Search Results (1,880)

Forests are considered one of the most valuable natural areas in metropolitan region landscapes. Considering the sensitivity and ecosystem services provided by trees, the definition of urban forest ecosystems is nowadays based on a comprehensive understanding of the entire urban ecosystem. The effective capturing of particulate matter is one of the ecosystem services provided by urban forests. These ecosystems function as efficient biological filters. Plants accumulate pollutants passively via their leaves. Therefore, another ecosystem service provided by city forests could be the use of tree organs as bioindicators of pollution. This paper aims to estimate differences in trace metal pollution between the wooded urban areas of Vienna and Krakow using leaves of evergreen and deciduous trees as biomonitors. An additional objective of the research was to assess the ability of the applied tree species to act as biomonitors. Plant samples of five species—Norway spruce, Scots pine, European larch, common white birch, and common beech—were collected within both areas, in seven locations: four in the “Wienerwald” Vienna forest (Austria) and three in the “Las Wolski” forest in Krakow (Poland). Concentrations of Cr, Cu, Cd, Pb, and Zn in plant material were determined. Biomonitoring studies with deciduous and coniferous tree leaves showed statistically higher heavy metal contamination in the “Las Wolski” forest compared to the “Wienerwald” forest. Based on the conducted analyses and the literature study, it can be concluded that among the analyzed tree species, only two: European beech and common white birch can be considered potential indicators in environmental studies. These species appear to be suitable bioindicators, as both are widespread in urban woodlands of Central Europe and have shown the highest accumulation levels of trace metals. Full article

To explore the characteristics of species diversity and phylogenetic diversity, as well as the dominant processes of community construction, in different forest types (deciduous broad-leaved forest, mixed coniferous and broad-leaved forest, and Chinese fir plantation) in subtropical regions, analyze the specific driving patterns of soil nutrients and other environmental factors on the formation of forest diversity in different forest types, and clarify the differences in response to environmental heterogeneity between natural forests and plantation forests. Based on 48 fixed monitoring plots of 50 m × 50 m in Shouchang Forest Farm, Jiande City, Zhejiang Province, woody plants with a diameter at breast height ≥5 cm were investigated. Species diversity indices (Margalef index, Shannon–Wiener index, Simpson index, and Pielou index), phylogenetic structure index (PD), and environmental factors were used to analyze the relationship between diversity characteristics and environmental factors through variance analysis, correlation analysis, and generalized linear models. Phylogenetic structural indices (NRI and NTI) were used, combined with a random zero model, to explore the mechanisms of community construction in different forest types. Research has found that (1) the deciduous broad-leaved forest had the highest species diversity (Margalef index of 4.121 ± 1.425) and phylogenetic diversity (PD index of 21.265 ± 7.796), significantly higher than the mixed coniferous and broad-leaved forest and the Chinese fir plantation (p < 0.05); (2) there is a significant positive correlation between species richness and phylogenetic diversity, with the best fit being AIC = 70.5636 and R² = 0.9419 in broad-leaved forests; however, the contribution of evenness is limited; (3) the specific effects of soil factors on different forest types: available phosphorus (AP) is negatively correlated with the diversity of deciduous broad-leaved forests (p < 0.05), total phosphorus (TP) promotes the diversity of coniferous and broad-leaved mixed forests, while the diversity of Chinese fir plantations is significantly negatively correlated with total nitrogen (TN); (4) the phylogenetic structure of three different forest types shows a divergent pattern in deciduous broad-leaved forests, indicating that competition and exclusion dominate the construction of deciduous broad-leaved forests; the aggregation mode of Chinese fir plantation indicates that environmental filtering dominates the construction of Chinese fir plantation; the mixed coniferous and broad-leaved forest is a transitional model, indicating that the mixed coniferous and broad-leaved forest is influenced by both stochastic processes and ecological niche processes. In different forest types in subtropical regions, the species and phylogenetic diversity of broad-leaved forests is significantly higher than in other forest types. The impact of soil nutrients on the diversity of different forest types varies, and the characteristics of community construction in different forest types are also different. This indicates the importance of protecting the original vegetation and provides a scientific basis for improving the ecological function of artificial forest ecosystems through structural adjustment. The research results have important practical guidance value for sustainable forest management and biodiversity conservation in the region. Full article

Microplastics are pervasive environmental pollutants that pose risks to human health through ingestion and inhalation. This review synthesizes current practices to reduce exposure and toxicity by examining major exposure routes and dietary interventions. More than 130 papers were analyzed to achieve this aim. The findings show that microplastics contaminate a wide range of food products, with particular concern over seafood, drinking water, plastic-packaged foods, paper cups, and tea filter bags. Inhalation exposure is mainly linked to indoor air quality and smoking, while dermal contact poses minimal risk, though the release of additives from plastics onto the skin remains an area of concern. Recommended strategies to reduce dietary exposure include consuming only muscle parts of seafood, moderating intake of high-risk items like anchovies and mollusks, limiting canned seafood liquids, and purging mussels in clean water before consumption. Avoiding plastic containers, especially for hot food or microwaving, using wooden cutting boards, paper tea bags, and opting for tap or filtered water over bottled water are also advised. To mitigate inhalation exposure, the use of air filters with HyperHEPA systems, improved ventilation, regular vacuuming, and the reduction of smoking are recommended. While antioxidant supplementation shows potential in reducing microplastic toxicity, further research is needed to confirm its effectiveness. This review provides practical, evidence-based recommendations for minimizing daily microplastic exposure. Full article

To address the degradation in localization accuracy caused by insufficient robustness of filter parameters and inefficient multi-trajectory data fusion in dynamic environments, this paper proposes a Kalman filter-based localization calibration method optimized by reinforcement learning and information matrix fusion (RL-IMKF). An actor–critic reinforcement learning network is designed to adaptively adjust the state covariance matrix, enhancing the Kalman filter’s adaptability to environmental changes. Meanwhile, a multi-trajectory information matrix fusion strategy is introduced, which aggregates multiple trajectories in the information domain via weighted inverse covariance matrices to suppress error propagation and improve system consistency. Experiments using both simulated and real-world sensor data demonstrate that the proposed method outperforms traditional extended Kalman filter approaches in terms of localization accuracy and stability, providing a novel solution for cooperative localization calibration of unmanned aerial vehicle (UAV) swarms in dynamic environments. Full article

The incorporation of residues into rubber composites has gained attention as a sustainable strategy to address waste management challenges while replacing commercial fillers. In this study, we investigated the potential use of water filter cartridge residue after exhaustion, composed of activated carbon, as a reinforcing filler in vulcanized natural rubber composites. Samples were prepared with 5, 10, 15, and 20 phr (per hundred rubber) of residue and compared to unfilled natural rubber. Stress vs. strain tests reached 13.9 MPa of tension at rupture for composites containing 10 phr of carbon-activated residues, representing a 21.9% increase compared to natural rubber. Interestingly, the tension at rupture for NR/AC_10phr reached values close to those of NR/CB_5phr (with carbon black N330) attaining 14.4 MPa. These results indicate that, even at relatively low concentrations, the carbon filter can offer partial substitution for commercial fillers. Moreover, the use of activated carbon from filter cartridges as filler in rubber composites provides an environmentally favorable alternative to energy-intensive regeneration processes for activated carbon. Full article

The intersection of green finance and artificial intelligence (AI) represents a rapidly emerging and high-impact research domain with the potential to reshape sustainable economic systems. This study presents a comprehensive bibliometric and network analysis aimed at mapping the scientific landscape, identifying research hotspots, and highlighting methodological trends at this nexus. A dataset of 268 peer-reviewed publications (2014–June 2025) was retrieved from the Web of Science Core Collection, filtered by the Business Economics category. Analytical techniques employed include Bibliometrix in R, VOSviewer, and science mapping tools such as thematic mapping, trend topic analysis, co-citation networks, and co-occurrence clustering. Results indicate an annual growth rate of 53.31%, with China leading in both productivity and impact, followed by Vietnam and the United Kingdom. The most prolific affiliations and authors, primarily based in China, underscore a concentrated regional research output. The most relevant journals include Energy Economics and Finance Research Letters. Network visualizations identified 17 clusters, with focused analysis on the top three: (1) Emission, Health, and Environmental Risk, (2) Institutional and Technological Infrastructure, and (3) Green Innovation and Sustainable Urban Development. The methodological landscape is equally diverse, with top techniques including blockchain technology, large language models, convolutional neural networks, sentiment analysis, and structural equation modeling, demonstrating a blend of traditional econometrics and advanced AI. This study not only uncovers intellectual structures and thematic evolution but also identifies underdeveloped areas and proposes future research directions. These include dynamic topic modeling, regional case studies, and ethical frameworks for AI in sustainable finance. The findings provide a strategic foundation for advancing interdisciplinary collaboration and policy innovation in green AI–finance ecosystems. Full article

Magnetic climbing robots hold significant value for operations in complex industrial environments, particularly for the inspection and maintenance of large-scale metal structures. High-precision spatial positioning is the foundation for enabling autonomous and intelligent operations in such environments. However, the existing literature lacks a systematic and comprehensive review of spatial positioning techniques tailored to magnetic climbing robots. This paper addresses this gap by categorizing and evaluating current spatial positioning approaches. Initially, single-sensor-based methods are analyzed with a focus on external sensor approaches. Then, multi-sensor fusion methods are explored to overcome the shortcomings of single-sensor-based approaches. Multi-sensor fusion methods include simultaneous localization and mapping (SLAM), integrated positioning systems, and multi-robot cooperative positioning. To address non-uniform noise and environmental interference, both analytical and learning-based reinforcement approaches are reviewed. Common analytical methods include Kalman-type filtering, particle filtering, and correlation filtering, while typical learning-based approaches involve deep reinforcement learning (DRL) and neural networks (NNs). Finally, challenges and future development trends are discussed. Multi-sensor fusion and lightweight design are the future trends in the advancement of spatial positioning technologies for magnetic climbing robots. Full article

Feed supplementation strategies are essential for optimizing cattle productivity, and the incorporation of non-conventional feed resources may reduce both production costs and environmental impact. This study evaluated the effects of pelletized protein concentrates (including Acacia farnesiana, A. schaffneri, and Agave duranguensis bagasse) on rumen fermentation parameters, microbial communities, and gas emissions. Fistulated bullocks received the concentrate daily, and ruminal contents were collected and filtered before and after supplementation to assess in vitro gas and methane production, pH, and microbial composition using high-throughput sequencing of 16S rRNA and mcrA amplicons. In addition, in situ degradability was evaluated during and after the supplementation period. Supplementation led to a significant (p < 0.05) reduction in degradability parameters and methane production, along with a marked decrease in the abundance of Methanobrevibacter and an increase in succinate-producing taxa. These effects were attributed to the enhanced levels of non-fiber carbohydrates, hemicellulose, crude protein, and the presence of bioactive secondary metabolites and methanol. Rumen microbiota composition was consistent with previously described core communities, and mcrA-based sequencing proved to be a valuable tool for targeted methanogen detection. Overall, the inclusion of non-conventional ingredients in protein concentrates may improve ruminal fermentation efficiency and contribute to methane mitigation in ruminants, although further in vivo trials on a larger scale are recommended. Full article

Due to the growing issue of plastic pollution over recent decades, it is essential to establish well-defined and appropriate methodologies for their extraction from diverse environmental samples. These particles can be found in complex agricultural matrices such as compost, sediments, agricultural soils, sludge, and wastewater, as well as in less complex samples like tap and bottled water. The general steps of MPs extraction typically include drying the sample, sieving to remove larger particles, removal of organic matter, density separation to isolate polymers, filtration using meshes of various sizes, oven drying of the filters, and polymer identification. Complex matrices with high organic matter content require specific removal steps. Most studies employ an initial drying process with temperature control to prevent polymer damage. For removal of organic matter, 30% H₂O₂ is the most commonly used reagent, and for density separation, saturated NaCl and ZnCl₂ solutions are typically applied for low- and high-density polymers, respectively. Finally, filtration is carried out using meshes selected according to the identification technique. This review analyzes the advantages and limitations of the different methodologies to extract microplastics from different sources, aiming to provide in-depth insight for researchers dedicated to the study of environmental samples. Full article

This research investigates resonator width optimization for simultaneously enhancing electrical performance and mechanical reliability in wideband RF MEMS filters through systematic evaluation of three configurations: 0% (L1), 60% (L2), and 100% (L3) matching ratios between cap and bottom wafers using Au-Au thermocompression bonding. The study demonstrates that resonator width alignment significantly influences both electromagnetic field coupling and bonding interface integrity. The L3 configuration with complete width matching achieved optimal RF performance, demonstrating 3.34 dB insertion loss across 4.5 GHz bandwidth (25% fractional bandwidth), outperforming L2 (3.56 dB) and L1 (3.10 dB), while providing enhanced electromagnetic wave coupling and minimized contact resistance. Mechanical reliability testing revealed superior bonding strength for the L3 configuration, withstanding up to 7.14 Kgf in shear pull tests, significantly exceeding L1 (4.22 Kgf) and L2 (2.24 Kgf). SEM analysis confirmed uniform bonding interfaces with minimal void formation (~180 nm), while Q-factor measurements showed L3 achieved optimal loaded Q-factor (Q_L = 3.31) suitable for wideband operation. Comprehensive environmental testing, including thermal cycling (−50 °C to +145 °C) and humidity exposure per MIL-STD-810E standards, validated long-term stability across all configurations. This investigation establishes that complete resonator width matching between cap and bottom wafers optimizes both electromagnetic performance and mechanical bonding reliability, providing a validated framework for developing high-performance, reliable RF MEMS devices for next-generation communication, radar, and sensing applications. Full article

The gastrointestinal tract mediates the absorption of nutrients from the diet, which is increasingly contaminated with toxic metal(loid) species (TMs) and thus threatens food safety. Evidence in support of the influx of TMs into the bloodstream of the general and vulnerable populations (babies, children, pregnant women, and industrial workers) has been obtained by accurately quantifying their blood concentrations. The interpretation of these TM blood concentrations, however, is problematic, as we cannot distinguish between those that are tolerable from those that may cause the onset of environmental diseases. Since TMs that have invaded the bloodstream may perturb biochemical processes therein that will eventually cause organ damage it is crucial to better understand their bioinorganic chemistry as these processes collectively determine their organ availability. Thus, bioinorganic processes of TMs in the bloodstream represent selectivity filters which protect organs from their influx and ultimately determine the corresponding exposure-response relationships. The need to better understand selectivity filters prompted us to mechanistically disentangle them into the major bioinorganic chemistry processes. It is argued that the detoxification of TMs in the bloodstream and the biomolecular mechanisms, which mediate their uptake into target organs, represent critical knowledge gaps to revise regulatory frameworks to reduce the disease burden. Full article

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. Full article

Hyperspectral image change detection (HSI-CD) provides substantial value in environmental monitoring, urban planning and other fields. In recent years, deep-learning based HSI-CD methods have made remarkable progress due to their powerful nonlinear feature learning capabilities, yet they face several challenges: mixed-pixel phenomenon affecting pixel-level detection accuracy; heterogeneous spatial scales of change targets where coarse-grained features fail to preserve fine-grained details; and dependence on high-quality labels. To address these challenges, this paper introduces WSCDNet, a weakly supervised HSI-CD network employing coarse-to-fine feature learning, with key innovations including: (1) A dual-branch detection framework integrating binary and multiclass change detection at the sub-pixel level that enhances collaborative optimization through a cross-feature coupling module; (2) introduction of multi-granularity aggregation and difference feature enhancement module for detecting easily confused regions, which effectively improves the model’s detection accuracy; and (3) proposal of a weakly supervised learning strategy, reducing model sensitivity to noisy pseudo-labels through decision-level consistency measurement and sample filtering mechanisms. Experimental results demonstrate that WSCDNet effectively enhances the accuracy and robustness of HSI-CD tasks, exhibiting superior performance under complex scenarios and weakly supervised conditions. Full article

Background: As a well-known environmental hazard, ambient fine particulate matter (PM_2.5, aerodynamic diameter ≤ 2.5 µm) has been positively correlated with an increased risk of digestive system diseases, including appendicitis, inflammatory bowel disease, and gastrointestinal cancer. Additionally, PM_2.5 exposure has been shown to alter microbiota composition and diversity in human and animal models. However, its impact on goblet cells and gut mucus barrier integrity remains unclear. Methods: To address this, 8-week-old male and female interleukin-10 knockout (IL10^−/−) mice, serving as a spontaneous colitis model, were exposed to concentrated ambient PM_2.5 or filtered air (FA) in a whole-body exposure system for 17 weeks. Colon tissues from the PM_2.5-exposed mice and LS174T goblet cells were analyzed using H&E staining, transmission electron microscopy (TEM), and transcriptomic profiling. Results: The average PM_2.5 concentration in the exposure chamber was 100.20 ± 13.79 µg/m³. PM_2.5 exposure in the IL10^−/− mice led to pronounced colon shortening, increased inflammatory infiltration, ragged villi brush borders, dense goblet cells with sparse enterocytes, and lipid droplet accumulation in mitochondria. Similar ultrastructure changes were exhibited in the LS174T goblet cells after PM_2.5 exposure. Transcriptomic analysis revealed a predominantly upregulated gene expression spectrum, indicating an overall enhancement rather than suppression of metabolic activity after PM_2.5 exposure. Integrated enrichment analyses, including GO, KEGG, and GSEA, showed enrichment in pathways related to oxidative stress, xenobiotic (exogenous compound) metabolism, and energy metabolism. METAFlux, a metabolic activity analysis, further substantiated that PM_2.5 exposure induces a shift in cellular energy metabolism preference and disrupts redox homeostasis. Conclusions: The findings of exacerbated gut barrier impairment and goblet cell dysfunction following PM_2.5 exposure provide new evidence of environmental factors contributing to colitis, highlighting new perspectives on its role in the pathogenesis of colitis. Full article

Muslims are religiously obligated to care for the Earth, yet little empirical research exists on how Muslim communities in the U.S. engage with climate change. This study used a mixed-methods approach to explore climate change knowledge, attitudes, and practices (KAP) among 82 Muslims in Knoxville, Tennessee, building on prior theoretical or internationally focused work. Results found that participants largely accepted anthropogenic climate change and were strongly willing to act, citing Islamic principles such as stewardship and divine accountability as key motivators. However, many felt underinformed and lacked clarity on how to take action. Religious texts, more than religious leaders, shaped environmental views, offering interpretations that both aligned with and diverged from scientific narratives. Education and personal experience were the most frequently cited sources of climate understanding. Religion emerged as an important source of climate knowledge and a filter through which scientific information was interpreted. The knowledge and environmental attitudes inspired by their religion guided many participants to mitigate climate impacts, although some expressed a more fatalistic view of climate change. These findings suggest that effective climate communication in Muslim communities should integrate faith-based teachings with scientific messaging and engage religious leaders as amplifiers. Expanding this research to include more diverse Muslim populations across the U.S. can provide deeper insight into how Islamic worldviews shape climate engagement and behavior. Full article