Search Results (191)

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

Remote sensing approaches to monitoring dryland ecosystem states and trends have been dominated by the binary distinction between degraded/non-degraded areas, leading to inconsistent results. We propose a different conceptual framework that better reflects the states and pressures of these ecosystems—ecological integrity—that is, the maintenance of ecosystem composition and its capacity to contribute to human needs and adapt to change. We systematically reviewed earth observation techniques for characterizing ecological integrity in trusted databases together with studies identified through expert-guided search. A total of 137 papers were included, and their metadata (i.e., location, year) and data (i.e., aspect of ecological integrity assessed, techniques employed) were analyzed. The results show that remote sensing ecological integrity is becoming an increasingly researched topic, especially in countries with extensive drylands. Vegetation was the most frequently monitored attribute and was often employed as an indicator of other attributes (i.e., soil and water quality) and as a key feature in approaches that aimed for a comprehensive ecosystem assessment. However, most of the literature employed the normalized difference vegetation index (NDVI) as a descriptor of vegetation characteristics (i.e., health, structure, cover), which has been shown not to be a good indicator of the litter/senescent vegetation components that tend to frequently dominate drylands. Methods to overcome this weakness have been identified, although more research is needed to demonstrate their application in ecological integrity monitoring. Specifically, knowledge gaps in the relationship between vegetation cover fractions (i.e., green, non-green, and bare soil), descriptors of ecosystem quality (e.g., soil condition or vegetation structure complexity), and management (i.e., how human intervention affects ecosystem quality) should be addressed. Notable potential has been identified in time series analysis as a means of operationalising remotely sensed vegetation fractional cover. Nevertheless, limitations in benchmarking must also be tackled for effective ecological integrity monitoring. Full article

Burgeoning air pollution is a pressing public health concern. However, due to the scarcity and sparsity of ground-based monitoring, its impact remains uncertain. This work demonstrates how satellite-derived NO₂ observations can identify persistent pollution hotspots and seasonal patterns in a data-scarce urban region. This work leveraged TROPOMI satellite data and Google Earth Engine to evaluate tropospheric NO₂ hotspot patterns in the Greater Accra Region of Ghana from 2019 to 2023. TROPOMI data revealed persistent NO₂ hotspots in urban and industrial areas, with overall peak concentrations reaching up to 3.3 × 10¹⁵ mol cm⁻². Seasonal analysis showed elevated NO₂ levels during the dry season, with a mean concentration of 2.3 × 10¹⁵ mol cm⁻², while lower levels were observed during the rainy season. Increased emissions and reduced dispersion influence this pattern due to stable atmospheric conditions. Google Earth imagery confirmed that the highest NO₂ concentrations were associated with the Heavy Industrial Area, highlighting the presence of extensive industrial facilities such as refineries, factories, and quarries. This integration of satellite observations with high-resolution geospatial tools provides a robust methodology for NO₂ source attribution, emphasizing the need for targeted emission control measures in industrial zones to mitigate air pollution and associated health risks. Full article

Background: Chinese Yellow Earth is a key subtropical agricultural resource in southwestern China; however, its productivity is limited by acidity and poor nutrient retention. This study examined how reduced nitrogen plus organic amendments affect its soil microbial structure and maize yield. Methods: A field experiment with four treatments evaluated reduced nitrogen fertilization amended with rice husk plus rapeseed cake (RS) or RS with biochar (BC). Soil properties (pH, nitrogen, organic matter) and maize yield were analyzed. Metagenomic analysis (NR database) characterized microbial communities, and correlation analysis with Mantel tests identified key relationships. Results: Combined organic amendments under reduced N significantly increased soil pH, nitrogen components, and organic matter, increasing maize yield by 4.41–8.97%. Metagenomics revealed enriched beneficial genera including Sphingomonas and Bradyrhizobium. Yield positively correlated with nitrate nitrogen and a beneficial microbial cluster containing Lysobacter and Reyranella, whereas Steroidobacter negatively correlated with key fertility indicators. Mantel tests revealed nitrate nitrogen as the primary correlate of functional gene community succession. Conclusions: This study reveals that reduced nitrogen with organic amendments promotes soil improvement and microbial modulation, demonstrating potential as a sustainable practice to maintain crop productivity in Chinese Yellow Earth. The observed trend toward yield improvement underscores its promise and warrants further validation through additional trials. Overall, the findings highlight the beneficial effects of these amendments on soil health and their role in supporting sustainable subtropical agriculture under reduced nitrogen input. Full article

Urban heat vulnerability is an increasing public health concern, particularly in rapidly urbanizing regions of southern France. This study aims to quantify and map the Heat Vulnerability Index (HVI) for Toulouse and to analyze its temporal trends to identify high-risk zones and influencing factors. The assessment integrates recent years’ remote sensing data of pollutant emissions, land use/land cover and land surface temperature, statistical data of climate-related mortalities, and socioeconomic and demographic factors. Following a detailed analysis of recent real-time air quality and weather data from multiple monitoring stations across the city of Toulouse, it was observed that Urban Pollution Island (UPI) and Urban Heat Island (UHI) are closely interlinked phenomena. Their combined effects can significantly elevate the annual mortality risk rate by an average of 2%, as calculated using AirQ+ particularly, in densely populated urban areas. Remote sensing data was processed using Google Earth Engine and all factors were grouped into three key categories: heat exposure, heat sensitivity, and adaptive capacity to derive HVI. Temporal HVI maps were generated and analyzed to identify recent trends, revealing a persistent increase in vulnerability across the city. Comparative results show that 2022 was the most critical summer period, especially evident in areas with limited vegetation and extensive use of heat-absorptive materials in buildings and pavements. The year 2024 indicates resiliency and adaptation although some areas remain highly vulnerable. These findings highlight the urgent need for targeted mitigation strategies to improve public health, enhance urban resilience, and promote overall human well-being. This research provides valuable insights for urban planners and municipal authorities in designing greener, more heat-resilient environments. Full article