Search Results (196)

Deep learning for precipitation forecasting remains constrained by complex meteorological factors affecting accuracy. To address this issue, this paper proposes TransMambaCNN, which is a spatiotemporal transformer network fusing state-space models and CNNs for short-term precipitation forecasting. The core of the model employs a Convolutional State-Space Module (C-SSM), which efficiently extracts spatiotemporal features from multi-source meteorological variables by replacing the self-attention mechanism in the Vision Transformer (ViT) with an Attentive State-Space Module (ASSM) and augmenting its feature extraction capacity with integrated depthwise convolution. Its dual-branch architecture consists of a global branch, where C-SSM captures long-range dependencies and global spatiotemporal patterns, and a local branch, which leverages multi-scale convolutions based on SimVP’s Inception structure to extract fine-grained local features. The deep fusion of these dual branches significantly enhances spatiotemporal feature representation.Experiments demonstrate that in southeastern China and adjacent marine areas (period of high precipitation: April–September), TransMambaCNN achieves a 13.38% and 47.67% improvement in Threat Score (TS) over PredRNN at thresholds of ≥25 mm and ≥50 mm, respectively. In the Qinghai Sanjiangyuan region of western China (a precipitation-scarce area), TransMambaCNN’s TS score surpasses SimVP by 11.86 times at the ≥25 mm threshold. Full article

Assessing ecological quality in mining areas is critical for environmental protection and sustainable resource management. However, most previous studies concentrate on large-scale analysis, overlooking fine-scale assessment in mining areas. To address this issue, this study proposed a novel analysis framework for mining areas by integrating high-resolution Landsat data, the Remote Sensing Ecological Index (RSEI), and the Random Forest regression method. Based on the framework, four decades of spatiotemporal dynamics and drivers of ecological quality were revealed in Youjiang River Valley. Results showed that from 1986 to 2024, ecological quality in Youjiang River Valley exhibited a fluctuating upward trend (slope = 0.004/year), with notable improvement concentrated in the most recent decade. Spatially, areas with a significant increasing trend in RSEI (48.71%) were mainly located in natural vegetation regions, whereas areas with a significant decreasing trend (9.11%) were concentrated in impervious surfaces and croplands in northern and central regions. Driver analysis indicates that anthropogenic factors played a crucial role in ecological quality changes. Specifically, land use intensity, precipitation, and sunshine duration were main determinants. These findings offer a comprehensive understanding of ecological quality evolution in subtropical karst mining areas and provide crucial insights for conservation and restoration efforts in Youjiang River Valley. Full article

Non-uniform corrosion cracking in reinforced concrete buildings constitutes a fundamental difficulty resulting in durability failure. This work develops a microscopic-scale multi-species electrochemical phase field model to tackle this issue. The model comprehensively examines the spatiotemporal coupling mechanisms of the full “corrosion-rust swelling-cracking” process by integrating electrochemical reaction kinetics, multi-ion transport processes, and a unified phase field fracture theory. The model uses local corrosion current density as the primary variable to accurately measure the dynamic interactions among electrochemical processes, ion transport, and rust product precipitation. It incorporates phase field method simulations of fracture initiation and propagation in concrete, establishing a bidirectional link between rust swelling stress and crack development. Experimental validation confirms that the model’s predictions about cracking duration, crack shape, and ion concentration distribution align well with empirical data, substantiating the efficacy of local corrosion current density as an indicator of electrochemical reaction rate. Parametric studies were performed to examine the effects of interface transition zone strength, oxygen diffusion coefficient, protective layer thickness, reinforcing bar diameter, and reinforcing bar configuration on cracking patterns. This model’s multi-physics field coupling framework, influenced by dynamic corrosion current density, facilitates cross-field interactions, offering sophisticated theoretical tools and technical support for the quantitative analysis, durability evaluation, and protective design of corrosion-induced cracking in reinforced concrete structures. Full article

This study investigates the long-term variability and extremes of monthly precipitation during 150 years or more at 4 locations in Argentina: Corrientes, Córdoba, Buenos Aires, and Bahía Blanca. Annual and seasonal trends, extreme dry and wet months over the whole period, and the relationships between large-scale climate drivers and monthly rainfall are considered. Results show that, except for Córdoba, the complete anomaly timeseries trend analysis for all other stations yielded null trends over the centennial study period. Considerable month-to-month variability is observed for all locations together with the existence of low-frequency decadal to interdecadal variability, both for monthly precipitation anomalies and for statistically significant excess and deficit months. Linear fits considering oceanic climate indicators as drivers of variability yield significant differences between locations, while not between full records and seasonally sampled. Issues regarding the use of linear analysis to quantify variability, the dispersion along the timeline of record extreme rainy months at each location, together with the evidence of severe daily precipitation events not necessarily coinciding with the ranking of the rainiest months at each location, highlights the challenges of understanding the drivers of variability of both monthly and severe daily precipitation and the need of using extended centennial timeseries whenever possible. Full article

The evaluation of climate change effects on airport infrastructures is important to maintain safety and flexibility in air travel operations. Airports are particularly vulnerable to extreme weather events and temperature fluctuations, which can disrupt operations, compromise passenger safety, and cause economic losses. Issues such as flooded runways and the disruption of power supplies highlight the need for strong adaptation strategies. The study focuses on the application of the high-resolution regional model COSMO-CLM to assess climate change impacts on Roma Fiumicino airport (Italy) under the IPCC RCP8.5 scenario. The complex topography of Italy requires fine-scale simulation to catch localized climate dynamics. By employing advanced statistical methods, such as fractal analysis, this research aims to increase an understanding of climate change and improve the model prediction capability. The findings provide valuable insights for designing resilient airport infrastructures and updating operational protocols in view of evolving climate risks. A consistent increase in daily temperatures is projected, along with a modest positive trend in annual precipitation. The use of advanced statistical methods revealed insights into the fractal dimensions and frequency components of climate variables, showing an increasing complexity and variability of future climatic patterns. Full article

Swine wastewater (SW) has a high chemical oxygen demand (COD) content and is difficult to degrade; an effective strategy to address this issue is through biodegradation, which poses negligible secondary pollution risks and ensures cost-efficiency. The objectives of this study were to isolate an effective COD-degrading strain of SW, characterize (at the molecular level) its transformation of SW, and apply it to practical production. A strain of Corynebacterium xerosis H1 was isolated and had a 27.93% ± 0.68% (mean ± SD) degradation rate of COD in SW. This strain precipitated growth in liquids, which has the advantage of not needing to be immobilized, unlike other wastewater-degrading bacteria. Based on analysis by Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS), this bacterium removed nitrogen-containing compounds in SW, with proteins and lipids decreasing from 41 to 10% and lignins increasing from 51 to 82%. Furthermore, the enhancement of the sequencing batch reactor (SBR) with strain H1 improved COD removal in effluent, with reductions in the fluorescence intensity of aromatic protein I, aromatic protein II, humic-like acids, and fulvic acid regions. In addition, based on 16S rRNA gene sequencing analysis, SBR_H1 successfully colonized some H1 bacteria and had a higher abundance of functional microbiota than SBR_C. This study confirms that Corynebacterium xerosis H1, as a carrier-free efficient strain, can be directly applied to swine wastewater treatment, reducing carrier costs and the risk of secondary pollution. The discovery of this strain enriches the microbial resource pool for SW COD degradation and provides a new scheme with both economic and environmental friendliness for large-scale treatment. Full article

In the wake of global energy transition and the “dual-carbon” goal, the rapid growth of electric vehicles has posed challenges for large-scale lithium-ion battery decommissioning. Retired batteries exhibit dual attributes of strategic resources (cobalt/lithium concentrations several times higher than natural ores) and environmental risks (heavy metal pollution, electrolyte toxicity). This paper systematically reviews pyrometallurgical and hydrometallurgical recovery technologies, identifying bottlenecks: high energy/lithium loss in pyrometallurgy, and corrosion/cost/solvent regeneration issues in hydrometallurgy. To address these, an integrated recycling process is proposed: low-temperature physical separation (liquid nitrogen embrittlement grinding + froth flotation) for cathode–anode separation, mild roasting to convert lithium into water-soluble compounds for efficient metal oxide separation, stepwise alkaline precipitation for high-purity lithium salts, and co-precipitation synthesis of spherical hydroxide precursors followed by segmented sintering to regenerate LiNi_1/3Co_1/3Mn_1/3O₂ cathodes with morphology/electrochemical performance comparable to virgin materials. This low-temperature, precision-controlled methodology effectively addresses the energy-intensive, pollutive, and inefficient limitations inherent in conventional recycling processes. By offering an engineered solution for sustainable large-scale recycling and high-value regeneration of spent ternary lithium ion batteries (LIBs), this approach proves pivotal in advancing circular economy development within the renewable energy sector. Full article

Portland cement is one of the most widely used construction materials employed in both large-scale structures and everyday applications. Although various materials are often added during production to enhance their performance, NaCl can be introduced in the process for various reasons. Despite this issue, existing studies lack sufficient quantitative data on the effects of NaCl on cement properties. Therefore, this study aims to investigate the physical and chemical degradation mechanisms in cement containing NaCl. Cement specimens were prepared by mixing cement, water, and NaCl, followed by stirring at 60 rpm and curing at room temperature for seven days. Microstructural changes as a function of the NaCl concentration were analyzed using scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). Electrochemical properties were evaluated via open-circuit potential (OCP) measurements, electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization tests. The results indicate that increasing the NaCl concentration leads to the formation of fine precipitates, the degradation of the cement matrix, and the reduced stability of major hydration products. Furthermore, the electrochemical analysis revealed that higher NaCl concentrations weaken the passive layer on the cement surface, resulting in an increased corrosion rate from 1 × 10⁻⁷ to 4 × 10⁻⁷ on the active polarization of the potentiodynamic polarization curve. Additionally, the pitting potential (E_pit) decreased from 0.73 V to 0.61 V with an increasing NaCl concentration up to 3 wt.%. This study quantitatively evaluates the impact of NaCl on the durability of Portland cement and provides fundamental data to ensure the long-term stability of cement structures in chloride-rich environments. Full article

The relationship between plants and their environment has always been a core issue in ecological research. This study about how plant architecture and leaf traits respond to environmental changes helps to more deeply understand the adaptive mechanisms of plants in diverse environments. Although there have been related studies, a systematic analysis on a China-wide scale is still lacking. To address this gap, we conducted a meta-analysis of 115 studies across China examining plant architectural and leaf trait responses to environmental changes. The dataset includes 849 observations across 11 ecological variables, such as the mean annual precipitation, mean annual temperature, soil type, and elevation, and evaluates their effects on seven key plant traits. The results indicated that variations in the plant height, diameter at breast height (DBH), and root-to-shoot ratio are primarily influenced by the soil type and mean annual precipitation. In contrast, the soil type and mean annual sunshine duration mainly affected the specific leaf area (SLA), leaf area, leaf thickness, and leaf dry matter content. Moreover, while the magnitude of trait responses varies across precipitation, temperature, elevation, and soil property gradients, the impacts of environmental change are particularly pronounced under more extreme conditions. This study provides robust scientific evidence for understanding the effects of environmental change on plant growth across China and offers valuable insights into ecological conservation and the sustainable use of plant resources. Full article

Accurate prediction of Global Navigation Satellite System-derived precipitable water vapor (GNSS-PWV), which is a crucial indicator for climate change monitoring, holds significant scientific value for climate disaster prevention and mitigation. In the study of GNSS-PWV prediction, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm within a decomposition–integration framework effectively addresses the non-stationarity and complexity of PWV sequences, enhancing prediction accuracy. However, residual noise and pseudo-modes from decomposition can distort signals, reducing the predictor system’s reliability. Additionally, independent modeling of all decomposed components decreases computational efficiency. To address these challenges, this paper proposes a hybrid model combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), autoregressive integrated moving average (ARIMA), and long short-term memory (LSTM) networks. Enhanced by local mean optimization and adaptive noise regulation, the ICEEMDAN algorithm effectively suppresses pseudo-modes and minimizes residual noise, enabling its decomposed intrinsic mode functions (IMFs) to more accurately capture the multi-scale features of GNSS-PWV. Sample entropy (SE) is used to quantify the complexity of IMFs, and components with similar entropy values are reconstructed into the following three sub-sequences: high-frequency, low-frequency, and trend. This process significantly reduces modeling complexity and improves computational efficiency. We propose different modeling strategies tailored to the dynamics of various subsequences. For the nonlinear and non-stationary high-frequency components, the LSTM network is used to effectively capture their complex patterns. The LSTM’s gating mechanism and memory cell design proficiently address the long-term dependency issue. For the stationary and weakly nonlinear low-frequency and trend components, linear patterns are extracted using ARIMA. Differencing eliminates trends and moving average operations capture random fluctuations, effectively addressing periodicity and trends in the time series. Finally, the prediction results of the three components are linearly combined to obtain the final prediction value. To validate the model performance, experiments were conducted using measured GNSS-PWV data from several stations in Hong Kong. The results demonstrate that the proposed model reduces the root mean square error by 56.81%, 37.91%, and 13.58% at the 1 h scale compared to the LSTM, EMD-LSTM, and ICEEMDAN-SE-LSTM benchmark models, respectively. Furthermore, it exhibits strong robustness in cross-month forecasts (accounting for seasonal influences) and multi-step predictions over the 1–6 h period. By improving the accuracy and efficiency of PWV predictions, this model provides reliable technical support for the real-time monitoring and early warning of extreme weather events in Hong Kong while offering a universal methodological reference for multi-scale modeling of geophysical parameters. Full article

Identifying regions with similar meteorological features is of both socioeconomic and ecological importance. Towards that direction, useful information can be drawn from meteorological stations, and spread in a broader area. In this work, a time series clustering procedure composed of two levels is proposed, focusing on clustering spatial units (meteorological stations) based on their temporal patterns, rather than clustering time periods. It is capable of handling univariate or multivariate time series, with missing data or different lengths but with a common seasonal time period. The first level involves the clustering of the dominant features of the time series (e.g., similar seasonal patterns) by employing K-means, while the second one produces clusters based on secondary features. Hierarchical clustering with Dynamic Time Warping for the univariate case and multivariate Dynamic Time Warping for the multivariate scenario are employed for the second level. Principal component analysis or Classic Multidimensional Scaling is applied before the first level, while an imputation technique is applied to the raw data in the second level to address missing values in the dataset. This step is particularly important given that missing data is a frequent issue in measurements obtained from meteorological stations. The method is subsequently applied to the available precipitation time series and then also to a time series of mean temperature obtained by the automated weather stations network in Greece. Further, both of the characteristics are employed to cover the multivariate scenario. Full article

The ability of a general circulation model (GCM) to capture the variability of El Niño–Southern Oscillation (ENSO) is not only a scientific issue of climate model performance, but also critical for climate change and variability impact studies. Here, we assess 48 CMIP5 GCMs for their skill in simulating ENSO interdecadal variability and its teleconnection with precipitation globally. The results show that (1) only 22 out of 48 GCMs display interdecadal variability that is similar to the observations; (2) the ensemble of the 48 GCMs captures the ENSO–precipitation teleconnection at the global scale; (3) no single GCM can capture the observed ENSO–precipitation teleconnection globally; and (4) a GCM that can realistically simulate ENSO variability does not necessarily capture the ENSO-precipitation teleconnection, and vice versa. The results could also be used by climate change impact studies to select suitable GCMs, especially for regions with a statistically significant teleconnection between ENSO and precipitation, as well as for the comparison of CMIP5 and CMIP6. Full article

Precipitation nowcasting is pivotal in monitoring extreme weather events and issuing early warnings for meteorological disasters. However, the inherent complexity of precipitation systems, coupled with their nonlinear spatiotemporal evolution, poses significant challenges for traditional numerical weather prediction methods in capturing multi-scale details effectively. Existing deep learning models similarly struggle to simultaneously capture local multi-scale features and global long-term spatiotemporal dependencies. To tackle this challenge, we propose SwinNowcast, a deep learning model based on the Swin Transformer architecture. Through the novel design of a multi-scale feature balancing module (M-FBM), the model dynamically integrates local-scale features with global spatiotemporal dependencies. Specifically, the multi-scale convolutional block attention module (MSCBAM) captures local multi-scale features, while the gated attention feature fusion unit (GAFFU) adaptively regulates the fusion intensity, thereby enhancing spatial structure and temporal continuity in a synergistic manner. Experiments were performed on the precipitation dataset from the Royal Netherlands Meteorological Institute (KNMI) under thresholds of 0.5 mm, 5 mm, and 10 mm. The results indicate that SwinNowcast surpasses six state-of-the-art approaches regarding the critical success index (CSI) and the Heidke skill score (HSS), while markedly reducing the false alarm rate (FAR). The proposed model holds substantial practical value in applications such as short-term heavy rainfall monitoring and urban flood early warning, offering effective technological support for meteorological disaster mitigation. Full article

Leghemoglobin (LegHb) is a plant-derived heme-containing globin found in the root nodules of legumes like soybean that can be used as a food additive for red color and meaty flavor as a plant-based meat alternative. However, conventional extraction methods face challenges of low yield and high costs. To address this issue, precision fermentation with recombinant microorganisms has been applied for the sustainable large-scale production of plant leghemoglobins. This study attempted the production of plant legHbs using recombinant yeast strains, Saccharomyces cerevisiae and Komagatella phaffii. The plant legHb genes were identified from the genome of legumes such as soybean, chickpea, mung bean and overexpressed in yeast via extracellular secretion by the signal peptide and inducible promoters. Subsequently, hemin as a heme provider was added to the fermentation, resulting in increased levels of plant legHbs. In S. cerevisiae, gmaLegHb expression reached up to 398.1 mg/L, while in K. phaffii, gmaLegHb showed the highest production level, reaching up to 1652.7 mg/L. The secretory production of plant legHbs was further enhanced by replacing the signal peptide in the recombinant yeast. The secreted plant legHbs were purified by His-Tag from a culture supernatant or concentrated via precipitation using ammonium sulfate. These results suggest that the production of plant legHbs is significantly influenced by hemin and signal peptide. This study successfully demonstrates the production of the various plant legHbs other than soy legHb that can be used as natural colors and flavors for plant-based meat alternatives. Full article

The northwestern region of China is characterized by loess soil and seasonal permafrost. Due to the combined effects of its unique climate and precipitation patterns, local roads frequently suffer from issues such as foundation settlement, erosion, and collapse, which pose significant risks to both road construction and safe operation. This study examines a typical high subgrade in Northwest China, where a scaled laboratory model experiment was conducted. The research investigates the impact of water infiltration at the slope foot, under the dual influences of extreme cold and precipitation, on changes in the internal moisture field and settlement deformation characteristics of both the foundation and subgrade. The results indicate that the variation in moisture content across the section follows an arc-shaped diffusion pattern. Settlement is influenced by both the amount of infiltrated water and cold air, with a noticeable lag effect. A settlement of 0.1 cm is considered the threshold for significant impact, with the minimum observed lag period approaching 4 days. The settlement is concentrated in the slope region, exhibiting a bending failure pattern. Numerical simulations reveal that the cross-sectional settlement distribution forms an inverted “S” shape, and the cumulative moisture content at each monitoring point exhibits a quadratic relationship with the cumulative settlement. The findings of this study provide scientific guidance and technical references for road construction and safe operation in the seasonal permafrost regions of Northwest China. Full article