Search Results (4)

The issue of sulfate (SO₄²⁻) pollution in groundwater has already attracted widespread attention from scientists. However, at the large-scale regional level, especially in areas with groundwater overexploitation, the pollution mechanisms and sources of sulfate remain unclear. This study innovatively investigates the spatial distribution characteristics and sources of SO₄²⁻ in the groundwater of the Hutuo River alluvial fan area, an understudied region facing significant environmental challenges due to overexploitation. Utilizing a combination of hydrochemical analysis, multivariate statistical methods, and geostatistical techniques, we reveal that the mean concentration of SO₄²⁻ is significantly higher (127 mg/L) in overexploited areas, with an exceedance rate of 5.1%. Our findings uncover substantial spatial heterogeneity in SO₄²⁻ concentrations, with particularly high levels in the river valley plain (RVP) (175 mg/L) and the upper area of the alluvial fan (UAF) (169 mg/L), which we attribute to distinct human activities. A novel contribution of our study is the identification of groundwater depth as a critical factor influencing SO₄²⁻ distribution (p < 0.001). We also demonstrate that the higher proportion of sulfate-type waters in overexploited areas is primarily due to the accelerated oxidation of sulfide minerals caused by overexploitation. Principal component analysis (PCA) and correlation analysis further identify the main sources of SO₄²⁻ as industrial wastewater, domestic sewage, the dissolution of evaporites, and the oxidation of sulfide minerals. By integrating geostatistical techniques, we present the spatial distribution of sulfate pollution sources at a fine scale, providing a comprehensive and spatially explicit understanding of the pollution dynamics. These results offer a novel scientific basis for developing targeted strategies to control sulfate pollution and protect the sustainable use of regional groundwater resources. Our study thus fills a critical knowledge gap and provides actionable insights for groundwater management in similar regions facing overexploitation challenges. Full article

Due to extensive groundwater exploitation, a groundwater funnel has persisted in the Hutuo River alluvial fan in Shijiazhuang since the 1980s, lasting nearly 40 years and significantly impacting the groundwater chemical characteristics. In this study, based on the groundwater level and chemistry data, the hydrochemical evolution processes and mechanisms of the groundwater during the 1980 groundwater funnel period and the post-2015 artificial governance period were investigated using traditional hydrogeochemical methods and inverse hydrogeochemical simulations. The results show the following: (1) The ion concentrations gradually increased along the groundwater flow path, where they displayed a pattern of lower levels in the northwest and higher levels in the southeast. From 1980 to 2021, the concentrations of major ions were increased. (2) In 1980s, the groundwater hydrochemical type predominantly exhibited HCO₃—Ca. From 1980 to 2015, the hydrochemical types diversified into HCO₃·Cl—Ca, HCO₃—Ca·Mg, and HCO₃·SO₄—Ca types. Following the artificial governance, the groundwater level rise led to an increase in the concentrations of SO₄²⁻ and Mg²⁺. Post-2015, the prevailing hydrochemical type changed to HCO₃·SO₄—Ca·Mg. (3) The changes in the groundwater level and ion concentrations were quantitatively strongly correlated and exhibited spatial similarity. (4) In the 1980s, the groundwater hydrochemical composition was primarily controlled by the dissolution of albite, dolomite, halite, and quartz; reverse cation exchange; and groundwater exploitation. Since 2015, the hydrochemical composition has mainly been influenced by the dissolution of albite, calcite, and quartz; positive cation exchange; river–groundwater mixing; and industrial activities, with increasing intensities of both water–rock interactions and human activities. Full article

Accurate groundwater level (GWL) prediction is crucial in groundwater resource management. Currently, it relies mainly on physics-based models for prediction and quantitative analysis. However, physics-based models used for prediction often have errors in structure, parameters, and data, resulting in inaccurate GWL predictions. In this study, machine learning algorithms were used to correct the prediction errors of physics-based models. First, a MODFLOW groundwater flow model was created for the Hutuo River alluvial fan in the North China Plain. Then, using the observed GWLs from 10 monitoring wells located in the upper, middle, and lower parts of the alluvial fan as the test standard, three algorithms—random forest (RF), extreme gradient boosting (XGBoost), and long short-term memory (LSTM)—were compared for their abilities to correct MODFLOW’s predicted GWLs of these 10 wells under two sets of feature variables. The results show that the RF and XGBoost algorithms are not suitable for correcting predicted GWLs that exhibit continuous rising or falling trends, but the LSTM algorithm has the ability to correct them. During the prediction period, the LSTM2 model, which incorporates additional source–sink feature variables based on MODFLOW’s predicted GWLs, can improve the Pearson correlation coefficient (PR) for 80% of wells, with a maximum increase of 1.26 and a minimum increase of 0.02, and can reduce the root mean square error (RMSE) for 100% of the wells with a maximum decrease of 1.59 m and a minimum decrease of 0.17 m. And it also outperforms the MODFLOW model in capturing the long-term trends and short-term seasonal fluctuations of GWLs. However, the correction effect of the LSTM1 model (using only MODFLOW’s predicted GWLs as a feature variable) is inferior to that of the LSTM2 model, indicating that multiple feature variables are superior to a single feature variable. Temporally and spatially, the greater the prediction error of the MODFLOW model, the larger the correction magnitude of the LSTM2 model. Full article

Groundwater quality deterioration has become an environmental problem of widespread concern. In this study, we used a water quality index (WQI) and multivariate statistical techniques to assess groundwater quality and to trace pollution sources in the Hutuo River alluvial-pluvial fan, China. Measurement data of 17 variables in 27 monitoring sites from three field surveys were obtained and pretreated. Results showed that there were 53.09% of NO₃⁻, 18.52% of SO₄²⁻ and 83.95% of total hardness (TH) in samples that exceeded the Grade III standard for groundwater quality in China (GB/T 14848-2017). Based on WQI results, sampling sites were divided into three types: high-polluted sites, medium-polluted sites and low-polluted sites. The spatial variation in groundwater quality revealed that concentrations of total dissolved solids (TDS), Cl⁻, TH and NO₃⁻ were the highest in high-polluted sites, followed by medium-polluted and low-polluted sites. The temporal variation in groundwater quality was controlled by the dilution of rainwater. A principal component analysis (PCA) revealed that the primary pollution sources of groundwater were domestic sewage, industrial sewage and water–rock interactions in the dry season. However, in the rainy and transition seasons, the main pollution sources shifted to domestic sewage and water–rock interactions, nonpoint pollution and industrial sewage. According to the absolute principal component scores-multivariate linear regression (APCS-MLR), most water quality parameters were primarily influenced by domestic sewage. Therefore, in order to prevent the continuous deterioration of groundwater quality, the discharge of domestic sewage in the Hutuo River alluvial-pluvial fan region should be controlled. Full article