Search Results (3,019)

Herbicides are widely used agrochemicals and are increasingly recognised as contaminants of emerging concern in aquatic environments due to their extensive application, environmental persistence, and potential ecological and human health impacts. Their determination in water presents significant analytical challenges, as these compounds occur at trace to ultra-trace levels and encompass a wide range of chemical properties, including highly polar and ionic species as well as transformation products. This review provides a critical overview of recent advances in separation technologies for the analysis of herbicides in water, based on peer-reviewed studies published between 2020 and 2025 retrieved from the PubMed and Scopus databases. The discussion focuses on developments in sample preparation, extraction strategies, chromatographic separation, and detection techniques, with particular attention to analytical performance and sustainability. The reviewed studies demonstrate that solid-phase extraction remains central to achieving the lowest detection limits, while miniaturised and greener extraction approaches are increasingly adopted to reduce solvent consumption and simplify workflows. Advances in chromatographic separation and detection, especially liquid chromatography coupled to tandem mass spectrometry, have further enhanced sensitivity and selectivity for a broad range of herbicides. Overall, this review highlights current analytical capabilities and emerging trends, outlining future directions for reliable and sustainable monitoring of herbicides in aquatic environments. Full article

Accurate prediction, forecasting and interpretability of air pollutant concentrations are important for sustainable environmental management and protecting public health. An integrated artificial intelligence (AI) framework is proposed to predict, forecast and analyse six major air pollutants, such as particulate matter concentrations (PM_2.5 and PM₁₀), ground-level ozone (O₃), carbon monoxide (CO), nitrogen dioxide (NO₂), and sulphur dioxide (SO₂), using a combination of ensemble and deep learning models. Five years of hourly air quality and meteorological data are analysed through correlation and Granger causality tests to uncover pollutant interdependencies and driving factors. The results of the Pearson correlation analysis reveal strong positive associations among primary pollutants (PM_2.5–PM₁₀, CO–nitrogen oxides NO_x and VOCs) and inverse correlations between O₃ and NO_x (NO and NO₂), confirming typical photochemical behaviour. Granger causality analysis further identified NO₂ and NO as key causal drivers influencing other pollutants, particularly O₃ formation. Among the 23 tested AI models for prediction, XGBoost, Random Forest, and Convolutional Neural Networks (CNNs) achieve the best performance for different pollutants. NO₂ prediction using CNNs displays the highest accuracy in testing (R² = 0.999, RMSE = 0.66 µg/m³), followed by PM_2.5 and PM₁₀ with XGBoost (R² = 0.90 and 0.79 during testing, respectively). The Air Quality Index (AQI) analysis shows that SO₂ and PM₁₀ are the dominant contributors to poor air quality episodes, while ozone peaks occur during warm, high-radiation periods. The interpretability analysis based on Shapley Additive exPlanations (SHAP) highlights the key influence of relative humidity, temperature, solar brightness, and NO_x species on pollutant concentrations, confirming their meteorological and chemical relevance. Finally, a deep-NARMAX model was applied to forecast the next horizons for the six air pollutants studied. Six formulas were elaborated using input data at times (t, t − 1, t − 2, …, t − n) to forecast a horizon of (t + 1) hours for single-step forecasting. For multi-step forecasting, the forecast is extended iteratively to (t + 2) hours and beyond. A recursive strategy is adopted for this purpose, whereby the forecast at (t + 1) is fed back as an input to generate the forecasts at (t + 2), and so forth. Overall, this integrated framework combines predictive accuracy with physical interpretability, offering a powerful data-driven tool for air quality assessment and policy support. This approach can be extended to real-time applications for sustainable environmental monitoring and decision-making systems. Full article

This review examines the status, challenges, and future trajectories of global mussel aquaculture within the blue food system. Despite steady production growth, mussels’ relative contribution to total bivalve output has significantly declined over recent decades due to disproportionate expansion of oyster, clam, and scallop sectors. A major geographical production shift has occurred, with Asia, spearheaded by China, emerging as the dominant region, supplanting traditional European producers while the Americas rapidly ascend. China’s overwhelming dominance in overall bivalve production starkly contrasts with its underdeveloped mussel sector, where growth lags behind other bivalves despite substantial absolute increases, reflecting a fundamental restructuring of species composition. The industry faces interconnected sustainability constraints: persistent vulnerabilities in spat supply stemming from environmental variability, hatchery limitations, and disease transmission risks; escalating environmental stressors including climate change impacts, harmful algal blooms, pollution, and pathogens; structural flaws in value chains characterized by fragmented production, market volatility, and underutilized byproducts; and governance challenges related to spatial access and licensing inefficiencies. This review advocates for a comprehensive strategy to boost the mussel aquaculture. These encompass advancing hatchery technology and genetic breeding programs, implementing ecosystem-based management such as multi-trophic systems and AI-enhanced environmental monitoring, restructuring value chains through producer cooperation and high value product diversification, and establishing science-based spatial planning frameworks with streamlined governance. Addressing these challenges holistically is critical to position mussel farming as a resilient pillar of sustainable blue food production capable of reconciling ecological integrity with economic viability and social equity. Full article

The growing demand for resources for production in intensive livestock farming requires research to operate with an environmentally sustainable perspective and respect for animal welfare, promoting circularity in the livestock industry. In this context, animal monitoring plays a key role in livestock management, not only to ensure their well-being but also to preserve the balance of the territory. In particular, early detection of oestrus events is one of the crucial elements in livestock monitoring. This study presents the development and on-farm validation of a low-power oestrus detection system for dairy cows, based on stand-alone smart pedometers (SASPs) connected through a Low-Power Wide-Area Network (LPWAN). The system implements an upgradeable, threshold-based algorithm that analyzes cow motor activity using a 24 h moving-mean approach and three behavioral indicators related to oestrus expression. Data are processed on board and transmitted to a cloud platform for visualization through a farmer-oriented WebApp, without requiring any fixed installation in the barn. The system was tested on a commercial free-stall dairy farm over three experimental campaigns (2021–2023). Oestrus events were validated through farmer visual observation and milk progesterone analysis, used as the reference method. A total of 22 confirmed oestrus events were analyzed. The system achieved a detection rate of 72.7% for certain oestrus events and 86.4% when including probable detections, with a mean oestrus duration of 18.1 ± 2.5 h, consistent with values reported in the literature. The proposed solution demonstrates the feasibility of a transparent, low-computational-cost oestrus detection approach compatible with LPWAN constraints. Its plug-and-play design, reduced infrastructure requirements, and upgradable firmware, although not able to self-update, limiting its potential compared to the machine learning-based methods present in the literature, make it suitable for practical adoption, particularly in farms where conventional connectivity and high-cost commercial systems are limiting factors. Full article

Sustainable management of indoor and outdoor air quality is essential for protecting public health, enhancing well-being, and supporting resilient urban environments. Low-cost air quality sensors enable continuous, real-time monitoring of key pollutants and, when combined with data analytics, provide scalable and cost-effective insights for smart building operation and environmental decision-making. This pilot study evaluates an indoor–outdoor air quality monitoring system deployed at the Faculty of Electrical Engineering and Information Technologies in Skopje, with a focus on: (i) PM2.5 and PM10 concentrations and their relationship with meteorological conditions and human occupancy; (ii) sensor responsiveness and reliability in an educational setting; and (iii) implications for sustainable building operation. From January to March 2025, two indoor sensors (a classroom and a faculty hall) and two outdoor rooftop sensors continuously measured PM2.5 and PM10 at one-minute intervals. All sensors were calibrated against a reference instrument prior to deployment, while meteorological data were obtained from a nearby station. Time-series analysis, Pearson correlation, and multiple regression were applied. Indoor particulate levels varied strongly with occupancy and ventilation status, whereas outdoor concentrations showed weak to moderate correlations with meteorological variables, particularly atmospheric pressure. Moderate correlations between indoor and outdoor PM suggest partial pollutant infiltration. Overall, this pilot study demonstrates the feasibility of low-cost sensors for long-term monitoring in educational buildings and highlights the need for adaptive, context-aware ventilation strategies to reduce indoor exposure. Full article

Brazil nut (Bertholletia excelsa Bonpl.) is a major non-timber forest product in the Amazon, supporting extractivist communities in Brazil, Bolivia, and Peru and contribute to forest conservation. Unlike other extractive products, Brazil nut production has not declined under commercial use and is recognized for its socioeconomic and environmental importance. Precision agriculture has been transformed by the use of unmanned aerial vehicles (UAVs) and artificial intelligence (AI), which enable monitoring efficiency and yield estimation in several crops, including the Brazil nut. This study assessed the potential of using UAV-based imagery combined with YOLOv8 object detection model to identify and quantify Brazil nut fruits in a native forest fragment in eastern Acre, Brazil. A UAV was used to capture canopy images of 20 trees with varying diameters at breast height. Images were manually annotated and used to train the YOLOv8 with an 80/20 split for training and validation/testing. Model performance was evaluated using precision, recall, F1-score, and mean Average Precision (mAP). The model achieved recall above 90%, with an F1-score of 0.88, despite challenges from canopy complexity and partial occlusion. These results indicate that UAV-based imagery combined with AI detection provides an approach for estimating Brazil nut yield, reducing manual effort and improving market strategies for extractivist communities. This technology supports sustainable forest management and socioeconomic development in the Amazon. Full article

This review synthesizes the state of the art on the integration of digital technologies, particularly machine learning, the Internet of Things (IoT), and advanced image processing techniques, for enhanced hazard monitoring. Focusing on air pollution, earthquakes, floods, and fires, we analyze articles selected from Scopus published between 2015 and 2024. This study classifies the selected articles based on hazard type, digital technology application, geographical location, and research methodology. We assess the effectiveness of various approaches in improving the accuracy and efficiency of hazard detection, monitoring, and prediction. The review highlights the growing trend of leveraging multi-sensor data fusion, deep learning models, and IoT-enabled systems for real-time monitoring and early warning. Furthermore, we identify key challenges and future directions in the development of robust and scalable hazard monitoring systems, emphasizing the importance of data-driven solutions for sustainable environmental management and disaster resilience. Full article

The Church of San Juan del Hospital in Valencia (Spain) is a Gothic church whose main architectural feature—the western façade—remained unresolved, posing structural and compositional challenges. The intervention addressed this issue while preserving the historical integrity of the building and its heritage context. A systematic methodology was applied, following principles of reversibility, sustainability, and compatibility with medieval ribbed-vault construction. The project resolved five key aspects: completion of the nave’s façade, coverage of the former atrium remains, access from the north courtyard, compositional coherence of the west courtyard front, and integration of the church and museum entrances. Contemporary materials and techniques, including aluminum, recycled wood, and handmade ceramic brick, were selected to harmonize with historic stonework, ensure durability, and minimize environmental impact. Design strategies guided visual perception, emphasizing the lower façade and resolving dispersive compositional elements, while creating functional spaces for ventilation, climate control, and circulation. This intervention demonstrates how a methodical, heritage-sensitive approach can solve complex architectural problems, combining innovation with historical authenticity, and enhancing both the functionality and aesthetic experience of the Church of San Juan del Hospital. Full article

This study examines the conceptualization of sustainability in heritage tourism in Saudi Arabia following the introduction of the Saudi Vision 2030 program and the country’s opening to tourism in 2019, both of which aim to diversify the economy and promote cultural heritage. A scoping review methodology based on the Arksey & O’Malley framework has been adopted; data were charted according to the Joanna Briggs Institute (JBI) charting method based on the PRISMA-ScR reporting protocol. Publications from 2019 to 2025 were systematically collected from the database and manual research, resulting in 25 fully accessible studies that met the inclusion criteria. Data were analyzed thematically, revealing six main areas of investigation, encompassing both sustainability outcomes and cross-cutting implementation enablers: heritage conservation and tourism development, architecture and urban planning, policy and governance, community engagement, marketing and technology, and geoheritage and environmental sustainability. The findings indicate that Saudi research in this field is primarily qualitative, focusing on ecological aspects. The studies reveal limited integration of social and technological dimensions, with significant gaps identified in standardized sustainability indicators, longitudinal monitoring, policy implementation, and digital heritage tools. The originality of this study lies in its comprehensive mapping of Saudi heritage tourism sustainability research, highlighting emerging gaps and future agendas. The results also provide a roadmap for policymakers, managers, and scholars to enhance governance policies, community participation, and technological integration, which can contribute to sustainable tourism development in line with Saudi Vision 2030 goals, thereby fostering international competitiveness while preserving cultural and natural heritage. Full article

The rapid expansion of Internet of Things (IoT)-enabled smart grids has intensified the need for reliable fault detection and autonomous self-healing under non-stationary operating conditions characterized by frequent concept drift. To address the limitations of static and single-strategy adaptive models, this paper proposes H-CLAS, a novel Hybrid Continual Learning for Adaptive Self-healing framework that unifies regularization-based, memory-based, architectural, and meta-learning strategies within a single adaptive pipeline. The framework integrates convolutional neural networks (CNNs) for fault detection, graph neural networks for topology-aware fault localization, reinforcement learning for self-healing control, and a hybrid drift detection mechanism combining ADWIN and Page–Hinkley tests. Continual adaptation is achieved through the synergistic use of Elastic Weight Consolidation, memory-augmented replay, progressive neural network expansion, and Model-Agnostic Meta-Learning for rapid adaptation to emerging drifts. Extensive experiments conducted on the Smart City Air Quality and Network Intrusion Detection Dataset (NSL-KDD) demonstrate that H-CLAS achieves accuracy improvements of 12–15% over baseline methods, reduces false positives by over 50%, and enables 2–3× faster recovery after drift events. By enhancing resilience, reliability, and autonomy in critical IoT-driven infrastructures, the proposed framework contributes to improved grid stability, reduced downtime, and safer, more sustainable energy and urban monitoring systems, thereby providing significant societal and environmental benefits. Full article

Very Low Earth Orbit (VLEO) satellites, operating at altitudes below 450 km, provide tremendous potential in the domain of remote sensing. Their proximity to Earth offers high resolution, low latency, and rapid revisit rates, allowing continuous monitoring of dynamic systems and real-time delivery of vertically integrated earth observation products. Nonetheless, the application of VLEO is not yet fully realized due to numerous complexities associated with VLEO satellite development, considering atmospheric drag, short satellite lifetimes, and social, political, and legal regulatory fragmentation. This paper reviews the recent technological developments supporting sustainable VLEO operations with regards to aerodynamic satellite design, atomic oxygen barriers, and atmospheric-breathing electric propulsion (ABEP). Furthermore, the paper provides an overview of the identification of regulatory and economic barriers that extort additional costs for VLEO ranging from frequency band allocation and space traffic management to life-cycle cost and uncertain commercial demand opportunities. Nevertheless, the commercial potential of VLEO operations is widely acknowledged, and estimated to lead to an economic turnover in the order of 1.5 B USD in the next decade. Learning from the literature and prominent past experiences such as the DISCOVERER and CORONA programs, the study identifies key gaps and proposes a roadmap to sustainable VLEO development. The proposed framework emphasizes modular and serviceable satellite platforms, hybrid propulsion systems, and globally harmonized governance in space. Ultimately, public–private partnerships and synergies across sectors will determine whether VLEO systems become part of the broader space infrastructure unlocking new capabilities for near-Earth services, environmental monitoring, and commercial innovation at the edge of space. Full article

Vehicular emissions are a major contributor to air pollution and respiratory morbidity in Ecuador’s urban centers. Despite increasing evidence of traffic-related health impacts, national research remains fragmented and unevenly distributed. This narrative review synthesizes 26 peer-reviewed studies published between 2000 and 2024 to characterize vehicular air pollution sources, pollutants, and respiratory health effects in Ecuador. The evidence shows a strong geographic concentration, with more than half of the studies conducted in Quito, followed by Guayaquil and Cuenca. National inventories indicate that the transport sector accounts for approximately 41.7% of Ecuador’s CO₂ emissions. Across cities, PM_2.5, PM₁₀, NO₂, CO, and SO₂ were the most frequently assessed pollutants and were repeatedly reported to approach or exceed international guideline values, particularly during traffic peaks and under low-dispersion conditions. Health-related studies documented substantial impacts, including up to 19,966 respiratory hospitalizations in Quito, with short-term PM_2.5 exposure associated with increased hospitalization risk in children. Among schoolchildren attending high-traffic schools, carboxyhemoglobin levels above 2.5% were linked to a threefold increase in the risk of acute respiratory infections. Occupationally exposed adults, such as drivers, traffic police officers, and outdoor workers with regular exposure to traffic-related air pollution, also showed a higher prevalence of chronic respiratory symptoms. Environmental evidence further highlighted the accumulation of traffic-related heavy metals (Zn, Cu, Pb, Cr) and pronounced spatial inequalities affecting low-income neighborhoods. Overall, the review identifies aging vehicle fleets and diesel-based transport as dominant contributors to observed pollution and health patterns, while underscoring methodological limitations such as the scarcity of longitudinal studies and uneven monitoring coverage. These findings provide integrated and policy-relevant evidence to support sustainable urban planning, cleaner transport strategies, and targeted respiratory health policies in Ecuador. Full article

Whey and its permeates constitute highly organic, low-alkalinity dairy streams whose management remains suboptimal in many processing facilities. This narrative review integrates recent evidence on the anaerobic digestion (AD) of whey, linking substrate composition and biodegradability with microbial pathways, inhibition mechanisms, biogas quality, and techno-economic and environmental feasibility in industrial settings. Data for sweet whey, acid whey, and their permeates are synthesized, with emphasis on operational windows, micronutrient requirements, and co-digestion or C/N/P/S balancing strategies that sustain resilient methanogenic communities. Options for biogas conditioning and upgrading towards combined heat and power, boiler applications, and compressed or liquefied biomethane are examined, and selection criteria are proposed based on impurity profiles, thermal integration, and methane-recovery performance. Finally, critical R&D gaps are identified, including mechanistic monitoring, bioavailable micronutrition, modular upgrading architectures, and the valorization of digestate as a recovered fertilizer. This review provides an integrated framework to guide the design and operation of technically stable, environmentally verifiable, and economically viable whey-to-biomethane schemes for the dairy industry. Full article

Heavy metal pollution in forest and agroforestry soils represents a persistent environmental challenge with direct implications for ecosystem functioning, food security, and human health. In tropical and subtropical regions, intense weathering, rapid organic matter turnover, and dynamic redox conditions strongly modulate metal mobility, bioavailability, and long-term soil vulnerability. This review synthesizes current knowledge on the sources, biogeochemical mechanisms, ecological impacts, monitoring approaches, and restoration strategies associated with heavy metal contamination in forest and agroforestry systems, with particular emphasis on tropical landscapes. We examine natural and anthropogenic metal inputs, highlighting how atmospheric deposition, legacy contamination, land-use practices, and soil management interact with mineralogy, organic matter, and hydrology to control metal fate. Key processes governing metal behavior include sorption and complexation, Fe–Mn redox cycling, pH-dependent solubility, microbial mediation, and rhizosphere dynamics. The ecological consequences of contamination are discussed in terms of soil health degradation, plant physiological stress, disruption of ecosystem services, and risks of metal transfer to food chains in managed systems. The review also evaluates integrated monitoring frameworks that combine field-based soil analyses, biomonitoring, and geospatial technologies, while acknowledging methodological limitations and scale-dependent uncertainties. Finally, restoration and remediation strategies—ranging from phytotechnologies and soil amendments to engineered Technosols—are assessed in relation to their effectiveness, scalability, and relevance for long-term functional recovery. By linking mechanistic understanding with management and policy considerations, this review provides a process-oriented framework to support sustainable management and restoration of contaminated forest and agroforestry soils in tropical and subtropical regions. Full article

Climate change and altered hydrological regimes are restructuring wetland habitats globally, triggering cascading effects on colonial waterbirds. This study investigates how environmental drivers, including thermal anomalies, water-level fluctuations, and aqueous surface extent, influence the distribution and size of waterbird colonies (Ardeidae, Threskiornithidae, and Phalacrocoracidae) in the Danube Delta Biosphere Reserve. We integrated colony census data (2016–2023) with remote-sensing-derived habitat metrics, in situ meteorological and hydrological measurements to model colony abundance dynamics. Our results indicate that elevated early spring temperatures and water level variability are the primary determinants of numerical population dynamics. Spatial analysis revealed a heterogeneous response to hydrological stress: while the westernmost colony exhibited high site fidelity due to its proximity to persistent aquatic surfaces, the central colonies suffered severe declines or local extirpation during extreme drought periods (2020–2022). A discernible eastward shift in bird assemblages was observed toward zones with superior hydrological connectivity and proximity to anthropogenic hubs, suggesting an adaptive spatial response that was consistent with behavioral flexibility. We propose an adaptive management framework prioritizing sustainable solutions for maintaining minimum lacustrine water levels to preserve critical foraging zones. This integrative framework highlights the pivotal role of remote sensing in transitioning from reactive monitoring to predictive conservation of deltaic ecosystems. Full article