Environmental Earth Sciences最新文献

The earthquake sequence that occurred on February 6, 2023, centered in Türkiye caused extensive loss of life and significant damage. In this study, the geotechnical properties of the central districts of Malatya province, one of the provinces affected by these earthquakes, were calculated using data obtained. In the calculations, the correlations suggested by the Turkish Building Earthquake Code (TBEC) and internationally recommended correlations were used. Thus, the difference between the methods proposed by TBEC and internationally recommended correlations was interpreted. Using 1890 drilling data, 1765 seismic data, and 1764 microtremor data, calculations were made to determine bearing capacity values for 3 m x 3 m pad foundation, liquefaction potentials of the soil and soil classifications around this region. The results obtained from the calculations were mapped with geographical information systems-based software. Results of the study revealed that 2.9% of the study area in Battalgazi district and 1.71% for Yeşilyurt district had liquefaction potential. Almost 80% of each district was found to have a soil class of ZD (medium dense gravel and sand or clay layers) according to TBEC. The findings of the study were compared with previous studies, satellite images of the study area and post-earthquake observations. In areas where damage caused by the earthquake sequence was observed intensively, bearing capacity values were relatively low. It was concluded that building on poor soil conditions poses a profoundly serious risk in terms of earthquakes and very serious precautions should be taken by gathering several disciplines during the construction of these structures.

Estuaries are critical components in the environmental risk assessment of anthropogenic contamination. They funnel the emissions from upstream terrestrial catchments and are often within historically established population and industrial centers. They are sensitive and biodiverse and increasingly acknowledged be subject to increasing risks and hazards from urban development and climate change. To understand these effects, regular monitoring is essential but needs to be appropriate to allow impact assessment and direct long-term mitigation strategies, building resilience under the advancing impacts of climate change. A One Health approach to environmental assessment is needed to counter the emergence of global public health threats, such as antimicrobial resistance (AMR) supporting the interaction between estuarine ecology, humans and the environment. We focus on Thane Creek, Mumbai, India as a critical case being recently designated a RAMSAR site and India’s only urban RAMSAR wetland. The necessity of a robust environmental monitoring system for regulatory policy development reflects impacts from historic and emerging pollution sources. It is a particularly sensitive environment, and one of the largest creeks in Asia, with ecosystem function identified to be highly vulnerable to the impacts of climate change. Rapid urbanization, causing alterations to creek geometry over relatively short timescales, has impinged on wetland habitats. Data from governmental monitoring and previous studies of environmental quality in Thane Creek are compared to data for other Indian estuaries. Overall, there is evidence of contamination from sources including domestic sewage and nearby industries, which may have chronic impacts on the ecosystem. Dissolved oxygen was lower, biochemical oxygen demand higher, and coliform counts similar in Thane Creek compared to other estuaries. The influence of tidal dynamics and sediment movement is likely to develop seasonal variation in AMR within water and sediments with potential impact on a rich and diverse ecology, especially for migratory birds. Subsets of organic contaminants and potentially toxic elements are currently monitored infrequently in water but have been found enriched in the creek’s sediments. These key geochemical parameters are likely to have significant impacts on environmental health and highlight the need for wider assessment of environmental stressors and the development of more robust estuarine health indicators. Given both the ecological and geographical sensitivity of the region focusing on one health is a more appropriate monitoring strategy to address the emerging ecosystem challenges.

Landslides significantly threaten human life and infrastructure, requiring accurate and timely identification for effective hazard assessment and management. This study proposes a new approach combining Geographic Object-Based Image Analysis (GEOBIA) and machine learning on the Google Earth Engine (GEE) platform, utilizing high-resolution Sentinel-2 imagery and NASADEM data. Our methodology begins with Simple Non-iterative Clustering (SNIC) segmentation, which divides the images into homogeneous super-pixels. This step is crucial for reducing 'salt and pepper' noise and enhances the differentiation of spectrally similar objects through advanced texture, shape, and contextual analysis. Following segmentation, Gray Level Co-occurrence Matrix (GLCM) feature extraction is employed to gather critical textural information, which is pivotal in discerning surface roughness, heterogeneity, and composition—key factors in identifying landslide-prone areas. To manage the high dimensionality of the data, Principal Component Analysis (PCA) is utilized for dimensionality reduction, transforming original variables into a set of uncorrelated principal components that facilitate more efficient subsequent analysis. Various machine learning algorithms are utilized, including Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART). We use the GEE platform to leverage extensive geospatial data and computational power. The performance of SVM, RF, and CART algorithms is evaluated for landslide detection. RF demonstrates superior accuracy in detecting landslides, achieving an overall accuracy of 87.41%, surpassing SVM (85.47%) and CART (68.45%). Integrating SNIC segmentation, GLCM feature extraction, PCA analysis, and RF algorithm within the GEOBIA framework using the GEE platform shows promising results for improving landslide identification, monitoring, and risk assessment.

Natural zeolites, known for their remarkable cation exchange capacity, can be used as effective minerals for sequestering cesium released from contaminated water and soil resulting from nuclear accidents. However, it is necessary to immobilize cesium after exchange to prevent dispersion. Structural modifications of these zeolites, particularly through thermal treatment, are emerging as a viable approach to effectively immobilize cesium. These modifications also offer insights into mineralogical and structural changes under significant radiation exposure, thereby serving as potential back-fill materials in nuclear waste repositories. This investigation focuses on the encapsulation of cesium by stilibite and heulandite, using XRD and thermal analyses and a leaching test after heat treatment over 500 °C after ion exchange of cesium. Cesium leaching experiments revealed variations dependent on the heat treatment temperature. Heulandite showed lower cesium leaching compared to stilbite at high temperatures, attributed to distinct dehydration characteristics and structural transformations at elevated temperatures, potentially correlated with different Si/Al ratios of the two zeolites. Both zeolites manifested decreased cesium leaching with increasing temperature of heat treatment, albeit exhibiting temperature ranges (700–800 °C) wherein cesium leaching initially increased before decreasing again, likely due to phase transformations. At temperatures over 1000 °C, both zeolites exhibited nearly negligible leaching, primarily attributable to the transformation of stilbite and heulandite into dehydrated zeolite CAS (Cs-aluminosilicate) and glass phases, respectively. This study highlights the dual capabilities of stilbite and heulandite as effective cesium ion exchange minerals and high-temperature encapsulants. Despite undergoing different phase transformations, these zeolites demonstrate significant potential as candidates for remediating radioactive cesium and as barrier materials in nuclear waste repositories.

Urbanization is a rapidly intensifying global phenomenon, reshaping landscapes and altering ecosystems across regions. In India, the foothills of the Himalayas, particularly Dehradun, has witnessed significant transformations due to urban expansion. Rapid urbanization has given rise to various adverse consequences affecting the environment, economy, and the well-being of its inhabitants. Among these challenges, land-use change stands out as a significant issue linked to urbanization. This research investigates alterations in land use and land cover (LULC) during the period from 2000 to 2020 within the Dehradun District of Uttarakhand. Forest cover degradation is one of the main threats to the area. LULC changes are determined with an interval of 5 years, that is, 2000, 2005, 2010, 2015, and 2020. Remote sensing (RS) and Geographical information system (GIS) methods detect changes in urban and forested areas using a multi-temporal supervised classification approach for generating LULC maps. The classifications reveal significant changes in the study area from 2000 to 2020. It was found that there was a decline in dense vegetation by 9.00% and an increase in built-up area by 2.32%, which may be due to the increase in urbanization and industrialization across 20 years. The outcome of current research can help significantly improve future development initiatives within the study area and is essential for the effective implementation of Sustainable Development Goal 15. The spatial analysis techniques contribute to a deeper understanding of potential strategies for prioritizing land use policies aimed at restoration at the priority level. These insights can then be extrapolated to benefit other socio-environmental tropical forest systems.

The South African Bushveld Complex, Upper Group 2 (UG2) Reef pillar design has been based on a strength formula originally determined for quartzite. This formula was modified to cater for the weaker UG2 chromitite material. However, there is evidence that the pillars are stronger than predicted by the modified formula. In this study, UG2 chromitite samples were tested at a wide range of width-to-height ratios to determine the material behaviour and the relationship between strength and width-to-height ratio. Such test results on this material have not been published in the past. All testing was performed in a servo-controlled stiff testing machine (MTS 815) at a constant deformation rate of 0.08 mm/min. Specially prepared load spreader end-pieces were used to minimise any bending effects of the loading platens on the sample behaviour. The analysis showed a non-linear strengthening with increasing width-to-height ratio, best described using a power formula. A change in failure mode from pure shear to extension was observed at a width-to-height ratio of one, which has been documented previously for other materials. The change in fracture mode did not appear to affect the strength of the material. Further tests were conducted to determine the critical strain at which extension fractures initiate in chromitite. It was found that extension fracturing initiated at a critical strain of between − 2.2 mε and 3.0 mε, which was about 10 times the strain calculated by existing formulae. This new information has not been previously published for chromitite. An additional useful product of the work is an equation that can be utilised to evaluate the true uniaxial strength from samples prepared with a w/h ratio of one. Strain softening was observed on all samples up to a w/h ratio of eight. Samples with w/h ratios of three and higher were able to sustain high stresses for large strains, but almost all samples finally failed with complete loss of load. The final sudden loss of load is believed to be associated with radial cracks observed in these samples after failure.

Karst aquifers are highly vulnerable to contamination due to their unique hydrogeological characteristics and increasing anthropogenic pressures. Given the challenges and costs associated with remediation, this study evaluates the groundwater vulnerability of karst formations in the Western Amazon Basin using three assessment methods: EPIK, DRASTIC, and DRASTIC-LUC. Geospatial data and sensitivity analysis were employed to assess the Napo Karst Formation. The results show that DRASTIC and EPIK classified 45.76% and 35.38% of the area as highly vulnerable, while DRASTIC-LUC classified 57.47% as moderately vulnerable. Sensitivity analysis identified depth to the water table and infiltration conditions as the most critical factors influencing vulnerability. The moderate-to-high vulnerability observed raises concerns about the risks to both surface and groundwater resources, which local populations heavily depend on. This study provides a valuable baseline for future research and offers essential guidance for decision-makers to mitigate activities that could degrade water quality in the Western Amazon Basin.

In this study, the ET in the Dawen River Basin for the year 2021 was estimated using the SEBS model, combined with meteorological data and Landsat 8 remote sensing images. By analyzing representative remote sensing images from spring, summer, autumn, and winter, the temporal and spatial distribution characteristics of ET in the region, as well as its patterns of change, were studied. The results indicate that ET in the study area exhibits a distinct seasonal variation, with the highest levels occurring in summer, followed by fall, spring, and winter. ET peaked in July and gradually decreased to its lowest point in January. Although different land cover types showed similar seasonal variation in ET, the differences were particularly pronounced in summer, with the highest ET observed in water bodies and the lowest in built-up areas. A one-way analysis revealed that elevation was the most significant factor influencing ET, with an explanatory power of 0.272, followed by mean annual temperature and land type. This study provides a scientific basis for optimizing water resource management in the Dawen River Basin and offers new insights into the spatial and temporal dynamics of ET and its driving mechanisms in the region.

The ongoing rise in global sea levels poses significant risks to coastal regions such as storms surges, floodings and necessitates accurate predictive models to inform the relevant government organizations that are responsible of mitigation strategies. This study leverages advanced hybrid deep learning techniques to forecast global sea level changes up to the year 2050. Utilizing a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, our model integrates historical global sea level data from climate.gov and global air temperature projections from the CMIP6 (Coupled Model Intercomparison Project Phase 6) model. Performance evaluation, based on metrics such as Nash-Sutcliffe Efficiency, Mean Squared Error (MSE), and the Diebold-Mariano Test, demonstrates the superior accuracy of the hybrid models over traditional deep learning models. Results show that the hybrid LSTM-CNN model outperforms the standalone models, achieving an MSE of 0.4644 mm and an NSE of 0.9994, compared to the LSTM model’s MSE of 2.4450 mm and NSE of 0.9970. These findings underscore the potential of deep learning methodologies in enhancing the precision of long-term sea level predictions, providing valuable insights for policymakers and researchers in climate science.

A nonlinear technique was used to analyse soil radon concentrations measured over a year and four months in Jiaosi, northeast Taiwan, to find precursory signals for regional earthquakes. The recorded data demonstrated that numerous meteorological and geophysical parameters influence radon gas emission from the soil, resulting in a complex soil radon time series. Empirical Mode Decomposition (EMD) was used to remove periodic factor components before applying the Hilbert-Huang Transform (HHT) to find pre-seismic anomalies. The discovered anomalous variations were associated with regional earthquakes of magnitude 5 or more that occurred within 100 km of the monitoring site. The effect of precipitation on soil radon emissions has also been discussed. The results show that the adopted technique can be used to analyse radon concentrations in soil gas for the prediction studies of regional earthquakes.