Search Results (493)

Global warming and rapid urbanization have increased population exposure to heatwaves, with compound day- and night-time heatwaves (CDNH) posing greater health risks than individual heatwave events. Although air conditioning (AC) adaptation effectively mitigates heat-related impacts, its role in reducing CDNH exposure under climate change remains unknown. Using meteorological and socioeconomic data, this study quantified population exposure to CDNHs and the impacts that could be avoided through AC adaptation across China and its regional variations. Results show that CDNH exposure risks were particularly high in the middle–lower Yangtze–Huaihe Basin and south China, with an increasing trend observed over the period of 2001–2022. AC adaptation has reduced the exposure risk and its upward trend by 5.85% and 37.87%, respectively, with higher mitigating effects in urban areas. By breaking down the total exposure changes into climatic, demographic, and AC-driven changes, this study reveals that increased AC contributes 10.16% to exposure reduction, less than the effect of climate warming (59.80%) on the exposure increases. These findings demonstrate that expanding AC adaptation alone is insufficient to offset climate-driven increases in exposure, highlighting the urgent need for more effective adaptation measures to address climate change and thereby alleviate its adverse impacts on human beings. 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

Asia minor bluegrass (Polypogon fugax), a widespread Poaceae weed, exhibits broad tolerance to abiotic stresses. Validated reference genes (RGs) for reliable RT-qPCR normalization in this ecologically and agriculturally significant species remain unidentified. This study identified eight candidate RGs using transcriptome data from seedling tissues. We assessed the expression stability of these eight RGs across various abiotic stresses and developmental stages using Delta Ct, BestKeeper, geNorm, and NormFinder algorithms. A comprehensive stability ranking was generated using RefFinder, with validation performed using the target genes COR413 and P5CS. Results identified EIF4A and TUB as the optimal RG combination for normalizing gene expression during heat stress, cold stress, and growth stages. EIF4A and ACT were most stable under drought stress, EIF4A and 28S under salt stress, and EIF4A and EF-1 under cadmium (Cd) stress. Furthermore, EIF4A and UBQ demonstrated optimal stability under herbicide stress. Additionally, application of validated RGs revealed higher acetyl-CoA carboxylase gene (ACCase) expression in one herbicide-resistant population, suggesting target-site gene overexpression contributes to resistance. This work presents the first systematic evaluation of RGs in P. fugax. The identified stable RGs provide essential tools for future gene expression studies on growth and abiotic stress responses in this species, facilitating deeper insights into the molecular basis of its weediness and adaptability. Full article

The rising frequency of heatwaves, combined with the urban heat island effect, increases the population’s exposure to high temperatures, significantly impacting the health of vulnerable groups and the overall well-being of residents. While mesoscale meteorological models can reliably forecast temperatures across urban neighbourhoods, dense networks of in situ measurements offer more precise data at the street scale. In this work, the Random Forest technique was used to predict street-scale temperatures in the downtown area of Thessaloniki, Greece, during a prolonged heatwave in July 2021. The model was trained using data from a low-cost sensor network, meteorological fields calculated by the mesoscale model MEMO, and micro-environmental spatial features. The results show that, although the MEMO temperature predictions achieve high accuracy during nighttime compared to measurements, they exhibit inconsistent trends across sensor locations during daytime, indicating that the model does not fully account for microclimatic phenomena. Additionally, by using only the observed temperature as the target of the Random Forest model, higher accuracy is achieved, but spatial features are not represented in the predictions. In contrast, the most reliable approach to incorporating spatial characteristics is to use the difference between observed and mesoscale temperatures as the target variable. Full article

Urbanization is accompanied by an increased use of building materials. However, the lack of high-resolution building material stock (BMS) maps limits our understanding of the relationship between BMS and urban heat. To address this, we estimated BMS across eight typical Chinese megacities using multi-source geographic data and investigated the relationship between BMS and land surface temperature (LST). The results showed that (1) the total BMS for the eight megacities was 9175.07 Mt, with Beijing and Shanghai having the largest shares. While BMS correlated significantly with population, growth patterns varied across cities. (2) Spatial autocorrelation between BMS and LST was evident. Around 16% of urban areas exhibited High–High clustering between BMS and LST, decreasing to 10% during the daytime. The relationship between BMS and LST is nonlinear, and also prominent at night, especially in Beijing. (3) Diverse building forms, especially building height, contribute to a nonlinear relationship between BMS and LST. Full article

Introduction: Unilateral cleft lip and palate (CLP) is a severe orofacial birth defect characterized by improper fusion of facial parts and disturbed orofacial functions. The defect manifests as a gap in the orofacial tissues that is accompanied by defective healing patterns and chronic inflammation. The immune system’s defense factors modulate immunity, inflammation, and healing. Angiogenesis factors control blood-vessel formation. Therefore, these factors are vital in the immunological assessment and understanding of CLP morphopathogenesis. The aim of the study is to assess the distribution of vascular endothelial growth factor (VEGF), transforming growth factor beta 1 (TGF- β1), the total macrophage population and the M2 subtype, heat-shock proteins (HSP) 60 and 70, and nuclear factor kappa B (NF-κB) p50 and p65 subtypes in the affected tissue of children with CLP before and during primary dentition. Materials and Methods: Tissue samples were obtained from 15 patients aged from 3 to 8 months during veloplastic surgery. Five controls were used for comparison of data. Immunohistochemistry, light microscopy, semi-quantitative evaluation (from 0 to ++++), and statistics (Mann–Whitney U test and Spearman’s rank correlation) were used to evaluate the data for statistically significant differences and correlations between the groups. Results: Epithelial tissues affected by CLP presented with statistically significant increases in levels of VEGF (p = 0.007), total macrophages (p = 0.007), HSP60 (p = 0.001), NF-κB p65 (p = 0.000), and p50 (p = 0.045), but with a decrease in M2 macrophages (p = 0.025). Blood vessels in CLP-affected tissues showed a statistically significant increase in levels of NF-κB p65 (p = 0.003) and a statistically significant decrease in M2 numbers (p = 0.014). Connective tissue presented with no statistically significant differences. Spearman’s rank correlation revealed multiple statistically significant correlations—26 positive and 5 negative. Conclusions: Statistically significant changes in levels of VEGF and both NF-κB subtypes and numbers of total macrophages and M2 macrophages suggest a possible alteration of variable immune and inflammatory reactions and macrophage functions associated with the initiation and maintenance of the chronic process and the resulting damage. Full article

Objectives: This study aimed to implement a high-resolution Saudi voxel-based anthropomorphic phantom in the OpenMC Monte Carlo (MC) simulation framework. The objective was to evaluate its applicability in radiological simulations, including radiographic imaging and effective dose calculations, tailored to the Saudi population. Methods: A voxel phantom comprising 30 segmented organs/tissues and over 32 million voxels were constructed from full-body computed tomography data and integrated into OpenMC. The implementation involved detailed voxel mapping, material definition using ICRP/ICRU-116 recommendations, and lattice geometry construction. The simulations included X-ray radiography projections using mesh tallies and anterior–posterior effective dose calculations across 20 photon energies (10 keV–1 MeV). The absorbed dose was calculated using OpenMC’s heating tally and converted to an effective dose using tissue weighting factors. Results: The phantom was successfully modeled and visualized in OpenMC, demonstrating accurate anatomical representation. Radiographic projections showed optimal contrast at 70 keV. The effective dose values for 29 organs were calculated and compared with MCNPX, the ICRP-116 reference phantom, and XGBoost-based machine learning (ML) predictions. OpenMC results showed good agreement, with maximum deviations of −35.5% against ICRP-116 at 10 keV. Root mean square error (RMSE) comparisons confirmed reasonable alignment, with OpenMC displaying higher RMSEs relative to other methods due to expanded organ modeling and material definitions. Conclusions: The integration of the Saudi voxel phantom into OpenMC demonstrates its utility for high-resolution dosimetry and radiographic simulations. OpenMC’s Python (version 3.10.14) interface and open-source nature make it a promising tool for radiological research. Future work will focus on combining MC and ML approaches for enhanced predictive dosimetry. Full article

Rapid population growth and urbanization have greatly impacted the environment, causing a sharp rise in city temperatures—a phenomenon known as the Urban Heat Island (UHI) effect. While previous research has extensively examined the influence of land use characteristics on urban heat islands, their impact on community demographics and UHI severity remains unexplored. Moreover, most previous studies have focused on specific locations, resulting in relatively homogeneous environmental data and limiting understanding of variations across different areas. To address this gap, this paper develops ensemble learning models to predict UHI severity based on demographic, meteorological, and land use/land cover factors in Midwestern United States. Analyzing over 11,000 data points from urban census tracts across more than 12 states in the Midwestern United States, this study developed Random Forest and XGBoost classifiers achieving weighted F1-scores up to 0.76 and excellent discriminatory power (ROC-AUC > 0.90). Feature importance analysis, supported by a detailed SHAP (SHapley Additive exPlanations) interpretation, revealed that the difference in vegetation between urban and rural areas (DelNDVI_summer) and imperviousness were the most critical predictors of UHI severity. This work provides a robust, large-scale predictive tool that helps urban planners and policymakers identify key UHI drivers and develop targeted mitigation strategies. Full article

The characteristics and types of the sky can greatly influence photovoltaic (PV) power generation, potentially leading to a reduction in both the lifespan and efficiency of the entire system. Driven by the challenge of addressing fluctuations in solar PV energy utilization, the aim was to assess the solar energy potential by analyzing the clear sky index $\left(K_t^{*}\right)$ across elevation profiles. To achieve this, a theoretical model for determining $K_t^{*}$ was employed, which encapsulated the solar energy analysis. Initially, solar energy data collected from approximately 16 stations in various provinces of Mozambique, as part of the solar energy measurement initiatives by INAM, FUNAE, AERONET, and Meteonorm, was processed. Subsequently, the clear sky radiation was calculated, and $K_t^{*}$ was established. The statistical findings indicate a reduction in energy contribution from the predictors, accounting for 28% of the total incident energy; however, there are progressive increases averaging around ~0.02, with $K_t^{*}$ values ranging from 0.4 to 0.9, demonstrating a strong correlation between 0.7 and 0.9 across several stations and predictor parameters. No significant climate change effects were noted. The radiation flux is directed from areas with higher $K_t^{*}$ to those with lower values, as illustrated in the heat map. The region experiences an increase in atmospheric parameter deposition, with concentrations around ~0.20, yet there remains a substantial energy flow potential of 92% for PV applications. This interaction can also be applied in other locations to assess the potential for available solar energy, as the analyzed solar energy spectrum aligns closely with the theoretical statistical calibration of energy distribution relevant to the global solar energy population process. Full article

In some regions, extreme heat can result in pavement temperatures that are high enough to cause severe burn injuries within seconds of skin contact. This risk is elevated for unhoused individuals who may lack adequate clothing and shelter and have susceptibility to other risk factors, including substance use and in turn loss of consciousness. While prior studies have shown worse outcomes for unhoused individuals due to delays in care and higher susceptibility, there is a lack of data on the impact of pavement burns specifically within this population. This single-institution retrospective cohort study aims to explore burn severity and hospital outcomes in housed vs. unhoused patients with pavement burns. The data were analyzed using independent samples t-tests and logistic regression when appropriate, with p < 0.05 considered statistically significant. A total of 305 individuals met the inclusion/exclusion criteria and comprised the final study cohort, 17.7% of which were unhoused. There was no significant difference in TBSA, survival to discharge, or hospital length of stay between housed and unhoused patients. While unhoused individuals may still be at heightened risk for pavement burns due to exposure to extreme heat and a lack of protective measures, these results may additionally suggest consistent emergency care for patients regardless of housing status. Furthermore, these results highlight the importance of developing targeted outreach and prevention programs and equitable emergency care protocols for vulnerable populations. Full article

Urban environments are increasingly affected by extreme weather conditions, posing significant risks to vulnerable populations, such as the homeless. This research applies geospatial analysis to identify areas of extreme heat and moisture within the Athens metropolitan area in Greece. The analysis utilizes satellite-derived land surface temperature (LST), vegetation density index (NDVI), build-up density index (NDBI), Topographic Wetness Index (TWI), and other terrain-based factors to develop high-fidelity risk zones. These zones are critical for informing targeted interventions and policy measures aimed at protecting vulnerable groups from heat waves and extreme moisture. This research integrates a geospatial analysis approach for mapping and evaluating heat and moisture vulnerability zones. This approach integrates remote sensing data, GIS-based modeling, and terrain analysis. The findings can provide local authorities and social services with the necessary information to design adaptive strategies for climate change resilience. Full article

The urban heat island (UHI) effect and its associated extreme weather events have adverse impacts on human environment-coupled systems. However, the spatiotemporal variations in the UHI effect, as well as potential influencing factors, across climate zones remain poorly understood. This study explored how climate zones influenced the spatiotemporal variation in, trends in, and drivers of summer daytime surface UHI intensity (SUHII) in 220 Chinese cities located in five climate zones from 2000 to 2020. SUHII was quantified using MODIS land surface temperature (LST) data and remote sensing-derived urban built-up area masks were used to quantify SUHII. The Mann–Kendall test was applied to detect long-term SUHII trends, while Pearson correlation and stepwise multiple regression analyses were performed to identify key climatic and geographic drivers across different climate zones. The results indicated summer daytime SUHII values of 1.75 °C ± 1.19 °C, 1.74 °C ± 0.81 °C, 2.37 °C ± 0.75 °C, 2.14 °C ± 1.00 °C, and 2.36 °C ± 0.91 °C for the middle temperate zone (MTZ), south temperate zone (STZ), north subtropical zone (NSZ), middle subtropical zone (MSZ), and south subtropical zone (SSZ), respectively. In most cities, the SUHII increased significantly over time (p < 0.05). Pearson’s correlation analysis indicated that the enhanced vegetation index (EVI) and net radiation (NR) were moderately correlated with the SUHII in the MTZ, with correlation coefficients (r) of 0.465 and 0.42 (p < 0.05). Using a multivariate stepwise regression model, the relative contributions of various influencing factors to the UHI effect were quantified, explaining 27.1% to 57.2% of the variation across different climate zones. In particular, the economic vulnerability index and population density were the main factors affecting the SUHII in the MTZ and SSZ. Our findings support the development of policies aimed at mitigating the UHI effect by addressing the specific requirements of different climate zones to reduce. Full article

Heat waves affect the health and quality of life of older adults, particularly in urban environments. However, there is limited understanding of how extreme temperatures influence their mobility. This research aims to understand the pedestrian mobility patterns of older adults during heat waves in Madrid, analyzing environmental and sociodemographic factors that condition such mobility. Geospatial data from the mobile phones of individuals aged 65 and older were analyzed, along with information on population, housing, urban density, green areas, and facilities during July 2022. Multiple linear regression models and Moran’s I spatial autocorrelation were applied. The results indicate that pedestrian mobility among older adults decreased by 7.3% during the hottest hours, with more pronounced reductions in disadvantaged districts and areas with limited access to urban services. The availability of climate shelters and health centers positively influenced mobility, while areas with a lower coverage of urban services experienced greater declines. At the district level, inequalities in the availability of urban infrastructure may exacerbate the vulnerability of older adults to extreme heat. The findings underscore the need for urban policies that promote equity in access to infrastructure and services that mitigate the effects of extreme heat, especially in disadvantaged areas. Full article

Air pollution is one of the most serious environmental threats, with particulate matter PM₁₀ and PM_2.5 representing its most harmful components, significantly affecting public health. These particles are primarily generated by transport, industry, residential heating, and agriculture, and are associated with increased incidence of respiratory and cardiovascular diseases, asthma attacks, and heart attacks, as well as chronic illnesses and premature mortality. The most vulnerable groups include children, the elderly, and individuals with pre-existing health conditions. This study focuses on the analysis of health risks associated with PM₁₀ and PM_2.5 air pollution in the city of Zvolen, which serves as a representative case due to its urban structure, traffic load, and industrial activity. The aim is to assess the current state of air quality, identify the main sources of pollution, and evaluate the health impacts of particulate matter on the local population. The results will be compared with selected Slovak cities—Banská Bystrica and Ružomberok—to understand regional differences in exposure and its health consequences. The results revealed consistently elevated concentrations of particulate matter (PM) across all analyzed cities, frequently exceeding the guideline values recommended by the World Health Organization (WHO), although remaining below the thresholds set by current national legislation. The lowest average concentrations were recorded in the city of Zvolen (PM₁₀: 20 μg/m³; PM_2.5: 15 μg/m³). These lower values may be attributed to the location of the reference monitoring station operated by the Slovak Hydrometeorological Institute (SHMÚ), situated on J. Alexy Street in the southern part of the city—south of Zvolen’s primary industrial emitter, Kronospan. Due to predominantly southerly wind patterns, PM particles are transported northward, potentially leading to higher pollution loads in the northern areas of the city, which are currently not being monitored. We analyzed trends in PM₁₀ and PM_2.5 concentrations and their relationship with hospitalization data for respiratory diseases. The results indicate a clear correlation between the concentration of suspended particulate matter and the number of hospital admissions due to respiratory illnesses. Our findings thus confirm the significant adverse effects of particulate air pollution on population health and highlight the urgent need for systematic monitoring and effective measures to reduce emissions, particularly in urban areas. Full article

Lettuce (Lactuca sativa) is the most consumed leafy vegetable in the world, with great economic and social importance in Brazil. In breeding programs, selecting genotypes with high agronomic potential is essential to meet market demands and cultivation conditions. In this context, plant phenotyping by means of multispectral imaging emerges as a modern, efficient and non-destructive tool, which enhances the analysis of phenotypic characteristics quickly and accurately. Therefore, the aim of the present study was to group different lettuce situations according to their group using image-based phenotyping, in addition to morphological descriptors and agronomic evaluations. The experiment was carried out in an experimental area of the Federal University of Uberlândia, Campus of Monte Carmelo, MG, Brazil, in randomized blocks with three replicates and 17 treatments (lettuce populations of the F₂ generation, resulting from the cross between different lettuce cultivars and/or lines). Morphological descriptors and agronomic characteristics were obtained in the field. The vegetation indices GLI, NDVI, GNDVI, NGRDI and NDRE were calculated from images acquired at 49 days after transplanting. Means were compared using the Scott–Knott test (p ≤ 0.05), and the results were presented in box plots. Genetic dissimilarity was confirmed by multivariate analysis, which resulted in a cophenetic correlation coefficient of 96.11%. In addition, validation between field-collected data and image-obtained data was performed using heat maps and Pearson’s correlation. Populations UFU 003, UFU 006, UFU 009, UFU 011, UFU 012, UFU 013, UFU 014, UFU 016 and UFU 017 stood out, with high agronomic potential. Image-based phenotyping was correlated with agronomic traits and, therefore, can be considered an alternative to grouping different lettuce populations. Full article