Search Results (1,865)

As lakes contribute significant amounts of methane (CH₄) to the atmosphere, they account for a significant share of the global greenhouse gases (GHGs) budget. Since lakes are ecosystems where physical and biological processes influencing CH₄ formation are concentrated, the study focuses on atmospheric CH₄ column concentrations over lake areas. This study aims to analyze the temporal variation in atmospheric CH₄ column concentrations (XCH₄) over Lake Eğirdir and Lake Burdur in Türkiye in 2023 and 2025 as well as the relationship between XCH₄ and environmental parameters such as Water Surface Temperature (WST), Normalized Difference Chlorophyll Index (NDCI), and Floating Algae Index (FAI). The temporal variability of XCH₄ observed over both lakes showed statistically significant positive correlations with lake-area-averaged WST, NDCI, and FAI (Pearson r = 0.49–0.65, p < 0.01). This outcome indicates consistent temporal patterns between XCH₄ and environmental conditions at the lake scale. Furthermore, time-series graphs show that monthly average XCH₄ values in both lakes reached their highest levels during the summer and autumn months. During these periods, XCH₄ concentrations exceeded 1860 ppb in Lake Eğirdir and 1900 ppb in Lake Burdur. The areas of land use/land cover (LULC) classes surrounding the lakes were evaluated together with XCH₄, and relatively higher XCH₄ values were observed over agricultural areas, which constitute the dominant class in the basins of both lakes. The distribution of XCH₄ throughout the lake depth showed higher values in the shallow and mid-depth zones and lower values in the deeper areas beyond 20 m, indicating that the distribution of XCH₄ varies throughout lake depth. The results obtained underline the importance of remote sensing data in monitoring XCH₄ in lake ecosystems. Full article

The rapid proliferation of connected vehicles equipped with both Vehicle-to-Vehicle (V2V) sidelink and cellular interfaces creates new opportunities for real-time vehicular applications, yet achieving ultra-reliable communication without prohibitive cellular costs remains challenging. This paper addresses reliable inter-vehicle video streaming for safety-critical applications such as See-Through for Passing and Obstructed View Assist, which require stringent Service Level Objectives (SLOs) of 50 ms latency with 99% reliability. Through measurements in Seoul urban environments, we characterize the complementary nature of V2V and Vehicle-to-Network-to-Vehicle (V2N2V) paths: V2V provides ultra-low latency (mean 2.99 ms) but imperfect reliability (95.77%), while V2N2V achieves perfect reliability but exhibits high latency variability ($P_{99}$: 120.33 ms in centralized routing) that violates target SLOs. We propose a hybrid framework that exploits V2V as the primary path while selectively retransmitting only lost packets via V2N2V. The key innovation is a dual loss detection mechanism combining gap-based and timeout-based triggers leveraging Real-Time Protocol (RTP) headers for both immediate response and comprehensive coverage. Trace-driven simulation demonstrates that the proposed framework achieves a 99.96% packet reception rate and 99.71% frame playback ratio, approaching lossless transmission while maintaining cellular utilization at only 5.54%, which is merely 0.84 percentage points above the V2V loss rate. This represents a $7\times$ cost reduction versus PLR Switching (4.2 GB vs. 28 GB monthly) while reducing video stalls by $10\times$. These results demonstrate that packet-level selective redundancy enables cost-effective ultra-reliable V2X communication at scale. Full article

One of the most significant impacts of climate change is sea-level rise, which is increasingly threatening to the coastal setting, infrastructure, and socioeconomic systems. Since a change at the sea level is spatially non-uniform and highly modulated by local oceanographic and climatic events, local or regional-scale measurements are necessary—especially in semi-enclosed basins. This paper examines the long-term variability of sea levels throughout the Persian Gulf and illustrates a strong spatial variance of the trends over the past and the future. Using three decades of satellite-derived observations, regional sea-level trends were estimated from monthly sea-level anomaly (SLA) data, which were also used to generate future projections to 2100. The analysis shows that the rate of sea-level rise along the UAE–Oman stretch is 3.88 mm year⁻¹ and that of the Strait of Hormuz is 5.23 mm year⁻¹, with a mean of 4.44 mm year⁻¹ in the basin. Statistical forecasts of sea-level change were projected by a statistical forecasting scheme with high predictive ability with the optimal configuration of an average of 0.0391 m, an RMSE of 0.0492 m, and an R² of 0.80 when independent validation was conducted. It is estimated that by 2100, the average rise of the sea level in the Persian Gulf is about 0.30–0.40 m, and the peak rise in sea level is at the Strait of Hormuz. Since these projections are based on statistical extrapolation rather than physics-based climate models, they are interpreted within the uncertainty envelope defined by IPCC AR6 scenarios. This study presents a unique, regionally resolved viewpoint on sea-level rise that is relevant to coastal risk management and adaptation planning in semi-enclosed marine basins by connecting robust statistical performance with physically interpretable regional patterns. Full article

Accurate electricity carbon emission factors are crucial for assessing overall social carbon emissions and achieving China’s “dual carbon” goals. This paper proposes a dynamic correction model that integrates lifecycle extension, power exchange networks, and multi-time-scale decomposition to address the limitations of static carbon emission factors. The model considers factors such as power generation structure, cross-regional transmission, clean energy proportion, line losses, and non-CO₂ greenhouse gas emissions, and achieves dynamic correction at quarterly and monthly scales, enhancing timeliness and regional adaptability. Results show that transmission losses, energy structure, and inter-provincial electricity exchange significantly impact carbon emission factors. For instance, in 2022, line losses in Xinjiang and Gansu raised the electricity carbon emission factor by over 0.06 kgCO₂/kWh. Monthly factors exhibit significant seasonal fluctuations, with some regions showing variations of up to 105% of the annual average. Areas rich in hydropower, such as Yunnan, Sichuan, and Qinghai, experience pronounced fluctuations, highly sensitive to changes in water volume, offering more accurate reflections of carbon emission changes during electricity consumption. This study presents a refined dynamic correction method for electricity carbon emission accounting, providing theoretical support for carbon emission policy development and performance evaluation. Full article

In regions with limited meteorological monitoring systems, spatial drought modeling is of importance for efficient water resource management. This study recommends an alternative drought modeling strategy for Standardized Precipitation Evapotranspiration Index (SPEI) prediction at multiple target stations using data from neighboring stations. The Multi-Output Random Forest (MORF) model is implemented in this study to consider the spatial correlations among stations for the simultaneous prediction of SPEI for multiple stations instead of training independent models for each station. The efficiency of MORF is further compared to Multi-Output Support Vector Regression (MOSVR) and three baselines; a single-output RF, a monthly climatology model, and a persistence model. In addition to statistical performance criteria, drought characteristics are evaluated using intensity–duration–frequency analysis for three temporal scales (SPEI-3, SPEI-6, and SPEI-12). Results demonstrate that MORF outperformed MOSVR and RF in approximating observed drought intensity, duration, and frequency under moderate, severe, and extreme drought scenarios. Furthermore, spatial analysis reveals that MORF accurately captured the seasonal evolution of drought conditions including onset and recovery phases. The remarkable success of MORF in contrast to MOSVR and three traditional baselines can be explained by its ability to detect nonlinear and complex interactions of drought condition among various neighboring stations. This study emphasizes the promise of multi-output machine learning algorithms for drought monitoring in water resource management and climate adaptation planning in data-scarce regions. Full article

Background/Objectives: Observational studies have found inverse associations between 25-hydroxyvitamin D concentration and risk of hypertension, hypercholesterolemia and type 2 diabetes (T2D). More robust evidence from large-scale randomized controlled trials, however, is limited or inconclusive. Methods: The D-Health Trial (N = 21,315) is a randomized, double-blind, placebo-controlled trial of supplementation with monthly doses of 60,000 international units of oral vitamin D₃, conducted in Australians aged 60–84 years. Commencing treatment with anti-hypertensive, lipid-modifying, or anti-diabetic drugs was used as a surrogate for incident hypertension, hypercholesterolemia, and T2D, respectively. Outcomes were ascertained via linkage with the Australian Pharmaceutical Benefits Scheme database. Follow-up began 6 months after randomization; we excluded participants without linked data, and those who were prevalent cases or who died prior to start of follow-up. Flexible parametric survival models were used to estimate the effect of vitamin D supplementation on each outcome. Results: We included 10,964 participants (vitamin D, n = 5456 [49.8%]; placebo, n = 5508 [50.2%]) in the analysis of hypertension, 12,126 participants (vitamin D, n = 6038 [49.8%]; placebo, n = 6088 [50.2%]) in the analysis of hypercholesterolemia, and 17,846 (vitamin D, n = 8931 [50.0%]; placebo, n = 8915 [50.0%]) in the analysis of T2D. Over a median follow-up of 4.6 years, 2672 (24.4%), 2554 (21.1%), and 779 (4.4%) participants developed hypertension, hypercholesterolemia, and T2D, respectively. Vitamin D supplementation had no material effect on the incidence of any of hypertension (HR 1.00; 95% CI 0.93 to 1.08), hypercholesterolemia (HR 1.05; 95% CI 0.97 to 1.13), or T2D (HR 0.97; 95% CI 0.84 to 1.12). Conclusions: Monthly supplements of vitamin D did not alter the incidence of any of the three conditions in older, largely vitamin D-replete Australians. Full article

Climate change increasingly threatens the sustainability of regional water resources; therefore, robust station-scale precipitation projections are essential for basin-level planning. This study aims to develop and evaluate a hybrid, machine-learning-based statistical downscaling framework to generate monthly precipitation projections for the 21st century in the Büyük Menderes Basin, western Türkiye, using the HadGEM3-GC31-LL global climate model from the CMIP6. Monthly observations from 23 rainfall observation stations and ERA5 reanalysis predictors were employed to train station-specific Random Forest (RF) models, with optimal predictor sets identified through a multistage selection procedure (MPSP). Coarse-resolution general circulation model (GCM) fields were harmonized with ERA5 data using a three-stage inverse distance weighting (IDW), Delta, and Variance rescaling approach. The downscaled projections were bias-corrected using Quantile Delta Mapping (QDM) to maintain the climate-change signal. The RF models exhibited strong predictive skill across most stations, with test Nash–Sutcliffe Efficiency (NSE) values ranging from 0.45 to 0.81, RSR values from 0.43 to 0.74, and PBIAS values from −21.99% to +5.29%. Future projections indicate a basin-wide drying trend under both scenarios. Relative to the baseline, mean annual precipitation is projected to decrease by approximately 12.2, 19.6, and 33.7 mm in the near (2025–2050), mid (2051–2075), and late (2076–2099) periods under SSP2-4.5 (Shared Socioeconomic Pathway 2-4.5, a moderate greenhouse gas scenario). Under the high-emission SSP5-8.5 scenario, projected decreases are 25.2, 53.2, and 86.9 mm, respectively. Late-century reductions reach approximately 15–22% in several sub-basins. These findings indicate a substantial decline in future water availability and underscore the value of RF-based hybrid downscaling and trend-preserving bias correction for water resources planning in semi-arid Mediterranean basins. Full article

Rainfall, as the main driving force of natural disasters such as floods and droughts, has strong non-linear and abrupt characteristics, which makes it difficult to predict. As extreme weather events occur frequently in the Yellow River Basin, it is especially critical to reveal the physical mechanism of rainfall in the basin and integrate monthly scale meteorological data to achieve monthly rainfall prediction. In this paper, we propose a rainfall prediction model coupled with a physical mechanism and a temporal convolutional network (TCN) to achieve the prediction of monthly rainfall in the basin, aiming to reveal the physical mechanism between rainfall factors in the basin based on the transfer entropy and the multidimensional Copula function and based on the physical mechanism which is embedded into the TCN to construct a dual-driven prediction model with both physical knowledge and data, while the SHAP is used to analyze the interpretability of the prediction model. The results are as follows: (1) Temperature, relative humidity, and evaporation are key characteristic factors driving rainfall. (2) The physical mechanism features between temperature, relative humidity, and evaporation can be described by the three-dimensional Gumbel–Hougaard Copula function, with a more concentrated data distribution of their joint distribution probability. (3) The PHY-TCN model can accurately fit the extremes of the rainfall series, improving the model accuracy in the training set by 3.82%, 1.39%, and 9.82% compared to TCN, CNN, and LSTM, respectively, and in the test set by 6.04%, 2.55%, and 8.91%, respectively. (4) Embedding physical mechanisms enhances the contribution of individual feature variables in the PHY-TCN model and increases the persuasiveness of the model. This study provides a new research framework for rainfall prediction in the YRB and analyzes the physical relationship between the input data and output results of the deep learning model. It has important practical significance and strategic value for guiding the optimal scheduling of water resources, improving the risk management level of the basin, and promoting the ecological protection and high-quality development of the YRB. Full article

Efficient irrigation planning in data-scarce regions remains challenging due to limited access to localized meteorological data, reliance on complex computer-based models, and the technical knowledge required to deploy them at the field scale. Hence, the need for accessible, smartphone-based tools that simplify soil water balance calculations using public data to support practical decision-making in resource-limited contexts. This smartphone-based application estimates Net and Gross Irrigation Requirements using a Soil Water Balance (SWB) framework. The app combines region-specific empirical formulations for Effective Rainfall (Pe) calculation. The application utilizes user-supplied crop and irrigation parameters and meteorological data available in the public domain and operates at multiple temporal scales (daily, 10-day, weekly, and monthly), thereby supporting flexible irrigation schedules. The performance of app was evaluated through simulation-based benchmarking against FAO-CROPWAT 8.0 using harmonized inputs across five representatives agro-climatic region: Central India, Southern Vietnam, Northern Thailand, Western Bangladesh, and Central Sri Lanka. Quantitative comparison showed deviations within ±5% for Effective Rainfall, crop evapotranspiration, Net Irrigation, and Gross Irrigation, and low mean bias values (−2.8% to +3.3%) show the absence of systematic over- or under-estimation compared to CROPWAT model. The application also demonstrated responsiveness to climatic variability. Although the validation is limited to few representative locations and assumed minimal runoff conditions, the results suggest that the proposed method is technically consistent and feasible in practice. This study demonstrates smartphone-based application as a decision support for field-level irrigation planning and water resource management, particularly in data-limited agricultural contexts. Full article

As the supply chain of the electric vehicle (EV) industry becomes increasingly complex and vulnerable, traditional supplier evaluation methods reveal inherent limitations. These approaches primarily emphasize static performance while neglecting dynamic future risks. To address this issue, this study proposes a comprehensive supplier evaluation model that integrates a hybrid Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) framework with the Extreme Gradient Boosting (XGBoost) algorithm, contextualized for the EV sector. The hybrid AHP-TOPSIS framework is first applied to rank suppliers based on multidimensional performance criteria, including quality, delivery capability, supply stability and scale. Subsequently, the XGBoost algorithm uses historical monthly data to capture nonlinear relationships and predict future supplier risk probabilities. Finally, a risk-adjusted framework combines these two components to construct a dynamic dual-dimensional performance–risk evaluation system. A case study using real data from an automobile manufacturer demonstrates that the hybrid AHP–TOPSIS model effectively distinguishes suppliers’ historical performance, while the XGBoost model achieves high predictive accuracy under five-fold cross-validation, with an AUC of 0.851 and an F1 score of 0.928. After risk adjustment, several suppliers exhibiting high performance but elevated risk experienced significant declines in their overall rankings, thereby validating the robustness and practicality of the integrated model. This study provides a feasible theoretical framework and empirical evidence for EV enterprises to develop supplier decision-making systems that balance performance and risk, offering valuable insights for enhancing supply chain resilience and intelligence. Full article

Springs fed by carbonate-fractured/karst aquifers support spring-dependent ecosystems and provide drinking water in the Italian Apennines, where complex hydro-meteorological environments are increasingly affected by prolonged droughts. The aim of this study was to investigate the hydrogeological behavior of two springs (Alzabove and Lupa) on the mountain ridge of Central Italy, using monthly reanalysis datasets to support sustainable water management. The Master Recession Curves based on the 1998–2023 recession periods highlighted a slightly higher average recession coefficient for Lupa (α = −0.0053 days⁻¹) than for Alzabove (α = −0.0020 days⁻¹). The hydrogeological settings of the Lupa recharge area led to a less resilient response to prolonged, extreme droughts as detected via the Standardized Precipitation-Evapotranspiration Index (SPEI) computed at different time scales using ERA-5 Land datasets. The SPEI computed at a 6-month scale (SPEI₆) showed the best correlation with monthly spring discharge, with a 1-month delay time. A parsimonious linear regression model was built using the antecedent monthly spring discharge values and SPEI₆ as independent variables. The best modeling performance was achieved for the Alzabove spring, with some overestimation of spring discharge during extremely dry conditions (e.g., 2002–2003 and 2012), especially for the Lupa spring. The findings are encouraging as they reflect the use of a simple tool developed to support decisions on the sustainable management of springs in mountain environments, although issues related to evapotranspiration underestimation during extreme droughts remain. Full article

Landfills serve as a primary disposal method for municipal solid waste in China, with over 20,000 operational sites nationwide; however, long-term operations risk leachate leakage and groundwater contamination. Amid intensifying climate change and human activities, understanding contaminant evolution mechanisms in landfills has become critically urgent. Focusing on a representative plain-based landfill in North China, this study integrated field investigations and groundwater monitoring to establish a monthly coupled groundwater flow–solute transport model (using MODFLOW and MT3DMS codes) based on site-specific hydrogeological boundaries and multi-year monitoring data, analyzing spatiotemporal plume evolution under the coupled impacts of precipitation variability (climate change) and intensive groundwater extraction (human activities), spanning the historical period (2021–2024) and future projections (2025–2040). Historical simulations demonstrated robust model performance with satisfactory calibration against observed water levels and chloride concentrations, revealing that the current contamination plume exhibits a distinct distribution beneath the site. Future projections indicate nonlinear concentration increases: in the plume core zone, concentrations rise with precipitation, whereas at the advancing front, concentrations escalate with extraction intensity. Spatially, high-risk zones (>200 mg/L) emerge earlier under wetter conditions—under the baseline scenario (S0), such zones form by 2033 and exceed site boundaries by 2037. Plume expansion scales positively with extraction intensity, reaching its maximum advancement and coverage under the high-extraction scenario. These findings demonstrate dual drivers—precipitation accelerates contaminant accumulation through enhanced leaching, while groundwater extraction promotes plume expansion via heightened hydraulic gradients. This work elucidates coupled climate–human activity impacts on landfill contamination mechanisms, proposing a transferable numerical modeling framework that provides a quantitative scientific basis for post-closure supervision, risk assessment, and regional groundwater protection strategies, thereby aligning with China’s Standard for Pollution Control on the Landfill Site of Municipal Solid Waste and the Zero-Waste City initiative. Full article

Precipitation in high mountain areas is of critical importance as these regions are major sources of freshwater, supporting river basins, ecosystems, and downstream communities. Changes in precipitation regimes in these regions can have cascading impacts on water availability, agriculture, hydropower, and biodiversity. The present study aims to give new information about precipitation variability in high mountain regions of Bulgaria (Musala, Botev Peak, and Cherni Vrah) and to assess the role of large-scale atmospheric circulation patterns for the occurrence of extreme precipitation months. The study period is 1937–2024, and the classification of extreme precipitation months is based on the 10th and 90th percentiles of precipitation distribution. The temporal distribution of extreme precipitation months was analyzed by comparison of two periods (1937–1980 and 1981–2024). The impact of atmospheric circulation was evaluated by correlation between the number of extreme precipitation months and indices for the North Atlantic Oscillation (NAO) and Western Mediterranean Oscillation (WeMO). Results show a statistically significant decrease in winter and spring precipitation at Musala and Cherni Vrah, and a persistent drying tendency at Cherni Vrah across all seasons. The frequency of extremely wet months in winter and autumn has sharply declined since 1981, whereas extremely dry months have become more common, particularly during the cold season. Precipitation erosivity also exhibits station-specific responses, with Musala and Cherni Vrah showing reduced monthly concentration, while Botev Peak retains pronounced warm-season erosive rainfall. Circulation analysis indicates that positive NAOI phases favor dry extremes, while positive WeMOI phases enhance wet extremes. These findings reveal a shift toward drier and more seasonally uneven conditions in Bulgaria’s alpine zone, increasing hydrological risks related to drought, water scarcity, and soil erosion. The identified shifts in precipitation seasonality and intensity offer essential guidance for forecasting hydrological risks and mitigating soil erosion in vulnerable mountain ecosystems. The study underscores the need for adaptive water-resource strategies and enhanced monitoring in high-mountain areas. Full article

To improve the accuracy of cable temperature anomaly prediction and ensure the reliability of urban distribution networks, this paper proposes a multi-scale spatiotemporal model called MSST-Net (MSST-Net) for medium-voltage power cables in underground utility tunnels. The model addresses the multi-scale temporal dynamics and spatial correlations inherent in cable thermal behavior. Based on the monthly periodicity of cable temperature data, we preprocessed monitoring data from the KN1 and KN2 sections (medium-voltage power cable segments) of Guangzhou’s underground utility tunnel from 2023 to 2024, using the Isolation Forest algorithm to remove outliers, applying Min-Max normalization to eliminate dimensional differences, and selecting five key features including current load, voltage, and ambient temperature using Spearman’s correlation coefficient. Subsequently, we designed a multi-scale dilated causal convolutional module (DC-CNN) to capture local features, combined with a spatiotemporal dual-path Transformer to model long-range dependencies, and introduced relative position encoding to enhance temporal perception. The Sparrow Search Algorithm (SSA) was employed for global optimization of hyperparameters. Compared with five other mainstream algorithms, MSST-Net demonstrated higher accuracy in cable temperature prediction for power cables in the KN1 and KN2 sections of Guangzhou’s underground utility tunnel, achieving a coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) of 0.942, 0.442 °C, and 0.596 °C, respectively. Compared to the basic Transformer model, the root mean square error of cable temperature was reduced by 0.425 °C. This model exhibits high accuracy in time series prediction and provides a reference for accurate short- and medium-term temperature forecasting of medium-voltage power cables in urban underground utility tunnels. Full article

Background: In recent years, electronic patient-reported outcome (ePRO) systems on electronic devices, such as smartphones, have been employed to collect patients’ self-assessments and symptom reports. However, these studies were limited to younger populations and patients with severe diseases. Objective: This study aimed to evaluate the ease of use and response continuity of an ePRO system used by healthy middle-aged and older adults. Methods: This prospective observational study included participants aged 40–74 years undergoing specific health checkups. The System Usability Scale (SUS) was used to assess ePRO usability. Response continuity was evaluated by assessing EuroQol 5-Dimensional 5-Level responses once a month for up to 3 months after the health checkup date. Results: Eleven participants, aged 47–73 years, participated in the study. The mean SUS on the screening date was 59.1 (95% CI: 50.0–68.1; a cut-off of 70 indicated “useful”). However, only one participant failed to complete the ePRO at one and two months post-examination, and responses were obtained from all participants at three months. Conclusions: Due to the small sample size, usability as measured by the SUS should be interpreted descriptively. While initial onboarding appeared to be a major implementation barrier, sustained monthly ePRO reporting over 3 months was achievable among participants who completed registration with support, suggesting the conditional feasibility of response continuity in this preventive health checkup setting. Full article