Environmental Earth Sciences最新文献

The Western Himalayas in India have witnessed increased geohazards, notably debris flows, due to increased precipitation and subsequent rapid landslides. These flows threaten flat landscapes, particularly through the deposition fans they form. The increase in debris flow hazards makes it essential to understand the changes in runout deposits with varying water content and coarser particles to better capture solid–liquid interactions at a small scale. Additionally, there is a need for prediction models to analyze key features such as coarse-grained particles and water content in shaping deposits. This study offers an experimental exploration of debris flow deposition kinematics in the Western Indian Himalayas context. Utilizing reconstituted debris material from the region, experiments were conducted using a flume setup to simulate debris flow. Subsequent machine learning and Particle Image Velocimetry (PIV) provided insights into flow dynamics and helped analyze sediment accumulation patterns. Extreme gradient boosting (XGBoost) analysis revealed the significant role of stony particles in influencing mobility, with compositions between 8 and 12% showing pronounced effects of increasing deposit thickness and width. XGBoost demonstrated high predictive accuracy, with an impressive correlation between predicted and actual values for length (r² = 0.95), thickness (r² = 0.91), and width (r² = 0.94) of deposit fans. Water content was found to negatively impact the thickness of the deposits, with a greater reduction in thickness at higher water content. However, it positively influenced the overall mobility of the debris flow. The study underscores the importance of understanding debris flow mechanisms to mitigate the associated geohazard risks.

Groundwater is a critical resource that supports agriculture and ecosystems which is increasingly threatened by anthropogenic activities and climate change. Despite the advancements in spatial analysis methods, there remains a lack of comprehensive reviews that synthesize these techniques specifically for groundwater vulnerability assessment as existing literature often focuses on isolated methodologies without integrating them into a cohesive framework that addresses the complexities of groundwater systems. Hence, the need for proactive conservation and mitigation strategies on how spatial analysis can enhance groundwater vulnerability assessments is crucial for developing effective policies and practices aimed at safeguarding this vital resource. This review evaluates various spatial analysis techniques used in assessing groundwater vulnerability, identify their strengths and limitations, and propose a strategic framework for their application in conservation efforts. A systematic literature review was conducted, focusing on peer-reviewed articles published in the last two decades. Techniques such as remote sensing (RS), Geographic Information Systems (GIS), Multi-Criteria Decision Analysis (MCDA), and statistical modelling were analyzed in terms of their applicability to groundwater vulnerability assessments. The findings reveal that spatial analysis techniques significantly enhance the accuracy of groundwater vulnerability assessments (GVAs) by incorporating diverse data sources such as land use, soil characteristics, and hydrological features. Key results indicate that GIS-based models provide robust frameworks for identifying vulnerable areas, while MCDA facilitates stakeholder engagement by integrating socio-economic factors into decision-making processes. The study concludes that a strategic approach combining various spatial analysis techniques offers a promising pathway for enhancing groundwater vulnerability assessments. This integrated methodology not only aids in identifying vulnerable areas but also supports informed decision-making processes regarding conservation efforts. However, future research should focus on developing standardized protocols for integrating diverse spatial analysis methods as well as longitudinal studies to assess the long-term effectiveness of implemented conservation strategies based on these assessments.

As the mining depth of coal mines continues to increase, the problem of mine heat damage becomes increasingly prominent. In response to the heat damage problem in deep mines, this paper presents a novel approach of mine heat damage control and geothermal resource exploitation under the collaborative effect of extraction and ventilation. Taking Sanhejian Coal Mine in Xuzhou as the research object, numerical simulation is conducted using finite element simulation analysis software to analyze the evolution law of the temperature field of roadway surrounding rock and air in the roadway during the variation of different key factors. Additionally, in the process of continuous tunneling, the optimal cooling scheme for roadways at different locations is obtained. The conclusions are as follows: (1) Treating mine heat damage under pure ventilation has the advantage of rapid cooling speed. The temperature at the observation point in the roadway can be reduced to approximately 283 K at its lowest. However, the disadvantage lies in the large temperature difference before and after the roadway (no less than 7 K) and the need for continuous ventilation. (2) During the extraction process, reducing the average injection water temperature and decreasing the distance from the roadway can effectively enhance the effectiveness of mine heat damage control. Nevertheless, under pure extraction, the temperature reduction rate of roadway surrounding rock is relatively slow. When the distance between the roadway and the injection well does not exceed 30 m and the average injection water temperature does not exceed 190 K, the surrounding rock temperature can be reduced to below 303.15 K within one year. (3) The extraction-ventilation synergy method not only can effectively narrow the temperature difference before and after the roadway but also can improve the temperature reduction speed in the roadway to a certain extent. Moreover, the geothermal resources generated by extraction can also yield certain economic benefits. This research provides a new perspective for cooling the coal mining face of coal mines.

Ras Sudr has garnered significant interest owing to various initiatives aimed at expanding and developing this area. It holds considerable strategic importance, serving as a key development hub in Sinai and a focal point for tourism destination. Moreover, it has a remarkable event with flashfloods which can be utilized for groundwater or direct usage. Integrating geophysical and geospatial analyses to study the surface and subsurface characteristics as well as identify groundwater potential areas in Ras Sudr, west-central Sinai is the main objective of this study. Firstly, geophysical data including gravity and magnetic methods have a crucial importance in qualitative and quantitative interpretation of the subsurface elements. Filtering techniques were implemented to distinguish between regional and residual anomalies. Geophysical data were subjected to the radial average power spectrum technique and 3D Euler deconvolution to identify the depth of the subsurface sources. The structure pattern that characterizes the interested region was defined by employing bandpass filter and edge detection appoarches using residual anomaly maps, regional anomaly maps, tilt derivatives and total horizontal gradient maps reflecting four distinct structural trends; NW-SE parallel to the Gulf of Suez, NEE-SWW parallel to the Syrian arc system, N-S parallel to the the Nile Valley, and NNE-SSW parallel to the Gulf of Aqaba. A basement relief map was constructed using 3D magnetic modelling showing that the depth of the basement ranges from 1.6 to 6.3 km. Secondly, the remote sensing data including Sentienl-2 and SRTM datasets were employed to extract the surface analyses in the GIS environment to develop the occurrence of groundwater potentiality utilizing seven factors; LU-LC, soil, geology, slope, drainage network and lineament density and rainfall data of the study area which were ranked from 1 to 5 and weighted according to their effective contribution to the infiltration of groundwater using AHP-GIS based multi criteria method. The resulting Groundwater Potential zone (GWPZ) was categorized into five zones from poor to excellent and validated using 41 observed wells. A significant quantity of wells was identified in the areas of high potential located to the west of the region, while five wells were situated within the moderate potential zone. Thereby, the GWPZ map identifying locations with viable groundwater resources suitable for habitation, development and economic plans in Sinai for decision makers.

Atmospheric processes play an important role in the deterioration of cultural stone heritage. In the development of these processes, the intensity of solar radiation and wind velocity, to which building stones are exposed, are the factors that directly determine the type and degree of deterioration. These factors play a determinative role in the development of atmospheric processes by varying according to the microclimatic environments around the cultural heritage. Solar and wind simulations can yield effective results in shedding light on the deterioration due to the microclimatic environments where historic buildings are located. In this study, the tomb of Şeyh Osman Rumi, where deteriorations resulting from the microclimatic environment effects created by trees, high buildings, and topography were observed, was examined. To this end, solar and wind effects in the building were investigated by De Luminae software and web-based computational fluid dynamics (CFD) simulation methods, respectively. According to the simulation results, the elements that make up the microclimatic environment caused variability in capillary levels by changing the wind direction and velocity with the solar radiation value on the monument facades and directly influenced the type and intensity of deterioration.

A flood susceptibility assessment is crucial for identifying areas that are susceptible to flooding. This task usually uses models, but prior flood susceptibility assessment models focused on the frequency or duration of floods, not both. Integrating the frequency and duration of floods in susceptibility assessment could provide a more accurate picture of flood susceptibility. This study aimed to utilise and assess a novel integrated model that considers the frequency and duration of floods to categorise vulnerability/susceptibility zones. This study focuses on the multi-hazard zone between Cuddalore and Sirkazhi on the east coast of Tamil Nadu, India. Sentinel-1 A and RISAT-1 A Synthetic Aperture Radar (SAR) images were analysed using the Classification and Regression Tree (CART) classifier. Eight SAR images were used to study the persistence and temporal evolution of flooding over 49 days in 2015, along with multi-temporal datasets for 2015, 2018, and 2019. The classification of flood-susceptibility zones based on the frequency and duration of flooding yielded an accuracy of 0.87, whereas the integrated model scored 0.96 in all matrices. The hybrid integrated analysis provided a comprehensive understanding of the area’s flooding system, identifying the southern part of the study area as the most susceptible. The proposed model recommends a frequency-duration-based approach to demarcate flood susceptibility zones and potentially improve flood susceptibility assessments and management strategies.

Uranium deposits are found in various geological environments; therefore, these exist in almost all geological phases: in high degree of metamorphic, metasomatic, Metamorphism, Surficial and Volcanic to Sedimentary perimeters. In this study, the amount of uranium in the Baghak mine (as a case study) was predicted using piecewise regression and Monte Carlo simulation at a 90% confidence level. To this end, 151 geochemical samples from the study area were analyzed, with rare earth elements, thorium, and yttrium selected as independent parameters due to their highest correlation with uranium. An equation was derived for estimating uranium levels, showing a high correlation (86%) with actual data. Based on the final model, 2,200 random data points for uranium were generated, with the mean differing by only 0.1 (logarithmic unit of concentration) from actual values. This indicates the model’s high accuracy in simulating real values. The simulated values closely matched actual values based on the density function, confirming the model’s validity. In this area, the confidence interval for uranium is from 1.8 to 2.6 based on logarithmic calculations. Finally, the independent parameters were prioritized based on their impact on predicting uranium levels. This research could be considered a practical tool for assessing and predicting uranium concentrations in various regions and could aid in improving mineral resource management.

The problem of toxic metal pollution in the soil of mining areas accompanied by coal mining cannot be ignored. Taking the mining area of Wucaiwan in Xinjiang as the research subject, the content characteristics of soil toxic metals were sampled and analyzed. We calculated the rotation factor loading coefficients and further performed principal component analysis to resolve the sources of toxic toxic metals. We analyze the individual environmental capacity index and integrated environmental capacity index to reflect the environmental capacity level. The temporal change trend of soil environmental capacity was studied, and the correlation between environmental capacity, pH and organic carbon was explored. The results showed that (1) the average value of Cd content in the soil of the study area exceeded the background value, and the size of the total environmental capacity was ranked as Cr > Ni > Pb > Cu > Cd; (2) the size of the average individual environmental capacity index was ranked as Cu (1.15) > Cr (1.10) > Ni (1.04) > Pb (1.03) > Cd (0.96), and the comprehensive environmental capacity index was 1.06 (3) the cumulative contribution of the first three main components of soil toxic metals reached 91.55%, and the presumed sources were soil parent material, coal combustion and dustfall, respectively; (4) the correlation between the existing capacities of different toxic metals was strong, and the existing capacity of Pb was highly significantly and positively correlated with the organic carbon content.

Soil is vital for food security and ecosystem nutrient recycling. Rapid infrastructure development projects requiring mineral resource extraction have led to an overall decrease in soil quality. Due to a higher environmental footprint, the soil quality in cities has declined quickly, necessitating continuous monitoring and evaluation. Educational institutions are traditionally not considered for such monitoring. Thus, the present study investigated the soil status under 10 different tree species at Maharshi Dayanand University (MDU) located in Rohtak city. Using various digital and volumetric methods, 20 physicochemical parameters including sand, silt, clay, pH, electrical conductivity (EC), organic matter (OM), macronutrients and micronutrients, etc., were analyzed. Sampling was performed at four depths (0–10, 10–20, 20–30 and 30–40 cm) to collect a total of 40 composite samples. Repeated measures of one-way analysis of variance (ANOVA) and pairwise comparison were used to detect significant differences. Soil parameters were significantly different among various tree species (p < 0.05). The soil nutrient index value (SNIV) classified sand (3), pH (2.62), Ca²⁺ (2.82), Cu (2.60) and Fe (2.65) in the high fertility class. Network analysis demonstrated the effects of physicochemical parameters on OM and nutrients. The structural stability index (SSI) appropriated 50% of the samples as thoroughly degraded (SSI < 5%). Principal component analysis (PCA) produced five significant components and designated N, P, Cu and OM as the most critical soil chemistry variables. Hierarchical cluster analysis (HCA) produced 3 clusters for tree species with similar soil properties. The soil under F. virens is the most productive. Overall, the campus soil is alkaline, nutrient deficient and surface layers are more fertile. The results obtained and the customized solutions provided in this article may help to improve the campus soil and aid in sustainable soil use, conservation and management. This may also encourage other campuses around the globe to assess their soil status.