Search Results (12,028)

Olive cultivation represents a key pillar of rural economies and cultural heritage in Mediterranean regions, including western Greece. Despite its socio-economic importance, the sector faces increasing pressures from climate change, market volatility, and technological transformation, while progress toward environmentally sustainable production remains uneven. This study investigates how olive farmers’ perceptions of carbon footprint and climate risks are influenced by their demographic characteristics. Primary data were collected through 402 structured questionnaires distributed to olive producers in the Aetolia–Acarnania region. The sample was designed to represent farmers directly engaged in olive production, ensuring the relevance and reliability of the collected data. The findings, based on descriptive statistics, reveal significant heterogeneity in producers’ perceptions of climate risks and their capacity to respond through sustainable practices. Demographic characteristics appear to play an important role in shaping awareness of carbon footprint and the potential adoption of environmentally responsible farming strategies. These results suggest that sustainability transitions in perennial cropping systems depend not only on technological availability but also on social, informational, and institutional capacities. Strengthening agricultural advisory services, farmer training, and climate adaptation strategies may therefore support the adoption of climate-smart practices in olive cultivation. Furthermore, cooperation and value-chain integration are identified as potentially important mechanisms for facilitating knowledge transfer and supporting the adoption of sustainable practices (e.g., efficient irrigation and optimized input use). However, their contribution to environmental performance and greenhouse gas mitigation cannot be directly inferred from the present perception-based analysis and should be examined in future research using appropriate quantitative or environmental assessment frameworks. Full article

Climate change disproportionately threatens low- and middle-income countries, yet integrated assessments combining socio-economic fragility with physical hazards remain limited. This study quantifies multi-dimensional climate vulnerability and derives optimized adaptation policies for six representative nations (Bangladesh, Colombia, Kenya, Morocco, Pakistan, Vietnam) by fusing socio-economic indicators with climate risk data (2000–2024). A computational framework integrating unsupervised learning, dimensionality reduction, and predictive modeling was employed. Principal Component Analysis synthesized eight indicators into a Compound Vulnerability Score (CVS), while K-Means and DBSCAN identified distinct vulnerability regimes. XGBoost quantified driver importance, and Graph Neural Networks captured systemic interconnections. XGBoost identified projected drought risk (31.2%), precipitation change (18.1%), and poverty headcount (14.3%) as primary drivers. Graph networks demonstrated significant risk amplification in African nations (Morocco SRS: 0.728–0.874; Kenya SRS: 0.504–0.641) versus damping in Asian countries. A Reinforcement Learning (RL) agent was trained using Deep Q-Networks with experience replay to optimize intervention portfolios under budget constraints. The RL policy achieved a 23% reduction in systemic risk compared to uniform allocation baselines, generating context-specific priorities: drought management for Morocco (score 50) and Pakistan (40); poverty alleviation for Kenya (40); coastal protection for Bangladesh (40); agricultural resilience for Vietnam (35); and institutional capacity building for Colombia (50). In conclusion, socio-economic fragility non-linearly amplifies climate hazards, with poverty and drought risk constituting critical vulnerability multipliers. The AI-driven framework demonstrates that targeted interventions in high-sensitivity systems maximize systemic risk reduction. This integrated approach provides a replicable, evidence-based foundation for strategic adaptation finance allocation in an increasingly uncertain climate future. Full article

Understanding how climate change alters the thermal environment experienced by dairy cattle is critical for guiding adaptation in rapidly warming regions. Using meteorological data from 1961 to 2020, this study quantified long-term trends in temperature, precipitation, relative humidity, and wind speed across Jiangsu Province, China, and assessed their impacts on thermal stress using a temperature–humidity index (THI). The results reveal pronounced spatial heterogeneity in climatic change, with accelerated warming in southern and coastal prefectures, and continued winter cold stress in the northern plain. Over the past six decades, the annual number of heat-stress days (THI > 72) increased substantially and expanded northward, while cold-stress days (THI ≤ 38) declined but remained non-negligible in northern Jiangsu. Although the total number of thermally comfortable days changed little at the provincial scale, their seasonal distribution became increasingly compressed between longer summer heat-stress periods and shorter winter cold-stress windows, indicating a narrowing of the effective comfort range for dairy cattle. To link historical analysis with operational applications, a forecasting platform was developed to generate short-term predictions of THI and associated meteorological conditions, enabling spatially explicit visualization and early identification of periods with elevated thermal risk. Overall, these findings demonstrate an intensification and redistribution of thermal stress in Jiangsu, while also illustrating a transferable climate-risk mechanism relevant to other warming, humid dairy regions worldwide. Full article

Background: Global water scarcity, intensified by climate change, demands optimization of irrigation systems consuming 70% of freshwater resources. Despite significant investments in modernizing irrigation infrastructure from open channels to pressurized networks, performance often falls below expectations. Objective: This study develops an integrated diagnostic and simulation framework for evaluating and improving large-scale pressurized irrigation systems by adapting the Mapping System and Services for Pressurized Irrigation (MASSPRES) methodology. Methods: The framework integrates three components: (1) demand flow dynamics determination using stochastic modelling; (2) hydraulic performance simulation incorporating multiple flow regimes; and (3) performance analysis using relative pressure deficit and reliability indicators. The methodology combines deterministic soil water balance calculations with stochastic farmer behaviour modelling. Results: Application to the Sinistra Ofanto irrigation scheme revealed localized pressure deficits during peak demand periods. The rehabilitation strategy restored full hydraulic feasibility of the network, increasing the proportion of hydraulically satisfied operating configurations from 62% to 100% under peak demand conditions and ensuring adequate pressure at all 317 hydrants across the system. Conclusions: The methodology provides robust decision support for cost-effective rehabilitation, ensuring reliable water delivery while promoting water-energy efficiency. Full article

Satellite-based precipitation estimates (SPE) provide essential spatial coverage and near real-time availability for hydrological applications but often exhibit systematic biases in regions characterized by complex terrain and strong climatic variability, limiting their reliability for flood-related studies. To address these limitations, this study proposes an Adaptive Regularization framework integrated within a Long Short-Term Memory (LSTM) model to enhance satellite–gauge rainfall fusion beyond conventional optimization strategies. The framework dynamically adjusts learning rate and weight decay during training based on validation performance and overfitting indicators, improving training stability, data efficiency, and model generalization across diverse precipitation regimes. The proposed approach was applied to refine Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-Final) daily rainfall estimates over the flood-prone east coast of Peninsular Malaysia. Model performance was assessed against ten optimization algorithms using correlation coefficient (CC), mean absolute error (MAE), normalized root mean squared error (NRMSE), percentage bias (PBias), and Kling–Gupta efficiency (KGE). Results show that the Adaptive Regularization framework consistently outperforms all benchmark optimizers, achieving an MAE of 6.87, CC of 0.68, NRMSE of 1.84, and KGE of 0.56. Overall, the proposed framework enhances spatial consistency and robustness across monsoon seasons, offering a scalable solution for improving SPE in flood-prone regions. Full article

Flooding is an increasingly frequent climate hazard with the potential to mobilize environmental contaminants and elevate human health risks. In this study, we assessed heavy metals and metalloids across five sites arranged along a flood-risk gradient from low to high. Six replicate samples per site (n = 30 per contaminant) were collected in a single sampling event. Contaminants were evaluated using the US Environmental Protection Agency (EPA) risk assessment framework to calculate chronic daily intake (CDI), hazard quotients (HQs), and lifetime cancer risk. Arsenic, chromium, and nickel emerged as the most concerning cancer drivers, with nickel cancer risk consistently exceeding 1 × 10⁻³ (equivalent to one additional cancer case per 1000 exposed individuals) and arsenic at 4.4 × 10⁻⁴ (about 1 in 2250). Lead posed non-cancer risks (HQ = 0.912, near the threshold of concern), while cobalt demonstrated a significant decreasing gradient with increasing flood-risk (p = 0.018). Arsenic and thallium more than doubled in concentration at high-flood sites relative to low-flood sites, while cadmium, cobalt, and nickel decreased. These findings suggest flooding may mobilize arsenic, lead, and thallium, while diluting or displacing other metals such as cadmium, cobalt, and nickel. Organs of concern include the liver and kidneys for arsenic, cadmium, nickel, and cobalt, the brain and bones for lead, and the lungs and liver for chromium. Although statistical significance was limited by the small sample size, effect sizes and fold-changes indicate meaningful flood-related differences. This study highlights the importance of considering flood-risk in contaminant hazard assessments and the need for flood-adaptive risk management strategies in vulnerable communities. Full article

Climate change and rapid urbanization are intensifying urban pluvial flooding and threatening sustainable urban development. This study proposes a three-stage, four-dimensional framework (TSFD-UPFR) to assess urban pluvial flood resilience across resistance, response, and recovery phases that integrate natural, infrastructural, social, and economic dimensions. Using a representative urban catchment affected by a typical extreme rainfall event, we couple hydrological–hydrodynamic simulations with multi-source remote sensing and socio-economic indicators at a 100 m grid resolution to enable spatially explicit assessment. The results indicate moderate overall resilience with pronounced spatial heterogeneity. Resistance is primarily constrained by drainage capacity and impervious surfaces, response is shaped by road connectivity and public service accessibility, and recovery is determined by essential facility restoration and economic support. Low-resilience clusters are concentrated in dense built-up areas and transport hubs, revealing structural weaknesses in adaptive capacity. By linking flood processes with socio-economic recovery dynamics, the framework captures cross-stage interactions within urban systems. The findings support climate-adaptive planning, targeted infrastructure investment, and resilience-oriented governance, contributing to sustainable and equitable urban transformation in megacities facing intensifying extreme rainfall. Full article

Climate variability and rising water scarcity are major challenges to agricultural sustainability, particularly in Mediterranean climates with high spatial heterogeneity. Agroclimatic zoning is a fundamental analytical tool for digital agriculture and climate-resilient agriculture. The current effort proposes an integrated agroclimatic and micro-agroclimatic zoning approach for Greece, based on the Aridity Index (AI), CORINE Land Cover 2018 land-use data, and topographic factors. Daily precipitation and reference evapotranspiration data from 139 meteorological stations and 382 rain gauges were spatially interpolated using Empirical Bayesian Kriging, identifying eight agroclimatic classes adapted to the country’s specific conditions. The results indicate a high degree of variability in space, with most agricultural areas being classified as dry to sub-humid, suggesting higher irrigation requirements and sensitivity to drought. Micro-agroclimatic zones have been identified by combining agroclimatic classes, land use, and elevation. Consequently, the derived zones can be used as groundwork for designing methodologies towards more efficient agrometeorological monitoring through the improved localization of IoT agrometeorological stations. Validation with the Köppen–Geiger climate classification reveals high spatial and statistical agreement (χ² = 248,454.09, df = 49, p < 0.001), proving the climatic validity of the proposed approach and its higher sensitivity to local water balance conditions. Full article

Elm (Ulmus pumila), an ecologically and economically valuable tree, exhibits significant tolerance to abiotic stress. However, the physiological and molecular mechanisms underlying its stress adaptabilities are largely unknown. Here, two elm salt-tolerant cultivars (ST-Y and ST-Q) and two salt-sensitive cultivars (SS-J and SS-JX) were identified in the 13 elm accessions collected from Shandong province, China via phenotypic salt tolerance screening. The key salt tolerance mechanisms were explored in ST-Y and SS-J via transcriptomic (RNA-Seq) assays, and subsequently validated in ST-Q and SS-JX via quantitative real-time polymerase chain reaction (RT-qPCR) analyses. Under salt treatment, ST-Y maintained leaf intactness and enhanced activation of antioxidant enzymes with a reduction in reactive oxygen species (ROS) accumulation, while SS-J suffered leaf defoliation and showed compromised antioxidant capacity with higher ROS levels. KEGG pathway analysis revealed that ST-Y leaves exhibited a unique enrichment of differentially expressed genes (DEGs) in the “oxidative phosphorylation (OXPHOS)” pathway after salt stress treatment. Both ST-Y and SS-J exhibited significant enrichment in the “metabolic pathway”, but the number of DEGs in the “arachidonic acid (AA) metabolism” pathway was much higher in ST-Y than in SS-J. Further RT-qPCR analysis verified the accuracy of the RNA-Seq data and revealed that genes related to the “OXPHOS” pathway were significantly up-regulated in ST-Y and ST-Q, but down-regulated in SS-J and SS-JX. Our results suggested that OXPHOS efficiency is critical to antioxidant capacity in elm salt tolerance, suggesting new avenues for forest tree improvement for climate change. Full article

Post-harvest losses of fruits and vegetables are an important bottleneck in food systems of countries around the world, with 30–50% of perishable food items lost between farm and consumer, smallholder farmers in low-and-middle income countries (LMICs) with poor cold chain infrastructures facing a disproportionate burden. Evaporative cooling (EC) is a low-cost and energy-efficient alternative to mechanical refrigeration; however, traditional systems are operated in one position and are dependent on climate, which restricts its performance. The combination of Internet of Things (IoT) sensing, machine learning (ML), and the advanced control theory has made intelligent evaporative cooling systems (IECS) adaptive, data-driven platforms that can regulate the environment in real-time and optimise autonomously. This is a systematic literature review that was carried out according to PRISMA 2020, summarising 94 peer-reviewed articles published in 2018–2025 to map the technological landscape, performance indicators, and research directions of the field of post-harvest fruit and vegetable preservation using IECS. Findings indicate that IECS can considerably lower the storage temperatures, increase the shelf life by 50–200%, and reduce energy consumption by 75–90% compared to traditional refrigeration, and the payback period is as short as 1.2 years. In arid conditions, ML models are accurate in prediction with an R² of 0.98. The gaps in the research identified are a lack of validation in wet climatic conditions, non-existent standardised Ag-IoT protocols, inadequate Food–Energy–Water (FEW) nexus calculation, and no explainable AI (XAI) interfaces. An example of a conceptual framework of four layers synthesised is proposed to direct next-generation research and implementation of the IECS. Full article

Urban trees deliver multiple ecosystem services. However, rapid climate change may alter species-specific growth suitability, necessitating climate-informed planting and management. We developed 1 km grid-based ensemble species distribution models (ensemble SDMS) for 18 tree species widely planted in South Korean cities and projected growth suitability under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 across four future periods (2021–2040, 2041–2060, 2061–2080, 2081–2100) relative to a historical baseline (2000–2019). We quantified multidimensional redistribution signals from SDM outputs, including binary suitable area changes, centroid displacement, latitudinal boundary shifts, and mean suitability changes, using multivariate climatic predictors and complementary environmental variables. These indicators were integrated to classify species responses into four management-relevant types: Stable, Northward Expansion, Poleward Shift, Range Contraction. Model performance was generally high (AUC = 0.74–0.97). Although the median change in suitable area remained near 0%, interspecific variability increased toward later periods and under stronger forcing, with the largest dispersion under SSP3-7.0 (2041–2060). Stable type was most frequent overall (36.8–63.2%), but Northward Expansion increased to 42.1% under late-century SSP3-7.0, and Range Contraction reached 36.8% under mid-century SSP3-7.0. This indicator-based typology provides a practical basis for decision-support tools to prioritize climate-adaptive urban tree selection, replacement, and monitoring. Full article

This study examines the gap concerning occupants’ perceived thermal comfort and objectively measured indoor conditions in a green university building in Riyadh. The purpose is to assess occupant satisfaction with thermal conditions, compare subjective responses with physical measurements, and derive design and operational implications for educational buildings in hot-arid climates. The primary aim was to assess occupant satisfaction with indoor thermal conditions and to measure key environmental parameters to provide a thorough assessment of thermal comfort. A cross-sectional approach was used, combining subjective data from the Center for the Built Environment (CBE) Occupant Indoor Environmental Quality (IEQ) survey with objective measurements of air temperature, relative humidity, mean radiant temperature, and air velocity, which were documented over five consecutive working days during the mid-winter period in Riyadh. These parameters were explored using the CBE Thermal Comfort Tool to calculate Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD) indices. Statistical analyses examined the relationship between occupant-reported comfort and measured environmental conditions. Results showed that only 36% of occupants reported satisfaction with thermal comfort, while 48% expressed dissatisfaction. In contrast, objective measurements indicated stable indoor conditions within recommended comfort ranges (average temperature 23 °C, humidity 30–34%, MRT 24 °C, air velocity 0.5–1.0 m/s), with PMV values near neutral (−0.2 to 0.0) and PPD below 6%. The observed discrepancy highlights the influence of regional climate, individual adaptability, and perceived control. These findings emphasize the need to integrate both subjective feedback and objective measurements to develop occupant-centered strategies that enhance comfort and well-being in sustainable educational buildings in hot-arid climates. Full article

Background: Although the concepts of meteoropathy and meteosensitivity are not included in official classifications, such as the ICD-11 or DSM-5, they are increasingly being studied as potential symptom complexes linking weather variability to health status. The METEO-Q questionnaire, originally developed in Italy, has been adapted in Japan and Turkey, where it has demonstrated satisfactory reliability parameters, although the authors emphasized the need for further verification of the tool’s temporal stability. The present study aimed to adapt METEO-Q to the Polish language and conduct a critical assessment of its factor structure, measurement invariance, and validity in clinical groups. Methods: This cross-sectional study involved 1128 adults: healthy individuals (n = 711), cardiac outpatients (n = 194), and subclinical group with diagnosed mental disorders (n = 223). Data from healthy participants were divided into a training sample (n = 426) for exploratory factor analysis (EFA) and a test sample (n = 285) for confirmatory factor analysis (CFA). Measurement invariance was assessed in the clinical groups. Validity was verified through correlations with a list of 21 symptoms and measures of anxiety and worry about climate change. Results: A two-factor model (meteoropathy and meteosensitivity) was better fitted to the data than a one-factor model, which is consistent with findings from Italian, Japanese, and Turkish studies. However, absolute fit indices in the test sample indicated significant model misfit [CFA: χ² (43) = 210.192, p < 0.001, RMSEA = 0.120, CFI = 0.927], suggesting the presence of local errors in the tool’s structure. The reliability of the subscales was high (α from 0.86 to 0.93). Multi-group analyses suggested metric and scalar invariance. Patients with mental disorders obtained the highest scores, while cardiac outpatients reported a lower level of meteoropathy (M = 6.13) than healthy individuals (M = 7.24). Conclusions: METEO-Q demonstrates a stable two-factor structure and high internal consistency. The obtained RMSEA index (0.12), although indicative of some misfit, is similar to results obtained in other adaptations, such as the Japanese (RMSEA = 0.10) and the Turkish (RMSEA = 0.11), which suggests it is a consistent feature of this tool across different cultural contexts. Accordingly, the instrument is suitable for research purposes; however, its clinical application requires considerable caution and further work to optimize the model. Full article

Urban socio-economic resilience to climate change has emerged as a central research theme as cities increasingly confront interconnected environmental, economic, and social risks. Despite the rapidly expanding body of literature, the conceptual boundaries, thematic evolution, and analytical priorities of this field remain fragmented across disciplines, and no prior study has systematically mapped the socio-economic dimension of urban resilience through a combined bibliometric and thematic analysis over a multi-decadal horizon. This study addresses that gap by providing a systematic review of global research on urban socio-economic resilience to climate change, integrating bibliometric and thematic analyses of peer-reviewed publications from 1990 to 2025. Following the PRISMA 2020 guidelines, records were retrieved from the Web of Science Core Collection and subjected to a multi-stage screening procedure that combined automated relevance scoring with mandatory manual validation of the socio-economic dimension, resulting in a final dataset of 5076 publications. The analysis examines conceptual interpretations of socio-economic resilience, dominant climate hazards affecting urban systems, methodological approaches and assessment indicators, adaptation strategies and governance responses, and emerging research gaps. The results reveal a marked acceleration of scientific output after 2015, driven by the Paris Agreement and the IPCC Special Report on Global Warming of 1.5 °C (2018). The bibliometric network analyses identify adaptation, vulnerability, flooding, and sustainability transitions as the core thematic clusters. The findings trace a paradigmatic trajectory from equilibrist recovery frameworks toward transformative, socio-economically grounded resilience models and reveal persistent gaps in the operationalization of governance, equity measurement, and geographic representation. By synthesizing three-and-a-half decades of scholarship, this review clarifies the intellectual structure of the field and proposes four specific post-2026 research pathways that emphasize longitudinal cross-city comparisons, mixed-methods assessments, sector-specific compound hazard analyses, and governance mechanism studies. Full article

Climate change presents a challenge for global viticulture due to rising temperatures and water stress, which accelerate grape ripening, increase sugar levels, and reduce acidity. This compromises wine quality and microbial stability, resulting in higher reliance on sulfur dioxide (SO₂). However, SO₂ can inhibit desirable fermentations, including those carried out by non-Saccharomyces yeasts, which are key biotechnological tools for climate adaptation due to their ability to modulate acidity, aroma, and ethanol. Therefore, alternative disinfection methods are needed to control wild microbiota without hindering inoculated yeasts. This review critically analyzes ozone (O₃) as a non-thermal disinfection technology for winemaking. It examines the antimicrobial mechanism of ozone, its efficacy against wine-related microorganisms, its impact on the physicochemical and aromatic parameters of grapes, and its practical viability. Ozone effectively reduces spoilage-causing microbiota, achieving inactivation of approximately 3–4 log CFU/mL for yeasts, while preserving crucial grape compounds and providing a favorable environment for novel fermentation biotechnologies. Compared to other emerging technologies and SO₂, ozone offers a balanced profile: effective disinfection, minimal residues, cost-effectiveness, and compatibility with sustainable winemaking. Ozone is emerging as a promising alternative to facilitate controlled fermentations and improve wine quality among the current climatic and oenological challenges. Full article