Search Results (383)

Understanding groundwater quality in karst environments is essential, particularly in semi-arid regions where water resources are highly vulnerable to both climatic variability and anthropogenic pressures. The Ghar Boumaaza karst aquifer, located in the semi-arid Tlemcen Mountains of Algeria, represents a critical yet understudied water resource increasingly threatened by climate change and human activity. This study integrates hydrochemical analysis, multivariate statistical techniques, and predictive modeling to assess groundwater quality and characterize the relationship between total dissolved solids (TDSs) and discharge (Q). An analysis of 66 water samples revealed that 96.97% belonged to a Ca²⁺–HCO₃⁻ facies, reflecting carbonate rock dissolution, while 3% exhibited a Cl⁻–HCO₃⁻ facies associated with agricultural contamination. A principal component analysis identified carbonate weathering (40.35%) and agricultural leaching (18.67%) as the dominant drivers of mineralization. A third-degree polynomial regression model (R² = 0.953) effectively captured the nonlinear relationship between TDSs and flow, demonstrating strong predictive capacity. Independent validation (R² = 0.954) confirmed the model’s robustness and reliability. This study provides the first integrated hydrogeochemical assessment of the Ghar Boumaaza system in decades and offers a transferable methodological framework for managing vulnerable karst aquifers under similar climatic and anthropogenic conditions. Full article

Change in sea level (SL) is an important indicator of global warming, since it reflects alterations in several components of the climate system at once. The main factors behind this phenomenon are the melting of glaciers and thermal expansion of ocean water, with the latter contributing about 40% to the overall rise in SL. Rising SL indirectly indicates an increase in ocean heat content and, consequently, its surface temperature. Previous studies have found that tropical sea surface temperature (SST) is critical to regulating the Earth’s climate and weather patterns in high and mid-latitudes. For this reason, SST and SL in the tropics can be considered as precursors of both global climate change and the emergence of climate anomalies in extratropical latitudes. Although SST has been used in this capacity in a number of studies, similar research regarding SL had not been conducted until recently. In this paper, we examine the links between SL in the tropical North Atlantic and North Pacific Oceans and surface air temperature (SAT) at mid- and high latitudes, with the aim of assessing the potential of SL as a predictor in forecasting SAT anomalies. To identify similarities between the variability of tropical SL and SST and that of SAT in high- and mid-latitude regions, as well as to estimate possible time lags, we applied factor analysis, clustering, cross-correlation and cross-spectral analyses. The results reveal a structural similarity in the internal variability of tropical SL and extratropical SAT, along with a significant lagged relationship between them, with a time lag of several years. Full article

The Flexible Combined Imager (FCI), developed as the next-generation imager for the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Third Generation (MTG) satellite series, represents a significant advancement over its predecessor, SEVIRI, on the Meteosat Second Generation (MSG) satellites. FCI offers more spectral bands, higher spatial resolution, and faster imaging capabilities, supporting a wide range of applications in weather forecasting, climate monitoring, and environmental analysis. On 13 January 2024, the FCI onboard MTG-I1 (renamed Meteosat-12 in December 2024) experienced a critical anomaly involving the failure of its onboard Calibration and Obturation Mechanism (COM). As a result, the use of the COM was discontinued to preserve operational safety, leaving the instrument dependent on alternative calibration methods. This loss of onboard calibration presents immediate challenges, particularly for the infrared channels, including image artifacts (e.g., striping), reduced radiometric accuracy, and diminished stability. To address these issues, EUMETSAT implemented an external calibration approach leveraging algorithms from the Global Space-based Inter-Calibration System (GSICS). The inter-calibration algorithm transfers stable and accurate calibration from the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral instrument aboard Metop-B and Metop-C satellites to FCI’s infrared channels daily, ensuring continued data quality. Comparisons with Cross-track Infrared Sounder (CrIS) data from NOAA-20 and NOAA-21 satellites using a similar algorithm is then used to validate the radiometric performance of the calibration. This confirms that the external calibration method effectively compensates for the absence of onboard blackbody calibration for the infrared channels. For the visible and near-infrared channels, slower degradation rates and pre-anomaly calibration ensure continued accuracy, with vicarious calibration expected to become the primary source. This adaptive calibration strategy introduces a novel paradigm for in-flight calibration of geostationary instruments and offers valuable insights for satellite missions lacking onboard calibration devices. This paper details the COM anomaly, the external calibration process, and the broader implications for future geostationary satellite missions. Full article

Disaster risk reduction (DRR) education is considered increasingly necessary, particularly for children. DRR educational interventions aim to enhance knowledge and attitudes related to self-protective capacity. However, comparative studies on students in areas prone to different disasters and comprehensive criteria covering both knowledge and attitudes toward behavior remain limited. A short DRR course was developed for primary schools across three regions (mountainous, low-lying, and coastal) in Hue City, one of Vietnam’s most vulnerable areas to extreme weather events. This study aimed to comprehensively evaluate student performance by applying Bloom’s taxonomy and treatment-control pre-post-follow-up design with panel analysis methods. From December 2022 to September 2023, three surveys, involving 517 students each, were conducted in six schools (three schools received the course and surveys, while the other three only participated in surveys). The intervention revealed similarities and differences between the groups. The course positively impacted on some elements of knowledge and preparedness intentions in students from low-lying and mountainous regions (including ethnic minorities). Higher-grade students in the mountainous region showed improvement in intentions, but not in attitudes toward self-protection. No gender differences in intentions were found. Although limited overall improvements, the study’s various methods, approaches and continuous assessment can be applied globally to design, implement, and assess DRR education courses effectively. Full article

In recent years, the increasing frequency and intensity of climate-related disasters have underscored the urgent need for resilient urban development. In cold-region cities, low temperatures pose a distinct and underexplored threat, with serious implications for human well-being, infrastructure performance, and ecological stability. Despite growing attention to climate resilience, existing urban risk assessments have largely focused on heatwaves and flooding, leaving a notable gap in research on cold-weather vulnerability. To address this gap, this study develops a fine-scale cold-climate vulnerability assessment framework grounded in the widely recognized “Exposure–Sensitivity–Adaptive Capacity” (ESA) model. Using subdistricts as the basic units of analysis, we integrate multi-source spatial data—including demographics, built environment, services, and ecological indicators—to construct a comprehensive evaluation system tailored to low-temperature conditions. The model is applied to the central urban area of Harbin, China, a representative cold-region city. The results reveal distinct spatial disparities in vulnerability: older urban districts exhibit higher vulnerability due to high population density and inadequate public services, while newly developed areas show relatively greater adaptive capacity. Further analysis identifies key drivers of vulnerability in different zones. Based on these insights, the study proposes differentiated, subdistrict-level planning strategies aimed at reducing exposure, mitigating sensitivity, and enhancing adaptive capacity. By extending the ESA model to cold-climate scenarios and operationalizing it at the subdistrict scale, this research contributes both methodologically and practically to the field of urban climate resilience. The findings offer actionable strategies for policymakers and provide a replicable framework applicable to other cold-region cities facing similar challenges. Full article

Photosynthetically Active Radiation (PAR) is critical for sustaining plant growth in the ground and on building surfaces, but how to accurately predict PAR availability in a complex urban environment can be a challenge. Using an advanced ray-tracing software (Radiance 4.0) and local weather data, this study presents a dynamic analysis of the effects of layout, façade orientation and height on PAR availability in four high density residential areas in Shanghai city, China. A metric system was also adopted using three light level requirements of outdoor plants (low, medium, high light levels). Key findings included: (1) the urban layout with the highest ratio of building height to north–south facing adjacent building separation achieved the higher levels of PAR availability for low/medium light level plants and the lower levels of PAR availability for high-light plants for middle and low façades and the ground, while high façades in all layouts could see similar PAR availability for all plants. (2) The PAR availability for low/medium-light plants decreased with the increasing façade height, while the PAR availability for high-light plants showed the opposite trend. (3) The north façade and its ground had higher levels of PAR availability for low/medium-light plants and lower levels of PAR availability for high-light plants than other façades. (4) All layouts offered more opportunities to apply high-light and medium-light plants at façades and the ground. Full article

Background: Beach volleyball (BVb) is a highly demanding Olympic sport characterized by intense physical activity and unique environmental challenges, including varying weather conditions and sandy, unstable court surfaces. Despite its popularity, there is a notable lack of scientific research addressing the metabolic and immune responses of elite female athletes in this sport. This study aims to address this gap by investigating two world-class Olympic medalists, female BVb players, who represent a country with a rich history in the sport. Methods: Two athletes underwent a simulated competition day consisting of two matches. A standardized protocol was utilized to collect blood and urine samples at seven time points, allowing for analysis throughout the competition and recovery phases. The analysis included various electrolytes, as well as hematological, metabolic, and inflammatory markers. Additionally, we assessed selected hormones, such as insulin, serotonin, ACTH, and cortisol, along with amino acids related to energy metabolism and neurotransmitter synthesis. Results: Both athletes presented a trend toward electrolyte disturbances, especially hypokalemia, with a mean decrease of 15% and individual values reaching as low as 3.3 mmol/L post-match. This indicates that BVb may pose a risk for such disturbances. Additionally, the matches led to 20% to 60% increases in muscle injury markers, with incomplete recovery even after a day of rest, signaling persistent physiological stress post-competition. This increase was matched by stimulating stress hormones (ACTH and cortisol rose up to 4-fold and 3-fold, respectively), and markers of exercise intensity, such as lactate and ammonium. Moreover, the simulated BVb competition day impacted the amino acid response, with the Fischer ratio (BCAA/AAA) and blood tryptophan decreasing to a minimum of 60% of the initial levels and blood serotonin increasing by up to 180%, which are signs of an increased risk of central fatigue onset, according to the Fischer and Newsholme theory. Conclusions: The responses examined in this exploratory study contribute to a deeper understanding of the metabolic and immune demands placed on elite female BVb players, suggesting practical applications. By addressing the similar physiological responses observed among the athletes and emphasizing their unique individual responses—despite following the same protocol under identical conditions and sharing similar life habits for an extended period—this study highlights the critical necessity for the n-of-1 monitoring of athletes. Full article

Understanding optical turbulence within the atmospheric boundary layer (ABL) is essential for refining atmospheric motion analyses, enhancing numerical weather prediction models, and improving light propagation assessments. This study develops an optical turbulence model for the boundary layer over the South China Sea (SCS) by integrating multiple observational and reanalysis datasets, including ERA5 data from the European Center for Medium-Range Weather Forecasts (ECMWF), radiosonde observations, coherent Doppler wind lidar (CDWL), and ultrasonic anemometer (CSAT3) measurements. Utilizing Monin–Obukhov Similarity Theory (MOST) as the theoretical foundation, the model’s performance is evaluated by comparing its outputs with the observed diurnal cycle of near-surface optical turbulence. Error analysis indicates a root mean square error (RMSE) of less than 1 and a correlation coefficient exceeding 0.6, validating the model’s predictive capability. Moreover, this study demonstrates the feasibility of employing ERA5-derived temperature and pressure profiles as alternative inputs for optical turbulence modeling while leveraging CDWL’s high-resolution observational capacity for all-weather turbulence characterization. A comprehensive statistical analysis of the atmospheric refractive index structure constant ($C_n^2$) from November 2019 to September 2020 highlights its critical implications for optoelectronic system optimization and astronomical observatory site selection in the SCS region. Full article

The analysis of sand distribution in a marine–continental rift basin is of practical value for hydrocarbon prediction. The primary objective of this study is to investigate the correlation between Paleoproterozoic sand development and paleomorphology in the Nanpu sag, and to focus on identifying the key factors controlling sand deposition in the marine–continental rift basin. Correspondence between the development of the Paleoproterozoic sand in the Nanpu sag and the paleogeomorphology shows that the gully limited the deposition of the sand into the lake. The differentiation and aggregation of the sand in the lake basin were influenced by two kinds of slope break zones (the syn-sedimentary fracture tectonic slope break zone and the paleo-topographic flexural depositional slope break zone). Due to tectonic movements in the marine–continental rift basin, as well as provenance supply and weather during chasmic stages, the impact of valley and syndeposit slope break zone on sand development varies. In areas where allocation is better as valley–syndeposit slope break zone, basal slope and its vicinity usually are favorable for delta (braided channel) and fan delta sand development, which extend basinward through hydraulic transport. Meanwhile, under the influence of syntectonic and gravitational disequilibrium, gravity flow sand can be seen sporadically distributed in the deep end of fan fronts. This study is of great significance for oil and gas exploration in the Bohai Bay Basin region and contributes to a better understanding of depositional processes in similar marine–continental rift basins around the globe. Full article

PM₁₀ samples were collected at an urban site of Zhuozhou, the southern gateway of Beijing, from 28 December 2021 to 29 January 2022, in order to explore the chemical composition, sources and physical and chemical formation processes of prominent components. The results showed that five trace elements (Mn, Cu, As, Zn and Pb) had high enrichment in PM₁₀ and were closely related with anthropogenic combustion and vehicle emissions; organic and element carbon had a high correlation due to the same primary sources and similar evolution; nitrate dominated SNA (sulfate, nitrate, ammonium) and nitrate/sulfate ratios reached 2.35 on the polluted days owing to the significant contribution of motor vehicle emissions. Positive matrix factorization analysis indicated that secondary source, traffic, biomass burning, industry, coal combustion and crustal dust were the main sources of PM₁₀, contributing 32.5%, 20.9%, 15.0%, 13.9%, 9.4% and 8.3%, respectively; backward trajectories and potential source contribution function analysis showed that short-distance airflow was the dominant cluster and accounted for nearly 50% of total trajectories. The Weather Research and Forecasting model with Chemistry, with integrated process rate analysis, showed that dominant gas-phase reactions (heterogeneous reaction) during daytime (nighttime) in presence of ammonia led to a significant enhancement of nitrate in Zhuozhou, contributing 12.6 μg/m³ in episode 1 and 22.9 μg/m³ in episode 2. Full article

With the growing global demand for energy optimization, particularly in the building sector, accurate daylight illuminance prediction plays a key role in enhancing energy efficiency through natural lighting and intelligent lighting systems. This study proposes a novel prediction model that integrates Meteorological Analog Intervals with a hybrid TCN-Transformer-BILSTM architecture to address the issue of insufficient prediction accuracy caused by the influence of various complex factors on daylight illuminance, as well as sudden weather changes, fluctuating meteorological conditions, and short-term variations. The model uses Grey Relational Analysis and Cosine Similarity to select historical data similar to the target moment in terms of meteorological conditions and time attributes, and constructs Meteorological Analog Intervals by combining the preceding and following time steps, providing high-quality data for the subsequent model development. The model effectively combines the multi-scale feature extraction capability of TCN, the global correlation-capturing advantage of Transformer, and the bidirectional temporal modeling characteristic of BILSTM to predict the temporal dynamics of daylight illuminance. Based on the measured data from Xi’an in 2023, experiments show that the proposed MAIL-TCN-Trans-BILSTM model achieves RMSEs of 1425.83 Lux and 2581.45 Lux under optimal and suboptimal daylight conditions, respectively, with MAPE reductions of 9–12% and 4–6% compared to baseline models. The proposed Meteorological Analog Intervals method significantly enhances the prediction accuracy and robustness of the model, especially in scenarios with complex and variable meteorological conditions, providing data support for intelligent lighting control systems. Full article

Hyperspectral remote sensing, which can acquire data in both spectral and spatial dimensions, has been widely applied in various fields. However, the available data are limited by factors such as revisit time, imaging width, and weather conditions. Three-dimensional (3D) hyperspectral simulation based on ray tracing can overcome these limitations by enabling physics-based modeling of arbitrary imaging geometries, solar conditions, and atmospheric effects. This type of simulation offers advantages in acquiring multi-angle and multi-condition quantitative results. However, the 3D hyperspectral simulation requires substantial computational resources. With the development of hardware, a graphics processing unit (GPU) offers a potential way to accelerate it. This paper proposes a 3D hyperspectral simulation model based on GPU-accelerated ray tracing, which is realized by modifying and using a common graphics API (OpenGL). Through experiments, we demonstrate that this model enables 600-band hyperspectral simulation with a computational time of just 2.4 times that of RGB simulation. Furthermore, we analyzed the balance between calculation efficiency and accuracy, and carried out a correlation analysis between ray count and accuracy. Additionally, we verified the accuracy of this model by using UAV-based data. The results demonstrate over 90% spectral curve similarity between simulated and UAV-acquired images. Finally, based on this model, we conducted additional simulation experiments under different environmental variables and observation conditions to analyze the model’s ability to characterize different situations. The results show that the model effectively captures the effects of environmental variables and observation conditions on the hyperspectral characteristics of vehicles. Full article

Rainfall-induced landslide mitigation remains a critical research focus in geotechnical engineering, particularly for safeguarding buildings and infrastructure in unstable terrain. This study investigates the stabilizing performance of slopes reinforced with negative Poisson’s ratio (NPR) anchor cables under rainfall conditions through physical model tests. A scaled geological model of a heavily weathered rock slope is constructed using similarity-based materials, building a comprehensive experimental setup that integrates an artificial rainfall simulation system, a model-scale NPR anchor cable reinforcement system, and a multi-parameter data monitoring system. Real-time measurements of NPR anchor cable axial forces and slope internal stresses were obtained during simulated rainfall events. The experimental results reveal distinct response times and force distributions between upper and lower NPR anchor cables in reaction to rainfall-induced slope deformation, reflecting the temporal and spatial evolution of the slope’s internal sliding surface—including its generation, expansion, and full penetration. Monitoring data on volumetric water content, earth pressure, and pore water pressure within the slope further elucidate the evolution of effective stress in the rock–soil mass under saturation. Comparative analysis of NPR cable forces and effective stress trends demonstrates that NPR anchor cables provide adaptive stress compensation, dynamically counteracting internal stress redistribution in the slope. In addition, the structural characteristics of NPR anchor cables can effectively absorb the energy released by landslides, mitigating large deformations that could endanger adjacent buildings. These findings highlight the potential of NPR anchor cables as an innovative reinforcement strategy for rainfall-triggered landslide prevention, offering practical solutions for slope stabilization near buildings and enhancing the resilience of building-related infrastructure. Full article

Photovoltaic (PV) power generation is characterized by high stochasticity, symmetry in daily power generation and low predictive accuracy. Enhancing the precision of power forecasting is crucial for improving symmetrical economic operation of the power grid. Due to Back-Propagation (BP) neural network prediction, there are problems such as difficulty in choosing network structure and high data requirements. A hybrid photovoltaic power forecasting model is introduced, utilizing the black-winged kite optimization algorithm (BKA) method to optimize the number of decompositions and maximum number of iterations in variational mode decomposition (VMD), as well as the critical parameters in the BP neural network. Initially, SHAP (Shapley Additive exPlanations) analysis identifies the primary factors used to serve as inputs for the K-means++ clustering of similar days, with the dataset segmented into samples of analogous days to reduce the asymmetric stochasticity of PV generation. Subsequently, the highly correlated features and PV power across different weather scenarios are decomposed using VMD, and a BKA-BP neural network prediction model is developed for each subcomponent. Ultimately, the predicted values are reconstructed through superimposition to yield the final prediction outcomes. The simulation findings indicate that VMD-BKA-BP neural network ensemble prediction model significantly enhances the short-term prediction accuracy of photovoltaic power relative to alternative models. This prediction model can be used in the future to optimize power dispatch and improve grid stability. Full article

Knowledge of surface water extent is critical for ecological and disaster monitoring. However, surface water extraction from optical satellite imagery is challenging due to the impact of weather. Synthetic Aperture Radar (SAR) can penetrate cloud cover and has significant advantages for surface water mapping, but the classification accuracy might be limited by SAR’s inherent properties and land cover, which have similar backscatter to surface water. This study finds that the accuracy of surface water extraction at the Prairie Pothole Region (PPR) can be improved by combining interferometric coherence and backscatter for machine learning classification. This study performs time-series analysis on surface water and land to investigate their discrimination at different seasonal periods. The accuracy improvement of this method on Sentinel-1 images reached 10% during the seasons of fall and winter, where the combination of backscatter and coherence was proven to be efficient for separating water and land. Hence, our approaches of combining backscatter and coherence provide new insights for surface water extraction from SAR images in future studies. Full article