Environmental Earth Sciences最新文献

The toxicity of arsenic (As) in drinking water poses a significant risk to public health, and its effective removal is essential to reduce the associated risks. Modified slow sand filter (SSF) has emerged as a promising decentralized water treatment method in developing countries due to its user friendliness, economic viability, and environment-friendly properties. The present study investigated the total arsenic removal efficiency and turbidity reduction in laboratory-scale SSF columns designed for a 60-day filtration period. For this purpose, SSF columns were modified with magnetic corncob biochar (MCCB) and zero-valent iron (ZVI) layers in different ratios. The characterization tests, including Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and X-ray Diffraction (XRD), were conducted before and after the filtration. Results revealed that the MCCB surface was porous with a honeycomb-like structure before adsorption, containing cave-like holes favourable for arsenic removal. Similarly, the ZVI surface exhibited a tabular and thread structure. The EDS results confirmed the presence of Fe in the MCCB and ZVI, indicating the magnetic properties of both adsorbents. Notably, maximum As removal efficiency of 80% was observed in SSF(b) with a 10 cm MCCB layer after 60 days, whereas SSF(d) with a 10 cm ZVI layer achieved 99% within just 10 days of filtration. In addition, SSF columns containing ZVI layers achieved a maximum turbidity removal of 98% and 99% after 10 days of filtration, while SSF(b) with a 10 cm MCCB layer reached a turbidity removal of 99.9% after 60 days. Statistical analyses indicated that these differences were significant (p < 0.05), demonstrating the superior efficacy of the ZVI-based SSF for arsenic removal and the strong performance of MCCB in turbidity reduction. Overall, SSF-amended MCCB and ZVI demonstrated effective removal of As and turbidity. The study suggests that the designed SSFs are durable and user-friendly filter made of locally avaible low-cost materials for water filtration.

Microplastic (MP) pollution in groundwater is a growing concern due to its toxic properties and harmful effects. Meanwhile, landfills and dumpsites act as storage areas for plastic materials, which gradually disintegrate into microplastics over time, leading to pollution of the surrounding environment. Knowledge of the presence of MPs in the groundwater is scarce, and it is the need of the hour. This article focuses on the MPs migration from the dumpsite to the surrounding groundwater by analyzing the MPs in leachate generated from the dumpsite and MPs found in the groundwater near the solid waste dumpsite region in Ariyamangalam, Tiruchirappalli, Tamil Nadu, India. In this study, the Nile Red staining method has been used to quantify the microplastics with sizes as small as 3.42 μm. The results indicated that the MPs abundance in groundwater is about 11 to 77 particles/L with an average size of 45.16 μm, and in leachate on average, 102 to 140 particles/L were identified with the average size of 152 μm. Based on appearance, most of the MPs are of a fragment’s nature; some films and fibers were also found in the groundwater. Meanwhile, in leachate, fragments (45%) and fibers (44%) were found to be in equal proportion, along with a smaller number of films (11%). From micro-Raman characterization, polyethylene was the dominating polymer, followed by polypropylene, polyethylene terephthalate, polystyrene, polyvinyl chloride, poly methyl methacrylate, polyamide, and polyvinyl alcohol in the groundwater. The risk assessment reveals that the groundwater near the dumpsite zone comes under risk category IV based on the polymer risk index, which means that there is a high risk due to the certain kind of highly toxic polymer present in the groundwater.

This study focused on the Daluxi River, a small watershed and a primary tributary of the Yangtze River. Based on the nonlinear characteristics of water quality parameters and environmental factors such as meteorological and hydrological influences, a comparative analysis was conducted using Kernel Principal Component Analysis (KPCA) and Principal Component Analysis (PCA). KPCA extracted four potential sources for both the upstream and downstream sections, accounting for 79% of the total variance in each case—an increase of 7% and 6% compared to PCA, respectively. To address the limitation of KPCA in directly revealing the relationship between principal components and the original water quality data, six machine learning algorithms—Extreme Learning Machine (ELM), Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT)—were employed to perform regression analysis between the kernel principal components and the original water quality parameters, thereby elucidating source characteristics. The results indicated that GBDT exhibited the best fitting performance (R² = 0.988, MAE = 0.05, RMSE = 7.13%). Based on the extracted KPC, the Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR) model was used to calculate the contribution rates of various pollution sources in the Wandang and Siming areas. The results indicate that combining KPCA with GBDT and APCS-MLR can effectively uncover the complex relationships among water quality, meteorological, and hydrological factors, thereby enhancing the accuracy and reliability of pollution source analysis. This study advances research by using KPCA to capture nonlinear relationships and integrating machine learning for enhanced pollution source analysis.

The rising demand for water in Punjab, fueled by swift urban growth, industrial development, and intensive agricultural practices, has resulted in significant groundwater depletion. In the state, more than 97% of cultivable land is irrigated, with groundwater accounting for approximately 70–75% of the total irrigation water supply. The present study analyzes fluctuations in groundwater levels within the S.A.S. Nagar district over a span of 26 years, from 1995 to 2021, utilizing comprehensive water level data. The findings indicate a significant decrease, with groundwater levels plummeting from 3.6 m in 1995 to 30.7 m in 2021, reflecting an average decline of over 1 m annually. The rate of depletion increased significantly after 1998, largely as a result of a broad transition from canal irrigation to tube wells, which offered farmers more convenient access to water. The findings indicate that 32% of the area exhibits high groundwater potential, whereas merely 3% shows low potential. Furthermore, 8% of the area is categorized as having a high flood risk, while 7% is identified as having a high drought risk. Despite the introduction of initiatives like underground pipeline systems and enhanced rice farming techniques, the groundwater table persists in its decline. The results underscore the critical necessity for revised irrigation policies, enhanced water conservation strategies, and greater public engagement to secure the enduring sustainability of groundwater resources.

The complexity of unsaturated cohesive soil behavior presents challenges in directly measuring unsaturated shear strength, making it a complex and time-consuming task. Scholars have proposed indirect models to estimate unsaturated strength using soil–water retention curves and saturated shear strength indicators. However, scholars lack consistency in defining parameters to characterize apparent cohesion, resulting in a lack of standardized expressions. To establish a unified model for predicting the strength of different types of unsaturated cohesive soils, existing unsaturated shear strength models based on soil–water retention curves were systematically reviewed. Fifteen sets of experimental data were collected and utilized to analyze and compare the predictive performance of these models. It was observed that existing predictive models partially reflect the strength of unsaturated cohesive soils to some extent. However, they have applicability limitations and fail to predict the unsaturated shear strength of all soil types fully. An improved model for the shear strength of unsaturated cohesive soil was developed to overcome these limitations based on the Khalili and Khabbaz (1998) model. This improvement involved replacing a fixed empirical value in the Khalili and Khabbaz (1998) model with the water loss obtained from the soil–water retention curve. The average relative error (ARE) and normalized sum of square error (SSE) were used to quantitatively evaluate the predictive accuracy of the unsaturated soil strength model, comparing the improved model with existing ones. The analysis revealed that the improved model demonstrated higher prediction accuracy across fifteen types of unsaturated soils. Furthermore, soil–water retention curve tests and unsaturated triaxial tests were performed on two types of test soils, with sand-clay mass ratios of 3:2 and 1:4, respectively. By comparing the test data, the effectiveness of the improved model in predicting shear strength was evaluated, affirming its generalizability and accuracy in estimating the shear strength of unsaturated clay soils.

The present study evaluates surface water and groundwater and explores their associated human health risk around dumpsites in four Nigerian cities. Hence, groundwater (GW), surface water (SW), dumpsites leachate (CW) and rainwater (RW) samples were collected and analyzed for physicochemical parameters, major ions trace and rare earth elements using standard field and laboratory methods. Moreover, the study applied multivariate statistics, geochemical modeling, scatter plots and pollution indices. Elevated concentration of TDS, TH, Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, HCO₃⁻, SO₄²⁻, NO₃⁻ and Al were obtained in the different water samples. REE data showed that the LREEs are higher compared to the HREEs, while the plots of REE data normalized to Post Archean Australian Shale (PAAS) revealed a middle REE enrichment relative to LREE and HREEs. Majority of the samples exhibits variable positive Europium, Cerium, Gadolinium and Erbium anomalies. The concentration of aluminum, iron and manganese were higher than MAL in some GW and SW samples, while in CW, Co, Cu and Zn were below their respective MAL. The major hydrochemical facies, were Ca²⁺–HCO₃⁻, Na⁺–Ca²⁺–HCO₃⁻, Na⁺–HCO₃⁻ and Na⁺–Cl⁻. The strong correlation between water pollution parameters suggests that those parameters were derived from common natural and anthropogenic sources. Furthermore, R-mode factor analysis and hierarchal cluster analysis indicated that the water chemistry was controlled by both water-rock interaction and anthropogenic activities. The pollution index for all the samples was low, suggesting that the water samples are suitable for human consumption, except for some samples with pollution index suggesting poor water quality for consumption and irrigation. Though the average daily dose for both adults and children were < 1, elevated hazard quotient > 1 values were observed in some samples, while hazard index values > 1 were also recorded. Carcinogenic values greater than 10⁻⁶ and 10⁻⁴ were observed for some samples due to the high Cd, Cr and Ni concentrations, suggesting potential health risk. The results showed that sustainable management measures are required to control open waste disposal so that water resources contamination can be effectively reduced.

Estimating reference evapotranspiration (ET₀), a vital hydrological parameter, is particularly challenging in regions with scarce meteorological data, such as developing countries. Remote sensing data is a valuable resource for obtaining climatic and vegetation parameters. By using MODIS data (LST and NDVI), we aim to improve ET0 estimation accuracy. Four interpolation methods (spline, cubic spline, Bezier, and composite Bezier) are used to enhance the temporal resolution of MODIS data for improved daily ET₀ estimation. Conducted at the Yazd station in Iran, using data from 2003 to 2024, this study implements the traditional XGBoost (eXtreme Gradient Boosting) model and its optimized variant, GSXG (GridSearch- XGBoost), which incorporates GridSearch for superior parameter tuning. The results demonstrate the GSXG model’s significant performance enhancements over the base XGBoost, with the Bezier function achieving an RMSE of 0.855 mm/day and R² of 0.531 using only remote sensing data, and the cubic spline method reaching an RMSE of 0.208 mm/day and R² of 0.972 when combining meteorological and remote sensing inputs. These findings underscore the potential of GSXG to minimize errors and improve predictive reliability. This study demonstrates the value of integrating remote sensing data with optimized machine learning for improved ET₀ estimation, providing a valuable approach for hydrological assessments in data-scarce regions.

The study put forward a data fusion approach for urban remote sensing that combines SAR (Synthetic Aperture Radar) and optical satellite data. By integrating datasets from different sensors and spatial–temporal scales, the technique aims to extract more accurate information. The fusion approach utilizes two methods: feature-based fusion, where relevant features are extracted and fused, and simple layer stacking (SLS), where the original datasets are directly stacked as multiple layers. This study extracted features using SAR textures (using Sentinel-1) and modified indices (using Landsat-8), and then classified these features using an XGBoost algorithm implemented in Python and Google Earth Engine. Researchers examined five cities, each representing a distinct climatic zone and urban dynamic: Cape Town, Guangzhou, Los Angeles, Mumbai, and Osaka. An accuracy assessment was conducted using random validation points, achieving an overall accuracy of 89.5% using the proposed MSFF method. A comparison was also performed with three well-known global products. The proposed approach, outperformed all three global products achived 89% accuracy while ESA (84%), ESRI (81%) and Dynamic World (82%). Additionally, Land surface temperature analysis was accomplished to investigate the relationship between extracted UIS and Land Surface Temperature (LST) across selected cities to show the practical use of proposed MSFF method. Los Angeles, a warm temperate city, showed the highest LST among all five cities. The datasets, along with the GEE and Python codes, are available at https://github.com/mnasarahmad/sls.

Groundwater is a vital resource in Saudi Arabia (KSA). The primary objectives are to explore the water quality status and its application for drinking, agriculture, livestock, and poultry and map the contamination zones (CZ) in the Khulais region, western KSA. Groundwater quality data (n = 53) depicts Ca-Mg-Cl (75%) and Na-Cl water types. The electrical conductivity (EC) and groundwater flow (GF) nexus indicate that EC and major ions increase from upstream to downstream along with GF. In the central region, groundwater is less mineralized with elevated HCO₃⁻ due to geogenic sources. NO₃⁻ and F⁻ distributions pose low values in the northern region. Further, 90% of groundwater samples surpassed the drinking water standards and are unfit for drinking. Irrigation suitability assessment explains that EC (38% of samples), SAR (58%), KR (77%), Na% (94%), PI (100%) and MH (81%) are recommended for irrigation applications. USSL classification suggests that groundwater is usable only for coarse textured (high permeability) soil and salt-tolerant plants. Based on salinity, 68% of samples are usable for livestock and poultry whereas multiple parameters (EC, NO₃⁻, Mg^2+, and F⁻) ensure that only 47% of samples are recommended to use for livestock and poultry. CZ mapping illustrates that groundwater is unsuitable in most of the area except for a few pockets in the southern and northern regions. CZ mapping can aid in locating freshwater zones for groundwater development for future needs in the study site. This study implies that groundwater quality monitoring and CZ mapping are inevitable for sustainable aquifer management in any region.

Attabad Lake is a debris-dammed lake formed due to a landslide disaster on 4th January 2010 in Gojal Valley. Multiple villages are located downstream of the lake along Hunza River. As this lake was formed by a massive landslide, there always remain questions about dam’s stability. The lake is in an active seismic zone which makes it prone to future earthquake and landslide disasters. Considering the sensitivity of region and ambiguities about dam’s structural integrity, this study adopts scenario-based approach for flood risk assessment and identification of potential inundation hotspots downstream. Seven hypothetical flow scenarios ranging from 5 to 50% flow were hydrologically modeled using HEC-RAS 6.5. Based on unsteady flow analysis, inundation boundary, depth, velocity, and product of depth and velocity were computed for all scenarios. The inundation boundary was highest (26.1 km²) in 50% flow scenario followed by 35% (19.6 km²), 25% (8.5 km²), 20% (5.1 km²), 15% (3.5 km²), 10% (3.1 km²), and 5% (2.5 km²). Threshold value of depth i.e., 0.35 m surpassed in all scenarios, whereas threshold velocity (1.5 m/s) was exceeded only in 50% flow scenario. In all flow scenarios, mean value of depth times velocity was higher than the threshold value of 0.52 m²/s. Based on analysis of flood critical parameters, flooding hotspots were mapped, and socio-economic impacts were evaluated. Using risk assessment maps, strategies for infrastructure development downstream, timely evacuation of villages in high-risk zones, and extensive disaster management plans can be prepared by administrative authorities to avoid casualties and economic loss.

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