Search Results (1,029)

The purpose of this study was to examine vegetable consumption and its relationship with lifestyle characteristics among children and adolescents. Data from a health survey administered to a representative sample of 177,091 schoolchildren between the ages of 8 and 17 were employed in this observational, cross-sectional investigation. Physical activity level, screen time, and sleeping patterns were assessed using self-completed questionnaires. Vegetable consumption and dietary habits were analyzed using the Mediterranean Diet Quality Index for Children and Adolescents. Participants consuming vegetables more than once daily were categorized as consumers vs. non-consumers. Physical education teachers measured anthropometric and physical fitness factors. Descriptive statistics and binary logistic regression analysis were conducted, and the odds ratio with the corresponding 95% confidence interval was calculated and adjusted for confounders. Vegetables were consumed once or more times a day by more females than males (25.5% vs. 24.0%, p < 0.001). In both sexes, vegetable consumers slept more, ate healthier, spent less time on screens, and had better anthropometric and aerobic fitness measurements than non-consumers. Healthy eating practices, such as regularly consuming fruits, legumes, nuts, and dairy products, were strongly correlated with vegetable intake. For every one-year increase in age, the odds of being a vegetable consumer decreased by 8% and 10% in boys and girls, respectively. Overweight/obese participants had lower odds of being a vegetable consumer by 20%. Increased screen time, inadequate physical activity, and insufficient sleeping hours decreased the odds of being a vegetable consumer by 22%, 30%, and 25%, respectively (all p-values < 0.001). Overall, a healthier lifestyle profile was associated with higher vegetable intake for both sexes among children and adolescents. Full article

This study investigated pesticide residues in 580 vegetable samples collected from markets in Serbia, encompassing potatoes, carrots, celery, radishes, horseradish, ginger, onions, and leeks. In total, 33 distinct pesticides were detected using validated HPLC-MS/MS and GC-MS/MS analytical methods. Multiple residues were identified in 19 samples, while 29 samples exceeded established maximum residue levels (MRLs). Acute and chronic dietary risks were assessed for both adults and children. Although individual hazard quotients (HQs) for adults and children remained below the threshold of concern (HQ < 1), the cumulative acute risk reached up to 63.1% of the Acute Reference Dose (ARfD) for children and 51.1% ARfD for adults, with ginger and celery posing the highest risks. Similarly, cumulative chronic risks remained below the safety threshold, with the Acceptable Daily Intake (ADI) percentages reaching a maximum of 5.9% ADI for adults and increased vulnerability of 11.0% ADI among children. Monte Carlo simulations were applied to account for variability and uncertainty in chronic exposure estimates. The hazard index (HI) results showed that adverse health effects for both population groups remained within acceptable safety limits (HI < 1), although higher susceptibility was observed in children. Sensitivity analysis identified body weight and vegetable consumption rates as the most influential factors affecting chronic risk variability. Full article

Urban heat islands (UHIs) are a growing sustainability challenge impacting public health, energy use, and climate resilience, especially in hot, arid cities like San Antonio, Texas, where land surface temperatures reach up to 47.63 °C. This study advances a data-driven, interdisciplinary approach to UHI mitigation by integrating Machine Learning (ML) with physical and socio-demographic data for sustainable urban planning. Using high-resolution spatial data across five functional zones (residential, commercial, industrial, official, and downtown), we apply three ML models, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), to predict land surface temperature (LST). The models incorporate both environmental variables, such as imperviousness, Normalized Difference Vegetation Index (NDVI), building area, and solar influx, and social determinants, such as population density, income, education, and age distribution. SVM achieved the highest R² (0.870), while RF yielded the lowest RMSE (0.488 °C), confirming robust predictive performance. Key predictors of elevated LST included imperviousness, building area, solar influx, and NDVI. Our results underscore the need for zone-specific strategies like more greenery, less impervious cover, and improved building design. These findings offer actionable insights for urban planners and policymakers seeking to develop equitable and sustainable UHI mitigation strategies aligned with climate adaptation and environmental justice goals. Full article

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

Background/Objectives: Understanding the impact of dietary habits in terms of obesity, health outcomes, and functional decline is critical in Europe’s growing elderly population. This study analyzed trends in Mediterranean diet (MD) adherence, obesity prevalence, and grip strength among middle-aged and elderly Europeans using data from the Survey of Health, Ageing and Retirement in Europe (SHARE). Methods: Data from four SHARE waves (2015–2022) across 28 countries were analyzed. Dietary patterns were assessed through food frequency questionnaires classifying participants as MD-adherent or non-adherent where adherent implies daily consumption of fruits and vegetables and occasional (3–6 times/week) intake of eggs, beans, legumes, meat, fish, or poultry (an unvalidated definition of the MD pattern). Handgrip strength, a biomarker of functional capacity, was categorized into low, medium, and high groups. Body mass index (BMI), self-perceived health (SPHUS), chronic disease prevalence, and CASP-12 scores (control, autonomy, self-realization, and pleasure evaluated on the 12-item version) were also evaluated. Statistical analyses included descriptive methods, logistic regressions, and multiple imputations to address missing data. Results: A significant majority (74–77%) consumed fruits and vegetables daily, which is consistent with MD principles; however, the high daily intake of dairy products (>50%) indicates limited adherence to the MD, which advocates for moderate consumption of dairy products. Logistic regression indicated that individuals with two or more chronic diseases were more likely to follow the MD (odds ratio [OR] = 1.21, confidence interval [CI] = 1.11–1.32), as were those individuals who rated their SPHUS as very good/excellent ([OR] = 1.42, [CI] = 1.20–1.69). Medium and high maximal handgrip were also strongly and consistently associated with higher odds of MD adherence (Medium: [OR] = 1.44, [CI] = 1.18–1.74; High: [OR] = 1.27, [CI] = 1.10–1.48). Conclusions: The findings suggest that middle-aged and older adults are more likely to adhere to the MD dietary pattern if they have more than two chronic diseases, are physically active, and have a medium or high handgrip. Although an unvalidated definition of the MD dietary pattern was used, the results highlight the importance of implementing targeted dietary strategies for middle-aged and elderly adults. Full article

The red imported fire ant (RIFA; Solenopsis invicta) is an invasive species that severely threatens ecology, agriculture, and public health in Taiwan. In this study, the feasibility of applying multispectral imagery captured by unmanned aerial vehicles (UAVs) to detect red fire ant mounds was evaluated in Fenlin Township, Hualien, Taiwan. A DJI Phantom 4 multispectral drone collected reflectance in five bands (blue, green, red, red-edge, and near-infrared), derived indices (normalized difference vegetation index, NDVI, soil-adjusted vegetation index, SAVI, and photochemical pigment reflectance index, PPR), and textural features. According to analysis of variance F-scores and random forest recursive feature elimination, vegetation indices and spectral features (e.g., NDVI, NIR, SAVI, and PPR) were the most significant predictors of ecological characteristics such as vegetation density and soil visibility. Texture features exhibited moderate importance and the potential to capture intricate spatial patterns in nonlinear models. Despite limitations in the analytics, including trade-offs related to flight height and environmental variability, the study findings suggest that UAVs are an inexpensive, high-precision means of obtaining multispectral data for RIFA monitoring. These findings can be used to develop efficient mass-detection protocols for integrated pest control, with broader implications for invasive species monitoring. Full article

Food safety concerns related to pesticide residues in fruits and vegetables have increased globally, particularly in regions where monitoring programs are scarce or inconsistent. This study provides the first multi-year evaluation of pesticide contamination and associated dietary risks in Cape Verde, an African island nation increasingly reliant on imported produce. A total of 570 samples of fruits and vegetables—both locally produced and imported—were collected from major markets across the country between 2017 and 2020 and analyzed using validated multiresidue methods based on gas chromatography coupled to Ion Trap mass spectrometry (GC-IT-MS/MS), and both gas and liquid chromatography coupled to triple quadrupole tandem mass spectrometry (GC-QqQ-MS/MS and LC-QqQ-MS/MS). Residues were detected in 63.9% of fruits and 13.2% of vegetables, with imported fruits showing the highest contamination levels and diversity of compounds. Although only one sample exceeded the maximum residue limits (MRLs) set by the European Union, 80 different active substances were quantified—many of them not authorized under the current EU pesticide residue legislation. Dietary exposure was estimated using median residue levels and real consumption data from the national nutrition survey (ENCAVE 2019), enabling a refined risk assessment based on actual consumption patterns. The cumulative hazard index for the adult population was 0.416, below the toxicological threshold of concern. However, when adjusted for children aged 6–11 years—taking into account body weight and relative consumption—the cumulative index approached 1.0, suggesting a potential health risk for this vulnerable group. A limited number of compounds, including omethoate, oxamyl, imazalil, and dithiocarbamates, accounted for most of the risk. Many are banned or heavily restricted in the EU, highlighting regulatory asymmetries in global food trade. These findings underscore the urgent need for strengthened residue monitoring in Cape Verde, particularly for imported products, and support the adoption of risk-based food safety policies that consider population-specific vulnerabilities and mixture effects. The methodological framework used here can serve as a model for other low-resource countries seeking to integrate analytical data with dietary exposure in a One Health context. Full article

The Atlantic Diet (AD) is based on traditional dietary patterns in Galicia (northwestern Spain) and northern Portugal and is known for its health benefits. The AD focuses on fresh, local, and seasonal foods, especially fish, seafood, vegetables, legumes, whole grains, fruit, olive oil, and a moderate consumption of wine. However, it has received less attention from researchers than other dietary patterns. The present study had two main objectives: (i) to evaluate the dietary habits of a Galician population in relation to the AD and (ii) to create a numerical index to measure adherence to the AD. In 2022, a validated food frequency questionnaire was administered to 500 healthy adults living in Galicia. The data on participants’ dietary habits showed notable deviations from the ideal AD, especially regarding consumption of fruits, grains, and seafood. However, an adequate intake of legumes and nuts was observed, along with a reduction in the consumption of processed foods (except among younger participants) relative to that revealed in previous surveys. To assess adherence to the diet, statistical and chemometric analyses were applied, leading to the development of a new index: the Atlantic Diet Scale (ADS). The ADS was compared with three existing tools and proved to be a simple, flexible, and effective method for assessing dietary adherence based on optimal intake levels across food groups. When applied to dietary data, the ADS yielded adherence levels similar to two of the three traditional methods, with some differences relative to the third. These findings highlight the need for standardized evaluation tools, including clear definitions of food groups and consistent scoring systems, to better assess and promote adherence to the Atlantic Diet. Full article

Vegetables and fruits grown in urban areas pose a potential threat to human health due to contamination with heavy metals (HMs). This study aimed to identify and quantify the concentrations of heavy metals (Fe, Mn, Zn, Cu, Pb, Cd) in tomatoes, leafy vegetables, and fruits collected from 16 allotment gardens (AGs) located in Warsaw. A total of 112 samples were analyzed (72 vegetable and 40 fruit samples). Vegetables from AGs accumulated significantly higher levels of HMs than fruits. Leafy vegetables, particularly those cultivated near high-traffic roads, exhibited markedly elevated levels of Pb, Cd, and Zn compared to those grown in peripheral areas. Lead concentrations exceeded permissible limits by six to twelve times, cadmium by one to thirteen times, and zinc by 0.7 to 2.4 times. Due to high levels of Pb and Cd, tomatoes should not be cultivated in urban environments. Regardless of location, only trace amounts of HMs were detected in fruits. The greatest health risk is associated with the consumption of leafy vegetables. Lettuce should be considered an indicator plant for assessing environmental contamination. The obtained Hazard Index (HI) values indicate that only the tested fruits are safe for consumption. Meanwhile, the values of the Hazard Quotient (HQ) indicate no health risk associated with the consumption of lettuce, cherries, and red currants. Among the analyzed elements, Pb showed a higher potential health risk than other metals. This study emphasizes the need for continuous monitoring of HM levels in urban soils and the establishment of baseline values for public health purposes. Remediation of contaminated soils and the implementation of safer agricultural practices are recommended to reduce the exposure of urban populations to the risks associated with the consumption of contaminated produce. In addition, the safety of fruits and vegetables grown in urban areas is influenced by the location of the AGs and the level of industrialization of the agglomeration. Therefore, the safety assessment of plant products derived from AGs should be monitored on a continuous basis, especially in vegetables. Full article

Forest drought monitoring tools are crucial for managing tree water stress and enhancing ecosystem resilience. The Forest Drought Response Index (ForDRI) was developed to monitor drought conditions in forested areas across the contiguous United States (CONUS), integrating vegetation health, climate data, groundwater, and soil moisture content. This study evaluated ForDRI using Pearson correlations with the Bowen Ratio (BR) at 24 AmeriFlux sites and Spearman correlations with the Tree-Ring Growth Index (TRSGI) at 135 sites, along with feedback from 58 stakeholders. CONUS was divided into four forest subgroups: (1) the West/Pacific Northwest, (2) Rocky Mountains/Southwest, (3) East/Northeast, and (4) South/Central/Southeast Forest regions. Strong positive ForDRI-TRSGI correlations (ρ > 0.7, p < 0.05) were observed in the western regions, where drought significantly impacts growth, while moderate alignment with BR (R = 0.35–0.65, p < 0.05) was noted. In contrast, correlations in Eastern and Southern forests were weak to moderate (ρ = 0.4–0.6 for TRSGI and R = 0.1–0.3 for BR). Stakeholders’ feedback indicated that ForDRI realistically maps historical drought years and recent trends, though suggestions for improvements, including trend maps and enhanced visualizations, were made. ForDRI is a valuable complementary tool for monitoring forest droughts and informing management decisions. Full article

Mangroves form the dominant coastal plant community of low-energy tropical intertidal habitats and provide critical ecosystem services to humans and the environment. However, more frequent and increasingly powerful hurricanes and storm surges are creating additional pressure on the natural resilience of these threatened coastal ecosystems. Advances in remote sensing techniques and approaches are critical to providing robust quantitative monitoring of post-storm mangrove forest recovery to better prioritize the often-limited resources available for the restoration of these storm-damaged habitats. Here, we build on previously utilized spatial and temporal ranges of European Space Agency (ESA) Sentinel satellite imagery to monitor and map the recovery of the mangrove forests of the British Virgin Islands (BVI) since the occurrence of back-to-back category 5 hurricanes, Irma and Maria, on September 6 and 19 of 2017, respectively. Pre- to post-storm changes in coastal mangrove forest health were assessed annually using the normalized difference vegetation index (NDVI) and moisture stress index (MSI) from 2016 to 2023 using Google Earth Engine. Results reveal a steady trajectory towards forest health recovery on many of the Territory’s islands since the storms’ impacts in 2017. However, some mangrove patches are slower to recover, such as those on the islands of Virgin Gorda and Jost Van Dyke, and, in some cases, have shown a continued decline (e.g., Prickly Pear Island). Our work also uses a linear ANCOVA model to assess a variety of geospatial, environmental, and anthropogenic drivers for mangrove recovery as a function of NDVI pre-storm and post-storm conditions. The model suggests that roughly 58% of the variability in the 7-year difference (2016 to 2023) in NDVI may be related by a positive linear relationship with the variable of population within 0.5 km and a negative linear relationship with the variables of northwest aspect vs. southwest aspect, island size, temperature, and slope. Full article

Mangrove forests serve as critical ecological barriers in coastal zones and play a vital role in global blue carbon sequestration strategies. In recent decades, China’s mangrove ecosystems have experienced complex interactions between degradation and restoration under intense coastal urbanization and systematic conservation efforts. However, the long-term spatiotemporal patterns and driving mechanisms of mangrove ecosystem health changes remain insufficiently quantified. This study developed a multi-temporal analytical framework using Landsat imagery (1986–2021) to derive kernel normalized difference vegetation index (kNDVI) time series—an advanced phenological indicator with enhanced sensitivity to vegetation dynamics. We systematically characterized mangrove growth patterns along China’s southeastern coast through integrated Theil–Sen slope estimation, Mann–Kendall trend analysis, and Hurst exponent forecasting. A Deep Forest regression model was subsequently applied to quantify the relative contributions of environmental drivers (mean annual sea surface temperature, precipitation, air temperature, tropical cyclone frequency, and relative sea-level rise rate) and anthropogenic pressures (nighttime light index). The results showed the following: (1) a nationally significant improvement in mangrove vitality (p < 0.05), with mean annual kNDVI increasing by 0.0072/yr during 1986–2021; (2) spatially divergent trajectories, with 58.68% of mangroves exhibiting significant improvement (p < 0.05), which was 2.89 times higher than the proportion of degraded areas (15.10%); (3) Hurst persistence analysis (H = 0.896) indicating that 74.97% of the mangrove regions were likely to maintain their growth trends, while 15.07% of the coastal zones faced potential degradation risks; and (4) Deep Forest regression id the relative rate of sea-level rise (importance = 0.91) and anthropogenic (nighttime light index, importance = 0.81) as dominant drivers, surpassing climatic factors. This study provides the first national-scale, 30 m resolution assessment of mangrove growth dynamics using kNDVI, offering a scientific basis for adaptive management and blue carbon strategies in subtropical coastal ecosystems. Full article

Ecological security underpins sustainable regional development and human well-being. Tianjin is in the eastern coastal area of China and features coastal wetlands and river systems. Over the past decade, Tianjin has undergone rapid urbanization. Tianjin faces the dual challenges of maintaining ecological security with economic growth, making it crucial to assess Tianjin’s ecological security status. This study constructed a comprehensive framework incorporating ecosystem health, services, and risk data to evaluate the ecological security status of Tianjin in 2012, 2017, and 2022. The results show the following: (1) Land use transfer mainly shows other land use types transferred to construction land. (2) The ecological security index of Tianjin ranges from 0.003 to 0.865, and the annual average values from 2012 to 2022 are 0.496, 0.493, and 0.499, with security levels dominated by medium, medium-high, and high security levels, respectively. The change in ecological security was relatively stable and was dominated by areas with unchanged levels, accounting for 63.72% of the total area. (3) The natural environment, human activities, and ecosystem status jointly influence Tianjin’s ecological security level. Shannon diversity, Shannon evenness, vegetation type, elevation, and mean annual temperature were the main factors affecting changes in ecological security in Tianjin, among which the interaction of Shannon diversity and vegetation type had the most significant influence. This study combines positive and negative aspects to assess ecological security, providing a reference for other regions to conduct ecological security assessments and a scientific basis for ecological management and urban planning decisions in similar regions. Full article

Crop growth monitoring at various growth stages is essential for optimizing agricultural inputs and enhancing crop yield. Nitrogen plays a critical role in plant development; however, its improper application can reduce productivity and, in the long term, degrade soil health. The aim of this study was to develop a non-invasive approach for nitrogen estimation through proxies (Nitrogen Flavanol Index) in Mentha arvensis using UAV-derived multispectral vegetation indices and machine learning models. Support Vector Regression, Random Forest, and Gradient Boosting were used to predict the Nitrogen Flavanol Index (NFI) across different growth stages. Among the tested models, Random Forest achieved the highest predictive accuracy (R² = 0.86, RMSE = 0.32) at 75 days after planting (DAP), followed by Gradient Boosting (R² = 0.75, RMSE = 0.43). Model performance was lowest during early growth stages (15–30 DAP) but improved markedly from mid to late growth stages (45–90 DAP). The findings highlight the significance of UAV-acquired data coupled with machine learning approaches for non-destructive nitrogen flavanol estimation, which can immensely contribute to improving real-time crop growth monitoring. Full article

Background: Sleep is a fundamental component of health, and deprivation has been linked to numerous adverse outcomes, including reduced academic and occupational performance, greater risk of accidents, and increased susceptibility to chronic diseases and premature mortality. Dietary and lifestyle behaviors are increasingly recognized as key determinants of sleep quality. Women are particularly susceptible to sleep disturbances due to hormonal fluctuations and psychosocial factors. However, women remain underrepresented in sleep research. This study aims to examine the associations between sleep quality, nutrition, and lifestyle in a large cohort of Spanish women. Methods: A cross-sectional study was conducted with 785 women aged 18–64. Participants completed the Pittsburgh Sleep Quality Index (PSQI) and the NutSo-HH questionnaire on dietary and lifestyle behaviors. Descriptive analyses, correlation matrices, Gaussian Graphical Models, and Principal Component Analyses were used to assess relationships between variables. Results: More than half of the participants rated their sleep quality as good or very good, although over 30% experienced frequent nighttime awakenings. Poor sleep quality was significantly associated with higher alcohol consumption, lower vegetable and white fish intake, and lower levels of physical activity. Diets rich in ultra-processed foods correlated moderately with subjective poor sleep and daytime dysfunction. However, no strong associations were found between stimulant consumption, late meals, or dietary patterns (e.g., Mediterranean diet) and sleep. Self-perceived health emerged as a protective factor, while nocturnal lifestyles were linked to longer sleep latency and fragmented sleep. Conclusions: In adult women, better sleep quality is linked to healthy dietary choices, regular physical activity, and a positive perception of general health. In contrast, alcohol use and irregular lifestyles are associated with poor sleep. Individual variability and cultural adaptation may moderate the impact of some traditionally harmful behaviors. Personalized, multidimensional interventions are recommended for promoting sleep health in women. Full article