in particular convolutional neural networks, physics-informed neural networks, and neural operators such as the Fourier neural operator, have become strong substitutes or enhancers of conventional solvers in recent years. These models have the potential to generalize across various permeability configurations and greatly speed up simulations. The purpose of this study is to examine and contrast the mentioned deep learning and numerical approaches to the problem of pressure distribution in single-phase Darcy flow, considering a 2D domain with mixed boundary conditions, localized sources, and sinks, and both homogeneous and heterogeneous permeability fields. The result of this study shows that the two-point flux approximation method is one of the best regarding computational speed and accuracy and the Fourier neural operator has potential to speed up more accurate methods like multi-point flux approximation. Different permeability field types only impacted each methods’ accuracy while computational time remained unchanged. This work aims to illustrate the advantages and disadvantages of each method and support the continuous development of effective solutions for porous medium flow problems by assessing solution accuracy and computing performance over a range of permeability situations. Full article

The rapid development of transportation infrastructure in mountainous terrains, soft-soil foundations, and high-fill embankments poses stability challenges for conventional embankments, driving the application of advanced three-dimensional reinforced soil technologies. Geogrid with Strengthened Nodes (GSN) is one such innovation, forming a three-dimensional structure by placing block-shaped nodes at geogrid rib intersections. Current research on GSN focuses mainly on pullout tests and numerical simulations, while model-scale studies of its load-bearing deformation behavior and soil pressure distribution remain scarce. This study presents laboratory model tests to assess the reinforcement performance of GSN-reinforced embankments under static and dynamic strip loads. Under static loading, the ultimate bearing capacity of GSN-reinforced embankments increased by 74.58% compared with unreinforced cases and by 26.2% compared with conventional geogrids. Under dynamic loading, cumulative settlement decreased by 32.82%, and lateral displacement at the slope crest was reduced by 64.34%. The strengthened node design improved soil shear strength and controlled lateral deformation via enhanced lateral resistance, creating a more stable “reinforced zone” that alleviated local stress concentrations. Overall, GSN significantly enhanced embankment bearing capacity and stability, outperforming traditional geogrid reinforcement under both static and dynamic conditions, and providing a promising solution for challenging geotechnical environments. Full article

Soil degradation poses a significant threat to the sustainability of agroecosystems, particularly in mountainous regions where environmental conditions are highly variable and management practices are often suboptimal. In this context, soil quality assessment emerges as a key tool for guiding sustainable land use and informing decision-making processes. This study aimed to develop and spatially evaluate a Soil Quality Index (SQI) tailored to the northeast sector of Jangas district, Ancash, Peru. A total of 24 soil indicators were initially considered and reduced using Spearman’s correlations to avoid multicollinearity. Depending on the weighting strategy applied, the final SQI configurations incorporated between 14 and 15 indicators. Two weighting strategies—Principal Component Analysis (PCA) and Expert Opinion (EO)—were combined with linear and non-linear (sigmoidal) scoring functions, resulting in four distinct SQI configurations. The spatial performance of each index was tested using Geographically Weighted Regression Kriging (GWRK), incorporating covariates like NDMI, elevation, slope, and aspect. The SQI constructed using PCA combined with non-linear scoring achieved the highest performance, effectively minimizing skewness and while achieving the highest predictive accuracy under GWRK. By contrast, although the EO-based index with linear scoring demonstrated similar statistical robustness, it failed to achieve comparable effectiveness in terms of spatial predictive accuracy. The SQIs generated offer a practical framework for local institutions to identify and prioritize areas requiring intervention. Through the interpretation of complex soil data into accessible, spatially explicit maps, these indices facilitate the targeted application of inputs—such as organic amendments in low-SQI zones—and support the implementation of improved management practices, including crop rotation and soil conservation, without necessitating advanced technical expertise. Full article

Due to extensive soft soil and high human activities, Wuhan is a hotspot for land subsidence. This study used the time-series InSAR to calculate the spatial and temporal distribution map of subsidence in Wuhan and analyze the causes of subsidence. An improved fuzzy analytic hierarchy process (GD-FAHP) was proposed and integrated with the Entropy Weight Method (EWM) to assess the hazard and vulnerability of land subsidence using multiple evaluation factors, thereby deriving the spatial distribution characteristics of subsidence risk in Wuhan. Results indicated the following: (1) Maximum subsidence rates reached −49 mm/a, with the most severe deformation localized in Hongshan District, exhibiting a cumulative displacement of −135 mm. Comparative validation between InSAR results and leveling was conducted, demonstrating the reliability of InSAR monitoring. (2) Areas with frequent urban construction largely coincided with subsidence locations. In addition, the analysis indicated that rainfall and hydrogeological conditions were also correlated with land subsidence. (3) The proposed risk assessment model effectively identified high-risk areas concentrated in central urban zones, particularly the Hongshan and Wuchang Districts. This research establishes a methodological framework for urban hazard mitigation and provides actionable insights for subsidence risk reduction strategies. Full article

Burrowing mammals function as ecosystem engineers by creating spatial heterogeneity in the soil structure and vegetation composition, thereby providing microhabitats for a wide range of organisms. These keystone species play a crucial role in maintaining local ecosystem functions and delivering ecosystem services. However, in Mongolia, where overgrazing has accelerated due to the expansion of a market-based economy, scientific knowledge remains limited regarding the impacts of human activities on such species. In this study, we focused on the Siberian marmot (Marmota sibirica), an ecosystem engineer inhabiting typical Mongolian steppe ecosystems. We assessed the relationship between the spatial distribution of marmot burrows and vegetation conditions both inside and outside Hustai National Park. Burrow locations were recorded in the field, and the Normalized Difference Vegetation Index (NDVI) was calculated, using Planet Lab, Dove-2 satellite imagery (3 m spatial resolution). Through a combination of remote sensing analyses and vegetation surveys, we examined how the presence or absence of anthropogenic disturbance (i.e., livestock grazing) affects the ecological functions of marmots. Our results showed that the distance between active burrows was significantly shorter inside the park (t = −2.68, p = 0.0087), indicating a higher population density. Furthermore, a statistical approach, using beta regression, revealed a significant interaction between the burrow type (active, non-active, off-colony area) and region (inside vs. outside the park) on the NDVI (e.g., outside × non-active: z = −5.229, p < 0.001). Notably, in areas with high grazing pressure outside the park, the variance in the NDVI varied significantly as a function of burrow presence or absence (e.g., July 2023, active vs. off-colony area: F = 133.46, p < 0.001). Combined with vegetation structure data from field surveys, our findings suggest that marmot burrowing activity may contribute to the enhancement of vegetation quality and spatial heterogeneity. These results indicate that the Siberian marmot remains an important component in supporting the diversity and stability of steppe ecosystems, even under intensive grazing pressure. The conservation of this species may thus provide a promising strategy for utilizing native ecosystem engineers in sustainable land-use management. Full article

The long-term durability of steel structures in contact with soil remains a critical challenge due to the complex and aggressive nature of many soil environments. This study presents a thorough evaluation of the corrosion resistance and microstructural evolution of Magnelis^® ZM430-coated steel exposed to highly aggressive, heterogeneous soils. Gravimetric analysis revealed that the Magnelis^® ZM430 coating exhibits low corrosion rates and enhanced initial barrier properties, even under severe soil conditions. Although the literature frequently reports that Zn–Al–Mg coatings outperform conventional hot-dip galvanized coatings, our results highlight that this superiority is not universal and may be limited under highly aggressive, heterogeneous soils. Microstructural characterization by optical microscopy, SEM/EDS, and XRD demonstrated that the as-received coating consists of a homogeneous layer with well-distributed Zn-, MgZn₂-, and Al-rich phases. Upon soil exposure, corrosion preferentially initiates in the Mg- and Al-rich interdendritic and eutectic regions, leading to selective phase depletion and localized breakdown of the protective layer. Despite these localized vulnerabilities, the overall performance of Magnelis^® ZM430 remains superior, especially during the early stages of exposure. While no direct comparisons were performed in this work, our findings align with previous literature reporting superior performance of Zn–Al–Mg coatings compared to conventional hot-dip galvanized coatings in similar environments. Importantly, the integration of precise corrosion rate data with detailed soil characterization enables accurate prediction of coating service life, allowing for optimized coating thickness selection and proactive maintenance planning. These findings underscore the value of combining advanced Zn–Al–Mg coatings with site-specific environmental assessment to ensure the long-term integrity of buried steel infrastructure. Full article