Search Results (997)

Addressing global warming and adapting to the impacts of climate change is a primary focus of climate change adaptation strategies at both European and national levels. Land surface temperature (LST) is a widely used proxy for investigating climate-change-induced phenomena, providing insights into the surface radiative properties of different land cover types and the impact of urbanization on local climate characteristics. Accurate and continuous estimation across large spatial regions is crucial for the implementation of LST as an essential parameter in climate change mitigation strategies. Here, we propose a deep-learning-based methodology for LST estimation using multi-source data including Sentinel-2 imagery, land cover, and meteorological data. Our approach addresses common challenges in satellite-derived LST data, such as gaps caused by cloud cover, image border limitations, grid-pattern sensor artifacts, and temporal discontinuities due to infrequent sensor overpasses. We develop a regression-based convolutional neural network model, trained on ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) mission data, which performs pixelwise LST predictions using 5 × 5 image patches, capturing contextual information around each pixel. This method not only preserves ECOSTRESS’s native resolution but also fills data gaps and enhances spatial and temporal coverage. In non-gap areas validated against ground truth ECOSTRESS data, the model achieves LST predictions with at least 80% of all pixel errors falling within a ±3 °C range. Unlike traditional satellite-based techniques, our model leverages high-temporal-resolution meteorological data to capture diurnal variations, allowing for more robust LST predictions across different regions and time periods. The model’s performance demonstrates the potential for integrating LST into urban planning, climate resilience strategies, and near-real-time heat stress monitoring, providing a valuable resource to assess and visualize the impact of urban development and land use and land cover changes. Full article

Oxalic acid is the most abundant low-molecular-weight dicarboxylic acid in the atmosphere, and it plays a crucial role in the formation of new particles and cloud condensation nuclei. However, most observational studies have focused on particulate oxalate, leaving a significant knowledge gap on oxalic acid vapor. This study investigated the concentrations and formation of oxalic acid vapor and oxalate in PM_2.5 at a rural tropical coastal island site in south China across different seasons, based on semi-continuous measurements using an Ambient Ion Monitor-Ion Chromatograph (AIM-IC) system. We replaced the default 25 μL sampling loop on the AIM-IC with a 250 μL loop, improving the ability to distinguish the signal of oxalic acid vapor from noise. The data revealed clear seasonal patterns in the dependent daytime and nighttime formation of oxalic acid vapor, benefiting from high signal-to-noise ratios. Specifically, concentrations were 0.059 ± 0.15 μg m⁻³ in February and April 2023, exhibiting consistent diurnal variations similar to those of O₃, likely driven by photochemical reactions. These values decreased to 0.021 ± 0.07 μg m⁻³ in November and December 2023, with higher nighttime concentrations likely related to dark chemistry processes, amplified by accumulation due to low mixing layer height. The concentrations of oxalate in PM_2.5 were comparable to those of oxalic acid vapor, but exhibited (3–7)-day variations, superimposed on diurnal fluctuations to varying degrees. Additionally, thermodynamic equilibrium calculations were performed on the coastal data, and independent size distributions of particulate oxalate in the upwind marine atmosphere were analyzed to support the findings. Full article

Complex mixtures of air pollutants, including ozone (O₃), carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), black carbon (BC), and fine particulate matter (PM_2.5), present significant health risks. To understand the factors influencing air pollution levels and their temporal variations, comprehensive high-resolution long-term air pollution data are essential. This study analyzed the characteristics, lagged meteorological effects, and temporal patterns of six air pollutant concentrations over a one-year period at an urban residential site in Kenitra, Morocco. The results reveal pronounced seasonal and diurnal variations in pollutant levels, shaped by meteorological factors, emission sources, and local geographic conditions. PM_2.5, SO₂, and CO concentrations peaked during winter, while NO₂ and CO exhibited consistent diurnal peaks during morning and evening rush hours across all seasons, driven by traffic emissions and nocturnal pollutant accumulation. In contrast, O₃ concentrations were highest during summer afternoons due to photochemical reactions fueled by strong UV radiation, while winter levels were the lowest due to reduced sunlight. Lagged meteorological effects further highlighted the complexity of air pollutant dynamics. Meteorological factors, including temperature, wind speed, humidity, and pressure, significantly influenced pollutant levels, with both immediate and lagged effects observed. Lag analyses revealed that PM_2.5 and BC levels responded to wind speed, temperature, and humidity over time, highlighting the temporal dynamics of dispersion and accumulation. CO is sensitive to temperature and pressure changes, with delayed impacts, while O₃ formation was primarily influenced by temperature and wind speed, reflecting complex photochemical processes. SO₂ concentrations were shaped by both immediate and lagged meteorological effects, with wind direction playing a key role in pollutant transport. These findings emphasize the importance of considering both immediate and lagged meteorological effects, as well as seasonal and diurnal variations, in developing air quality management strategies. Full article

The application of regenerative therapy through stem cell transplantation has emerged as a promising avenue for the treatment of diabetes mellitus (DM). Transplanted tissue homeostasis is affected by disturbances in the clock genes of stem cells. The aim of this study is to investigate the diurnal variation in mitochondrial genes and function after transplantation of adipose-derived mesenchymal stem cells (T2DM-ADSCs) from type 2 diabetic patients into immunodeficient mice. Diurnal variation in mitochondrial genes was assessed by next-generation sequencing. As a result, the diurnal variation in mitochondrial genes showing troughs at ZT10 and ZT22 was observed in the group transplanted with adipose-derived mesenchymal stem cells derived from healthy individuals (N-ADSC). On the other hand, in the group transplanted with T2DM-ADSCs, diurnal variation indicative of troughs was observed at ZT18, with a large phase and amplitude deviation between the two groups. To evaluate the diurnal variation in mitochondrial function, we quantified mitochondrial DNA copy number using the Human mtDNA Monitoring Primer Set, measured mitochondrial membrane potential using JC-1, and evaluated mitophagy staining. The results showed a diurnal variation in mitochondrial DNA copy number, mitophagy, mitochondrial membrane potential, and NF-kB signaling in the N-ADSC transplant group. In contrast, no diurnal variation was observed in T2DM-ADSC transplants. The diurnal variation in mitochondrial function revealed in this study may be a new marker for the efficiency of T2DM-ADSC transplantation. Full article

Sporadic E (Es) layers are irregular structures that occur at the E-layer height of the ionosphere, significantly affecting the reliability and accuracy of wireless communications, navigation, and satellite remote sensing. This study utilized the S4max data collected from the Constellation Observing System for the Meteorology, Ionosphere, and Climate (COSMIC) occultation observations from 2007 to 2016 to identify the Es layer and investigate its climatological variations. The Horizontal Wind Field model (HWM14), in conjunction with the International Geomagnetic Reference Field model (IGRF13), is used to calculate vertical ion convergence (VIC) and analyze its correlation to the Es layers. The results of this study showed that the occurrence of Es has apparent hemispheric asymmetry. In the mid- and low latitudes, Es layer activity is more intense in the summer hemispheres, with center peak altitudes of around 105 km. The summer hemisphere exhibits a semi-diurnal periodic pattern, whereas the winter hemisphere shows a weakened diurnal variation. Simulation studies indicate that VIC induced by neutral wind shear contributes to the asymmetry in Es layer activities observed between the Northern and Southern hemispheres, and the zonal wind shear plays a more critical role than the meridional wind one. Full article

Circadian misalignment, due to shiftwork and/or individual chronotype and/or social jetlag (SJL), quantified as the difference between internal and social timing, may contribute to cardiovascular disease. Markers of endothelial dysfunction and activation of the coagulation system may predict cardiovascular pathology. The present study aim was to investigate the effects of shift work, SJL, and chronotype on endothelial function and coagulation parameters. One hundred female nurses underwent endothelial function testing using the EndoPAT and blood sampling for coagulation markers, repeated at 06:00–9:00 and 18:00–21:00. We found that compared with day workers, endothelial function and fibrinogen levels were lower (p = 0.001, p = 0.005, respectively) and the procoagulant parameters of plasminogen activator inhibitor-1 (PAI-1) and heparanase level and activity were higher amongst shift workers (p = 0.009, p = 0.03, p = 0.029, respectively). High SJL was associated with lower endothelial function (p = 0.002) and higher PAI-1, heparanase procoagulant activity, heparanase level, and D-Dimer level (p = 0.004, p = 0.003, p = 0.021, p = 0.006, respectively). In the late chronotype, PAI-1 and heparanase procoagulant activity were higher than in the early chronotype (p = 0.009, p = 0.007, respectively). Diurnal variation was found for PAI-1, von-Willebrand factor (vWF), heparanase, and heparan-sulfate with higher levels in the mornings. The correlation between shift/day workers and SJL or chronotype was moderately strong, indicating that SJL and chronotype are independent factors. In conclusion, findings suggest endothelial impairment and increased thrombotic risk in nurses working in shifts or with high SJL or late chronotype. The thrombotic risk is increased in the morning independent of circadian misalignment cause. These findings strengthen the importance of the alliance to the biological daily rhythm in daily life. Further research is needed to evaluate inhibitors of heparanase to attenuate the thrombotic risk in individuals with circadian misalignment. Full article

Power plants remain major contributors to air pollution, and while their impact on air quality and atmospheric chemistry have been extensively studied, there are still uncertainties in quantifying their precise contributions to PM_2.5 and O₃ formation under varying environmental conditions. This study employs the WRF/CMAQ modeling system to quantify the impact of power plant emissions on PM2.5 and O₃ levels across eastern China in June 2019. We investigate the spatial and temporal patterns of pollutant formation, analyze contributions to secondary PM2.5 components, and assess process-specific influences on O₃ concentrations. Results show that power plant emissions contribute up to 2.5–3.0 μg m⁻³ to PM_2.5 levels in central and eastern regions, with lower impacts in coastal and southern areas. O₃ contributions exhibit a more complex pattern, ranging from −4 to +4 ppb, reflecting regional variations in NOx saturation. Among secondary PM_2.5 components, nitrate formation is most significantly influenced by power plant emissions, emphasizing the critical role of NOx. Diurnal O₃ patterns reveal a transition from widespread morning suppression to afternoon enhancement, particularly in southern regions. Process analysis indicates that vertical transport is the primary mechanism enhancing surface O₃ from power plant emissions, while dry deposition acts as the main removal process. This comprehensive assessment provides crucial insights for developing targeted air quality management strategies, highlighting the need for region-specific approaches and prioritized NOx emission controls in the power sector. Our findings contribute to a deeper understanding of the complex relationships between power plant emissions and regional air quality, offering a foundation for more effective pollution mitigation policies. Full article

There are numerous lakes in the Tibetan Plateau (TP) that significantly impact regional climate and aquatic ecosystems, which often freeze seasonally owing to the high altitude. However, the special warming mechanisms of lake water under ice during the frozen period are poorly understood, particularly in terms of solar radiation penetration through lake ice. The limited understanding of these processes has posed challenges to advancing lake models and improving the understanding of air–lake energy exchange during the ice-covered period. To address this, a field experiment was conducted at Qinghai Lake, the largest lake in China, in February 2022 to systematically examine thermal conditions and radiation transfer across air–ice–water interfaces. High-resolution remote sensing technologies (ultrasonic instrument and acoustic Doppler devices) were used to observe the lake surface changes, and MODIS imagery was also used to validate differences in lake surface conditions. Results showed that the water temperature under the ice warmed steadily before the ice melted. The observation period was divided into three stages based on surface condition: snow stage, sand stage, and bare ice stage. In the snow and sand stages, the lake water temperature was lower due to reduced solar radiation penetration caused by high surface reflectance (61% for 2 cm of snow) and strong absorption by 8 cm of sand (absorption-to-transmission ratio of 0.96). In contrast, during the bare ice stage, a low reflectance rate (17%) and medium absorption-to-transmission ratio (0.86) allowed 11% of solar radiation to penetrate the ice, reaching 11.70 W·m⁻², which increased the water temperature across the under-ice layer, with an extinction coefficient for lake water of 0.39 (±0.03) m⁻¹. Surface coverings also significantly influenced ice temperature. During the bare ice stage, the ice exhibited the lowest average temperature and the greatest diurnal variations. This was attributed to the highest daytime radiation absorption, as indicated by a light extinction coefficient of 5.36 (±0.17) m⁻¹, combined with the absence of insulation properties at night. This study enhances understanding of the characteristics of water/ice temperature and air–ice–water solar radiation transfer through effects of different ice coverings (snow, sand, and ice) in Qinghai Lake and provides key optical radiation parameters and in situ observations for the refinement of TP lake models, especially in the ice-covered period. Full article

The microwave polarization index (PI), defined as the difference between vertically polarized (V-pol) and horizontally polarized (H-pol) brightness temperature divided by their average, is independent of land surface temperature. Since soil emission is stronger at V-pol than H-pol and vegetation attenuates this polarized soil signal primarily because of liquid water stored in vegetation tissue, a lower PI will be indicative of more water in vegetation if vegetation emits a mostly unpolarized signal and changes in soil moisture within the emitting depth are small (like during periods of drought) or accommodated by averaging over long periods. We hypothesize that the L-band PI will reveal diurnal changes in vegetation water related to whether plants have adequate soil water. We compare 6 a.m. and 6 p.m. L-band PI from NASA’s Soil Moisture Active Passive (SMAP) satellite to the evaporative stress index (ESI) in the U.S. Corn Belt during the growing season. When ESI<0 (there is not adequate plant-available water, also called plant water stress), the L-band PI is not significantly different at 6 a.m. vs. 6 p.m. On the other hand, when ESI≥0 (no plant water stress), the L-band PI is greater in the evening than in the morning. This diurnal behavior can be explained by transpiration outpacing root water uptake during daylight hours (resulting in a decrease in vegetation water from 6 a.m. to 6 p.m.) and continued root water uptake overnight (that recharges vegetation water) only when plants have adequate soil water. Consequently, it may be possible to use L-band PI to identify plant water stress in the Corn Belt. Full article

In April 2023, a major dust storm event in Lanzhou attracted widespread attention. This study provides a comprehensive analysis of the causes, progression, and dust sources of this event using multiple data sources and methods. Backward trajectory analysis using the HYSPLIT model was employed to trace the origins of the dust, while FY-2H satellite data provided high-resolution dust distribution patterns. Additionally, the MAIAC AOD product was used to analyze Aerosol Optical Depth, and concentration-weighted trajectory (CWT) analysis was used to identify key dust source regions. The study found that PM₁₀ played a dominant role in the storm, and the AOD values during the storm in Lanzhou were significantly higher than the annual average, highlighting the severe impact on regional air quality. Key meteorological conditions influencing the storm’s occurrence were analyzed, including the formation and eastward movement of a high-potential ridge, convection driven by diurnal temperature variations, and surface temperature increases coupled with decreased relative humidity, which together promoted the generation and development of dust. Backward trajectory and dust distribution analyses revealed that the dust primarily originated from Central Asia, western Mongolia, Xinjiang, and Gansu. From the 19th to the 21st, the dust distribution showed similarities between day and night, with a noticeable increase in dust concentration from night to day due to strong vertical atmospheric mixing. To mitigate the impacts of future dust storms, this study highlights both short-term and long-term strategies, including enhanced monitoring systems, public health advisories, and vegetation restoration in key source regions. Strengthening regional and international cooperation for transboundary dust management is also emphasized as critical for sustainable mitigation efforts. These findings are significant for understanding and predicting the causes, characteristics, and environmental impacts of dust storms in Lanzhou and the Northwestern region. Full article

Because PM_2.5 concentrations are not regularly monitored in Mbarara, Uganda, this study was implemented to test whether correlations exist between weather parameters and PM_2.5 concentration, which could then be used to estimate PM_2.5 concentrations. PM_2.5 was monitored for 24 h periods once every week for eight months, while weather parameters were monitored every day. The mean dry and wet season PM_2.5 concentrations were 70.1 and 39.4 µg/m³, respectively. Diurnal trends for PM_2.5 levels show bimodal peaks in the morning and evening. The univariate regression analysis between PM_2.5 and meteorological factors for the 24 h averages yields a significant correlation with air pressure when all data are considered, and when the data are separated by season, there is a significant correlation between PM_2.5 concentration and wind speed in the dry season. A strong correlation is seen between diurnal variations in PM_2.5 concentration and most weather parameters, but our analysis suggests that in modeling PM_2.5 concentrations, the importance of these meteorological factors is mainly due to their correlation with underlying causes including diurnal changes in the atmospheric boundary layer height and changes in sources both hourly and seasonally. While additional measurements are needed to confirm the results, this study contributes to the knowledge of short-term and seasonal variation in PM_2.5 concentration in Mbarara and forms a basis for modeling short-term variation in PM_2.5 concentration and determining the effect of seasonal and diurnal sources on PM_2.5 concentration. Full article

A hybrid attention transformer (HAT) was developed for accurate daily PM_2.5 predictions in Seoul. The performance of the HAT was evaluated through a comparative analysis of its predictions against ground-based observations and those from a three-dimensional chemical transport model (3-D CTM). The results demonstrated that the HAT outperformed the 3-D CTM, achieving a 4.60% higher index of agreement (IOA). Additionally, the HAT exhibited 22.09% fewer errors and 82.59% lower bias compared to the 3-D CTM. Diurnal variations in PM_2.5 predictions from both models were also analyzed to explore the characteristics of the proposed model further. The HAT predictions closely aligned with observed PM_2.5 throughout the day, whereas the 3-D CTM exhibited significant diurnal variability. The importance of the input features was evaluated using the permutation method, which revealed that the previous day’s PM_2.5 was the most influential feature. The robustness of the HAT was further validated through a comparison with the long short-term memory (LSTM) model, which showed 18.50% lower errors and 95.91% smaller biases, even during El Niño events. These promising findings highlight the significant potential of the HAT as a cost-effective and highly accurate tool for air quality prediction. Full article

This study explores a prediction system for global horizontal irradiance and cloudiness in a humid subtropical terrestrial region. This system consists of regional simulations performed with the Weather Research and Forecasting model using the initial and boundary conditions from the Global Forecast System. The predictions show significant biases for the variable of interest, with notable variations within the daily and annual cycles. This study also finds significant biases in cloud incidence and clarity index predictions, with relevant diurnal and seasonal variations. During austral summer, multiplying the relative humidity of initial and boundary conditions by a fixed factor improves the forecasts of global horizontal irradiance and cloud incidence for the central hours of the day and the afternoon. During austral winter, an empirical correction of the clarity index obtained from the simulation’s outputs also shows the potential to improve the forecasts’ biases. This work proposes a hypothesis about the causes of the forecast biases. Full article

This study aims to investigate the impact of ionospheric models on Global Navigation Satellite System (GNSS) positioning and proposes an ionospheric prediction method based on a Transformer deep learning model. We construct a Transformer-based deep learning model that utilizes global ionospheric maps as input to achieve spatiotemporal prediction of Total Electron Content (TEC). To gain a deeper understanding of the model’s prediction mechanism, we employ integrated gradients for explainability analysis. The results reveal the key ionospheric features that the model focuses on during prediction, providing guidance for further model optimization. This study demonstrates the efficacy of a Transformer-based model in predicting Vertical Total Electron Content (VTEC), achieving comparable accuracy to traditional methods while offering enhanced adaptability to spatial and temporal variations in ionospheric behavior. Furthermore, the application of advanced explainability techniques, particularly the Integrated Decision Gradient (IDG) method, provides unprecedented insights into the model’s decision-making process, revealing complex feature interactions and spatial dependencies in VTEC prediction, thus bridging the gap between deep learning capabilities and explainable scientific modeling in geophysical applications. The model achieved positioning accuracies of −1.775 m, −2.5720 m, and 2.6240 m in the East, North, and Up directions respectively, with standard deviations of 0.3399 m, 0.2971 m, and 1.3876 m. For VTEC prediction, the model successfully captured the diurnal variations of the Equatorial Ionization Anomaly (EIA), with differences between predicted and CORG VTEC values typically ranging from −6 to 6 TECU across the study region. The gradient score analysis revealed that solar activity indicators (F10.7 and sunspot number) showed the strongest correlations (0.7–0.8) with VTEC variations, while geomagnetic indices exhibited more localized impacts. The IDG method effectively identified feature importance variations across different spatial locations, demonstrating the model’s ability to adapt to regional ionospheric characteristics. Full article

Bumblebee species are vital wild pollinators, providing essential pollination services for various crops, fruits, and vegetables. However, their biodiversity is vulnerable to decline due to climate change, particularly in regions like northern Pakistan. Despite this, no research has yet been conducted on the distribution patterns of bumblebee species in this region. The current study aimed to model the spatial distribution of three important bumblebee species: Bombus haemorrhoidalis, B. rufofasciatus, and B. subtypicus in northern Pakistan. Habitat suitability and the contribution of bioclimatic variables to the spatial distribution of species were assessed using the MaxEnt approach. Current and future bioclimatic variables, along with presence-only records of three bumblebee species, were incorporated into the species distribution model. The results indicated that nearly 96% of the area (43 out of 45 cities in northern Pakistan) showed habitat suitability for all three species in the current scenario. Among these 43 cities, five exhibited a 100% overlap in suitable areas for the three species. However, this overlap area is expected to decrease in the future, particularly by the middle of the 21st century, highlighting these regions as prime candidates for conservation. In terms of bioclimatic factors influencing spatial distribution, the study found that temperature-related variables played a more significant role than precipitation-related ones in current and future scenarios. Specifically, bio3 (isothermality) contributed 48% to B. haemorrhoidalis and 43% to B. rufofasciatus, while bio2 (mean diurnal range) was the most influential factor for B. subtypicus. Temperature-related variables accounted for more than 80%, 69.4%, and 78.3% of the spatial variation in B. haemorrhoidalis, B. rufofasciatus, and B. subtypicus, respectively. This study demonstrates the critical influence of temperature on the spatial distribution of bumblebee species in northern Pakistan, underscoring the need for climate-focused conservation strategies to protect these important wild pollinators. Full article