Environmental Earth Sciences最新文献

Magnetic susceptibility (MS) is a magnetic parameter that reflects the intensity and nature of a material’s response to an external magnetic field. It can sensitively indicate magnetic particles associated with anthropogenic activities and serves as an important proxy for soil heavy metal pollution. This study focused on the topsoil of Yinchuan City, a representative urban area in northwestern China. A total of 366 surface soil samples were collected via grid and profile-layering methods. MS was measured to analyze spatial distribution, vertical variation patterns, and correlations with heavy metal concentrations. By integrating MS data from cities of different administrative levels across Northwest China, the applicability of the Dual-Threshold Magnetic Susceptibility Evaluation Map (DTMS Map) was validated, and the regional soil pollution status was assessed. The results showed: (1) the mass-specific low-frequency magnetic susceptibility (χ_lf) of topsoil in Yinchuan was generally low but exhibited higher values in traffic, industrial, and commercial zones; (2) the χ_lf was significantly correlated with Cd, Cu, Pb, and Zn, with Cd identified as the dominant pollutant (70% above background values). Cr and Ni mainly originated from natural sources, while Cd, Cu, Pb, and Zn were primarily attributed to traffic and agricultural inputs. The overall ecological risk was low. (3) Across Northwest China, 26.8% of topsoil exhibited contamination characteristics, with MS increasing from northwest to southeast. This confirms that MS as an effective tool for rapid heavy metal assessment, and demonstrates the suitability of the DTMS Map for identifying urban soil pollution in northwestern China.

Mining-induced seismicity caused by the fracture of extra-thick strata at high positions (ETSHP) presents significant challenges in terms of both prevention and control. This study highlights the systematic application of the Reissner–Mindlin thick plate theory to analyze the fracture behavior of the ETSHP and the spatial evolution of delamination in relation to the mining-induced “two zones” (fracture and bending–subsidence zones). Spatial evolution equations for delamination were derived, elucidating the mechanical relationships among the ETSHP geometry, coal–rock spacing, and goaf development. Numerical simulations and field monitoring revealed that reduced coal seam thickness and greater coal–rock spacing increase the extent of delamination and the amount of stored elastic energy, thereby increasing the likelihood of high-energy seismic events. On the basis of the spatial position of the ETSHP, in this study, differentiated, combined prevention and control technologies are proposed: underground deep-hole blasting for the ETSHP within the fractured zone and surface vertical-well hydraulic fracturing for the ETSHP spanning both fractured and bending-subsidence zones. Field applications validated that these integrated measures significantly reduced high-energy events and stress concentrations. These findings provide new theoretical and engineering frameworks for seismic risk mitigation in deep mines containing ETSHP.

As an essential component of global change, atmospheric nitrogen deposition exerts profound impacts on ecosystems. Since ~ 1900 AD, the rapid increase in global nitrogen deposition has become an environmental concern. Lakes, as key components of terrestrial ecosystems, warrant further investigation regarding their responses to atmospheric nitrogen deposition. Here, we selected lakes from the Loess Plateau and the arid Northwest China to examine their potential responses to atmospheric nitrogen deposition since ~ 1900 AD. Our results show that: (1) The persistently negative δ¹⁵N values in lake sediments from the Loess Plateau since the 1900s may be linked to enhanced atmospheric nitrogen deposition; (2) The responses from lakes in the arid Northwest China to atmospheric nitrogen deposition may occur decades later than the lakes in Loess Plateau; (3) Compared to the effects of atmospheric nitrogen deposition, human activities (such as road construction, agricultural practices) near or within the lake catchments have caused stronger variability in sedimentary δ¹⁵N values. Our findings indicate that gradual increases in atmospheric nitrogen deposition may lead to significant responses in lakes and a shift in nutrient limitation.

Microplastic (MP) contamination in aquatic environments poses a persistent and serious threat to ecosystem health, necessitating comprehensive investigations. This study examines MP pollution in the surface waters of the northern beaches of Goa, including Baga, Anjuna, and Vagator. Results revealed that all three beaches are contaminated with MPs, with an average abundance of 0.47 MP/L. Among them, Baga Beach exhibited the highest concentration (0.6 MP/L), followed by Anjuna (0.5 MP/L) and Vagator (0.3 MP/L). Fibres were the most dominant shape of MPs, while colourless (56.5%) and 0.3–1 mm (68.62%) MPs were prevalent across all beaches. Common polymers such as polyethylene terephthalate (PET), polyamide (PA), polypropylene (PP), polyethylene (PE), and polystyrene (PS) were identified in the water samples. Risk assessment indices, including the Pollution Load Index (PLI), Polymer Hazard Index (PHI), and Coefficient of Microplastic Impact (CMPI), highlighted varying levels of ecological risk. PLI values exceeding 1 indicated pollution at all beaches, while PHI categorized the risk at level V due to hazardous polymers like PA and PS. CMPI emphasized the extreme impact of fibres on coastal waters. Scanning Electron Microscopy (SEM) analysis revealed surface degradation and weathering of MPs, providing insights into their environmental fate. This study indicates the presence of MP contamination along the northern beaches of Goa, highlighting the need for continued monitoring and the implementation of preventive measures to mitigate potential environmental impacts. Furthermore, these findings provide a critical baseline for future research and contribute to the formulation of strategies to manage and mitigate plastic pollution in coastal environments.

Glaciers in the Himalayan region have undergone rapid shrinkage owing to climate change, posing significant challenges to regional hydrology, dependent ecosystems, and socioeconomic sectors. In addition to temperature warming, surface albedo is a key parameter that influences glacier melt. In this study, spatiotemporal variability of albedo on four benchmark glaciers, Kolahoi, Machoi, Parkachik, and Drang Drung, in the northwest Himalaya was analyzed using MODIS (Moderate Resolution Imaging Spectroradiometer) and Landsat-8 OLI data over 22 years (2001–2022) in Google Earth Engine (GEE). Furthermore, changes in glacier albedo were correlated with geodetic surface lowering. The glacier albedo varied with time and space. Drang Drung showed the highest mean albedo at 55% (MODIS) and 65.3% (Landsat-8 OLI), whereas Machoi recorded the lowest at 46.2% (MODIS) and 52% (Landsat-8 OLI), likely due to debris cover and slope of glaciers. Seasonal analysis revealed a marked decline in albedo during summer and fall as seasonal snow melts and exposes darker bare ice, while winter and spring recorded higher albedo owing to fresh snowfall. The highest seasonal albedo of 68.8% was observed for the Drang Drung glacier in spring, and the lowest (32.2%) for Machoi in summer. Although climate is typically considered the dominant driver of glacier surface lowering, the results highlight the substantial role of albedo. Reduced albedo in summer enhances ice melt, contributing to surface lowering, whereas higher winter albedo aids in mass preservation by reflecting greater solar radiation. These findings underscore the importance of monitoring albedo variability for understanding glacier behaviour and improving projections of future melt rates. 

Data Science (Digitalization and Artificial Intelligence) became more than an important facilitator in various domains in fundamental and applied sciences as well as industry and is disrupting the way of research already to a large extent. Originally, data sciences were viewed to be well-suited, especially, for data-intensive applications such as image processing, pattern recognition, etc. In the recent past, particularly, data-driven and physics-inspired machine learning methods have been developed to an extent that they accelerate numerical simulations and became directly applied in the nuclear waste management cycle. In addition to process-based approaches for creating surrogate models, other disciplines such as virtual reality methods and high-performance computing are leveraging the potential of data sciences more and more. The present challenge is utilizing of the best experimental and monitoring data as well as model concepts and tools to integrate multi-chemical-physical, coupled processes, multi-scale and probabilistic simulations in Digital Twins (DT) able to mirror or predict the performance of its corresponding existing or future physical implementations including workflows. The call for the Topical Collection was initiated from different actors, including research entities, technical support organizations and nuclear waste management organizations of the European projects EURAD (European Joint Programme on Radioactive Waste Management) and PREDIS (Pre-disposal Management of Radioactive Waste). The Topical Collection attracted a large number of manuscripts, more than eighty of which were published. These articles reveal a strong academic focus on using machine learning to map and assess soil and groundwater resources, hydrology and land use, landslides, and climate protection. They also highlight the core theme of nuclear waste management.

Water resources of the Tagus River, the longest river in the Iberian Peninsula, are under increasing pressure. One of the main sources of pressure is the Tagus-Segura Water Transfer (TSWT), which has operated since the early 1980s and withdraws water from a reservoir system in the Tagus headwaters to meet southeastern Spain’s demands. Since its commissioning, the TSWT has significantly altered the river’s natural flow regime, a process further intensified by recent climate changes. This study analyses the evolution of climate and water availability in the Tagus headwaters, the transferred volumes, the streamflow patterns downstream of the reservoir system and the associated impacts on water quality. The results show that sustained increases in temperature and reductions in precipitation since the 1980s have led to a marked 48% decline in reservoir inflows. Combined with an average annual transfer of 332 hm³, these changes have resulted in a 76% reduction in streamflow in the middle reaches of the Tagus River, representing a severe disruption of its natural hydrological regime. This hydrological alteration has had direct consequences on water quality downstream of the headwaters. The exiguous flows in the Tagus River cannot dilute the pollutants from the tributaries flowing through the Madrid metropolitan area, contributing to a diminished ecological potential and reduced self-purification capacity of the river system.

The spatial and temporal heterogeneity of precipitation on the Qinghai-Tibetan Plateau has significantly delayed the natural recovery of alpine desertified grasslands, sometimes leading to irreversible degradation. Amphiphilic hydrogels, as novel soil stabilization materials, demonstrate considerable potential for vegetation restoration due to their ability to improve soil structure and regulate moisture. This study investigated the effects of an independently developed amphiphilic hydrogel on the improvement of sandy soil and the growth of five pioneer herbaceous species (alfalfa, Astragalus adsurgens, oats, tall fescue, and ryegrass) across a concentration gradient (0%-1% w/w). The primary mechanism by which the hydrogel operated was to postpone the onset of drought stress by markedly enhancing soil water retention. This delayed soil drying meant that at the same point in time, plants in hydrogel-treated soils experienced less intense water deficit than those in untreated controls. Consequently, application at the optimal concentration range of 0.5%-0.75% sustained plant growth and physiological function, mitigated oxidative stress, and slowed the wilting process. Legumes (Astragalus adsurgens and alfalfa) exhibited higher inherent drought tolerance than gramineous species. This study clarified how amphiphilic hydrogels enhance plant drought adaptation primarily by modulating the soil water environment and delaying drought stress. It provides a quantitative, technically viable strategy based on the synergistic regulation of the “material-soil-plant” system for ecological restoration in alpine sandy lands.

Soil erosion is known as one of the main factors contributing to soil health degradation and as one of the most common hazards associated with land degradation worldwide. Soil erosion threatens the sustainability of our world. The mechanisms, the process, and their spatial patterns of soil erosion can be analyzed and assessed by models. Models are efficient tools for understanding nature and the human impact. To model soil erosion, in this study, the SWAT and RUSLE models were evaluated to estimate soil loss and sediment yield in the Zayandeh-Rood dam watershed in central Iran. For runoff and sediment estimation using the SWAT model, the coefficient of determination (R²) and Nash-Sutcliffe efficiency (NSE) for runoff calibration and validation were obtained as 0.69, 0.64, 0.72, and 0.66, respectively. Similarly, these coefficients for sediment calibration and validation were found to be 0.62, 0.60, 0.58, and 0.55, respectively. The estimated sediment yield at the outlet point, based on the SWAT model’s calibration data, was 4.7 t ha^-1 yr^-1. By implementation of the slope-based sediment delivery ratio (SDR), average soil erosion at the watershed was estimated at 14.16 t ha^-1 yr^-1. Additionally, the prediction of eroded soil using the RUSLE model was estimated as about 177,908 t yr^-1 with an average value of 7.97 t ha^-1 yr^-1. The midterm data at the hydrometric station showed sediment production of 3.93 t ha^-1 yr^-1; with exerting SDR, the real soil erosion was estimated at approx. 11.83 t ha^-1 yr^-1. The results indicated that the RUSLE and SWAT models underestimated and overestimated, respectively, soil loss compared to measured data. The soil erosion classification of the study area was developed using the SWAT and RUSLE models, identifying regions with severe and very severe erosion classes. These areas require urgent biological and mechanical conservation measures to reduce sediment delivery to the Zayandeh-Rood Dam reservoir.