Search Results (61)

Wind energy is an important form of clean energy, and its rational utilization represents a crucial solution for mitigating the energy crisis and global warming. In this study, wind energy potential and its long-term changes in the South China Sea (SCS) are evaluated using ERA5 100 m wind data from 1944 to 2023, validated against ASCAT observations. High wind speeds and high wind power density (WPD) are concentrated southwest of Taiwan and southeast of Vietnam. Annual wind availability exceeds 6457 h across most regions, reaching up to 8283 h in optimal locations. WPD and capacity factor peak in winter (up to 2.4 × 10⁸ Wh·m⁻² and >50% capacity factor), with the most stable conditions occurring in the southwestern Taiwan Strait, southeast of the Pearl River Delta, and the Beibu Gulf. Empirical orthogonal function analysis reveals that the first mode of winter WPD accounts for 65.7% of the total variance, with a statistically significant increasing trend since 1990. The interannual variation in wind energy resources in the SCS during winter is controlled by the combined effects of sea surface temperature (SST) anomalies in the tropical Pacific and the Arctic Barents Sea. Specifically, in the years with strong wind anomalies in the SCS, mega-La Niña-type SST patterns in the tropical Pacific trigger anomalous cyclonic circulation in the SCS and the eastern Philippine Sea, while warm anomalies in the Arctic Barents Sea surface drive a wave-like structure of “anticyclone–cyclone–anticyclone” from Siberia to South China. The coupling of the two systems jointly promotes the strengthening of the South China Sea monsoon, leading to increased wind speeds and elevated WPD in the northern SCS. These findings provide a scientific basis for wind farm siting and long-term operational planning in the region. Full article

Since September 2021, numerous seismic events with spectral peaks below 1 Hz occurred on the island of Vulcano, Italy, 131 years after its last eruption. The local monitoring network recorded microseismicity mostly in the form of months-long swarms, concurrent with anomalous values of other geophysical and geochemical parameters. By applying a machine learning technique (Self-Organizing Maps, SOMs), we obtained an inventory of ~6600 seismic signals, identifying and separating exogenous signals (anthropic noise) from distinct families of events. These families were located below La Fossa Crater (where the last eruption of the volcano happened) from the surface to a depth of 2.2 km b.s.l. Based on the seismic signature and source location of these events, we hypothesize unsealed/sealed processes through a network of shallow fractures favored by fluid pressure. After the return to background values of geochemical and geophysical parameters in 2023, a resumption of microseismicity occurred between May and June 2024. A test application of the SOM to the new data confirmed the non-destructive source of the new recorded signals, which shared families, location, and depths with our previous inventory. This test showed that SOM can be an effective tool for supporting real-time monitoring and warning of future unrest at Vulcano. Full article

With the acceleration of global climate change and urbanization, air pollution, particularly ozone pollution, has become a critical environmental issue, especially in the Beijing–Tianjin–Hebei region of China. This study investigates the spatiotemporal distribution of ozone pollution and its precursors, focusing on formaldehyde as a key indicator of volatile organic compounds. Utilizing high-resolution remote sensing data from the China High-Resolution Air Pollutants dataset and TROPOMI HCHO observations from 2013 to 2022, we employed advanced techniques such as the Kolmogorov–Zurbenko filter and high-value area identification to analyze ozone pollution trends, meteorological influences, and the spatial distribution of HCHO concentrations. Our findings reveal a significant increase in ozone concentrations across BTH, with an annual growth rate of 2.51 μg/m³, peaking during the summer months. The KZ filter decomposition highlighted that short-term and seasonal variations dominate ozone fluctuations, driven by meteorological factors such as solar radiation and temperature. Furthermore, the identification of HCHO HVAs demonstrated that urban agglomeration and expansion zones exhibit higher HCHO concentrations, with VOCs-limited zones showing the most pronounced HCHO levels. The study also introduced the PHV (Percentage Higher than Vicinity) index to quantify anomalous HCHO emissions, providing a robust tool for pinpointing pollution hotspots. Based on these insights, we propose targeted emission control strategies for key regions, including urban expansion zones in Zhangjiakou and non-urban zones in Qinhuangdao, to mitigate ozone pollution effectively. This research offers valuable scientific support for regional air quality management and the formulation of precise pollution control measures in the Beijing–Tianjin–Hebei region. Full article

Understanding the effects of mixing-driven precipitation on solute transport behavior is critical for reactive transport predictions, yet its complexity, arising from the interplay of flow dynamics, solute transport, and geochemical reactions, remains a significant challenge. In particular, mineral precipitation modifies the hydraulic properties of porous media. The impact of this process on the solute transport behavior remains largely unexplored and is crucial for accurate reactive transport predictions. This study presents a controlled laboratory investigation of mixing-driven calcite precipitation (MDP) in an intermediate-scale Hele-Shaw cell, simulating a coarse-sand porous medium. The experiment allowed for direct visualization of the spatiotemporal evolution of precipitation while continuously monitoring hydraulic properties. Self-organized heterogeneities in the precipitate structure were observed, with calcite layers forming symmetric patterns aligned with the main flow, contrasting with the asymmetry predicted by a semi-analytical model under idealized conditions. Tracer tests conducted before and after precipitation demonstrated significant impacts on solute transport, including the emergence of strong anomalous transport features, such as earlier solute arrival, a distinct double peak, and pronounced tailing. These findings highlight the critical role of precipitation-induced heterogeneities in shaping transport behavior, emphasizing the need to integrate these dynamics into reactive transport models for improved predictive accuracy. Full article

Carbon fiber-reinforced polyetheretherketone (CF/PEEK) thermoplastic composites are increasingly applied in aerospace structures due to their outstanding mechanical and thermal properties. However, their strengthening mechanism and damage evolution under repeated low-velocity impacts remains inadequately explored. This study systematically investigates the mechanical response and failure mechanisms of CF/PEEK laminates subjected to sequential single and second impacts at energy levels of 10 J, 20 J, and 30 J. Through comprehensive analysis of impact parameters (peak load, energy absorption, residual displacement), optical microscopy and ultrasonic C-scan, this study reveals that the load-bearing increase under repeated low-velocity impacts results from the combined effects of multiple mechanisms, including matrix plastic deformation, local compaction, matrix damage, and interlaminar failure. Under initial impacts, laminates exhibit high load-bearing capacity and energy dissipation, which are dominated by plastic deformation and matrix failure at 10 J and 20 J, whereas the 30 J impact causes pronounced fiber failure. An anomalous increase in peak load is observed during secondary impacts, which is attributed to matrix compaction-induced strengthening resulting from the initial impact. Optical microscopy and C-scan quantification demonstrate that, while the initial impact induces compaction-related strengthening, it also causes internal damage, which leads to aggravated damage evolution during the subsequent impact. The findings provide fundamental insights into damage accumulation in thermoplastic composites and directly inform impact-resistant design strategies. Full article

Recent laboratory experiments in an intermediate-scale Hele-Shaw cell, designed to represent a coarse sand aquifer, demonstrate that mixing-induced calcite precipitation leads to the formation of a self-organized, heterogeneous porous medium. This morphology, characterized by elongated carbonate structures and internal preferential flow channels, induces strong anomalous transport features, including early solute arrival, distinct double-peak breakthrough curves, and pronounced tailing. In this article, we investigate the link between this precipitation-induced heterogeneity and solute transport by implementing varying permeability scenarios, derived from experimental image analysis, into a transport model. Our analysis reveals that while a standard dual-permeability approach, which simply delineates the total precipitated area, captures the flow diversion responsible for the emergence of the double peak, it fails to reproduce the transition between peaks and the late-time tailing. To address this, we introduce a novel triple-permeability model that incorporates internal preferential flow channels within the high-precipitation zones. By resolving the internal structure of these zones, the triple-permeability model accurately captures the dual-peak transition and tailing behavior. These findings provide critical insights for applications such as geological carbon sequestration and enhanced oil recovery. Although determining exact internal structures in field settings is challenging, our results demonstrate that effective transport models must account for the internal heterogeneity of high-precipitation zones, rather than treating them as uniform barriers, to accurately predict the channeling effects that govern injectivity and long-term storage security. Full article

Existing macroscopic finite element models for electron beam welding (EBW) typically assume isotropic material behavior, often failing to accurately predict residual stresses induced by strong crystallographic textures. To address this limitation, this study established a sequential dual-scale coupled numerical model bridging micro-texture to macro-mechanics by combining the crystal plasticity finite element method (CPFEM) with thermal-elastic-plastic theory. Representative volume elements (RVEs) incorporating α and β dual-phase characteristics were constructed based on electron backscatter diffraction (EBSD) data from the TA15 weld cross-section. Through simulated tensile and shear calculations on the RVEs, homogenized orthotropic stiffness matrices and Hill yield constitutive parameters were derived and mapped onto the macroscopic model. Simulation results indicate that the proposed model maintains the prediction error for molten pool morphology within 16.3%, while effectively correcting the stress overestimation inherent in isotropic models. Specifically, it adjusts the peak longitudinal residual stress at the weld center from 800 MPa to approximately 350 MPa, significantly reducing the anomalous “M-shaped” stress distribution. By successfully capturing shear stress components, this work provides a high-fidelity computational approach for predicting complex stress states in welded joints, offering critical insights for structural integrity assessment. Full article

The analysis of ground-penetrating radar data generally relies on the visual identification of structures on selected profiles and their interpretation in terms of buried features. In simple cases, inverse modelling of the acquired data set can facilitate interpretation and reduce subjectivity. These methods suffer from severe restrictions due to antenna resolution limits, which prevent the identification of tiny structures, particularly in forensic, stratigraphic, and engineering applications. Here, we describe a technique to obtain a high-resolution characterization of the underground, based on the forward modelling of individual traces (A-scans) of selected radar profiles. The model traces are built by superposition of Ricker wavelets with different polarities, amplitudes, and arrival times and are used to create reflectivity diagrams that plot reflection amplitudes and polarities versus depth. A thin bed is defined as a layer of higher or lower permittivity relative to the surrounding material, such that the top and bottom reflections are subject to constructive interference, determining the formation of an anomalous peak in the trace (tuning effect). The proposed method allows the detection of ultra-thin layers, well beyond the Rayleigh vertical resolution of GPR antennas. This approach requires a preliminary estimation of the instrumental uncertainty of common monostatic antennas and takes into account the frequency-dependent attenuation, which causes a spectral shift of the dominant frequency acquired by the receiver antenna. Such a quantitative approach to analyzing radar data can be used in several applications, notably in stratigraphic, forensic, paleontological, civil engineering, heritage protection, and soil stratigraphy applications. Full article

Dendrochronology serves as a vital tool for analyzing the long-term interactions between commercial timber growth and environmental variables such as soil, water, and climate. This study presents Dendro-AutoCount, an innovative image processing framework designed for identifying obscured tree rings in cross-sectional images of Pinus taeda L. The methodology integrates Hessian-based ridge detection with a weighted radial voting gradient method to precisely locate the pith. Following pith detection, the system performs radial cropping to generate directional sub-images (north, east, south, west), where rings are identified via intensity profile analysis, signal smoothing, and peak detection. By filtering outliers and averaging directional counts, the system effectively mitigates common visual interference from black molds, fungus, structural cracks, buds, knots, and cracks. Experimental results confirm the high efficacy of Dendro-AutoCount in processing anomalous tree ring images. Full article

The tidal mudflat of the South Yellow Sea is characterized by complex sediment environments that preserve rich paleoenvironmental signals, making it an important area for understanding land–sea interactions and promoting sustainable coastal development. Thus, accurate identification of sediment sequences and layer thicknesses becomes crucial for interpreting sediment dynamics and paleoenvironmental reconstruction. While borehole data have elucidated local sediment facies, their spatially discontinuous nature hinders a holistic reconstruction of regional depositional history. To overcome this limitation, ground-penetrating radar (GPR) surveys were conducted across the tidal mudflat of the South Yellow Sea, enabling systematic correlation between radar reflection patterns and sediment architectures. Based on the relationship between the dielectric permittivity and wave velocity, short-time Fourier transform (STFT) was applied to derive the peak-weighted average frequency in the frequency domain for individual soil layers, revealing its dependence on dielectric properties. Sediment interfaces and layer thicknesses were determined using three methods: the radar image waveform method, the Hilbert spectrum instantaneous phase method, and the generalized S-transform time–frequency analysis method. The results indicate the following: (1) GPR enables high-fidelity imaging of subsurface stratigraphy, successfully resolving three distinct radar facies: F1: high-amplitude, horizontal, continuous reflections with parallel waveforms; F2: moderate-to-high-amplitude, sinuous continuous reflections with parallelism; and F3: medium-amplitude, discontinuous chaotic reflections. (2) All three methods effectively characterize subsurface soil stratification, but positioning accuracy decreases systematically with depth. Excluding anomalous errors at one site, the relative error for most layers within the 1 m depth is below 15%, and remains ≤25% at the 1–2 m depth. Beyond the 2 m depth, reliable stratification becomes unattainable due to severe signal attenuation. (3) Comparative analysis demonstrates that the Hilbert spectral instantaneous phase method significantly enhances GPR signals, achieving an optimal performance with positioning errors consistently below 5 cm for most soil layers. The application of this approach along the tidal mudflat of the South Yellow Sea significantly enhances the precision of sediment layer boundary identification. Our analysis systematically interpreted radar facies, demonstrating the effectiveness of the Hilbert spectrum instantaneous phase method in delineating soil stratification. These findings offer reliable technical support for interpreting GPR data in comparable sediment environments. Full article

We propose a novel distributed demodulation optimization algorithm for optical frequency domain reflectometry (OFDR). This algorithm applies discrete-time analytic (DTA) signals to the Rayleigh backscattered signal (RBS) reconstruction. The DTA-RBS algorithm utilizes only positive-frequency components in the distance domain and employs a frequency-domain construction method to generate DTA-RBS, thereby improving performance without increasing the computational complexity of the OFDR demodulation algorithm. By leveraging the envelope property, DTA-RBS enhances spectral feature information and intensity while effectively suppressing high-frequency noise and spurious oscillations introduced during reconstruction, thereby maintaining a higher correlation between the reference and test data. Comprehensive experimental validation demonstrates significant performance improvements across multiple metrics. Cross-correlation intensity analysis shows that the average peak intensity of DTA-RBS reaches 0.9527, compared to 0.9096 for the conventional method. Standard deviation measurements on unstrained fiber segments demonstrate a 63% improvement. Large-strain demodulation experiments show that DTA-RBS exhibits superior strain demodulation performance and robustness, whereas the conventional method produces anomalous data points due to false peaks obscuring genuine correlation peaks. These results confirm that the DTA-RBS method provides a theoretically rigorous and practically effective approach for enhancing the sensing accuracy, stability, and robustness of OFDR in high-precision distributed measurement applications. Full article

The identification of anomalous data objects within massive datasets is a critical technique in financial auditing. Most existing methods, however, focus on global outlier anomalies detection with less effective in contexts such as Chinese financial budget auditing, where local outliers are often more prevalent and meaningful. To overcome this limitation, a unified outlier detection framework is proposed that integrates both global and local detection mechanisms using k-nearest neighbors (KNN) and kernel density estimation (KDE) methodologies. The global outlier score is redefined as the sum of the distances to the k-nearest neighbors, while the local outlier score is computed as the ratio of the average cluster density to the kernel density—replacing the cutoff distance employed in Density Peak Clustering (DPC). Furthermore, an adaptive adjustment coefficient is further incorporated to balance the contributions of global and local scores, and outliers are identified as the top-ranked objects based on the combined outlier scores. Extensive experiments on synthetic datasets and benchmarks from Python Outlier Detection (PyOD) demonstrate that the proposed method achieves superior detection accuracy for both global and local outliers compared to existing techniques. When applied to real-world Chinese financial budget data, the approach yields a substantial improvement in detection precision-with 38.6% enhancement over conventional methods in practical auditing scenarios. Full article

Dust storms have a significant impact on the Martian atmosphere and climate. Previous studies have found that regional and global dust storms mainly occur in the Mars perihelion season. However, an anomalous spring regional dust storm occurred in the aphelion season of Martian year 35 (MY 35). The occurrence and evolution of this new type of large dust storm and its impact on the Martian atmosphere are not yet fully understood. Using Mars Climate Sounder (MCS) dust observations, this study investigates the evolutionary characteristics of the MY 35 anomalous spring storm during its pre-storm, onset, expansion, and decay phases, by comparing it with other types of regional dust storms. The evolution of the MY 35 anomalous spring dust storm is more similar to that of the MY 35 C storm, showing north–south mirror symmetry relative to the equator, suggesting that the two storms may have similar evolutionary mechanisms. Additionally, we analyze the effects of the anomalous MY 35 storm on the atmospheric thermal and dynamical structures using a combination of MCS temperature observations and LMD-GCM wind simulation results. Eastward winds in the high latitudes of both hemispheres and westward winds in the low-to-mid latitudes are significantly enhanced during the storm, corresponding to the change in the atmospheric thermal structure and the global circulation. Finally, we performed a preliminary analysis of changes in the wind field during the spring dust storm in the Chryse and Utopia plains, which are two potential landing areas for China’s Tianwen-3 Mars sample-return mission. The vertical profiles of the simulated horizonal wind in the two plains show that, during the E storm peak time, the change in daily mean wind speed is significant above 20 km, but relatively small in the atmospheric boundary layer below ~5 km. Within the boundary layer, the horizontal wind speed shows remarkable diurnal variation, remaining relatively low during the midday hours (10:00 a.m. to 4:00 p.m.). These results can provide necessary environmental parameters related to spring dust storms for China’s Tianwen-3 mission. Full article

The extraction of ocean wave wavelengths from optical imagery via Fast Fourier Transform (FFT) exhibits significant potential for Wave-Derived Bathymetry (WDB). However, in practical applications, this method frequently produces anomalously large wavelength estimates. To date, there has been insufficient exploration into the mechanisms underlying image spectral leakage to low wavenumbers and its suppression strategies. This study investigates three plausible mechanisms contributing to spectral leakage in optical images and proposes a subimage-based preprocessing framework: prior to executing two-dimensional FFT, the remote sensing subimages employed for wavelength inversion undergo three sequential steps: (1) truncation of distorted pixel values using a Gaussian mixture model; (2) application of a polynomial detrending surface; (3) incorporation of a two-dimensional Hann window. Subsequently, the dominant wavenumber peak is localized in the power spectrum and converted to wavelength values. Water depth is then inverted using the linear dispersion equation, combined with wave periods derived from ERA5. Taking 2 m-resolution WorldView-2 imagery of Sanya Bay, China as a case study, 1024 m subimages are utilized, with validation conducted against chart-sounding data. Results demonstrate that the proportion of subimages with anomalous wavelengths is reduced from 18.9% to 3.3% (in contrast to 14.0%, 7.8%, and 16.6% when the three preprocessing steps are applied individually). Within the 0–20 m depth range, the water depth retrieval accuracy achieves a Mean Absolute Error (MAE) of 1.79 m; for the 20–40 m range, the MAE is 6.38 m. A sensitivity analysis of subimage sizes (512/1024/2048 m) reveals that the 1024 m subimage offers an optimal balance between accuracy and coverage. However, residual anomalous wavelengths persist in near-shore subimages, and errors still increase with increasing water depth. This method is both concise and effective, rendering it suitable for application in shallow-water WDB scenarios. Full article

Urban heat exposure has become an increasingly critical environmental issue under the dual pressures of global climate warming and rapid urbanization, posing significant threats to public health and urban sustainability. However, conventional linear regression models often fail to capture the complex, nonlinear interactions among multiple environmental factors, and studies confined to single LCZ types lack a comprehensive understanding of urban thermal mechanisms. This study takes the central urban area of Zhengzhou as a case and proposes an integrated “Local Climate Zone (LCZ) framework + random forest-based multi-factor contribution analysis” approach. By incorporating multi-temporal Landsat imagery, this method effectively identifies nonlinear drivers of heat exposure across different urban morphological units. Compared to traditional approaches, the proposed model retains spatial heterogeneity while uncovering intricate regulatory pathways among contributing factors, demonstrating superior adaptability and explanatory power. Results indicate that (1) high-density built-up zones (LCZ1 and E) constitute the core of heat exposure, with land surface temperatures (LSTs) 6–12 °C higher than those of natural surfaces and LCZ3 reaching a peak LST of 49.15 °C during extreme heat events; (2) NDVI plays a dominant cooling role, contributing 50.5% to LST mitigation in LCZ3, with the expansion of low-NDVI areas significantly enhancing cooling potential (up to 185.39 °C·km²); (3) LCZ5 exhibits an anomalous spatial pattern with low-temperature patches embedded within high-temperature surroundings, reflecting the nonlinear impacts of urban form and anthropogenic heat sources. The findings demonstrate that the LCZ framework, combined with random forest modeling, effectively overcomes the limitations of traditional linear models, offering a robust analytical tool for decoding urban heat exposure mechanisms and informing targeted climate adaptation strategies. Full article