Search Results (461)

Protecting groundwater against pollution from agricultural sources is a key aspect of sustainable management of soil and water resources. Implementation of sustainable strategies for agricultural production can be supported by modeling tools, which allow us to quantify the effects of different agricultural practices in the context of groundwater vulnerability to contamination. In this study we present a method to assess groundwater vulnerability to nitrate pollution based on a combination of the SWAT agro-hydrological model and the DRASTIC index method. SWAT modeling was applied to assess different scenarios of agricultural practices and identify solutions for sustainable management of soil and groundwater and reduction of nitrate pollution. The developed method was implemented for groundwater resources in a study area (Puck Bay region, southern Baltic coast), which represented a complex multi-aquifer system formed in Quaternary fluvioglacial deposits (sand and gravel) separated by moraine tills. In order to investigate the effects of different agricultural practices, 12 scenarios have been defined, which were grouped into four classes: crop type, fertilizer management, tillage, and grazing. An overlay index structure was applied, and ratings and weights to several factors were assigned. All analyses were processed using GIS tools, and the results are presented in the form of maps, which categorize groundwater vulnerability to nitrate pollution into five classes, ranging from very low to very high. The results reveal significant variability in groundwater vulnerability to nitrate pollution in the study area. Agricultural practices have a very strong influence on groundwater vulnerability by controlling both recharge rates and nitrogen losses from the soil profile. The most pronounced increases in vulnerability were associated with scenarios involving excessive fertilization and intensive grazing. Among crop types, potato cultivation appears to pose the greatest risk to groundwater quality. Full article

This study investigated the influence of groundwater flux and temperature on denitrification in a steep coastal agricultural Island in western Japan. Push–pull tests (PPTs) were conducted at depths of 3 m, 15 m, and 30 m, during winter, spring, and summer to assess denitrification under varying hydrogeological and seasonal conditions. The 3 m layer is silty loam, 15 m is granitic weathered soil, and 30 m is granitic weathered rock, each with distinct hydraulic conductivities and fluxes. The objectives were to assess denitrification rates and fluxes, assess depth- and season-related variability, and determine the relative roles of hydraulic flux and temperature on denitrification. Denitrification was higher at shallow (3 m) and deep (30 m) boreholes during low-flux periods, while low at the intermediate depth (15 m) where fluxes were highest. Temperature variation had weak correlations compared to hydraulic flux, which showed a strong inverse correlation with denitrification. These findings demonstrate that residence time, controlled by groundwater flux, is the dominant factor influencing nitrate attenuation in this steep coastal aquifer. The PPTs results indicate that denitrification rates derived from PPTs decrease under higher hydraulic fluxes, as these conditions promote more oxic conditions. The study highlights the potential for natural denitrification to mitigate nitrate contamination during low-flux periods, providing insights for sustainable groundwater management in agricultural island environments. Full article

Understanding the spatiotemporal evolution of soil salinization is essential for elucidating its driving mechanisms and supporting sustainable land and water management in arid regions. In this study, the Alar Reclamation Area in Xinjiang, a typical desert–oasis transition zone, was selected to investigate the drivers of spatiotemporal variation in soil salinization. GRACE gravity satellite observations for the period 2002–2022 were used to estimate groundwater storage (GWS) fluctuations. Contemporaneous Landsat multispectral imagery was employed to derive the normalized difference vegetation index (NDVI) and a salinity index (SI), which were further integrated to construct the salinization detection index (SDI). Pearson correlation analysis, variance inflation factor analysis, and a stepwise regression framework were employed to identify the dominant factors controlling the occurrence and evolution of soil salinization. The results showed that severe salinization was concentrated along the Tarim River and in low-lying downstream zones, while salinity levels in the middle and upper parts of the reclamation area had generally declined or shifted to non-salinized conditions. SDI exhibited a strong negative correlation with NDVI (p ≤ 0.01) and a significant positive correlation with both irrigation quota and GWS (p ≤ 0.01). A pronounced collinearity was observed between GWS and irrigation quota. NDVI and GWS were identified as the principal drivers governing spatial–temporal variations in SDI. The resulting regression model (SDI = 0.946 − 0.959 × NDVI + 0.318 × GWS) established a robust quantitative relationship between SDI, NDVI and GWS, characterized by a high coefficient of determination (R² = 0.998). These statistics indicated the absence of multicollinearity (variance inflation factor, VIF < 5) and autocorrelation (Durbin–Watson ≈ 1.876). These findings provide a theoretical basis for the management of saline–alkali lands in the upper Tarim River region and offer scientific support for regional ecological sustainability. Full article

Understanding hydrochemical evolution and apportioning pollution sources are prerequisites for effective groundwater protection at the regional scale; nevertheless, the governing processes and anthropogenic drivers in arid regions remain poorly constrained. Here, we present a comprehensive geochemical survey of the Datong River Basin, a representative arid catchment in north-western China. Thirty-seven groundwater samples were analyzed with hydrochemical methods and Positive-Matrix Factorization (PMF) to delineate natural controls and contaminant sources. Results showed that the aquifer is dominated by HCO₃–Ca(Mg) water controlled predominantly by silicate and carbonate weathering, modified locally by evapo-concentration and human activities. Water-quality indices classify 70.3% of the samples as excellent, but spatially restricted degradation is evident. PMF resolved three independent sources: a natural end-member enriched in Mn, Na⁺ and Cl⁻; a mixed source reflecting domestic wastewater, agricultural fertilizers and rock weathering; and an industrial source dominated by Fe. The mixed source contributes most major ions and chemical oxygen demand (COD), whereas the industrial source accounts for 75.7% of total Fe. These findings provide a robust scientific basis for groundwater management and pollution mitigation in arid regions under similar hydrogeological settings. Full article

To explore the hydrogeochemical characteristics and dominant water–rock interaction processes of thermal water in Lindian geothermal field (northern Songliao Basin, NE China), this study analyzed 16 thermal water samples (1900–3000 m depth) and 3 shallow groundwater samples using hydrochemical indices, water isotopes, and statistical methods (Pearson Correlation and Principle Component Analysis). Results show that the thermal water originates from precipitation and exhibits an “oxygen shift” indicating a long-time water–rock interaction under low to medium reservoir temperature. The thermal water is alkaline with a high TDS and dominated by Na⁺, Cl⁻, and HCO₃⁻, and its hydrochemical facies changes from HCO₃·Cl–Na to Cl·HCO₃–Na and Cl–Na along the groundwater flow path. Leaching of halite, silicates, and carbonates is the primary process controlling solute accumulation. The geothermal reservoir is in a relatively closed, strong reducing environment, and thermal water reached water–rock equilibrium with respect to Na-, K-, Ca-, and Mg-alumino silicates. Principle Component Analysis identifies three key controlling factors, including mineral leaching, organic matter degradation, and sulfate reduction/mineral precipitation. This study establishes a hydrogeochemical baseline for the initial exploitation stage, providing a scientific basis for predicting long-term water quality changes and formulating differentiated sustainable development strategies for the Lindian geothermal field. Full article

Health risks associated with groundwater deterioration have become increasingly prominent worldwide. Accurate assessment of human health risks associated with groundwater is a critical component of groundwater development and utilization, particularly in large metropolitan areas with high water resource demands. In our study, 37 groundwater samples were collected from the main urban areas of Chongqing, the largest city in southwest China, to identify the groundwater driving factors and their associated human health risk. The primary hydrochemical facies in the study area is Ca–HCO₃. Groundwater hydrochemistry is primarily controlled by silicate weathering, carbonate (dolomite and calcite) dissolution, and anthropogenic activities such as industrial and agricultural activities. The hazard index (HI) caused by NO₃⁻ and NO₂⁻ was higher than the safety standard and exhibited potentially noncarcinogenic risk for children in the north and the west of the study area. The KDE-based Monte Carlo simulation method showed a high reliability in human health risk assessment, with all mean values of the original dataset falling within their corresponding 95% confidence intervals (CIs) of generated data. The achievement can provide valuable insights for groundwater risk mitigation and resource management in Chongqing’s main urban areas, as well as in other metropolitan regions worldwide. Full article

The enrichment laws and key controlling factors of coalbed methane (CBM) in the Xishanyao Formation of the Hedong mining area remain unclear, restricting exploration progress. Based on well data and experimental analyses, this study investigates CBM enrichment characteristics and geological controls using genetic identification diagrams. Results demonstrate that CBM exhibits a “high in northwest and low in southeast” planar distribution. Vertically, CBM content is extremely low above 360 m due to weathering oxidation and burnt zone effects, increases within the 360–950 m interval (peaking at 750–950 m), and declines from 950 to 1200 m because of limited gas contribution. Genetic analysis indicates predominantly primary biogenic gas, with a minor component of early thermogenic gas. Enrichment is controlled by structure and hydrogeology: the medium-depth range (358–936 m) on the northern syncline limb and western part of the northern monoclinal zone forms a high-efficiency enrichment zone due to compressive stress from reverse faults and high mineralization groundwater (TDS > 8000 mg/L). While the southern limb, characterized by high-angle tensile fractures and active groundwater runoff, suffers gas loss and generally low gas content (<3.5 m³/t). This study clarifies CBM enrichment laws and enrichment mechanisms, supporting exploration of low-rank CBM in the Hedong mining area. Full article

This study aims to investigate hydrogeochemical characteristics and groundwater quality in the Bashang Area in Chengde and to discuss factors controlling the groundwater quality. A total of 91 groundwater samples were collected and a fuzzy synthetic evaluation (FSE) method was used for assessing groundwater quality. Results show the groundwater chemistry in the study area is predominantly characterized by HCO₃⁻-Ca type waters. Rock weathering processes dominate the hydrogeochemical processes within the study area, while also being influenced by evaporation and concentration effects. The results of the fuzzy evaluation indicate that 94.5% of groundwater samples are of good quality and suitable for drinking (Classes I, II, and III), while 5.5% are of poor quality and unsuitable for drinking (Class IV). Among these, bedrock fissure water exhibited superior quality. Within clastic rock pore water, elevated levels of NO₃⁻ and F⁻ ions were observed in certain localized areas. The exceedance of NO₃⁻ concentrations stems from agricultural expansion, where the application of nitrogen fertilizers constitutes the primary driver of local nitrate pollution. Excessive F⁻ levels correlate with the region’s indigenous geological background. Fluoride-bearing minerals such as fluorite and biotite are widely distributed throughout the study area. Intensive evaporation concentrates groundwater, while the region’s slow groundwater flow facilitates the accumulation and enrichment of F⁻ within aquifers. Full article

The long-term stability of deep underground excavations near aquifer-bearing strata is primarily controlled by the time-dependent deformation and permeability changes in the surrounding rock mass under the combined effects of mechanical loading and groundwater seepage. This study experimentally investigates the creep behavior and permeability evolution of tuff specimens subjected to stepwise reductions in confining pressure under coupled seepage and stress conditions. Conventional triaxial compression tests were conducted to determine the peak strength at confining pressures of 10, 15, and 20 MPa. Subsequently, triaxial creep tests were performed, maintaining axial stress at 70% of the previously established peak strength, with a constant seepage pressure of 4 MPa, while progressively decreasing the confining pressure. The results clearly reveal a three-stage creep process—with instantaneous, steady-state, and accelerated phases—with the radial strain exceeding axial strain and ultimately dominating at failure. This indicates that failure is characterized by significant volumetric expansion. At the specified initial confining pressures of 10 MPa, 15 MPa, and 20 MPa, the tuff specimens exhibited volumetric strains of −1.332, −1.119, and −0.836 at failure, respectively. Permeability evolution depends on the creep stage, showing a pronounced increase during the accelerated creep phase that often surpasses the cumulative permeability changes observed earlier. The specimen’s permeability at failure increased by factors of 3.97, 3.21, and 3.61 compared to the initial stage of the experiment, respectively. Additionally, permeability evolution exhibits a strong functional relationship with volumetric strain, which can be effectively modeled using an exponential function. The experimental findings further indicate that, as the confining pressure is gradually reduced, the permeability evolves following a clear exponential trend. Additionally, a higher initial confining pressure slows the rate at which permeability increases. These findings clarify the three-stage creep behavior and the associated evolution of the permeability index in tuff under coupled seepage–stress conditions. Additionally, they present a quantitative model linking permeability to volumetric strain, offering both a theoretical foundation and a new approach for assessing the long-term stability risks of deep underground engineering projects. Full article

Soil salinization is an escalating global concern threatening agricultural productivity and ecological sustainability, particularly in coastal regions where complex interactions among hydrological, climatic, and anthropogenic factors govern salt accumulation. The vertical differentiation and spatial heterogeneity of salinity drivers remain poorly resolved. We present an integrated modeling framework combining ensemble machine learning and spatial statistics to investigate the depth-specific dynamics of soil salinity in the Yellow River Delta, a vulnerable coastal agroecosystem. Using multi-source environmental predictors and 220 field samples harmonized to 30 m resolution, the hybrid Gray Wolf Optimizer–Random Forest–XGBoost model achieved high predictive accuracy for surface salinity (R² = 0.91, RMSE = 0.03 g/kg, MAE = 0.02 g/kg). Spatial autocorrelation analysis (Global Moran’s I = 0.25, p < 0.01) revealed pronounced clustering of high-salinity hotspots associated with seawater intrusion pathways and capillary rise. The results reveal distinct vertical control mechanisms: vegetation indices and soil water content dominate surface salinity, while total dissolved solids (TDS), pH, and groundwater depth increasingly influence middle and deep layers. By applying SHAP (SHapley Additive Explanations), we quantified nonlinear feature contributions and ranked key predictors across layers, offering mechanistic insights beyond conventional correlation. Our findings highlight the importance of depth-specific monitoring and intervention strategies and demonstrate how explainable machine learning can bridge the gap between black-box prediction and process understanding. This framework offers a generalizable framework that can be adapted to other coastal agroecosystems with similar hydro-environmental conditions. Full article

Groundwater pollution risk assessment is crucial for sustainable groundwater management. However, accurately evaluating groundwater pollution risk presents significant challenges due to the complexity and multitude of influencing factors. In this study, the traditional DRASTIC model for groundwater vulnerability assessment was improved. By integrating groundwater vulnerability, pollution load, and value function, a holistic assessment framework was developed to analyze pollution risks of shallow groundwater across the plain region of Baoji City. The analytic hierarchy process method was used to determine indicator weights. Results indicate that the general level of groundwater pollution risk in the Baoji Plain is comparatively low. Compared with the traditional DRASTIC model, the proportion of high and relatively high pollution risk areas based on the improved DRSTICW model superimposed pollution risk model accounts for 3.72%, which is reduced by 12.57% but is more reasonable. These high and relatively high pollution risk areas are predominantly located in the western floodplain area, where the groundwater vulnerability, pollution load, and value function are all high. Although the distribution range is limited, they are located in densely populated and industrial agglomeration areas. Accurately identifying such high-risk areas and implementing priority control are of great significance for curbing pollution diffusion, safeguarding groundwater resources, and guiding future land use development and management. Full article

Permeability, thermal conductivity, and porosity distribution are key factors to control groundwater flow and heat transport in porous media. The parameter estimation procedure is widely used to understand flow and transport behavior in geothermal systems. As recognized in most studies, this parameter estimation relies on the quality and quantity of spatiotemporal measurements. With the typically limited resources for conducting field investigations, understanding suitable sampling strategies is crucial before applying a model to site-specific conditions. This study aims to quantify uncertainties in hydro-thermal properties using Monte Carlo Simulation (MCS) and Ensemble Kalman Filter (EnKF). A synthetic two-dimensional aquifer profile is used to evaluate the accuracy of the estimated hydrothermal properties in accounting for variations in groundwater temperature resulting from cross-hole pumping and injection events. Based on the calculations of the mean absolute and squared errors for estimated hydrothermal properties, EnKF generally leads to more accurate estimates of hydrothermal properties than MCS. Furthermore, EnKF strikes a balance between accuracy and efficiency, making it the most effective method. This study highlights the strengths and limitations of each method, providing valuable insights for selecting appropriate inversion techniques to quantify uncertainties in geothermal systems. Additionally, well spacing and open screen locations are recommended to obtain optimal thermal energy in the geothermal system Full article

Heavy metal contamination in agricultural soils poses serious threats to food safety, ecosystem integrity, and public health. This study investigates the concentrations, ecological risks, and human health impacts of nine heavy metals Cr, Mn, Co, Ni, Cu, Zn, Pb, As, and V in homestead agricultural soils collected from two depths, surface (0–20 cm) and subsurface (21–50 cm), across industrial and non-industrial regions of Bangladesh, using inductively coupled plasma mass spectrometry (ICP-MS). Results revealed that surface soils from industrial areas exhibited the highest metal concentrations in order of Mn > Zn > Cr > Pb > V > Ni > Cu > As > Co. However, maximum As levels were detected in non-industrial areas, suggesting combined influences of local geology, intensive pesticide application, and prolonged irrigation with As-contaminated groundwater. Elevated concentrations in surface soils indicate recent contamination with limited downward migration. Multivariate statistical analyses indicated that industrial and urban activities are the major sources of contamination, whereas Mn remains primarily geogenic, controlled by natural soil forming processes. Contamination factor (CF) and pollution load index (PLI) analyses identified Pb and As as the principal pollutants, with hotspots in Nairadi, Majhipara (Savar), Gazipur sadar, and Chorkhai (Mymensingh). Ecological risk (ER) assessment highlighted As and Pb as the dominant environmental stressors, though overall risk remained low. Human health risk analysis showed that ingestion is the primary exposure pathway, with children being more susceptible than adults. Although the hazard index (HI) values were within the acceptable safety limits, the estimated carcinogenic risks for As and Cr exceeded the USEPA thresholds, indicating potential long term health concerns. Therefore, the cumulative carcinogenic risk (CCR) results demonstrate that As is the primary driver of lifetime carcinogenic risk in homestead soils, followed by Cr, while contributions from other metals are minimal. These findings emphasize the urgent need for continuous monitoring, improved industrial waste management, and targeted mitigation strategies to ensure safe food production, a cleaner environment, and better public health. Full article

Heavy metal contamination in agricultural soils is a critical global concern, threatening ecosystem safety and food security. The wheat–corn rotation system, vital for food production in regions like Northern China, is particularly vulnerable. However, comprehensive studies investigating vertical migration, future dynamics under climate change, and predictive modeling of heavy metals within this system are still limited. This study combined field sampling of soil profiles (0–200 cm) with geochemical modeling (the PROFILE and SSCL models) and machine learning techniques (Multiple Regression, Neural Networks, and Random Forest). Key findings revealed that atmospheric deposition was the primary input source for most heavy metals, contributing 49.50–93.27%. The release rates (Rm) of heavy metals were significantly higher during the corn season than the wheat season and are projected to increase by 1.2–1.5 times under the RCP4.5 climate scenario. Vertical distribution analysis showed a significant accumulation of heavy metals in the middle soil layer (20–120 cm), with Arsenic (As) and Cadmium (Cd) exhibiting the strongest migration potential, posing a threat to groundwater. The Random Forest model demonstrated superior performance (R² > 0.95) in predicting heavy metal behavior, identifying Fed and soil TOC as the dominant controlling factors. This study provides a unique and significant contribution by integrating geochemical fate modeling with climate projections and advanced machine learning to offer a predictive, multi-faceted risk assessment framework, thereby supplying a scientific basis for targeted pollution control and sustainable soil management in wheat–corn rotation systems under a changing climate. Full article

Identifying the causes and mechanisms of heat hazards in mining operations is essential for effective heat hazard prevention and control. In recent years, hydrothermal phenomena have frequently occurred in the eastern part of the Chenghe Mining Area, located in the central Weibei Coalfield. However, research on the geothermal fluid migration patterns and heat generation mechanisms in this region remains limited. This study comprehensively explores the geothermal field characteristics in the area, based on well temperature logging data, rock thermal conductivity, temperature control models, temperature curve analysis, and numerical simulations. It reveals the key controlling factors and mechanisms behind the formation of geothermal anomalies in the region. The results show that the overall geothermal heat flow trend in the area is characterized by low heat in the northwest and high heat in the southeast. The formation of geothermal anomalies is primarily influenced by water-conducting faults and coal seams. Based on this, the temperature control models are classified into two types: the fault + deep circulating thermal water uplift model and the coal seam heat-resistant-folded temperature control model. Heat transfer occurs through groundwater convection along the F1 fault and its secondary faults, which transport heat. The heat generation mechanism in the study area involves the heating of groundwater during deep circulation, followed by the upward migration of the heated water along the F1 fault, which adds an additional heat source to the surrounding rock of the fault, creating localized thermal anomalies. The findings of this study provide direct guidance for safe production in the Chenghe Mining Area and offer a universal theoretical framework for understanding the causes of heat hazards in mining areas with strong tectonic activity in northwestern China. Full article