Natural-human driving factors of groundwater salinization in a long-term irrigation area

Hydrochemical characteristics, control factors and health risk assessment of groundwater in typical arid region Hotan Area, Chinese Xinjiang

The Hotan region of Xinjiang is an arid region in northwest China, where water resources are scarce, and groundwater is the main water supply. In this study, a self-organizing map (SOM), positive matrix factorization (PMF), hydrochemical diagrams, and health risk assessment model were used to analyze the sources and controlling factors of groundwater chemistry, and evaluate health risks of nitrate and fluoride. The results showed that the evaporation process and water-rock interaction were the main factors influencing groundwater chemistry in the region. Based on the SOM, 239 groundwater samples were divided into six clusters. The main hydrochemical types were Cl-Na, HCO3-Na, and SO4-Ca. Natural factors such as evaporation, water-rock interaction and cation exchange play important roles in Cluster 1-2 and 4-6, while Cluster 3 is mainly polluted by nitrate. Fluoride pollution, primarily caused by geological processes, and nitrate pollution, caused by human activities, cannot be ignored. Attention should be paid to the high non-carcinogenic risk of fluoride and nitrate exposure through drinking water, especially for children. These results provide a theoretical basis for the rational development and utilization of local water resources and ecological environmental protection. The study suggested that the combined method of the SOM and PMF provides a reliable approach for interpreting nonlinear and high-dimensional hydrochemical data.

Optimized groundwater quality evaluation using unsupervised machine learning, game theory and Monte-Carlo simulation

Assessing groundwater quality is essential for achieving sustainable development goals worldwide. However, it is challenging to conduct hydrochemical analysis and water quality evaluation by traditional methods. To fill this gap, this study analyzed the hydrochemical processes, drinking and irrigation water quality, and associated health risks of 93 groundwater samples from the Sichuan Basin in SW China using advanced unsupervised machine learning, the Combined-Weights Water Quality index, and Monte-Carlo simulations. Groundwater samples were categorized into three types using the self-organizing map with the K-means method: Cluster-1 was Ca-HCO3 type, Cluster-2 was dominated by Ca-HCO3, Na-HCO3, and mixed Na-Ca-HCO3 types, Cluster-3 was Ca-Cl and Ca-Mg-Cl types. Ion ratio diagrams revealed that carbonate dissolution and silicate weathering primarily influenced the hydrochemical characteristics. Cluster-1 samples exhibited high NO3- contents from intensive agricultural activities. Cluster-2 samples with high Na+ contents were characterized by positive cation exchange, while Cluster-3 samples with elevated Ca2+ and Mg2+ contents were influenced by reverse cation exchange. Combined-Weights Water Quality Index indicated that 62.37% of total samples were suitable for drinking, predominantly located in the central part of the study area. Irrigation Water Quality Index revealed that 33.34% of total samples were suitable for irrigation, mainly in the northeastern region. NO3- concentration and electrical conductivity (EC) value were the main indicators with the highest sensitivity for drinking and irrigation suitability, respectively. Probabilistic health risk assessments suggested that a significant portion of the groundwater samples posed a health risk greater than 1 to children (63%) and adults (52%) by Monte-Carlo simulation. The high-risk areas (hazard index >4), primarily in the eastern region, are closely associated with nitrate distribution. Sensitivity analysis demonstrated that NO3- concentration is the primary indicator accounting for health risks. Reducing the application of nitrogen-based fertilizers on cultivated land is the most effective approach to improve drinking quality and mitigate the associated health risks to the population. This study's findings aim to produce a novel groundwater quality evaluation for promoting the sustainable management and utilization of groundwater resources.

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.

Integrated approach to understand the multiple natural and anthropogenic stresses on intensively irrigated coastal aquifer in the Mediterranean region

Understanding the major factors influencing groundwater chemistry and its evolution in irrigation areas is crucial for efficient irrigation management. Major ions and isotopes (δD-H2O together with δ18O-H2O) were used to identify the natural and anthropogenic factors contributing to groundwater salinization in the shallow aquifer of the Wadi Guenniche Plain (WGP) in the Mediterranean region of Tunisia. A comprehensive geochemical investigation of groundwater was conducted during both the low irrigation season (L-IR) and the high irrigation season (H-IR). The results show that the variation range and average concentrations of almost all the ions in both the L-IR and H-IR seasons are high. The groundwater in both seasons is characterized by high electrical conductivity and CaMgCl/SO4 and NaCl types. The dissolution of halite and gypsum, the precipitation of calcite and dolomite, and Na-Ca exchange are the main chemical reactions in the geochemical evolution of groundwater in the Wadi Guenniche Shallow Aquifer (WGSA). Stable isotopes of hydrogen and oxygen (δ18O-H2O and δD-H2O) indicate that groundwater in WGSA originated from local precipitation. In the H-IR season, the δ18O-H2O and δD-H2O values indicate that the groundwater experienced noticeable evaporation. The enriched isotopic signatures reveal that the WGSA's groundwater was influenced by irrigation return flow and seawater intrusion. The proportions of mixing with seawater were found to vary between 0.12% and 5.95%, and between 0.13% and 8.42% during the L-IR and H-IR seasons, respectively. Irrigation return flow and the associated evaporation increase the dissolved solids content in groundwater during the irrigation season. The long-term human activities (fertilization, irrigation, and septic waste infiltration) are the main drives of the high nitrate-N concentrations in groundwater. In coastal irrigation areas suffering from water scarcity, these results can help planners and policy makers understand the complexities of groundwater salinization to enable more sustainable management and development.

Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, and health risk model

An in-depth understanding of nitrate-contaminated surface water and groundwater quality and associated risks is important for groundwater management. Hydrochemical characteristics and driving forces of groundwater quality and non-carcinogenic risks of nitrate were revealed by the integrated approaches of self-organizing map analysis, spatial visualization by geography information system, entropy and irrigation water quality indices, and human health risk model. Groundwater samples were categorized into two clusters by SOM analysis. Cluster I including three samples were Ca-SO4 type and cluster II of remaining 136 samples were Ca-HCO3 type. Hydrochemical compositions of two cluster samples were dominated by water-rock interaction: (1) calcite and gypsum dissolution for cluster I samples and (2) calcite dissolution, silicate weathering, and positive cation exchange for cluster II samples. Nitrate contamination occurred in both cluster I and II samples, primarily induced by agricultural nitrogen fertilizer. The EWQI results showed that 90.97% in total groundwater samples were suitable for drinking purpose, while the IWQI results demonstrated that 65.03% in total groundwater samples were appropriate for irrigation purpose. The HHR model and Monte Carlo simulation indicated that the non-carcinogenic nitrated risk was highest in children. Exposure frequency was the most sensitive factor (86.33% in total) influencing the total non-carcinogenic risk, indicated by sensitivity analysis. Compared with the two clusters of groundwater, surface water has a shorter circulation cycle and lower ion concentrations resulting in better water quality. This study can provide scientific basis for groundwater quality evaluation in other parts of the world.

Tracing spatial patterns of lacustrine groundwater discharge in a closed inland lake using stable isotopes

Tracing lacustrine groundwater discharge (LGD) is essential for understanding the hydrological cycle and water chemistry behaviour of lakes. LGD usually exhibits large spatial variability, but there are few reports on quantitatively revealing the spatial patterns of LGD at the whole lake scale. This study investigated the spatial patterns of LGD in Daihai Lake, a typical closed inland lake in northern China, based on the stable isotopes (δ2H and δ18O) of groundwater, surface water, and sediment pore water (SPW). The results showed that there were significant differences between the δ2H and δ18O values of different water bodies in the Daihai Lake Basin: groundwater < SPW < lake water. The LGD through SPW was found to be an important recharge pathway for the lake. Accordingly, stable isotopes of SPW showed that LGD in the northeastern and northwestern of Daihai Lake was significantly greater both horizontally and vertically than that in the other regions, and the proportions of groundwater in SPW in these two regions were 55.53% and 29.84%, respectively. Additionally, the proportion of groundwater in SPW showed a significant increase with profile depth, and the proportion reached 100% at 50 cm below the sediment surface in the northeastern of the lake where the LGD intensity was strongest. The total LGD to Daihai Lake was 1.47 × 107 m3/a, while the LGD in the northeastern and northwestern of the lake exceeded 1.9 × 106 m3/a. This study provides new insights into assessing the spatial patterns of LGD and water resource management in lakes.

Multi-scale failure mechanisms of hydraulic engineering exposed to seasonally frozen salinization environment: Integrating SBAS-InSAR and mechanical experiments

Constructing hydraulic engineering ensures agricultural development and improves salinization environments. However, in seasonally frozen salinization regions, hydraulic engineering is prone to deformation failure. Leakage from canal raises the regional groundwater level, triggering secondary salinization environmental issues. Exploring the instability mechanisms is thus necessary for hydraulic engineering. Traditional deformation monitoring techniques and soil experiments are constrained by observation scale and timeliness. In this study, Sentinel-1B data from November 2017 to August 2019 were acquired. The small baseline subset (SBAS) InSAR approach was employed to interpret the seasonal deformation characteristics in both the vertical and slope directions of a damaged canal segment in Songyuan, Northeast China. The mechanical properties of saline-alkali soil under varying water contents were quantified by integrating unconfined compression experiment (UCE). In May, as the soil thawed downward, a frozen lenses with poor permeability formed at a depth of approximately 100 cm, causing the accumulation of meltwater and infiltrated precipitation between the frozen layer and the melting layer in the canal. The soil water content at a depth of 80 to 140 cm exceeded 22 %, reaching a threshold for rapid reduction in unconfined compression strength (UCS). Consequently, in spring, the low soil strength between the frozen layer and the melting layer resulted in interface sliding, with a displacement of -133.88 mm in the canal slope direction. Furthermore, the differential projection of freeze-thaw deformation in the slope direction caused continuous creep of the canal towards the free face, with a value of -23.27 mm, exacerbating the formation of the late spring landslide. Integrating InSAR and engineering geological analysis is beneficial for addressing deformation issues in hydraulic engineering. Ensuring the sustainable operation of hydraulic engineering holds important implications for mitigating the salinization process.

Salinity stress results in ammonium and nitrite accumulation during the elemental sulfur-driven autotrophic denitrification process

In this study, we investigated the effects of salinity on elemental sulfur-driven autotrophic denitrification (SAD) efficiency, and microbial communities. The results revealed that when the salinity was ≤6 g/L, the nitrate removal efficiency in SAD increased with the increasing salinity reaching 95.53% at 6 g/L salinity. Above this salt concentration, the performance of SAD gradually decreased, and the nitrate removal efficiency decreased to 33.63% at 25 g/L salinity. Approximately 5 mg/L of the hazardous nitrite was detectable at 15 g/L salinity, but decreased at 25 g/L salinity, accompanied by the generation of ammonium. When the salinity was ≥15 g/L, the abundance of the salt-tolerant microorganisms, Thiobacillus and Sulfurimonas, increased, while that of other microbial species decreased. This study provides support for the practical application of elemental sulfur-driven autotrophic denitrification in saline nitrate wastewater.

Distribution characteristics, source identification and health risk assessment of trace metals in the coastal groundwater of Taizhou City, China

This study was carried out to evaluate and analyze the fluctuations in groundwater for certain trace metals (Fe, Mn, Cu, Zn, Al, Cd, Cr, Pb, As, and Se) in Taizhou City over three years (2020-2022), evaluate the potential human health risks due to the consumption of groundwater. To quantify the spatiotemporal changes in groundwater trace metals, the heavy metal pollution index (HPI) and heavy metal evaluation index (HEI) were utilized. Furthermore, multivariate statistical methods were utilized to distinguish the sources of trace elements. Deterministic health risk assessment and Monte Carlo health risk simulation methods were employed to evaluate human health risks associated with exposure to trace metals. The results indicate that areas with higher pollution are in the south-central region, with low HPI increasing from 50% to 75% and low HEI from 68.75% to 81.25%, reflecting improved water quality. Correlation matrix analysis and principal component analysis (PCA) pinpointed anthropogenic sources as major trace metal contributors. Cr and As concentrations were associated with farming activities, Cd and Pb concentrations showed links to local industries such as e-waste recycling and shipbuilding. Furthermore, Cu levels in groundwater was influenced by the combined effects of industry, agriculture, and urban sewage discharge. Based on the hazard quotient (HQ) and hazard index (HI) calculations, the majority of groundwater samples did not exceed the reference values, indicating acceptable noncarcinogenic risks for both adults and children. However, the analysis of carcinogenic risk (CR) and uncertainty revealed an overall decreasing trend in carcinogenic risk, with Cr and Cd possessing the highest potential for causing carcinogenic risks. The sensitivities were 46.3%, 53.3%, and 70.3% for Cr, and 18.8%, 27.6%, and 9.3% for Cd.