Environmental Earth Sciences最新文献

The soil in Northern Iran exhibits significant variability, showing notable responses to variations in climatic and environmental conditions. In a representative area covering 936.7 km², we measured 18 soil properties at 73 sampling locations. These properties were then screened using Principal Component Analysis (PCA) which identified five PCs with eigenvalues greater than 1.0 and correlation analysis to construct the Nemro Soil Quality Index (SQI) with a mean of 0.27 ± 0.04. The predictability of SQI was modeled using the Generalized Additive Model (R² = 0.669, explained deviance = 69.7%), indicating that elevated Normalized Difference Vegetation Index (NDVI; p value = 0.000) and daytime Land Surface Temperature (LST; p value = 0.000) enhance SQI, while higher slopes (p value = 0.020) have a diminishing effect. The model was also utilized to illustrate potential future alterations in SQI for the year 2040. For this purpose, the 2040 MODIS data (NDVI and LST) were projected using various regression models (0.09 < R² < 0.69) applied to historical mean annual MODIS data spanning from 2002 to 2022. The majority of projected changes in SQI exhibited a negative trend, primarily attributed to the depletion of vegetation cover at the peripheries of forest borders. These findings underscore the imperative need for strategic future management plans, emphasizing the preservation of soil in marginal forest lands.

Avalanches are natural events that can lead to significant risks to both human life and property. The creation of an avalanche susceptibility map is a valuable tool for effectively managing the avalanche prone areas. The primary objective of this paper is to utilize and analyse machine learning models for susceptibility mapping, with the goal of classifying avalanche-prone regions based on terrain parameters extracted from a digital elevation model. In other word, to explore the capability of Tree-based machine learning methods to handle the GIS dataset. Fifteen data layers have been extracted, stacked, and processed to create training and testing data using the avalanche inventory. Three tree-based machine learning models has been trained and tuned using grid search on dataset that has been split into 80:20 for model calibration and validation. Results indicated that both advanced models had an excellent performance in terrain-based avalanche modelling (ROC-AUC > 85%), although true positive and true negative analysis demonstrated the superior performance of Random Forest. Feature importance analysis indicated that elevation and aspect are the top effective and most common feature among all the variables and models, respectively. Building a high-quality and informative database is a crucial part, and avalanches inventory classification before susceptibility assessment is a key step in enhancing the accuracy of the model. The study’s findings can offer valuable insights for land use planning, enabling the control of avalanche paths and mitigating potential hazards. Additionally, these results can serve as a valuable reference for future studies focused on snow avalanche hazards modelling.

This study investigated the efficacy of biochar as a soil amendment for enhancing soil physicochemical properties and solute transport dynamics, with implications for agricultural sustainability and environmental stewardship. Batch laboratory experiments and column studies were conducted to assess the effects of biochar application on soil parameters and solute transport under saturated conditions. The saturation soil extraction approach was employed in batch leaching tests, while column experiments replicated subsurface conditions. Transport modeling using CXTFIT 2.1 elucidated solute dispersion dynamics in biochar-amended soils. Batch experiments revealed significant alterations in soil pH, electrical conductivity, and nutrient release following biochar addition. Biochar exhibited adsorption capacity for fluoride ions and released dissolved organic carbon, highlighting its potential for soil carbon sequestration and microbial activity. Column studies demonstrated enhanced solute dispersion and increased microbial activity in biochar-amended soils, as evidenced by changes in breakthrough curves and degradation rates of nitrate. Indeed, nitrate first-order degradation coefficients were 9.08E-06 for the column with only sandy soil, 3.09E-05 and 1.47E-04 for the columns with minimum and maximum doses of biochar respectively. Biochar application significantly influenced soil physicochemical properties and solute transport dynamics, with potential implications for nutrient management and contaminant attenuation in agricultural systems. Despite limitations in laboratory-scale experiments, this research provides valuable insights into biochar-soil interactions. It underscores the need for further investigation under field conditions to validate findings and optimize biochar management practices for sustainable soil and environmental management.

Mercury intrusion porosimetry (MIP) is widely used for coal pore structure characterization, however, the matrix compressibility (MC) can lead to overestimated measurement results. Determination of MC is crucial for revealing the influence of pore structure on coalbed methane (CBM) flow behavior. In this study, MIP and low temperature N₂ adsorption (LT-N₂A) were conducted on 15 coal samples from major coal-producing regions in Northern China. The MIP data were corrected using MC theory, and the effects of coal rank and pore structure on coal MC were analyzed. The influence of MC on fractal dimension was elucidated, and the sensitivity of three fractal models to MC was effectively evaluated. Finally, the impact of MC on the coalbed methane (CBM) exploitation was discussed. The results show that low-rank coals have higher MC than medium/high-rank coals, and the MC coefficient follows a cubic polynomial relationship with coal rank, with two inflection points located at 1.4–2.5%, respectively. Micropores and transition pores are the main contributors to MC, for corrected data, the pore volume of both types of pores decreases significantly. The corrected pore size distribution exhibits better agreement with the LT-N₂A measurement results, particularly in peak position and size for pores between 5 and 50 nm. This suggests the potential of corrected MIP data to supersede the combined use of MIP and LT-N₂A data. MC can lead to overestimation of the fractal dimension, with the thermodynamic model showing the lowest sensitivity to MC. After the microfractures in medium/high-rank coal are greatly compressed, the compressional deformation of micropores and transition pores begins to have a significant impact on the CBM transport. The research results are of great significance for deeply understanding the mechanism of CBM transport.

The disturbance of shallow-buried tunnel excavation with large section is the main factor causing the ground subsidence, and the coupling effect between double-line tunnels makes the ground subsidence regularity more complicated. Based on the actual project, the law of ground subsidence and its influencing factors during the excavation of double-line tunnel under different compressive states are discussed by numerical simulation method. The results show that the ground subsidence curve of the double-line tunnel is asymmetrically W-shaped, and the maximum ground subsidence is inclined to the side with larger buried-depth; Under the non-biased state, the axial distance of the double-line tunnel has the most significant effect on the ground subsidence, while under the biased state, the influence factor is the transverse bias angle, and the maximum ground subsidence increases with the increase of the transverse bias angle. Based on the numerical results, three indexes are proposed to quantitatively describe the relationship respectively between the ground subsidence and the excavation section area, the axial distance of the double-line tunnel and the bias angle during the excavation of the double-line tunnel, and the indexes can be used to predict the ground subsidence triggered by the excavation of shallow-buried tunnel under the biased state. Furthermore, the study results can provide reference values for tunnel construction under similar conditions.

A deep geological repository is considered an appropriate option for disposal of radioactive waste containing long-lived radionuclides. Engineered barriers’ degradation and radionuclide transport strongly depend on the conditions in the repository. This work presents the assessment of the geochemical evolution in a radioactive long-lived intermediate-level waste disposal cell constructed in granite. The considered cell consists of cemented waste packages, cementitious backfill and several meters of host rock. Three abstracted reactive transport models of different complexity were developed: a 1D model and a 2D model considering transport by advection and diffusion and a 2D model with diffusive transfer only. The changes in the pH, the pore water composition and the materials’ mineralogical composition were observed. The modelling results indicate an increase in the pH in the disposal cell due to leaching of alkalis, which is followed by the dissolution of portlandite and the precipitation of calcite at the granite-vault backfill boundary. The obtained changes in the pH indicate that the geochemical alterations in the disposal cell proceed very slowly. Such a slow degradation could be the result of the formation of the higher pH zone upstream from the disposal tunnel. The advection–diffusion models in 1D and 2D geometries produced similar results. However, the 2D model also identified spatial peculiarities in the changes of the geochemical environment. Comparison of the pure diffusive case results with the advection–diffusion cases demonstrated that both processes are relevant in the analysed disposal cell.

Hydrothermal resources are used for health prevention and treatment worldwide based on drinking, bathing, and immersing in thermal mineral waters. In some places, ²²²Rn inhalation is also present. In Brazil, several locations apply thermal and hydrotherapies integrated into the Unified Health System as in Águas de Lindóia and Thermas Antonio Carlos in Poços de Caldas Town. The effective dose due to ²²²Rn inhalation by the public and balneary workers, as well as the chemical and radiological characterization for these spas were evaluated. ²²²Rn activity concentration was measured by using CR-39, elemental water composition by neutron activation, and radionuclides, by gamma espectrometry. Results showed that the ²²²Rn activity concentration varied from 21 to 71 Bq m⁻³ in the public areas of the balnearies and from 407 to 16,451 Bq m⁻³ in the closed springs. Effective doses varied from 0.01 to 0.02 mSv y⁻¹ for the public, from 0.10 to 0.33 mSv y⁻¹ for works and from 0.03 to 4.95 mSv y⁻¹ for maintenance. No risk occurs for members of the public and workers due to radon inhalation, but care must be taken on behalf of the maintenance workers from Águas de Lindóia balneary. The water, for both balnearies, do not exceed the recommendation for drinking water for gross alpha and gross beta activities. Chemically, the water from Águas de Lindóia is classified as hypo saline and the water from Poços de Caldas, as low mineral content.

Ultrasonic waves have been explored for fracturing coal seams to enhance Coalbed Methane (CBM) permeability, yet the underlying mechanics of ultrasonic cavitation are not fully understood. This study investigates the effect of water-based ultrasonic cavitation on coal pores microstructure by employing fluid invasion methods and non-destructive X-ray testing to reconstruct coal pore microstructures. Additionally, numerical simulations of ultrasonic cavitation development were conducted. The research examines how ultrasonic power influence evolution mechanism of cavitation effects in pores microstructure of water-bearing coal. Such insights lay a theoretical groundwork for improved Water-Based Ultrasonic Cavitation Enhanced Coalbed Methane Recovery (WUC-ECBM). Findings suggest that coal pores microstructure with a fully connected pore topology are more conducive to ultrasonic cavitation. As ultrasonic exposure increases, the accumulated waves cause coal pore microstructure cavitation bubbles to oscillate violently and non-linearly, leading to their growth, development, and collapse. This results in high-energy microjets and shockwaves, creating a high-temperature, high-pressure environment (reaching up to 35.69 MPa and 2729.77 K) favorable for improving gas desorption and migration. Ultrasonic power adjusts the cavitation threshold and intensity, facilitating gas migration. This research aims to improve gas desorption and migration in coal by capitalizing on the mechanical, physical, and thermal effects of ultrasonic cavitation. These findings offer theoretical support for the effective implementation of WUC-ECBM.

The study focuses on assessing groundwater quality, with a special emphasis on fluoride contamination, in the Muktsar, Bathinda, and Moga of Punjab, India. Groundwater being a crucial resource for the region, faces contamination from both natural processes and anthropogenic activities. The study employs advanced techniques, including Geographic Information Systems (GIS) and machine learning models to predict fluoride contamination and assess the water quality index (WQI). The groundwater samples were systematically collected from 281 locations using GIS at approximately 5 km distance to ensure uniform distribution. The study aims to predict fluoride levels, various hydrochemical parameters and WQI to identify high-risk areas. Using Inverse Distance Weighting (IDW), the distribution of fluoride level and WQI was mapped, revealing varying concentrations across the study area. From the study, the Random Forest (RF) model identified key hydrochemical parameters influencing fluoride contamination. The RF model demonstrates high predictive accuracy for fluoride contamination, using the receiver operating characteristic (ROC) curves for validation and yield area under the curve (AUC) values of 82%, 81%, and 94% for Muktsar, Bathinda, and Moga districts, respectively. The novel integration of GIS with machine learning provides a robust framework offering valuable insights for water resource management. The results showed significant fluoride contamination in many areas, posing serious health risks like dental and skeletal fluorosis. The findings highlight the importance of addressing both natural and human-induced factors in managing groundwater quality, ensuring safe drinking water, and protecting public health in affected regions.

The government agency BGE started a site selection process for a high-level nuclear waste repository in 2017. The first phase of data screening was completed in 2020 and resulted in the identification of search areas covering 54% of the national territory. The actual second phase will be completed by 2027 or later, and the areas for aboveground investigations will be identified. The obligatory nongovernment input is provided by follow-up regional conferences, which are best supported by external scientific expertise. The large amount of text documents and maps and the interactive internet tools such a 3D viewer provide a suitable basis for external evaluation. The rationale is to apply stricter criteria than those used in the first phase to drastically reduce the size of the search area. With this updated set of criteria, the size of the search area can be reduced to 2,662 km² or 0.7% of the national territory. This size is convenient for detailed aboveground investigations such as high-density 3D seismic surveys.