Environmental Earth Sciences最新文献

Screening of soil organic matter (SOM) is necessary because it is a key indicator of soil health. For this reason, thermal analysis (TG/DTG, DTA) was used as a more effective alternative to conventional soil analysis. Due to these methods, we can rapidly, non-destructively, and accurately determine key parameters of global warming, mainly soil organic matter (SOM) and its key component, soil organic carbon (SOC). Soil organic matter was monitored in five park urban soil samples collected from the A horizon at a depth of 30 cm in one part of Košice, specifically in the concrete Košice city district called “Above the Lake”. The soil type established according to the Reference Base for Soil Resources was ANTHROSOLS in all studied soils. The studied soils were predominantly moderately alkaline, which was likely caused by higher carbonate contents, as confirmed in some cases via thermal analysis. DTG curves of the studied samples show several peaks, indicating that the SOM has different thermal sensitivities, which depend on the SOM’s chemical composition, including the presence of aliphatic and aromatic parts. Thermal analysis, coupled with mass spectrometry, provided comparable results to other techniques, such as loss on ignition (LOI), in determining the quantity of soil organic matter. SOM determined via LOI strongly correlated with the amount of SOM established by TG/DTG methods as the sum of thermogravimetry weight losses in two temperature intervals (188–400 °C) and (400–620 °C) (R² = 0.88). It is known that this temperature interval corresponds to labile and stable SOM. The presented study shows that a combination of thermal analysis coupled with mass spectrometry, assuming that about 50% of all SOM exists as carbon, seems to be suitable for a quick assessment of SOM and SOC quantity, as well as carbonate content, with a single analysis.

Vertical cut-off walls made of soil-bentonite materials are often used as engineered barriers to prevent the spread of heavy metal pollution. In this study, the potential use of two common active porous materials (APM), active carbon and zeolite, as modifiers for the soil-bentonite barrier to retard hexavalent chromium Cr(VI) in contaminated sites, and the microscopic mechanism was investigated by consolidation permeability tests, isothermal adsorption tests, soil column tests, and scanning electron microscope tests. Additionally, to evaluate the effectiveness of the modified materials in practical engineering, the migration process of heavy metal pollutants in engineered barriers and contaminated sites was simulated using finite element numerical software. The results show that the active porous material can only improve the impermeability of the soil-bentonite barrier to a limited extent. However, the barrier material modified by the APMs showed a greatly enhanced adsorption capacity for Cr(VI), and the Langmuir and the Freundlich models described the nonlinear adsorption process well. The active porous material improved the retardation of the engineering barrier to Cr(VI), and the effect of adding active carbon was better than that of zeolite. The microscopic mechanism lies in the incorporation of active porous materials can impede pore development and combine with soil particles to form agglomerates possessing a large specific surface area, thereby enhancing the heavy metal ion adsorption. The results of the simulation show that increasing the thickness of the engineering barrier can effectively prolong the breakthrough time of Cr(VI), and the breakthrough time of the APMs-modified barriers was more sensitive to the change in thickness. Based on the migration simulation of the actual site, the 10% active carbon-modified soil-bentonite barrier can protect the surrounding densely populated areas from pollution for 37.14 years and improve the service life of the engineered barrier by at least 51.1%.

The spa waters of Ciechocinek (Poland) are internationally recognized for their healing properties and their distinctive Na–Cl–I composition of Mesozoic origin. This study provides comprehensive modern assessments of these waters, integrating analytical chemistry, toxicology and spa medicine. It delivers a detailed physicochemical and microbiological characterization of the four springs, clearly distinguishing the drinking mineral water source S19A (bottled as Krystynka) from the highly mineralized therapeutic brines S11, S14 and S16. In the case of S19A, a direct comparison with the parametric limits and labelling requirements of Commission Directive 2003/40/EC shows that all constituents regulated by the Directive (e.g. F⁻, Mn, NO₃⁻) remain below the maximum limits. However, the elevated sodium and selected trace metal content limits its suitability for long-term consumption in large quantities, emphasizing the importance of appropriate labelling, medical advice for sodium-sensitive individuals, and systematic monitoring. In contrast, for therapeutic brines an intake-based assessment is not appropriate; instead, a dermal exposure model adapted to spa practice is used. The hazard ratios for six trace elements (Cr, Mn, Fe, Co, Cu, Li) are all below unity, with values of approximately 0.05–0.06, confirming that the systemic toxicological risk from dermal absorption remains negligible under realistic balneotherapy conditions. By adapting the assessment criteria to the intended use, this study offers a reliable and clinically relevant assessment of the Ciechocinek waters: S19A is considered a microbiologically and chemically safe natural mineral water within the current European framework, provided its high salinity is taken into account in clinical recommendations and consumer information, while S11, S14 and S16 are safe for therapeutic use in spa treatment.

As a prevalent form of geological hazard, landslides have been a key focus of numerical simulation research aimed at elucidating failure mechanisms, assessing slope stability, and supporting disaster prevention and mitigation. This study applies bibliometric analysis to systematically evaluate the evolution of landslide numerical simulation research from 1995 to 2024, using data from the Web of Science Core Collection. A total of 906 publications were identified, representing contributions from 56 countries, 921 institutions, and 3190 authors. The development of the field is characterized by three distinct phases: the embryonic stage, the exploratory stage, and the developmental stage. Research hotspots primarily center on triggering mechanisms, stability analysis, landslide dynamics, and simulation methodologies. The findings highlight China and Italy as leading contributors in both publication volume and academic impact. Core journals such as Landslides and Engineering Geology have served as principal platforms for disseminating key advances. This study provides a comprehensive theoretical and data-driven foundation to support future research and development in landslide numerical simulation.

Accurately estimating fine particulate matter (PM_2.5) is challenging because many data-driven studies either rely on coarse or low-accuracy datasets, weakening prediction reliability, or focus on ultra-fine resolution mapping that requires dense monitoring, detailed emissions data, and high computational cost, making them impractical for large regions. To address this gap, we developed a spatiotemporal random forest (ST-RF) model to reconstruct monthly PM_2.5 concentrations at 1 km resolution across the Yangtze River Delta (YRD) during 2015–2020. The model integrates satellite-derived aerosol optical depth (AOD), meteorological variables, vegetation index (NDVI), digital elevation model (DEM), and spatiotemporal features to capture both spatial autocorrelation and seasonal dynamics. Evaluation results showed robust predictive ability (R² = 0.764, RMSE = 11.135 µg/m³, MAE = 7.238 µg/m³), outperforming conventional random forest, XGBoost, and support vector machine models. AOD and spatial context were the most influential predictors, followed by seasonal effects, temperature, and NDVI. The reconstructed dataset reveals a significant decline in PM_2.5 from 2015 to 2020, with winter peaks, summer troughs, and persistent hotspots in inland industrial cities. This study provides a practical and transferable framework that balances accuracy and feasibility for regional PM_2.5 mapping and long-term air quality assessment.

Physics-informed neural networks (PINNs) have recently emerged as a promising machine learning paradigm that embeds physical laws into data-driven modeling, offering new opportunities for addressing long-standing challenges in groundwater science. Distinct from previous reviews, this study presents a dual-track framework that integrates bibliometric analysis with critical methodological synthesis to systematically review 178 articles published between 2019 and 2025 in the Web of Science Core Collection. The results show that current research hotspots center on groundwater flow and seepage simulation, multi-physics coupling, parameter inversion, and the characterization of porous media flow processes, with a clear shift from idealized synthetic cases toward more complex heterogeneous aquifer applications. Furthermore, this review summarizes the major strengths of PINNs in groundwater applications and identifies key methodological bottlenecks related to training stability, computational efficiency, boundary condition enforcement, and cross-scenario generalization. Building on these insights, a development roadmap is proposed to advance PINNs in groundwater science, emphasizing weak-form formulations, neural operators, hybrid physics–numerical solvers, high-performance computing, and integrated frameworks for multi-source data fusion and uncertainty quantification. These directions provide guidance for evolving PINNs into scalable and reliable tools for groundwater modeling and management.

Electrical Resistivity Tomography (ERT) is a common method for assessing saltwater intrusion in hydrogeological studies, but its application is often limited to small areas due to the need for physical contact with the ground, resulting in long data acquisition times. In contrast, electromagnetic induction techniques (EMI) offer a promising alternative, but face ongoing debates regarding the robustness and calibration of their data. This study compares data from two different Frequency Domain Electromagnetic (FDEM) instruments - a multi-offset, constant frequency, constant induction number instrument and a single-offset, multi-frequency, variable induction number instrument - with ERT surveys. The comparison was made at four different sites of varying salinity and morphology, including both saline and freshwater aquifers. The results show that FDEM is effective for mapping shallow salinity when ERT data are used for calibration. In fact, while the qualitative conductivity trends obtained by FDEM are consistent, discrepancies in the range and values of electrical conductivity persist between different FDEM instruments and when compared to ERT data. Calibrations achieved through statistical analysis of cross plots can correct these discrepancies, but remain site- and season-dependent. The study highlights the potential of FDEM to detect seawater intrusion, even for long-distance profiles and 3-D spatial analysis, offering a faster, cost-effective alternative to traditional ERT approaches. The proposed technique is not limited to hydrogeological studies as it can be easily extended to other applications and geological backgrounds.

Soil physicochemical and hydrothermal properties are reshaped by freeze–thaw, yet dynamic responses of key hydrothermal parameters and the mechanisms governing water–salt evolution remain insufficiently validated under natural conditions. A representative saline farmland in northern China was investigated combining hourly in-situ monitoring with laboratory freeze–thaw experiments. Key periods were identified, and the spatiotemporal evolution of coupled water–heat–salt dynamics and parameter responses was systematically quantified. Air temperature and snow cover jointly induced vertical differentiation in heat and mass transfer. Water retention by frozen soil and snow increased shallow soil water content by 32.20% relative to early winter, whereas snowmelt infiltration reduced salt content by 15.44–34.02%. During freezing, relative permeability decreased by up to eight orders of magnitude in surface layers and by approximately three orders of magnitude in deeper layers. After thawing, permeability exceeded pre-freeze level and facilitated snowmelt infiltration and downward salt leaching. Thermal conductivity increased by 20.76–56.78%, whereas volumetric heat capacity decreased by 22.30–39.41%, leading to faster cooling than warming. The heat absorbed during thawing could not offset freezing losses. Soil temperature gradient (STG) and matrix potential gradient (MPG) dominated water–salt migration and intensified toward the cold end. Maximum STG (0.775 °C/cm) and MPG (134.9) in shallow soils were 9–10 and 3–4 times higher, respectively, than those in deeper layers. The mechanisms underlying moisture recovery and salt redistribution during freeze–thaw were elucidated, providing a reference for parameterized characterization and quantitative assessment of water–salt processes in comparable saline soils.

Graphical Abstract

Groundwater investigation in the Bengal Basin requires standardized characterization and naming of hydrostratigraphic units to ensure consistency in research, interpretation, and management. The existing aquifer nomenclature in Bangladesh remains inconsistent, with names derived variably from lithologic, stratigraphic, geographic, or depth-based classifications. Consequently, the same aquifer is often referred to by multiple names across studies and agencies, creating ambiguity and limiting comparability of hydrogeological data. This study establishes a standardized, physiography-based aquifer mapping and nomenclature framework for the Bengal Basin. Approximately 5,000 borehole lithologic logs were compiled from national hydrogeological archives, of which 3,506 were standardized and analyzed through stratigraphic correlation, three-dimensional modeling, and interpolated maps using RockWorks 16 and ArcGIS. The study identifies Seventeen physiographic units with distinct hydrostratigraphic characteristics and introduces a uniform three-part coding system integrating district abbreviations, physiographic identifiers, and aquifer depth sequences (e.g., BG-BT-Aqf1 for Bogura Barind Tract Aquifer 1). Results show that transmissivity and permeability are highest in the floodplain and deltaic regions, while uplands and hilly areas exhibit lower hydraulic properties due to finer sediments and limited recharge. The proposed framework provides a consistent scientific basis for aquifer delineation, mapping, and data integration. It enhances groundwater resource assessment, supports arsenic and salinity management, and strengthens coordination among agencies for sustainable groundwater governance in Bangladesh.

This study presents the application of the CE-QUAL-W2 model to simulate the temperature, water quality, and ecological conditions of the Doroudzan dam reservoir, the primary drinking water source for Shiraz, Iran. Physicochemical and ecological water quality variables were collected from July 2019 to September 2020 with monthly and seasonal frequencies, respectively. The reservoir geometry was divided into 29 segments and 52 layers to reflect the hydrodynamic and water quality conditions of the reservoir within the simulation model. Key calibrated parameters are temperature and dissolved oxygen, with calibration performance quantified by mean absolute error and root mean square error values of respectively 0.73 and 0.84 °C for temperature, 1.21 and 1.37 mg/l for DO, and close agreement for phytoplankton and macrophytes. Results demonstrated a significant thermal stratification in the reservoir of the Doroudzan dam, characterized by a temperature difference of 15 °C within the water column. Comparative statistical analyses between the simulated and field observations revealed that the model accurately captured both spatial and temporal dynamics of water quality indicators within the deep reservoir. Additionally, the influence of nutrient loading on the reservoir eutrophication status was assessed using the tropic state index, which is equal to 56.1, demonstrating a eutrophic condition in the reservoir. Sustaining the reservoir’s ecological integrity and water quality requires proactive management to mitigate potential stressors and ensure the reservoir’s long-term functional stability.