Search Results (8,788)

Atmospheric CO₂ concentrations have reached significant levels during the industrial era, necessitating the implementation of effective carbon dioxide removal (CDR) technologies. Enhanced Rock Weathering (ERW) using basalt has emerged as a high-potential strategy, leveraging its mafic composition to sequester CO₂ as stable carbonates. This review analyzes ERW’s geochemical processes, application methods, and multifaceted co-benefits, such as restoring “background fertility” and improving soil structure. The literature indicates that while small-scale applications range from 1.5 to 6 Mg·ha⁻¹·yr⁻¹, intensive agricultural rates typically reach 40–100 Mg·ha⁻¹·yr⁻¹. Global models estimate a sequestration potential of up to 4.9 × 10⁹ Mg CO₂·yr⁻¹ for basalt, although field-scale results vary significantly, reaching uptake rates of up to 4 Mg CO₂·ha⁻¹ depending on pedological conditions and crop types. Despite this promise, transitioning to large-scale deployment faces critical hurdles, including operational difficulties in mechanized spreading and a scarcity of audited, long-term field data. Future research must prioritize standardized protocols and comprehensive economic analyses to bridge the gap between theoretical models and empirical evidence. Ultimately, ERW represents a multifaceted solution for climate stabilization and sustainable food security, provided that sequestration efficacy and environmental safety are rigorously verified through high-application field trials. Full article

The objective of this study was to identify the factors that most strongly influence the postharvest quality of ‘Gala Schniga^® SchniCo Red(s)’ apples under conditions of simulated transport and simulated trading at elevated temperature following long-term storage. The study was conducted over two storage seasons (2022/2023 and 2023/2024) on fruit originating from the experimental orchard of the Warsaw University of Life Sciences (SGGW-WULS) in Warsaw. The effects of harvest date (optimal—OHD and delayed by 14 days—DH), four variants of 1-MCP (1-methylcyclopropene) application: (control, Harvista™—preharvest, SmartFresh™—postharvest, and Harvista™ + SmartFresh™), controlled-atmosphere storage technology (ULO 1: 1.2% CO₂ and 1.2% O₂; ULO 2: 0.6% CO₂ and 0.6% O₂), storage period (5, 7, and 9 months), duration of simulated transport (4 or 6 weeks at 1 °C in normal atmosphere), and shelf life (0, 7, and 14 days at 25 °C) were analyzed. Five quality parameters were evaluated: firmness (F), soluble solids content (SSC), titratable acidity (TA), SSC/TA ratio, and 1-aminocyclopropane-1-carboxylic acid (ACC) content. Stepwise regression with backward elimination was applied to identify significant predictors, and partial eta squared (η²) was calculated to compare the relative strength of effects. Postharvest 1-MCP application had the greatest impact on maintaining firmness and TA (F: η² = 75.8%; TA: η² = 56.3%), whereas shelf life was the key factor in the deterioration of quality parameters after removal from storage (F: η² = 55.5%; TA: η² = 30.1) and in increasing the SSC/TA ratio (η² = 29.6%). Harvest date strongly differentiated firmness (η² = 51.3) and significantly affected TA (η² = 14.4), while storage period had the greatest effect on ACC content (η² = 14.2) and TA decline (η² = 15.6). Preharvest 1-MCP application had a smaller effect on F and TA but significantly reduced SSC (η² = 24.9), highlighting the importance of the timing of ethylene inhibitor application. The effects of simulated transport and preharvest weather indicators were statistically significant but relatively small compared with the effects of postharvest technological decisions and exposure time under retail conditions. The results indicate that maintaining target quality parameters throughout an extended supply chain requires precise determination of the harvest date, prioritizing postharvest 1-MCP application, and limiting shelf life under elevated-temperature conditions. Full article

Icing and galloping disasters threaten the safe operation of power transmission lines, and effective response depends on multi-agent collaboration. To address the insufficient attention paid in existing studies to grassroots execution constraints, this paper constructs a tripartite evolutionary game model involving local governments, power grid enterprise O&M management, and grassroots O&M teams. The model integrates collaborative investment, agency costs, benefit sharing, and multi-layer reward–punishment mechanisms into a unified framework. Replicator dynamics and numerical simulations are then used to analyze the evolution of collaborative strategies; the parameters are non-dimensional benchmark values rather than empirically calibrated estimates. The results show that the system exhibits multi-stability and path dependence, with fully non-collaborative and fully collaborative equilibria possibly remaining stable simultaneously. The combination of strong government regulation and incentives with high-level enterprise collaborative management is the key mechanism for overcoming the low-collaboration trap, and strict accountability has higher marginal policy efficiency than equivalent subsidies. Reducing grassroots execution costs and moderately increasing their share of disaster mitigation benefits can accelerate collaborative convergence and expand the attraction basin of the high-collaboration equilibrium. This study provides theoretical support and mechanism-design implications for enhancing the resilience of collaborative emergency response for power transmission lines under extreme weather conditions. Full article

Recurrent meteorological droughts, projected to intensify under climate change, affect the cross-border Lima River Basin shared between Portugal and Spain, highlighting the need for robust early warning systems to support proactive water management. Within the EU-funded RISC_PLUS project—aimed at strengthening resilience to hydro-climatic risks in the cross-border Minho–Lima River Basins—this study develops a regionalised forecasting framework to evaluate meteorological drought forecast skill using precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Seasonal Forecasting System 5 (SEAS5) for the Portuguese section of the Lima River Basin. A precipitation-only 12-month Standardized Precipitation Index (SPI12) is employed to isolate the contribution of seasonal precipitation forecasts. SPI12 is computed from hybrid 12-month accumulations combining observed monthly precipitation (October 1979 to February 2025) and SEAS5 forecasts (October 2018 to February 2025). Four hybrid configurations (1 to 6 months lead time) are evaluated: 11 obs + 1 fcst, 10 obs + 2 fcsts, 9 obs + 3 fcsts, and 6 obs + 6 fcsts. Forecast performance is assessed from October 2018 to February 2025. Deterministic SPI12 forecasts and categorical drought classifications are evaluated using regression-based metrics (e.g., Pearson correlation and RMSE) and contingency-table metrics (e.g., FAR and F1-score), across SEAS5 ensemble members, percentiles, and spread-based indicators. The 11 obs + 1 fcst configuration, particularly when using the Dry Spread (SpD; Q10 + Q25 percentiles) and the Q75 percentile, exhibits the highest skill, achieving a Pearson correlation coefficient of $r=0.97$ and an RMSE of approximately 0.17, alongside near-perfect categorical performance (POD = 1.00; FAR = 0.00), although these scores are partly conditioned by the shared observed accumulation window. Conversely, longer lead-time configurations exhibit degraded performance, with the 6 obs + 6 fcsts configuration showing weak or negative skill relative to climatology, indicating that 6-month lead forecasts should be interpreted with caution. These results demonstrate that SEAS5 precipitation forecasts can provide skilful drought predictions at lead times of several months in the Lima River Basin within the SPI12 framework. The proposed blending methodology provides a transparent benchmark and a technical basis for the early-warning system being developed under the RISC_PLUS project to support drought risk management in the Minho–Lima region and complement data-driven drought forecasting approaches. Full article

Electricity systems are increasingly exposed to demand variability driven by extreme weather conditions, creating significant challenges for maintaining grid reliability and operational stability. Conventional forecasting approaches focus primarily on prediction accuracy and provide limited support for operational decision-making under dynamic conditions. This study proposes a hybrid deep learning framework for smart grid stress prediction and adaptive mitigation under extreme weather. The framework reformulates demand forecasting using residual learning. It further integrates grid stress modelling with control-oriented decision support. A sequence learning architecture with attention is employed to capture temporal demand dynamics, while a continuous Grid Stress Index (GSI) translates predictions into operational indicators of system stress. The model demonstrates stable performance on real-world UK electricity demand data, achieving a mean absolute error of 1827.51 MW and a root mean squared error of 2505.22 MW. Peak demand and ramp behaviour are captured with improved consistency, and grid stress is predicted with a mean absolute error of 0.1246. An adaptive mitigation module translates predicted stress into actionable control, resulting in approximately 5.37% peak demand reduction, with limited impact on ramp smoothing. The results demonstrate that integrating forecasting, stress modelling, and control delivers greater operational value than standalone predictive models. The proposed framework provides a scalable and practical approach for grid-aware decision support under increasing climate-driven demand uncertainty. Full article

Rapid increase in mining activities, outdated management approaches, and climate change pose risks to the safe operation of mines. We explore public databases on mine tailings storage facilities (TSF) in Kazakhstan, a major mineral producer. We proceed to an in-depth analysis of a representative TSF, located in an area that has been affected by spring flooding. Our geospatial analysis and review of company reports reveal serious challenges related to the TSF design, tailings deposition patterns, and changing weather conditions. Despite modifying the TSF design in response to its failure, the company has struggled with persistent TSF overtopping and seepage in the subsequent years. Our findings from both the country-level review of TSF and the case study highlight the urgency of adopting best practices of TSF management. Specifically, our study demonstrates that risks stemming from spring flooding in Kazakhstan call for proactive TSF management, transparency, and stakeholder engagement. Such changes in TSF governance are essential for achieving a number of Sustainable Development Goals, in particular, SDG 12 Responsible Consumption and Production and SDG6 Clean Water and Sanitation. Full article

Natural lacquer, a bio-based polymer derived from Toxicodendron vernicifluum, has attracted renewed scientific interest as a sustainable coating material with exceptional mechanical durability, chemical resistance, and aesthetic qualities. This review synthesizes current knowledge on the chemical composition, enzymatic curing mechanisms, and structure–property relationships of lacquer-based polymer systems, with particular focus on recent advances in functional modification and processing technology. Key findings indicate that laccase-catalyzed oxidative polymerization, operating optimally at pH 6.0–7.5 and 20–30 °C, governs the formation of a highly cross-linked urushiol network whose properties are fundamentally determined by side-chain unsaturation and emulsion stability. Mechanistic analysis reveals that polyurethane hybridization improves weathering resistance by introducing flexible aliphatic segments and additional hydrogen-bonding cross-links, while graphene oxide incorporation enhances anticorrosion performance through a physical barrier mechanism that prolongs ionic diffusion pathways. UV-curable LPEA derivatives achieve an 83% reduction in curing time relative to ambient-cured lacquer, enabling integration with industrial spray-coating lines. Despite these advances, several critical limitations remain inadequately resolved. Allergen reduction strategies have not yet achieved sufficient quantitative efficiency for large-scale commercial deployment, and the long-term stability of nanocomposite lacquer films under sustained UV exposure and hydrothermal conditions is not well established. Furthermore, most high-performance modification systems reported in the literature are demonstrated only on laboratory scale, with scalability, substrate compatibility, and lifecycle performance remaining largely unvalidated. The review identifies the absence of standardized performance evaluation protocols and the fragmentation of structure–property data across studies as key barriers to systematic progress, and proposes that future work prioritize the development of integrated processing–modification–performance frameworks to guide the rational design of next-generation lacquer-based functional materials. Full article

Robust perception in adverse weather conditions remains a significant challenge for autonomous vehicles. Short-wave infrared (SWIR) sensors offer specific physical properties that enable them to penetrate atmospheric disturbances like fog, rain, and snow. However, effectively combining this robustness with the textural and colour information of visible (VIS) cameras is difficult due to signal decorrelation and the limitations of static fusion schemes. To address this, we present VISWIR (Visible and SWIR Weighted Image Reconstruction), a pixel-level fusion method based on a multi-scale pyramid architecture. We introduce an automated strategy for scheduling parameters based on weather conditions using an optimisation framework. Rather than relying on static weights, our method applies offline parameter scheduling to adjust fusion hyperparameters based on the meteorological context. We focus on a multi-objective optimisation approach that maximises perceptual image quality via No-Reference Image Quality Assessment (NR-IQA) metrics. Validated in controlled environment scenarios with varying weather severities, our results confirm the potential of VISWIR as a robust, lightweight algorithmic baseline to enhance the perception capabilities of autonomous vehicles in adverse weather conditions. Full article

The rapid development of renewable energy generators (REGs) has increased the uncertainties and security risks in power systems. Furthermore, extreme weather conditions impose higher demands on the secure operation range of power systems. Energy storage systems (ESSs), with fast power regulation capability, can smooth fluctuations of REGs and mitigate risks of power deficits and power flow violations under extreme events. To this end, this paper proposes an ESS siting and sizing model that considers the economic efficiency, security, and resilience requirements. First, to overcome drawbacks of existing ESS planning methods that ignore the resilience requirement under extreme events and the strong nonlinearity of power flow entropy indicator reflecting system security margins, the loading rate balance (LRB) indicator is developed to describe the safety and resilience of transmission grid and is incorporated into the ESS planning model in a first-order dispersion form to keep the optimization model linear. Second, a coordinated ESS planning and dispatch optimization model is formulated to minimize the equivalent daily planning cost, daily dispatch cost, and LRB, subject to secure operation constraints of the power system under renewable generation uncertainties. Third, a sample average approximation -based chance-constrained approach is proposed in the ESS planning model to characterize the uncertainties of wind and solar power to avoid distributional dependence and the curse of dimensionality. Detailed simulations validate the effectiveness of the proposed ESS planning method in terms of improving economic efficiency while ensuring system security and resilience. Full article

The increasing demand for intelligent transportation systems (ITS) and advanced driver assistance system (ADAS) significantly demands a real-time and robust perception to recognize road-side obstacles in varying different weather settings. This paper presents DriveEdgeAI, a lightweight YOLO11 based embedded deep learning framework for efficient road anomaly detection with the emphasis on potholes, speed bumps and relevant traffic sign detection. We have prepared a custom dataset consisting of 17,061 annotated images to train and test the model under different lighting conditions, weather conditions, and roads configurations. The proposed system also managed to demonstrate good convergence and generalization with a precision@50 of 95.8%, recall@50 of 89.7%, mAP@50 of 95.4%, surpassing previous YOLO versions. The stability and robustness of the model at different thresholds were also substantiated by Precision-Recall and F1-Confidence analyses. DriveEdgeAI was also deployed on a number of edge devices, such as Jetson Nano, Raspberry Pi 5, Intel Movidius VPU and Hailo-8L NPU respectively reaching 9.5 FPS/W and 28.5 FPS for the Raspberry Pi 5 + Hailo-8L version. From these results, one can conclude that DriveEdgeAI is an energy-efficient and scalable solution for real-world ADAS applications. Full article

Rainfall-induced landslides are a major geohazard in Japan, yet the contrasting triggering mechanisms of short-duration convective storms and prolonged frontal rainfall remain poorly quantified. This study examines rainfall conditions associated with the August 2014 Hiroshima and July 2018 Western Japan landslide disasters using intensity–duration (I–D) threshold analysis, percentile-based thresholds, Monte Carlo Receiver Operating Characteristic (ROC) analysis, and evaluation of topographic and geological controls. The 2014 event produced approximately 600 landslides with 376 mm of rainfall over 40 h, whereas the 2018 event produced 1960 landslides after > 1000 mm of rainfall over 11.6 days. Distinct I–D thresholds were established for the events: the 2014 threshold (I = 95.795D^−0.574, R² = 0.94) indicates intensity-controlled landslide initiation, whereas the 2018 threshold (I = 61.76D^−0.42, R² = 0.97) reflects duration-controlled slope saturation. ROC analysis identified 2 h cumulative rainfall as the most effective rainfall indicator (AUC = 0.61 and 0.69 for the 2014 and 2018 events). Statistical analyses showed that lithology was a stronger influence on rainfall-triggering conditions than slope angle, with weathered granitic terrains requiring rainfall thresholds for landslide initiation. These results underscore the need to integrate rainfall characteristics with geological conditions in the development of landslide early-warning systems for humid mountainous regions. Full article

Extreme heat poses health risks during pregnancy, but little is known about how pregnant individuals seek weather information to engage in heat-health protective actions. This study examined associations between routine and event-driven weather information seeking and both routine physiological heat-health protective actions (i.e., limiting sun exposure, staying hydrated, and spending time in air conditioning) and higher-threshold adaptive behaviors (i.e., changing plans due to heat). A cross-sectional survey of 195 pregnant individuals in Bexar County, TX, USA, was conducted during the summer and fall of 2024. Descriptive and nonparametric analyses explored relationships across trimesters. Participants demonstrated high routine weather information seeking and greater weather information needs since becoming pregnant. Over half (51.3%) reported increased weather information seeking during excessive heat, with lower increases during the first trimester. During extreme heat, most respondents increased heat-health protective actions. Increased information needs during pregnancy were significantly related to heat-health protective actions. Routine weather checking showed weak or inverse relationships with changing plans, suggesting that routine weather awareness alone may not prompt changing plans. Trimester patterns indicated heightened information seeking and protective actions later in pregnancy. Findings highlight the importance of pregnancy-specific heat risk communication with trimester-specific guidance provided in clinical counseling, public health messaging, and meteorological communication. Full article

Temperature variability and weather-related fluctuations significantly affect the energy, agricultural, and industrial sectors that are highly sensitive to meteorological changes. These conditions may lead to financial losses caused by demand fluctuations and operational disruptions. This study aims to develop a fractional weather-derivative pricing model based on temperature dynamics by integrating the Ornstein–Uhlenbeck (OU) process, the classical Black–Scholes model (BSM), and the fractional Black–Scholes model (fBSM). Daily temperature data from 2016 to 2025 obtained from the Bandung Geophysical Station, West Java, Indonesia, were used as the basis of analysis. Temperature dynamics were modeled using an OU process, and parameter estimation was conducted using Ordinary Least Squares (OLS). The strike price was determined using Historical Burn Analysis (HBA), whereas weather-derivative pricing was performed using call and put option approaches under both the BSM and fBSM frameworks, incorporating the Hurst parameter to capture long-term memory effects. The results indicate that the fractional Black–Scholes model analytical solution is obtained using the Daftardar–Gejji Aboodh method. Furthermore, the OU process successfully captured daily temperature dynamics, yielding a Mean Absolute Percentage Error (MAPE) of 4.344% and a Root Mean Square Error (RMSE) of 1.396 C, indicating high predictive accuracy across both relative and absolute error measures. In addition, the fBSM consistently generated higher option values than the classical BSM, particularly under higher observed temperatures during the study period and at higher strike prices. These findings demonstrate that long-term memory significantly influences effective volatility and option valuation. This study is expected to contribute to the development of weather derivative models that more realistically represent temperature dynamics and to serve as a reference for weather derivative pricing, hedging, and decision-making, as well as for more measurable, systematic, and sustainable climate-related financial analysis using derivative pricing frameworks. Full article

Foggy traffic scenes pose significant challenges for object detection because reduced contrast, blurred object boundaries, and the loss of local details weaken discriminative feature representations. These degradations are particularly detrimental to lightweight detectors used in intelligent transportation and vehicle perception systems, where both accuracy and real-time efficiency are required. To address this problem, this paper proposes YOLO-GCM, a lightweight detector-side feature enhancement framework built upon YOLO11n. Instead of relying on an external image dehazing stage, YOLO-GCM improves the internal feature representation of the detector through three complementary modules: a gated additive feature block (GAFB) for adaptive channel-wise feature selection and noise suppression, a context-aware feature enhancement module (CAFEM) for strengthening high-level semantic context, and a multi-scale adaptive fusion (MSAF) module for enhancing cross-scale feature interaction. By integrating these modules into a unified one-stage detector, the proposed method improves detection robustness under low-visibility traffic conditions while maintaining a compact architecture. Experiments on the FoggyCar dataset show that YOLO-GCM achieved 89.81% mAP@0.5 and 67.99% mAP@0.5:0.95, outperforming standard YOLO baselines and dehazing-assisted detection pipelines under a consistent evaluation protocol. Additional evaluation on Foggy Cityscapes further verified the generalization capability of the proposed method under domain shift. The results demonstrate that detector-side feature enhancement provides an effective and efficient alternative to multi-stage dehazing-plus-detection pipelines for foggy traffic object detection. These findings can provide useful guidance for the development of robust and efficient perception modules in roadside monitoring, intelligent transportation systems, and vehicle-assisted driving applications under adverse weather conditions. Full article

Urban heat stress is intensifying under climate change, particularly in outdoor public spaces where conventional mechanical cooling is impractical. This study develops a climate-driven, system-level numerical framework to evaluate the pre-deployment feasibility of modular, solar-powered spray cooling canopies across 110 cities in Türkiye. Hourly Typical Meteorological Year (TMYx) weather files, representing a single typical year constructed from 2009 to 2023 source data, are used to estimate photovoltaic (PV) energy yield, electrical load, feasible misting duration, water demand, and PV-to-load autonomy under summer daytime conditions. The misting operation is governed by a rule-based adaptive control strategy based on air temperature, relative humidity, and plane-of-array irradiance. To support transferable comparison, the cities are classified into six summer climate-archetype zones using k-means clustering of standardized climate variables, including temperature, humidity, irradiance, wind speed, and summer precipitation. Results show that evaporative cooling feasibility is governed primarily by humidity rather than temperature alone. Hot–Dry Inland cities exhibit the longest mean misting duration (501.90 h) and highest water demand (30,152 L per module), but the lowest PV-to-load autonomy ratio (1.55) because of high pump-driven electrical demand. In contrast, Humid Black Sea cities show minimal misting duration (11.43 h) and water use (465 L per module), but the highest autonomy ratio (39.68) due to very limited system activation. Thus, high autonomy does not necessarily indicate high cooling usefulness. The proposed framework provides a reproducible screening tool for identifying where PV-powered spray cooling canopies are climatically suitable, where water and PV sizing become limiting, and where alternative outdoor heat-mitigation strategies may be more appropriate. Full article