Search Results (632)

Due to global warming, extreme temperature events have become increasingly prevalent, posing significant threats to both socioeconomic development and human safety. While previous studies have extensively examined the influence of individual climatic circulation systems on extreme temperature, the combined effects of multiple concurrent circulation patterns remain poorly understood. Using daily temperature data from 29 meteorological stations in Chongqing (1960–2019), this study employs linear trend analysis, correlation analysis, and random forest (RF) models to analyze spatiotemporal variations in the intensity and frequency of extreme temperature. We selected 21 climate indicators from three categories—atmospheric circulation, sea surface temperature (SST), and sea-level pressure (SLP)—to identify the primary drivers of extreme temperatures and quantify their respective contributions. The key findings are as follows: (1) All extreme intensity indices exhibited an increasing trend, with the TXx (annual maximum daily maximum temperature) showing the higher trend (0.03 °C/year). The northeastern region experienced the most pronounced increases. (2) Frequency indices also displayed an upward trend. This was particularly evident for the TD35 (number of days with maximum temperature ≥35 °C), which increased at an average rate of 0.16 days/year, most notably in the northeast. (3) The Western Pacific Subtropical High Ridge Position Index (GX) and Asia Polar Vortex Area Index (APV) were the dominant climate factors driving intensity indices, with cumulative contributions of 26.0% to 33.4%, while the Western Pacific Warm Pool Strength Index (WPWPS), Asia Polar Vortex Area Index (APV), North Atlantic Subtropical High Intensity Index (NASH), and Indian Ocean Warm Pool Strength Index (IOWP) were the dominant climate factors influencing frequency indices, with cumulative contributions of 46.4 to 49.5%. The explanatory power of these indices varies spatially across stations, and the RF model effectively identifies key circulation factors at each station. In the future, more attention should be paid to urban planning adaptations, particularly green infrastructure and land use optimization, along with targeted heat mitigation strategies, such as early warning systems and public health interventions, to strengthen urban resilience against escalating extreme temperatures. Full article

Improving outdoor thermal comfort is a critical objective in urban design, particularly in densely built urban environments. Elevated semi-open spaces—outdoor areas located beneath raised building structures—have been recognized for enhancing pedestrian comfort by improving airflow and shading. However, previous studies primarily focused on warm or temperate climates, leaving a significant research gap regarding their thermal performance in cold climate zones characterized by extreme seasonal variations. Specifically, few studies have investigated how these spaces perform under conditions typical of northern Chinese cities like Xi’an, which is explicitly classified within the Cold Climate Zone according to China’s national standard GB 50176-2016 and experiences both severe summer heat and cold winter conditions. To address this gap, we conducted field measurements and numerical simulations using the ENVI-met model (v5.0) to systematically evaluate the microclimatic performance of elevated ground-floor spaces in Xi’an. Key microclimatic parameters—including air temperature, mean radiant temperature, relative humidity, and wind velocity—were assessed during representative summer and winter conditions. Our findings indicate that the height of the elevated structure significantly affects outdoor thermal comfort, identifying an optimal elevated height range of 3.6–4.3 m to effectively balance summer cooling and winter sheltering needs. These results provide valuable design guidance for architects and planners aiming to enhance outdoor thermal environments in cold climate regions facing distinct seasonal extremes. Full article

This study investigates the viability of a native Scenedesmus sp. strain for use in a 50 L bubble column photobioreactor designed to reduce greenhouse gas emissions under simulated spring, extreme summer, and winter conditions. The experiments were conducted by placing the reactor in a controlled climatic chamber, which allowed us to regulate the temperature, light intensity, and day–night cycles throughout the entire experiment. The results showed that under simulated spring conditions (a maximum temperature of 22 °C), the algal culture grew continuously for 61 days. Under extreme summer conditions (a maximum temperature of 39 °C), an initial drop in cell density was followed by recovery and continued growth over 75 days, although biomass production was 35% lower. Under winter conditions (a maximum temperature of 10 °C), the culture failed, indicating the need to prevent temperatures below 10 °C. In terms of biomass production, the culture densities achieved were 1.04 g L⁻¹ and 0.68 g L⁻¹ in the spring and summer trials, respectively. The Scenedesmus sp. strain demonstrated high carbon capture efficiency, tolerance to extreme heat, and sustained growth without the need for fresh medium or pH adjustments for over 60 days during spring and extreme summer conditions, confirming its potential for outdoor applications. Full article

The National Adaptation Plan of Bangladesh identifies eleven climate-stressed zones, placing nearly 100 million people at high risk of climate-related hazards. Vulnerable groups such as the poor, floating populations, daily laborers, and slum dwellers are particularly affected. However, there is a lack of data on climate-sensitive diseases and related hospital visits in these areas. This study explored the prevalence of such diseases using the Delphi method through focus group discussions with 493 healthcare professionals from 153 hospitals in 156 upazilas across 21 districts and ten zones. Participants were selected by district Civil Surgeons. Key climate-sensitive diseases identified included malnutrition, diarrhea, pneumonia, respiratory infections, typhoid, skin diseases, hypertension, cholera, mental health disorders, hepatitis, heat stroke, and dengue. Seasonal surges in hospital visits were noted, influenced by factors like extreme heat, air pollution, floods, water contamination, poor sanitation, salinity, and disease vectors. Some diseases were zone-specific, while others were widespread. Regions with fewer hospital visits often had higher disease burdens, indicating under-reporting or lack of access. The findings highlight the need for area-specific adaptation strategies and updates to the Health National Adaptation Plan. Strengthening resilience through targeted investment and preventive measures is crucial to reducing health risks from climate change. Full article

A (NiCoCr)_99.25C_0.75 medium entropy alloy (MEA) was developed via laser powder bed fusion (LPBF) using pre-alloyed powder feedstock containing 0.75 at%C, followed by a precipitation heat treatment. The as-built alloy exhibited high density (>99.9%), columnar grains, fine substructures, and strong <111> texture. Heat treatment at 700 °C for 1 h promoted the precipitation of Cr-rich carbides (Cr₂₃C₆) along grain and substructure boundaries, which stabilized the microstructure through Zener pinning and the consumption of carbon from the matrix. The heat-treated alloy achieved excellent cryogenic tensile properties at 77 K, with a yield strength of 1230 MPa and an ultimate tensile strength of 1.6 GPa. Compared to previously reported LPBF-built NiCoCr-based MEAs, this alloy exhibited superior strength at both room and cryogenic temperatures, indicating its potential for structural applications in extreme environments. Deformation mechanisms at cryogenic temperature revealed abundant deformation twinning, stacking faults, and strong dislocation–precipitate interactions. These features contributed to dislocation locking, resulting in a work hardening rate higher than that observed at room temperature. This study demonstrates that carbon addition and heat treatment can effectively tune the stacking fault energy and stabilize substructures, leading to enhanced cryogenic mechanical performance of LPBF-built NiCoCr MEAs. Full article

This study investigates the seasonal dynamics of energy balance, evapotranspiration (ET), and Net Ecosystem Production (NEP) in the Dengkou desert ecosystem of Inner Mongolia, China. Using eddy covariance and meteorological data from 2019 to 2022, the research focuses on understanding how these processes interact in one of the world’s most water-limited environments. This arid research area received an average of 109.35 mm per annum precipitation over the studied period, classifying the region as a typical arid ecosystem. Seasonal patterns were observed in daily air temperature, with extremes ranging from −20.6 °C to 29.6 °C. Temporal variations in sensible heat flux (H), latent heat flux (LE), and net radiation (Rn) peaked during summer season. The average ground heat flux (G) was mostly positive throughout the observation period, indicating heat transmission from atmosphere to soil, but showed negative values during the winter season. The energy balance ratio for the studied period was in the range of 0.61 to 0.80, indicating challenges in achieving energy closure and ecological shifts. ET exhibited two annual peaks influenced by vegetation growth and climate change, with annual ET exceeding annual precipitation, except in 2021. Net ecosystem production (NEP) from 2019 to 2020 revealed that the Dengkou desert were a net source of carbon, indicating the carbon loss from the ecosystem. In 2021, the Dengkou ecosystem shifted to become a net carbon sink, effectively sequestrating carbon. However, this was sharply reversed in 2022, resulting in a significant net release of carbon. The study findings highlight the complex interactions between energy balance components, ET, and NEP in desert ecosystems, providing insights into sustainable water management and carbon neutrality strategies in arid regions under climate change effect. Full article

The process of urbanization has intensified the urban heat environment, with the degradation of thermal conditions closely linked to the morphological characteristics of different functional zones. This study delineated urban functional areas using a multivariate dataset and investigated the seasonal and threshold effects of landscape and architectural features on land surface temperature (LST) through boosted regression tree (BRT) modeling and Spearman correlation analysis. The key findings are as follows: (1) LST exhibits significant seasonal variation, with the strongest urban heat island effect occurring in summer, particularly within industry, business, and public service zones; residence zones experience the greatest temperature fluctuations, with a seasonal difference of 24.71 °C between spring and summer and a peak temperature of 50.18 °C in summer. (2) Fractional vegetation cover (FVC) consistently demonstrates the most pronounced cooling effect across all zones and seasons. Landscape indicators generally dominate the regulation of LST, with their relative contribution exceeding 45% in green land zones. (3) Population density (PD) exerts a significant, seasonally dependent dual effect on LST, where strategic population distribution can effectively mitigate extreme heat events. (4) Mean building height (MBH) plays a vital role in temperature regulation, showing a marked cooling influence particularly in residence and business zones. Both the perimeter-to-area ratio (LSI) and frontal area index (FAI) exhibit distinct seasonal variations in their impacts on LST. (5) This study establishes specific indicator thresholds to optimize thermal comfort across five functional zones; for instance, FVC should exceed 13% in spring and 31.6% in summer in residence zones to enhance comfort, while maintaining MBH above 24 m further aids temperature regulation. These findings offer a scientific foundation for mitigating urban heat waves and advancing sustainable urban development. Full article

This study assessed the effectiveness of stress-based cognitive-behavioral training compared to standard training in firefighters, emphasizing their ability to distribute extinguishing water and cool environments evenly during enclosure fires. Experiments took place at the Zbiroh training facility with two firefighter teams (Team A with stress-based training and Team B with standard training) under realistic conditions. Using 58 thermocouples and 4 radiometers, temperature distribution and radiant heat flux were measured to evaluate water distribution efficiency and cooling performance during interventions. Team A consistently achieved temperature reductions of approximately 320 °C in the upper layers and 250–400 °C in the middle layers, maintaining stable conditions, whereas Team B only achieved partial cooling, with upper-layer temperatures remaining at 750–800 °C. Additionally, Team A recorded lower radiant heat flux densities (e.g., 20.74 kW/m² at 0°) compared to Team B (21.81 kW/m²), indicating more effective water application and adaptability. The findings confirm that stress-based training enhances firefighters’ operational readiness and their ability to distribute water effectively during interventions. This skill is essential for safer and effective management of indoor fires under extreme conditions. This study supports the inclusion of stress-based and scenario-based training in firefighter education to enhance safety and operational performance. Full article

Occupational heat stress represents an increasing challenge to safety and operational performance in underground mining, where elevated temperatures, humidity, and limited ventilation are common. This study proposes an integrated framework to analyze the systemic impact of heat stress on human reliability in mining operations. We conducted a systematic literature review to identify empirical studies addressing thermal exposure, extracting key operational functions for modeling. These functions were structured using the Functional Resonance Analysis Method (FRAM) to reveal interdependencies and performance variability. Human reliability was evaluated using Fuzzy CREAM, which quantified the degree of contextual control associated with each function. Finally, we applied the Gaussian Analytic Hierarchy Process (AHP) to prioritize functions based on thermal impact, contextual reliability, and systemic connectivity. The results showed that functions involving subjective or complex judgment, such as assessing thermal stress or identifying psychophysiological indicators, exhibited lower reliability and higher vulnerability. In contrast, monitoring and control functions based on standardized procedures were more stable and resilient. This combined approach identified critical points of systemic fragility and offers a robust decision-support tool for prioritizing thermal risk mitigation. The findings contribute to advancing the scientific understanding of heat stress impacts in mining and support the development of targeted interventions to enhance human performance and safety in extreme environments. Full article

Extreme heat is a pressing public health threat. This study assesses and describes the interrelationships between beliefs about heat waves, individuals’ precautionary behaviors during heat waves, and demographic factors. In May 2022, we surveyed 1051 residents (aged 25–90 years) in Southern Spain, a region that experiences frequent heat waves. We found that many participants engaged in heat wave avoidance (80.5%, e.g., spending more time indoors), impact reduction (63.7%, e.g., drinking more water), or prosocial behavior (31.6%, e.g., helping others). However, one in four (25.9%) respondents also indicated that they personally do not need to worry about heat waves. Heat wave beliefs and behaviors were modestly correlated with demographic characteristics. Individuals who view themselves as less vulnerable to heat-related health risks (“impervious” beliefs) were less likely to report altering their behavior during heat waves. Public health efforts aiming to change behavior during heat waves may anticipate “impervious” beliefs and demographic differences in risk perception and heat-related behaviors. Full article

Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean (Phaseolus vulgaris L.) × tolerant Tepary bean (P. acutifolius A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their β regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation. Full article

Thermal resilience is critical for building safety in cold climates during heating outages. This study presents full-scale experimental data from two residential buildings in Poland, tested during the winter of 2024–2025 under both typical and extreme outdoor conditions. The buildings—a lightweight timber-frame structure and a mediumweight masonry structure with ground coupling—were exposed to multi-day heating blackouts, and their thermal responses were monitored at a high temporal resolution. Several resilience indicators were used, including the resistance time (RT), degree of disruption (DoD), and hours of safety threshold (HST). Additionally, two time-based metrics—the time to threshold (T_x) and temperature at X-hours (T(t_x))—were introduced to improve classification in long-duration scenarios. The weighted unmet thermal performance (WUMTP) index was also implemented and validated using experimental data. The results show that thermal mass and ground coupling significantly improved passive resilience, enabling the mediumweight building to maintain temperatures above 15 °C for over 60 h without heating. This study provides new empirical evidence of passive survivability in blackout conditions and supports the development of time-sensitive assessment tools for cold climates. The findings may inform future updates to building codes and retrofit guidelines. Full article

Heatwaves intensified by climate change increasingly threaten urban populations, especially the elderly. However, most existing studies have concentrated on short-term or single-scale analyses, lacking a comprehensive understanding of how land cover changes and urbanization affect the vulnerability of the elderly to extreme heat. This study aims to investigate the spatiotemporal distribution patterns of heat-related health risks among the elderly in Nanchang City and to identify their key driving factors within the context of rapid urbanization. This study employs Crichton’s risk triangle framework to the heat-related health risks for the elderly in Nanchang, China, from 2002 to 2020 by integrating meteorological records, land surface temperature, land cover data, and socioeconomic indicators. The model captures the spatiotemporal dynamics of heat hazards, exposure, and vulnerability and identifies the key drivers shaping these patterns. The results show that the heat health risk index has increased significantly over time, with notably higher levels in the urban core compared to those in suburban areas. A 1% rise in impervious surface area corresponds to a 0.31–1.19 increase in the risk index, while a 1% increase in green space leads to a 0.21–1.39 reduction. Vulnerability is particularly high in economically disadvantaged, medically under-served peripheral zones. These findings highlight the need to optimize the spatial distribution of urban green space and control the expansion of impervious surfaces to mitigate urban heat risks. In high-vulnerability areas, improving infrastructure, expanding medical resources, and establishing targeted heat health monitoring and early warning systems are essential to protecting elderly populations. Overall, this study provides a comprehensive framework for assessing urban heat health risks and offers actionable insights into enhancing climate resilience and health risk management in rapidly urbanizing regions. Full article

Mango is highly sensitive to heat stress, which directly affects the yield and quality. The extreme heat waves of 2024, with temperatures reaching 41–47 °C over 25 days, caused significant impacts on sensitive cultivars. The impact of heat waves on ten commercial cultivars from subtropical regions viz.,‘Dashehari’, ‘Langra’, ‘Chausa’, ‘Bombay Green’, ‘Himsagar’, ‘Amrapali’, ‘Mallika’, ‘Sharda Bhog’, ‘Kesar’, and ‘Rataul’, and thirteen selected elite hybrids H-4208, H-3680, H-4505, H-3833, H-4504, H-1739, H-3623, H-1084, H-4264, HS-01, H-949, H-4065, and H-2805, is reported. The predominant effects that were observed include the following: burning symptoms or blackened tips, surrounded by a yellow halo, with premature ripening in affected parts and, in severe cases, tissue mummification. Among commercial cultivars, viz., ‘Amrapali’ (25%), ‘Mallika’ (30%), ‘Langra’ (30%), ‘Dashehari’ (50%), and ‘Himsagar’ and ‘Bombay Green’ had severe impacts, with ~80% of fruits being affected, followed by ‘Sharda Bhog’. In contrast, mid-maturing cultivars like ‘Kesar’, ‘Rataul’, and late-maturing elite hybrids, which were immature during the stress period, showed no symptoms, indicating they are tolerant. Biochemical analyses revealed significantly elevated total soluble solids (TSS > 25 °B) in affected areas of sensitive genotypes compared to non-affected tissues and tolerant genotypes. Aroma profiling indicated variations in compounds such as caryophyllene and humulene between affected and unaffected parts. The study envisages that the phenological maturity scales are indicators for the selection of climate-resilient mango varieties/hybrids and shows potential for future breeding programs. Full article

Transient increments of X-ray radiation and extreme ultraviolet (EUV) during solar flares are strong drivers of thermospheric dynamics on Mars, yet their class-dependent impacts remain poorly measured. This work provides the first direct, side-by-side study of Martian thermospheric reactions to flares X8.2 on 10 September 2017 and M6.0 on 17 September 2017. This study shows nonlinear, class-dependent effects, compositional changes, and recovery processes not recorded in previous investigations. Species-specific responses deviated significantly from irradiance proportionality, even though the soft X-ray flux in the X8.2 flare was 13 times greater. Argon (Ar) concentrations rose 3.28× (compared to 1.13× for M6.0), and radiative cooling led CO₂ heating to approach a halt at ΔT = +40 K (X8.2) against +19 K (M6.0) at exobase altitudes (196–259 km). N₂ showed the largest class difference, where temperatures rose by +126 K (X8.2) instead of +19 K (M6.0), therefore displaying flare-magnitude dependent thermal sensitivity. The 1.95× increase in O concentrations during X8.2 and the subsequent decrease following M6.0 (−39 K cooling) illustrate the contradiction between photochemical production and radiative loss. The O/CO₂ ratio at 225 km dropped 46% during X8.2, revealing compositional gradients boosted by flares. Recovery timeframes varied by class; CO₂ quickly re-equilibrated because of effective cooling, whereas inert species (Ar, N₂) stabilized within 1–2 orbits after M6.0 but needed >10 orbits of the MAVEN satellite after the X8.2 flare. The observations of the X8.2 flare came from the western limb of the Sun, but the M6.0 flare happened on the far side. The CME shock was the primary driver of Mars’ EUV reaction. These findings provide additional information on atmospheric loss and planetary habitability by indicating that Mars’ thermosphere has a saturation threshold where strong flares induce nonlinear energy partitioning that encourages the departure of lighter species. Full article