Search Results (6,424)

Amid intensifying global climate change, coastal cities are facing increased heat stress. The sea breeze plays a crucial role in mitigating the urban heat island effect and improving outdoor thermal comfort, warranting detailed investigation of its spatiotemporal impacts. This research, conducted in Sendai, Japan, combines the Weather Research and Forecasting (WRF) model with the Rayman thermal comfort model to assess the spatiotemporal evolution of the Physiological Equivalent Temperature (PET) on typical sea breeze days, exploring heat stress patterns. The findings indicate significant PET reductions in the area due to sea breeze influence, although high heat stress persists in urban centers. The coastal zone (0–4 km) experiences the longest period of low heat stress, whereas the inland zone (20–26 km) suffers from poor thermal comfort. Heat stress intensifies in the northwestern inland regions, while improvement progresses from the coast inland. Vegetated areas reach low heat stress states earlier than built-up areas; both coastal and urban zones quickly revert to “no heat stress” conditions. The results demonstrate that the cooling effect of sea breezes decreases with distance, its efficacy hindered by urban environments, whereas vegetated lands prolong comfort inland. These insights are crucial for planning thermal environments in coastal cities. Full article

Drought stress is a major constraint on plant photosynthesis, growth, and yield, particularly in the context of increasingly frequent and severe extreme weather events driven by global climate change. Enhancing photosynthetic efficiency and abiotic stress tolerance is therefore essential for sustaining crop productivity. In this study, we functionally characterized Kalanchoë fedtschenkoi NAC83 (KfNAC83), a transcription factor derived from a heat-tolerant obligate crassulacean acid metabolism (CAM) species, by constitutively overexpressing it in the C₃ model plant Arabidopsis thaliana. Transgenic Arabidopsis lines overexpressing KfNAC83 exhibited significantly enhanced tolerance to water-deficit and NaCl stress, along with improved photosynthetic performance, biomass accumulation, and overall productivity. Transcriptomic analysis revealed that KfNAC83 overexpression increased key components of the jasmonate (JA) signaling pathway in both roots and shoots, suggesting a mechanistic link between KfNAC83 activity and enhanced abiotic stress responses. Additionally, the transgenic lines displayed increased nighttime decarboxylation activity, indicative of partial CAM-like metabolic traits. These findings demonstrate that KfNAC83 functions as a positive regulator of abiotic stress tolerance and growth, likely through modulation of jasmonate-mediated signaling and photosynthetic metabolism. This work highlights the potential of CAM-derived transcription factors for bioengineering abiotic stress-resilient crops in the face of climate change. Full article

Climate change threatens coastal biodiversity, necessitating proactive conservation for endangered species like the mangrove Acanthus ebracteatus. This study integrated the MaxEnt and OPGD models to simulate its potential suitable habitats under current and three future SSP scenarios (SSP126, SSP245, and SSP585). Based on the MaxEnt model, sea surface salinity (SSS_range), sea surface temperature (SST_max), soil texture (T_silt, T_sand), and annual precipitation (Bio12) were identified as the dominant factors influencing its distribution, with SSS_range emerging as the key constraint. Furthermore, interaction analysis using the OPGD model revealed significant synergistic effects, particularly between salinity and soil properties (q > 0.8), underscoring the importance of multi-factor interactions in ecological niche modeling. Under the three SSP scenarios, the suitable habitat is projected to expand northeastward, accompanied by a poleward shift in the distribution centroid, driven predominantly by warming temperatures and altered rainfall patterns. KDE analysis revealed that existing protected areas do not fully cover regions with high habitat suitability. We propose a stratified conservation strategy that enhances in situ protection in core zones, initiates assisted restoration in potential habitats, and promotes experimental outplanting in future climatically suitable areas. This study provides scientific insights for the conservation and management of Acanthus ebracteatus under global climate change. Full article

Physical and chemical processes related to global and regional climate changes are important factors in shaping the Earth’s surface. These processes form various erosion and deposition landforms on the Earth’s surface. These landforms reflect the traces of past and present climate conditions. This study shows that geomorphometric parameters can effectively distinguish between geomorphometrically and climatically distinct geomorphic provinces. In this context, supervised machine learning models were developed using geomorphometric parameters and the Köppen-Geiger climate classes observed in Türkiye. These models, Random Forest, Support Vector Machines, and K-Nearest Neighbor algorithms, were developed using a training data set. Classification analysis was performed using these models and a test dataset that was independent of the training dataset. According to the classification results, the overall accuracy values for the Random Forest, Support Vector Machines, and K-Nearest Neighbor models were calculated as 99.27%, 99.70%, and 99.30%, respectively. The corresponding kappa values were 0.99, 0.99, and 0.99, respectively. This study shows that among the geomorphometric parameters used in the analyses, maximum altitude, elevation, and valley depth were determined as important parameters in distinguishing geomorphic provinces. Full article

This study analyses the potential of artificial neural networks (ANN) in accurately predicting potato yields based on 11 parameters characterising the soil environment. Accurate yield forecasting is crucial for optimising potato production, especially in the context of potato processing. Due to the significant impact of soil properties on yield, there is a need for comprehensive predictive models that take these factors into account. The field studies (2021–2024) included an analysis of soil parameters determining potato tuber yield. The developed ANN model was highly accurate, as evidenced by the following indicators: R² = 0.8227, RMSE = 4.19 t∙ha⁻¹, MAE = 3.35 t∙ha⁻¹, MAPE = 7.34%. Global sensitivity analysis showed that cation exchange capacity (CEC), base saturation percentage (V), and sum of exchangeable bases (S) are key parameters influencing tuber yield. The results indicate that neural networks are effective in modelling complex relationships between soil parameters and potato yield, and that soil properties play a fundamental role in increasing yields and improving potato quality. The approach used may contribute to optimizing the nutrient content of potato tubers intended for French fry production. Future studies should incorporate climate data and micronutrients to enhance the accuracy of predictive models, potentially leading to a 10–15% improvement in yield predictions. Full article

Reducing the negative effects associated with floods in cities constitutes one of the highest-priority contemporary social challenges on the global sustainability agenda. In general, most historical studies focus on the consequences, but not on the causes of the phenomenon, which is essential for moving towards sustainable and resilient territories. The aim of this research is to quantify the effect that urban expansion has exerted on floods, taking the city of Belo Horizonte as a critical and representative case study. To this end, an integrative, qualitative, and quantitative approach has been developed, based on previous studies and on distributed hydrological modelling for the period 1940–2024. The results show that urban growth has contributed to a 7%, 14%, and 21% increase in the first three quartiles of annual floods. Likewise, the increase in the magnitude and frequency of the floods is also attributable, since it is more noticeable in the events of higher frequency than in those of lower frequency, in a range from 15% to 7%. The above results show the way in which the application of quantitative knowledge derived from the environmental history is highly useful for decision-making regarding the measures required to increase resilience, considering the possible effects of climate change. Thus, the recovery of the infiltration capacity of the soil constitutes a priority measure to reverse the effect that urban growth has exerted on the hydrological cycle. Full article

With the aggravation of global climate change and the increasing frequency and intensity of extreme weather events, power systems with a high proportion of renewable energy are under threat. In response, in traditional wind–solar–storage–hydrogen multi-energy systems, it is difficult to balance power supply resilience, economy, and environmental protection, and such systems cannot meet actual demand due to the lack of a dynamic meteorological integration mechanism. Therefore, a dynamic collaborative optimization model of a multi-energy system driven by meteorological risk perception is proposed. The dynamic meteorological risk factor integrating various meteorological elements is introduced, and the risk response mechanism is established based on the system’s energy storage state to realize the adaptive adjustment of coupled weight parameters and achieve the goal of collaborative optimization of power supply resilience, economy, and environmental protection. The case analysis results show that, compared with other models, the proposed model can reduce the power supply shortage by 23.1% in extreme weather periods, and the system’s survival probability can reach 97.1% at most. The proposed model minimizes the assembly while ensuring that carbon emissions meet standards, and achieves the collaborative optimization of power supply toughness, economy, and environmental protection. It provides a theoretical tool for solving the collaborative optimization problem that energy systems with a high proportion of renewables face in coping with climate risks. Full article

Marine plastic pollution has become a critical transboundary environmental issue, particularly affecting coastal regions with insufficient waste management infrastructure. This study applies a modified Lagrangian hydrodynamic model, TrackMPD v.1, to simulate the movement and accumulation of macroplastics in the West Africa Coastal Area. The research investigates three case studies: (1) the Liberia–Gulf of Guinea region, (2) the Mauritania–Gulf of Guinea coastal stretch, (3) the Cape Verde, Mauritania, and Senegal regions. Using both forward and backward simulations, macroplastics’ trajectories were tracked to identify key sources and accumulation hotspots. The findings highlight the cross-border nature of marine litter, with plastic debris transported far from its source due to ocean currents. The Gulf of Guinea emerges as a major accumulation zone, heavily impacted by plastic pollution originating from West African rivers. Interesting connections were found between velocities and directions of the plastic debris and some of the characteristics of the West African Monson climatic system (WAM) that dominates the area. Backward modelling reveals that macroplastics beached in Cape Verde largely originate from the Arguin Basin (Mauritania), an area influenced by fishing activities and offshore oil and gas operations. Results are visualized through point tracking, density, and beaching maps, providing insights into plastic distribution and accumulation patterns. The study underscores the need for regional cooperation and integrated monitoring approaches, including remote sensing and in situ surveys, to enhance mitigation strategies. Future work will explore 3D simulations, incorporating degradation processes, biofouling, and sinking dynamics to improve the representation of plastic behaviour in marine environments. This research is conducted within the Global Development Assistance (GDA) Agile Information Development (AID) Marine Environment and Blue Economy initiative, funded by the European Space Agency (ESA) in collaboration with the Asian. Development Bank and the World Bank. The outcomes provide actionable insights for policymakers, researchers, and environmental managers aiming to combat marine plastic pollution and safeguard marine biodiversity. Full article

West Nile Virus (WNV) poses a recurring public health threat in Greece, with outbreaks influenced by meteorological conditions. This study examines associations between key weather variables (e.g., temperature and total precipitation) and WNV incidence from 2010 to 2024 using ERA5 environmental data and available epidemiological data. Distributed lag nonlinear models were applied to identify time-lagged impacts of climate on WNV transmission. The results aim to reveal critical meteorological thresholds that could improve early warning systems and vector control. Understanding these climate–infection links can support predictive modeling and enhance public health preparedness amid growing climate variability and global vector-borne disease risks. Full article

As one of the main sources of global greenhouse gas emissions, the low-carbon transformation and emission reduction in the construction industry are inevitable requirements for addressing climate change. Under cap-and-trade regulations, Carbon emission rights have become a key production factor. However, the price of carbon emission rights is highly random. Taking the EU carbon market in 2024 as an example, the carbon price fluctuated by more than 35%, soaring from 65 euros per ton to 80 euros per ton and then falling back. Such sharp fluctuations not only increase the cost uncertainty of enterprises but also complicate the investment decisions for emission reduction. Therefore, enterprises can enhance the flexibility of carbon emission rights trading decisions through option strategies, helping them hedge against the risks of carbon price fluctuations, and at the same time improve market liquidity and risk management capabilities. Against this background, based on the carbon cap-and-trade policy, this paper introduces the two-way option strategy into the construction supply chain game model composed of general contractors and subcontractors, and studies to obtain the optimal carbon reduction volume, carbon option purchase volume, maximum expected profit of general contractors, subcontractors and profit distribution ratio. This study shows that two-way options play a crucial role in optimizing supply decision-making and emission reduction strategies. Under the decentralized model, emission reduction responsibilities are often shifted to subcontractors by the general contractor, resulting in a decline in overall mitigation effectiveness. Furthermore, appropriately lowering the carbon emission benchmark can strengthen enterprises’ incentives for emission reduction and significantly enhance the profitability of the supply chain. The study further suggests that general contractors should enhance their competitiveness by developing environmentally friendly technologies and improving their ability to reduce emissions on their own. Meanwhile, subcontractors need to actively participate in the collaborative efforts through revenue-sharing contracts. This study reveals the strategic value of two-way carbon options in construction supply chain carbon trading and provides theoretical support for the formulation of carbon market policies, contributing to the low-carbon transition of the construction supply chain. Full article

Long-term renovation strategies (LTRSs) play a central role in achieving the European Union’s objective of a climate-neutral building stock by 2050. In Poland, the challenge is particularly acute: a majority of the building stock was constructed before 1990 and does not even meet basic thermal performance standards. In view of the state of the buildings in Poland and the assumptions made about obtaining the necessary energy parameters in the coming years, it is necessary to undertake thermal modernization measures. The purpose of the paper is to assess the economic efficiency of the variants of modernization of building stock in Poland, taking into account the constraints related to improving energy efficiency. Additionally, the article also points out the problem of discrepancies resulting from climate zones that may significantly affect the final primary energy results (on average, 5–15%). In order to achieve the objectives, the paper focuses on the analysis of energy sources. According to the overall score in the analytic hierarchy process (AHP) method, the best solutions, with a global priority of 0.46, are renewable energy sources (RESs). The evaluation of selected fuel types in the 2055 perspective, using the technique for order preference by similarity to ideal solution (TOPSIS) method, indicate favorable environmental performance by sources based on electricity, i.e., air-source heat pumps, ground-source heat pumps, and electric heating, which achieved the highest relative closeness to the ideal solution. Heat pump systems can reduce energy consumption by 26–41% depending on the building and heat pump type. The final analysis in the paper concerns different options for thermal modernization of a model single-family house, taking into account different energy sources and stages of thermal modernization work. The scenario involves the simultaneous implementation of all renovation measures at an early stage, resulting in the lowest investment burden over time and the most favorable economic performance. Full article

With the dual pressures of accelerating urbanization and global climate warming, understanding the evolution and connectivity of cold island networks has become crucial for managing urban thermal risks. This study explores the spatiotemporal dynamics, driving mechanisms, and scenario-based projections of cold island networks in a rapidly urbanizing region of Southwest China. Using multi-temporal Landsat imagery (2000–2024), ecological resistance surface modeling, and least-cost path analysis, the study evaluated historical changes and simulated future scenarios for 2035 and 2050 under both Natural Development (ND) and Park City (PC) planning interventions. The findings reveal that: (1) Between 2000 and 2024, rapid urbanization significantly expanded high-temperature areas, fragmented cooling sources, and reshaped cold island connectivity into a hierarchical corridor network centered on a dominant ventilation axis; (2) Since 2019, ecological restoration measures have notably enhanced the structural cohesion and connectivity of cooling corridors, partially mitigating previous fragmentation; (3) Scenario simulations indicate that proactive ecological planning could reduce the extent of high-temperature zones by approximately 20% by 2050, demonstrating strong potential for mitigating future thermal risks. Overall, the results emphasize the necessity of incorporating continuous cold island corridors and connectivity principles into urban spatial planning to enhance climate resilience and support sustainable development. Full article

Extreme climate events—such as heatwaves, floods, and droughts—are increasingly affecting ecosystems, with the global average temperature projected to rise by up to 3 °C (IPCC, 2023) due to anthropogenic greenhouse gas emissions. These changes pose critical challenges to food security, as evidenced by 733 million people facing hunger in 2024. In response, crop modeling considering different climate change scenarios has become a valuable tool to guide the development of climate-resilient agricultural strategies. Despite its nutritional importance and capacity to thrive across diverse environments, Ipomoea batatas (sweet potato) remains understudied in terms of potential spatial distribution forecasting, particularly in regions of high agrobiodiversity such as northwestern South America. Therefore, in this study we modeled the projected distribution of wild and landrace sweet potato genepools in the northern Andes under four future timeframes using seven machine learning algorithms. Our results predicted a 50% reduction in the climatically suitable range for the wild genepool by 2081, coupled with an average altitudinal shift from 1537 to 2216 m above sea level (a.s.l.). For landraces, a 36% reduction was projected by 2080, with a shift from 62 to 1995 m a.s.l. By the end of the century, suitable zones for both wild and cultivated genepools are expected to converge in high-altitude regions such as the Colombian Massif, with additional remnants of wild populations near the mountain range of Farallones de Cali. This modeling approach provides essential insights into the spatial dynamics of I. batatas under climate change, highlighting the need for ex situ conservation planning in vulnerable regions as well as assisted migration to more suitable areas. Future research should integrate edaphic and biotic interaction data to better approach the realized niche of the species and understand potential responses under a niche conservatism assumption, as well as genomic data to account for the species’ intrinsic adaptative potential, overall informing conservation, germplasm mobilization, and pre-breeding strategies that may ultimately secure the role of sweet potato in resilient food systems. Full article

The traditional construction industry significantly contributes to global resource consumption and climate change. Conventional methods limit the development of complex and multifunctional architectural forms. In contrast, 3D concrete printing (3DCP), an additive manufacturing technique, enables the creation of intricate building envelopes that integrate architectural and energy-efficient functions. Bioinspired design, recognized for its sustainability, has gained traction in this context. This study investigates the thermal and energy performance of various bioinspired and regular 3DCP infill patterns compared to conventional concrete building envelopes in tropical climates. A three-stage methodology was employed. First, bioinspired patterns were identified and evaluated through a literature review. Next, prototype models were developed using Rhino and simulated in ANSYS to assess thermal performance. Finally, energy performance was analyzed using Ladybug and Honeybee tools. The results revealed that honeycomb, spiral, spiderweb, and weaving patterns achieved 35–40% higher thermal and energy efficiency than solid concrete, and about 10% more than the 3DCP sawtooth pattern. The findings highlight the potential of bioinspired spiral infill patterns to enhance the sustainability of 3DCP building envelopes. This opens new avenues for integrating biomimicry into 3DCP construction as a tool for performance optimization and environmental impact reduction. Full article

With the continuous acceleration of industrialization, air pollution has become increasingly severe and has, to some extent, contributed to the progression of global climate change. Against this backdrop, accurate temperature forecasting plays a vital role in various fields, including agricultural production, energy scheduling, environmental governance, and public health protection. To improve the accuracy and stability of temperature prediction, this study proposes a hybrid modeling approach that integrates convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, and random forests (RFs). This model fully leverages the strengths of CNNs in extracting local spatial features, the advantages of LSTM in modeling long-term dependencies in time series, and the capabilities of RF in nonlinear modeling and feature selection through ensemble learning. Based on daily temperature, meteorological, and air pollutant observation data from Wuhan during the period 2015–2023, this study conducted multi-scale modeling and seasonal performance evaluations. Pearson correlation analysis and random forest-based feature importance ranking were used to identify two key pollutants (PM_2.5 and O₃) and two critical meteorological variables (air pressure and visibility) that are strongly associated with temperature variation. A CNN-LSTM model was then constructed using the meteorological variables as input to generate preliminary predictions. These predictions were subsequently combined with the concentrations of the selected pollutants to form a new feature set, which was input into the RF model for secondary regression, thereby enhancing the overall model performance. The main findings are as follows: (1) The six major pollutants exhibit clear seasonal distribution patterns, with generally higher concentrations in winter and lower in summer, while O₃ shows the opposite trend. Moreover, the influence of pollutants on temperature demonstrates significant seasonal heterogeneity. (2) The CNN-LSTM-RF hybrid model shows excellent performance in temperature prediction tasks. The predicted values align closely with observed data in the test set, with a low prediction error (RMSE = 0.88, MAE = 0.66) and a high coefficient of determination (R² = 0.99), confirming the model’s accuracy and robustness. (3) In multi-scale forecasting, the model performs well on both daily (short-term) and monthly (mid- to long-term) scales. While daily-scale predictions exhibit higher precision, monthly-scale forecasts effectively capture long-term trends. A paired-sample t-test on annual mean temperature predictions across the two time scales revealed a statistically significant difference at the 95% confidence level (t = −3.5299, p = 0.0242), indicating that time granularity has a notable impact on prediction outcomes and should be carefully selected and optimized based on practical application needs. (4) One-way ANOVA and the non-parametric Kruskal–Wallis test were employed to assess the statistical significance of seasonal differences in daily absolute prediction errors. Results showed significant variation across seasons (ANOVA: F = 2.94, p = 0.032; Kruskal–Wallis: H = 8.82, p = 0.031; both p < 0.05), suggesting that seasonal changes considerably affect the model’s predictive performance. Specifically, the model exhibited the highest RMSE and MAE in spring, indicating poorer fit, whereas performance was best in autumn, with the highest R² value, suggesting a stronger fitting capability. Full article