Search Results (11,461)

Ecolabelling has emerged as a key instrument to communicate environmental performance to consumers, particularly in the agri-food sector where resource use and ecological pressures are highly interlinked. Conventional Life Cycle Assessment (LCA)-based ecolabels often suffer from methodological discretion, lack of territorial specificity, and limited consumer trust. This study investigates how the Water–Energy–Food–Ecosystem (WEFE) Nexus could be integrated into LCA-based ecolabelling, with a specific focus on pasta production as a representative case in the food industry. Indicators were collected from recent literature on LCA and Nexus applications, selected for simplicity and clear attribution to one WEFE dimension, and then evaluated by experts from COST Action CA20138 (NexusNet) through a two round Delphi protocol. The process yielded 23 indicators distributed across the four dimensions, which were subsequently compared with six Environmental Product Declarations to assess data availability and compatibility. The results suggest that many indicators can be computed with standard LCA inventories, while the Nexus perspective adds value by capturing multidimensional impacts and regional resource pressures. Further refinement and empirical testing are expected to enhance the framework’s applicability, but the findings already indicate that incorporating WEFE-based indicators into pasta ecolabelling could represent a promising pathway to improve analytical depth and consumer relevance, aligning circular economy principles with corporate assessment practices. Full article

Rail transport is central to achieving sustainable and energy-efficient mobility, and its digitalization is accelerating the adoption of condition-based maintenance (CBM) strategies. However, existing maintenance practices remain largely reactive or rely on limited rule-based diagnostics, which constrain safety, interoperability, and lifecycle optimization. This survey provides a comprehensive and structured review of Artificial Intelligence techniques applied to the preventive, predictive, and prescriptive maintenance of railway infrastructure. We analyze and compare machine learning and deep learning approaches—including neural networks, support vector machines, random forests, genetic algorithms, and end-to-end deep models—applied to parameters such as track geometry, vibration-based monitoring, and imaging-based inspection. The survey highlights the dominant data sources and feature engineering techniques, evaluates the model performance across subsystems, and identifies research gaps related to data quality, cross-network generalization, model robustness, and integration with real-time asset management platforms. We further discuss emerging research directions, including Digital Twins, edge AI, and Cyber–Physical predictive systems, which position AI as an enabler of autonomous infrastructure management. This survey defines the key challenges and opportunities to guide future research and standardization in intelligent railway maintenance ecosystems. Full article

Mosquitoes (Diptera: Culicidae) exhibit vertical stratification patterns in forest environments, a fundamental ecological aspect for understanding niche occupation patterns, host-seeking behavior, and consequently arbovirus transmission mechanisms. Despite the relevance of this topic, available studies mostly focus on genera such as Aedes, Haemagogus, and Sabethes which are traditionally associated with arbovirus transmission. There are still important gaps regarding stratification and seasonality in the Mansoniini tribe, whose biology and epidemiological role remain underexplored, especially in highly biodiverse ecosystems such as the Atlantic Forest. This study evaluated the influence of seasonality and vertical stratification on the mosquito community, with a detailed focus on the Mansoniini tribe, in an Atlantic Forest fragment in Brazil, between May 2023 and December 2024. Captures were performed monthly using CDC light traps positioned at 1.5 m and 10 m heights, and specimens were morphologically identified. A total of 880 mosquitoes from nine genera and 24 species were captured, of which 91 (10.3%) belonged to the Mansoniini tribe. The most abundant species were Coquillettidia fasciolata and Mansonia titillans, recorded in both strata. Our results indicate no marked vertical segregation for the studied mosquito community in this specific location, but a strong influence of seasonality, particularly for the Mansoniini tribe, reinforcing the role of meteorological data on the population structure of these species. These site-specific findings offer a foundational ecological portrait and a robust methodological template for a neglected taxon. They generate critical, testable hypotheses about niche partitioning in fragmented forests and underscore the necessity for broader spatial replication to disentangle the relative influence of seasonal versus vertical drivers in similar ecosystems. Full article

With the rapid development of the sharing economy and the progress of social ecological civilization, social media influencers (SMIs) have garnered significant from academia and practitioners for their pivotal role in fostering pro-environmental behavioral intentions within the tourism consumption context. Drawing on the two-step flow theory, social influence theory, and social learning theory, this study establishes an integrated analytical framework to elucidate how SMIs facilitate the balance between tourism development and ecosystem preservation by activating pro-environmental behavioral behavior. This study conceptualizes the SMIs’ multi-traits as a higher-order construct (a third-order reflective structure), which integrates content-determined and personality-determined attributes, viewing SMIs’ effectiveness as a coherent system of influence rather than a series of fragmented traits. Based on survey data collected from 598 Chinese social media users, the study utilized Covariance-Based Structural Equation Modeling (CB-SEM) to test the proposed model. The results demonstrate that SMIs’ multi-traits exert significant positive effects on parasocial relationships and wishful identification, which in turn enhance individuals’ willingness to mimic. This willingness to mimic serves as a core behavioral conversion mechanism, bridging digital influence on three pro-environmental behavioral intentions: general, specific and online advocacy intentions. Furthermore, robustness analyses reveal marked heterogeneity across education- and income-based groups, indicating that the efficacy of SMI traits and the psychological-to-behavioral conversion efficiency are contingent upon the recipients’ socioeconomic resources and cognitive capital. Overall, this study characterizes social media influencer marketing as a scalable, socially driven phenomenon that can effectively activate and promote pro-environmental behavioral intentions, providing valuable insights for environmental education and sustainable tourism development in the digital age. Full article

Urban green spaces increasingly serve as sites of human–tick contact, yet long-term data on tick activity and host associations in urban recreational areas remain limited. This study investigated the seasonal activity patterns of Ixodes ricinus and Dermacentor reticulatus and the host spectrum of juvenile tick stages in an urban park in eastern Poland over a five-year period (2015–2019). Questing ticks were collected from vegetation using the flagging method, while small mammals were live-trapped to assess tick infestation of juvenile stages. The effects of air temperature, relative humidity, and seasonality on tick activity were analysed using generalized additive models (GAMs). D. reticulatus was the dominant tick species throughout the study, exhibiting pronounced autumn activity peaks, whereas I. ricinus occurred at lower densities with peak activity in late spring and early summer. GAM analyses revealed that apparent temperature effects observed in uncorrected models disappeared after accounting for seasonality, while seasonal timing remained a strong and consistent predictor of tick activity across species, developmental stages, and sexes. Juvenile ticks of both species were most frequently associated with Apodemus agrarius, indicating that urban-adapted rodent hosts play a key role in sustaining tick life cycles in simplified urban ecosystems. These findings demonstrate that urban recreational areas can function as persistent foci of tick hazard, with tick activity driven primarily by intrinsic seasonal dynamics rather than short-term weather variation. Full article

Achieving sustainable agricultural intensification in inland valleys while limiting the adverse environmental impacts and uncertainties related to water availability requires an analysis of the long-term hydrological behavior of the catchment. Such a task is particularly challenging in West Africa and Benin due to the limited availability of climate and hydrological data. This study evaluates the applicability of the lumped GR4J model for simulating streamflow in three inland valleys of the Sudanian zone of Benin (Lower-Sowé, Bahounkpo and Nalohou). Additionally, we test the reliability of satellite-based rainfall data (GPM-IMERG, CHIRPS or GSMAP) in modeling hydrological dynamics in these small catchments. The results demonstrate that the GR4J model is effective in simulating daily discharge in the three inland valleys (KGE > 0.5 during both calibration and validation periods), with particularly interesting performance in mean-flow conditions. The modeling using GPM-IMERG and GSMAP rainfall data shows mitigated results with acceptable performance at Nalohou and less accurate results at Bahounkpo and Lower-Sowé. CHIRPS emerged as the most consistent among the evaluated products, providing a sound basis for reconstructing general trends and seasonal variations in historical streamflow time series. The approach of combining historical CHIRPS data and the GR4J model provides insights and can support decision-making related to water resource management in terms of resource capacity and volume in the study area. Except for Nalohou (KGE = 0.19 with GPM-IMERG data), we observe limitations in predicting high flows with satellite-based climatic data at Bahounkpo (KGE = 0.02 with GPM-IR) and Lower-Sowé (KGE = −0.01 with CHIRPS), where the near-zero KGE scores indicate marginal improvement over a mean-flow benchmark. Future work should explore how hybrid or flexible modeling approaches can improve the accuracy of runoff simulations in inland valleys, particularly for extreme (low- and high-) flow conditions. Additionally, the analysis of the trends of indicators of hydrological alteration (IHA) must be deepened in these important ecosystems, especially under climate and land-use change scenarios. Full article

Accurate estimation of Leaf Area Index (LAI) is essential for monitoring vegetation structure and ecosystem services in urban and peri-urban environments, particularly in small, heterogeneous patches typical of semi-arid cities. This study presents a comparative assessment of four empirical LAI estimation methods—NDVI-based, NDVI-advanced, SAVI-based, and EVI-based methods—applied to atmospherically corrected Sentinel-2 Level-2A imagery (10 m spatial resolution) over a 0.045 km² urban–agricultural polygon in the Tashkent region, Uzbekistan. Multi-temporal observations acquired during the 2023 growing season (June–August) were used to examine intra-seasonal vegetation dynamics. In the absence of field-measured LAI, a Random Forest regression model was implemented as an inter-method consistency analysis to assess agreement among index-derived LAI estimates rather than to perform external validation. Statistical comparisons revealed highly systematic and practically significant differences between methods, with the EVI-based approach producing the highest and most dynamically responsive LAI values (mean LAI = 1.453) and demonstrating greater robustness to soil background and atmospheric effects. Mean LAI increased by 66.7% from June to August, reflecting irrigation-driven crop phenology in the semi-arid study area. While the results indicate that EVI provides the most reliable relative LAI estimates for small urban–agricultural patches, the absence of ground-truth data and the influence of mixed pixels at 10 m resolution remain key limitations. This study offers a transferable methodological framework for comparative LAI assessment in data-scarce urban environments and provides a basis for future integration with field measurements, higher-resolution imagery, and LiDAR-based 3D vegetation models. Full article

Dissolved oxygen (DO) is a cornerstone of aquatic ecosystem vitality, yet conventional in situ monitoring methods, reliant on field probes, buoys, and lab analyses, struggle to capture the spatiotemporal variability of DO at regional or global scales. Satellite remote sensing has revolutionized water quality assessment by enabling systematic, high-frequency, and spatially continuous monitoring of surface waters, transcending the logistical and financial constraints of traditional approaches. This systematic review critically evaluates satellite-based methodologies for estimating DO concentrations, emphasizing their capacity to address global environmental challenges such as eutrophication, hypoxia, and climate-driven deoxygenation. Following the PRISMA 2020 guidelines, large bibliographic databases (Scopus, Web of Science, and Google Scholar) identified that studies on satellite-derived DO concentrations are focused on both spectral and thermal foundations of DO retrieval, including empirical relationships with proxy variables (e.g., Chlorophyll-a, sea surface temperature, and turbidity) as well as direct optical signatures linked to oxygen absorption in the red and near-infrared spectra. The 77 results included in this review (accessed on 27 November 2025) indicate that the reported advances in sensor technologies (e.g., Sentinel-2,3’s OLCI and MODIS) have greatly expanded the ability to monitor DO levels across different types of water bodies, and that there has been a significant paradigm shift towards more complex and sophisticated machine learning and deep learning architectures. Recent work demonstrates that advanced machine learning and deep learning models can effectively estimate DO from remote sensing proxies, achieving high predictive performance when validated against in situ observations. Overall, this review indicates that their effectiveness depends heavily on high-quality training data, rigorous validation, and careful recalibration. Global case studies illustrate applications showcasing the scalability of remote sensing solutions. An OSF project was created to enhance transparency, while the review protocol was not prospectively registered, which is consistent with the PRISMA 2020 guidelines for non-registered reviews. Full article

The development of urban green infrastructures (UGI) is considered among the main nature-based solutions for climate mitigation in cities; however, the role of soils in the carbon (C) balance of UGI ecosystems remains largely overlooked. Urban green spaces are typically dominated by constructed Technosols, created by adding organic materials on top of former natural or agricultural subsoils. The combined effects of land-use history and current UGI management result in a high spatial variation of soil organic carbon (SOC) stocks and soil CO₂ emissions. Our study aimed to explore this variation for the case of Wageningen University campus. Developed on a former agricultural land, the campus area includes green spaces dominated by trees, shrubs, lawns, and herbs, with well-documented management practices for each vegetation type. Across the campus area (~32 ha), a random stratified topsoil sampling (n = 90) was conducted to map the spatial variation of topsoil (0–10 cm) SOC stocks. At the key sites (n = 8), representing different vegetation types and time of development (old, intermediate, and recent), SOC profile distribution was analyzed including SOC fractionation in surface and subsequent horizons, as well as the dynamics in soil CO₂ emissions, temperature, and moisture. Topsoil SOC contents on campus ranged from 1.1 to 5.5% (95% confidence interval). On average, SOC stocks under trees and shrubs were 10–15% higher than those under lawns and herbs. The highest CO₂ emissions were observed from soil under lawns and coincided with a high proportion of labile SOC fraction. Temporal dynamics in soil CO₂ emissions were mainly driven by soil temperature, with the strongest relation (R² = 0.71–0.88) observed for lawns. Extrapolating this relationship to the calendar year and across the campus area using high-resolution remote sensing data on surface temperatures resulted in a map of the CO₂ emissions/SOC stocks ratio, used as a spatial proxy for C turnover. Areas dominated by recent and intermediate lawns emerged as hotspots of rapid C turnover, highlighting important differences in the role of various UGI types in the C balance of urban green spaces. Full article

Asterothamnus centraliasiaticus is a key species within the desert ecosystems of the Qinghai–Tibet Plateau. To elucidate the physiological responses and underlying molecular mechanisms of drought tolerance in A. centraliasiaticus, this study employed high-throughput RNA sequencing of leaf tissues to identify key pathways and drought resistance-related genes associated with adaptation to water deficit conditions. Physiological analyses revealed that drought stress significantly enhanced the activities of antioxidant enzymes, increased the accumulation of osmotic adjustment substances and membrane damage indicators, and elevated relative electrical conductivity in leaves. In contrast, total ROS levels were significantly reduced under drought stress, indicating effective activation of antioxidant defense systems. Transcriptome analysis identified 15,010 differentially expressed genes (DEGs) in response to drought stress. GO and KEGG enrichment analyses revealed that these DEGs were predominantly involved in phenylpropanoid biosynthesis, plant hormone signal transduction, and zeatin biosynthesis pathways, which are closely associated with stress perception, signal transduction, and adaptive metabolic regulation. Moreover, qPCR validation of 15 randomly selected genes corroborated the RNA-seq results, confirming the reliability of the transcriptomic data. Collectively, these findings provide a valuable molecular framework for understanding drought response pathways and identifying drought resistance genes in A. centraliasiaticus, thereby offering theoretical support for future studies on xerophytic plant adaptation and molecular breeding for drought tolerance. Full article

Climate change affects surface water quality parameters, including river quality. This study analyses changes in climate parameters, specifically air temperature and solar radiation, and their impact on river water temperature. It also examines how changes in river water temperature and organic matter load affect oxygen saturation levels, a key indicator of river water quality. Using water quality data, the status as well as temporal and spatial trends of the analysed parameters were assessed for the period between 2007 and 2024 on the three largest Slovenian rivers: the Drava, Mura, and Sava. Relative importance analysis of temperature and biochemical oxygen demand (BOD) using the Random Forest machine learning method showed that water temperature in the analysed rivers has an impact ranging from 51% to 66% on predicting oxygen saturation. The selected approach to analysing watercourse quality parameters enables the assessment of the impact of these parameters on river water quality. Based on these results, it will be possible to implement appropriate measures promptly to achieve sustainable river management by establishing a strategy that, under climate change conditions, safeguards water quality and maintains ecosystem protection, ensuring long-term ecological and socio-economic benefits. Full article

The noctuid moth Dioszeghyana schmidtii (Dioszeghy, 1935) is a geographically restricted and poorly known species associated with xerothermic oak ecosystems of Central, Eastern, and Southeastern Europe and Asia Minor. Despite its inclusion in European conservation frameworks, information on its distribution, biology, and ecological requirements remains fragmented, regionally uneven, and scattered across the faunistic literature in multiple languages. This review synthesizes published records, taxonomic sources, ecological observations, and curated occurrence data to provide an updated and critically assessed overview of the species’ biology, habitat associations, and biogeographic pattern. Distributional information was compiled exclusively from the literature and vetted public databases, with mapped occurrences representing confirmed regional presence rather than fine-scale occupancy. The species exhibits a patchy but ecologically coherent distribution closely linked to open, thermophilous Quercus woodlands, particularly those dominated by Q. cerris and related oak species. Major threats include habitat loss, forest densification, fragmentation, and phenological mismatches associated with climate change. By identifying persistent knowledge gaps and sources of uncertainty, this review highlights priorities for future research, monitoring, and habitat-based conservation of D. schmidtii and similar early-spring, oak-associated Lepidoptera. Full article

Habitat fragmentation is reshaping ecosystems worldwide, reducing connectivity, eroding genetic diversity, and limiting species’ capacity to adapt to rapid environmental change. Conservation management responses to fragmentation generally follow three pathways: restoring habitats to rebuild connectivity, translocating individuals to bolster declining populations, and, more recently, directly managing adaptive genetic variation. We synthesize the ecological and genetic consequences of fragmentation and evaluate these management pathways along a continuum from landscape-scale interventions to genome-level strategies. Habitat restoration can reconnect patches and improve demo-graphic stability, but its genetic outcomes remain uncertain without baseline and post-restoration monitoring. Translocation offers a more immediate means of restoring gene flow but introduces demographic risks, potential impacts on source populations, and uncertainties in establishment and long-term fitness. Emerging genomic technologies now support a third approach: Targeted Genetic Intervention (TGI), which aims to accelerate the spread of beneficial genetic variants or enhance adaptive potential directly. Although promising, TGI faces significant challenges, including polygenic trait architecture, risks to genome-wide diversity, and the need for robust ethical and governance frameworks. Across all pathways, genetic data are essential for prioritizing actions, diagnosing vulnerable species and populations, and restoring the evolutionary potential necessary for long-term persistence in increasingly fragmented landscapes. Full article

The phyllosphere microbiome is an important component of plant-associated ecosystems, and its structure is susceptible to biotic stress and agricultural interventions. However, the non-target effects of plant-derived pesticides and their nanoformulations on the phyllosphere microbial community remain unclear. By using 16S rRNA amplicon sequencing, we investigated the non-target effects of azadirachtin (Aza) and its nanoformulation (O-carboxymethyl chitosan-loaded azadirachtin, O-cmc-aza) on the phyllosphere microbial community of maize, including Spodoptera frugiperda herbivory stress (Attack) as an additional treatment. The results showed that all three treatments significantly altered the phyllosphere microbial community structure, while the overall microbial diversity indices remained stable. Specifically, the Attack treatment significantly enriched bacterial genera such as Akkermansia and Burkholderia-Caballeronia-Paraburkholderia; the Aza treatment mainly increased the abundance of taxa such as Stenotrophomonas and Herbaspirillum, which have been associated in the literature with plant growth promotion; and the O-cmc-aza treatment specifically enriched microbial groups such as Ralstonia and Sphingomonas, which have been reported to include strains involved in pollutant degradation and nitrogen cycling, while reducing the ACE index but maintaining high community evenness. Our results indicated that azadirachtin and its nanoformulations induced compositional changes in the phyllosphere microbiome, without causing marked decline in microbial diversity. This study provides data support for evaluating plant-derived pesticides and nanoformulations with respect to their non-target effect on phyllosphere microbial communities in green agricultural systems. Full article

The widespread adoption of mobile health applications has increased the volume of sensitive personal and physiological data processed through interconnected devices. Ensuring privacy compliance in this context remains a challenge, as existing app stores and privacy labeling systems rely heavily on self-declared information. App-PI is a data-driven ecosystem designed to offer end users with tools they can easily manage and privacy researchers with structured and reliable app metadata. It is designed to automate the collection, analysis, and visualization of privacy-related metadata from mobile applications. Heterogeneous data sources are integrated into a unified repository (App-PIMD), enabling the empirical assessment of privacy risks. The data flow design is critical to ensure that the data used to assess privacy impact is of good quality, as well as the privacy indicators that end users will be offered. It is shown on a popular mHealth application, demonstrating the importance of data flow design in order to be able to obtain, from documents and files created for consumption by an operating system, a set of data and tools ready for consumption by the true recipients of health apps: people. Full article