Search Results (5,990)

This research presents a comprehensive analysis of the spatial extent and temporal change in benthic habitats within the Puck Lagoon in the southern Baltic Sea, utilizing integrated machine learning classification and multi-sourced remote sensing. Object-based image analysis was integrated with Random Forest, Support Vector Machine, and K-Nearest Neighbors algorithms for benthic habitat classification based on airborne bathymetric LiDAR (ALB), multibeam echosounder (MBES), satellite bathymetry, and high-resolution aerial photography. Ground-truth data collected by 2023 field surveys were supplemented with long temporal datasets (2010–2023) for seagrass meadow analysis. Boruta feature selection showed that geomorphometric variables (aspect, slope, and terrain ruggedness index) and optical features (ALB intensity and spectral bands) were the most significant discriminators in each classification case. Binary classification models were more effective (93.3% accuracy in the presence/absence of Zostera marina) compared to advanced multi-class models (43.3% for EUNIS Level 4/5), which identified the inherent equilibrium between ecological complexity and map validity. Change detection between contemporary and 1957 habitat data revealed extensive Zostera marina loss, with 84.1–99.0% cover reduction across modeling frameworks. Seagrass coverage declined from 61.15% of the study area to just 9.70% or 0.63%, depending on the model. Seasonal mismatch may inflate loss estimates by 5–15%, but even adjusted values (70–94%) indicate severe ecosystem degradation. Spatial exchange components exhibited patterns of habitat change, whereas net losses in total were many orders of magnitude larger than any redistribution in space. These findings recorded the most severe seagrass habitat destruction ever described within Baltic Sea ecosystems and emphasize the imperative for conservation action at the landscape level. The methodology framework provides a reproducible model for analogous change detection analysis in shallow nearshore habitats, creating critical baselines to inform restoration planning and biodiversity conservation activities. It also demonstrated both the capabilities and limitations of automatic techniques for habitat monitoring. Full article

The rapid development of unmanned aerial vehicles (UAVs) and deep learning has accelerated the application of semantic segmentation in precision agriculture (SSPA). A key driver of this progress lies in multimodal fusion, which leverages complementary structural, spectral, and physiological information to enhance the representation of complex agricultural scenes. Despite advancements, the efficacy of multimodal fusion in SSPA is limited by modality heterogeneity and the difficulty of simultaneously retaining fine details and capturing global context. To address these challenges, we propose AgriFusion, a dual-encoder framework based on convolutional and transformer architectures for SSPA tasks. Specifically, convolutional and transformer encoders are first used to extract crop-related local structural details and global contextual features from multimodal inputs. Then, an attention-based fusion module adaptively integrates these complementary features in a modality-aware manner. Finally, a MLP-based decoder aggregates multi-scale representations to generate accurate segmentation results efficiently. Experiments conducted on the Agriculture-Vision dataset demonstrate that AgriFusion achieves a mean Intersection over Union (mIoU) of 49.31%, Pixel Accuracy (PA) of 81.72%, and F1 score of 67.85%, outperforming competitive baselines including SegFormer, DeepLab, and AAFormer. Ablation studies further reveal that unimodal or shallow fusion strategies suffer from limited discriminative capacity, whereas AgriFusion adaptively integrates complementary multimodal features and balances fine-grained local detail with global contextual information, yielding consistent improvements in identifying planting anomalies and crop stresses. These findings validate our central claims that modality-aware spectral fusion and balanced multi-scale representation are critical to advancing agricultural semantic segmentation, and establish AgriFusion as a principled framework for enhancing remote sensing-based monitoring with practical implications for sustainable crop management and precision farming. Full article

Background: Although the micronutrients (vitamins and trace minerals) essential for growth and normal physiological function are obtained from the diet, a substantial fraction of the human population is deficient in one or more micronutrients due to inadequate nutrition and/or malabsorption. Methods: This narrative review examines evidence that airborne micronutrients (‘aeronutrients’) are readily absorbed by the lungs, and preclinical and clinical evidence that inhaled iodine and vitamins A, B12 and D can enter the bloodstream. Results: Inhaled vitamin B12 resolves the symptoms and haematological features of pernicious anaemia with a bioavailability comparable to intramuscular injections and superior to oral formulations. Inhaled nebulised vitamin A restores serum levels in children with retinol deficiency. Randomised controlled trials have reported that inhalation of nebulised preparations of vitamins A, B12, magnesium and zinc are well tolerated and not associated with adverse health effects. Aeronutrient formulations have untapped potential for the therapeutic treatment of nutritional deficits, particularly in individuals with malabsorption or a low tolerance of injections. Aeronutrient therapy should be regarded as a medical intervention and be regulated accordingly, with efficacy and safety supported by scientific evidence, unlike the ‘vitamin vapes’ marketed by the wellness industry. Conclusions: Before this potential can be realised, a regulatory framework will need to be developed for aeronutrients. The high effectiveness of the pulmonary route introduces concerns regarding overdosing and toxicity which can best be addressed by categorising these formulations as prescription drugs that require regular monitoring of nutritional and health status. Full article

This study investigates the environmental impact of combined missile and drone attacks on Kyiv, the capital of Ukraine, with a focus on the release of particulate matter (PM) into the urban atmosphere. These military strikes frequently result in the destruction of residential and industrial infrastructure, as well as fires, leading to acute increases in ambient concentrations of fine particulate matter (PM2.5). Observational data were collected between 1 and 30 June 2025 using a distributed network of low-cost air quality monitoring stations aggregated by the SaveEcoBot platform. The optical particle counters, based on light scattering technology, enable real-time monitoring of airborne particulate fractions of PM2.5 along with meteorological parameters and gas pollutants. The study period included two significant attacks (10 and 17 June), during which the temporal and spatial dynamics of PM2.5 concentrations were analyzed in comparison to baseline levels observed under non-attack conditions. Raw concentrations of PM2.5 up to 241 μg/m³ were observed in the epicenters of air-strike-induced fires, while smog plumes covered half of the city area. Elevated PM2.5 concentrations were recorded during and for several hours following the attacks and corresponding air raid alerts. The findings show days of PM2.5 exceedances above the World Health Organization (WHO) daily threshold of 15 μg/m³. These results underscore the acute environmental and public health hazards posed by military assaults on urban centers. Furthermore, this research highlights the role of citizen-driven environmental monitoring as a valuable tool for both scientific documentation and potential evidentiary support in assessing the environmental impacts of warfare. Full article

Verticillium wilt (VW) is a soil-borne disease that threatens cotton growth and yield. Resistance assessment is crucial for breeding VW-resistant varieties. Drone remote sensing technology has been used to achieve high-throughput resistance evaluation based on late-stage disease severity. However, the timing and progression of disease onset vary considerably among varieties with different resistance levels, and current methods do not adequately address the influence of the disease development rate during the early stage, making it difficult to systematically assess variety resistance levels. We employed temporal differential feature analysis methods (Cohen’s d and Sequential Backward Selection), combined with dynamic development rates, to identify hyperspectral characteristics that indicate the dynamic responses of resistant cotton varieties during VW disease progression. We observed that the bottom-up development characteristics of VW resulted in challenges in early-stage disease evaluation, as symptoms were not apparent on the upper leaves. After incorporating evaluation features that combined the dynamic disease development rate, the accuracy of the evaluation model during the early stage of the disease significantly improved, with a precision of 100%, a recall of 66.7%, and an F1-Score of 80%, effectively distinguishing varieties with different resistance levels. This study presents an efficient and accurate screening method for assessing VW resistance in cotton, thereby establishing a reliable foundation for informed disease-resistant breeding strategies. Full article

Swimming is one of the most popular forms of recreational sport worldwide, recommended for people of all ages as a healthy activity. While numerous studies have focused on the impact of indoor air quality on the health of pool users, relatively few have addressed how specific airborne parameters in indoor swimming facilities affect the durability of construction materials. This article analyzes the current state of knowledge on the influence of the pool indoor environment on structural reliability, with trichloramine (NCl₃) emphasized as a degradation factor. Indoor pool environments are classified as chemically aggressive, due to elevated air temperature (~30 °C), high humidity (often exceeding 60%), and the presence of volatile chlorine compounds released from disinfected water. Our case study demonstrates that during swimming competitions, the average concentration of airborne NCl₃ reached a value of 900 µg/m³, with peaks up to 1200 µg/m³, i.e., about ten times higher than on typical usage days. The median trichloramine concertation during the competition was 1071 µg/m³. Such exposure conditions accelerate corrosion processes in stainless steels and other building materials, reducing service life and requiring targeted monitoring and preventive maintenance. Based on the findings, recommendations are provided regarding material selection, highlighting the importance of surface texture, ventilation strategies, and protective measures tailored to periods of intensive facility use. Full article

Accurate remote sensing scene classification is essential for applications such as environmental monitoring and disaster management. In real-world scenarios, particularly during emergency response and disaster relief operations, acquiring nadir-view satellite images is often infeasible due to cloud cover, satellite scheduling constraints, or dynamic scene conditions. Instead, off-nadir images are frequently captured and can provide enhanced spatial understanding through angular perspectives. However, remote sensing scene classification has primarily relied on nadir-view satellite or airborne imagery, leaving off-nadir perspectives largely unexplored. This study addresses this gap by introducing Off-nadir-Scene10, the first controlled and comprehensive benchmark dataset specifically designed for off-nadir satellite image scene classification. The Off-nadir-Scene10 dataset contains 5200 images across 10 common scene categories captured at 26 different off-nadir angles. All images were collected under controlled single-day conditions, ensuring that viewing geometry was the sole variable and effectively minimizing confounding factors such as illumination, atmospheric conditions, seasonal changes, and sensor characteristics. To effectively leverage abundant nadir imagery for advancing off-nadir scene classification, we propose an angle-aware active domain adaptation method that incorporates geometric considerations into sample selection and model adaptation processes. The method strategically selects informative off-nadir samples while transferring discriminative knowledge from nadir to off-nadir domains. The experimental results show that the method achieves consistent accuracy improvements across three different training ratios: 20%, 50%, and 80%. The comprehensive angular impact analysis reveals that models trained on larger off-nadir angles generalize better to smaller angles than vice versa, indicating that exposure to stronger geometric distortions promotes the learning of view-invariant features. This asymmetric transferability primarily stems from geometric perspective effects, as temporal, atmospheric, and sensor-related variations were rigorously minimized through controlled single-day image acquisition. Category-specific analysis demonstrates that angle-sensitive classes, such as sparse residential areas, benefit significantly from off-nadir viewing observations. This study provides a controlled foundation and practical guidance for developing robust, geometry-aware off-nadir scene classification systems. Full article

Forest height is a critical parameter for understanding ecosystem functions, assessing carbon stocks, and supporting sustainable forest management. Its accurate measurement is essential for climate change mitigation and understanding the global carbon cycle. While traditional methods like field surveys and airborne LiDAR provide accurate measurements, their high costs and limited spatial coverage make them impractical for the large-scale, dynamic monitoring required for effective sustainability initiatives. This research presents a multi-source remote sensing fusion approach to tackle this problem. For regional forest height inversion, it includes Sentinel-1 SAR, Sentinel-2 multispectral images, ICESat-2 lidar, and SRTM DEM data. Sentinel-1 + ICESat-2 + SRTM, Sentinel-2 + ICESat-2 + SRTM, and Sentinel-1 + Sentinel-2 + ICESat-2 + SRTM were the three data combination methods built using Shangri-La Second-class Category Resource Survey data as ground truth. An accuracy assessment was performed using three machine learning models: Light Gradient Boosting (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Based on the results, the ideal configuration using the LightGBM model and the following sensors: Sentinel-1, Sentinel-2, ICESat-2, and SRTM yields a correlation coefficient of 0.72, an $RMSE$ of 5.52 m, and an $MAE$ of 4.08 m. The XGBoost model obtained $r$ = 0.716, $RMSE$ = 5.55 m, and $MAE$ = 4.10 m using the same data combination as the Random Forest model, which produced $r$ = 0.706, $RMSE$ = 5.63 m, and $MAE$ = 4.16 m. The multi-source comprehensive fusion technique produced the greatest results; however, including either Sentinel-1 or Sentinel-2 enhances model performance, according to comparisons across multiple data combinations. This work presents an efficient technological strategy for monitoring forest height in complex terrains, thereby providing a scalable and robust methodological reference for supporting sustainable forest management and large-scale ecological assessment. The proposed multi-source spatiotemporal fusion framework, coupled with systematic model evaluation, demonstrates significant potential for operational applications, especially in regions with limited LiDAR coverage. Full article

Airborne particulate matter (PM), particularly PM_2.5, contributes to pulmonary injury by inducing oxidative stress and inflammation. Thyme (Thymus vulgaris L.) contains bioactive compounds with anti-inflammatory, antioxidant, and expectorant properties. Here, we evaluated the dose-dependent protective effects of thyme extract (TV) against PM_2.5-induced pulmonary injury in mice, using dexamethasone (DEXA) as a reference anti-inflammatory drug. Subacute pulmonary injury was induced in male Balb/c mice via intranasal administration of PM_2.5 (1 mg/kg, twice at 48 h intervals). Mice received oral TV (50, 100, or 200 mg/kg) or DEXA (0.75 mg/kg) daily for 10 days. Assessments included lung weight, serum AST/ALT, bronchoalveolar lavage fluid (BALF) leukocyte counts, cytokines (TNF-α, IL-6), chemokines, oxidative stress markers (ROS, lipid peroxidation, antioxidant enzymes), histopathology, and mRNA expression of genes related to inflammation (PI3K/Akt, MAPK, and NF-κB), mucus production (MUC5AC, MUC5B), and apoptosis (Bcl-2, Bax). Exposure to PM_2.5 caused oxidative stress, pulmonary inflammation, mucus hypersecretion, and histopathological changes. TV treatment dose-dependently reduced leukocyte infiltration, cytokine/chemokine release, ROS generation, and mucus overproduction, while enhancing antioxidant defenses and improving tissue pathology. Effects were comparable but slightly less potent than DEXA. Notably, unlike DEXA, TV reduced mucus hyperplasia and enhanced expectorant activity. No hepatotoxicity was observed. These results indicate that thyme extract could serve as a promising natural candidate for alternative respiratory therapeutics or functional food development. Full article

A tiered approach is presented for evaluating occupational risks during liquid deposition modelling (LDM) using ceramic materials for manufacturing complex geometries in construction. The ceramic paste is comprised of kaolin/zeolite powders mixed with deionised water at a specific ratio. The tiered occupational risk analysis covered (i) the material evaluation and information gathering, (ii) on-site exposure measurements to ultrafine and micro-size particles, and (iii) morphological and toxicological analyses of raw and collected air samples. Results indicated an increase in PM4 (particle diameter < 4 μm) concentrations during powder preparation, reaching up to 1 mg/m³ during powder preparation, although below the corresponding substance-specific and general dust occupational exposure limit and with no increased exposure to ultrafine particles, as supported by morphological analysis. In toxicity assessment, reactive oxygen species production (ROS) reached approximately 300% for 50 μg/mL raw kaolin powder, while inducing high upregulation of TNF-α and IL-6 mRNA expression genes, indicating activation of pro-inflammatory pathways. Airborne samples resulted in cell viability reduction by ~50% at 40 μg/mL, showing significance (p-value < 0.001). Translating these findings to human risk remains difficult, yet the findings highlight an urgent requirement for continuous exposure surveillance, tailored toxicity evaluations, and robust protective strategies throughout ceramic manufacturing. Full article

Airborne microplastics (MPs) are a global issue, and there is an urgent need to prevent their spread in the environment. Sensitive and reliable methods are also needed to assess their deposition and effectively evaluate risk in terrestrial ecosystems. Current automated monitoring devices are expensive and do not enable large-scale mapping of MP deposition. As with other persistent atmospheric contaminants, developing accurate, cost-effective and easily applicable biomonitoring methods would therefore be highly beneficial. Cryptogams are among the most suitable biomonitors of airborne contaminants, and preliminary surveys show that epiphytic lichens accumulate higher concentrations of MPs in urban areas and near landfills than in control sites. However, the interaction between lichen thalli and MPs is weak and, as discussed in this review, the anthropogenic fibres and plastic fragments intercepted and retained by lichens probably do not reflect the levels in bulk atmospheric deposition. While emphasizing the need for studies evaluating the effectiveness of cryptogams in accumulating different types of airborne MPs under various meteorological conditions, this review also suggests directing future research efforts toward mosses, which seem to accumulate much higher concentrations of MPs than lichens in both active and passive biomonitoring surveys. Full article

Occupational exposure to airborne polymethacrylate (PMMA) particles during dental laboratory procedures poses an underexplored health risk. This study presents the first integrated Computational Fluid Dynamics (CFD) and real-time particle monitoring investigation of 0.5 µm PMMA particle dispersion during mechanical polishing in an actual clinic. We quantitatively assessed particle behavior in 30 s exposure scenarios by examining the effects of dental professional work orientations and comparing two mitigation strategies, rear-inlet portable air cleaners (PACs) and a Box Dust Collector (BC), with an emphasis on the safety of both personnel and patients. The findings establish that operatory airflow is a primary safety determinant: aligning the workflow with the main airflow (0°). Furthermore, the combined use of PACs and BC demonstrated synergistic superiority, achieving the optimal reduction in peak concentrations and airborne residence time. PACs alone reduced working zone concentrations by up to 80%, while BC provided a crucial 40–60 s delay in initial plume dispersion. We conclude that effective exposure control requires a proactive, two-stage engineering defense: source confinement augmented by continuous ambient filtration. This research provides a robust, evidence-based foundation for defining airflow-aware ergonomic and combined engineering standards in the evolving digital era of dentistry. Full article

Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) provides an advanced proxy for global vegetation productivity. Recently, new high-quality remote sensing SIF datasets and reanalysis products have significantly advanced the application of SIF. However, the lack of long-term, daily resolution datasets continues to limit the precise exploration of vegetation dynamics, primarily due to challenges in daily modeling accuracy, substantial data volume, and computational demands. In this study, supported by the Google Earth Engine (GEE) platform, we developed a data-driven approach based on the Moving Spatial–Temporal Window Sampling (MSTWS) strategy for reconstructing long-term daily SIF. By learning the relationship between high-spatial-resolution Orbiting Carbon Observatory (OCO)-3 SIF and MODIS surface reflectance, we established a spatially and temporally specific daily prediction model for each day of the year (DOY), reconstructing the long-term daily OCO-3 SIF (DOSIF) from 2001 to the present with a global contiguous distribution. The prediction framework demonstrated robust performance with an R² of 0.92 on the training set and 0.81 on the validation set, indicating strong predictive ability and resistance to overfitting. Systematic evaluation of the dataset showed that DOSIF accurately captures the expected spatiotemporal distribution patterns. Cross-sensor validation with independent airborne SIF measurements further enhanced the reliability of the DOSIF dataset. Full article

The soil condition of Norilsk, a large industrial city located in the Arctic zone of Russia, was assessed for the first time using pollution indices calculated based on the gross content of Pb, Zn, Co, Cd, Cu, Ni, Cr, Mn, As, and petroleum products. The Nemerov Pollution Index (NPI) classifies all Norilsk soil samples as polluted. According to the PLI index, 86% of the soil samples were characterized as polluted, and according to the total pollution index (Zc), 56% of the soil samples were classified as moderately hazardous and hazardous polluted. All soil samples had a medium, high, or very high environmental risk. The high level of soil pollution in Norilsk and the crucial role of nonferrous metallurgy as the primary source of these metals are confirmed. Pollutant content in the soil varied in different districts of Norilsk, with Mn and petroleum products being significant. The maximum heavy metal pollution occurred in the soils of the enterprise protection zones and in the soil of the industrial zones. Airborne pollutants from industrial enterprises are the main cause of heavy metal soil pollution in the Norilsk agglomeration. The contribution of other sources of pollution, typical for various functional areas of the city (e.g., motor transport and waste), is not expressed. Simultaneously, the hydrocarbon content is determined by the location of areas near roads, which is typical for districts with a high population and intensive traffic. Using the example of the Central District of Norilsk, the landscaping of the territory was shown to play a role in reducing the total content of heavy metals. Based on the physicochemical properties of Norilsk’s urban soils, the following key measures are proposed to improve soil quality: increasing organic matter content; ensuring a neutral pH and a high cation exchange capacity; and reducing soil density, which will reduce the toxic load on plants and negative impact on human health. Full article

Antimicrobial resistance (AMR) in bacteria is a critical global health threat, yet the impact of environmental stressors such as aerosolization on resistance remains unclear. We previously showed that aerosolization can induce antibiotic resistance in Escherichia coli MG1655, a gram-negative pathogen simulant. Here, we investigated Bacillus globigii, a surrogate for the gram-positive pathogen Bacillus anthracis, to assess how aerosolization affects bacterial survival and antibiotic resistance. B. globigii vegetative cells and spores were aerosolized under varying conditions and durations (5, 10, 15, 30, and 45 min) into a sterile, airtight chamber and collected using the wetted wall cyclone (WWC) system. Samples were analyzed via antibiotic susceptibility testing, culture-based assay, and quantitative polymerase chain reaction (qPCR). Vegetative cells exhibited the lowest culturability after 5 and 30 min aerosolization, while spores showed reduced culturability at 15–45 min. Both vegetative cells and spores displayed lowest antibiotic susceptibility profiles after 15 min of aerosolization. Our findings suggest that aerosolization duration and bacterial state (vegetative vs. spores) can influence bacterial survival and development of antibiotic resistance. Understanding these dynamics is essential for designing strategies to mitigate the airborne spread of antibiotic-resistant bacteria. Full article