Environmental Earth Sciences最新文献

Coastal land erosion and accretion (EA) are dynamic processes influenced by local climate variability, often resulting in land loss and displacement of communities in Bangladesh. This study examines the interplay between climatic factors and coastal land changes by analyzing daily rainfall and temperature data from multiple meteorological stations over a 33-year period (1985–2017). To establish robust statistical relationships, we employ three correlation techniques—Spearman rank correlation, Pearson correlation, and Kendall’s tau—after applying a moving average smoothing method. Our findings reveal a strong correlation between rainfall and temperature (ranging from 0.74 to 0.80), with consistent patterns across the mainland and islands, although slight regional variations exist. To quantify land changes, we derive calibration factors by comparing year-by-year correlations with previously documented erosion and accretion rates. The analysis indicates that Bhola is experiencing significant erosion at a rate of 0.29 km² per year, whereas Barishal, Patuakhali, Khepupara, and Mongla exhibit relatively stable conditions, with accretion rates ranging from 0.5 to 7.6 km² per year. Beyond these climate-driven correlations, we integrate ecological factors by assessing the role of mangrove forests in coastal stability. Our results demonstrate that mangrove-dominated areas exhibit enhanced sediment retention, facilitating land accretion and mitigating erosion. This stabilizing effect becomes particularly crucial in regions affected by rising sea levels, where mangroves act as natural buffers, reducing coastal vulnerability. By synthesizing climatic correlations with ecological influences, this study presents a comprehensive, cost-effective, and adaptable methodology for evaluating coastal land dynamics. The findings offer valuable insights for sustainable land management and climate resilience strategies in vulnerable coastal regions.

This study numerically models energy production in hydrothermal reservoirs using both CO(_{varvec{2}}) and water as working fluids. Unlike past studies, this work considers multi-phase flow to model the effect of water displacement by supercritical CO(_{varvec{2}}) (ScCO(_{varvec{2}})). Several simulations are performed to systematically evaluate the influence of reservoir and injection temperatures as well as fracture networks on energy production rates. Furthermore, the efficacy of water and CO(_{varvec{2}}) as working fluids is compared by evaluating their respective energy production rates in moderate- to high-enthalpy reservoirs. For identical mass injection rates, energy production rates at early stages are higher in reservoirs with ScCO(_{varvec{2}}) than in those with water, due to the higher thermal expansivity of ScCO(_{varvec{2}}). By contrast, at later stages, energy production rates in CO(_{varvec{2}})-based reservoirs are lower than in water-based reservoirs due to the lower heat capacity of ScCO(_{varvec{2}}), although this difference can be minimized by carefully calibrating the injection temperature. Additionally, the injection pressure required for CO(_{varvec{2}})-based reservoirs is significantly lower than for water-based reservoirs, because ScCO(_{varvec{2}}) is less viscous than water. The findings from this study provide useful insights into the use of CO(_{varvec{2}}) as a working fluid in enhanced geothermal systems.

This study investigates and compares the characteristics and potential sources of carbonaceous aerosols in fine particulate matter (PM_2.5) between urban and suburban areas of Hanoi. Daily samples were collected at two sites in Hanoi, Vietnam, including HUCE (urban) and Thuong Tin (suburban), during June, July, and August 2024, corresponding to the summer period in Hanoi. The average concentrations of organic carbon (OC) were 10.80 µg/m³ at HUCE and 16.16 µg/m³ at Thuong Tin, while elemental carbon (EC) averaged 1.65 µg/m³ and 1.75 µg/m³, respectively. Carbonaceous aerosols accounted for over 30% of PM_2.5 mass at both sites, emphasizing the importance of carbonaceous matter in Hanoi’s atmospheric particulate composition. The average OC/EC ratios exceeded 2.0, suggesting a significant contribution from secondary organic carbon (SOC). SOC accounted for 59.69% of the total OC at HUCE and 48.29% at Thuong Tin, highlighting its significant presence. Source apportionment using the UNMIX model revealed three carbonaceous emission sources in Hanoi: traffic emissions, coal combustion, and biomass burning. The obtained results showed that traffic emissions were the primary contributor at HUCE (62%), whereas biomass burning was most influential at Thuong Tin (45%). Traffic emissions contributed 35% of PM_2.5 at Thuong Tin. Coal combustion contributed 35% at HUCE and 20% at Thuong Tin, underscoring its significant and consistent role in PM_2.5 pollution across urban and suburban Hanoi. These findings provide a scientific basis for targeted air quality management strategies focused on controlling combustion-related emissions.

This study investigates the effectiveness of triangular toe filters in controlling seepage and enhancing slope stability in homogeneous earth dams through flume-scale experiments and numerical analyses. The influence of key filter parameters, including median particle size, height, and length, was systematically assessed. Results indicated that internal erosion began when the reservoir level exceeded 58% of the dam height and became critical at 66%. The incorporation of granular toe filters significantly improved downstream slope stability, increasing the factor of safety by up to 40% under static loading and 46% under seismic loading. The most effective configuration featured a median particle size of 24 mm, a filter height equal to 36% of the dam height, and a length equivalent to 17% of the base width. Filters with a length-to-embankment bottom width ratio of 0.23 achieved the highest drawdown efficiency, reducing water levels by 70% within three days and by 83% by day 30. In comparison, shorter filters with a ratio of 0.05 initially reduced water levels by only 30% and overall, by 79%. These findings underscore the crucial role of toe filter design in enhancing seepage control and improving stability, offering practical guidance aligned with established geotechnical engineering standards.

This study investigates how biological processes, particularly riparian vegetation, regulate nitrogen (N) dynamics under varying redox conditions in the riparian zone of an agricultural fluvial island. Groundwater characterized by high nitrate (NO₃⁻) concentrations and oxidizing conditions flows toward adjacent surface waters through the riparian zone. Along this subsurface pathway, NO₃⁻ concentrations declined significantly, whereas bicarbonate (HCO₃⁻) concentrations increased, reflecting enhanced reducing conditions. The strong positive relationship between the molar ratio of HCO₃⁻ to total anions and dissolved organic carbon (DOC), together with the δ¹³C-DIC signatures consistent with CO₂ derived from C₃ vegetation, indicates that root and microbial respiration are major contributors to biogenic carbon production. Depth-resolved hydrochemical monitoring further revealed that areas with tall vegetation sustained reducing conditions and low NO₃⁻ concentrations at both shallow and deep depths, while deeper zones with short vegetation exhibited oxidizing conditions and elevated NO₃⁻ concentrations. These findings demonstrate that vegetation-driven biological activity influences vertical and lateral redox heterogeneity, thereby influencing NO₃⁻ attenuation efficiency within the riparian zone. Overall, the riparian zone functions as a significant biogeochemical filter mediating N and C fluxes across groundwater–riparian–surface water interfaces, offering important implications for nutrient management and the sustainability of agricultural watersheds.

Streamflow generation in cold and arid regions is more complex and sensitive to climate warming than other climatic zones. This study investigated the responses of streamflow components to future climate change (2021–2050) in the headwater catchment of the Manas River in northwest China. We employed an integrated modelling framework that combined trend-preserving bias-corrected outputs from five Regional Climate Models (RCMs) of the CORDEX-East Asia project with Snowmelt Runoff Model (SRM) simulations. The SRM exhibited strong performance in streamflow simulation (Nash-Sutcliffe efficiency > 0.82), while the bias correction significantly enhanced the reliability of climate projections, reducing precipitation biases by 50–90%. Climate projections indicated a pronounced shift toward warmer and wetter, with mean annual precipitation increasing by 20 ± 30% and temperature rising by 2.1 ± 0.6℃. Streamflow was projected to increase by 23 ± 7%, with larger seasonal variations in spring and autumn than winter and summer. Snowmelt runoff contribution increased from 41 ± 1% (historical) to 45 ± 3%, particularly under higher emissions. Rainfall runoff exhibited more variable trends, and its contribution increased from 29 ± 2% to 34 ± 5% under RCP4.5, but declined to 28 ± 3% under RCP8.5. These findings demonstrate substantial shifts in streamflow components, underscoring the need for adaptive water resource management in cold and arid regions under climate change.

Soil erosion, which is caused by both natural processes and human activities, still has serious effects on ecosystems, farming, and water quality in watersheds all over the world. Identifying areas most at risk of erosion and in need of prompt action requires understanding and measuring the physical features of a watershed. This study is about figuring out which sub-watersheds in the Dantewara watershed are most likely to erode. This study introduces a new way of doing things that combines morphometric analysis with advanced multi-criteria decision-making (MCDM) methods, including grey relational analysis (GRA) and the technique for order of preference by similarity to ideal solution (TOPSIS). Further strengthening the prioritization process, machine learning (ML)-primarily the support vector machine (SVM) method-is a new way to improve the process of ranking areas that are at risk of soil erosion. The keynovelty of this study lies in integrating morphometric parameters, land use and land cover (LULC), MCDM methods like TOPSIS and GRA, and machine learning to develop a unique model for sub-watershed prioritization. The sub-watersheds designated as high priority are SW1, SW6, and SW9, whilst SW2, SW3, and SW7 are categorized as medium priority. SW4, SW5, SW8, and SW10 are classified as low-priority sub-watersheds. The findings guide planners and policymakers in implementing sustainable watershed management practices.

Hot springs emerging along fault zones provide valuable evidence of fluid circulation, fault activity, and possible seismic precursors. In the seismically active Central Tianshan Mountains (CTM), a variety of indicators from 17 hot springs were analysed between 2017 and 2023 to identify potential geochemical signals associated with seismic events. Analyses of major and trace elements, hydrogen and oxygen isotopes, strontium isotopes, silica (SiO₂), and dissolved gases indicate meteoric recharge at depths of 1.3–4.1 km, Na-Cl-dominated compositions primarily derived from evaporite dissolution, and additional inputs from deep fluids, with reservoir temperatures ranging from 13.45 to 96.69 °C. Helium isotope ratios (0.02–0.05 Ra) measured in spring waters indicate a predominantly crustal origin. Springs located within the central sections of the North Tianshan and South Tianshan fault zones exhibit the deepest circulation pathways and correlate with areas with the highest seismic activity. This finding confirms that water-rock interactions have a measurable influence on fault activity. Continuous monitoring from 2021 to 2023 captured pre-seismic multi-index hydrogeochemical anomalies occurring 1–4 months before the 2023 Shaya M_S6.1 and 2024 Wushi M_S7.1 earthquakes (M_S: Surface wave Magnitude). These anomalies included abrupt declines in Na⁺, Cl^-, and SO₄^2- concentrations (13.3–99.3%) and shifts in isotope signatures (δD, δ¹⁸O, δ¹³C_DIC), which are interpreted as indicators of stress-induced microfracturing and fluid mixing. The negative abnormal post-seismic fluctuations may indicate subsequent fault healing and hydrological reorganisation. This study presents the first identified relationship between variations in multiple hydrochemical indicators in hot springs of the CTM and seismic activity. The integration of multiple elemental and isotopic analyses provides a more comprehensive framework for understanding fault processes, fluid circulation, and pre-seismic anomalies.

Industrial solid wastes present severe disposal challenges, while hexavalent chromium [Cr(VI)]-contaminated wastewater threatens ecological and human health. This study developed a phosphogypsum-coal ash composite (MTPC) through synergistic mechanical-thermal activation to serve as a cost-effective impermeable liner for Cr(VI) environments. Orthogonal experiments assessed the influence of calcination temperature, duration, and Cr(VI) concentration on material’s performance. Optimal results were obtained at 700 °C for 120 min with a Cr(VI) concentration of 1 mg/L. The 28-day compressive strength reached 36.80 MPa, a 248.5% increase compared with the untreated samples. The Cr(VI) leaching concentration was reduced to 0.91 µg/L, while permeability coefficients as low as 2.66 × 10^− 8 cm/s. Microstructural and chemical characterization was performed using scanning electron microscope and energy dispersive X-ray spectroscopy (SEM-EDS), Fourier-transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). Findings demonstrate that mechanical-thermal activation enhances the release and hydration reactions of active components in MTPC, promotes the formation of calcium silicate hydrate (C-S-H) gel and ettringite (AFt) crystals. These results highlight mechanical-thermal activation as an effective and practical approach to valorize industrial wastes into sustainable, high-performance cementitious materials for environmental protection.

The Mannarkad Watershed is a subtropical mountain catchment in the Western Ghats, Kerala, facing critical soil erosion issues due to climate change and anthropogenic activities, which cause adverse impacts on soil infertility and agricultural productivity. This study investigates the spatio-temporal variability of soil erosion rate (SE) and erosion hotspots for 2000, 2010, and 2023 using the GIS-based Revised Universal Soil Loss Equation (RUSLE) model. Sediment yield estimates were derived at detailed spatial scales using the Sediment Delivery Ratio (SDR) method. The results show that annual soil erosion rates have grown from 14.9 t/ha in 2000 to 23.02 t/ha in 2023, occurring in the sloped upland valleys and foothills across the watershed have seen the most severe erosion. Wherein, rainfall erosivity is at a higher rate of 1129–1669 MJ·mm/ha·hr·yr in 2000, and 2344–3076 MJ·mm/ha·hr·yr in 2023, causing sediment transport towards downslope and deposited along the valley fills and channel levees. Over the last two decades, the findings revealed that the erosion rate has progressively increased in various hotspots owing to rainfall variability and land-use changes. The RUSLE model exhibits that the erosion hotspots, mainly found in middle laterite plateaus, and sparsely vegetated uplands, consist of steep-sloped valleys, upland stream orders, barren lands, down-sloped fallows and proximity of built-up areas. Significantly, the sediment yield is at a higher rate (0.27–23.81 t/ha/yr) in the valley fills, downpour streams of the foothills, with an average rate of 6.75 t/ha/yr, contributing to alluvial plains and channel levees. The RSULE model calculates the AUC value of 0.823 based on the spatial correlation of eroded sites of GPS survey and map-derived data, indicating the model’s reliable predictability. The sediment yield was validated with rain gauge-based sedimentation data from the Kanjirapuzha Dam station, showing close agreement with the derived SDR rate (predicted 2.79 t/ha/yr vs. observed 5.8 t/ha/yr). GIS-based RUSLE–SDR is widely used for soil erosion rate and sediment yield at the pixel scale, and in heterogenic landscapes, and lacks this in other conventional approaches. However, the output of this model is subject to uncertainties that depend on the quality of input parameters, scale, and resolution. Despite these limitations, the outcomes provide a spatio-temporal pattern of soil erosion and sediment yield and are a valuable database for soil and water conservation planning and management activities.