Environmental Earth Sciences最新文献

Characterization of soil infiltration is crucial for designing and assessing hydrological processes. The study aimed to characterize the infiltration across various soil types (ranging from fine to moderately coarse in texture), agricultural land uses (including paddy fields, fallow lands, and upland crops) and major soil groups in India using Kostiakov, Philip, Horton, Holtan, Green and Ampt, and modified Kostiakov models. The mean basic infiltration rate was maximum in medium-texture soil (7.0 cm h^− 1), followed by moderately fine (4.6 cm h^− 1), fine (3.7 cm h^− 1), and moderately coarse (3.2 cm h^− 1) soils. Under paddy, fallow land uses and upland crops, the values of basic infiltration rate were 2.5, 4.1 and 5.3 cm h^− 1, respectively. Across major soil groups, a significant variations in basic infiltration rates were observed among different soil types. Particularly, the highest infiltration rate was recorded in brown hill soil (8.1 cm h^− 1); conversely, the lowest infiltration rate was in black soil (3.2 cm h^− 1), attributed to the predominance of montmorillonite clay. By using rank-based multicriteria decision making, the Philip model was found to be the overall best-performing model. The Kostiakov model came in close second phase. Moreover, depending on the kind of land cover, Philip model outperformed other models, while Kostiakov fared better in upland crops and paddy fields. Additionally, Philip’s model also performed better than other infiltration models across a range of soil textures. The study emphasized the significane of choosing suitable models to appropriately quantify the infiltration across a range of hydrological and agricultural contexts by highlighting the effects of soil texture, land use, and soil type on infiltration rates.

Subsidence is one of the main environmental impacts of underground mining worldwide. Besides, the increasing complexity of underground mining due to greater depths and interaction with inhabited and environmentally sensitive areas can lead to challenges that may threaten the viability of mining due to phenomena such as subsidence. This research aims to increase the knowledge about surface subsidence due to underground mining, characterising the main factors that rule mining subsidence utilising an actual mine that extracts potash. The calculation methodology was based on 74 sections of the subsidence basin, using GPS measurements and the InSAR technique, with data collected over twelve nonconsecutive years from 1995 to 2021. Thus, three different active areas and one residual area were determined. Average boundary angles and their average distances of influence for the active regions have also been determined. Furthermore, using the least squares method, the subsidence basin curve was defined using a Gaussian function. The algorithm that governs the subsidence process has been successfully calculated, allowing the approximation of the deformation of any point within an area of interest. The novelty of this paper is twofold: the results obtained provide a detailed subsidence behaviour and a prediction model of the case study. Furthermore, the methodology implemented can be applied to other subsidence basins with mines in their area of influence. Hence improving the surface mining area’s safety levels and managing the environmental impacts.

Alluvial rivers are dynamic landscapes on the earth’s surface that evolve with time. While many studies have examined the immediate effects of floods on river channels, there is a lack of research that investigates the longer-term evolution of river morphology following such events. The present study was carried out on the Upper Krishna River which flows between the southern part of Maharashtra and the northern part of Karnataka states in India for 375 Km. The morphological parameters were analyzed for three decades (1991–2021) and the year 2019 with the highest flood level was also considered for change analysis. The assessment was done for change in active channel area, mean width, bank line migration, sinuosity index, and erosion-accretion. The land use classification was also analyzed for the study period to understand the exposure to future floods. The spatial data was retrieved from different satellite missions and analyzed with the help of Remote Sensing (RS) and Geographical Information System (GIS). The river was divided into seven segments (R1, R2, R3, R4, R5, R6, and R7) and bank lines were digitised manually to minimise possible errors. The results show that during the study period, the river channel has been modified in terms of active channel area expansion in the R1, R5, R6, and R7, and erosion was found the dominating process while the left bank was more erosive than the right bank of the river. The built-up area was seen going through a major expansion than any other land use class. The discharge and sediment data confirm the flood years (1994, 2005, 2006, and 2019) which accelerated the morphological activity in the river segment. The results of the study provide new insights related to short-term morphological changes in the Upper Krishna River and can be used by policymakers and managers to carry out future development plans and river training work at affected sites.

Landslides are devastating geological disasters in mountainous terrains around the world. The Chitral Valley, situated in the Eastern Hindu Kush Mountain ranges, is highly exposed to recurrent landslides posing a significant threat to urban growth, society, and the surrounding ecosystem. To assist in understanding and mitigating the landslide impacts, this study utilized statistical and geospatial modeling tools to develop a landslide hazard map and subsequently evaluate the vulnerability of the infrastructure and settlements to derive a risk map. A comprehensive landslide inventory was developed through on-screen digitization and field verification and compared with the predominant causative parameters to establish a landslide susceptibility index (LSI). The LSI map was analyzed with the precipitation and peak ground acceleration (PGA) data to generate the landslide hazard index (LHI). A semi-quantitative multi-criterion evaluation (MCE) technique was applied for the vulnerability assessment. Physical, environmental, and social vulnerability indexes were determined using direct and pairwise comparison matrices and integrated into a landslide vulnerability index (LVI). The landslide risk index map was developed by combining the hazard and vulnerability. The findings reveal that 37.25% of the region is categorized as shallow risk, with the remaining sections classified as low (62.22%), moderate (0.37%), high (0.11%), and very high risk (0.05%). The study findings are critical for the local authorities and policymakers to develop plans and policies for landslide-induced disaster risk reduction, landuse planning, and public involvement to reduce the vulnerabilities.

Water-bearing fractured rock masses are prone to geological hazards due to freeze–thaw (FT) damage, which brings adverse effects on the stability of rock engineering. In order to study the FT damage characteristics of rocks, the intact and pre-cracked cyan sandstone samples were taken as the research objects, with pre-crack inclination angles β of 0°, 45°, and 90°, respectively. The effects of FT cycle on stress–strain curve, peak strength, apparent stiffness and FT coefficient of cyan sandstone samples were studied by uniaxial compression test. Based on macroscopic damage variables, a damage constitutive model of cyan sandstone is proposed combined with strain equivalence hypothesis and Weibull distribution hypothesis. Considering that the strain equivalence hypothesis is difficult to reflect the compaction effect of microcracks, the damage constitutive equation is modified by taking the ratio of the secant modulus of the actual stress–strain curve to that of the classical Lemaitre damage constitutive curve as the correction coefficient. The application results show that the modified constitutive model can well describe the stress–strain relationship of cyan sandstone before the peak strength, which verifies the reliability of the model parameters derived from the test data, and the practicability of the damage characterization method and correction coefficient. The results can provide theoretical reference for the study of FT damage of rocks in cold regions.

The purpose of this research was to model areas prone to erosion in the Gol-Mehran catchment in southern Iran. For this purpose, the soil erosion map was determined using membership functions and analytic hierarchy process (AHP) determined the soil erosion map. Additionally, using the self-organizing map (SOM) and principal component analysis (PCA) methods, the most crucial parameters affecting gully erosion were extracted. Finally, soil erosion was predicted using a multilayer perceptron (MLP) and radial basis function. The results of the fuzzy AHP method with all data and the selected data with SOM and PCA demonstrated that areas located in the center of the region were prone to gully erosion. The results of this research also demonstrated that urban lands have expanded significantly, while vegetation has decreased from 1990 to 2019, which has had a significant impact on soil erosion. The results also showed that the MLP model, with R² = 0.97, could accurately predict soil erosion.

Carbon capture and storage (CCS) is a method of reducing carbon dioxide (CO₂) emissions, which could be crucial in addressing global warming. Accurately estimating the pH of CO₂-saturated water/brine binary systems is an important physical–chemical property of reservoir fluids at CO₂ injection sites. Although there has been substantial research on the pH of CO₂-saturated brines, it mainly focuses on brine systems containing monovalent cations. Research on the pH of brine systems containing bivalent cations is lacking. The aim of this paper is to develop a chemical equilibrium model based on pressure–volume–temperature (PVT) data around the mechanism of CO₂ dissolution–acidification and the CO₂ chemical equilibrium process in brine aquifers, which can be used to predict the pH value of the CO₂-saturated brine system (0–4 mol kg⁻¹) under high-temperature (273–473 K) and high-pressure (up to 100 MPa) conditions. The model employs phase equilibrium equations to describe the CO₂-water/brine dissolution–acidification process, with corrections to the CO₂ activity coefficient part of the Spycher and Pruess model. The results showed that the predicted pH values were in good agreement with the experimental data. The pH of the CO₂-saturated CaCl₂(aq) and MgCl₂(aq) systems was predicted using both a chemical equilibrium model and the PHREEQC geochemical simulator based on the Pitzer model. The differences in the predicted values of the two models were analyzed. Based on the process of CO₂ dissolution acidification, a reliable empirical equation was established to calculate the pH of CO₂-saturated water/brine system, and it was applied to the storage of CO₂ brine layer.

Copper compounds in the form of fungicides are most commonly used as a measure of protection against vine diseases. Typically, around a dozen treatments are done throughout a year with a dispersion of approximately 5–10 kg of Cu per hectare. For many years, the remediation of heavy metals has often involved the use of ex-situ soil washing with chelating reagents. Amino acids have a lower metal chelation capability compared to EDTA and its derivatives, but they have numerous other advantages in comparison. The main goal of this research was to investigate the ability of 9 amino acids and one dipeptide to extract Cu from various vineyard soil samples and compare their chelating ability with other ‘green’ chelating agents. The average content of Cu extracted with amino acids is 34.7 ± 16.7 mg/kg or 30.3 ± 5.43 wt% relative to the pseudo-total content. This is more than what was extracted with carboxylic acid salts (9.91 ± 7.49 mg/kg or 8.45 ± 5.56 wt%) but less than with EDTA (98.5 ± 42.7 mg/kg or 79.9 ± 7.12 wt%). The descending order of tested amino acids relative to the removed Cu (mg/kg) is: His > Ser > Thr > Leu > Gly > Val > Phe > Gly-Gly ~ Ala > Arg. The results of this paper show that the amino acid structure is the most important factor for efficient Cu extraction, while the physicochemical properties of the vineyard soil have less impact.

Ouagadougou, the capital city of Burkina Faso, is facing significant economic and social damages due to recurring floods. This study aimed to develop a flood susceptibility map for Ouagadougou using a logistic regression (LR) model and 14 flood conditioning factors, including elevation, slope, aspect, profile curvature, plan curvature, topographic position index (TPI), topographic roughness index (TRI), flow direction, topographic wetness index (TWI), distance to river, rainfall, land use/land cover (LULC), normalized difference vegetation index (NDVI) and soil type. A historical flood inventory map was created from household survey data, identifying 1026 flooded sites which were divided into a training dataset (70%) and a validation dataset (30%). The factors that had a statistically significant influence (p-value < 0.05 and │Z│ > 1.96) at the 95% confidence level were, in order of importance, elevation, distance to river, rainfall, plan curvature and NDVI. The receiver operating characteristic (ROC) curve method was used to validate the model. The area under the curve (AUC) values of the model were 81% for the prediction rate and 82% for the success rate indicating its effectiveness in identifying areas susceptible to flooding. The results showed that 18.48% of the city is very high susceptible to flooding, 18.99% has high susceptibility, 18.43% has moderate susceptibility, and 19.98% and 24.18% have low and very low susceptibility, respectively. This research provides valuable information for policy makers to develop effective flood prevention and urban development strategies.

Uncertainty of a geoscientific map derived from data sampled by sensory engagement of human experts cannot be quantified from within the processing chain followed to produce the map due to subjective components in the underlying data. Nevertheless, quantified uncertainty associated with such maps would be essentially required for correct information retrieval from such maps. An approach mimicking an expert elicitation through digital agents in an information fusion procedure is suggested to quantify the trustworthiness of a partially subjective map (such as a soil map or any other geoscientific map inherently linked to partial subjectivity, e.g., a geological map). This procedure links a partially subjective map with collocated maps resulting from technical measurements provided with quantified uncertainty. Variation of methodology in this approach results in an ensemble of maps with variable degree of matching with the partially subjective map which allows for probabilistic statements about the trustworthiness of the partially subjective map. This enables including partially subjective maps in further data integration relying on numerical analysis, e.g., in the context of computational modeling or machine learning. By being able to assign a quantitative trustworthiness to a partially subjective map the risk of over-utilization of this map is reduced.