Search Results (170)

Aquatic vegetation plays a crucial role in freshwater ecosystems by providing habitats, regulating water quality, and supporting biodiversity. This study aims to monitor and analyze the dynamics of aquatic vegetation in Chimaditida Shallow Lake, Greece, using Sentinel-2 satellite imagery, with validation from field measurements. Data processing was performed using Google Earth Engine and QGIS. The study focuses on discriminating and mapping two classes of aquatic surface conditions: areas covered with Floating and Emergent Aquatic Vegetation and open water, covering all seasons from 1 March 2024, to 28 February 2025. Spectral bands such as B04 (red), B08 (near infrared), B03 (green), and B11 (shortwave infrared) were used, along with indices like the Modified Normalized Difference Water Index and Normalized Difference Vegetation Index. The classification was enhanced using Otsu’s thresholding technique to distinguish accurately between Floating and Emergent Aquatic Vegetation and open water. Seasonal fluctuations were observed, with significant peaks in vegetation growth during the summer and autumn months, including a peak coverage of 2.08 km² on 9 September 2024 and a low of 0.00068 km² on 28 December 2024. These variations correspond to the seasonal growth patterns of Floating and Emergent Aquatic Vegetation, driven by temperature and nutrient availability. The study achieved a high overall classification accuracy of 89.31%, with producer accuracy for Floating and Emergent Aquatic Vegetation at 97.42% and user accuracy at 95.38%. Validation with Unmanned Aerial Vehicle-based aerial surveys showed a strong correlation (R² = 0.88) between satellite-derived and field data, underscoring the reliability of Sentinel-2 for aquatic vegetation monitoring. Findings highlight the potential of satellite-based remote sensing to monitor vegetation health and dynamics, offering valuable insights for the management and conservation of freshwater ecosystems. The results are particularly useful for governmental authorities and natural park administrations, enabling near-real-time monitoring to mitigate the impacts of overgrowth on water quality, biodiversity, and ecosystem services. This methodology provides a cost-effective alternative for long-term environmental monitoring, especially in regions where traditional methods are impractical or costly. Full article

The Tabu River Basin (TRB) is one of the most ecologically fragile areas in the arid regions of northern China; it is a key component of the desert steppe north of the Yinshan Mountains. The fractional vegetation coverage (FVC) represents a vital indicator of ecological health in the TRB. In this study, we explored the impacts of climate change and human activities on vegetation growth and utilized Landsat data (30 m) from the Google Earth Engine to generate a long-term FVC dataset (1986–2023) in the TRB. Furthermore, we established a framework for quantitatively identifying the effects of climate change and anthropogenic activities on the FVC in desert steppe regions. The results revealed that: (1) the FVC exhibits considerable spatial heterogeneity, with higher values observed in the southeastern and southwestern areas and lower values in the northern part; (2) over the past 38 years, the annual average FVC has shown fluctuations, with a slight declining trend, while the Hurst exponent indicates a reverse persistence pattern in the FVC across the TRB; and (3) the correlation between the FVC and the temperature is marginally stronger than that with precipitation, and the influence of climate change on promoting the FVC outweighs the role of human activities. These results offer valuable insights for ecological restoration and sustainable development efforts and provide scientific support for monitoring vegetation in the region. Full article

Artisanal and small-scale gold mining (ASGM) in Burkina Faso increasingly relies on cyanide, intensifying concerns about environmental contamination and human exposure. This study assessed free cyanide levels in water and soil across three ASGM sites—Zougnazagmiline, Guido, and Galgouli. Water samples (surface and groundwater) and topsoil (0–20 cm) were analyzed using the pyridine–pyrazolone method. Data were statistically and spatially processed using SPSS version 29.0 and the Google Earth Engine in conjunction with QGIS version 3.34, respectively. A site conceptual model (SCM) was also developed, based on the literature review, field observations, and validation by multidisciplinary experts in public health, toxicology, ecotoxicology, environmental engineering, and the mining sector, through a semi-structured survey. The results showed that 9.26% of the water samples exceeded the WHO guideline (0.07 mg/L), with peaks of 1.084 mg/L in Guido and 2.42 mg/L in Galgouli. At Zougnazagmiline, the water type differences were significant (F = 64.13; p < 0.001), unlike the other sites. In the soil, 29.36% of the samples exceeded 0.5 mg/kg, with concentrations reaching 9.79 mg/kg in Galgouli. A spatial analysis revealed pollution concentrated near the mining areas but spreading to residential and agricultural zones. The validated SCM integrates pollution sources, transport mechanisms, exposure routes, and vulnerable populations, offering a structured tool for environmental monitoring and health risk assessment in cyanide-impacted mining regions. Full article

Cyanobacterial blooms and aquatic vegetation dynamics are critical indicators of freshwater ecosystem health, increasingly shaped by climate change, nutrient enrichment, and ecological restoration efforts. Here, we present an automated monitoring system optimized for small- and medium-sized lakes. This system integrates phenology-based algorithms with Sentinel-2 MSI imagery, leveraging the AI Earth (AIE) platform developed by Alibaba DAMO Academy. Applied to monitor 12 ecologically sensitive lakes and reservoirs in Jiangsu Province, China, the system enables multi-year tracking of spatiotemporal changes from 2019 to 2024. A clear north-south gradient in cyanobacterial bloom intensity was observed, with southern lakes exhibiting higher bloom levels. Although bloom intensity decreased in lakes such as Changdang, Yangcheng, and Dianshan, Ge Lake displayed fluctuating patterns. In contrast, ecological restoration efforts in Cheng and Yuandang Lakes led to substantial increases in bloom intensity in 2024, with affected areas reaching 33.16% and 33.11%, respectively. Although bloom intensity remained low in northern lakes, increases were recorded in Hongze, Gaoyou, and Luoma Lakes after 2023, particularly in Hongze Lake, where bloom coverage surged to 3.29% in 2024. Aquatic vegetation dynamics displayed contrasting trends. In southern lakes—particularly Cheng, Dianshan, Yuandang, and Changdang Lakes—vegetation coverage significantly increased, with Changdang Lake reaching 44.56% in 2024. In contrast, northern lakes, including Gaoyou, Luoma, and Hongze, experienced a long-term decline in vegetation coverage. By 2024, compared to 2019, coverage in Gaoyou, Luoma, and Hongze Lakes decreased by 11.28%, 16.02%, and 47.32%, respectively. These declines are likely linked to increased grazing pressure following fishing bans, which may have disrupted vegetation dynamics and reduced their ability to suppress cyanobacterial blooms. These findings provide quantitative evidence supporting adaptive lake restoration strategies and underscore the effectiveness of satellite-based phenological monitoring in assessing freshwater ecosystem health. Full article

Analyzing the population’s access to ecosystem services offered by urban greening constitutes a measure of environmental justice, as it directly affects the quality of life and health of the population living in cities. This article is committed to proposing a geoenvironmental model in a geographic information system (GIS), envisaged to estimate the share of urban forests and green spaces in territorial planning units (TPUs), corresponding to neighborhoods of a pilot city, using high-spatial-resolution images of the China–Brazil Earth Resources Satellite (CBERS-4A) and the normalized difference vegetation index (NDVI). These data were combined by means of a Boolean analysis with social vulnerability indicators assessed from census data related to income, education, housing, and sanitation. This model ultimately aims to identify priority areas for urban afforestation in the context of environmental justice and is thus targeted to improve the inhabitants’ quality of life. The municipality of Goiânia, the capital of Goiás state, located in the Brazilian Central–West Region, was chosen as the study area for this experiment. Goiânia presents 19.5% of its urban territory (82.36 km²) covered by vegetation. The analyses indicate an inequity in the distribution of urban forest patches and green areas in this town, where 7.8% of the total TPUs have low priority, 28.2% have moderate to low priority, 42.2% have moderate to high priority, and 21.8% have high priority for urban afforestation. This urban greening imbalance is particularly observed in its most urbanized central nuclei, associated with a peripheralization of social vulnerability. These findings are meant to support initiatives towards sound territorial planning processes designed to promote more sustainable and equal development to ensure environmental justice and combat climate change. Full article

Buildings of historic neighborhoods of Santiago de Chile are protected by a coating system composed of different layers of earth-based mortars, as part of a building culture that has been neglected and forgotten since the introduction of industrialized materials but still exists in many buildings. This study presents preliminary results from ongoing research that explores the hygroscopic capacity of this vernacular coating system and the impact of incorporating recent finishing layers into traditional construction practices. The investigation focuses on identifying materials and techniques typical of traditional Chilean coatings, highlighting their role in enhancing the durability of historic buildings, improving user comfort, and promoting environmental sustainability. It contributes to the conservation of historic buildings and their reuse, as well as to the health of its inhabitants, due to its contribution to hygrometric regulation. This article focuses on this last purpose, through the identification and characterization of the coating system and its finishing layer materials, and a comparative sorption/desorption test of four case studies with these vernacular coatings. This study began with the sample extraction in situ, followed by its observation and cataloguing. Stratigraphic and stereo microscope analysis of the finishing layers were carried out to identify them. The characterization of the finishing materials was performed using FTIR-ATR and SEM-EDX tests. The sorption/desorption test was performed with a set of original complete samples of the four case studies. Subsequently, another set was prepared with the removal of the finishing layers in order to compare their influence on the hygroscopicity of the coating systems. The results elucidate the variety of materials employed on the finishing layer of these coatings, which are often superimposed, revealing renovations and reparations over time. The influence of these finishing materials on sorption properties of the coating system (the scratch and base coats) is exposed by comparing the samples with and without them. Full article

In this study, we aimed to investigate the occurrence of rare earth elements (REEs) in commonly consumed foods and assess the dietary exposure risks among different age groups in Zhejiang Province. The results showed that tea and shrimp had the highest REE detection rates, reaching 100%. Of all the food categories examined, tea exhibited the highest REE concentrations, significantly exceeding those in other foods. This may be attributed to differences in moisture content, root absorption mechanisms, and processing methods. The concentration pattern of REEs in all samples occurred in the following order: cerium > lanthanum > yttrium > neodymium > neodymium > scandium > praseodymium > gadolinium > dysprosium. The light REEs/heavy REEs (HREEs) ratio was consistently > 2 but remained lower than the ratios observed in the soil and sediments, indicating a potential risk of HREE enrichment. Dietary exposure assessments revealed that the total REE intake among Zhejiang residents was below the established safety threshold (51.3 µg/kg BW/day), with children experiencing the highest exposure (3.71 µg/kg BW/day), primarily due to their lower body weight. In the assessment of individual rare earth elements, Ce exposure in children aged ≤ 6 years exceeded the toxicological reference value. However, this threshold was established based on studies in pregnant and lactating populations and might not be directly applicable to young children. Therefore, overall dietary exposure to individual REEs remains within safe limits. REE exposure from tea consumption did not pose a health risk, even for habitual tea drinkers. These findings underscore the importance of continuous monitoring of REE accumulation in food and additional research on the potential long-term health effects, even though the current exposure levels of REEs are below the established safety limit. This is especially important considering the bioaccumulative nature of REEs and the limited paucity of toxicological data, particularly in vulnerable populations. Full article

Current food systems pose serious threats to both human health and environmental sustainability. Market forces stimulate food production to such an extent that life on Earth is endangered, and an eating pattern based on calorie-dense, nutrient-poor, and ultra-processed foods is promoted. In terms of health, this situation has caused obesity to become a pandemic, which is a major risk factor for non-communicable diseases, one of the current leading causes of death. The EAT-Lancet Commission (EAT-LC) argues that providing a growing world population with healthy diets through sustainable food systems is an immediate challenge. Thus, we propose to compare: (a) the consumption patterns described by this commission for Latin America and the Caribbean (LA&C) with those determined for Argentina; (b) Argentine dietary habits with the healthy and sustainable dietary pattern proposed by the EAT-LC; and (c) the availability of each food group in Argentina with the pattern proposed by the EAT-LC. A total of 11 food groups proposed by the Commission were taken as a reference. The dietary patterns described by the EAT-LC for LA&C are not representative of Argentina, where meat consumption is much higher (200%) and legume consumption is lower (78%). Very marked differences are observed between the consumption pattern in Argentina and the recommendations made by the EAT-LC. Regarding food availability, Argentina exceeds the requirements for meat (13.4 times), starchy vegetables (2.4 times), and eggs (2.3 times); by contrast, it does not reach the levels proposed for other products such as vegetables, nuts, legumes, or fish. Promoting a change in the eating habits of the population from a human-rights perspective represents an enormous challenge. The main aim of this work is to take a first step towards that transformation by comparing international recommendations for health and sustainability—paying special attention to culture-specific aspects—and dietary patterns in Argentina. Full article

Background/Objectives: Rare earth elements (REEs) are emerging environmental pollutants, with human exposure increasing due to recent industrial and technological activities. While most studies have focused on detecting REEs in human fluids, their presence in tissues remains understudied. Aortic valve degeneration is known to facilitate the adsorption of various chemical elements; however, the occurrence of REEs in human valves has not yet been investigated. This exploratory study aimed to determine the presence of REEs in the aortic valves of patients with aortic stenosis undergoing surgical valve replacement. It also analyzed potential correlations between REE levels in the valves, epicardial adipose tissue, serum, and selected disease markers. Methods: Samples of aortic valve, epicardial adipose tissue, and serum were collected from 20 adult patients undergoing elective aortic valve replacement. The concentrations of 14 REEs in these samples were measured using inductively coupled plasma mass spectrometry. Biochemical and clinical parameters of the patients were also considered to explore potential associations with the determined REE levels. Results: Total REEs, heavy REEs, and light REEs in aortic valves, epicardial fat, and serum were not intercorrelated. Moreover, for any sample type, they were not significantly related to the patient’s demographics (age and sex), clinical characteristics (body mass index, heart failure severity, and systolic pressure gradients), kidney function (estimated glomerular filtration rate), and biochemical markers (creatinine, lipoprotein(a), total cholesterol, HDL, LDL, and fibrinogen). Smoking was the only factor influencing REE burden in studied patients, with active smokers revealing 61% higher serum REE concentrations and past smokers exhibiting 133% higher REE valvular deposition. Conclusions: The findings suggest that REE accumulation in aortic valve tissues occurs independently of systemic and clinical parameters but may be promoted by smoking, highlighting the need to investigate the underlying mechanisms of REE deposition. Given the small sample size and the cross-sectional, hypothesis-generating design, these observations should be interpreted with caution and treated as preliminary. Larger, longitudinal studies are needed to validate these results and explore potential causal relationships. Further research should also include the tissue originating from individuals without aortic stenosis for comparison. A deeper understanding of the pathways and health risks associated with REEs in cardiovascular tissues may offer valuable insights into their broader implications for human health. Full article

Intensive mining activities in the Zhungeer open-pit coal mining area of China have resulted in drastic changes to land use and landscape patterns, severely affecting the ecological quality and stability of the region. This study integrates 36 years (1985–2020) of Landsat multiband remote sensing imagery with 30 m resolution CLCD land cover data, establishing a “Sky–Earth–Space” integrated monitoring system. This system allows for the calculation of ecological indices and the creation of land use transition matrices for internal and external regions of the mining area, ultimately completing an assessment of the ecological stability of the Zhungeer open-pit coal mining region. By overcoming the limitations posed by a singular data source, it facilitates a dynamic analysis of the interrelationships among mining activities, vegetation responses, and engineering remediation efforts. The findings reveal a significant transformation among various land types within the mining area, with both the area of mining pits and the area rehabilitated through artificial restoration undergoing rapid increases. By 2020, the area of the mining pits had reached 2630.98 hectares, while the area designated for rehabilitation had expanded to 2204.87 hectares. Prior to 2000, bare land and impermeable surfaces dominated the internal area of the mine; however, post-2000, the Normalized Difference Built-up Index (NDBI) value continuously decreased to −0.0685, indicative of an ecological transition where vegetation became predominant. The beneficial impacts of rehabilitation efforts have effectively mitigated the adverse environmental consequences of open-pit coal mining. Since 2000, the mean Normalized Difference Vegetation Index (NDVI) within the mining area has shown a consistent increase, recovering to 0.2246, signifying a restoration of the internal ecological environment. Moreover, this area exerts a notable radiative influence on the vegetation conditions outside the mining zone, with a contribution value of 1.016. Following rehabilitation efforts, the landscape patch density, landscape separation, and landscape fragmentation in the Zhungeer open-pit coal mining area exhibited a declining trend, leading to a more uniform distribution of landscape patches and improved structural balance. By 2020, the adaptability index had risen to 0.35836, achieving 93.69% of the restoration level observed prior to mining operations in 1985, thus indicating an improvement in ecosystem stability and the restoration of ecological functions, although rehabilitation efforts display a temporal lag of 10 to 15 years. The adverse impacts of open-pit coal mining on the regional ecological environment are, in fact, predominantly short-term. However, human intervention has the potential to reshape the ecology of the mining area, enhance the quality of the ecological environment, and foster the sustained development of regional ecological health. Full article

The DC global electric circuit, GEC, was conceived by C.T.R. Wilson more than a century ago. Powered by thunderstorms and electrified shower clouds, an electric current I ~1 kA flows up into the ionosphere, maintaining the ionospheric potential V ~250 kV with respect to the Earth’s surface. The circuit is formed by the current I, flowing through the ionosphere all around the world, down through the atmosphere remote from the current sources (J ~2 pA/m² through a resistance R ~250 Ω), through the land and sea surface, and up to the thunderstorms as point discharge currents. This maintains a downward electric field E of magnitude ~130 V/m at the Earth’s surface away from thunderstorms and a charge Q ~−6.10⁵ C on the Earth’s surface. The theoretical modelling of ionospheric currents and the miniscule geomagnetic field perturbations (ΔB ~0.1 nT) which they cause, as derived by Denisenko and colleagues in recent years, are reviewed. The time constant of the GEC, τ = RC, where C is the capacitance of the global circuit capacitor, is estimated via three different methods to be ~7 to 12 min. The influence of stratus clouds in determining the value of τ is shown to be significant. Sudden excitations of the GEC by volcanic lightning in Iceland in 2011 and near the Tonga eruption in 2022 enable τ to be determined, from experimental observations, as ~10 min and 8 min, respectively. It has been suggested that seismic activity, or earthquake precursors, could produce large enough electric fields in the ionosphere to cause detectable effects, either by enhanced radon emission or by enhanced thermal emission from the earthquake region; a review of the quantitative estimates of these mechanisms shows that they are unlikely to produce sufficiently large effects to be detectable. Finally, some possible links between the topics discussed and human health are considered briefly. Full article

In agriculture, manuring offers several benefits, which include improving soil fertility, structure, water retention, and aeration; all these factors favor plant health and productivity. However, improper handling and application of manure can pose risks, such as spread of pathogens and water pollution. Mitigation of such risks requires not only proper storage and composting practices, but also compliance with correct application periods and techniques. Spaceborne Earth observation can contribute to mapping manure applications and identifying possible critical situations, yet manure detection from satellite data is still a largely open question. The aim of this research is an automated, machine learning (ML)-based approach to detecting manure application on crop fields in time sequences of spaceborne, multi-source optical Earth Observation data. In the first stage of this research, multispectral data alone was considered; a pool of different spectral indexes were analyzed to identify the ones most impacted by manure application. Increments of the selected indexes from one satellite acquisition to the next were used as features to train and test various machine learning models. Two agricultural areas—one in Spain and one in Italy—were considered. Fair levels of accuracy were achieved when training and testing were carried out in the same geographical context, whereas ML models trained on one context and tested on the other reported significantly lower—albeit still acceptable—accuracy levels. In the stage that followed, thermal data was integrated and used alongside multispectral indexes. This addition led to significant improvements in accuracy levels, despite possible thermal-to-multispectral sampling mismatch in time series. Our results appear to indicate that ML-based approaches to manuring detection from space require training on the targeted geographical context, although transfer learning can probably be leveraged and only fine-tuning training will be needed. Spaceborne thermal data, where available, should be included in the input data pool to improve the quality of the final result. The proposed method is meant as a first step towards a suite of techniques that should enable large-scale, consistent monitoring of agricultural activities to check compliance with environmental regulations and provide enhanced traceability information for food products. Full article

The emissions of reactive nitrogen (Nr) from cropland links the pedosphere and atmosphere, playing a crucial role in the Earth’s nitrogen cycle while significantly impacting regional climate change, air quality, and human health. Among various Nr species, nitrogen oxide (NO) and nitrous acid (HONO) have garnered increasing attention as critical precursors to surface ozone (O₃) formation due to their participation in photochemical reactions. While most studies focus on Nr emissions from soils, the specific contributions of cropland Nr emissions considering planting activities to regional O₃ pollution remain insufficiently investigated. This study applied the enhanced process-based agroecological model (FEST-C*) coupled with the air quality (CMAQ) model to quantify cropland Nr emissions and assess their contributions to regional O₃ formation across China in June 2020. The simulated results indicated that the fertilizer-induced total Nr emission was estimated at 1.26 Tg in China, with NO emissions accounting for 0.66 Tg and HONO emissions for 0.60 Tg. North China was identified as a hotspot for cropland Nr emissions, contributing 43% of the national total. The peak emissions of cropland NO and HONO occurred in June, with emissions of 169 and 192 Gg, respectively. Cropland Nr emissions contributed approximately 8% to the national monthly mean MDA8 O₃ concentration, with localized enhancements exceeding 9% in agricultural hotspots in summer. North China experienced the largest MDA8 O₃ increase, reaching 11.71 μg m⁻³, primarily due to intensive fertilizer application and favorable climatic conditions. Conversely, reductions in nighttime hourly O₃ concentrations were observed in southern North China and northern Southeast China due to the rapid titration of O₃ via NO. In this study, the contributions of cropland Nr emissions to MDA8 O₃ concentrations across different regions of China have been further constrained. Incorporating cropland Nr emissions into the CMAQ model improved simulation accuracy and reduced mean biases in MDA8 O₃ predictions. This study offers a detailed quantification of the contribution of cropland Nr emissions in regional ozone formation across China and highlights the critical need to address cropland NO and HONO emissions in air quality management strategies. Full article

With the rising frequency and severity of wildfires that cause significant threats to ecosystems, public health and livelihoods, it is essential to have tools for evaluating and monitoring their impacts and the effectiveness of policy initiatives. This paper presents the development and implementation of a new calculation pipeline integrated with a web-based platform designed to provide georeferenced data on the burn severity of wildfires in mainland Portugal. The platform integrates a modular architecture that comprises a module in R and Google Earth Engine to compute standardized satellite-derived datasets on observed/historical severity for burned areas, integrated with a web portal module to facilitate the access, search, visualization, and downloading of the generated data. The platform provides open-access, multisource data from satellite missions, including MODIS, Landsat-5, -7, and -8, and Sentinel-2. It offers multitemporal burn severity products, covering up to 12 months post-fire, and incorporates three severity indicators, the delta NBR, relative difference NBR, and relativized burn ratio, derived from Normalized Burn Ratio (NBR) quarterly median composites. The platform’s modular and scalable framework also allows the integration of more spectral indices, burn severity indicators, and other wildfire perimeter databases. These design features also enable the platform to adapt to other contexts or regions beyond its current scope and regularly update burn severity products. Results from exploratory data analyses revealed the ability of satellite-based severity products to diagnose trends, assess interannual variability, and enable regional comparisons of burn severity, providing a basis for further research. In the face of climate change and societal challenges, the platform aims to support decision-making processes by providing authorities with standardized and updated information while promoting public awareness of wildfire challenges and, ultimately, contributing to the sustainability of rural landscapes. Full article

Invasive species negatively affect ecosystems, economies, and human health by outcompeting native species and altering habitats. Ailanthus altissima, also known as the tree of heaven, an invasive species native to China that has spread to North America and Europe. Commonly found in urban areas and forestland, these invasive plants cause ecological and economic damage to local ecosystems; they are also the preferred host of other invasive species. Ecological stability refers to the balance and harmony in species populations. Invasive species like A. altissima disrupt this stability by outcompeting native species, leading to imbalances, and there was a lack of research and data on the tree of heaven. To address this issue, this study leveraged deep learning and satellite imagery recognition to generate reliable and comprehensive prediction maps in the USA. Four deep learning models were trained to recognize satellite images obtained from Google Earth, with A. altissima data obtained from the Life Alta Murgia project, LIFE12 BIO/IT/000213. The best performing fine-tuned model using binary classification achieved an AUC score of 90%. This model was saved locally and used to predict the density and probability of A. altissima in the USA. Additionally, multi-class classification methods corroborated the findings, demonstrating similar observational outcomes. The production of these predictive distribution maps is a novel method which offers an innovative and cost-effective alternative for extensive field surveys, providing reliable data for concurrent and future research on the environmental impact of A. altissima. Full article