Search Results (84)

Research on raw materials for Al₂O₃-SiC-C refractory castables used in blast furnace troughs is relatively well established. However, gaps remain in both laboratory and industrial trials concerning the performance of castables incorporating SiC-modified flake graphite and alternative carbon sources. This study investigated the sintering behavior, mechanical properties, and service performance of Al₂O₃-SiC-C castables utilizing varying contents of modified flake graphite, pitch, and carbon black as carbon sources. Samples were characterized using SEM, XRD, and EDS for phase composition and microstructural morphology analysis. Key findings revealed that the thermal expansion mismatch between the SiC coating and flake graphite in SiC-modified graphite generated a microcrack-toughening effect. This effect, combined with the synergistic reinforcement from both components, enhanced the mechanical properties. The SiC modification layer improved the wettability and oxidation resistance of the flake graphite. This modified graphite further contributed to enhanced erosion resistance through mechanisms of matrix pinning and crack deflection within the microstructure. However, the microcracks induced by thermal mismatch concurrently reduced erosion resistance, resulting in an overall limited net improvement in erosion resistance attributable to the modified graphite. Specimens containing 1 wt.% modified flake graphite exhibited the optimal overall performance. During industrial trials, this formulation unexpectedly demonstrated a water reduction mechanism requiring further investigation. Full article

Erosion gullies can reduce arable land area and decrease agricultural machinery efficiency; therefore, automatic gully extraction on a regional scale should be one of the preconditions of gully control and land management. The purpose of this study is to compare the effects of the grey level co-occurrence matrix (GLCM) and topographic–hydrologic features on automatic gully extraction and guide future practices in adjacent regions. To accomplish this, GaoFen-2 (GF-2) satellite imagery and high-resolution digital elevation model (DEM) data were first collected. The GLCM and topographic–hydrologic features were generated, and then, a gully label dataset was built via visual interpretation. Second, the study area was divided into training, testing, and validation areas, and four practices using different feature combinations were conducted. The DeepLabV3+ and ResNet50 architectures were applied to train five models in each practice. Thirdly, the trainset gully intersection over union (IOU), test set gully IOU, receiver operating characteristic curve (ROC), area under the curve (AUC), user’s accuracy, producer’s accuracy, Kappa coefficient, and gully IOU in the validation area were used to assess the performance of the models in each practice. The results show that the validated gully IOU was 0.4299 (±0.0082) when only the red (R), green (G), blue (B), and near-infrared (NIR) bands were applied, and solely combining the topographic–hydrologic features with the RGB and NIR bands significantly improved the performance of the models, which boosted the validated gully IOU to 0.4796 (±0.0146). Nevertheless, solely combining GLCM features with RGB and NIR bands decreased the accuracy, which resulted in the lowest validated gully IOU of 0.3755 (±0.0229). Finally, by employing the full set of RGB and NIR bands, the GLCM and topographic–hydrologic features obtained a validated gully IOU of 0.4762 (±0.0163) and tended to show an equivalent improvement with the combination of topographic–hydrologic features and RGB and NIR bands. A preliminary explanation is that the GLCM captures the local textures of gullies and their backgrounds, and thus introduces ambiguity and noise into the convolutional neural network (CNN). Therefore, the GLCM tends to provide no benefit to automatic gully extraction with CNN-type algorithms, while topographic–hydrologic features, which are also original drivers of gullies, help determine the possible presence of water-origin gullies when optical bands fail to tell the difference between a gully and its confusing background. Full article

Natural water content affects many geotechnical parameters and geological properties of soils, which can reduce cohesion and friction, leading to potential failures in structures such as foundations, retaining walls, and slopes. Identification of the water content helps in designing effective drainage and water management systems to prevent flooding and erosion. In tunnel engineering, soil water content plays an important role as the stability of the tunnel face depends on it. This research solves the problem of classifying soil images depending on the natural water content by computer vision technology. First, laboratory soil tests were carried out, and the relationship between the amount of torque on the screw conveyor and the moisture content of the soil was established; photographs of the soil at different conditions were taken at each step of the experiment. Second, the resulting dataset after preprocessing was processed by convolutional neural network algorithms during deep learning; the transfer learning technique was used to obtain better results. As a result, seven algorithms were obtained that allow classifying the soil images, which can later be used to optimize the tunnel construction process. The best classification ability is demonstrated by the algorithm based on the DenseNet architecture (accuracy 0.9302 and loss 0.1980). The proposed model surpasses traditional approaches due to its increased automation and processing speed. Laboratory tests can be carried out only once for one type of soil in order to determine the boundaries of water content for classes labeling, after which only a cheap camera is required from the equipment to transmit new images for processing by the algorithm. Full article

Water security is a basic requirement of a region’s residents and also an important point of discussion worldwide. The middle route of the south-to-north water diversion project (MR-SNWDP) represents the most extensive inter-basin water allocation scheme globally. It is the major water resource for the Beijing–Tianjin–Hebei region, and its security is of great significance. In this study, 28 indicators including society, nature, and economy were selected from the water sources of the MR-SNWDP from 2000 to 2017. According to the Drivers-Pressures-States-Impact-Response (DPSIR) framework principle, the entropy weight method was used for weight calculation, and the comprehensive evaluation method was used for evaluating the water security of the water sources of the MR-SNWDP. This study showed that the total loss of nonpoint source pollution (NPSP) in the water source showed a trend of slow growth, except in 2007. Over the past 18 years, the proportion of pollution from three NPSP sources, livestock, and poultry (LP) breeding industry, planting industry, and living sources, were 44.56%, 40.33%, and 15.11%, respectively. The main driving force of water security in all the areas of the water source was the total net income per capita of farmers. The main pressure was the amount of LP breeding and the amount of fertilizer application. The largest impact indicators were NPSP gray water footprint and soil erosion area, and water conservancy investment was the most effective response measure. Overall, the state of the water source safety was relatively stable, showing an overall upward trend, and it had remained at Grade III except for in 2005, 2006, and 2011. The state of water safety in all areas except Shiyan City was relatively stable, where the state of water safety had fluctuated greatly. Based on the assessment findings, implications for policy and decision-making suggestions for sustainable management of the water sources of the MR-SNWDP resources are put forward. Agricultural cultivation in water source areas should reduce the application of chemical fertilizers and accelerate the promotion of agricultural intensification. Water source areas should minimize retail livestock and poultry farming and promote ecological agriculture. The government should increase investment in water conservancy and return farmland to forests and grasslands, and at the same time strengthen the education of farmers’ awareness of environmental protection. The evaluation system of this study combined indicators such as the impact of agricultural nonpoint source pollution on water bodies, which is innovative and provides a reference for the water safety evaluation system. Full article

Agroecosystems play a key role in the global carbon cycle, with CO₂ exchange driven by photosynthesis and respiration. Indicators such as gross primary productivity (GPP), net primary productivity (NPP), and carbon, water, and light use efficiency (CUE, WUE, LUE) are essential for assessing resource use in agricultural systems. Conventional tillage depletes carbon, water, and nutrients, negatively impacting the environment, while conservation practices aim to improve soil health and biodiversity. This study evaluated the effects of a cover crop in a wheat–maize rotation on sloped arable land prone to water erosion. The experiment involved minimum contour tillage combined with cover cropping, and its impact on carbon balance components and resource use efficiency was assessed. The results demonstrated that the inclusion of a cover crop significantly improved GPP and NPP. Water and light use efficiency also increased, particularly in 2022 and 2023, which were characterized by summer drought. However, carbon use efficiency remained unchanged over the study period. These findings highlight the potential of conservation practices, such as cover cropping and reduced tillage, to enhance productivity and resource efficiency in sloped agricultural landscapes under water stress conditions. Full article

This review discusses the evolution and integration of open-access remote sensing technology in shoreline detection and coastal erosion monitoring through the use of Geographic Information Systems (GIS), Artificial Intelligence (AI), Unmanned Aerial Vehicles (UAVs), and Ground Truth Data (GTD). The Sentinel-2 and Landsat 8/9 missions are highlighted as the primary core datasets due to their open-access policy, worldwide coverage, and demonstrated applicability in long-term coastal monitoring. Landsat data have allowed the detection of multi-decadal trends in erosion since 1972, and Sentinel-2 has provided enhanced spatial and temporal resolutions since 2015. Through integration with GIS programs such as the Digital Shoreline Analysis System (DSAS), AI-based processes such as sophisticated models including WaterNet, U-Net, and Convolutional Neural Networks (CNNs) are highly accurate in shoreline segmentation. UAVs supply complementary high-resolution data for localized validation, and ground truthing based on GNSS increases the precision of the produced map results. The fusion of UAV imagery, satellite data, and machine learning aids a multi-resolution approach to real-time shoreline monitoring and early warnings. Despite the developments seen with these tools, issues relating to atmosphere such as cloud cover, data fusion, and model generalizability in different coastal environments continue to require resolutions to be addressed by future studies in terms of enhanced sensors and adaptive learning approaches with the rise of AI technology the recent years. Full article

Accurate shoreline extraction is critical for coastal engineering applications, including erosion monitoring, disaster response, and sustainable management of island ecosystems. However, traditional methods face challenges in large-scale monitoring due to high costs, environmental interference (e.g., cloud cover), and poor performance in complex terrains (e.g., bedrock coastlines). This study developed an optimized DeepLabV3+ model for the extraction of island shorelines, which improved model performance by replacing the backbone network with MobileNetV2, introducing a strip pooling layer into the ASPP module, and adding CBAM modules in both the shallow and deep stages of feature extraction from the backbone network. The model accuracy was verified using a self-built drone dataset of the shoreline of Koh Lan, Thailand, and the results showed: (1) Compared with the control model, the improved DeepLabV3+ model performs excellently in pixel accuracy (PA), recall, F1 score, and intersection over union (IoU), reaching 98.7%, 97.7%, 98.0%, and 96.2%, respectively. Meanwhile, the model has the lowest number of parameters and floating-point operations, at 6.61 M and 6.7 GFLOPS, respectively. (2) In terms of pixel accuracy (PA) and intersection over union (IoU), the CBAM attention mechanism outperforms the SE-Net and CA attention mechanisms. Compared with the original DeepLabV3+ network, PA increased by 3.1%, and IoU increased by 8.2%. (3) The verification results of different types of coastlines indicate that the improved model can effectively distinguish between shadows and water bodies, reducing the occurrence of false negatives and false positives, thereby lowering the risk of misclassification and obtaining better extraction results. This work provides a cost-effective tool for dynamic coastal management, particularly in data-scarce island regions. Full article

Siltation around the harbour entrance poses significant challenges to the navigational safety and operational stability of coastal ports. Previous research has predominantly focused on sedimentation mechanisms in sandy coastal environments, while studies on silt-muddy coasts remain scarce. This paper investigates the causes of siltation around the entrance of Binhai Port in Jiangsu Province, China, utilising field observation data and a two-dimensional tidal current numerical model, with emphasis on hydrodynamic variations and sediment dynamics. Observations reveal that tidal currents induce sediment deposition in the outer harbour entrance area, whereas pronounced scouring occurs near breakwater heads. During extreme weather events, such as Typhoons Lekima (2019) and Muifa (2022), combined wind–wave interactions markedly intensified sediment transport and accumulation, particularly amplifying siltation at the entrance, with deposition thicknesses reaching 0.5 m and 1.0 m, respectively. The study elucidates erosion–deposition patterns under combined tidal, wave, and wind forces, identifying two critical mechanisms: (1) net sediment transport directionality driven by tidal asymmetry, and (2) a lagged dynamic sedimentary response during sediment migration. Notably, the entrance zone, functioning as a critical conduit for water– sediment exchange, exhibits the highest siltation levels, forming a key bottleneck for navigational capacity. The insights gleaned from this study are instrumental in understanding the morphodynamic processes triggered by artificial structures in silt-muddy coastal systems, thereby providing a valuable reference point for the sustainable planning and management of ports. Full article

Steep slopes, characterized by their high gradient and limited soil and water resources, pose significant challenges to plant colonization. Consequently, the ecological restoration of steep slopes is one of the major challenges in the field of mine site rehabilitation. This study evaluated the impact of slope conditions on the restoration effectiveness during the early stages of ecological restoration. Two ecological restoration slopes with different slope conditions, excavated slope and filled slope, were selected, and restored by hanging net and soil spraying measures. The unrepaired slope was used as the control. The results showed that ecological restoration has a significant effect for soil and water conservation; runoff and sediment were reduced by 61.38% and 99.28%, respectively, and infiltration increased by 104.26%, compared to untreated slopes. Furthermore, ecological restoration could effectively reduce runoff erosion dynamics and soil erodibility, and alter the runoff–sediment relationship on slopes, thereby substantially influencing the yield processes of runoff and sediment of the slopes. Notably, the reduction effect of ecological restoration measures on runoff and sediment was more significant on excavated slopes than on filled slopes. The runoff and sediment yield of excavated slopes were 19.06% and 53.77% lower than that of filled slopes, respectively. From a soil and water conservation perspective, the ecological restoration measures of hanging net and soil spraying were more suitable for application to steep excavated rock slopes. However, further research is needed to evaluate its applicability to filled slopes. Full article

Conservation tillage involves covering the soil surface with crop residues after harvest, typically through reduced or no-tillage practices. This approach increases the soil organic matter, improves the soil structure, prevents erosion, reduces water loss, promotes microbial activity, and enhances root development. Therefore, accurate information on crop residue coverage is critical for monitoring the implementation of conservation tillage practices. This study collected “crop–crop residues–soil” images from wheat-soybean rotation fields using mobile phones to create calibration, validation, and independent validation datasets. We developed a deep learning model named crop–crop residue–soil segmentation network (CCRSNet) to enhance the performance of cropland “crop–crop residues–soil” image segmentation and proportion extraction. The model enhances the segmentation accuracy and proportion extraction by extracting and integrating shallow and deep image features and attention modules to capture multi-scale contextual information. Our findings indicated that (1) lightweight models outperformed deeper networks for “crop–crop residues–soil” image segmentation. When CCRSNet employed a deep network backbone (ResNet50), its feature extraction capability was inferior to that of lighter models (VGG16). (2) CCRSNet models that integrated shallow and deep features with attention modules achieved a high segmentation and proportion extraction performance. Using VGG16 as the backbone, CCRSNet achieved an mIoU of 92.73% and a PA of 96.23% in the independent validation dataset, surpassing traditional SVM and RF models. The RMSE for the proportion extraction accuracy ranged from 1.05% to 3.56%. These results demonstrate the potential of CCRSNet for the accurate, rapid, and low-cost detection of crop residue coverage. However, the generalizability and robustness of deep learning models depend on the diversity of calibration datasets. Further experiments across different regions and crops are required to validate this method’s accuracy and applicability for “crop–crop residues–soil” image segmentation and proportion extraction. Full article

The Abu Darag sub-basin in Egypt is a significant hydrocarbon province. This study provides the first thorough evaluation of the petroleum system in the Northern Gulf of Suez, specifically targeting regions with tectonically influenced paleo highs. The research is novel in its holistic approach, linking tectonic activity with hydrocarbon generation and accumulation, particularly in the Nukhul Formation. In the NDARAG-1 well, with the Nukhul Formation serving as its main reservoir, petrophysical analysis estimates an average net pay of 126 ft in the Nukhul Formation, with 19% average shale volume, 17% average effective porosity, and 57% average water saturation. Geochemical evaluation of the shales in the Thebes, Matulla, Raha, and Nubia-A formations indicate source rock potential ranging from fair to very good, with TOC values between 0.5 wt% and 5.4 wt%. The burial history model outlines gradual subsidence and sediment deposition from the Paleozoic to the Early Cretaceous, followed by significant compression and uplift during the Late Cretaceous. Early oil generation in the Nubia-A Lower shales began during the Early Cretaceous (~132 Ma) at a depth of 4000 ft while it occurred in the Early Miocene (~22 Ma) at a depth of 7400 ft. The Nubia-A Lower Member is identified as the key source rock, with vitrinite reflectance values above 0.70%. Continuous subsidence during the Eocene led to the deposition of the Nukhul, Rudeis, and Kareem formations. Oil generation in the Nubia-A Lower shales occurred during both the Early Cretaceous and Early Miocene. The main risk to hydrocarbon accumulation in the Abu Darag sub-basin is related to potential seal failures due to the erosion and/or non-deposition of the Belayim, South Gharib, and Zeit evaporites. The only producing wells are situated in the northwest of the study area, where conditions are conducive to hydrocarbon entrapment and preservation, and secondary migration has occurred in a northwestward direction. Full article

The Yellow River Basin (YRB) is an important grain production base, and exploring the spatiotemporal heterogeneity and driving factors of soil erosion in the YRB is of great significance to the ecological environment and sustainable agricultural development. In this study, we employed the Revised Universal Soil Loss Equation (RUSLE) in conjunction with Transport-Limited Sediment Delivery (TLSD) to explore a modified RUSLE-TLSD for use assessing net water erosion. This modification was performed using sediment data, and the explanatory power of driving factors was assessed utilizing an optimal parameters-based geographical detector (OPGD). The results demonstrated that the modified RUSLE-TLSD can accurately simulate the spatiotemporal distribution of net water erosion (NSE = 0.5766; R² = 0.6708). From 2000 to 2020, the net water erosion modulus in the YRB ranged between 1.62 and 5.33 t/(ha·a). Specifically, the net water erosion modulus decreased in the YRB and the middle reaches of the YRB (MYRB), but it increased in the upper reaches of the YRB (UYRB). The erosion occurred mainly in the Loess Plateau region, while the deposition occurred mainly in the Hetao Plain and Guanzhong Plain. The Normalized Difference Vegetation Index (NDVI) and slope emerged as significant driving factors, and their interaction explained 31.36% of YRB net water erosion. In addition, the redistribution of precipitation by vegetation and the slope weakened the impact of precipitation on the spatial pattern of net water erosion. This study provides a reference, offering insights to aid in the development of soil erosion control strategies within the YRB. Full article

Land degradation is a critical issue in Ethiopia, exacerbating food insecurity by reducing agricultural productivity. Soil and water conservation (SWC) practices are essential to control erosion and increase food production. However, there is a lack of comprehensive evaluations on the impact of Ethiopia’s Productive Safety Net Program (PSNP) on SWC practices. This study aimed to assess the contribution of the PSNP to SWC in the Amhara Sayint Woreda. The researchers used a mixed-method approach, combining quantitative and qualitative data. Multistage sampling was used to select households, and data were collected through questionnaires, interviews, focus groups, and observations. The study provided empirical evidence that the PSNP has a positive impact on SWC practices. Key factors influencing SWC participation include age, family size, education, plot size, livestock ownership, credit service, and access to extension services. The results suggest that the PSNP should improve payment for public work participants implementing SWC, undertake institutional reform, and increase public awareness of the benefits of SWC in reversing land degradation and improving food security. This study uniquely contributes to the understanding of how the PSNP influences the varying degrees of participation in SWC practices, filling a critical research gap. The findings can inform policymakers and program managers to enhance the PSNP’s effectiveness in promoting sustainable land management and food security in Ethiopia. Full article

The dynamic replacement of soil organic carbon represents a pivotal mechanism through which water erosion modulates soil–atmosphere CO₂ fluxes. However, the extent of this dynamic replacement of soil inorganic carbon within this process remains unclear. In our study, we focused on Yuanmou County, China, a prototypical region afflicted by water erosion, as our study area. We leveraged the WaTEM/SEDEM model to quantify the dynamic replacement of soil carbon, accounted for the average annual net change in soil carbon pools, and used isotope tracer techniques to track and measure the process of the coupled carbon–water cycling. This comprehensive approach enabled us to scrutinize the dynamic replacement of soil carbon under water erosion and delineate its ramifications for the carbon cycle. Our findings unveiled that the surface soil carbon reservoir in the Yuanmou area receives an annual replacement of 47,600 ± 12,600 tons following water erosion events. A substantial portion, amounting to 39,700 ± 10,500 tons, stems from the dynamic replacement of soil inorganic carbon facilitated by atmospheric carbon. These results underscore the critical role of the dynamic replacement of soil inorganic carbon in altering the soil–atmosphere CO₂ fluxes under water erosion, thereby influencing the carbon cycle dynamics. Consequently, we advocate for the integration of water erosion processes into regional carbon sink assessments to attain a more comprehensive understanding of regional carbon dynamics. Full article

The main objective of protecting ecosystems and enhancing the supply of ecosystem services (ESs) is to quantify the value of ecological services. This article calculates the ecological service value (ESV) of the Tarim Basin over the past 40 years using the improved benefits transfer method of satellite remote sensing data, such as Landsat, analyzes the spatiotemporal evolution characteristics of ESV, and studies the driving mechanism of ESV changes using GeoDetector. Finally, the FLUS model was selected to predict the ecosystem service value until 2030, setting up three scenarios: the Baseline Scenario (BLS), the Cultivated Land Protection Scenario (CPS), and the Ecological Protection Scenario (EPS). The results indicate that (1) the ESV in the Tarim Basin decreased by USD 1248.21 million (−2.29%) from 1980 to 2020. The top three contributors are water bodies, wetlands, and grassland. (2) Waste treatment and water supply functions had the highest service value, accounting for 44.53% of the total contribution. The rank order of ecosystem functions in terms of their contribution to the total value of ESV was as follows, refining from high to low importance: water supply, waste treatment, biodiversity protection, climate regulation, soil formation, recreation and culture, gas regulation, food production, raw material. (3) The spatial differentiation driving factors of ESV were detected, with the following Q-values in descending order: net primary productivity (NPP) > normalized difference vegetation index (NDVI) > precipitation > aspect > temperature > slope > soil erosion > GDP > land use intensity > per capita GDP > population > human activity index. (4) The ESVs simulated under the three scenarios (BLS, CPS, and EPS) for 2030 were USD 51,133.9 million, USD 53,624.99 million, and USD 54,561.26 million, respectively. Compared with 2020, the ESVs of the three scenarios decreased as follows: BLS (USD 4209.33 million), CPS (USD 1718.24 million), and EPS USD (−781.97 million). These findings are significant for maintaining the integrity and sustainability of the large-scale ecosystem, where socioeconomic development and the fragile features of the natural ecosystem interact. Additionally, the study results provide a crucial foundation for governmental decision-makers, local residents, and environmental researchers in northwest China to promote sustainable development. Full article