Environmental Earth Sciences最新文献

Detecting past characteristic of hydro-climatic variables can provide a deeper understanding of the effects of climate change on water resources. Among the hydro-meteorological parameters, salinity is one of the most critical factors. This study explored the average monthly salinity trends of the six stations in Ho Chi Minh City for the period from 2007 to 2021, using the Innovative Polygon Trend Analysis (IPTA). IPTA is a simple approach that allows for fine-trend interpretation and avoids the fundamental assumptions in classical methods, such as normality, independence, and pre-whitening. Polygon graphics for monthly salinity data were analyzed using arithmetic mean and standard deviation to assess trend length and slope. The outcomes showed that most of the statistically significant transitions (April–May, November–December) occurred in consecutive months between dry and rainy seasons, with the highest average trend length values of 9.88 g/l and 8.31 g/l at Ong Thin and Nha Be stations, respectively. Particularly, the maximum length trend in the dry season also highlighted a characteristic of the salinity transition over time. Therefore, the application of IPTA provides insights into many months’ cycles, which are essential for the salinization management of the study area.

A novel high-permeability counterfort retaining wall (HPRW) was proposed for improved control of rainfall-induced landslides, and its working performance and mechanism were studied by thorough numerical simulations. The numerical simulations revealed that the retaining effect of the HPRW was significantly better than that of the conventional counterfort retaining wall (CRW) under the effect of rainfall. Relative to the CRW, the pore water pressure and groundwater table decreased owing to the excellent drainage capacity of the HPRW, in turn leading to the decreases in the hydrodynamic pressure and earth pressure. Consequently, the slope deformation decreased and stability of the slope increased with the application of the HPRW. Furthermore, the stress and displacement of the HPRW and the earth pressure acting on the HPRW were lower than those of the CRW under identical working conditions. Parametric analysis indicated that the rainfall intensity, property of the sliding mass and gravel filling in the catchment tank affected the retaining effect of the HPRW and the stability of the slope to varying degrees. The results of this study can provide a significant basis for the design, application and subsequent research on the HPRW.

The visual impact of the chromatic contrast between quarry faces and rocky outcrops represents one of the prominent disturbances to natural environments. This study, therefore, aims to quantify color changes over time in quarries by analyzing three faces of an active limestone quarry in Santullán, Cantabria, that were exposed to atmospheric conditions in 1978, 2003 and 2021. To achieve this, the contribution of biological colonisation to natural darkening, along with the physicochemical changes occurring on the quarry faces, have been evaluated using scanning electron microscopy in secondary and backscattered electron mode, UV–Vis spectrophotometric techniques, Raman spectroscopy and XRD and XRF analysis. The analysis revealed that the color change was primarily due to microbial colonisation rather than oxidative chemical reactions. Although color change does not follow a direct and progressive relationship with exposure time, biological colonisation, identified primarily as microbial communities dominated by phototrophic microorganisms, shows a clear increase in microbial presence, bioalteration, and penetration into the rock substrate in older samples. The most significant visual changes appear to occur during the first years of exposure of the massif to atmospheric conditions.

Integrating dynamic factors such as rainfall and land use/cover (LULC) changes into landslide predictions is often overlooked. A combination of aforementioned dynamic factors, mountainous terrain and fragile geology increase risk of landslides in the Himalayan region. This study assesses the impact of both dynamic and static factors on landslide prediction. The XGBoost machine learning (ML) algorithm is employed for generating landslide susceptibility maps due to its superior performance and accuracy in the study area. Base map is prepared for the period from 1995 to 2020, taking into account significant changes in urbanization and climatic impacts observed in the study area. Results suggest that the ML algorithm performs well based on metrics such as accuracy (96.6%), precision (98.4%), recall (94.8%), Matthew’s correlation coefficient (93.2%), Cohen’s kappa coefficient (92%), F1 score (96.6%), and area under receiver-operating-characteristic (ROC) curve (99.3%). For future landslide susceptibility predictions, maps under different climate change scenarios are prepared using rainfall alone and both rainfall and LULC as dynamic factors. Results indicate an increase in high and very high susceptibility classes; the most significant increase (approximately 60% of the baseline) is observed in scenarios considering both the dynamic factors. It infers that including dynamic parameters in landslide prediction enhances the accuracy of landslide susceptibility analysis and improves reliability of disaster management strategies.

Most current research focuses on the remediation of heavy metal contamination by biochar in one type of soil. However, there are few comparative studies on the remediation effects in fresh and historically contaminated soils. To simulate the eight-year natural aging process, we measured the transformation of Cd in both freshly contaminated soil (NS) and historically contaminated soil (OS) treated with rice straw biochar made at 400 °C (BC400) and 700 °C (BC700) during combined dry-wet and freeze-thaw cycles for 72 days. Standing comparison with CK, the availability of Cd in NS-BC400 and NS-BC700 reduced from 0.16 mg·kg^− 1 to nearly 0, and OS-BC400 and OS-BC700 reduced from 1.7 mg·kg^− 1 to 0.6 and 0.2 mg·kg^− 1 at day 72. During the 72-day aging process, the average Tessier exchangeable Cd percent of OS, OS-BC400, and OS-BC700 were 70%, 59%, and 52%; and those of NS, NS-BC400, and NS-BC700 were 65%, 62%, and 51%. Compared with CK, the Tessier exchangeable Cd (F1) of NS-BC700, OS-BC400 and OS-BC700 significantly reduced by 20.3%, 10.7%, and 22.2% at day 72, respectively, but that of NS-BC400 had no obvious change. The contribution of biochar and soil to the reduction of F1 was different in OS and NS with different biochars. For OS treated with biochar, the relative percent contribution of biochar to the reduction of F1 was nearly 100%, and that of OS was almost no contribution. However, for NS treated with biochar, the relative percent contribution of BC400 and BC700 to the reduction of F1 decreased from 86.1% to 89.5% at day 3 to 21.2% and 49% at day 72.

Investigating the influence of creep–slip behavior on the deformation and faulting development near the fracture is essential for elucidating the deformation and failure mechanism of fractured rock masses. In this study, uniaxial compression experiments were conducted on rock-like models with different creep–slip fracture lengths prefabricated from gypsum, quartz sand, and barite powder. The experimental results showed that both the uniaxial compressive strength and elastic modulus of models diminished as fracture length increased. Based on the stress–strain curve characteristics, the model deformation process could be divided into four stages: medium compaction stage, elastic deformation stage, crack propagation stage, and failure stage. The fracture modes of the model were wing cracks and anti-wing cracks, and the degree of crack development increased with the increase in the fracture length. The distribution location of the local peak maximum principal strain was not fixed. The local strain deflection angle exhibited significant regional characteristics. The peak value of the relative displacement rate decreased with increasing fracture length. The acoustic emission hit count rate of models with a small fracture length increased significantly after entering the crack propagation stage. Microfracture events on the active loading plate increased and became more dispersed with increasing fracture length. These findings can serve as a basis for better understanding the failure characteristics of geological bodies, particularly in the context of geotechnical engineering.

Heavy and intense rainfall commonly triggers debris flow events, but the relationship between gentle rainfall, snowmelt water, and freeze–thaw erosion in high-altitude cold regions of northern Pakistan remains unexplored. A devastating debris flow from Bicharh Nallah hit the village of Sherqilla in northern Pakistan on July 5, 2022, and killed 8 people and destroyed 10 houses completely and 250 houses partially. The Bicharh Nallah debris flow (Sherqilla village) in Ghizer district is used as a case study to find out what makes this size debris flow happen in a cold region and how it is affected by rainfall, snowmelt water, and freeze–thaw erosion. This work was done through fieldwork investigation, laboratory work, and statistical analysis. The debris flow dynamic characteristics such as peak discharge, velocity, and density were calculated as 430.82 m³/s, 5.11 m/s, and 1.74 g/cm³, respectively. We determined that rainfall acts as a direct triggering factor. Snowmelt water was quantitatively calculated, which contributed to triggering the debris flow. This study revealed that gentle rainfall and snowmelt water are direct triggering factors in the debris flow outbreak in Bicharh Nallah of Sherqilla village. Long-duration, extreme, and severe freeze–thaw erosion provided enough loose soil conditions for the triggering and formation of debris flow. The regional tectonics, geology, and local topography indirectly contributed to the development and amplification of Bicharh Nallah debris flow.

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.