Search Results (17,028)

Water supply networks in both developed and developing major cities worldwide were constructed many years ago. Currently, these systems face numerous challenges, including population growth, climate change, emerging technologies, and the policies implemented by local governments. Such factors can impact the design life of water infrastructure, leading to service pressure deficiencies. Consequently, water infrastructure must be reinforced to ensure an adequate and reliable service. This research presents the development of an analytical formulation for hydraulic installations with a pumping station, enabling the calculation of requirements for a new parallel pipeline within an existing water system without altering the current pipe resistance class. To implement the proposed solution, it is essential to maintain the initial pump head by adjusting the impeller size. A construction cost assessment is also undertaken to identify the most cost-effective reinforcement strategy, acknowledging that pipe costs vary significantly with diameter and material, and are proportional to the square of the diameter. The proposed methodology is applied to a 30 km pipeline with a 10% increase in demand, showing that a new parallel pipe of the same diameter as the existing hydraulic installation must be installed to minimise construction costs. A multi-parametric analysis was conducted employing machine learning presets with 309 dataset points. Full article

Lake Qinghai, the largest closed-basin lake on the Qinghai–Tibet Plateau, plays a crucial role in maintaining regional ecological stability through its hydrological functions. In recent decades, the lake has exhibited a continuous rise in water level and lake area expansion, sparking growing interest in the mechanisms driving these changes and their future evolution. This study integrates the Soil and Water Assessment Tool (SWAT), simulations under future Shared Socioeconomic Pathways (SSPs) and statistical analysis methods, to assess runoff dynamics and lake level responses in the Lake Qinghai Basin over the next 30 years. The model was developed using a combination of meteorological, hydrological, topographic, land use, soil, and socio-economic datasets, and was calibrated with the sequential uncertainty fitting Ver-2 (SUFI-2) algorithm within the SWAT calibration and uncertainty procedure (SWAT–CUP) platform. Sensitivity and uncertainty analyses confirmed robust model performance, with monthly R² values of 0.78 and 0.79. Correlation analysis revealed that runoff variability is more closely associated with precipitation than temperature in the basin. Under SSP 1-2.6, SSP 3-7.0, and SSP 5-8.5 scenarios, projected annual precipitation increases by 14.4%, 18.9%, and 11.1%, respectively, accompanied by temperature rises varying with emissions scenario. Model simulations indicate a significant increase in runoff in the Buha River Basin, peaking around 2047. These findings provide scientific insight into the hydrological response of plateau lakes to future climate change and offer a valuable reference for regional water resource management and ecological conservation strategies. Full article

Accurate monitoring of vegetation phenology is vital for understanding climate change impacts on terrestrial ecosystems. While global vegetation greenness phenology (VGP) products are widely available, vegetation productivity phenology (VPP), which better reflects ecosystems’ carbon dynamics, remains largely inaccessible. This study introduces a novel global 500 m VPP dataset (GLASS VPP) from 2001 to 2020, derived from the GLASS gross primary productivity (GPP) product. Validation against three ground-based datasets—Fluxnet 2015, PhenoCam V2.0, and PEP725—demonstrated the dataset’s superior accuracy. Compared to the widely used MCD12Q2 VGP product, GLASS VPP reduced RMSE and bias by 35% and 63%, respectively, when validated against Fluxnet data. It also showed stronger correlations than MCD12Q2 when compared with PhenoCam (195 sites) and PEP725 (99 sites) observations, and it captured spatial and altitudinal phenology patterns more effectively. Overall, GLASS VPP exhibits a higher spatial integrity, stronger ecological interpretability, and improved consistency with ground observations, making it a valuable dataset for phenology modeling, carbon cycle research, and ecological forecasting under climate change. Full article

This paper offers content priorities, justifications, and pedagogical approaches for the integration of climate change education into the training of teachers, and thus into public schooling. To meet urgent imperatives presented by the polycrisis of the Anthropocene, climate change education must be inclusive, comprehensive, flexible, and regionally responsive. Climate change education can be achieved by adapting regional programs for teacher education to meet those requirements. An example is the Climate Education in Teacher Education (CETE) project in northern British Columbia, Canada. Using the Education Design-Based Research methodology, the project addresses critical questions for curricular and pedagogical development of teachers to address the following three questions: (a) what content and outcomes to prioritize, (b) why these elements matter, and (c) how to implement them effectively. Over two years, CETE engaged pre-service and in-service teachers through workshops, reflective practices, and consultations with Indigenous communities. Our tentative answers emphasize the importance of adapting curriculum and pedagogy to foster community resilience, address climate anxiety, and promote an ethical renewal toward sustainability. The iterative development of objectives as “High-Level Conjectures” provides flexibility and reflexivity in the design process in the face of rapid contextual change. CETE developed practical pedagogical tools and workshop strategies that align educational priorities with local and global needs. This study offers a replicable framework to empower educators and communities in diverse locations to navigate the complexities of the climate crisis in their quest for a more secure and sustainable future. Full article

The Inner Mongolian Plateau is a critical region for the development of herbivorous animal husbandry in China. However, its harsh climate and poor soil quality have constrained the sustainable growth of the alfalfa industry. This 3-year field study investigated the effects of potassium (K) fertilizer on the productivity and forage quality of alfalfa (Medicago sativa L. cv. ‘WL168’) in such specific conditions of the region. Five rates of K fertilizer (0 (CK), 100, 200, 300, and 400 kg ha⁻¹ of K₂O) were applied in three split applications. Forage harvests occurred three times annually in 2023 and 2024, and yield, yield components, and forage quality were determined. The results showed that the forage yield of alfalfa increased initially and then decreased with the rising K application rates, which paralleled the changes in the plant density, and plant height, especially the mass shoot⁻¹; forage yield was mainly correlated with mass shoot⁻¹. Appropriate K fertilizer improved forage quality, especially in 2024. With increasing application, crude protein (CP) and total digestible nutrients (TDNs) first rose then declined, whereas neutral detergent fiber (NDF) and acid detergent fiber (ADF) decreased steadily, leading to a consistent rise in the relative feeding value (RFV). Comprehensively considering both yield and quality under such condition, a K fertilizer application rate of 273.2 kg ha⁻¹ of K₂O is suggested as a reference for this region. Full article

Adverse climatic dynamics in recent years have intensified the need for resilient and multifunctional agricultural systems that integrate productivity, ecological sustainability, and socio-economic viability. This study evaluates the harvesting performance of three cropping systems: intercropping of cardoon and safflower (IT) and monocultures of cardoon (DC) and safflower (DS). Field trials were conducted during three following growing seasons to assess key harvesting parameters, including working speed, effective field capacity, harvesting costs, biomass yield, seed yield, seed losses, and seed moisture content. DC demonstrated the better performance, with a working speed of 6.35 ha h⁻¹ and a field capacity of 2.56 ha h⁻¹, also resulting in the lowest harvesting cost (EUR 70.24 ha⁻¹). In contrast, IT exhibited the lowest performance and the highest cost (EUR 98.61 ha⁻¹). DS achieved the highest effective seed yield (1.394 Mg ha⁻¹), while IT produced the greatest biomass (22.96 Mg ha⁻¹). Seed losses were lowest in DS (0.020 Mg ha⁻¹) and highest in IT (0.425 Mg ha⁻¹). Moisture content ranged from 5.82% in DC to 9.40% in DS. These findings highlighted the trade-offs between productivity, efficiency, and system complexity, offering valuable insights into the comparative performance and sustainability of innovative cropping systems under changing climatic conditions. Full article

With the increasing frequency and intensity of forest fires driven by climate change and human activities, efficient detection and rapid response have become critical for forest fire prevention. Effective fire detection, swift response, and timely rescue are vital for forest firefighting efforts. This paper proposes an unmanned aerial vehicle (UAV)-based forest fire inspection system that integrates a ground support system (GSS), aiming to enhance automation and flexibility in inspection tasks. A three-layer mixed-integer linear programming model is developed: the first layer focuses on the site selection and capacity planning of the GSS; the second layer defines the coverage scope of different GSS units; and the third layer plans the inspection routes of UAVs and coordinates multi-UAV collaborative tasks. For planning UAV patrol routes and collaborative tasks, a goal-driven greedy algorithm (GDGA) based on traditional greedy methods is proposed. Simulation experiments based on a real forest fire case in Turkey demonstrate that the proposed model reduces the total annual costs by 28.1% and 16.1% compared to task-only and renewable-only models, respectively, with a renewable energy penetration rate of 68.71%. The goal-driven greedy algorithm also shortens UAV patrol distances by 7.0% to 12.5% across different rotation angles. These results validate the effectiveness of the integrated model in improving inspection efficiency and economic benefits, thereby providing critical support for forest fire prevention. Full article

Microgrids are becoming an innovative piece of modern energy systems as they provide locally sourced and resilient energy opportunities and enable efficient energy sourcing. However, microgrid operations can be greatly affected by sudden environmental changes, deviating demand, and unexpected outages. In particular, extreme climatic events expose the vulnerability of microgrid infrastructure and resilience, often leading to increased risk of system-wide outages. Thus, successful microgrid operation relies on timely and accurate outage predictions. This research proposes a data-driven machine learning framework for the optimized operation of a microgrid and predictive outage detection using a Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM) architecture that reflects inherent temporal modeling methods. A time-aware embedding and masking strategy is employed to handle categorical and sparse temporal features, while mutual information-based feature selection ensures only the most relevant and interpretable inputs are retained for prediction. Moreover, the model addresses the challenges of experiencing rapid power fluctuations by looking at long-term learning dependency aspects within historical and real-time data observation streams. Two datasets are utilized: a locally developed real-time dataset collected from a 5 MW microgrid of Maple Cement Factory in Mianwali and a 15-year national power outage dataset obtained from Kaggle. Both datasets went through intensive preprocessing, normalization, and tokenization to transform raw readings into machine-readable sequences. The suggested approach attained an accuracy of 86.52% on the real-time dataset and 84.19% on the Kaggle dataset, outperforming conventional models in detecting sequential outage patterns. It also achieved a precision of 86%, a recall of 86.20%, and an F1-score of 86.12%, surpassing the performance of other models such as CNN, XGBoost, SVM, and various static classifiers. In contrast to these traditional approaches, the RNN-LSTM’s ability to leverage temporal context makes it a more effective and intelligent choice for real-time outage prediction and microgrid optimization. Full article

The COVID-19 pandemic caused the world to pause temporarily on an almost planetary scale. The creation and diffusion of knowledge about environmental planning and public health are now almost taken for granted. However, such processes were rather different in pre-pandemic times. It took a substantial dose of labor and resources to generate the information needed to produce useful and usable knowledge, and especially to make it available to others in a timely and effective way. As automobility has come to occupy center stage in the lives of an increasing number of suburbanized dwellers, it has taken multiple energy and public health crises, bold leadership, and the real threat of climate change to create the conditions needed to bolster sustainable Non-Motorized Transportation (NMT) as a complement to cleaner and more convenient mass transit options in cities. How does knowledge about sustainable NMT get created? How are sustainable NMT innovations diffused? How can technological and societal transitions to more sustainable realities be nurtured and augmented? This article utilizes a longitudinal and integrated knowledge creation and diffusion model with a Participatory Planning Process to analyze the adoption of measures aimed at reducing the negative consequences of too much automobility and encouraging higher levels of walking, cycling, and mass transportation. The research methods comprised autoethnographic, qualitative, and policy evaluation techniques. The study makes use of the means and ends matrix to discuss cases from five distinct realms: personal, academic, institutional, volunteering NGO, and private sector. The key findings and lessons learned promote scenarios of managed degrowth and sustainable urban transitions. Full article

Cave systems are a kind of natural laboratory for interdisciplinary research on karstogenesis in the context of global changes. In this study, we investigate the concentration of ²²²Rn at 65 points in 37 representative caves of Bulgarian karst through continuous monitoring with passive and active detectors with a duration of 1 to 13 years. The concentration changes strongly both in the long term and seasonally, with values from 0.1 to 13 kBq m⁻³. These variations are analyzed from different perspectives (location and morphological features of the cave system, cave climate, ventilation regime, etc.). The seasonal change in the direction and intensity of ventilation is a leading factor determining the gas composition of the cave atmosphere during the year. Parallel measurements of ²²²Rn and CO₂ concentrations in the cave air show that both gases have a similar seasonal fluctuation. Cases of coincidences of an anomalous increase in the concentration of ²²²Rn with manifestations of seismic activity and micro-displacements along tectonic cracks in the caves have also been registered. The dependencies between the ²²²Rn concentration in the caves and in the soil above them are also discussed, as well as the possible connections between global trends in climate change and trends in ²²²Rn emissions. Special attention is paid to the risks of radiation exposure in show caves. A calculation procedure has been developed to achieve the realistic assessment of the effective dose of cave guides. It is based on information about the annual course of the ²²²Rn concentration in the respective cave and the time schedule of the guides’ stay in it. The calculation showed that the effective dose may exceed the permitted limits, and it is thus necessary to control it. Full article

The temperature-based indicators heating and cooling degree days, are frequently utilized to quantitatively link indoor energy demand and outdoor thermal conditions, especially in the context of climate change. We present a comprehensive study of the heating and cooling degree-days and the degree-days categories for the near past (1976–2005), and the AR5 RCP4.5 and RCP8.5 scenario-driven future (2066–2095) over Southeast Europe based on an elaborated methodology and performed using a 19 combinations of driving global and regional climate models from EURO-CORDEX with horizontal resolution of 0.11°. Alongside the explicit focus of the degree-days categories and the finer grid resolution, the study benefits substantially from the consideration of the monthly, rather than annual, time scale, which allows the assessment of the intra-annual variations of all analyzed parameters. We provide evidences that the EURO-CORDEX ensemble is capable of simulating the spatiotemporal patterns of the degree-days and degree-day categories for the near past period. Generally, we demonstrate also a steady growth in cooling and a decrease in heating degree-days, where the change of the former is larger in relative terms. Additionally, we show an overall shift toward warmer degree-day categories as well as prolongation of the cooling season and shortening of the heating season. As a whole, the magnitude of the projected long-term changes is significantly stronger for the ’pessimistic’ scenario RCP8.5 than the ’realistic’ scenario RCP4.5. These outcomes are consistent with the well-documented general temperature trend in the gradually warming climate of Southeast Europe. The patterns of the projected long-term changes, however, exhibit essential heterogeneity, both in time and space, as well as among the analyzed parameters. This finding is manifested, in particular, in the coexistence of opposite tendencies for some degree-day categories over neighboring parts of the domain and non-negligible month-to-month variations. Most importantly, the present study unequivocally affirms the significance of the anticipated long-term changes of the considered parameters over Southeast Europe in the RCP scenario-driven future with all subsequent and far-reaching effects on the heating, cooling, and ventilation industry. Full article

The purpose of this study was to examine the role of traditional fire management practices in the general mitigation of wildfire risk in Greece. Major emphasis was placed on assessing people’s opinions about the perceived effectiveness of traditional fire management strategies that were historically and culturally employed by local communities—such as weather condition monitoring, prescribed burning, proper land use planning, and mosaic burning—in the general mitigation of wildfire risks. An online questionnaire was used to collect data from 397 environmental experts in Greece. The study shows that traditional fire control methods reduce wildfire risk. First, weather monitoring was found to be crucial to wildfire forecasting and prevention. The results showed that early warning, successful firefighting, and fire prevention depend on meteorological data. Additionally, prescribed burning was revealed to have reduced wildfire risk. Respondents accepted that they could reduce unprescribed fires, protect natural ecosystems, remove wildfire-prone areas, and regulate flame intensity. This suggests that scheduled burning in Greece may reduce wildfire damage. The study underlines the importance of including conventional fire management in the wildfire mitigation strategy of Greece. The aforementioned activities may help the environment and civilization progress by safeguarding ecosystems and reducing wildfire damage. These techniques, combined with community engagement and improved early warning systems, may help manage climate change-induced wildfires. Overall, the study contributes to wildfire management in Greece and other Mediterranean countries. The study emphasizes the need to incorporate traditional fire practices into Greece’s wildfire risk reduction strategies. Taking into account the success rates of these practices in other areas, as well as Greece’s old tradition of conducting fire, this paper stresses that further studies and policy developments be made in order to reinstate these practices in today’s wildfire management. Full article

Climate change poses serious challenges to Mediterranean crops such as the olive tree (Olea europaea L. subsp. europaea), underscoring the need for cultivars with improved drought tolerance and disease resistance. This study investigates variability in leaf and wood traits among Moroccan and introduced olive cultivars and their crossbreed genotypes grown under similar conditions. Specifically, we assessed (1) variation in key functional traits, (2) the effects of crossbreeding combinations, and (3) trait syndromes shaped by selection. Results showed substantial intraspecific variation in leaf traits, including specific leaf area (SLA), specific leaf water content (SLWC), stomatal size (SS), and density (SD), indicating diverse strategies for resource use and plasticity. Crossbreed genotypes generally displayed higher SLWC and lower SLA, reflecting adaptation to water stress. Wood traits, particularly vessel size (SVS) and number (NVS), also varied, revealing trade-offs between hydraulic efficiency and safety. Notably, an increase in vessel size and hydraulic conductivity was correlated with oil content (OC%), while OC% increased with higher vessel and stomatal densities. Larger stomata increased conductance and fruit growth, while lower SLA was linked to higher yield. Multivariate analysis distinguished two genotype groups, consistent with parental combinations. Overall, crossbreeding generated novel functional diversity that may enhance adaptive potential. These findings highlight the value of integrating functional and anatomical traits into olive breeding programs to improve resilience and productivity under climate change. Full article

Land degradation in Iraq’s Mesopotamian plain threatens food security and rural livelihoods, yet the relative roles of climatic water deficits versus anthropogenic pressures remain poorly attributed in space. We test the hypothesis that multi-timescale climatic water deficits (SPEI-03/-06/-12) exert a stronger effect on vegetation degradation risk than anthropogenic pressures, conditional on hydrological connectivity and irrigation. Using Babil and Al-Qadisiyah (2000–2023) as a case, we implement a four-part pipeline: (i) Fractional Vegetation Cover with Mann–Kendall/Sen’s slope to quantify greening/browning trends; (ii) LandTrendr to extract disturbance timing and magnitude; (iii) annual LULC maps from a Random Forest classifier to resolve transitions; and (iv) an XGBoost classifier to map degradation risk and attribute climate vs. anthropogenic influence via drop-group permutation (ΔAUC), grouped SHAP shares, and leave-group-out ablation, all under spatial block cross-validation. Driver attribution shows mid-term and short-term drought (SPEI-06, SPEI-03) as the strongest predictors, and conditional permutation yields a larger average AUC loss for the climate block than for the anthropogenic block, while grouped SHAP shares are comparable between the two, and ablation suggests a neutral to weak anthropogenic edge. The XGBoost model attains AUC = 0.884 (test) and maps 9.7% of the area as high risk (>0.70), concentrated away from perennial water bodies. Over 2000–2023, LULC change indicates CA +515 km², HO +129 km², UL +70 km², BL −697 km², WB −16.7 km². Trend analysis shows recovery across 51.5% of the landscape (+29.6% dec⁻¹ median) and severe decline over 2.5% (−22.0% dec⁻¹). The integrated design couples trend mapping with driver attribution, clarifying how compounded climatic stress and intensive land use shape contemporary desertification risk and providing spatial priorities for restoration and adaptive water management. Full article

Surface pretreatment significantly influences the hygroscopic behavior of wood, which in turn affects surface stability when exposed to variable climatic conditions. This study focuses on how different surface pretreatment methods impact the performance of protective coating applied on spruce wood (Picea abies (L.) Karst.) during one year of natural weathering. Samples were prepared using various surface treatments: milling and sanding with grit sizes P40, P80, and P120, respectively. Two types of coatings were applied: a solventborne coating (ADLER Pullex Plus-Lasur) and a waterborne coating (DColor FK 47 UV Protect). The samples were exposed for 12 months at an outdoor testing site in Suchdol, Czech. Surface properties were assessed through color changes in the CIE Lab* space, gloss measurements (ISO 2813), contact angle analysis, and visual inspection. The results showed that exposure to UV radiation and microbial activities led to the gradual degradation of the optical properties and aesthetic appearance of the wood. Surfaces with greater roughness preserved their aesthetic properties more effectively, indicating a higher absorption of the coating. Untreated wood exhibited low water repellency, while the coated surface demonstrated enhanced hydrophobicity. Notably, the waterborne coating showed a temporary increase in contact angle around the sixth month, indicating surface clogging by dust particles. In contrast, the solventborne coating had a rapid decrease in wettability during the first nine months. These findings suggested the importance of surface pretreatment and coating type in maintaining the long-term performance and aesthetic appearance for wood used in exterior conditions. Full article