Environmental Earth Sciences最新文献

Oil pollution can modify the compression characteristics of soil, impacting its bearing capacity and stability. In addition, oil pollutants can spread to the surrounding environment through soil pores and groundwater systems, causing extensive environmental pollution. Simultaneously, it elevates the likelihood of fire and other accidents, posing a risk to the safe production of oil. This article aims to explore the compression characteristics and resistivity response of oil contaminated soil. It conducts uniaxial compression tests on oil contaminated soil under varying oil contents (0%, 3%, 6%, 9%, 12%, 15%) and moisture contents (5%, 10%, 15%), monitors the changes in resistivity simultaneously. The results indicate that the compressive strength of the soil decreases with increasing moisture content in oil contaminated soils. The compressive strength exhibits a trend of initially increasing and then decreasing with higher oil content, reaching its peak value when the oil content is 3%. During uniaxial compression, the soil resistivity generally decreases at first and then stabilizes as strain increases. Before the uniaxial compression test, soil resistivity decreases with increasing moisture content, but the variation differs under different oil content conditions. When the moisture content is 5%, the resistivity generally increases with rising oil content. However, when the moisture content reaches 15%, the dominant conductive mechanism shifts from oil-controlled to water-controlled, leading to a decrease in resistivity with increasing oil content. After the uniaxial compression test, soil resistivity is primarily influenced by moisture content. The resistivity gradually decreases as moisture content increases and shows little sensitivity to oil content. By monitoring changes in soil electrical resistivity, the impact of oil pollution on the foundation of structures and the surrounding environment can be predicted in a timely manner, providing a basis for preventive maintenance.

Climate change and human activity have placed substantial pressure on water resources, underscoring the critical importance of understanding the surface water and groundwater interactions (SGIs). This article reviews the development and application of the coupled SWAT-MODFLOW model for characterizing SGIs. It highlights three key developmental stages, drawing on the seminal works of researchers. These models are powerful tools for simulating hydrological processes, nutrient transport, and climate change impacts on water resources. However, uncertainties related to parameterization, input databases, and model structure constrain their predictive accuracy. The integrated SWAT-MODFLOW model enhances integrated water resource simulations, but this comes at the cost of increased complexity; by contrast, standalone SWAT and MODFLOW models optimize analyses of surface water and groundwater systems, respectively. A further strength is its ability to accurately simulate SGIs by accounting for diverse influencing factors, though it requires more extensive input data and incurs higher computational costs than individual standalone models. Accordingly, for practical applications, researchers should select an appropriate model and a reasonable set of influencing factors based on the specific research topic and requirements.

The Nyong River sub-watershed in the southern Cameroon plateau has been prioritized for urgent conservation action due to its erodibility, using the morphometric and hydrological criteria. Six sub -watershed were delimited from the Shuttle Radar Topography Mission (SRTM) data in a geographic information system environment. The Sub-Watershed Prioritization Tool (SWPT) was used to analyze the weighted sum (WSA) parameters of these sub-watersheds. Field investigations, including river water sampling, were carried out from January to August 2024 to estimate the suspended solids (SS) rate in each sub-catchment. Key morphometric parameters, such as compactness index, drainage density and form factor, were calculated and weighed. Based on the results, particularly the CPV, the Ayos, Mbalmayo and Olama sub-watersheds with CPV ranging between − 853 to -3.6 were classed as high priority and therefore most erodible. On the contrary, Abong-Mbang, Akonolinga, and Pont So’o sub-catchments with CPV of -275.27, -311.36 and − 33.86, respectively, showed moderate vulnerability. The CPV results were generally coherent with suspended solid content from river water samples in the area. The suspended solids content (in mg/l) obtained from river water samples was as follows in the sub-catchments: Ayos (18.12); Mbalmayo (14.24); So’o (10.1); Olama (6.24); Abong-Mbang (6.17), and Akonolinga (2.81). Except for Pont Soo, the sub-basin prioritization CPV ranking ties closely with the SS values. This study provides decision-makers with strategic information for land and water resource management in tropical ecosystems.

Previous studies have mainly focused on liquid-induced slope failures, whereas gas-induced failures, which are common in municipal solid waste (MSW) landfills, remain insufficiently understood. This study presents the 1 g model tests of slope failures caused by rising gas pressure. To reveal the triggering mechanism and improve the accuracy of stability assessment, pore liquid pressure and pore gas pressure during two-phase flow were monitored independently. Experimental and numerical comparisons were further conducted between foam flow (disconnected phase) and gas flow (connected phase) to examine their different effects on slope instability. The results show that cracks started to develop on the slope surface at the peak of the pore gas pressures, which were larger than the peak liquid pressures. Under the same injection pressure, foam and gas have significant differences in the failure mode and degree of slope. Foam could partially block the pore throat, thereby trapping a large volume of gas inside the model, causing the slope to fail through penetrating cracks. However, there was only some erosion failure occurred locally on the slope surface under gas flow. The critical gas pressure ratio (gas pressure/earth pressure) were determined to be 0.87 for foam flow and 0.99 for gas flow, indicating that slope failures were more likely to occur under foam conditions. By monitoring in-situ gas and earth pressures, the current gas pressure ratio can be evaluated as a practical safety early-warning indicator for MSW landfills.

This study develops a new regional flood frequency analysis (RFFA) model using Generalized Additive Models (GAM), Random Forest (RF), and XGBoost (XG) within the Peaks Over Threshold (POT) modelling framework. These machine learning techniques attempt to overcome the limitations associated with traditional linear regression-based RFFA models by better capturing complexity in non-linear rainfall-runoff process. Analysing data from 145 catchments in south-east Australia, we assess each of three model’s ability to predict flood quantiles across various return periods. GAM is found to be superior in accuracy, with a median absolute relative error of 33%, compared to 37% for RF and 40% for XG. Spatial analysis shows GAM’s robustness, significantly reducing errors in regions with high stream densities. It is also found that RF and XG models tend to overestimate flood quantiles in catchments with high stream densities. This research demonstrates that the integration of advanced machine learning methods within the POT framework significantly enhances the accuracy of flood quantile estimation, supporting more resilient flood risk management and infrastructure planning in flood affected regions. The findings of this study will assist upgrading Australian Rainfall and Runoff (ARR) – the national guideline. Unlike prior POT-RFFA studies based on linear/regularised regressions (and AM/GEV-focused GAM/ML work), we provide the first comprehensive comparison of GAM, RF, and XGBoost in a POT framework across 12EY–10ARI, with consistent cross-validation and spatial error diagnostics for SE Australia.

Understanding long-term spatiotemporal changes of drought and its linkage with climate modes is important from an agricultural perspective. Spatiotemporal trends of meteorological drought, quantified using the Standardized Precipitation Index (SPI), over Agro Climatic Zones (ACZs) of India from 1933–2022 were analyzed using the graphical Innovative Trend Analysis (ITA) along with traditional Mann-Kendall (MK)/modified Mann-Kendall (m-MK), Sen’s slope and simple linear regression. This study also analyzed the linkage between El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) with monsoon season meteorological drought over ACZs for 1903–2022. Variations in both SPI-4 of September and SPI-12 of December showed almost equal percentage of wet (SPI > 0, 49.9%) and dry (SPI < 0, 51.1%) years. Monsoon and annual drought frequencies varied from 12.5 to18.3%. The trend slopes of monsoon SPI-4 varied from − 0.14 to 0.11/10a, while the annual SPI-12 varied from − 0.14 to 0.19/10a. Long-term trends of both monsoon SPI-4 and annual SPI-12 showed significantly increasing drying tendencies in the central, northern and eastern parts of the country, while the peninsular India showed wetting trends, except in western coastal plains, where significant drying is observed. Monsoon SPI-4 in most ACZs was closely linked with ENSO (Niño 3.4 and SOI), while it showed almost no linkage to IOD (DMI). This suggests that the ENSO remains the dominant driver of drought variability in ACZs of India, whereas the IOD’s role appears marginal in modulating long-term drought risk. The outcomes of this work gives valuable insights for agricultural planning and water resource management strategies to mitigate drought risks in different ACZs of India.

Remote regions in India often lack systematic groundwater quality assessment, resulting in elevated risks to public and environmental health. This study presents a comprehensive evaluation of groundwater in the Sahibganj district of Jharkhand, employing random sampling and precise GPS-based site selection. Forty groundwater samples were analysed for physicochemical parameters and heavy metals, with a distinct focus on uranium. The novelty of the work lies in the analytic framework which integrates advanced multivariate statistical methods, such as the Pearson correlation matrix, with the entropy-weighted Water Quality Index (EWQI) and spatial interpolation via ArcGIS-IDW to enable robust quantitative and spatial appraisal of water quality. Results indicate elevated concentrations of Ca, Na, Mg, and Al associated with local lithology, and uranium levels generally below permissible limits, though some samples reach 24 ppb. Heavy metals are also found in exceeding concentrations at few regions. EWQI scores range from excellent to poor, demonstrating substantial spatial variability in groundwater quality and highlighting the need for continued monitoring and targeted management strategies in Sahibganj district. Correlation study shows weak correlation of uranium with depth as deeper groundwater would have lower uranium concentrations due to more reducing conditions and lesser uranium solubility. Uranium health risk evaluation shows value of 1.342 × 10^− 5 for adults and 5.115 × 10^− 7 for children.

The River Niger floodplain is among the most stressed floodplains in the world, arising from the impacts of agriculture, and urban and industrial development, especially in the oil and gas sectors which have altered the ecosystem structures and functions. Thus, organochlorine pesticide (OCP) concentrations were evaluated in floodplain soils from the lower sections of the River Niger to explore their distribution patterns and interrelationships with soil depth, sources, and ecosystem and human health risks. The Σ20 OCP concentrations in the soils from depths of 0–15, 15–30 and 30–45 cm varied from 5.0 to 592, 7.1–281 and 8.12–507 ng g⁻¹, respectively. The average Σ20 OCP concentrations decreased with depth. Chlordane was the predominant OCP in the soil profiles. The risk assessment suggested that the concentrations of OCPs in the soil profiles can adversely affect the ecosystem and farmers in the floodplain. The source apportionment investigation showed the predominance of historical sources over recent use of OCPs in the floodplain soils.

We employ a partially saturated groundwater modeling sensitivity analysis to investigate slope instability and landslides due to flood irrigation in the Siguas River Valley of the Majes Irrigation Project region in Peru. Using this approach, we test the hypothesis that flood irrigation practices created a perched water table, and that this perched water table significantly impacts slope stability. We then used our modeling approach to explore how intentional modifications to the topsoil’s hydraulic conductivity, such as those from biochar amendment, impact timing of slope failure. Our modeling approach demonstrates the importance of hydraulic conductivity on slope failure and that modest increases in soil hydraulic conductivity could extend the time to failure for nearby slopes by up to 2 years. Additional simulations predict that a 12% increase in hydraulic conductivity, which could be achieved through biochar amendment to the soil combined with a 25% reduction in irrigation volume, could stabilize slopes for up to 6.87 years longer than without mitigation, while still maintaining sufficient near-surface water to support agricultural productivity. Our findings are directed at the Majes region, while also demonstrating a novel approach to applying biochar amendments to help balance irrigation needs, agricultural productivity, and slope stabilization in vulnerable and/or arid environments.

This study investigates the leaching behavior and medium-term stability of lead (Pb) in phosphate-amended, cement-based solidified/stabilized (S/S) soils under both initial-pH and constant-pH conditions. An advanced automated leaching system was developed, which employs feedback control to maintain constant pH levels throughout the 47-day experiment. Semi-dynamic and modified TCLP leaching tests reveal that sustained acidic or alkaline conditions lead to significant increases in Pb leaching, primarily due to the progressive degradation of the cement matrix, which reduces its ability to immobilize Pb. In contrast, maintaining a pH near 9 resulted in optimal Pb immobilization, with minimal release observed. Sequential extraction demonstrated that Pb was predominantly present in the residual fraction or bound to Fe-Mn oxides. These findings underscore the critical role of pH stability in controlling Pb mobility, highlighting the importance of incorporating constant-pH leaching protocols and chemical speciation analysis for accurate medium-term performance evaluations and effective risk management in S/S systems. From a practical perspective, maintaining near-neutral to slightly alkaline conditions (around pH 9) and co-applying phosphate with cement-based binders are recommended for field-scale stabilization of highly mobile Pb-contaminated soils to ensure enhanced immobilization efficiency and medium-term environmental safety.