Search Results (195)

Climate change poses increasing threats to urban populations, with rising temperatures exacerbating heat-related health risks and reducing outdoor thermal comfort. Although cities occupy only a small fraction of the Earth’s surface, they function as complex ecosystems where human transformations significantly influence environmental quality and public health. This research introduces a trans-scalar methodological framework to assess the impacts of climate change on human well-being in urban areas, integrating qualitative and quantitative approaches. The methodology combines direct field observations, instrumental surveys using infrared thermography, quantitative land cover analysis (Runoff and RIE indices), and predictive microclimatic simulations using ENVI-met 5.8. The framework is applied to two case studies in Italy, the municipalities of Bibbiena and Borgo San Lorenzo, focusing on pedestrian accessibility to community healthcare facilities, known as Community Houses. Results reveal critical environmental vulnerabilities, including surface temperatures reaching up to 60 °C on impervious materials, runoff coefficients exceeding 80%, and Physiological Equivalent Temperature (PET) values exceeding 56 °C during summer peak hours, indicating extreme heat stress conditions. The proposed framework enables the identification of priority intervention areas for climate adaptation strategies, supporting evidence-based urban design that prioritises public health and thermal comfort. The methodology offers replicability for other Mediterranean cities facing similar climate challenges, providing a decision-support tool for planners and public administrations. Full article

The sustainable management of land resources and the formulation of land policies are closely linked to the stability and health of terrestrial ecological systems, which in turn underpin sustainable regional economic, social, and environmental development. However, land use change has a time effect on the environment and requires long-term observation to discover its impact on landscape patterns. The Yellow River Basin functions as a critical ecological barrier in northern China, where land use changes are particularly intense in the transitional zone between its middle and lower reaches. Using Landsat imagery as the data source, this study adopts the Random Forest (RF) algorithm to classify eight sets of sequential data covering a 35-year period from 1990 to 2025 in the study area. Landscape pattern metrics and transfer matrices are employed to conduct qualitative and quantitative analyses of the spatiotemporal dynamics of land use changes. Additionally, land expansion analysis strategies and the RF algorithm are applied to identify the relative importance of different driving factors. The results show that: (1) The classification accuracy based on the Google Earth Engine (GEE) cloud platform remains consistently high, exceeding 90% across all phases. (2) Patch density decreases significantly, while the largest patch index continues to decline; the Shannon diversity index shows a fluctuating upward trend, and the aggregation index exhibits a slight increase. (3) Mutual conversions among farmland, forest, and grassland are the dominant processes driving land use changes in the region. (4) The Digital Elevation Model (DEM), construction land area distribution, and distance to primary roads are the key factors influencing land use patterns, with human activities acting as the primary driver of land use type transformations in the area. Full article

Mangrove forests provide essential ecosystem services but are increasingly threatened by anthropogenic pressures and climate-related disturbances. Effective and accessible tools for assessing mangrove ecosystem condition are therefore needed to soundly support their conservation and management. This study adapted the “Coastal Health” framework originally proposed for assessing coastal ecosystems health to evaluate the health status of mangrove forests along the Caribbean coast of Colombia. Using freely available high-resolution imagery from Google Earth Pro 7.3, complemented by field observations, technical reports, and unpublished literature, 56 mangrove sites distributed across eight coastal departments were assessed according to their ecological integrity, hydrological connectivity, sediment dynamics, freshwater and marine inputs, mangrove species condition, and their potential for landward and seaward migration. The results showed that 54% of the evaluated sites were classified as being in “Good Health”, while 2% were categorized as “Health Warning”, 3% as “Surface Wounds”, 14% as “Minor Injury”, 25% as “Major Injury”, and 2% as “Deceased”. Mangroves in good condition were generally associated with protected areas, river mouths, estuaries, and relatively isolated coastal systems, whereas degraded ecosystems were affected by urban expansion, tourism infrastructure, hydrological alterations, coastal engineering works, and reduced freshwater inputs. The proposed methodology proved to be a simple, low-cost, and easily replicable tool for large-scale mangrove health assessment. It provides valuable information for prioritizing conservation and restoration actions and can be adapted to support mangrove monitoring and ecosystem-based coastal management in other regions worldwide. Full article

The quality of edible oils is determined not only by the raw material, but also by the applied processing technology. Rapeseed oil is widely valued for its favorable fatty acid profile, however, the effects of combined pressing conditions and low-temperature bleaching on its lipid profile and calculated nutritional quality indices remain insufficiently characterized. This study aimed to evaluate the effects of oil production method and bleaching earth addition on the fatty acid profile, nutritional quality indices, and oxidizability-related indices of rapeseed oil from an applied food processing perspective. Rapeseed oil samples were obtained from a commercial producer using cold pressing, hot pressing, and mixed two-stage pressing. The oils were subjected to low-temperature bleaching with 1–5% bleaching earth. The fatty acid profile was determined by gas chromatography, and selected fatty acid-based indices were calculated and grouped into three categories: fatty acid class ratios, nutritional quality indices, and oxidizability-related indices. These included ratios describing the balance between omega-6 s omega-3 polyunsaturated fatty acids (Σ PUFA n-6/n-3), unsaturated and saturated fatty acids (Σ UFA/Σ SFA), and polyunsaturated and saturated fatty acids (Σ PUFA/Σ SFA). Health-oriented indices such as the Desirable Fatty Acids/Undesirable Fatty Acids ratio (Σ DFA/Σ OFA), hypocholesterolemic/hypercholesterolemic ratio (h/H), Nutritive Value Index (NVI), Atherogenic Index (AI), and Thrombogenic Index (TI), and oxidizability-related indices including the Unsaturation Index (UI), Peroxidizability Index (PI), Oxidizability Index (OI), calculated Oxidizability Value (Cox), and Oxidative Stability Index (OS). The results showed that the production method significantly influenced saturated fatty acid content and selected fatty acid-based nutritional quality indices. Cold-pressed oil was characterized by the lowest saturated fatty acid content and the most beneficial values of selected nutritional quality indices. Hot-pressed oil showed higher saturated fatty acid content and less favorable values for selected indices, whereas mixed oil generally showed values between those of cold-pressed and hot-pressed oils. However, differences in the main unsaturated fatty acid fractions were limited and, for most monounsaturated and polyunsaturated fatty acid parameters, were not statistically significant. Across all processing variants, the nutritional quality indices remained advantageous, with omega-6/omega-3 polyunsaturated fatty acid ratio values of 2.1–2.4, atherogenic index values of approximately 0.04–0.05, and thrombogenic index values of approximately 0.08–0.09. Although significant interactions between production method and bleaching earth addition were observed for selected oxidizability-related indices, the magnitude of bleaching-induced changes was limited. Overall, the findings indicate that low-temperature bleaching may be applied as a mild processing step without substantially compromising the fatty acid-based quality characteristics of rapeseed oil. The study provides practical insight into the relationship between pressing technology, bleaching conditions, and lipid quality assessment in industrial rapeseed oil production. Full article

Child malnutrition remains a critical global health challenge, yet most existing studies rely on static risk estimates and overlook the spatial–temporal nature of environmental exposures and localized socioeconomic disparities. To address this gap, we integrated Earth observation-derived environmental indicators, geolocated conflict events, socioeconomic variables, and child health outcomes, and applied a Fixed Effects Two-Stage Least Squares Spatial Durbin Error Model (FE-2SLS-SDEM). We found distinct hotspots of joint vulnerability, where areas experiencing both high conflict intensity and recurrent droughts show significantly higher rates of childhood stunting. High conflict intensity, drought severity, diarrheal prevalence, and inadequate sanitation significantly increase stunting, while maternal and paternal education, improved sanitation, economic development (proxied by nighttime light intensity), and agricultural productivity reduce it. Among these determinants, female education demonstrated the most pronounced inverse relationship with childhood stunting. Additionally, exposure to both drought severity and high conflict intensity independently and in combination worsens childhood stunting not only within affected regions but also in nearby localities. Our results underscore the urgency of geographically targeted, multisectoral, and action-oriented policies aimed at strengthening community and health system capacities to mitigate the converging risks of climate change and conflict on child malnutrition. Full article

The operationalization of the One Health (OH) approach remains a major challenge due to persistent fragmentation across human, animal, and environmental data systems. This gap is exacerbated by climate change, which acts as a risk multiplier for pathogen transmission and agri-food system vulnerability. Drawing on more than a decade of research, including the re-emergence of brucellosis in Italy and the 2024 Salmonella Umbilo outbreak, this perspective discusses key weaknesses in current data management, particularly the lack of real-time, interoperable data sharing. To address these challenges, we propose an AI-enabled One Health Information System (OH-IS), grounded in FAIR data principles and privacy-preserving architectures. The proposed conceptual framework integrates multi-matrix data streams, combining Earth observation data, genomic surveillance through whole-genome sequencing (WGS), and livestock mobility within a geospatially integrated architecture to support timely decision-making in vulnerable settings. By analyzing the constraints of siloed databases, we discuss how automated semantic harmonization could conceptually support improved risk assessment and outbreak reconstruction in recent zoonotic events. This approach may facilitate a transition from descriptive to anticipatory surveillance, providing a scalable model to move One Health from a conceptual paradigm toward a more integrated and data-driven surveillance framework aligned with EU digital health policies and global health security priorities. Full article

Wildfires pose serious risks to ecosystems, air quality, and human health. Effective wildfire monitoring requires accurate detection and timely validation, but current approaches are often constrained by fragmented data sources, false alarms, and delays in field verification. This study presents WildfireGO, a multi-source wildfire detection and validation system that integrates crowdsourced observations, satellite hotspot data, and image-based classification in a geospatial monitoring environment. The system combines user-submitted images, Sentinel-2 imagery, and Moderate Resolution Imaging Spectroradiometer (MODIS) hotspot data processed through Google Earth Engine (GEE) to support wildfire detection and verification. Four classification models, namely Convolutional Neural Network (CNN), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting (GB), were evaluated using 10-fold cross-validation and an independent test dataset of 800 wildfire-related images. The CNN model produced the best result, with an accuracy of 97.5% on the independent test dataset. By combining image-based classification with crowdsourced reporting, the system helps screen user-submitted wildfire information and reduce false detections. Satellite-derived hotspot data provide spatial evidence for cross-checking reported events and improving spatial situational awareness for wildfire monitoring and response planning. WildfireGO supports near real-time data submission, automated processing, and interactive map-based visualization through a web-based interface. The findings indicate that combining crowdsourced reports, satellite observations, and image classification in a single geospatial system has the potential to support more reliable wildfire detection and provide practical support for environmental monitoring, disaster response, and spatial decision-making. Full article

This study presents an integrated framework for agricultural drought monitoring in data-scarce regions, utilizing the Google Earth Engine (GEE) platform to analyze multisource Earth observation data over the South Wollo highlands, Ethiopia, from 2001 to 2024. The analysis was complemented by Mann–Kendall trend testing, Sen’s slope estimation, and Pettitt change-point detection to identify and quantify long-term trends and abrupt shifts in drought dynamics. The methodology integrates climatic and satellite-derived indicators within a hybrid analytical framework. It incorporates the standardized precipitation evapotranspiration index (SPEI), vegetation condition index (VCI), vegetation health index (VHI), temperature condition index (TCI), and land surface temperature (LST), which are derived from MODIS (NDVI, LST, PET) and CHIRPS precipitation datasets. The analysis focused on the main growing season (June–September) to capture critical crop growth and moisture-sensitive periods for agricultural production in the study area. The findings reveal pronounced interannual variability in drought occurrence and intensity across the study period. Severe agricultural drought conditions were most extensive in 2009 and 2014, with VHIs indicating 15% and 4% of the area under severe and extreme drought in 2009, respectively, and 2.6% and 2% in 2014, respectively. In contrast, 2001, 2005, 2020, and particularly 2024 were characterized by predominantly no-drought to mild-drought conditions, with no-drought coverage increasing from 86.7% (2009) to 98.0% (2024). Vegetation-based indices demonstrate that drought impacts are episodic rather than persistent and strongly controlled by rainfall timing and early-season moisture availability. The LST exhibited marked year-to-year variability (28.8 °C to 33.8 °C), with elevated temperatures coinciding with drought periods and suppressed evaporative cooling. Correlation analysis confirmed a strong positive relationship between the SPEI and VHI (r = 0.77), with moderate correlations for the VCI (r = 0.40) and TCI (r = 0.36), underscoring the sensitivity of integrated vegetation health to the climatic water balance. The study concludes that combining the SPEI with satellite-derived vegetation and thermal indices provides a robust, scalable approach for agricultural drought assessment in regions with limited ground-based observations. The integrated framework effectively captures both moisture deficits and thermal stress components, offering a scientific basis for improving drought early warning systems and climate-resilient agricultural planning in Ethiopia and similar environments. Full article

Copernicus, the Earth Observation component of the European Union Space Programme, plays a key role in monitoring planetary health and informing global sustainability agendas. Enhancing its uptake offers a strategic opportunity to translate climate information into actionable knowledge for sustainable institutional governance. This study examines how data visualization, translating complex climate information into context-relevant formats, can strengthen the uptake of Copernicus Climate Change and Atmosphere Monitoring Service by national institutions. Using the Italian initiative for the National Collaboration Programme of the Copernicus Climate Change Service as an empirical setting, we adopt a mixed-method design to bridge expert visualization practices with institutional stakeholders tasked with sustainability transitions. The findings show that users widely recognize the value of Copernicus. Nonetheless, uptake depends largely on how easily visual outputs can be integrated into workflows and decision procedures. By linking uptake to visualization practices, the study reveals a previously underexplored user–expert gap between production and use contexts. We introduce “adaptive translation” as a framework to align scientific integrity with usability through progressive disclosure, defensibility-oriented design, and iterative feedback loops. The results provide context-sensitive guidance for designing “workflow-ready” visual products in similar national institutional settings, enhancing the capacity of institutional actors to design the climate-resilient actions that are essential for a sustainable future. Full article

The level of pharmaceutical contaminants is increasing exponentially on planet Earth. Despite the vital role of medicines in life, pharmaceutical effluents have severe environmental impacts and cause health issues. In order to treat pharmaceutical effluents, a variety of methods are adopted globally. The conventional techniques lack the capability of effective removal of these hazardous effluents. This review focuses on the methods currently used to treat pharmaceutical wastewater. Both individual and hybrid treatment approaches have been investigated. Optimum and sustainable treatment methods have been presented. Their advantages and limitations have been discussed in detail. Modern treatment techniques are designed to be more sustainable and cost-effective, with a target to achieve high to near-complete removal of contaminants. No single technique is sufficient individually for the purpose. A suitable combination of biological treatment processes with a membrane system and advanced oxidation processes has been observed to be a highly effective method. However, such hybrid methods are designed according to the quality and quantity of wastewater, target pollutants, and several other crucial parameters. Full article

Air pollution caused by fine particulate matter (PM_2.5) poses a serious public health threat in many South Asian megacities where monitoring networks remain limited. Lahore, Pakistan—frequently ranked among the world’s most polluted cities—still lacks reliable short-term PM_2.5 forecasting systems. This study develops a performance-weighted ensemble machine learning framework that integrates satellite observations, meteorological reanalysis data, and ground monitoring measurements to improve daily PM_2.5 prediction. Eleven predictor variables were processed using a unified Google Earth Engine pipeline, including MODIS aerosol optical depth, Sentinel-5P trace gases (CO, NO₂, SO₂), and ERA5 meteorological parameters. Four tree-based machine learning algorithms—Random Forest, XGBoost, LightGBM, and CatBoost—were trained using daily observations from 2019 to 2023. Model evaluation using an independent 2024 dataset showed strong predictive capability, with Random Forest achieving R² = 0.77 (RMSE = 24.75 µg m⁻³), XGBoost R² = 0.76 (RMSE = 26.32 µg m⁻³), CatBoost R² = 0.73 (RMSE = 30.39 µg m⁻³), and LightGBM R² = 0.70 (RMSE = 32.75 µg m⁻³). To further enhance performance, the best models were combined into a weighted ensemble (RF 0.5, XGBoost 0.3, and CatBoost 0.2), which produced the highest validation accuracy (R² = 0.77; RMSE = 23.37 µg m⁻³). Statistical testing using paired t-tests and Diebold–Mariano tests confirmed that the ensemble significantly reduced forecast errors compared with individual models. Feature importance analysis revealed that surface pressure, temperature, CO, and NO₂ were the most influential predictors of PM_2.5 variability. The proposed framework demonstrates that combining satellite data, reanalysis meteorology, and ground observations through ensemble learning can provide accurate and scalable air quality forecasting for data-limited urban environments. Full article

Accurate mapping of near-surface air temperature ($T_{\mathrm{air}}$) at the fine spatial resolution is required for city-scale monitoring and remains a critical challenge in Earth Observation (EO). Reliance on ground-based measurements is constrained by their sparse spatial coverage and high operational costs. We present a novel, scalable machine learning framework designed to overcome this limitation. Our method utilizes interpretable Convolutional Neural Networks (CNNs) to fuse high-resolution Landsat data, integrating both thermal and reflective spectral bands, with contextual spatiotemporal metadata. This approach allows for inference, at 30 m resolution, of $T_{\mathrm{air}}$ fields without relying on dense, localized ground monitoring networks. Our hybrid CNN architecture is optimized for spatial generalization, maintaining strong and transferable performance (station-wise $R^2\approx 0.88$) across diverse environments from humid coasts ($R^2\approx 0.89$) to arid interiors ($R^2\approx 0.84$). Although focused on a specific geographical region, our results suggest a robust and reproducible pathway for generating spatially consistent temperature fields from globally available EO archives, directly supporting urban heat island mitigation, climate policy development, and high-resolution public health assessment worldwide. Full article

Current sustainability discourse promotes sustainable yard practices as a means for residents to contribute to urban environmental health and soil conservation. Social–ecological research suggests that yard practices are shaped by multiscale social drivers, including social contagion, whereby visible expressions of individuality in front yard design are copied by nearby neighbors. This study evaluated residential areas within the Río Piedras Watershed (RPWS) in the San Juan metropolitan area to assess evidence of social contagion in front yard configuration and vegetation structure, and to examine whether these variables were associated with socio-demographic and economic characteristics when spatial effects were considered. A total of 6858 front yards across six highly urbanized sites were analyzed using Google Earth Street View imagery. Housing lot sizes were quantified, and yards were classified into eight landscape configurations based on green and gray cover elements. Woody vegetation structures, including trees, shrubs, and palms, were also quantified to generate estimates of functional diversity and a front yard quality index. Significant differences in yard characteristics were observed among sites. Spatial analyses revealed significant clustering at distances of 65–80 m, particularly for front yard configuration, while clustering of woody vegetation density was weaker. Local clustering patterns and the distribution of outliers varied across sites. Spatial lag models indicated that lot area positively influenced yard configuration and quality, and the density and diversity of woody vegetation. While socio-economic variables were not significant predictors of yard quality, their effects cannot be discarded. Overall, results are consistent with social contagion processes but also highlight neighborhood design as a key driver of clustering, alongside widespread conversion of green to paved front yards, with implications for soil and green infrastructure loss as well as environmental and human health in the RPWS. Full article

This work focuses on the study of traditional Andean therapeutic knowledge of spirituality, understood as current practices that articulate health, territory, and sacredness. In a setting invaded by modernity and conventional medicine, Pampapu survives as a healing ritual that expresses a symbolic and spiritual relationship with the Earth and Andean deities. The objective is to understand the religious, cultural, and symbolic meanings that the inhabitants attribute to this ritual. It was carried out using qualitative research methods with an ethnographic and interpretive approach, based on participant observation and in-depth interviews with traditional healers, older adults, and patients’ families. Thematic and hermeneutic analysis confirmed categories such as sacredness, illness of the Earth, generational transmission, and religious syncretism. The results show that the ritual fulfills therapeutic functions, identity, and social cohesion, and is transmitted through generations. It is concluded that this practice constitutes a living expression of the Andean religious worldview and an essential component of intangible cultural heritage. Full article

The increased mobilization of Rare Earth Elements (REEs), due to emerging technologies, could impact human health. The study assessed the effects of CeO₂ nanopowder (100 μg/mL) in human intestinal cells (HT-29) following both acute (24 h) and, a novelty for in vitro study, sub-chronic exposure, treating subcultures of exposed cells to CeO₂ NP up to 35 days. Recovery was also examined in exposed cells’ progeny. CeO₂ NP internalization and acute cytotoxicity were dose and time dependent. A significant pro-oxidant effect was observed for up to 14 days. The highest mitochondrial impairment was detected after 7 days, but in post-exposure experiments the recovery was observed. Conversely, genotoxicity highlighted the saturation of the DNA repair mechanisms. The irreversible cell damage of sub-chronic exposure was highlighted by the percentage of death cells (p = 0.011) and by the weekly cell replication index (5.68 vs. 7.41). The homeostatic mitophagy pathway was able to counteract ROS-induced mitochondrial dysfunction, as shown by overexpression of ATG5, LC3, and BECN1 genes throughout the examined times. Instead, the overexpression of the pro-apoptotic gene Bax was very brief, highlighting that prolonged exposure might cause more widespread adverse effects, also involving cells that are not directly exposed to nanoceria. Full article