Search Results (593)

The study investigates the influence of atmospheric pressure fluctuations on methane emissions in a decommissioned coal mine in Poland (SRK S.A., KWK “Krupiński”). Continuous measurements of methane concentrations and atmospheric pressure were analyzed to identify periods of dynamic pressure drops, which were then correlated with recorded methane levels. Strong linear relationships were observed, with correlation coefficients ranging from 0.88 to 0.97 and determination coefficients exceeding 0.85, indicating that pressure changes are a primary factor influencing methane release. Individual regression models for each identified case showed the lowest mean absolute errors compared to generalized models, highlighting the impact of atypical cases on predictive performance. Key findings align with previous studies, confirming that both the magnitude and the gradient of pressure decline directly affect the rate and scale of methane release and that threshold effects may limit further concentration increases despite continued pressure drops. The results suggest the potential to develop a predictive model linking atmospheric pressure variations to methane emissions, which could support forecasting of methane capture in decommissioned mines or ventilation methane levels in active mines. Understanding these mechanisms is crucial for both occupational safety and for effective methane emission reduction strategies in the mining sector. Full article

The aviation sector is one of the largest sources of greenhouse gas emissions, and the European Union (EU) is calling for a rapid transition to sustainable aviation fuels (SAFs). This study aims to assess market dynamics and regulatory challenges of sustainable aviation fuels (SAFs) in the European Union, with emphasis on economic feasibility and the role of policy frameworks. Using econometric methods: Autoregressive Integrated Moving Average (ARIMA) and Vector Autoregression (VAR) models, forecasts of SAF infrastructure development trajectories were produced, while regression analysis was applied to assess the relationship between national GDP and the scale of SAF deployment. The results revealed a statistically significant positive link between higher economic development and faster expansion of SAF infrastructure, highlighting the policy-driven nature of market dynamics. Germany and France demonstrate the greatest growth potential, while countries such as Italy and Denmark show slower progress. The findings confirm that clear regulatory frameworks and targeted economic incentives are essential to stimulate SAF uptake; however, additional investment and stronger policy harmonization across Member States are required to achieve large-scale commercialization and long-term sustainability. The empirical analysis utilizes data from 2015 to 2023 to estimate SAF infrastructure trajectories and policy effects, ensuring sufficient temporal coverage for robust econometric modeling and forecasting. Full article

Carbon sequestration by vegetation and soil conservation are vital components in balancing greenhouse gas emissions and enhancing terrestrial ecosystem carbon sinks. They also represent an efficient pathway towards achieving carbon neutrality objectives and addressing numerous environmental challenges arising from global warming. Soil and water conservation, as crucial elements of ecological civilisation development, constitute a key link in realising carbon neutrality. This study systematically quantifies and forecasts the spatiotemporal characteristics of carbon sink capacity in soil and water conservation within the study area of Puding County, a typical karst region in Guizhou Province, China. Following a research approach of “mechanism elucidation–model construction–categorised estimation”, we established a carbon sink calculation system based on the dual mechanisms of vertical biomass carbon fixation via vegetative measures and horizontal soil organic carbon (SOC) retention using engineering measures. This system combines forestry, grassland, and engineering, with the aim of quantifying regional carbon sinks. Machine learning regression algorithms such as Random Forest, ExtraTrees, CatBoost, and XGBoost are used for backtracking estimation and optimisation modelling of soil and water conservation as carbon sinks from 2010 to 2022. The results show that the total carbon sink capacity of soil and water conservation in Puding County in 2017 was 34.53 × 10⁴ t, while the contribution of engineering measures was 22.37 × 10⁴ t. The spatial distribution shows a pattern of “higher in the north and lower in the south”. There are concentration hotspots in the central and western regions. Model comparison demonstrates that the Random Forest and extreme gradient boosting regression models are the best models for plantations/grasslands and engineering measures, respectively. The LSTM model was applied to predict carbon sink variables over the next ten years (2025–2034), showing that the overall situation is relatively stable, with only slight local fluctuations. This study solves the problem of the lack of quantitative data on soil and water conservation as carbon sinks in karst areas and provides a scientific basis for regional ecological governance and carbon sink management. Our findings demonstrate the practical significance of promoting the realisation of the “double carbon” goal. Full article

Prediction of coal and rock dynamic disasters is essential for ensuring the safety, efficiency, and long-term sustainability of deep mining operations. To improve the accuracy of acoustic methods for forecasting coal instability, acoustic emission (AE) source localization experiments are conducted on coal samples under uniaxial compression, and the multidimensional correlations among AE events together with the evolution characteristics of the corresponding complex network are investigated. The results show that the temporal correlations of AE events exhibit nonlinear decay with increasing time intervals, the spatial correlations display fractal clustering that transcends Euclidean geometry, and the energetic correlations reveal hierarchical transitions controlled by intrinsic material properties. To capture these interactions, a multidimensional correlation calculation method is developed to quantitatively characterize these multidimensional coupled relationships of AE events, and a complex network of AE events is constructed. The network evolution from sparse to highly interconnected is quantified using three parameters: average degree, clustering coefficient, and modularity. A rapid rise in the first two metrics, accompanied by a sharp decline in the latter, indicates the rapid strengthening of AE event correlations, the aggregation of local microcrack clusters, and their transition into a global fracture network, thereby providing a clear early warning of impending compressive failure of the coal sample. The study establishes a mechanistic link between microcrack evolution and macroscopic failure, offering a robust real-time monitoring tool that supports sustainable mining by reducing disaster risk, improving resource extraction stability, and minimizing socio-economic and environmental losses associated with dynamic failures in deep underground coal operations. Full article

This study examines the impact of Environmental, Social, and Governance (ESG) strategies on reducing air pollution in the West Kazakhstan region, a major hub for Kazakhstan’s oil and gas industry. A spatial analysis of atmospheric emissions reveals an uneven distribution of emission sources, predominantly concentrated in the northern industrialized part of the region, where the Karachaganak oil and gas condensate field is located. The ESG model of Karachaganak Petroleum Operating b.v. (KPO), implemented as an integrated management system based on Global Reporting Initiative (GRI) standards, is compared with the ESG strategies of leading oil and gas companies in Kazakhstan and globally, aligning with current international research trends. The analysis underscores the interdependence of technological and social aspects in the transition to a low-carbon economy, confirming the importance of integrating the environmental, social, and governance components of ESG into a unified strategic planning framework for sustainable development. Using econometric modeling, the study establishes a relationship between ESG indicators and the reduction in atmospheric pollution and provides a forecast for emission reductions by 2030. The key measures proposed to improve regional air quality are linked to long-term decarbonization strategies within the context of the sustainable development of the entire region. The proposed algorithm for implementing ESG principles helps to identify the concentration of functions and associated risks at different management levels within Highly Polluting Enterprises (HPEs) and optimizes business processes by focusing efforts on air pollution mitigation. The findings are applicable to other countries, as oil and gas producers worldwide face a number of common air pollution challenges. Full article

Mine-induced surface subsidence threatens infrastructure and can trigger cascading geohazards, so accurate and computationally efficient monitoring and forecasting are essential for early warning. We integrate Persistent Scatterer InSAR (PS-InSAR) time series with a Bayesian-optimized CNN–LSTM designed for spatiotemporal prediction. The CNN extracts spatial deformation patterns, the LSTM models temporal dependence, and Bayesian optimization selects the architecture, training hyperparameters, and the most informative exogenous drivers. Groundwater level and backfilling intensity are encoded as multichannel inputs. Endpoint anchoring with affine calibration aligns the historical series and the forward projections. PS-InSAR indicates a maximum subsidence rate of 85.6 mm yr⁻¹, and the estimates are corroborated against nearby leveling benchmarks and FLAC^3D simulations. Cross-site comparisons show acceleration followed by deceleration after backfilling and groundwater recovery, which is consistent with geological engineering conditions. A symmetry-aware preprocessing step exploits axial regularities of the deformation field through mirroring augmentation and documents symmetry-breaking hotspots linked to geological heterogeneity. These choices improve generalization to shifted and oscillatory patterns in both the spatial CNN and the temporal LSTM branches. Short-term forecasts from the BO–CNN–LSTM indicate subsequent stabilization with localized rebound, highlighting its practical value for operational planning and risk mitigation. The framework combines automated hyperparameter search with physically consistent objectives, reduces manual tuning, enhances reproducibility and generalizability, and provides a transferable quantitative workflow for forecasting mine-induced deformation in complex goaf systems. Full article

Accurate prediction of significant wave height (SWH) is central to coastal ocean dynamics, wave–climate assessment, and operational marine forecasting, yet many high-performing machine-learning (ML) models remain opaque and weakly connected to underlying wave physics. We propose an explainable, feature engineering-guided ML framework for coastal SWH prediction that combines extremal wave statistics, temporal descriptors, and SHAP-based interpretation. Using 30 min buoy observations from a high-energy, wave-dominated coastal site off Australia’s Gold Coast, we benchmarked seven regression models (Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regression, K-Nearest Neighbors, and Neural Networks) across four feature sets: (i) Base (Hmax, Tz, Tp, SST, peak direction), (ii) Base + Temporal (lags, rolling statistics, cyclical hour/month encodings), (iii) Base + a physics-informed Wave Height Ratio, WHR = Hmax/Hs, and (iv) Full (Base + Temporal + WHR). Model skill is evaluated for full-year, 1-month, and 10-day prediction windows. Performance was assessed using R², RMSE, MAE, and bias metrics, with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) employed for multi-criteria ranking. Inclusion of WHR systematically improves performance, raising test R² from a baseline range of ~0.85–0.95 to values exceeding 0.97 and reducing RMSE by up to 86%, with a Random Forest|Base + WHR configuration achieving the top TOPSIS score (1.000). SHAP analysis identifies WHR and lagged SWH as dominant predictors, linking model behavior to extremal sea states and short-term memory in the wave field. The proposed framework demonstrates how embedding simple, physically motivated features and explainable AI tools can transform black-box coastal wave predictors into transparent models suitable for geophysical fluid dynamics, coastal hazard assessment, and wave-energy applications. Full article

Accurate wind power forecasting is crucial for the secure and efficient integration of renewable energy into the power grid. However, the inherent intermittency and non-stationary nature of wind power pose significant challenges to prediction models. To address these issues, this paper proposes a novel hybrid forecasting framework named VMD-IPCA-IHSO-FSRVFL. This model synergistically combines variational mode decomposition (VMD), incremental principal component analysis (IPCA) for feature selection, an improved holistic swarm optimization (IHSO) algorithm, and a feature space-regularized random vector functional link (FSRVFL) network. The VMD first decomposes the complex original wind power signal into several stable sub-sequences to simplify the prediction task. The IPCA then identifies and selects the most relevant features, reducing data redundancy and noise. Subsequently, the IHSO algorithm is employed to automatically optimize the hyperparameters of the FSRVFL model, enhancing its performance and convergence speed. Finally, the optimized FSRVFL, a computationally efficient semi-supervised learning model, performs the final prediction. The proposed model was validated using four seasonal datasets from a Chinese offshore wind farm. Experimental results demonstrate that our VMD-IPCA-IHSO-FSRVFL model significantly outperforms other benchmark models, including BP, ELM, RVFL, and their variants, across all evaluation metrics (MSE, RMSE, MAE, and R²). The findings confirm that the integration of signal decomposition, effective feature selection, and intelligent parameter optimization substantially improves forecasting accuracy and stability under different seasonal conditions. This study provides a robust and effective solution for wind power prediction, offering valuable insights for wind farm operation and grid management. Full article

Accurate daily runoff forecasting is essential for flood control and water resource management, yet existing models struggle with the seasonal non-stationarity and inter-basin variability of runoff sequences. This paper proposes a Season-Aware Ensemble Forecasting (SAEF) method that integrates SVM, LSSVM, LSTM, and BiLSTM models to leverage their complementary strengths in capturing nonlinear and non-stationary hydrological dynamics. SAEF employs a seasonal segmentation mechanism to divide annual runoff data into four seasons (spring, summer, autumn, winter), enhancing model responsiveness to seasonal hydrological drivers. An Improved Arctic Puffin Optimization (IAPO) algorithm optimizes the model weights, improving prediction accuracy. Beyond numerical gains, the framework also reflects seasonal runoff generation processes—such as rapid rainfall–runoff in wet seasons and baseflow contributions in dry periods—providing a physically interpretable perspective on runoff dynamics. The effectiveness of SAEF was validated through case studies in the Dongjiang Hydrological Station (China), the Elbe River (Germany), and the Quinebaug River basin (USA), using four performance metrics (MAE, RMSE, NSEC, KGE). Results indicate that SAEF achieves average Nash–Sutcliffe Efficiency Coefficient (NSEC) and Kling–Gupta efficiency (KGE) coefficients of over 0.92, and 0.90, respectively, significantly outperforming individual models (SVM, LSSVM, LSTM, BiLSTM) with RMSE reductions of up to 58.54%, 55.62%, 51.99%, and 48.14%. Overall, SAEF not only strengthens predictive accuracy across diverse climates but also advances hydrological understanding by linking data-driven ensembles with seasonal process mechanisms, thereby contributing a robust and interpretable tool for runoff forecasting. Full article

The building sector accounts for a significant portion of the global energy consumption and carbon dioxide (CO₂) emissions, making it a critical area for improving energy efficiency. In Africa, the rapid energy demand and costs have further emphasized the urgency of developing effective solutions for reducing building energy use. This paper presents a comprehensive review of data-driven and physics-based modeling approaches for forecasting and optimizing energy performance in non-domestic buildings. The review highlights the evolution of statistical models, classical machine learning methods, deep learning, and hybrid approaches across various application scenarios. Emphasis is placed on the role of data pre-processing techniques, including data fusion and transfer learning, as strategies to address data limitations and improve model generalization. Furthermore, the study evaluates the strengths and limitations of different modeling methods in terms of accuracy, scalability, and applicability in real-world contexts. By integrating insights from recent literature, this paper identifies key research gaps such as the need for standard datasets, physics-informed hybrid modeling, and policy-oriented frameworks. The findings aim to guide building managers, policymakers, and researchers toward adopting robust data-driven solutions that enhance energy resilience, reduce operational costs, and support environmental sustainability in the built environment. The review also justifies the importance of these models for practical applications like energy benchmarking, retrofit planning, and CO₂ reduction, providing a clear link between research and industry implementation. Full article

In the highly volatile realm of global security, the necessity for leading-edge and effectual border resilience tactics has never been more imperative. This PRISMA 2020 guided systematic literature review (SLR) examines the intersection of artificial intelligence (AI), open-source intelligence (OSINT), and social media intelligence (SOCMINT) for enhancing border protection. Our systematic investigation across major databases (IEEE Xplore, Scopus, SpringerLink, MDPI, ACM) and grey literature sources yielded 3932 initial records and, after screening and eligibility assessment, 73 studies and reports from acknowledged organizations, contributing to the evidence synthesis. Three research questions (RQ1–RQ3) were addressed concerning the following: (a) the effectiveness and application of AI in OSINT/SOCMINT for border protection, its (b) data, technical, and operational limitations, and its (c) ethical, legal, and societal implications (GELSI). Evidence matrices summarize the findings, while narrative syntheses underline and thematically group the extracted insights. Results indicate that AI techniques—fluctuating from machine learning (ML) and natural language processing (NLP) to computer vision and emerging large language models (LLMs)—produce quantifiable improvements in forecasting irregular migration, detecting human trafficking, and supporting multimodal intelligence fusion. However, limitations include misinformation, data bias, adversarial vulnerabilities, governance deficits, and sandbox-to-production gaps. Ethical and societal concerns highlight risks of surveillance overreach, discrimination, and insufficient oversight, among others. To our knowledge, this is the first SLR at this intersection. We conclude that, AI-assisted OSINT/SOCMINT presents transformative potential for border protection requiring, nonetheless, balanced governance, robust validation, and future research on LLM/agentic AI, human–AI teaming, and oversight mechanisms. Full article

Accurate solar radiation forecasting with numerical weather prediction (NWP) is critical for optimizing photovoltaic power generation. This study evaluates short-term (<36 h) performance of the Weather Research and Forecasting model (WRF-Solar) and its ensemble version (WRF-Solar EPS) for global horizontal irradiance (GHI) and direct horizontal irradiance (DIR) over East Asia (December 2019–November 2020) against geostationary satellite retrievals. Both models effectively capture GHI spatial patterns but exhibit systematic overestimation (biases: 17.27–17.68 W/m²), with peak errors in northwest China and the North China Plain. Temporal mismatches between bias (maximum in winter-spring) and RMSE/MAE (maximum in summer) may indicate seasonal variability in error signatures dominated by aerosols and clouds. For DIR, regional biases prevail: overestimation in the Tibetan Plateau and northwest China, and underestimation in southern China and Indo-China Peninsula. Errors (RMSE and MAE) are larger than for GHI, with peaks in southeast and northwest China, likely linked to poor cloud–aerosol simulations. WRF-Solar EPS shows no significant bias reduction but modest RMSE/MAE improvements in summer–autumn, particularly in southeast China, indicating limited enhancement of short-term predictive stability. Both WRF-Solar and WRF-Solar EPS require further refinements in cloud–aerosol parameterizations to mitigate systematic errors over East Asia in future applications. Full article

Saltwater intrusion increasingly jeopardizes groundwater in low-lying coastal plains worldwide, where the combined effects of sea-level rise, land subsidence, and hydraulic regulation further exacerbate aquifer vulnerability and threaten the long-term sustainability of freshwater supplies. To move beyond sparse and fragmented piezometric observations, we propose “integrated coastal supersites”: wells equipped with multiparametric sensors and multilevel piezometers that couple high-resolution vertical conductivity–temperature–depth (CTD) profiling with continuous hydro-meteorological time series to monitor the hydrodynamic behavior of coastal aquifers and saltwater intrusion. This study describes the installation of two supersites and presents early insights from the first monitoring period, which, despite a short observation window limited to the summer season (July–September 2025), demonstrate the effectiveness of this approach. Two contrasting supersites were deployed in the coastal plain between the Brenta and Adige Rivers (Italy): Gorzone, characterized by a thick, laterally persistent aquitard, and Buoro, where the aquitard is thinner and discontinuous. Profiles and fixed sensors at both sites reveal a consistent fresh-to-saline transition in the phreatic aquifers and a secondary freshwater lens capping the confined systems. At Gorzone, the confining layer hydraulically isolates the deeper aquifer, preserving low salinity beneath a saline, tidally constrained phreatic zone. Groundwater heads oscillate by about 0.2 m, and rainfall events do not dilute salinity; instead, pressure transients—amplified by drainage regulation and inland-propagating tides—induce short-lived EC increases via upconing. Buoro shows smaller water-level variations, not always linked to rainfall, and, in contrast, exhibits partial vertical connectivity and faster dynamics: phreatic heads respond chiefly to internal drainage and local recharge, with rises rapidly damped by pumping, while salinity remains steady without episodic peaks. The confined aquifer shows buffered, delayed responses to surface forcings. Although the monitoring window is currently limited to 2025 through the summer season, these results offer compelling evidence that coastal supersites are reliable, scalable, and management-critical relevance platforms for groundwater calibration, forecasting, and long-term assessment. Full article

Tropical cyclones (TCs) induce pronounced sea surface temperature (SST) cooling, which strongly influences their intensity. Accurate prediction of TC intensity is particularly important in coastal regions where landfall occurs. While SST cooling has been extensively studied in the open ocean, its characteristics in coastal seas remain less understood. Using satellite and reanalysis data from 2004 to 2021, this study systematically characterizes SST cooling in China’s coastal seas—the Yellow Sea, East China Sea, Taiwan Strait, and northern South China Sea—and compares the cooling with adjacent offshore regions. Composite analyses of about 6300 TC track points reveal that coastal SST cooling shows significant differences relative to their offshore cooling. Regionally, the Yellow Sea exhibits significantly stronger coastal cooling (−2.5 °C vs. −1.8 °C), whereas the Taiwan Strait shows weaker coastal cooling. Further analyses using a statistical subsampling method reveal that coastal–offshore cooling differences result from the combined effects of TC attributes and pre-TC oceanic conditions, with temperature stratification exerting the dominant control. Furthermore, an increasing trend in coastal cooling is linked to enhanced temperature stratification. These findings highlight the critical role of pre-TC temperature structure in modulating coastal SST responses, with implications for improving intensity forecasts and risk assessments. Full article

Accurate typhoon track forecasting is vital for disaster mitigation in East China, a region frequently impacted by landfalling typhoons. Despite advances in numerical weather prediction, uncertainties remain high, especially within 48 h of landfall, due to complex interactions among tropical cyclones, the subtropical high, and mesoscale systems. This study applies Ensemble-based Sensitivity Analysis (ESA) within a high-resolution regional ensemble prediction system (Shanghai Weather And Risk Model System-Ensemble Prediction System, SWARMS-EN) to investigate forecast uncertainties of three representative typhoons—Gaemi, Bebinca, and Kong-rey—that made landfall in East China in 2024. Our results reveal consistent sensitivity patterns across diverse large-scale environments, particularly around the western flank of the subtropical high and in proximity to nearby low-pressure systems. Track uncertainty was closely tied to fluctuations in the steering flow, notably its zonal component. Moreover, binary typhoon interactions emerged as key drivers of forecast divergence. ESA effectively identified sensitive regions where small initial perturbations exert significant downstream influence on typhoon tracks. This study demonstrates the operational value of ESA for diagnosing forecast error sources and guiding targeted observations. By linking forecast uncertainty to physical mechanisms, this research enhances our understanding of typhoon predictability and supports the development of more adaptive and accurate regional forecasting systems. Full article