Environmental Earth Sciences最新文献

Potential Evapotranspiration (PET) is a core parameter for assessing the water and heat balance of alpine grassland ecosystems. Its accurate estimation is crucial for water resource evaluation, carbon flux simulation, and vegetation productivity prediction. Considering the strong climatic heterogeneity and limited applicability of single-source PET data in Northwest Sichuan, this study integrates three types of PET data—MODIS, ERA5-Land, and Hargreaves—using the Bayesian Triangle Cap (BTCH) method to construct a high-accuracy fusion product. The fusion product’s accuracy is validated against data from the FLUXNET Hongyuan site and pan evaporation observations from regional meteorological stations, and the applicability of the BTCH method in alpine grassland regions is systematically assessed. The results demonstrate that BTCH-PET outperforms single-source datasets in terms of Nash-Sutcliffe Efficiency and Root Mean Square Error, exhibiting greater stability and regional representativeness. Additionally, spatial distribution analysis and wavelet analysis are employed to reveal the seasonal patterns and dominant periodic variations of PET, providing valuable data support and methodological references for evapotranspiration monitoring and water resource management in alpine grassland ecosystems.

The integration of artificial intelligence (AI) in remote sensing and satellite image processing has significantly transformed the field, offering advanced tools for data analysis, feature extraction, and environmental monitoring. With the growing availability of high-resolution satellite imagery, AI applications such as machine learning and deep learning have been applied to automate the process of interpreting complex spatial data. This paper intends to explore the current state of AI in remote sensing, focused on some applications such as land cover classification, object detection, climate change monitoring, and disaster management. Additionally, the challenges and future directions for AI-driven remote sensing are discussed, emphasizing the need for better generalizability, data fusion, and improved computational efficiency.

At present, the exploitation of shallow coal seams in China is nearly exhausted, while the exploitation of deep coal seams is confronted with a high-temperature and high-pressure reservoir environment, which directly affects the adsorption performance of coal seams. The adsorption characteristics of coal seams are a crucial factor influencing the effectiveness of coalbed methane control. To obtain the gas adsorption characteristics of coal under high-temperature and high-pressure conditions, this study combines pore structure experiments, isothermal adsorption experiments, and molecular dynamics isothermal adsorption simulations to investigate the gas adsorption performance of coal under different temperature and pressure conditions from both macroscopic and microscopic perspectives. The research results show that: the effect of pressure on pores is greater than that of temperature. When coal is stored in a high-temperature and high-pressure environment, the pore volume decreases, the pore area increases, and the connectivity of coal deteriorates. When the gas temperature rises from 303.15 K to 363.15 K, the average maximum adsorption capacity of coal decreases by 2 cm³/g for every 20 K increase; when the gas pressure rises from 0 MPa to 10 MPa, the average maximum adsorption capacity of coal increases by 1 cm³/g for every 1 MPa increase. The Langmuir volume (V) exhibits a significant linear relationship with the pore volume and specific surface area of micropores; the Langmuir pressure (P) also has a certain correlation with the pore volume and specific surface area of mesopores. This indicates that the micropore structure in coal is the key factor controlling its methane ultimate adsorption capacity, while the mesopore structure, although not directly determining the adsorption capacity, affects the morphological characteristics of the methane adsorption isotherm to a certain extent. The affinity distribution function curves under different pressures and temperatures show “multiple peaks”, indicating the surface inhomogeneity of deep coal macromolecules and the presence of multiple adsorption sites. During the adsorption process of coal macromolecules, gas molecules first occupy the strong adsorption regions, and then gradually fill the weak adsorption regions. The adsorption of deep coal seams is no longer a single monolayer adsorption, but also involves micropore filling adsorption. When the temperature increases from 303.15 K to 363.15 K and the pressure increases from 0 MPa to 10 MPa, the adsorption heat is always less than 42 kJ/mol, indicating that the adsorption on the coal molecular surface belongs to physical adsorption. These findings clarify the adsorption characteristics of deep coal seams and provide a theoretical basis for the exploitation of deep coalbed methane.

Lateritic soil slopes are prone to desiccation cracking and surface erosion, particularly in the early stages of vegetation establishment, which compromises their long-term stability and resilience. This study aims to address these issues by applying an optimized environmentally friendly agent using the surface spraying method. A series of laboratory tests were conducted to examine the effects of various potential agents on the tensile strength, desiccation cracking, surface erosion, and plant growth in lateritic soil. Finally, the protective mechanism of the optimal agent was analyzed. The results show that untreated lateritic soil at its natural dry density has a low tensile strength of 15.1 kPa after drying, resulting in a high crack intensity factor of 8.30% after wet-dry cycles and a large erosion ratio of 2.92 g/cm² under heavy rainfall. Soil stabilizer, sodium silicate, and alkaline lignin are effective in enhancing strength, mitigating cracks, and controlling erosion at appropriate concentrations, but they inhibit plant growth in lateritic soil. Polyaluminum chloride improves tensile strength and crack resistance without inhibiting plant growth, but it cannot prevent surface erosion. Polyacrylamide exhibits the best overall performance as it can increase the tensile strength to 57.7 kPa, reduce the crack intensity factor to 3.44% and the erosion ratio to within 0.10 g/cm², without affecting plant growth. Given its accessibility, cost-effectiveness and rapid action, polyacrylamide stands out as the superior surface spraying agent for protecting lateritic soil slopes.

Many slopes remain stable due to the strength imparted to them by suction stress. Thus, the necessity for stability charts to aid in design considerations in scenarios involving partial saturation has become essential. Therefore, this paper proposes a set of straightforward and quick stability charts for uniform unsaturated slopes using the kinematic approach of limit analysis, upper bound theorem. The charts covered various soil strength which ensured a case of frictional and c-(phi)slopes. The stability charts, which account for changes in suction due to infiltration, evaporation, or fluctuations in the water table level, eliminate the need for iterations. Validation against case studies from the literature showed generally excellent agreement, demonstrating the efficacy of the approach in assessing slope stability under unsaturated conditions. The significance of stability charts becomes evident when they are required in situations where conventional stability charts do not account for the effect of the suction stress on the stability.

Assessing landscape ecological risk based on land use is crucial for managing regional ecological security and guiding urban development. This study investigates the urban agglomeration around Poyang Lake (UAAPL) and employs the landscape ecological risk model and the PLUS model to analyze the distribution and spatial patterns of landscape ecological risks from 2000 to 2020, and projects risks for 2030. The main findings are as follows: (1) From 2000 to 2020, the area was dominated by forestland and cropland, covering over 85% of the total. The area of construction land increased by 95.71%, while arable land, forestland, and grassland decreased by 3.1%, 1.3%, and 9.76%. Land-use changes primarily occurred through transitions between arable land, forestland, and construction land. (2) The Moran’s I for landscape ecological risk remained above 0.9 during this period, indicating strong spatial clustering. High-risk, medium-high-risk, and medium-risk areas expanded by 258 km², 2,016.75 km², and 1,342.5 km², respectively, while low-risk and medium-low-risk areas contracted. The expansion of construction land was the main driver of this trend, increasing landscape ecological risks and degrading the environment. (3) By 2030, under the ecological protection scenario, the area of ecological improvement is expected to reach 1,899.5 km², while the growth of ecologically deteriorated areas will be significantly reduced. (4) The area of construction land constitutes the dominant driver influencing the spatial differentiation of landscape ecological risk in the UAAPL, and the interaction between construction land area and slope serves as the primary factor shaping the spatial variation of landscape ecological risk across the basin. These findings provide valuable implications for ecological management and planning in the UAAPL.

Exploring water resources is crucial for sustaining life in arid-hyperarid regions. Multicriteria derived from remote sensing, geologic, and climatic were integrated into a GIS-based data-driven frequency ratio (FR), and evidential belief function (EBF) techniques for demarcating water resources in Wadi Numan and its surroundings, west of Saudi Arabia. Remote sensing data including SRTM, Sentinel-1, and Landsat-8 have allowed for characterizing the geologic, hydrologic, structural features, and meteoric conditions of the study area. Fourteen GIS- layers include topography, slope, curvature, depression, TRI, drainage density, TWI, Distance to River, SPI, InSAR CCD, Lithology, Vegetation, lineament density, and rainfall intensity. These layers were prepared, normalized, and computed using FR and EBF, then fused and overlayed using GIS methods. The two outputs of FR and EBF revealed that the excellent areas that hold water were covering 3.63%, and 6.08%, respectively. Assessing and validation of the two models using groundwater wells and area under the curve (AUC). The AUC for validated, and all samples of FR are 0.80, and 0.82%, but EBF is 0.77, and 0.79, respectively. This has proven an accepted implementation than the EBF method. Using the InSAR CCD technique derived from Sentinel-1, two different date images revealed that areas of incoherence have high potential. Overall, implementing data-driven techniques is crucial for modeling water resources in arid regions and the applied methods are important and can be used in other areas of environmental conditions.

Artificial Intelligence integration with Synthetic Aperture Radar technologies provides significant advantages for monitoring ground deformation on a large scale and with high precision, but current applications face important limitations that reduce both reliability and practical usability. This systematic review demonstrates that generalizability remains a central challenge, as models often show decreased performance when applied to different geological and geographical settings. Furthermore, many studies do not comprehensively incorporate various deformation forces such as geological, hydrological, or human-induced factors into the analysis, and the definition of deformation regions frequently relies on subjective judgments. A review of 62 relevant articles clearly shows that adaptability to local conditions is among the most persistent weaknesses. In response to these findings, this study introduces the Adaptive Regional Artificial Intelligence System, a new conceptual framework designed to dynamically select and apply the most suitable algorithms based on the specific geological features and external triggers present in each deformation region. This approach provides a flexible and context-aware analysis by overcoming the constraints of single-model strategies. The main contribution of this review is to highlight the need for a shift from uniform, static models toward locally adaptive and scalable methodologies, thereby increasing scientific reliability, transparency, and operational value. These advancements support the development of more targeted and effective strategies for disaster risk management and sustainable infrastructure planning.

In contaminated soil, the heavy metal Pb is characterized by strong toxicity, concealment, hysteresis, and irreversibility. This study aimed to explore the effects of heavy metal concentrations and cathode-anode spacing conditions on the electricity generation performance and lead remediation efficiency of soil microbial fuel cells (SMFCs). Under an external resistance of 1000 Ω, dual-chamber SMFCs with different Pb concentrations (300, 580, and 780 mg/kg) and cathode-anode spacings (2.5, 5.0, and 7.5 cm) were constructed. The results showed that the maximum output voltage and power density were 310.74 mV and 0.78 W/m² in the SMFC-580 mg/kg, and 369.95 mV and 0.92 W/m² in the SMFC-2.5 cm, respectively. When the concentration of Pb in the soil was 580 mg/kg and the electrode spacing was 2.5 cm, the highest remediation efficiency of Pb was achieved at 16.67%. Characterization of the SMFCs indicated the deposition of Pb on the cathodic surface. According to the improved continuous extraction BCR method, the acid-extractable Pb in the SMFC-580 mg/kg and SMFC-2.5 cm was 10.61% and 12.59%, respectively. The internal resistance of SMFCs can be reduced by increasing the lead concentration and shortening electrode spacing. However, an excessively high concentration of Pb may hinder the electrochemical performance of SMFCs and the Pb remediation effect. After 30 days of operation, the lead migrated toward the cathode.

This study investigates the hydrogeochemical characteristics, water quality, and potential human health risks of groundwater in the Tavas Plain (Denizli, SW Türkiye). The groundwater of the alluvial aquifer, the main freshwater resource in the region, exhibits significant contamination by arsenic (As) and nitrate (NO₃⁻). Arsenic (As) concentrations ranged from 12.3 to 58.6 µg/L, exceeding the World Health Organization (WHO) guideline value of 10 µg/L in all sampled groundwater. Nitrate (NO₃⁻) concentrations varied between 10.9 and 104.4 mg/L, with 36% of the samples exceeding the WHO drinking-water limit of 50 mg/L. Hydrogeochemical data indicate that the dominant groundwater facies are Ca²⁺- Mg²⁺- HCO₃⁻ and Mg²⁺- Ca²⁺- HCO₃⁻. These facies are primarily controlled by carbonate dissolution and silicate weathering, with additional influences from ion-exchange reactions and localized agricultural inputs. According to the Groundwater Quality Index (GWQI), all samples were classified as unsuitable for drinking, while Nitrate Pollution Index (NPI) values indicated moderate contamination in 86% of the samples. Health risk assessment revealed that all groundwater samples exceeded the acceptable limits for both carcinogenic and non-carcinogenic risks, mainly driven by arsenic exposure. Children exhibited significantly higher risk levels than adults, highlighting their increased vulnerability.The findings emphasize that groundwater in the Tavas Plain is unsuitable for drinking purposes and highlight the urgent need to identify alternative and sustainable water resources for the region.