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Fluoride and nitrate contamination of groundwater is a major environmental issue in the world's arid and semiarid regions. This issue is severe in both developed and developing countries. This study aimed at assessing the concentration levels, contamination mechanisms, toxicity, and human health risks of NO 3 - in the groundwater within the coastal aquifers of the eastern part of Saudi Arabia using a standard integrated approach. Most of the tested physicochemical properties of the groundwater exceeded their standard limits. The water quality index and synthetic pollution index evaluated the suitability of the groundwater and showed that all the samples have poor and unsuitable quality for drinking. The toxicity of F - in the groundwater within the coastal aquifers of the eastern part of Saudi Arabia using a standard integrated approach. Most of the tested physicochemical properties of the groundwater exceeded their standard limits. The water quality index and synthetic pollution index evaluated the suitability of the groundwater and showed that all the samples have poor and unsuitable quality for drinking. The toxicity of F - was estimated to be higher than NO 3 - . Also, the health risk assessment revealed higher risks due to F - than NO 3 - . Younger populations had higher risks than elderly populations. For both F - and NO 3 . Na-Cl and Ca-Mg-Cl water types predominate in the area. Pearson's correlation analysis, principal component analysis, regression models, and graphical plots were used to determine the possible sources of the water contaminants and their enrichment mechanisms. Geogenic and geochemical processes had greater impact he groundwater chemistry than anthropogenic activities. For the first time, these findings provide public knowledge on the overall water quality of the coastal aquifers and could help the inhabitants, water management authorities, and researchers to identify the groundwater sources that are most desirable for consumption and the human populations that are vulnerable to non-carcinogenic health risks.- , the order of health risk was Infants > Children > Adults. Most of the samples posed medium to high chronic risks due to F - and NO 3 - ingestion. However, negligible health risks were obtained for potential dermal absorption of NO 3 - . Na-Cl and Ca-Mg-Cl water types predominate in the area. Pearson's correlation analysis, principal component analysis, regression models, and graphical plots were used to determine the possible sources of the water contaminants and their enrichment mechanisms. Geogenic and geochemical processes had greater impact he groundwater chemistry than anthropogenic activities. For the first time, these findings provide public knowledge on the overall water quality of the coastal aquifers and could help the inhabitants, water management authorities, and researchers to identify the groundwater sources that are most desirable for consumption and the human populations that are vulnerable to non-carcinogenic health risks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The growing increase in groundwater (GW) salinization in the coastal aquifers has reached an alarming socio-economic menace in Saudi Arabia and various places globally due to several natural and anthropogenic activities. Hence, evaluating the GW salinization is paramount to safeguarding the water resources planning and management. This study presents three different scenarios viz.: real field investigation, experimental laboratory analysis (using ion chromatography (IC) and inductively coupled plasma mass spectrometry (ICP-MS), etc.), and artificial intelligence (AI) based metaheuristic optimization (MO) algorithms in Saudi Arabia. The main purpose of this study is to validate the obtained experimental-based analysis using hybrid MO techniques comprising of adaptive neuro-fuzzy inference system (ANFIS) hybridized with genetic algorithm (GA), particle swarm optimization (PSO), and biogeography-based optimization (BBO) for identification of GW salinization in the coastal region of eastern Saudi Arabia. Additionally, ArcGIS 10.3 software generates the prediction map based on ANFIS-GA, ANFIS-PSO, and ANFIS-BBO. Feature selection was assessed using the PSO algorithm, and four indices evaluated the estimated models, namely, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and standard deviation (SD). The simulated results are based on three variable input combinations, which showed that the ANFIS-PSO (MAE = 0.00439) algorithm had the highest accuracy (99 %), followed by the ANFIS-GA (MAE = 0.00767) and ANFIS-BBO (MAE = 0.0132) algorithms. Besides, Ca 2+ , Na + , Mg 2+ , and Cl - were the most influential parameters. The accuracy also demonstrated the potential reliability of MO algorithms based on spatial distribution mapping. The employed approach proved to be merit and reliable tool for water resources decision-makers in the coastal aquifer of Saudi Arabia. This approach is believed to improve water scarcity as one of the essential targets for Goal 6 of Sustainable Development Vision 2030 and the Kingdom in general., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Seawater intrusion (SWI) is the main threat to fresh groundwater (GW) resources in coastal regions worldwide. Early identification and delineation of such threats can help decision-makers plan for suitable management measures to protect water resources for coastal communities. This study assesses seawater intrusion (SWI) and GW salinization of the shallow and deep coastal aquifers in the Al-Qatif area, in the eastern region of Saudi Arabia. Field hydrogeological and hydrochemical investigations coupled with laboratory-based hydrochemical and isotopic analyses ( 18 O and 2 H) were used in this integrated study. Hydrochemical facies diagrams, ionic ratio diagrams, and spatial distribution maps of GW physical and chemical parameters (EC, TDS, Cl - , Br - ), and seawater fraction ( f sw ) were generated to depict the lateral extent of SWI. Hydrochemical facies diagrams were mainly used for GW salinization source identification. The results show that the shallow GW is of brackish and saline types with EC, TDS, Cl - , Br - concentration, and an increasing f sw trend seaward, indicating more influence of SWI on shallow GW wells located close to the shoreline. On the contrary, deep GW shows low f sw and EC, TDS, Cl - , and Br - , indicating less influence of SWI on GW chemistry. Moreover, the shallow GW is enriched in 18 O and 2 H isotopes compared with the deep GW, which reveals mixing with recent water. In conclusion, the reduction in GW abstraction in the central part of the study area raised the average GW level by three meters. Therefore, to protect the deep GW from SWI and salinity pollution, it is recommended to implement such management practices in the entire region. In addition, continuous monitoring of deep GW is recommended to provide decision-makers with sufficient data to plan for the protection of coastal freshwater resources., Competing Interests: The authors declare no conflict of interest.

The degradation of groundwater (GW) quality due to seawater intrusion (SWI) is a major water security issue in water-scarce regions. This study aims to delineate the impact of SWI on the GW quality of a multilayered aquifer system in the eastern coastal region of Saudi Arabia. The physical and chemical properties of the GW were determined via field investigations and laboratory analyses. Irrigation indices (electrical conductivity (EC), potential salinity (PS), sodium adsorption ratio (SAR), Na%, Kelly's ratio (KR), magnesium adsorption ratio (MAR), and permeability index (PI)) and a SWI index (fsea) were obtained to assess the suitability of GW for irrigation. K-mean clustering, correlation analysis, and principal component analysis (PCA) were used to determine the relationship between irrigation hazard indices and the degree of SWI. The tested GW samples were grouped into four clusters (C1, C2, C3, and C4), with average SWI degrees of 15%, 8%, 5%, and 2%, respectively. The results showed that the tested GW was unsuitable for irrigation due to salinity hazards. However, a noticeable increase in sodium and magnesium hazards was also observed. Moreover, increasing the degree of SWI (fsea) was associated with increasing salinity, sodium, and magnesium, with higher values observed in the GW samples in cluster C1, followed by clusters C2, C3, and C4. The correlation analysis and PCA results illustrated that the irrigation indices, including EC, PS, SAR, and MAR, were grouped with the SWI index (fsea), indicating the possibility of using them as primary irrigation indices to reflect the impact of SWI on GW quality in coastal regions. The results of this study will help guide decision-makers toward proper management practices for SWI mitigation and enhancing GW quality for irrigation. [ABSTRACT FROM AUTHOR]

Groundwater (GW) salinization is among several hydro-environmental processes that essentially influence water resources management, freshwater supply, water scarcity and sustainability. Machine Learning (ML) is an effective and robust tool for GW monitoring, primarily when chaotic and interrelated parameters influence the system. The current study presents the first inverse modelling of GW salinization in sandstone aquifer using stand-alone (Elman NN, GRNN, BPNN, and ILR) models coupled with nonlinear ensemble ML (GPR-E, SVR-E, and ANFIS-E) techniques. For this purpose, 25 input parameters were imposed on data mining; subsequently, inversely correlated variables including bicarbonate (HCO 3 −), nitrate (NO 3 −), Temperature (Temp), Lithium (Li), and Manganese (Mn) were used for modelling the Electrical conductivity (EC). Neuro-sensitivity feature selection (NSFS) ranking was used to determine the input combinations. The Modeling schema was based on the actual field and experimental data. The results indicated the potential of GRNN-C1 with a value of NSE = 0.998, and 0.928 in both calibration and validation phases, respectively. The outcomes revealed weaknesses in all other stand-alone models with low efficiency ranged from 0 to 60%. The attained ensemble results proved promising for all, with ANFIS-E-C1 (RMSE = 2.993, MAE = 0.879, and NSE = 0.999) emerging best in the validation phase. The overall proposed ensemble learning proved the excellent capability of ML to handle complex GW processes and served as a reliable predictive tool for water resources management. The contribution-based methodologies explored in this study involved inverse modelling, nonlinear feature selection, and nonlinear ensemble techniques, which was the first study in GW salinization. [ABSTRACT FROM AUTHOR]

The global need for freshwater resources by densely populated regions has resulted in the excessive exploitation of groundwater (GW), subsequently leading to seawater intrusion (SWI) predisposed to the influences of rising sea levels and changing climates. This research proposes a state-of-the-art review of SWI to develop proper coastal integrated water resource management. For these purposes, a detailed review of the long research period on SWI in terms of conceptual advancement, identification techniques, and protection and mitigation procedures is presented. Past, present, and future improvements of SWI were analysed from the available literature and comprehensive bibliographic analysis based on the largest available database (Scopus) was performed from 1970 to March 2022. We found that hydrogeological, hydrochemical, isotopic, and geophysical methods accounted for 7.2%, 8.1%, 3.7%, and 9.2% for SWI assessment studies, respectively. Moreover, SWI modelling, and simulation received the closest attention, accounting for 49.4% and 22.3%, respectively. The outcomes also revealed that China, the United States of America (USA), Australia, India, and Italy are the countries with the highest contributions to the field of SWI, accounting for 17%, 14%, 5.6%, 5%, and 4.5% of research, respectively. The obtained results also suggest further developments for enhancing the management of SWI in the affected regions. For Saudi Arabia, we found that more research is required for SWI assessment and management. [ABSTRACT FROM AUTHOR]

Reliable modelling and simulation of groundwater management are crucial for sustainable development. Groundwater salinization is considered challenging and has recently led to the development of several emerging advancements and technologies, which grant a feasible solution to integrated water management and desalination processes. For this purpose, Electrical Conductivity (EC) as the early Salinization sign is modelled using various computational techniques, namely, Least Square-boost (LSQ-Boost), Gaussian Process regression (GPR), support vector regression (SVR) and stepwise linear regression (SWLR). The experiment data from sandstone aquifers include parameters from the physical, chemical and hydrogeochemical aspects. Four different input combinations (C1-C4) were developed using linear and ranking nonlinear feature selection and validated modelling results weres assessed by mean square error (MSE), mean absolute error (MAE), root means square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (R). The analysis also considers the effect of multicollinearity, and the variables affected, such as TDS (mg/L), were not included in the first three combinations. The novel GPR proved superior to other models, with GPR-C1 justified quantitatively (MSE = 0.0255, MAE = 25260.49 and RMSE = 0.1595) in the verification phase. Other intelligent models (SVR, LSQ-Boost) depicted promising for C3 and C4 combinations with more than 88–90% predictive accuracy. The explored novel GPR algorithm offered an excellent and reliable EC prediction tool. The study also suggested using direct correlated positive variables, including hydrochemical and topographic factors, in modelling groundwater salinization. This would lead to more effective water-resources-related planning and decision making. [ABSTRACT FROM AUTHOR]

Groundwater (GW) is a critical freshwater resource for billions of individuals worldwide. Rapid anthropogenic exploitation has increasingly deteriorated GW quality and quantity. Reliable estimation of complex hydrochemical properties of GW is crucial for sustainable development. Real field and experimental studies in an agricultural area from the significant sandstone aquifers (Wajid Aquifer) were conducted. For the modelling purpose, three types of computational models, including the emerging Hammerstein–Wiener (HW), back propagation neural network (BPNN), and statistical multi-variate regression (MVR), were developed for the multi-station estimation of total dissolved solids (TDS) (mg/L) and total hardness (TH) (mg/L). A geographic information system (GIS) was used for the spatial variability assessment of 32 hydrochemical and physical properties of the GW aquifer. A comprehensive visualized literature review spanning several decades was conducted in order to gain an understanding of the existing research and debates relevant to a particular GW and artificial intelligence (AI) study. The experimental data, pre-processing, and feature selection were conducted to determine the most dominant variables for AI-based modelling. The estimation results were evaluated using determination coefficient (DC), mean bias error (MBE), mean square error (MSE), and root mean square error (RMSE). The outcomes proved that TDS (mg/L) and TH (mg/L) correlated more than 90% and 70–85% with Ca2+, Cl−, Br−, NO3−, and Fe, and Na+, SO42−, Mg2+, and F− combinations, respectively. HW-M1 justified promising among all the models with MBE = 1.41 × 10−11, 1.14 × 10−14, and MSE = 7.52 × 10−2, 3.88 × 10−11 for TDS (mg/L), TH (mg/L), respectively. The accuracy proved merit for the overall development of and practical estimation of hydrochemical variables (TDS, TH) (mg/L) and decision-making benchmarks. [ABSTRACT FROM AUTHOR]

The Wajid aquifer is considered the main source of water for drinking and irrigation in Wadi Al-Dawasir and Najran, the southern region of Saudi Arabia. This aquifer has been used since the 1960s, and due to the expansion in agricultural activities, the aquifer has been overexploited. The study aims to understand the origin, hydrochemical processes of the groundwater in the shallow unconfined, deep unconfined, and confined parts of the Wajid aquifer in the Wadi Al-Dawasir area. In-situ hydrochemical parameters (pH, temperature, EC, and TDS) were measured in the field, and groundwater samples were collected for major ions and stable isotopes (2H and 18O) measurements in the laboratory. The results show that the groundwater in shallow unconfined, and confined aquifers are of two types; Cl.SO4-Ca. Na and Cl.SO4-Na. Ca; however, groundwater in deep, unconfined aquifers is characterized as HCO3-Ca. Na, and Cl. HCO3-Ca. Na; types of groundwater. The isotopic analysis results reveal that all groundwater samples have values of δ18Oand δ2Hclose to the local and global meteoric water lines, indicating the meteoric origin of Wajid groundwater. Three major hydrochemical processes, including rock weathering, ion exchange, and evaporation, have been identified as key controls on the chemical composition of water in the studied aquifer. The evaporation and ion exchange processes have more influence on the chemical composition of groundwater in the shallow unconfined and confined aquifers. On the contrary, weathering of carbonate minerals affected more the chemistry of groundwater in a deep unconfined aquifer. The unconfined section of the Wajid aquifer shows a reverse pattern of salinity with higher salinity in the recharge area, which is most probably related to the return irrigation water and leaching of salty soil. The open fractures in the upper part of Wajid sandstone most likely act as conduits to percolated saline water to the Wajid aquifer. [ABSTRACT FROM AUTHOR]

In arid countries such as Saudi Arabia, aeolian sand often covers a large area of the country. Understanding the variations of sand properties in dunes, including grain size, sorting, mineral composition and water content, can be important for groundwater recharge, environmental, and construction applications. Earlier studies examined properties of sand dunes by collecting samples from the surface. This study aims to investigate variations of sand properties within a Barchan sand dune in the coastal area of Saudi Arabia, by collecting samples and measurements from two vertically drilled boreholes up to the ground water level; one drilled in the dune crest and another one in the limb. Representative samples were collected and analyzed for their texture parameters, water content, and mineralogy. Electrical resistivity survey data was also acquired to map water content variation in the dune limb, and for comparison with well bore data. The reported results show no vertical variations in grain size or sorting in the dune crest. In contrast, the upper 0.5 m of the dune limb shows a relatively poorer sorting than found in deeper parts of the dune. Laterally, no variations in minerology were observed between crest and limb sands while grain size tended to be slightly coarser in the dune limb compared to the crest. Regarding the water content, it was found to vary vertically, probably due to previous cycles of rainfall infiltration through the sand body. Such observed variation in water content is consistent with the measured resistivity profile which could clearly identify the water table and areas with higher water content. This study concludes that beyond the upper 0.5 m, the Barchan sand dune body can be treated as a homogeneous medium in terms of mineralogy and sorting while grain size increases slightly toward the limb side. [ABSTRACT FROM AUTHOR]

Worldwide, water management decisions are usually depending on the in-situ observation networks, which decline in coverage recently. The decision-makers need information about the groundwater resources for optimum management. Due to the incompleteness of ground-based observations, space-based observation of global freshwater resources is critical. GRACE (GRAVITY RECOVERY AND CLIMATE EXPERIMENT) satellite mission has become very important water management tool since its initiation many years ago. GRACE detects lateral variations in the gravitational potential in the earth, which is mostly caused by changes in terrestrial water storage or ΔTWS (Terrestrial Water Storage variation). The main objective of this study was to analyze the spatiotemporal groundwater variations using GRACE in the Saudi Arabia from 2007 to 2016 and to compare the GRACE result with the in-situ observations. The result of this study revel that the groundwater depletion in north-west and central Saudi Arabia estimated to be about 7.848 ± 0.44 mm/year per year. Groundwater Depletion in the Eastern Province Arabia estimated to be about 6.384 ± 0.409 mm/year. In this study, spatial down-scaling of the coarse scale GRACE data was applied directly using spatial correlation through variograms analysis. Sequential Gaussian Simulation (SGS) was used and multi realizations were apply for uncertainty quantification for different scenarios. In Saudi Arabia, freshwater over-exploitation for irrigation purpose attributed to groundwater depletion instead of climate variability. It is worth to say that the use of GRACE and other satellite data for monitoring the groundwater charge and discharge regionally could enhance by integrating them with the land surface models. [ABSTRACT FROM AUTHOR]