Spatial distribution and driving factors of groundwater chemistry and pollution in an oil production region in the Northwest China

Groundwater hydrochemistry, source identification and health assessment based on self-organizing map in an intensive mining area in Shanxi, China

The Changzhi Basin in Shanxi is renowned for its extensive mining activities. It's crucial to comprehend the spatial distribution and geochemical factors influencing its water quality to uphold water security and safeguard the ecosystem. However, the complexity inherent in hydrogeochemical data presents challenges for linear data analysis methods. This study utilizes a combined approach of self-organizing maps (SOM) and K-means clustering to investigate the hydrogeochemical sources of shallow groundwater in the Changzhi Basin and the associated human health risks. The results showed that the groundwater chemical characteristics were categorized into 48 neurons grouped into six clusters (C1-C6) representing different groundwater types with different contamination characteristics. C1, C3, and C5 represent uncontaminated or minimally contaminated groundwater (Ca-HCO3 type), while C2 signifies mixed-contaminated groundwater (HCO3-Ca type, Mixed Cl-Mg-Ca type, and CaSO4 type). C4 samples exhibit impacts from agricultural activities (Mixed Cl-Mg-Ca), and C6 reflects high Ca and NO3- groundwater. Anthropogenic activities, especially agriculture, have resulted in elevated NO3- levels in shallow groundwater. Notably, heightened non-carcinogenic risks linked to NO3-, Pb, F-, and Mn exposure through drinking water, particularly impacting children, warrant significant attention. This research contributes valuable insights into sustainable groundwater resource development, pollution mitigation strategies, and effective ecosystem protection within intensive mining regions like the Changzhi Basin. It serves as a vital reference for similar areas worldwide, offering guidance for groundwater management, pollution prevention, and control.

Is redox zonation an appropriate method for determining the stage of natural remediation in deep contaminated groundwater?

Groundwater contamination resulting from petroleum development poses a significant threat to drinking water sources, especially in developing countries. In situ natural remediation methods, including microbiological processes, have gained popularity for the reduction of groundwater contaminants. However, assessing the stage of remediation in deep contaminated groundwater is challenging and costly due to the complexity of diverse geological conditions and unknown initial concentrations of contaminants. This research proposes that redox zonation may be a more convenient and comprehensive indicator than the concentration of contaminants for determining the stage of natural remediation in deep groundwater. The combination of sequencing microbial composition using the high-throughput 16S rRNA gene and function predicted by FAPROTAX is a useful approach to determining the redox conditions of different contaminated groundwater. The sulfate-reducing environment, represented by Desulfobacteraceae, Peptococcaceae, Desulfovibrionaceae, and Desulfohalobiaceae could be used as characteristic early stages of remediation for produced water contamination in wells with high concentrations of SO42-, benzene, and salinity. The nitrate-reducing environment, enriched with microorganisms related to denitrification, sulfur-oxidizing, and methanophilic microorganisms could be indicative of the mid stages of in situ bioremediation. The oxygen reduction environment, enriched with oligotrophic and pathogenic Sphingomonadaceae, Caulobacteraceae, Syntrophaceae, Legionellales, Moraxellaceae, and Coxiellaceae, could be indicative of the late stages of remediation. This comprehensive approach could provide valuable insights into the process of natural remediation and facilitate improved environmental management in areas of deep contaminated groundwater.

Prediction of sulfate concentrations in groundwater in areas with complex hydrogeological conditions based on machine learning

The persistent and increasing levels of sulfate due to a variety of human activities over the last decades present a widely concerning environmental issue. Understanding the controlling factors of groundwater sulfate and predicting sulfate concentration is critical for governments or managers to provide information on groundwater protection. In this study, the integration of self-organizing map (SOM) approach and machine learning (ML) modeling offers the potential to determine the factors and predict sulfate concentrations in the Huaibei Plain, where groundwater is enriched with sulfate and the areas have complex hydrogeological conditions. The SOM calculation was used to illustrate groundwater hydrochemistry and analyze the correlations among the hydrochemical parameters. Three ML algorithms including random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN) were adopted to predict sulfate levels in groundwater by using 501 groundwater samples and 8 predictor variables. The prediction performance was evaluated through statistical metrics (R2, MSE and MAE). Mine drainage mainly facilitated increase in groundwater SO42- while gypsum dissolution and pyrite oxidation were found another two potential sources. The major water chemistry type was Ca-HCO3. The dominant cation was Na+ while the dominant anion was HCO3-. There was an intuitive correlation between groundwater sulfate and total dissolved solids (TDS), Cl-, and Na+. By using input variables identified by the SOM method, the evaluation results of ML algorithms showed that the R2, MSE and MAE of RF, SVM, BPNN were 0.43-0.70, 0.16-0.49 and 0.25-0.44. Overall, BPNN showed the best prediction performance and had higher R2 values and lower error indices. TDS and Na+ had a high contribution to the prediction accuracy. These findings are crucial for developing groundwater protection and remediation policies, enabling more sustainable management.

Using unsupervised machine learning models to drive groundwater chemistry and associated health risks in Indo-Bangla Sundarban region

Groundwater is an essential resource in the Sundarban regions of India and Bangladesh, but its quality is deteriorating due to anthropogenic impacts. However, the integrated factors affecting groundwater chemistry, source distribution, and health risk are poorly understood along the Indo-Bangla coastal border. The goal of this study is to assess groundwater chemistry, associated driving factors, source contributions, and potential non-carcinogenic health risks (PN-CHR) using unsupervised machine learning models such as a self-organizing map (SOM), positive matrix factorization (PMF), ion ratios, and Monte Carlo simulation. For the Sundarban part of Bangladesh, the SOM clustering approach yielded six clusters, while it yielded five for the Indian Sundarbans. The SOM results showed high correlations among Ca2+, Mg2+, and K+, indicating a common origin. In the Bangladesh Sundarbans, mixed water predominated in all clusters except for cluster 3, whereas in the Indian Sundarbans, Cl--Na+ and mixed water dominated in clusters 1 and 2, and both water types dominated the remaining clusters. Coupling of SOM, PMF, and ionic ratios identified rock weathering as a driving factor for groundwater chemistry. Clusters 1 and 3 were found to be influenced by mineral dissolution and geogenic inputs (overall contribution of 47.7%), while agricultural and industrial effluents dominated clusters 4 and 5 (contribution of 52.7%) in the Bangladesh Sundarbans. Industrial effluents and agricultural activities were associated with clusters 3, 4, and 5 (contributions of 29.5% and 25.4%, respectively) and geogenic sources (contributions of 23 and 22.1% in clusters 1 and 2) in Indian Sundarbans. The probabilistic health risk assessment showed that NO3- poses a higher PN-CHR risk to human health than F- and As, and that potential risk to children is more evident in the Bangladesh Sundarban area than in the Indian Sundarbans. Local authorities must take urgent action to control NO3- emissions in the Indo-Bangla Sundarbans region.

Ecological and health risk assessment of heavy metals in agricultural soils from northern China

Soil pollution by heavy metals can cause continuing damage to ecosystems and the human body. In this study, we collected nine fresh topsoil samples and 18 maize samples (including nine leaf samples and nine corn samples) from agricultural soils in the Baiyin mining areas. The results showed that the order of heavy metal concentrations (mg/kg) in agricultural soils was as follows: Zn (377.40) > Pb (125.06) > Cu (75.06) > Ni (28.29) > Cd (5.46) > Hg (0.37). Cd, Cu, Zn, and Pb exceeded the Chinese risk limit for agricultural soil pollution. The average the pollution load index (4.39) was greater than 3, indicating a heavy contamination level. The element that contributed the most to contamination and high ecological risk in soil was Cd. Principal component analysis (PCA) and Pearson's correlation analysis indicated that the sources of Ni, Cd, Cu, and Zn in the soil were primarily mixed, involving both industrial and agricultural activities, whereas the sources of Hg and Pb included both industrial and transportation activities. Adults and children are not likely to experience non-carcinogenic impacts from the soil in this region. Nonetheless, it was important to be aware of the elevated cancer risk presented by Cd, Pb, and especially Ni. The exceedance rates of Cd and Pb in corn were 66.67% and 33.3%, respectively. The results of this research provide data to improve soil protection, human health monitoring, and crop management in the Baiyin district.

Identifying the hydrochemical features, driving factors, and associated human health risks of high-fluoride groundwater in a typical Yellow River floodplain, North China

Fluoride enrichment (> 1.5 mg/L) in groundwater has become a global threat, particularly given the hazards to human health. This study collected 58 unconfined groundwater samples from Fengpei Plain in June 2022 for hydrochemical and stable isotope analyses combined with multiple methods to explore sources, influencing factors, and potential health hazards of groundwater F-. The results showed that groundwater F- concentration ranged from 0.08 to 8.14 mg/L, with an average of 1.91 mg/L; over 41.4% of them exceeded the acceptable level of 1.5 mg/L prescribed by the World Health Organization (WHO). The dominant hydrochemical facies changed from Ca·Mg-HCO3 and Ca·Mg-SO4·Cl type in low-F- groundwater to Na-HCO3 and Na-SO4·Cl water types in high-F- groundwater. The Self-Organizing Map (SOM) and ionic correlation analysis indicated that F- is positively correlated to pH, EC, Na+, K+, SO42-, and TDS, but negatively to Ca2+ and δ18O. Groundwater F- accumulation was primarily driven by F--bearing minerals dissolution such as fluorite. Simultaneously, the carbonates precipitation, positive cation exchange processes, and salt effect were conducive to groundwater F- enrichment. However, competitive adsorption between OH-/HCO3- and F-, evaporation, and anthropogenic activities only had a weak effect on the F- enrichment in groundwater. The hazard quotient (HQ) assessment results show that 67.2% of groundwater samples pose a non-carcinogenic risk (HQ > 1) for infants, followed by 53.4% for children, 32.8% for females, and 25.9% for males. The Monte Carlo simulation results agreed with those of the deterministic model that minors are more susceptible than adults. These findings are vital to providing insights into the geochemical behavior, driving factors, and drinking water safety of high-F- groundwater worldwide.

Characterization of trace and heavy metal concentration in groundwater: A case study from a tropical river basin of southern India

This study investigates the hydrogeochemistry of groundwater samples collected from the Shiriya River Basin (SRB), a tropical watershed located in Kasaragod, Kerala, southern India, with a special focus on trace elements. Fifty-four groundwater samples were collected from deep aquifers, which constitute weathered and fractured granitoids and mafic rocks, and the groundwater is tapped by bore wells from a fractured zone at a depth range of 60-100 m. Concentrations of Sr, Li, Ba, Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Cd, Ag, Au, Te, Pb, Re, and PGEs in groundwater were determined by using Q-ICPMS. Out of the 25 analysed trace elements in groundwater, only Sr (489.6 μg/L), Ba (226 μg/L), Li (11.76 μg/L) Mn (396.8 μg/L), Ni (68 μg/L) and Fe (2438.5 μg/L) show anomalous values. The PGEs and the majority of trace elements show values within the permissible limit. Raman spectral studies reveal the presence of celsian in aquifer rocks and are the source of Ba in groundwater. Further, XRF data of the rocks show a high enrichment of Fe and Mn in mafic dyke, basalt, and syenite, and Ba and Sr in granite, pegmatite, and granitic gneiss. Therefore, this study proved that the source of these elements is geogenic, i.e., they are released from the crystalline aquifer through rock-water interaction under alkaline conditions. The results of this study show that the groundwater of the basin has enough metals such as Na, K, Mg, Ca, Mn, Fe, Co, Cu, and Zn, which are good for health. Nevertheless, a few metals (Fe, Mn, Ba, Sr, Li, Ni) that may exert toxic effects on humans are also present in the groundwater of the SRB. As groundwater is found to be a dependable source of drinking water in such watersheds, a comprehensive study on the hydrogeochemistry of all watersheds in tropical regions is recommended.