Groundwater Model Research Articles

Assessing groundwater behavior and future trends in the Ardabil Aquifer: A comparative study of groundwater modeling system and categorical gradient boosting hybrid model

Rapid population growth and rising demand have led to increased groundwater extraction, underscoring the importance of effective water resource management. While groundwater models are practical tools, their reliance on extensive hydrogeological and hydrological data can pose challenges when such data is unavailable or inaccessible. In this study, two modeling approaches were employed for the prediction and characterization of groundwater behavior in Ardabil Plain from October 2010 to March 2021. Initially, groundwater behavior was modeled using Modflow. Subsequently, the groundwater behavior was modeled within the same time frame using the hybrid CatBoost-AOA model. Groundwater levels were then determined using these two models until the year 2031. The CatBoost-AOA hybrid method showed the highest agreement (correlation coefficient: 0.9977). Modflow and CatBoost models predicted a decline of 0.77 and 0.85 m, respectively, until 2031. This study aims to guide sustainable development planning, with CatBoost-AOA providing a simplified and efficient alternative for accurately forecasting groundwater fluctuations.

Groundwater Salinisation by Irrigation Practices in More Arid Regions

Groundwater Salinisation by Irrigation Practices in More Arid Regions

Assessing the impact of surface water and groundwater interactions for regional-scale simulations of water table elevation

Groundwater flow models are increasingly considered for the regional scale simulation of hydraulic heads and water table elevation. In the most complete configuration, models explicitly simulate two-way interactions between surface water (SW) and groundwater (GW) to reproduce and forecast both SW and GW water levels. In most regional scale groundwater models, however, SW-GW interactions are represented by simplified boundary conditions that only allow one-way interaction from SW to GW, neglecting most of the dynamic exchange fluxes between SW and GW. To evaluate the potential consequences of such simplifications on the simulation of regional GW levels, we compare two models on a 36,900 km2 regional aquifer system in Southern Quebec. One model explicitly simulates both SW and GW flow with two-way SW-GW feedback and the other model only simulates GW flow with a surface boundary flux to represent a one-way interaction with the land surface. Both models are developed with the same numerical code to ensure that the only differences are the representation of SW water flow and SW-GW feedback. The one-way model simulates overall deeper water tables because it removes all exfiltrated groundwater from the system once it exits the subsurface, therefore not allowing exfiltrating groundwater to re-infiltrate. This effect is most pronounced in areas where the water table is close to the surface and for low-flow periods. The inclusion of two-way feedback also reduces the sensitivity of simulated GW levels to the magnitude of the hydraulic conductivity. This result highlights the need for additional data on other system states to improve the calibration of regional scale models that explicitly simulate two-way SW-GW interactions.

Anomaly detection in groundwater monitoring data using LSTM-Autoencoder neural networks.

Groundwater monitoring data can be prone to errors and biases due to various factors like borehole and equipment malfunctions, or human mistakes. These inaccuracies can jeopardize the groundwater system, leading to reduced efficiency and potentially causing partial or complete failures in the monitoring system. Traditional anomaly detection methods, which rely on statistical and time-variant techniques, struggle to handle the complex and dynamic nature of anomalies. With advancements in artificial intelligence and the growing need for effective anomaly detection and prevention across different sectors, artificial neural network methods are emerging as capable of identifying more intricate anomalies by considering both temporal and contextual aspects. Nonetheless, there is still a shortage of comprehensive studies on groundwater anomaly detection. The intricate patterns of sequential data from groundwater present numerous challenges, necessitating sophisticated modeling techniques that combine mathematics, statistics, and machine learning for viable solutions. This paper introduces a model designed for high accuracy and efficient computation in detecting anomalies in groundwater monitoring data through a probabilistic approach. We employed the Monte Carlo method and SEAWAT numerical simulation to ascertain the uncertainty in groundwater salinity. Subsequently, a Long Short-Term Memory (LSTM)-Autoencoder model was trained and evaluated, forming the basis of an anomaly detection framework. Each piece of training data was assessed by the LSTM-Autoencoder using the Negative Log Likelihood (NLL) score and a predefined threshold to determine the data's abnormality percentage. The accuracy evaluation of the proposed LSTM-Autoencoder algorithm revealed that this approach achieved commendable performance, with an accuracy of 98.47% in anomaly detection.

Enhancing Florida red tilapia aquaculture: biofloc optimization improves water quality, pathogen bacterial control, fish health, immune response, and organ histopathology across varied groundwater salinities.

Freshwater scarcity poses challenges to aquaculture worldwide, including countries like Egypt. In this study, we investigate the feasibility of integrating underground saline water (USW) with varying salinities into a Biofloc (BFT) system for desert mariculture of Florida red tilapia (FRT) and its impacts on water quality, fish performance and health. Four BFT treatments (C/N ratio = 15) were examined in triplicate using four salinity levels 0 ppt, 12 ppt, 24 ppt and 36 ppt, expressed as S0, S12, S24 and S36, respectively. For 75 days, a total of 12 fiberglass tanks (each 250L-1 water) were used to store FRT fry (average weight of 1.73 ± 0.01g/fish). The fish were fed an experimental diet (protein/fat = 30/5) and an additional carbon source of rice bran. The results revealed that group S12 showed better growth indicators, higher survival rate, lower FCR, and lower ammonia levels, while group S0 exhibited lower growth indicators (final weight, weight gain, and specific growth rate) than all groups. The serum kidney, liver, and antioxidant indices performed better in the S12 group. At 12 ppt, the immune-related parameter (IgM) increased by 22.5%, while the stress parameter (cortisol) decreased by 40.8% compared to the S0 group. The liver and intestinal histopathological results revealed that the S12 and S24 groups performed better. Pathogenic bacterial load counts favored the S24 group, which had the lowest number among the groups studied. The recommended salinity for FRT cultivation in USW and BFT is 19.94-20 ppt, determined by polynomial regression of FW and FCR.

The effects of land subsidence and its mitigating measures on shallow groundwater salinization in the low-lying coastal plain of East Japan

Land subsidence in low-lying coastal regions results from geological and human factors, causing inundation during high tides. Mitigation measures, like pumping stations and ditch systems, aim to address this challenge. However, their impact on groundwater salinity near tidal rivers is understudied. Using a coupled surface-subsurface model, we investigate this issue in the lower Nabaki River region, Shirako Town, Japan. The simulation reveals adverse effects of pumping stations that induce intrusion of saline water from the tidal river into surrounding groundwater. While they are designed to prevent floods, these stations and ditches may inadvertently raise groundwater vulnerability to saltwater contamination. Despite 2D model limitations, it offers valuable insights into coastal groundwater dynamics and salinization. This study provides important information for policymakers and land managers to better understand the consequences of flood mitigation strategies on groundwater quality in vulnerable coastal areas.

Estimating natural groundwater recharge taking into consideration temporal and spatial variability for arid unconfined aquifer

ABSTRACT Water scarcity is one of the major critical affecting arid climate countries. In addition, population growth and climate change are threatening the sustainable development of water resources in the upcoming decades. Therefore, managing groundwater can solve water scarcity such that sustainable development is ensured for future generations. Hence, it is crucial to assess the current quantity of groundwater by analyzing groundwater variables. Thus, natural groundwater recharge could be utilized as an indicator to assess groundwater quantity conditions. This study aims to estimate natural groundwater recharge in an arid unconfined aquifer. El-Qaa plain in Sinai, Egypt was chosen as a case study. Recharge was estimated taking into consideration spatial and temporal variability. To accomplish this, the WetSpass (Water and Energy Transfer between Soil, Plants, and Atmosphere under quasi-Steady State) model was applied. The model was run using morphological, landuse – land cover and climate data from 1986 to 2015 with a monthly time step. To calibrate the model, the isotope signature of δ18O and δ2 H of water was analyzed to determine the natural groundwater recharge of the aquifer. The obtained recharge value was utilized as a reference value for the calibration process. The results were validated using a finite-difference groundwater flow model (MODFLOW). The recharge values were imported to MODFLOW followed via a transient run from 2000 to 2011. The validation was conducted by comparing the simulated groundwater level with the observed ones. After validation process, the inter-annual and seasonal variability of recharge were determined, and a relationship between recharge and other hydrological parameters was deduced. In addition, the recharge distribution was mapped to depict the recharge spatial variability. The results show that the current recharge rate ranges from approximately 8.10 to 16.62 mm/yr, with a mean annual recharge rate of nearly 12.36 mm/yr. The study might be useful for hydrogeologists for determining potential recharge in the study area, groundwater modelers for developing a reliable groundwater flow model, and decision-makers for planning appropriate future sustainable development through water resources management.

Quantifying the Impacts of Density-Dependent Flow on Surface Water–Groundwater Interaction in a Riparian Setup

Surface Water–Groundwater (SW–GW) interaction is a crucial aspect of the hydrological cycle and requires accurate modeling for reliable predictions. In many basic hydrological models and calculations, it is common to assume that the water density is constant. However, density-dependent flow, which accounts for changes in water density, plays a significant role in various hydrological processes. This study aims to quantify the effects of density-dependent flow on SW–GW interaction and evaluate the sensitivity of dominant hydrological drivers to density-dependent flow. Our simulations using the HydroGeoSphere model revealed that neglecting density-dependent flow in SW–GW interaction can lead to inaccurate estimations of water and solute balances. In particular, including density-dependent flow in the model yielded more realistic salinity distributions under gaining river scenarios and captured the gradual expansion of freshwater lenses under losing river scenarios. The results also indicated that under non-density-dependent flow, more saline groundwater is exposed to evapotranspiration, resulting in higher solute mass storage and a more saline unsaturated zone. Further, surface recharge and pumping rates played crucial roles in salinity distribution. This study highlights the critical importance of incorporating density-dependent flow in simulations, providing valuable insights for improving the accuracy of predictions and effectively managing water and solute balances in floodplain aquifers.

The Determination of Ground Water Balance (GW) Using Modeling Flow, A Case Study West of Karbala Province

The Determination of Ground Water Balance (GW) Using Modeling Flow, A Case Study West of Karbala Province

Assessment of Soil Enzyme Activities in Plant Root Zone of Saline Soil Reclaimed by Drip Irrigation with Saline Groundwater

Drip irrigation with saline water is frequently adopted to realize the sustainable utilization of saline–sodic soil with high water tables, and soil enzyme activities can be used to indicate changes in soil quality. In the current study, spatiotemporal changes in soil urease enzyme (URE), alkaline phosphatase (ALP) and invertase (INV) activities were investigated during consecutive growing seasons. Soil in beds was sampled before planting (0 y) and one, two, three and four years after the growing season (1 y, 2 y, 3 y, 4 y), and these samples were distributed at four horizontal distances from the drip line (0, 10, 20 and 30 cm) and four vertical soil depths (0–10, 10–20, 20–30 and 30–40 cm). The results showed that a distribution pattern of URE and ALP activities formed during the first growing season, while the distribution of INV activity formed until the third growing season. All three soil enzyme activities in the upper soil layers and positions close to the drip line were more greatly affected by planting year. The average URE activity of the soil profile decreased slightly during the first year and increased by about 220% and decreased by 20% after reclamation for two and three years, and finally, it increased to 4.9 μg NH4+·g−1·h−1 at the end of the fourth growing season. ALP activity remained stable during the first two years and rapidly increased in the following years; in particular, in the fourth year, it reached 32.7 μg ph(OH)·g−1·h−1. INV activity increased continually with the number of years after planting and reached 1009.0 μg glu·g−1·h−1 at the fourth season’s end. An analysis of variance indicated that URE, ALP and INV activities varied insignificantly among the time points of 0 y, 1 y, 2 y and 3 y (p < 0.05), while they were significantly higher for 4 y than for 0 y and 1 y. In addition, all three enzyme activities of the soil profile had an exponentially increasing trend with the number of years after planting. These results indicated the soil quality in saline–sodic soils could be improved with time under drip irrigation with local saline groundwater, especially around the drip line.

Evaluating the groundwater salinization processes in Zarabad coastal aquifer (southeastern Iran) using hydrogeochemical and isotopic techniques

Hydrogeochemical, multivariate statistical analysis, and multi-isotopic (δ18O, δD, and δ34S) approaches were used to identify the cause and process of groundwater salinization in the Zarabad coastal aquifer. The hydrochemical facies evolution (HFE) diagram suggests that the Na–Cl facies is the dominant hydrochemical facies. Groundwater chemistry is mostly influenced by cation exchange and its interaction with silicate rocks, as shown by the Gibbs plot. The isotopic composition of δ18O, δD, and δ34S varies from −3.17‰ to −1.35‰ (with an average of −1.69‰), −25.5‰ to −9‰ VSMOW (with an average of −18.09‰) and −7.7‰–16.7‰ V-CDT (with an average of 0.54‰), respectively. The salinization of groundwater may be caused by the evaporation of water or the dissolution of evaporites. This can be inferred from the δ18O to δD data, which indicates that a majority of water falls below the GMWL, IMWL, and LMWL. The d-excess value, ranging from −19.8‰ to 5.36‰, further suggests that the groundwater has undergone evaporation before infiltration. In addition, the comparison between the δ34S–SO42- and SO42− plots suggested that the dissolution of evaporites is the primary source of SO42−. Water chemistry changes in this aquifer is primarily caused by water-rock interaction, ion exchange, and evaporation.

Assessment of spatio-temporal variations in groundwater quality for the groundwater-dependent Maltese islands

Study regionMaltese islands Study focusThe Maltese islands are situated in the Mediterranean Sea, characterized by a semi-arid climate, and encounter increased levels of water stress. This study conducted a hydrochemical analysis of 35 groundwater samples to evaluate the suitability of the current groundwater for agricultural and domestic purposes. Additionally, three-dimensional numerical modeling was conducted to estimate long-term changes in groundwater levels and saline water distributions over 30 years, under the assumption that groundwater pumping will persist at the current rate. New hydrological insights for the regionSalinity factors that classify groundwater in this region can potentially be used for the irrigation of salt-tolerant crops. The output of the modeling calculations suggests that groundwater levels were expected to decrease significantly across most of the island, particularly around gallery areas. Descending the water table may compromise the discharge of coastal groundwater and potentially accelerate seawater intrusion into the inner island areas. Hydrochemical calculations indicated that an increase in salinity induced variations in saturation conditions, leading to a decrease in calcite and an increase in anhydrite, dolomite, and gypsum. The water quality characteristics and contamination evaluations conducted in this study are valuable for the development of long-term alternatives for water resource conservation in this region.

Features of underground flow formation to the Golovesnya river (Desna basin) during the dry period 2007-2021

Purpose. To analyze the peculiarities of the dynamics of underground flow on the catchment of the Golovesnya River, a right-bank tributary of the Desna River, during the dry period of 2007-2021 in comparison with the period of 1960-1985 - before the beginning of noticeable climatic changes. Methods. The hydrodynamic finite-difference method of calculating the specific consumption of groundwater based on the data of routine observations of the levels of groundwater and surface water, the hydrodynamic method of calculating the components of the groundwater balance. Results. The specific flow of groundwater to the Golovesnya River was calculated based on the data of observations for 2007-2021; the share of groundwater flow in the total flow of the river was determined, changes in the dynamics of infiltration nutrition and the inflow of groundwater to the river were revealed, which can be associated with the sign of the charge of the static electric field of the surface layer of the atmosphere, an increase in air temperature, and an increase in the volume of moisture retention in the aeration zone after reducing GWT. Underground flow to the river largely compensates for the losses and stabilizes the river flow in low-water years and during prolonged hydrological drought. Important factors in the regulation of the groundwater regime in low-water periods (in addition to precipitation) are the level and flow of water in the river, as well as the loss of moisture due to the saturation of the aeration zone, and therefore, the lithological and granulometric composition of this zone. Losses in the aeration zone are also an important regulatory factor of balance and river flow in the scale of the Desna River catchment. The largest volumes of underground runoff to the Golovesnya River for 2007-2021 were set for the multi-water year 2016, the smallest - for 2021. By 2016, the chronological graphs show clearer seasonal patterns: repeatability of underground flow fluctuations in the spring and autumn seasons; opposite flow fluctuations in winter and spring and a relatively stable summer flow; typical for autumn are maximum, and for summer always minimum values ​​of reserves and infiltration recharge of groundwater. In the abundant water year of 2016, infiltration recharge and groundwater reserves during autumn and winter sharply decrease and acquire negative values ​​due to a significant decrease in GWT in the previous period, an unfavorable course of temperature and precipitation distribution, and increased outflow to the river. In 2017-2021, the seasonal differences in the recharge of the river with groundwater are almost equalized: the role of recharge increases during the summer period, and decreases during the spring and autumn seasons. The role of the condensation mechanism of moisture transfer in the aeration zone is increasing. During the summer, the lateral inflow of groundwater to the river was the highest in 2016-2018. Conclusions. Calculations and analysis of the dynamics of infiltration feeding and groundwater flow to the Golovesnya River have been performed. Changes in groundwater supply and discharge, especially in the period after 2015, have been identified, which can be interpreted as the consequences of adverse weather and climate conditions in 2014-2015. In 2018-2021, there was a decrease in groundwater and surface water resources, which corresponds to signs of hydrological drought.

An Imputation Method for Simulating 3D Well Screen Locations from Limited Regional Well Log Data.

In groundwater modeling studies, accurate spatial and intensity identification of water sources and sinks is of critical importance. Precise construction data about wells (water sinks) are particularly difficult to obtain. The collection of well log data is expensive and laborious, and government records of historic well log data are often imprecise and incomplete with respect to the precise location or pumping rate. In many groundwater modeling studies, such as groundwater quality assessments, a precise representation of the horizontal and vertical distribution of well screens is required to accurately estimate contaminant breakthrough curves. The number of wells under consideration may be very large, for example, in the assessment of nonpoint source pollution. In this paper, we propose an imputation framework that allows for proper reconstruction of missing well data. Our approach exploits available information and tolerates data gaps and imprecisions. We demonstrate the value of this method for a subregion of the Central Valley aquifer (California, USA). We show that our framework imputes missing values that preserve statistical properties of available data and that remain consistent with the known spatial distribution of well screens and pumping rates in the three-dimensional aquifer system.

Assessment of Soil Nutrients in Plant Root Layer of a Saline-Sodic Soil Cropped with Lycium barbarum L. When Drip Irrigated with Saline Groundwater

Drip irrigation is an effective method to utilize waste saline-sodic land with a high water table. For reasonable and sustainable utilization of saline-sodic soil under such conditions, spatiotemporal changes in total nitrogen (TN), total phosphorus (TP), and soil organic matter (SOM) were investigated during the utilization process. The soil was sampled from newly built raised beds before planting (0 y) and beds in three adjacent plots had been planted with Lycium barbarum L. for one (1 y), two (2 y), and three years (3 y), respectively, at the end of the growing season. Soil samples were obtained at four horizontal distances from the drip line (0, 10, 20, and 30 cm) and four vertical soil depths (0–10, 10–20, 20–30, and 30–40 cm). The results showed that the average TN and TP of the soil profile increased with the planting year and were approximately 0.68 and 1.81 g·kg−1 in the soils of 3 y, approximately 84.9 and 42.4% higher than that of 0 y, respectively. SOM decreased in the first growing season and then continuously increased in the following planting years, reaching 8.26 g·kg−1 in the soils of 3 y, which was approximately 38.2% higher than that of 0 y. TN, TP, and SOM contents were high in soils around the drip line and decreased with distance from the drip line. In both horizontal and vertical directions, TN, TP, and SOM varied slightly in soils of 0, 1, and 2 y, while in soils of 3 y, TN and SOM decreased with increased distance in both horizontal and vertical directions and TP decreased obviously only within 10 cm in both directions. This indicated that the contents and distributions of soil nutrients in such saline-sodic soils could be improved with planting year under drip irrigation with local saline groundwater and especially around the drip line as the soil around the dripper was quickly ameliorated.

Characterization and Mapping of the Potential Area of Oil Palm Using Multi-Criteria Decision Analysis in a Geographic Information Systems Environment

This study presents a GIS-based Multi-Criteria Decision Analysis (MCDA) spatial model to assess land suitability for oil palm (OP) cultivation in rainfed conditions. Initially, twelve parameters, viz., rainfall, number of rainy days, mean temperature, RH, ground water level, soil pH, salinity, soil depth, surface texture, stoniness, slope, and drainage, were selected for assessing OP suitability in one of the states (Kerala). However, subsequent ground verification revealed significant discrepancies, which prompted refining the model by focusing on key parameters with greater accuracy and relevance. Accordingly, only five the most critical parameters affecting OP cultivation under rainfed conditions were selected through the rank sum method, and weights were assigned ac-cording to their significance. This study was aimed at creating a comprehensive tool for informed decision making in agricultural planning. District-level spatial data from reliable sources were utilized for Multi-Criteria Decision Analysis. Thematic rasters, representing key factors influencing land suitability, were created in a GIS. Utilizing MCDA techniques, a digital suitability map was generated in ArcGIS 10.3, delineating three distinct classes over an extensive area of 10.5 million hectares. Further, with an aim to focus on actual locations that can be readily planted with oil palm, the suitable locations identified were restricted to eight selected land use/land cover (LULC) classes. This strategic limitation aimed to facilitate the expansion of OP cultivation exclusively to areas deemed most suitable based on the identified criteria. The validation of this developed model involved comparing the suitability map generated with the performance of existing oil palm plantations across diverse locations. The reasonable similarity between the model’s predictions and real-world plantation outcomes validated the effectiveness of this MCDA spatial model. This model not only helps identify suitable locations for rainfed oil palm cultivation but also serves as a valuable tool for strategic decision making in agricultural land use planning.

A comprehensive framework for stochastic calibration and sensitivity analysis of large-scale groundwater models

Abstract. We introduce a comprehensive and robust theoretical framework and operational workflow that can be employed to enhance our understanding, modeling and management capability of complex heterogeneous large-scale groundwater systems. Our framework encapsulates key components such as the three-dimensional nature of groundwater flows, river–aquifer interactions, probabilistic reconstruction of three-dimensional spatial distributions of geomaterials and associated properties across the subsurface, multi-objective optimization for model parameter estimation through stochastic calibration, and informed global sensitivity analysis (GSA). By integrating these components, we effectively consider the inherent uncertainty associated with subsurface system characterizations as well as their interactions with surface waterbodies. The approach enables us to identify parameters impacting diverse system responses. By employing a coevolutionary optimization algorithm, we ensure efficient model parameterization, facilitating simultaneous and informed optimization of the defined objective functions. Additionally, estimation of parameter uncertainty naturally leads to quantification of uncertainty in system responses. The methodology is designed to increase our knowledge of the dynamics of large-scale groundwater systems. It also has the potential to guide future data acquisition campaigns through an informed global sensitivity analysis. We demonstrate the effectiveness of our proposed methodology by applying it to the largest groundwater system in Italy. We address the challenges posed by the characterization of the heterogeneous spatial distribution of subsurface attributes across large-scale three-dimensional domains upon incorporating a recent probabilistic hydrogeological reconstruction specific to the study case. The system considered faces multiple challenges, including groundwater contamination, seawater intrusion, and water scarcity. Our study offers a promising modeling strategy applicable to large-scale subsurface systems and valuable insights into groundwater flow patterns that can then inform effective system management.

Spatio-temporal assessment of soil salinization utilizing remote sensing derivatives, and prediction modeling: Implications for sustainable development

This study aims to investigate the combined use of multi-sensor datasets (Landsat 4–5 &8 OLI satellite imagery, spatial resolution = 30 m) coupled with field studies to evaluate spatio-temporal dynamics of soil salinization along the coastal belt in West Bengal, India. This study assesses soil salinization by mapping the salinity and electrical conductivity of saturation extract (ECe) and utilizing spectral signatures for estimating soil salinity. The SI change (%) was analyzed (2021–1995), categorizing increases in salinity levels into 5%, 10%, and 50% changes possibly due to salt encrustation on the soil layers. The land use land cover (LULC) change map (2021–1995) demonstrates that the study area is continuously evolving in terms of urbanization. Moreover, in the study area, soil salinity ranges from 0.03 ppt to 3.87 ppt, and ECe varies from 0.35 dSm−1 to 52.85 dSm−1. Additionally, vulnerable saline soil locations were further identified. Classification of soil salinity based on ECe reveals that 26% of samples fall into the non-saline category, while the rest belong to the saline category. The Spectral signatures of the soil samples (n = 19) acquired from FieldSpec hand spectrometer show significant absorption features around 1400, 1900, and 2250 nm and indicate salt minerals. The results of reflectance spectroscopy were cross-validated using X-ray fluorescence and scanning electron microscopy. This study also employed partial least square regression (PLSR) approach to predict ECe (r2 = 0.79, RMSE = 3.29) and salinity parameters (r2 = 0.75, RMSE = 0.51), suggesting PLSR applicability in monitoring salt-affected soils globally. This study’s conclusion emphasizes that remote sensing data and multivariate analysis can be crucial tools for mapping spatial variations and predicting soil salinity. It has also been concluded that saline groundwater used for irrigation and aqua-cultural activities exacerbates soil salinization. The studywill help policymakers/farmers identify the salt degradation problem more effectively and adopt immediate mitigation measures.

Multi-isotopes (δD, δ18Owater, 87Sr/86Sr, δ34S and δ18Osulfate) as indicators for groundwater salinization genesis and evolution of a large agricultural drainage lake basin in Inner Mongolia, Northwest China

Groundwater salinization, a major eco-environmental problem in arid and semi-arid areas, can accelerate soil salinization, reducing crop productivity and imbalances in ecosystem diversity. This study classified water samples collected from the Ulansuhai Lake basin into five clusters using self-organizing maps (SOM). On this basis, multiple isotopes (δ18Owater, δD, 87Sr/86Sr, δ18Osulfate and δ34S) and isotopic models (Rayleigh fractionation and Bayesian isotope mixing models) were used to identify and quantify the genesis and evolution of groundwater salinization. The results showed that the samples were brackish or saline water, and the hydrochemical types were dominated by Na + K-Cl (SO4). It has been proved that the processes associated with groundwater salinization in the Ulansuhai Lake basin were dominated by water-rock interaction and human inputs. Among them, evaporite dissolution contributed substantially to groundwater salinity. Furthermore, salt inputs from human activities cannot be negligible. Based on the model calculations, evaporite dissolution accounted for the most significant proportion of all sources, with a mean value of 53 %. In addition, human inputs from regular agricultural activities (28 % from sewage and manure and 8 % from fertilizers) constituted another vital source of groundwater salinization associated with extensive agricultural activities in the study area. This study's results can deepen our understanding of the genesis of groundwater salinization and the evolution of the agricultural drainage lake basin. This knowledge will assist the Environmental Protection Department in developing effective policies for groundwater management in the Yellow River Basin.

Study on the Removal Effect of 3DEPs on Microplastics in Groundwater and Desalination Mechanism

Owing to water scarcity, groundwater quality has become an important constraint on sustainable regional development, especially in coastal areas. Coastal groundwater is subject to natural conditions and intense human activities, and the evolution of water quality is extremely complex, with the possibility of seawater intrusion into the groundwater, leading to the salinisation of freshwater aquifers. At present, the commonly used methods for removing groundwater pollution include coagulation, adsorption, biological method, membrane technology, etc., among which the coagulation method is more commonly used. At present, three-dimensional electrode electrochemical technology is widely used in high salinity organic wastewater and many organic wastewaters, but there are few studies related to microplastic removal by three-dimensional electrode electrochemical technology using metallic aluminium particles as particle electrodes. The treatment of microplastics in coastal groundwater is a relatively novel issue, and with the increasing application of three-dimensional electrochemical technology in the study of desalination, the three-dimensional electrode electrochemical technology will be promising due to the high salinity of coastal groundwater, the increasingly serious pollution of microplastics, and the fluctuation of water quality and quantity.