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Original article
11 2023
:35;
102847
doi:
10.1016/j.jksus.2023.102847

Assessment of the Biyadh groundwater quality and geochemical process in Saudi Arabia using statistical, modelling, and WQI methods

 Geology and Geophysics Department, College of Science, King Saud University, Saudi Arabia

⁎Corresponding author at: Department of Geology and Geophysics, College of Science, King Saud University, P.O. box 2455, Riyadh 11451, Saudi Arabia. tgalharbi@ksu.edu.sa (Talal Alharbi)

Received: , Accepted: ,
© The Author(s) 2023
Disclaimer:
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.

Peer review under responsibility of King Saud University.

Abstract

This research aimed to understand the variations in the groundwater quality and hydrochemical processes in the Biyadh aquifers in central Saudi Arabia. The Biyadh Aquifer in the Riyadh area is the primary natural water resource for Wadi Sahba and Wasia Well Fields. The present study collected thirty groundwater samples from the Biyadh aquifer in the outcrop and confined parts. The samples were evaluated by multivariate statistical methods and hydrochemical modelling to understand the geochemical processes that control the groundwater. Also, it used various indexes to find the groundwater's suitability for drinking, agricultural, and industrial purposes. The analysis revealed a general evolution in groundwater quality as groundwater flowed east and northeast. The TDS increases from 1730 mg/l in WS to 2370 mg/l in WWF. The groundwater facies developed from Ca-Mg-SO4-Cl to Ca-Na-Mg-SO4-Cl in WS and ended with Ca-Na-SO4-Cl in WWF. Applying Pearson's correlation matrix, cluster analyses, and factor analyses indicate that the ions significantly influence groundwater mineralization. The geochemical modelling revealed that the dissolution of the calcite, halite, anhydrite, and gypsum minerals increases with the direction of groundwater flow. In addition, the agriculture activities in Wadi Sahba mixed with irrigation return water to groundwater and increase nitrate (NO3) concentration. Biyadh groundwater has a better quality for drinking in the confined aquifer than in the unconfined aquifer, and both areas are suitable for irrigation. The TH indicates that the groundwater becomes more suitable for industrial purposes as it flows toward the confined part of the aquifer.

Keywords

Groundwater quality
Geochemical process
Water Quality Index
Saudi Arabia
Biyadh aquifer
1

1 Introduction

Groundwater is a significant component of the Earth's water cycle and is essential for sustaining human life. This critical resource is being challenged by urbanization, climate change, and population growth. As there are issues obtaining sufficient water to meet the increasing demand, groundwater research is becoming more crucial to understanding the long-term sustainability of water resources, how climatic change affects the water cycle, how natural disasters affect groundwater sources, and how groundwater quality affects human health (Richey et al., 2015; DeNicola et al., 2015).

Groundwater plays a vital role in the economic and social development of arid countries like Saudi Arabia. With rising demands for water, especially from agriculture and urban sectors, groundwater resources must be managed to ensure balanced use and sustainable withdrawal. Riyadh, the capital of Saudi Arabia, is experiencing rapid economic growth due to tourism projects, resulting in abundant jobs and increased internal migration rates (Almatar, 2022). This continuous increase in population was accompanied by high groundwater exploitation, which affected its quantities and qualities (Alzahrani et al., 2022; Alharbi & Zaidi, 2018).

Wasia Well Field (WWF) and Wadi Sahba (WS) are the two important sites for providing water to Riyadh. They extract groundwater from the Biyadh aquifer in central Saudi Arabia. WWF provides 20% of the city's drinking and domestic needs, besides the 80% provided by desalination plants in the Arabian Gulf. Wadi Sahba is the main irrigation water for the Al Kharj area's agricultural fields and the primary food product supplier for Riyadh city (Al-Omran et al., 2015; Al-Harbi & Hussain, 2009). After decades of production, we need to check the groundwater quality in the Biyadh aquifer (Fallatah, 2020).

Previous studies of groundwater in Wadi Sahba and Wasia Well Field (Al-Harbi & Hussein, 2009; Alharbi & Zaidi, 2018; Khogali et al., 2020; Zaidi et al., 2016; Alfaifi et al., 2017) focused on ionic relationships and groundwater types to understand the hydrogeochemical processes that influence the Biyadh groundwater. Multivariate statistical analysis, particularly cluster and factor analysis, was used to determine the underlying processes driving groundwater chemistry in the aquifer. None of these studies investigates the differences in groundwater quality in Biyadh between the unconfined aquifer in WS and the confined beds in WWF.

It is crucial to conduct research tracking the spatial changes in the Biyadh groundwater quality from the outcrop to the confined beds. The current research addresses how the groundwater quality changed from the WS to the WWF regarding chemical processes and mineral saturation. Besides, the study will determine the groundwater's suitability for drinking, agricultural, and industrial uses. These objectives were achieved by determining the groundwater types using a Piper diagram, using various statistical techniques, identifying hydrogeochemical trends, and comparing the geochemistry in WWF and WS.

2

2 Study area

The research region is located in Saudi Arabia's centre, between 47° − 48oE and 24°−25.30oN. The research focuses on two sites, Wadi Sahba and Wasia Well Field (Fig. 1).

(a) Map showing the location of the study area and the drainage system. (b) Map showing the sampling locations of the Wasia Well Field (WWF) and the groundwater level map for the Biyadh aquifer in WWF. (c) Map showing the sampling locations of the Wadi Sahba (WS) and groundwater level map for the Biyadh aquifer in Wadi Sahba.
Fig. 1
(a) Map showing the location of the study area and the drainage system. (b) Map showing the sampling locations of the Wasia Well Field (WWF) and the groundwater level map for the Biyadh aquifer in WWF. (c) Map showing the sampling locations of the Wadi Sahba (WS) and groundwater level map for the Biyadh aquifer in Wadi Sahba.

2.1

2.1 Topography and geology

The study area is underlain by series of sedimentary formations interrupted by Wadi Sahba, major depression in the south of WWF. This wadi primarily consists of recent deposits such as silt, sand, and conglomerate (Powers et al. 1966; BRGM, 1976; Alharbi & Zaidi, 2018). The area's elevation from west to east ranges from 954 m to 337 m AMSL. The lowest height in the area is the WS's alluvial sediments in the south and southeast (Fig. 1a). The Biyadh formation outcrop in the Wadi Dawasir is about 515 m; it decreases in thickness to 360 in the north of WS, then gradually disappears in the north. (Powers et al. 1966; BRGM, 1976). The Biyadh formation consists of conglomeratic to fine-grained sandstone with siltstone, mudstone, and claystone beds (Fig. 2). The Biyadh formation average thickness in WS is 400 m and 425 m in WWF (Keller et al., 2019; Jaju et al. 2016).

Geological map of the Biyadh formation and the main formation in the study area.
Fig. 2
Geological map of the Biyadh formation and the main formation in the study area.

2.2

2.2 Hydrogeology and climate

The Biyadh aquifer is recharged by rainfall from the outcrop area and adjoining valleys in the area (Fig. 1a). As a result that the Biyadh is in an arid region, a low amount of rainfall is received yearly. Researches revealed that the yearly average rainfall in Saudi Arabia is 59 mm/y, and about 1.8 mm is recharged to the aquifers (Uitto & Schneider, 1997; Alsharhan et al., 2001).

The groundwater level of the Biyadh Aquifer in WS ranges from 325 to 275 m AMSL, with groundwater flowing from the west to east (Fig. 1c). In the WWF, the Biyadh Aquifer water level is below 285 m AMSL (Fig. 1b). From the WS to WWF, the water level is decreasing and follows with the regional topography. The Biyadh groundwater depth below ground level varies in WS from 92 to 130 m and 270 to 285 m in WWF.

The study area has a high seasonal record range. In the winter season, from December to March, the temperature in day and night times varies between 21 and 28 °C and 6–12 °C, respectively. This season, the humidity has high records reach 54%, but the evaporation records in its lowest levels, reaching 3 mm. From May to August, the temperature and evaporation records are very high, 41 °C and 12 mm, respectively, and the humidity has a minimum record rate of 27% because there is little to no rain during the summer (Alharbi & Zaidi, 2018).

3

3 Data sources and methods

The hydrochemical and hydrological data used in this study were collected from two sources; the first was obtained from geological field trips to WS in the Al Kharj area. In these trips, thirteen groundwater samples were collected, and hydrogeological records were measurements for this study. The Ministry of Environment, Water, and Agriculture provided the second data source. These data include hydrological and hydrochemical data for seventeen groundwater wells in WWF. These wells are mainly pumped from the Biyadh aquifer. The two sources provide 30 groundwater samples that were studied in this research.

Using portable meters, TDS, EC, and pH were measured on field trips. Besides, groundwater samples were collected in polyethylene bottles from the wells after they had been pumped for 10 min to prevent contamination. Chemical tests were done on the groundwater samples in the Central Lab Facility in the College of Science at King Saud University. These tests were done in line with APHA (2005) standards. The ionic balance equation is used to confirm the chemical analysis's correctness and remove all groundwater samples with errors exceeding ±5%. After this process, all the 30 groundwater samples in this study were approved (Supplementary table).

Several software programs were used to analyze the groundwater chemical analysis data in order to identify the groundwater facies, compute the minerals' saturation indices, and suggest their suitability for different purposes. They were also employed in the statistical analysis to show the correlations between the chemical ions and determine the key components in groundwater chemistry. The water quality was calculated and statistically analyzed using IBM SPSS 23, a software package that enables advanced statistical analysis, and Microsoft Excel, which provides spreadsheets to create formulas for data calculations. Geochemical modeling was done with the help of PHREEQC, a computer program that simulates chemical reactions in water, and Aquachem, a program that analyzes data about water quality. The digitizing, creating, and displaying maps were done with ArcGis 10.7.

4

4 Results and discussion

4.1

4.1 Hydrochemical characteristics

The physical and chemical hydrochemical parameters determined at WWF and WS sites are summarized in Table 1. The total dissolved solids mean in WWF is 2370 mg/l and 1729 mg/l in WS. Calcium, with a mean content of 340 mg/l in WWF, is the most abundant cation at both locations, followed by sodium, with a mean concentration of 203 mg/l, and magnesium, with a mean concentration of 117 mg/l. Sulfate is the most abundant anion in the WWF and WS, with mean values of 340 and 771 mg/l, respectively. Sulfate has a wide range in WS, where the minimum value is 888 mg/l, and the maximum is 4752 mg/L. After sulfate, chloride had a mean concentration of 501 mg/l in WWF and 825 mg/l in WS. The bicarbonate ion follows up chloride ion with mean concentrations value 179 mg/l in WWF and 231 mg/l in WS. Nitrate varies from minor amounts 4 to 17 mg/l in WWF to higher values reach 46 mg/L in WS, which exceeds the prescribed limits by the Saudi Standards, Metrology and Quality Organization (SASO 2000) and WHO (2011).

Table 1 Showing descriptive statistics of groundwater samples (N = 30) in the study area.
Site Statistic TDS EC Ca Mg Na Cl HCO3 SO4 NO3
WWF Mean 2370 3121 340 117 203 501 179 883 7
Median 2493 3278 344 107 224 520 179 900 6
Standard Deviation 364 459 44 37 66 76 17 129 3
Range 1207 1558 192 136 228 252 81 475 11
Minimum 1520 2006 240 53 70 338 149 625 4
Maximum 2727 3564 432 190 298 590 229 1100 15
Count 17 17 17 17 17 17 17 17 17
WS Mean 1729 3527 771 65 422 825 231 1807 37
Median 1888 3850 609 54 378 708 224 1430 41
Standard Deviation 520 1060 520 37 325 566 25 1321 14
Range 1571 3204 1524 111 969 1727 83 3864 40
Minimum 875 1786 406 36 156 323 202 888 6
Maximum 2446 4990 1930 147 1125 2050 285 4752 46
Count 13 13 13 13 13 13 13 13 13

4.2

4.2 Hydrochemical classification

In this research, the groundwater facies of the Biyadh Aquifer were categorized using a Piper diagram (Fig. 3). The cationic triangle shows that all the samples from WWF and WS fall into the Ca-dominant type and SO4-dominant in the anionic triangle, indicating the dissolution of gypsum, anhydrite, and calcium minerals. Nevertheless, most groundwater facies of the Biyadh aquifer in the WWF site classified into the Ca-Na-SO4-Cl type (15 samples), and only two samples have Ca-Mg-SO4-Cl type. These groundwater facies show the influence of the dissolution of gypsum and anhydrite minerals. In the WS site, the groundwater types shift with the direction of groundwater flow. In well number 18, the water type is Ca-Mg-SO4-Cl and becomes Ca-Mg-Na-SO4-Cl in well number 24 and finally develops Ca-Na-Mg-SO4-Cl in well number 28. This shift suggests that the dissolving rate of halite minerals in WS rises with the direction of groundwater flow.

Piper’s Trilinear Diagram for wells located in Wadi Sahba and Wasia Well Field.
Fig. 3
Piper’s Trilinear Diagram for wells located in Wadi Sahba and Wasia Well Field.

4.3

4.3 Correlation matrix

The Correlation coefficients among ions could help identify the processes contributing to groundwater salinization (Helsel & Hirsch,1992). Table 2 presents the results of the analyses. The WS groundwater exhibit a typical positive relationship between EC-TDS and a strong positive relationship between Ca-Cl, Ca-SO4, Mg-Cl, Mg-SO4, Cl-SO4, and HCO3-NO3, which demonstrates that the mineralization has a major impact on groundwater. The strong relationship between Ca and Cl may occur because of limestone rocks' reaction with hydrochloric acid to increase Ca and Cl ions in the groundwater. Calcium and sulfate have strong positive relationships, which could mean that Ca and SO4 ions come from the dissolution of anhydrite and gypsum.

Table 2 Showing the Pearson correlation matrix of the groundwater samples in WS and WWF.
Site Statistic EC TDS Ca Mg Na Cl HCO3 SO4 NO3
WS EC 1.00
TDS 1.00 1.00
Ca 0.20 0.22 1.00
Mg 0.68 0.70 0.58 1.00
Na 0.01 0.03 0.38 −0.05 1.00
Cl 0.52 0.54 0.87 0.71 0.55 1.00
HCO3 −0.27 −0.25 0.54 0.30 0.56 0.51 1.00
SO4 0.38 0.41 0.87 0.71 0.58 0.93 0.65 1.00
NO3 −0.25 −0.23 0.51 0.12 0.41 0.39 0.71 0.47 1.00
WWF EC 1.00
TDS 0.73 1.00
Ca 0.67 0.28 1.00
Mg 0.38 0.76 0.35 1.00
Na 0.73 0.35 0.98 0.31 1.00
Cl 0.77 0.44 0.98 0.45 0.98 1.00
HCO3 0.61 0.01 0.89 −0.07 0.87 0.84 1.00
SO4 0.68 0.32 1.00 0.40 0.98 0.99 0.85 1.00
NO3 0.17 −0.37 0.33 −0.38 0.40 0.25 0.45 0.33 1.00

Moreover, the strong relationship between Mg with Cl and SO4 may imply that fertilizers are a dominant contributor of these ions. However, there is no strong negative relationship found between all ions. HCO3 and NO3 have a strong relationship. The possible interpretation of this relation is the agriculture activities in WS. When mixed with fertilizers in agricultural areas, irrigation water infiltrates through the rock layers and may reach the groundwater, which increases its concentration of NO3. The presence of organic matter increases carbon dioxide and is then mixed with groundwater to produce carbonic acid and bicarbonate.

The results of Pearson's correlation matrix for WWF wells are shown in Table 2. It demonstrates a significant positive correlation between Ca-Na, Ca-Cl, Ca-HCO3, Na-Cl, Na-HCO3, Na-SO4, Cl-HCO3, Cl-SO4, HCO3-SO4; and a typical strong relationship between Ca-SO4. These relationships revealed that groundwater mineralization is affected by these ions. The strong relationship between Ca and Cl may occur because of limestone rocks' reaction with hydrochloric acid to increase Ca and Cl ions in the groundwater. The Ca against SO4 shows typical strong positive relationships, indicating that anhydrite and gypsum's dissolutions are the Ca and SO4 ions' natural sources.

Furthermore, Na and Cl display a strong correlation, indicating that the dissolution of the halite mineral is the source of the two ions. In the WWF, the relationship between HCO3 and NO3 was different from the WS site. It is characterized as medium to week relationship. Since the WWF samples were taken from a greater depth than the WS samples, the shallow aquifer's groundwater had no effect on the NO3 concentration.

4.4

4.4 Multivariate statistical methods

Multivariate statistical analyses consist of several methods applied to several practical investigations, including the classification of hydrochemical facies (Belkhiri & Narany, 2015). These methods were proposed to be used for the classification of hydrochemical facies. This analysis involves Cluster Analysis (CA) and Factor Analysis (FA) (Pathak, 2012). These two techniques better understand the groundwater systems and the hydrochemical processes that govern them through data reduction and classification (Kolsi et al., 2013; Yidana et al., 2010). The analysis in these methods involves concentrations of chemical species (cations: calcium (Ca), magnesium (Mg), sodium (Na); and anions: bicarbonate (HCO3), chloride (Cl), sulfate (SO4), and Nitrate (NO3).

Cluster analysis attempts to find the group of variables based on attribute information about the variables and display them in groups on a tree diagram known as a dendrogram, which showing the grouping according to the order in which they were joined during clustering (Aggarwal & Reddy, 2013). Groundwater samples from Biyadh aquifer in WWF and WS sites were clustered using the SPSS software package. Fig. 4 revealed the resulting dendrogram for the variables. It was interpreted to have classified the major ions in 30 groundwater samples into four groups using seven variables. The first group shows a similarity between Mg, NO3, and HCO3, which probably represents the effects of agriculture fertilizers on groundwater in WS. The second group is represented by Na, which derive from the dissolution of halite mineral in the WWF and Sahba groundwater. The third group includes Ca, Cl, which probably means the effects of limestone rocks' reaction with hydrochloric acid. Finally, the fourth group is represented by SO4, which could correspond to the dissolution of anhydrite and gypsum.

A Dendrogram showing the clustering of elements linked by their geochemical similarity within the samples.
Fig. 4
A Dendrogram showing the clustering of elements linked by their geochemical similarity within the samples.

Factor analysis was used to find out what ions have in common and group them into small components (Kim et al., 2005). The most used approach to factor analysis is Principal Component Analysis (PCA). It works to find a linear set of variables. Then the maximum variance is removed from the variables and seeks a second linear combination that explains the maximum proportion of the remaining variance (Senapathi et al., 2019). The analysis yields two factors that are responsible for about 93% of the variation in groundwater chemistry. Factor 1 controlled 71% of the groundwater chemistry and has high loadings on Ca, Na, Cl, HCO3, SO4 (Fig. 5). This component indicates the effect of these ions on the overall mineralization of groundwater and the dissolution of calcite and gypsum minerals. Factor 2 has high positive loading on Mg that anhydrite can explain its significant effect on 22% of groundwater chemistry variation.

Principal component analysis (PCA) plot showing the similarities and dissimilarities among the elements.
Fig. 5
Principal component analysis (PCA) plot showing the similarities and dissimilarities among the elements.

4.5

4.5 Hydrochemical modelling

PHREEQC is a Hydrochemical model extensively used to understand and calculate the saturation indices of groundwater with respect to mineral phases (Parkhurst & Appelo, 1999). Through thermodynamic calculations of ionic activities, it could indicate the equilibrium state between groundwater and aquifer materials. The United States Geological Survey created this program for the aqueous elements’ dissolution and precipitation to solid phases.

4.6

4.6 Saturation indices

The saturation index (SI) can measure the equilibrium state between minerals and groundwater. When the SI of a mineral is zero, it means that the aquifer's groundwater is in an equilibrium state concerning that mineral. If it is less than zero, groundwater is undersaturated and can dissolve the mineral. However, if SI is more than zero, this means the water is oversaturated for that mineral and can precipitate it (Saleh et al., 1999).

The following equation calculates the saturation index:

(1)
Saturation i n d e x SI = l o g IAP / K s where IAP is the ionic activity of the ions and Ks is the mineral's solubility. The saturation state of the minerals: anhydrite, aragonite, calcite, dolomite, and gypsum are shown in Fig. 6. The SI of calcite ranges in the Biyadh aquifer from −0.13 to 0.58 with an average of 0.09. Most of the samples are oversaturated on the WWF site. The exception is that well number 14 is in an equilibrium state, and wells number 15, 16, and 17 are undersaturated. And vice versa, all the samples in the WS site are undersaturated for calcite minerals. The saturation index for the aragonite mineral shows that three samples are saturated, nine are undersaturated, and well number 22 is oversaturated. The calculated average of dolomite SI is −0.27, where the minimum value is −0.71, and the maximum is 0.33. Most of the samples are undersaturated, except wells number 6, 12, and 22 are oversaturated. The SI for gypsum and anhydrite minerals are all under saturated except for two wells, 6 and 12. The average value for gypsum is −0.28, and it is −0.51 for anhydrite. The saturation state for these minerals imply that the groundwater has the potential to dissolve them. Furthermore, Fig. 6 shows that as the groundwater moves away from the outcrop site in WS towards the WWF, it undergoes more dissolution processes until it reaches saturation.
X-Y plot showing the saturation state for anhydrite, aragonite, calcite, gypsum, and anhydrite minerals in WWF and WS sites.
Fig. 6
X-Y plot showing the saturation state for anhydrite, aragonite, calcite, gypsum, and anhydrite minerals in WWF and WS sites.

4.7

4.7 Groundwater quality for Drinking, Irrigation, and industrial purposes

4.7.1

4.7.1 Groundwater quality for drinking purpose

At WWF and WS sites, the study evaluated Biyadh groundwater for drinking, irrigation, and industry usage (Bhunia et al., 2018). The Water Quality Index (WQI) is widely used to define and rank the suitability of groundwater to drink (Abbasnia et al., 2018; Gabr et al., 2021). This equation required calculation in several steps and evaluated based on Saudi Standards, Metrology, and Quality Organization (Table 3) (SASO 2000; Bhunia et al., 2018; Lateef, 2011).

Table 3 Weight and Relative Weight for Each Parameter in the study area.
Physicochemical parameter SASO (2000) Weight (wi) Relative weight (Wi)
pH 6.5–8.5 4 0.12
TDS 1,000 5 0.15
Ca2 200 3 0.09
Mg2 150 3 0.09
Na 200 4 0.12
Cl 250 5 0.15
SO4 250 5 0.15
NO3 50 5 0.15

The parameters used for the calculations involve pH, TDS, Ca, Mg, Na, K, Cl, SO4, and NO3 (Annapoorna & Janardhana, 2015). These parameters are assigned weights depending on their importance on the water quality for drinking and health effects (Ugochukwu et al., 2019). The parameters have the highest rank of five, including TDS, Cl, SO4, and NO3, since they have a significant role in assessing water quality for drinking (Lateef, 2011); Na and pH are given weight four and three for Ca and Mg (Table 3). TDS, Cl, SO4, and NO3, are the primary parameters in determining the quality of drinking water (Gabr et al., 2021; Abbasnia et al., 2018). SASO 2000 defined that concentrations that exceed the maximum permissible level of these ions in water are not toxic to drinking but may affect people's health (Shil et al., 2019). After weighting the concentrations, the Wi values calculated using the following equation:

(2)
Wi = w i / ∑ i = 1 n w i

The equation computes the relative weight of each variable, wi is the variable weight, Wi is the relative weight, and n is the number of variables in the equation.

The quality rating scale (qi) for the variables is calculated next by dividing the water sample concentration level by the SASO 2000 standard and multiplying by 100, as indicated in the following equation:

(3)
qi = Ci / S i × 100

In the equation, qi indicates the quality rating, Si is the SASO 2000 standard for drinking water parameters in mg/l, and Ci is the parameter concentration in mg/l (Lateef, 2011). SIi computes the chemical parameter based on the two flowing equations, and SI is then used to calculate WQI.

(4)
SIi = W i × q i
(5)
WQI = ∑ i = 1 n S I i

SIi represents the parameter's subindex, qi represents the quality rating, and WQI is the water quality type (Mahmud et al., 2020). Based on the Water Quality Index, the groundwater categorize as excellent, good, poor, very poor, and unsuitable when its value range <50, 50–100, 100–200, 200–300, and >300, respectively. The result of the WQI calculations presents in Table 4. It shows WQI values range from 120 to 184 on the WWF site, and all its samples are classified as poor drinking water. In contrast, the WQI for WS ranges from 134 to 618 and has 38% of the samples classified as poor water, 46% as very poor water, and 15% water unsuitable for drinking. Generally, The WWF shows poor water in its groundwater related to the strong water–rock interaction in the Biyadh Aquifer. WS site has poor drinking water quality compared to the WWF because of the groundwater's overexploitation and agricultural activities.

Table 4 Showing the quality of the groundwater in the study area for drinking, irrigation, and industrial purpose.
No. Aquifer WQI Type of water SAR SAR Classif. Na% Na% Classif. MH MH Classif. PI PI Classif. TH TH Classif.
1 WWF 120 Poor 2.79 Very good 32 Good 32 Suitable 35 Suitable 53 Soft
2 WWF 173 Poor 1.51 Very good 16 Excellent 45 Suitable 18 Unsuitable 103 Moderately hard
3 WWF 120 Poor 2.11 Very good 26 Good 23 Suitable 29 Suitable 54 Soft
4 WWF 169 Poor 3.52 Very good 34 Good 38 Suitable 36 Suitable 76 Moderately hard
5 WWF 130 Poor 1.67 Very good 20 Excellent 32 Suitable 23 Suitable 68 Soft water
6 WWF 155 Poor 2.90 Very good 30 Good 34 Suitable 32 Suitable 72 Soft water
7 WWF 168 Poor 2.64 Very good 26 Good 35 Suitable 29 Suitable 83 Moderately hard
8 WWF 168 Poor 2.91 Very good 29 Good 34 Suitable 31 Suitable 78 Moderately hard
9 WWF 158 Poor 1.45 Very good 16 Excellent 41 Suitable 18 Unsuitable 92 Moderately hard
10 WWF 169 Poor 3.53 Very good 33 Good 26 Suitable 36 Suitable 72 Soft
11 WWF 165 Poor 1.52 Very good 16 Excellent 45 Suitable 19 Unsuitable 98 Moderately hard
12 WWF 166 Poor 3.15 Very good 31 Good 35 Suitable 33 Suitable 76 Moderately hard
13 WWF 165 Poor 0.74 Very good 8 Excellent 46 Suitable 11 Unsuitable 109 Moderately hard
14 WWF 171 Poor 2.73 Very good 27 Good 32 Suitable 29 Suitable 82 Moderately hard
15 WWF 184 Poor 2.41 Very good 23 Good 38 Suitable 25 Suitable 97 Moderately hard
16 WWF 183 Poor 2.68 Very good 26 Good 36 Suitable 28 Suitable 92 Moderately hard
17 WWF 178 Poor 3.32 Very good 30 Good 29 Suitable 32 Suitable 90 Moderately hard
18 WS 228 Very poor 3.93 Very good 32 Good 12 Suitable 34 Suitable 93 Moderately hard
19 WS 614 Unsuitable 6.64 Very good 31 Good 11 Suitable 32 Suitable 290 Hard
20 WS 230 Very poor 3.94 Very good 32 Good 13 Suitable 34 Suitable 94 Moderately hard
21 WS 199 Poor 2.50 Very good 23 Good 11 Suitable 25 Suitable 96 Moderately hard
22 WS 201 Poor 2.51 Very good 23 Good 11 Suitable 25 Suitable 97 Moderately hard
23 WS 140 Poor 1.98 Very good 22 Good 13 Suitable 25 Suitable 65 Soft water
24 WS 134 Poor 1.99 Very good 23 Good 13 Suitable 26 Suitable 63 Soft water
25 WS 142 Poor 1.99 Very good 22 Good 14 Suitable 25 Suitable 66 Soft water
26 WS 231 Very poor 3.94 Very good 32 Good 14 Suitable 34 Suitable 96 Moderately hard
27 WS 232 Very poor 3.94 Very good 32 Good 14 Suitable 34 Suitable 97 Moderately hard
28 WS 618 Unsuitable 6.64 Very good 31 Good 11 Suitable 32 Suitable 291 Hard
29 WS 228 Very poor 3.97 Very good 32 Good 13 Suitable 34 Suitable 94 Moderately hard
30 WS 231 Very poor 3.94 Very good 32 Good 14 Suitable 34 Suitable 96 Moderately hard

4.7.2

4.7.2 Groundwater quality for irrigation purpose

The sodium concentration of groundwater is the primary factor determining its appropriateness for irrigation. Irrigating farms with a high concentration of Na water will negatively affect soil permeability and crop productivity. Several equations have been recommended to scale the sodium content in water for irrigation purposes (Singh, 2019). These equations compare the Na concentration to total cations within the water, and all expressed in meq/l. The current study examines and categorizes the appropriateness of Biyadh groundwater for irrigation using the Sodium Adsorption Ratio (SAR), Sodium Percentage (Na%), Permeability Index (PI), and Magnesium Ratio (MR). Classifying the Biyadh groundwater can be identified based on the SAR equation:

(6)
SAR = N a / Ca + M g / 2 1 / 2

Based on the equation, any SAR values below ten are classified as very good water, the values between 10 and 18 are good water, the SAR values from 18 to 26 are classified as poor water, and anything above 26 means the groundwater is very poor for irrigation (Glover, 1996). The calculations presented in Table 4 shows SAR values in the WWF range from 0.75 to 3.5 and in the WS from 3 to 4. Consequently, the results indicate that all the Biyadh Aquifer groundwater in WWF and WS sites are suitable for irrigation purposes.

Another critical evaluator in determining water suitability for irrigation is the Sodium Percentage (Na %). The percentage of sodium in groundwater is vital in defining irrigation quality since sodium results in soil hardness, thus reducing soil permeability (Singh, 2020). The calculation of Na % can be done using the below equation:

(7)
Na % = Na + K x 100 / C a + M g + N a + K

Table 4 shows the classification of groundwater samples based on Na% findings. The WWF calculations vary from 8 to 33 and in the WS from 22 to 32. The majority of the samples in both locations are rated as excellent to good for irrigation use.

The permeability index (PI) is also applied to determine the suitability of groundwater for irrigation, and it is calculated as follows:

(8)
PI = 100 x ( N a + H C O 3 1 / 2 / 2 ) / N a + C a + Mg ]

The classification of PI includes three classes based on permeability levels. Class I values above 75 %, Class II between 25% and 75 %, and class III involve values below 25 % (Singh, 2020). Since Class II and Class I show at least 25% permeability, there are suitable for irrigation. On the other hand, Class III waters are not ideal for irrigation since the maximum permeability level is below 25%. The results show that all PI in WS classified in Class II. Consequently, the equation revealed 13 samples of WWF have fallen in Class II and four samples classified as class III (Table 4).

The magnesium ratio (MR) has also been proposed as a metric for determining groundwater suitability for irrigation (Rawat et al., 2018). A high Mg level harms the soil when groundwater has increased salinity levels and results in more alkalinity levels in soil, influencing agriculture production (Abbasnia et al., 2018). The MR equation is represented as the ratio of Mg ion concentration to the concentration of Mg and Ca ions combined (Bhunia et al., 2018). The result is then multiplied by 100, as shown in the equation.

(9)
MR = Mg / Ca + M g × 100

If the equation's outcome is more than 50, the groundwater is considered unsuitable for irrigation since it would cause adverse effects on agriculture production (Rawat et al., 2018). All Biyadh groundwater samples are categorized as suitable for irrigation (Table 4).

4.7.3

4.7.3 Groundwater quality for industrial purpose

Various formulae help determine the acceptability of water for industrial use; in this study, the total hardness (TH) was employed to determine the industrial suitability of the Biyadh groundwater.

Due to the presence of Ca and Mg, total hardness is produced. Hard water will be coating in the pipes with deposits such as CaCO3, CaSO4, and Mg(OH)2. Water hardness is categorized into four classes, over 300 as very hard water, from 150 to 300 as hard water, 75 to 150 as moderately hard water, and below 75 soft water (Todd & Mays, 2018). Using the following formula, the TH can be determined:

(10)
TH = 2.5 × C a + 4.1 × M g

The calculations classify 23% of the WS groundwater as soft water, 62% as moderately hard water, and 15% as hard water. Therefore, 30% of WWF samples are classified as soft water, and 70% as moderately hard water (Table 4).

5

5 Conclusions

Thirty groundwater samples from the Biyadh aquifer in Wasia Well Field and Wadi Sahba were analyzed to assess the evolution of groundwater quality, hydrochemical processes, and its consumption for drinking, agriculture, and industry. The groundwater TDS values increase from the outcrop toward the WWF, and the water type changes from Ca-Mg-SO4-Cl to Ca-Na-SO4-Cl in WWF groundwater. The correlation matrix calculations of Pearson indicate that ions have a considerable effect on groundwater mineralization. Cluster and factor studies suggest that agricultural fertilizers and the dissolution of halite, anhydrite, and gypsum minerals may affect the groundwater in Wadi Sahba. The geochemical modeling reveals that the direction of groundwater flow increases the dissolution processes involving anhydrite, aragonite, calcite, dolomite, and gypsum minerals. According to the WWF, Biyadh's drinking water quality is poor. In addition, 38% of water samples in WS are of poor quality, 46% are of very poor quality, and 15% are unsuitable for consumption. The aquifer is classified as appropriate for irrigation and industrial reasons.

The importance of this research lies in providing sufficient information for decision-makers in the city of Riyadh to determine the appropriate groundwater sites for residential, agricultural, and industrial projects. For future work, the study recommends investigating the amount of rainfall recharge to the Biyadh aquifer and comparing the outcome with the volume of groundwater withdrawn.

Acknowledgement

The authors extend their appreciation to Researchers Supporting Project number (RSPD2023R791), King Saud University, Riyadh, Saudi Arabia. Moreover, the authors thank the anonymous reviewers for their valuable suggestions and constructive comments.

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.

References

  1. , , , , , , , . Evaluation of groundwater quality using water quality index and its suitability for assessing water for drinking and irrigation purposes: case study of Sistan and Baluchistan province (Iran) Hum. Ecol. Risk Assessment: An Int. J.. 2018;25(4):988-1005.
    [CrossRef] [Google Scholar]
  2. , , . Data Clustering: Algorithms and Applications (first ed.). Chapman and Hall/CRC; .
  3. , , , , , , . Groundwater management scenarios for the Biyadh-Wasia aquifer systems in the eastern part of Riyadh region, Saudi Arabia. J. Geol. Soc. India. 2017;89:669-674.
    [Google Scholar]
  4. Al-Harbi, T.G., Hussein, M.T., 2009. Statistical Evaluation of Groundwater Quality in Wasia Well Field, Saudi Arabia. J. King Saud Univ., vol. 21, Science, pp. 125-135.
  5. , , . Hydrochemical classification and multivariate statistical analysis of groundwater from Wadi Sahba area in central Saudi Arabia. Arab. J. Geosci.. 2018;11(20)
    [CrossRef] [Google Scholar]
  6. , . Transit-oriented development in Saudi Arabia: Riyadh as a case study. Sustainability. 2022;14(23):16129.
    [CrossRef] [Google Scholar]
  7. , , , , , . Hydrochemical characterization of groundwater under agricultural land in arid environment: a case study of Al-Kharj. Saudi Arabia. Arabian J. Geosci.. 2015;9(1)
    [Google Scholar]
  8. , , , , , . Hydrogeology of an arid region: the Arabian Gulf and adjoining areas. Amsterdam: Elsevier; .
  9. , , , . Use of geoelectrical resistivity method for detecting near-surface groundwater potential zones at Riyadh city, Saudi Arabia. J. King Saud Univ.-Sci.. 2022;34(7):102253
    [Google Scholar]
  10. , , . Assessment of groundwater quality for drinking purpose in rural areas surrounding a defunct copper mine. Aquat. Procedia. 2015;4:685-692.
    [CrossRef] [Google Scholar]
  11. APHA, 2005. Standard Methods for the Examination of Water and Wastewater, twentyfirst Edition, American Public Health Association/American Water Works Association/Water Environment Federation, Washington DC.
  12. , , . Using multivariate statistical analysis, geostatistical techniques and structural equation modeling to identify spatial variability of groundwater quality. Water Resour. Manage.. 2015;29(6):2073-2089.
    [Google Scholar]
  13. , , , . Evaluation of groundwater quality and its suitability for drinking and irrigation using GIS and geostatistics techniques in semiarid region of Neyshabur, Iran. Appl. Water Sci.. 2018;8:168.
    [CrossRef] [Google Scholar]
  14. BRGM, 1976. Hydrogeological Investigations of the Al Wasia Aquifer in the Eastern Province of Saudi Arabia; Ministry of Agriculture and Water, Water Resources Development Department: Riyadh, Saudi Arabia, p. 109.
  15. , , , , , . Climate change and water scarcity: the case of Saudi Arabia. Ann. Glob. Health 2015
    [CrossRef] [Google Scholar]
  16. , . Groundwater quality patterns and spatiotemporal change in depletion in the regions of the Arabian shield and Arabian Shelf. Arab. J. Sci. Eng.. 2020;45:341-350.
    [CrossRef] [Google Scholar]
  17. , , , . Groundwater quality evaluation for drinking and irrigation uses in Dayrout city Upper Egypt. Ain Shams Eng. J.. 2021;12(1):327-340.
    [CrossRef] [Google Scholar]
  18. Glover, C., 1996. Irrigation water classification systems. Cooperative Extension Service. College of Agriculture and Home Economics, New Mexico State University.
  19. , , . Statistical methods in water resources. Stud. Environ. Sci.. 1992;49:522.
    [Google Scholar]
  20. , , , , . Optimal aquifers and reservoirs for CCS and EOR in the Kingdom of Saudi Arabia: an overview. Arab. J. Geosci.. 2016;9(12)
    [CrossRef] [Google Scholar]
  21. Keller, M., Bohnsack, D., Koch, R., Hinderer, M., Hornung, J., Al Ajmi, H., Amarah, B.A, 2019. Outcrop Analog Studies of the Wasia-Biyadh and Aruma Aquifers in the Kingdom of Saudi Arabia. In: AlAnzi, H.R., Rahmani, R.A., Steel, R.J., Soliman, O.M. (Eds.) Siliciclastic reservoirs of the Arabian plate: AAPG Memoir 116, pp. 317–382.
  22. Khogali, A., Birkle, P., Al-Shaibani, A., Keller, M., Tawabini, B., Makkawi, M., 2020. Geochemical Assessment of Potential Sources for Nitrate in the Wasia Aquifer, Al Kharj Area, Central Saudi Arabia. Water, 12(5), 1479. MDPI AG. Retrieved from https://doi.org/10.3390/w12051479.
  23. , , , , , , . Multivariate statistical analysis to identify the major factors governing groundwater quality in the coastal area of Kimje, South Korea. Hydrol Process.. 2005;19(6):1261-1276.
    [Google Scholar]
  24. , , , , . Implementation and evaluation of multivariate analysis for groundwater hydrochemistry assessment in arid environments: a case study of Hajeb ElyounJelma, Central Tunisia. Environ. Earth Sci.. 2013;70:2215-2224.
    [Google Scholar]
  25. , . Evaluation of groundwater quality for drinking purpose for tikrit and samarra cities using water quality index. Eur. J. Sci. Res.. 2011;58:472-481.
    [Google Scholar]
  26. , , , . Assessment of groundwater quality in Khulna city of Bangladesh in terms of water quality index for drinking purpose. Appl. Water Sci.. 2020;10(11)
    [CrossRef] [Google Scholar]
  27. , , . User's guide to PHREEQC version 3 - a computer program for speciation, batch reaction, one-dimensional transport, and inverse geochemical calculations. USGS. 1999
    [Google Scholar]
  28. , . Multivariate statistics: An approach for Water quality assessment. LAP LAMBERT Academic Publishing; .
  29. , , , , . Geology of the Arabian peninsula. Geol. Surv. Prof. Pap. 1966;560:1-147.
    [Google Scholar]
  30. , , , . Assessment of groundwater quality for irrigation use: a peninsular case studyApplied Water. Science. 2018;8(8)
    [CrossRef] [Google Scholar]
  31. , , , , , , , , . Quantifying renewable groundwater stress with GRACE. Water Resour. Res.. 2015;51
    [CrossRef] [Google Scholar]
  32. , , , . Hydrogeochemical processes operating within the main aquifers of Kuwait. J. Arid Environ.. 1999;42:195-209.
    [Google Scholar]
  33. SASO, 2000. Saudi standards, metrology and quality, unbottled drinking water 701, p. 9.
  34. , , , . GIS and geostatistical techniques for groundwater science. Elsevier; .
  35. , , , . Water quality assessment of a tropical river using water quality index (WQI), multivariate statistical techniques and GIS. Appl. Water Sci.. 2019;9(7)
    [Google Scholar]
  36. , . Prediction of the sodium absorption ratio using data-driven models: a case study in Iran. Geol. Ecol. Landscapes. 2019;4(1):1-10.
    [CrossRef] [Google Scholar]
  37. , . Evaluation of groundwater quality for suitability of irrigation purposes: a case study in the Udham Singh Nagar. Uttarakhand. J. Chem.. 2020;2020:15. (Article ID 6924026)
    [CrossRef] [Google Scholar]
  38. , , . Groundwater Hydrology. Wiley India Pvt Ltd; .
  39. , , , . Water quality evaluation of Ekulu river using Water Quality Index (WQI) J. Environ. Stud.. 2019;5(1):1-4.
    [Google Scholar]
  40. Uitto, J.I., Schneider, J., 1997. Freshwater resources in arid lands. In: UNU global environmental forum V, United Nation University Headquarters, Tokyo, 13 June 1996.
  41. WHO, 2011. Guidelines for drinking-water quality, fourth edn. WHO, Geneva.
  42. , , , . Analysis of groundwater quality using multivariate and spatial analyses in the Keta Basin, Ghana. J. Afr. Earth Sci.. 2010;58(2):220-234.
    [Google Scholar]
  43. , , , , . Evaluation of groundwater chemistry and its impact on drinking and irrigation water quality in the eastern part of the Central Arabian graben and trough system, Saudi Arabia. J. Afr. Earth Sc.. 2016;120:208-219.
    [CrossRef] [Google Scholar]

Appendix A

Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jksus.2023.102847.

Appendix A

Supplementary material

The following are the Supplementary data to this article:

Supplementary data 1

Supplementary data 1 Showing the hydrochemical data for the 30 groundwater samples in the study area with the ionic balance errors.


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