Groundwater quality assessment in Harrat Khaybar, western Saudi Arabia

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El-Sorogy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1899157/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Evaluating the suitability of groundwater for human use is essential for water supply and health in arid and semi-arid regions. In this work, a total of 68 groundwater samples were collected from Harrat Khaybar, western Saudi Arabia to evaluate the suitability of groundwater for drinking and irrigation purposes and to document the controlling mechanisms using pollution indices and multivariate statistical methods. The results showed that the average values of the ions Cl – , SO 4 2– , HCO 3 – , NO 3 – , Na + , Ca 2+ , Mg 2+ , and total dissolved solids (TDS) were greater than the permissible limit for drinking water while the average values of heavy metals (HMs) were less than the permissible limit, with exceeding limits of Cr, Se, As, Zn, and Pb in some individual samples. Piper diagram indicated that 47.10% of the water samples are of Na-K-SO 4 -Cl type, 23.51% of Ca-Mg-CO 3 -HCO 3 type, 23.51% of Ca-Mg-SO 4 -Cl type, and 5.88% of Na-K-CO 3 -HCO 3 type. Based on the groundwater quality index (GWQI), 29 of the groundwater samples were categorized as excellent and good water for drinking purposes, while 29 samples fell under poor, very poor water, and unsuitable for drinking. Additionally, results of heavy metal pollution index (HPI) indicated that all water samples fell within the low pollution category, while the metal index (MI) results indicated that 35 samples fell within very pure, pure, and slightly affected categories, while 33 samples fell in the moderately, strongly, and seriously affected categories. Results of electrical conductivity (EC), sodium adsorption ratio (SAR), sodium percentage (%Na), Kelly’s ratio (KR), and magnesium ratio (MR) revealed that 33.82–98.5 % of the water samples are suitable for irrigation depending on the parameter type. Ions exchange reactions and dissolution of carbonates, evaporites, and silicates, as well as industrial and domestic effluents and intensive use of fertilizers and pesticides were the natural and athropogenic factors controlling the groundwater geochemistry in the study area and HM pollution in some wells. Quality evaluation Groundwater contamination Harrat Khaybar Saudi Arabia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Groundwater is the main source of water used for drinking, agriculture, industrial, and domestic uses (Delgado et al. 2010; Li et al. 2013; El Maghraby 2015; Singh et al. 2020; Mallick et al. 2021). The quality and quantity of groundwater, which is an important natural resource must be evaluated and monitored to ensure access to water of good quality especially in areas with urban development (Khan et al. 2020; Alghamdi et al. 2020). Anthropogenic activities close to boreholes and shallow hand dug wells such as domestic practices (waste disposal and poor sanitation), agriculture, mining, industrialization, and urbanization deteriorate the groundwater system (Salifu et al. 2015; Ashehri et al. 2021). Overexploitation of groundwater resources due to intense agricultural and industrial activities and population growth have caused an extensive declining of the groundwater level and putting these resources at a greater risk of contamination (Jiang and Yun 2010; Tayfur et al. 2008; Aghazadeh and Moghaddam 2010; Nagarajan et al. 2010). The climate, rock weathering, and evapotranspiration were the natural geochemical characteristics affecting groundwater quality, while, sewage disposal, agriculture and industrial wastes were the anthropogenic ones (Singh and Chandel 2006; Nisi et al. 2008; Jiang and Yan 2010). Globally, Saudi Arabia is one of the driest regions with scarce water resources and is considered to be the largest country on the planet without perennial streams or lakes (Al-Harbi et al. 2009; Mallick et al. 2021). Groundwater is a valuable source of water in Saudi Arabia. Saudi Arabia depends largely on the desalination of seawater (Red Sea and Arabia Gulf) and groundwater for drinking, irrigation, and industry purposes (Saud and Abdullah 2009). The shallow groundwater aquifers near the major cities in Saudi Arabia are becoming polluted due to agriculture and domestic sewerage and industrial effluent discharge (Mallick et al. 2021). Various agricultural farms around Al-Madinah and Khaybar cities conduct important agricultural activities. Groundwater is originated from the fractured basement and shallow alluvial aquifer, and from deep aquifers (Abderrahman and Al-Harazin 2008; Al-Shaibani 2008). The groundwater in western Saudi Arabia is subjected to many hydrogeological and hydrogeochemical studies (e.g., Matsah and Hossain, 1993; Al Harbi et al., 2006; Al-Shaibani et al., 2007; Khashogji and El Maghraby, 2013; Shraim et al., 2013; ElMaghraby et al., 2013; El Maghraby, 2015; Sonbul, 2016; Alghamdi et al., 2020; Khan et al., 2020; Alshehri et al., 2021; Mallick et al., 2021; Alfaifi et al., 2021). The source of renewable groundwater in Khaybar region is a shallow aquifer made up of the weathered basement, sands and gravels, and fractured basalts with free top surface and minor confined zones. This shallow aquifer is becoming polluted through agriculture, domestic, and industrial discharges. Since the groundwater is sometimes used for irrigation, drinking, and cooking without pretreatment, the continuous evaluation of water quality is an important health issue. Therefore, the main objectives of the present work are to evaluate the status of the overall pollution level of the groundwater in the Harrat Khaybar, western Saudi Arabia with respect to physicochemical properties and to document the possible sources of HM contamination using pollution indices and multivariate analyses. The outcomes of this work provide essential information on the suitability of the water source for different uses and its results can be used by decision-makers as a guide for managing the aquifer in the study area. Material And Methods Geology of the study area Harrat Khaybar is located north of Medina in western Saudi Arabia, at 25°44ʹ04″ N and 39°58ʹ51″ E, and covers approximately 14,000 km 2 (Fig. 1 ). It is a Cenozoic lava field which is mainly composed of basaltic lava flows and created during the formation of Red Sea (Pint 2006 ; Sonbul 2016 ; Alhejji 2019 ). Geologically, the following rock units were described from Harrat Khaybar (Fig. 2 ): Al Ays volcanic and sedimentary group, the Khanzirah complex, Hamra Badi—partly covered by lower Paleozoic sandstone and Cenozoic flood basalt—Cambrian-Ordovician thick-bedded and pink weathering Saq sandstone, Cenozoic Harrat Khayber and Tertiary boulder conglomerates and fissile shales, unconsolidated Quaternary deposits of wadi alluvium, eolian sand, and sabkhah deposits (Kemp 1981 ; Pellaton 1981 ; Fairer 1986 ; Johnson 2005 ; Sonbul 2016 ). Harrat Khaybar differs from all other harrats in Saudi Arabia because of the presence of white felsic rocks present as tuff rings and domes with pyroclastic aprons (Sonbul 2016 ). On the eastern edge of Harrat Khaybar, there are many villages and small towns, such as Al-Nakheel, Al-Hanaquiyah, Al-Huwait, Al-Hayit, and Ash-Shamly, while Khaybar and Al-Ashash lie on its western edge. The climate in Harrat Khaybar varies between wet periods during the Pliocene and some parts of the Pleistocene to dry-arid conditions during the Holocene and desert at present (Peel et al. 2007; Parker et al. 2010; Sulieman et al. 2021). The Harrat Khayber is considered as an arid region with high temperatures throughout the whole year with high evaporation and relatively less infiltration rates. The major source of any natural water storage is rainfall. The annual rainfall varied from year to year with high percentage in the winter and spring seasons. The mean of precipitation is less than 13 mm rainfall per year. Sampling, Analytical, And Multivariate Analyses A total of 68 groundwater samples were sampled—from 4.2–130 m depth dugwells and boreholes in Harrat Khaybar, western Saudi Arabia (Fig. 1 ). The investigated groundwater is almost used for irrigation through dug wells and boreholes in farms, as well as livestock, domestic, drinking, and industrial benefits. The major source of domestic water is desalinated water that is pumped from Yanbu Power and Desalination plant at the Red Sea coast. Data were obtained from the Saudi Ministry of Water and Electricity reports (MoWE 2015), including hydrogeochemical parameters (pH, EC, and TDS), the ions (SiO 2 , Cl − , NO 3 − , F − , SO 4 2− , HCO 3 − , Mg 2+ , Ca 2+ , K + , and Na + ), and HMs (Hg, Al, Sb, Cu, Cr, B, Pb, Ni, Se, Cd, As, and Zn). The EC and pH were determined in the field using a portable EC/pH meter (Hanna HI 9811-5). Mg 2+ and Ca 2+ were determined using the titration method with ethylenediaminetetraacetic acid. K + and Na + were determined by a flame photometer (Corning 400). HCO 3 − was determined using acid titration. NO 3 − and B were established utilizing phenoldisulfonic acid and azomethine-H, respectively. Cl − was determined by using silver nitrate titration. SO 4 2− was estimated using a turbidity procedure. F − was determined by using a fluoride selective electrode. HMs were determines using Inductively Coupled Plasma-Mass Spectrometer (ICP-MS). Supplementary Table 1 shows the coordinates of groundwater wells, hydrogeochemical parameters, major anions, major cations, and HMs. The Piper plot is prepared to determine the groundwater facies. The principal component analysis (PCA), hierarchical cluster analysis, Q and R-modes (HCA), and correlation analysis (CA) were used as multivariate analyses in combination with hydrogeochemical analysis to identify groundwater hydrochemical characteristics and hydrogeochemical evolution processes and for understanding groundwater quality (Ayed et al. 2017 ; Zhang et al. 2021 ; Heydarirad et al. 2019 ; Patil et al. 2020 ). Pollution Indices And Criteria The GWQI, HPI, and MI are used as pollution indices to document water quality, while SAR, %Na, KR, and MR are used as criteria to identify the characteristics of water used for irrigation. The following are the procedures and classification of these indices and criteria: Groundwater Quality Index (Gwqi) Each of the 11 parameters has been assigned a weight (wi) according to its relative importance vis-à-vis the overall quality of drinking water as shown in Table 1 . The relative weight (Wi) is computed from the following equation: Table 1 The minimum, maximum, averages, standard deviation and the maximum allowable concentration of the measured physical and chemical parameters. Minimum Maximum Average SD MAC EC (µS/cm) 347 12870 3333 3109.54 1500 pH 6.54 8.07 7.44 0.35 6.5–8.5 TDS (mg/L) 225 8340 2165 2018.16 1000 TH (mg/L) 96.19 3167.82 758.60 718.42 Ca 2+ (mg/L) 11.00 746.90 150.95 158.85 75 Mg 2+ (mg/L) 9.10 711.60 99.86 127.28 30 Na + (mg/L) 9.60 2244.52 427.90 454.40 200 K + (mg/L) 0.20 130.57 12.42 20.95 12 Cl − ( mg/L) 5.50 3700.00 635.74 842.60 250 HCO3 − ( mg/L) 106.00 876.00 361.00 174.89 200 NO3 − ( mg/L) 1.00 450.00 67.87 71.78 50 SO4 2− ( mg/L) 8.00 2200.00 518.34 555.04 250 F − ( mg/L) 0.01 1.71 0.53 0.38 1.5 B (µg/L) 35.87 1888.38 454.33 382.61 2400 Al (µg/L) 0.10 266.86 6.47 32.98 Cr (µg/L) 0.10 78.93 12.90 19.85 50 Mn (µg/L) 0.09 735.85 11.68 89.16 Ni (µg/L) 0.10 34.77 4.39 6.95 70 Cu (µg/L) 0.13 9.69 1.61 1.77 2000 Zn (µg/l) 0.11 123.31 7.51 19.90 50 As (µg/L) 0.13 25.39 2.84 3.85 10 Se (µg/L) 0.10 110.72 14.27 17.76 40 Cd (µg/L) 0.10 0.42 0.11 0.05 3 Sb (µg/L) 0.10 0.49 0.11 0.06 20 Ba (µg/L) 0.10 311.13 23.76 41.59 700 Pb (µg/L) 0.10 18.97 0.40 2.29 10 U (µg/L) 0.18 21.20 4.40 4.04 30 W i = w i /Σ w i where W i is the relative weight and w i is the weight of each parameter. The quality rating scale (q i ) for each parameter is calculated by dividing the parameter concentration in each water sample by its respective standard (WHO 2011) multiplied by 100: q i = (C i /S i ) × 100 where q i is the quality rating score, C i is the concentration of each chemical parameter in each water sample in mg/L, and Si is the WHO (2011) standard for each chemical parameter. Finally, the W i and qi are used to calculate the SI i for each chemical parameter, and then the GWQI is calculated from the following equation (Bodrud-Doza et al. 2016): SI i = W i × q i GWQI = ΣSI i where SI i is the sub index of each parameter and qi is the rating based on concentration of each parameter. The computed GWQI values are classified into five categories (Ramakrishnalah et al. 2009; Ketata-Rokbani 2011; Aly et al. 2014 ): GWQI 300 (Unsuitable for drinking purposes). Heavy Metal Pollution Index (Hpi) The HPI index is calculated as follows (Mohan et al. 1996 ): W i = 1/MAC where W i is the relative weight of each parameter and MAC is the maximum allowable concentration in drinking water. An individual Q i is computed for each parameter using the following equation: Q i = Σ (M i - I i /S i – M i ) × 100 where M i is the monitored value of HM in the water sample, I i is the ideal value of the parameter, and S i is the standard value of the parameter. the overall index is computed using the following equation: HPI = \(\sum\) W i Q i / \(\sum\) W i Based on the HPI, the groundwater quality is classified into three categories (Mohan et al. 1996 ; Bodrud-Doza et al. 2016): HPI 90 (high pollution). Metal Index (Mi) This index can be expressed by the following equation (Islam et al. 2017): MI = \(\sum Ci/MAC\) where MI is the metal index, C is the concentration of each element in the solution, and MAC is the maximum allowed concentration of each element. MI is classified into six categories (Siegel 2002): MI 6.0 (seriously affected). Sodium Adsorption Ratio (Sar) It is used to indicate the degree of hazard that irrigation water sodium causes to soil or plants (Karanth 1987 ; Ghouili et al. 2018 ). SAR = Na+/(√ Ca 2+ + Mg 2+ )/2 All concentrations are stated in meq/L. The ratio classifies groundwater quality into four groups (Richards 1954): SAR 26 (unsuitable). Sodium Percentage (%na) It is used to evaluate the degree of sodium damage (Kumar et al. 2016 ). Na% = Na + / (Na + + K + + Ca 2+ +Mg 2+ ) × 100 All the values are expressed in meq/L. It classifies groundwater quality into five groups (Saha et al. 2017): Na% 80% (unsuitable). Kelly’s Ratio (Kr) It evaluates the suitability of water for irrigation by examining the balance between sodium ions, calcium ions, and magnesium ions (Zhang et al. 2021 ). KR = Na + /(Ca 2+ + Mg 2+ ) All the values are expressed in meq/L. The ratio classifies groundwater quality into two groups: KR 1 (unsafe). Magnesium Ratio (Mr) It is one of the important criteria which is used to assess the suitability of irrigation water (Szabolcs 1964 ). MR = Mg 2+ / (Ca 2+ + Mg 2+ ) × 100 It is proposed by Raghunath (1987) and classifies groundwater quality into two groups: MR ˂ 50% (suitable) and MR > 50% (unsuitable). Results And Discussion Groundwater chemistry The hydrochemistry of groundwater is influenced by different factors, such as geology and hydrogeology of the study area, chemical weathering, and human activities (Li et al. 2017; Wu et al. 2019). The pH of the groundwater varies from 6.54 to 8.07 (Table 1 ), implying slightly acidic to slightly basic waters, and fall within the standards prescribed for drinking water (WHO 2014). The 8.00 values reflect the possible silicate mineral and carbonate minerals dissolution, which is accompanied by high HCO 3 contents in the groundwater (Appelo and Postma 2005 ; Maghraby 2015 ). TDS varied from 225–8340, with an average of 2165 mg/L, indicating values greater than the acceptable limits of WHO (2005, 1000 mg/L). According to Freeze and Cherry ( 1979 ), 24 groundwater samples (35.29%) fall under the freshwater category (e.g., samples 14, 15, 46, 52, 63, 64, 65, and 68) with TDS less than 1000 mg/L, and 44 samples (64.71%) fall under the brackish to saline water category (e.g., samples 3, 4, 19, 34, 40, 41, and 54) with TDS greater than 1000 mg/L. Na + was the most abundant cations (average of 427.90 mg/L), followed by Ca 2+ (average of 150.95 mg/L), Mg 2+ (average of 99.86 mg/L), and K + (average of 12.42 mg/L), and B 3+ (average of 0.45 mg/L). Cl − was the most abundant anions (average of 635.74 mg/L), followed by SO 4 2− (average of 518.34 mg/L), HCO 3 − (average of 361 mg/L), NO 3 − (average of 67.87 mg/L), and F − (average of 0.53 µg/L). Table 1 The minimum, maximum, averages, standard deviation and the maximum allowable concentration of the measured physical and chemical parameters. The quality of groundwater in its natural state indicates the hydrogeochemical nature of groundwater with respect to aquifers (Maghraby 2015 ). Figure 3 illustrates the hydrogeochemical facies and groundwater types. The triangle diagram of cations shows that sodium and potassium are the leading cations in 52.94% of the groundwater samples, 32.35% fall within the no dominant type, and 14.71% of samples have cations that are dominated by calcium and magnesium. On the anions plot, 70.59% of groundwater samples fall under the sulphate and chloride type, which may be due to the effect of halite dissolution and human activities, while the remaining 29.41% are of the bicarbonate type, indicating a leading role for bicarbonate in groundwater. The investigated groundwater is characterized by the dominance of the alkalines (sodium and potassium) over the alkaline earth elements (calcium and magnesium), and the strong acids (chloride and sulphate) and the nearly balance of the weak acids (bicarbonate). The diamond diagram shows that 32 samples (47.10%) represent the (Na-K)-(SO 4 -Cl) type, 16 samples (23.51%) account (Ca-Mg)-(CO 3 -HCO 3 ) type, 16 samples (23.51%) represent (Ca-Mg)-(SO 4 -Cl) type, and 4 samples (5.88%) represent (Na-K)-(CO 3 -HCO 3 ) type (Fig. 3 ). These water types indicated that the geological composition in the area was mainly gypsum, anhydrite, and halite. The 24 fresh water samples (e.g., samples 14–17, 46, 51,52, 63, 65, 67, 68) showed the lowest values of TDS and Na + , and Cl − (sample 15), Mg 2+ (sample 52), K + and SO 4 2− (sample 14), F − (sample 67), and B 3+ (sample 46). Suitability Of Groundwater For Drinking The average values of Cl − , SO 4 2− , HCO 3 − , NO 3 − , Na + , Ca 2+ , and Mg 2+ were greater than the permissible limit for drinking water (Table 1 ), especially in most Na-K-SO 4 -Cl and Ca-Mg-SO 4 -Cl water sample types, indicating ion exchange reactions and dissolution of carbonates, evaporites, and silicates (Li et al. 2016 a). Moreover, some of these ions might originate anthropogenically. High Ca 2+ and Mg 2+ may originate from wastewater, domestic effluents, and the metal industry (Reimann and Caritat 1998 ; Pitt et al. 1999 ). Cl − ions can be used as an effective indicator of pollution from fertilizers and sewage. High HCO 3 − concentration results from leaky industrial and domestic sewage (Canter, 1997 ). SO 4 2− may be derived from industrial effluents and phosphate fertilizers (Subbarao et al. 1996 ; Alghamdi et al. 2020 ). Water samples from wells 1 and 13 showed F − levels that were greater than the permissible limit in drinking water (1.5 mg/L), implying extensive use of phosphatic fertilizers in agricultural areas and leaching of F − -rich minerals (Aswathanarayana et al. 1985 ; Dissanayake and Chandrajith 2009 ). Fluoride can also come from runoff and infiltration of chemical fertilizers in agricultural areas, septic and sewage treatment system discharges, and from waste from industrial sources (Smedley et al. 2002 ; Edmunds and Smedley, 2013 ). Notably, 54.41% and 48.53% of the water samples had SO 4 2− and Cl − concentrations greater than the permissible limit (250 mg/L), respectively. Further, 91.18% and 67.65% of the water samples had HCO 3 − and Na + concentrations greater than the permissible limit (200 mg/L), respectively. Furthermore, 70.59%, 55.88%, and 45.59% had Mg 2+ , Ca + and NO 3 − concentrations greater than the permissible limit (30, 75, 50 mg/L), respectively. GWQI is a mathematical application to transfer large amounts of water quality-related data into a single number, which indicates the suitability of water for drinking purposes (Patel and Vadodaria 2015 ; Sahu and Sikdar 2008 , Alfaifi et al. 2021 ; Alshehri et al. 2021 ). It ranged from 24.25 in sample 15 to 637.20 in sample 40 (Supplementary Table 2). Based on the calculated values of the GWQI, 13 of the water samples (19.12%) and 16 samples (23.53%) fell under excellent water and good water, respectively (fresh water category). 24 samples (35.29%) fell under poor water, 4 samples (5.88%) fell under very poor water, and the remaining 11 samples (16.18%) were categorized as unsuitable for drinking purposes. The excellent quality samples (samples 14–17, 46, 51–53, 63–65, 67, 68) showed the lowest values of EC, TDS, Na + , and Cl − (sample 15), Mg 2+ (sample 52), K + and SO 4 2− (sample 14), F − (sample 67), and B 3+ (sample 46). In the other hand, the wells of unsuitable water for drinking purposes (samples 3, 4, 19, 34, 35, 39–41, 54, 61, and 62) are mainly due to the highly dissolved soluble ions and characterized by the highest values of EC, TDS, Na + , Cl − (sample 40), TH (sample 62), Ca 2+ (sample 61), Mg 2+ (sample 34), K + (sample 39), NO 3 − (sample 54), SO 4 2− and B 3+ (sample 4), and F − (sample 13). Ba was the abundant HMs (average 23.76 µg/l), followed by Se (average 14.27 µg/l), Cr (average 12.90 µg/l), Mn (average 11.68 µg/l), Zn (average 7.51 µg/l), Al (average 6.47 µg/l), U (average 4.40 µg/l), Ni (average 4.39 µg/l), As (average 2.84 µg/l), Cu (average 1.61 µg/l), Pb (average 0.40 µg/l), Cd (average 0.11 µg/l), and Sb (average 0.11 µg/l). The average values of these HMs were less than the permissible limit of WHO standards for drinking water (Table 1 ). Figure 4 illustrated the spatial distribution of HMs in the groundwater samples. Chromium levels exceeded the permissible limit of WHO standards (50 µg/L) in seven water samples 31, 32, 36, 37, and 43–45 (53.79, 56.39, 50.91, 57.28, 61.69, 78.93, 69.13 µg/L, respectively) in the east central part of the study area, which is covered by basalt and andesite. Selenium levels exceeded the permissible limit (40 µg/L) in six water samples 23, 25–27, 40 and 54 (110.72, 48.68, 44.69, 41.11, 40.21, 50.06 µg/L, respectively) in the central part of the study area, which is covered by basalt and andesite. Arsenic levels exceeded the permissible limit (10 µg/L) in the water samples 10 (25.39 µg/L), in the southeastern side of the study area, which is covered by Haliban Formation, and samples 40 and 41 (13.89, 10.21 µg/L, respectively) in the central part of the study area which is covered by basalt and andesite. Zinc levels exceeded the permissible limit (50 µg/L) in water samples 29 and 50 (123.31, 101.02 µg/L, respectively) in the central eastern side of the study area, which is covered by basalt and andesite (Fig. 2 ). Lead levels exceeded the permissible limits (10 µg/L) in water sample 36 (18.97 µg/L) in the central eastern side of the study area, which is covered by basalt and andesite. Leakage of industrial wastewater might be the main point source of the pollution with Cr, Se, As, Zn, and Pb in some groundwater wells. HPI is a powerful tool for ranking the composite influence of individual HMs on overall water quality (Rizwan et al. 2011 ; Rezaei et al. 2017 : Alfaifi et al. 2021 ). HPI values varied from 2.17 in sample 65 to 43.55 in sample 10, with an average of 10.07 (Supplementary Table 2). Accordingly, all water samples fell within the low pollution category (HPI < 45). This is due to the fact that the average values of HMs were less than the permissible limit of WHO standards for drinking water. The higher levels of HPI in same water samples, e.g., 10, 36, and 40 (43.55, 41.37, and 28.67, respectively) contribute to the exceeding of the As value (sample 10), Cr and Pb values (sample 36), and As and Se values (sample 4) in comparison with MAC values of drinking water. MI helps evaluate the overall quality of drinking water quickly and takes into account the possible additive effects of HMs on human health (Enaam Abdullah 2013 ; Rezaei et al. 2017 ). MI values varied from 0.22 in sample 65 to 16.38 in sample 44, with an average of 3.83 (Supplementary Table 2). Based on the calculated values of MI, two water samples (46 and 65) fell within very pure category, 19 samples as pure, 14 samples as slightly affected, 13 samples as moderately affected, 7 samples as strongly affected, and 13 samples as seriously affected. The high concentrations of Se in samples 23, Cr in samples 30 and 32 and in samples 36, 37, and 43–45, and Pb in sample 36 were the reasons for the higher values of MI in these samples. Suitability Of Groundwater For Irrigation Irrigation and drainage have often been associated with a loss of water quality caused by salt, pesticides and fertilizer runoff, and leaching (Mateo-Sagasta et al. 2017 ). Since Khaybar city is characterized by various agricultural farms produce—vegetables, dates, and alfalfa—it is important to evaluate the quality and suitability of groundwater for agricultural usage. Supplementary Table 2 presented the results and classification of EC, %Na, SAR, KR, and MR in the present study. The acceptable limit of pH for irrigation water is between 6.5 and 8.4 (Ayers and Westcot 1985). pH varied from 6.54 to 8.07, and accordingly, all water samples fell within the acceptable limit. EC is a good measurement of salinity hazard to crops as it reflects the TDS in groundwater (Dumaru et al. 2021). EC ranged between 347 to 12870 µS/cm, with an average value of 3333 µS/cm. According to the classification by Ayers and Westcot (1985), 8 water samples showed no degree of restriction for irrigation purpose, 34 samples showed slight to moderate restriction, and 26 samples showed severe restriction, particularly those of the Na-K-SO 4 -Cl and Ca-Mg-SO 4 -Cl types. The % Na values, which indicated that the soluble sodium content varied from 9.52 to 85.66, with an of average 50.55. Water quality classification based on % Na showed that 51 of the groundwater samples (75%) were suitable for irrigation, (3 samples were of excellent quality, 11 samples were of good quality, 37 samples belonged to the permissible category), and 17 samples (25%) were doubtful and unsuitable for irrigation (Supplementary Table 2). The doubtful and unsuitable groundwater samples for irrigation, e.g., samples 3, 4, 27, 31, and 40 showed higher levels of Na + . Long-term use of water for irrigation with excessive sodium will destroy the soil structure and permeability, leading to soil compaction and reduction of crop yields (Salifu et al. 2017 ; Marghade et al. 2021 ). Based on SAR results, 85.3% (58 samples) were categorized as excellent for irrigation, 9 samples as good for irrigation, and only 1 (sample 53) as doubtful for irrigation, which of the Na-K-SO 4 -Cl type. The KR is based on the ratio of the concentration of sodium to calcium and magnesium (Kale et al. 2021). It ranged from 0.11 to 6.50, with an average of 1.56 (Supplementary Table 2). 23 of the groundwater samples (33.8%) are categorized under safe for irrigation (KR ˂ 1), while 45 water samples (66.2%) fell within unsafe for irrigation (KR > 1). Increasing magnesium content in groundwater results in the alkaline nature of the soil and thereby reduces the crop yield (Kumar et al. 2007; Dumaru et al. 2021). MR varied from 16.65 to 84.62%, with an average of 48.07%, suggesting that 37 of the water samples (54.41%) are suitable for irrigation (MR ˂ 50%) and 31 samples (45.59%) are not suitable for irrigation. The unsuitable water samples for irrigation, e.g., samples 12, 25–27, 40–42, 54, and 60 showed higher concentrations of Mg 2+ (Supplementary Table 1). Multivariate Analysis And Possible Sources Of Contamination The most common multivariate statistical techniques used to identify hydrogeochemical processes and solute sources and for interpretation of datasets are HCA, PCA, and correlation analysis CA (Yidana et al. 2008 ; Khan et al. 2017 , 2020 ). HCA is an effective tool to divide water samples into different clusters based on groundwater chemistry data (Belkhiri et al. 2010 ). Q mode HCA categorizes the 68 groundwater samples into three clusters, mainly based on TDS and ion levels (Fig. 5 ). Cluster 1 includes 7 samples (3, 4, 19, 34, 40, 41, and 54), which account for higher levels of TDS (ranged from 5490 to 8340 mg/L, with an average of 6950 mg/L), and the highest values of EC, Na + , Cl − , and Ni (sample 40), Mg 2+ , Sb, and Ba (sample 34), NO 3 − (sample 54), SO4 2− and B (sample 4), and U (sample 19). Increasing NO 3 − might be related to the intensive use of fertilizers and pesticides (Alshahri and El‑Taher 2018). All groundwater samples of cluster 1 belong to Na-K-SO 4 -Cl type, except sample 34, which belongs to Ca-Mg-SO 4 -Cl type. Cluster 2 includes 8 samples (26, 27, 35, 39, 42, 55, 61, and 62), which have medium TDS levels in the study area (ranged from 3250 to 5250 mg/L, with an average of 4018.75 mg/L). Samples 35, 42, 55, 61, and 62 belong to Ca-Mg-SO 4 -Cl type, while samples 26, 27, and 39 belong to Na-K-SO 4 -Cl type. Cluster 3 includes the remaining 53 samples, accounting the lower values of TDS (ranged from 225 to 2930 mg/L, with an average of 1264.64 mg/L), and the lowest levels of EC, TDS, and Na + (sample 15), pH, HCO 3 − , and U (sample 66), TH (sample 30), Ca 2+ (sample 29), Mg 2+ (sample 52), K + , Cl − , SO 4 2− , and Zn (sample 14), NO 3 − (sample 31), F (sample 6), B (sample 46), Cr (sample 2), Mn (sample 43), Ni (sample 22), Cu (sample 11), As (sample 65), Se (sample 67), Ba (sample 44). 23 samples of the cluster 3 belong to Na-K-SO 4 -Cl type, 16 samples to Ca-Mg-CO 3 -HCO 3 type, 10 samples to Ca-Mg-SO 4 -Cl type and 4 samples to Na-K-CO 3 -HCO 3 type. 24 groundwater samples of cluster 3 were of freshwater category (TDS less than 1000 mg/L). R mode HCA is used to classify the parameters into groups based on the similarity of each other (Banoeng-Yakubo et al. 2009 ). It classifies the hydrogeochemical parameters into two clusters (Fig. 6 ). The first cluster includes EC and TDS, while the second one accounts the remaining hydrogeochemical parameters and HMs. Pearson correlation is a technique used to identify similar sources of major ions and HMs with good correlation (Fisher and Mullican 1997 ; Yin et al. 2021 ). The correlation coefficient (r) 0.7 indicates strong correlation (Oinam et al. 2012). Table 2 showed strong and moderate correlations between EC and TDS, Ca 2+ , Mg 2+ , Na + , Cl − , NO 3 − , SO 4 2− , B, Ni, Cu (r = 1.00, 0.75, 0.85, 0.94, 0.98, 0.55, 0.93, 0.70, 0.77, and 0.66, respectively), which indicates a similar origin related to rock-water interaction and evaporation (Khan et al. 2020 ). Ca 2+ showed moderate and strong correlations with Mg 2+ , Na + , Cl − , SO 4 2− , Ni, and Cu (r = 0.64, 0.55, 0.78, 0.69, 0.70, and 0.56, respectively), reflecting the rock–water interaction is possible source of these ions in groundwater (Li et al. 2016 a; Zhang et al. 2018 b; Wu 2020). SO 4 2− is strongly and moderately correlated with Ca 2+ , Mg 2+ , Na + , Cl − , and NO 3 − (r = 0.69, 0.78, 0.89, 0.86, and 0.50, respectively), indicating the possibility of dissolution of halite, gypsum, sulfur-bearing minerals, as well as agricultural and industrial wastewater (Jalali 2010 ; Zhang et al. 2021 ). Moreover, Ni and Cu are strongly and moderately correlated with Ca 2+ , TH, Mg 2+ , Na + , Cl − , and SO 4 2− . Moreover, Ba is moderately correlated with Mg, Ni, and Sb. Moreover, U is moderately correlated with SO 4 2− and B, suggesting that soluble sulphate minerals and excessive use of phosphate-containing fertilizers are sources of these metals (Sharma and Singh 2016; Kale et al. 2021). Principal component analysis (PCA) divides the hydrochemical parameters according to the relationship between the different variables and identifies the factors that control the chemistry of the groundwater (Cloutier et al. 2008 ; Cortes et al. 2016 ; Wen et al. 2019 ). Nine principal components, accounting for 34.56%, 9.15%, 7.80%, 6.65%, 5.51%, 5.41%, 4.74%, 4.01%, and 3.83% of the total variance, were extracted with eigenvalues greater than 1 (Table 3 ). PC1 is the main factor affecting the hydrochemistry of the groundwater in the study area and showing high positive loading of EC, TDS, Ca 2+ , Mg 2+ , Na + , Cl − , NO 3 − , SO 4 2− , B, Ni, Cu, and As, reflecting a natural process of the dissolution of rocks in the study area and the increase in groundwater salinity (Rezaei et al. 2017 ; Kim et al. 2020 ; Wu 2020; Alshehri et al. 2021 ). Moreover, the high positive loading with NO 3 − indicates an anthropogenic factor from the agricultural activity (Li et al. 2018 , 2019 ; Alfaifi et al. 2021 ). PC3 shows high loading for Pb and Cd, implying soil leaching from usage of fertilizers and pesticides (Kukrer and Mutlu 2019 ; Wen et al. 2019 ; Alharbi et al. 2021 ). PC5 and PC8 showed high loading for Zn and Mn, respectively, which might originate from mixed anthropogenic and natural factors (Nour et al. 2019 ; Al-Hashim et al. 2021 ). Table 3 Principal component loadings and explained variance of the analyzed parameters with varimax normalized rotation. Component 1 2 3 4 5 6 7 8 9 EC .984 .080 − .032 .001 .058 .016 .038 .007 − .010 pH − .279 .392 − .188 .340 .031 − .008 .546 − .261 .166 TDS .984 .079 − .032 .002 .058 .017 .038 .007 − .011 TH .287 .000 − .161 − .233 − .532 − .136 .068 .265 .117 Ca .781 − .213 − .152 − .102 − .081 − .180 − .011 .136 .283 Mg .855 − .119 − .097 .070 − .025 .301 − .007 − .144 .123 Na .911 .226 .034 .023 .138 − .037 .075 .007 − .181 K .488 − .027 .214 − .247 .015 .019 .495 .088 − .549 Cl .965 − .028 − .103 − .036 .027 − .016 .113 .013 − .040 HCO 3 − .086 .356 .300 .418 .423 .264 − .018 .053 − .107 NO 3 .554 .223 − .141 − .092 − .217 .324 .319 − .315 .192 SO 4 .928 .201 .033 − .009 .037 − .052 − .122 − .022 .056 F .273 .355 .136 .353 .175 − .478 .088 − .156 .174 B .678 .373 .261 .105 .224 − .077 − .159 .156 .161 Al − .104 − .217 .207 − .633 .430 − .017 .200 − .174 .168 Cr − .040 .278 .446 − .224 − .225 .497 − .365 .080 − .072 Mn − .055 − .185 − .083 .238 .413 .358 .228 .608 .072 Ni .819 − .336 − .040 − .105 − .018 − .021 .037 .076 − .260 Cu .694 .016 − .134 − .144 − .090 − .051 − .054 .135 .212 Zn − .017 − .095 .104 − .615 .566 − .061 − .127 − .204 .130 As .555 − .412 .329 .246 − .024 − .268 − .016 − .164 − .308 Se .471 .428 .102 − .058 − .105 .494 − .118 − .226 − .035 Cd .009 − .239 .876 .161 − .192 − .114 .084 .006 .132 Sb .307 − .698 .226 .346 − .044 .148 − .184 − .349 .091 Ba .408 − .634 − .226 .289 .187 .273 − .028 .064 .214 Pb − .003 .000 .696 − .126 − .187 .034 .359 .221 .343 U .475 .360 .062 .042 .090 − .311 − .360 .143 .026 % of Variance 34.56 9.15 7.80 6.65 5.51 5.41 4.74 4.01 3.83 Cumulative % 34.56 43.70 51.51 58.16 63.67 69.09 73.83 77.84 81.67 Conclusions In this work, GWQI, HPI, MI, SAR, % Na, KR, and MR were used as pollution indices and criteria to evaluate the suitability of groundwater for drinking and irrigation purposes in Harrat Khaybar, western Saudi Arabia. Hydrogeochemical methods and multivariate statistical methods are employed to identify the hydrochemistry characteristics and controlling mechanisms of the groundwater in the study area. The findings were the following: 1. The average concentration of the ions and HMs was in the following descending order: Cl – > SO 4 2– > Na + > HCO 3 – > Ca 2+ > Mg 2+ > NO 3 – > K + > F – > B 3+ > Ba > Se > Cr > Mn > Zn > Al > U > Ni > As > Cu > Pb > Cd > Sb. Average values Cl – , SO 4 2– , HCO 3 – , NO 3 – , Na + , Ca 2+ , Mg 2+ , and TDS were greater than the permissible limit for drinking water while values of Cr, Se, As, Zn, and Pb were greater than the permissible limit in some individual samples. 2. On the whole, 3 5 .29% of the groundwater samples fell under the freshwater category and 64.71% fell under the brackish to saline water category. The freshwater category includes all samples of the Ca-Mg-CO 3 -HCO 3 and Na-K-CO 3 -HCO 3 types, except one sample while most samples of the Ca-Mg-SO 4 -Cl the Na-K-SO 4 -Cl types were in the brackish to saline category. 3. GWQI indicated that 42.65% of the groundwater samples fell under excellent and good water for drinking purposes, and 57.15% fell under poor, very poor water, and unsuitable for drinking. MI results indicated that 51.47% samples fell within very pure, pure, and slightly affected categories and 48.53% fell under moderately affected, strongly affected, and seriously affected categories. The irrigation criterion revealed that more than half of the groundwater wells were suitable for irrigation. 4. Multivariate statistical methods revealed that the dissolution of rock forming minerals as well as domestic, agricultural, and industrial effluents are the factors that control the geochemistry of groundwater and HM pollution in some wells in the study area. Declarations Acknowledgements : The authors extend their appreciation to the Abdullah Alrushaid Chair for Earth Science Remote Sensing Research for funding. Ethical approval Not applicable Consent to participate Not applicable. Consent to Publish All authors have read the manuscript and agreed to publish the manuscript. Authors Contributions FA and AS. designed the study and was responsible for the data collection as well as analysis of the data and wrote the initial draft; FA and AM were responsible for the data analysis, data curation and modeling as well as editing of the initial draft and supervised the project; BP helped in the data preparation as well as provided technical support; AR provided software guidance, helped in validation as well as reviewed the final manuscript. Funding Research has been funded through Abdullah Alrushaid Chair for Earth Science Remote Sensing Research. Competing Interest No potential conflict of interest has been reported among the authors on any issue. Availability of Data and Materials The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Conflict of interest We wish to confirm that there are no known conflicts of interest associated with this publication, and there has been no significant financial support for this work that could have influenced its outcome. References AbderrahmanWA, Al-Harazin IM (2008) Assessment of climate changes on water resources in the Kingdom of Saudi Arabia, GCC Environment and Sustainable Development Symposium, 28–30 Aghazadeh N, Moghaddam AA (2010) Investigation of hydrochemical characteristics of groundwater in the Harzandat aquifer, Northwest of Iran. 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J Hydrol 592:125604 Zhang X, Zhao R, Wu X, Mu W (2021) Identification of hydrogeochemical evolution using integrated multivariate statistical and geochemical methods and assessment of groundwater quality in the southwestern Ordos Basin, China. Environmental Science and Pollution Research (Accepted) Zhang Y, Xu M, Li X et al (2018) Hydrochemical characteristics and multivariate statistical analysis of natural water system: a case study in Kangding County, Southwestern China. Water 10:80 Table Table 2 is available in the Supplementary Files section Supplementary Files Table2.docx SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1899157","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":130343661,"identity":"5a66789d-f24f-44c8-a34d-726367daadca","order_by":0,"name":"Fahad Alshehri","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fahad","middleName":"","lastName":"Alshehri","suffix":""},{"id":130343662,"identity":"8723dd9f-3937-4f55-88c2-0a61e0add165","order_by":1,"name":"Abdelbaset S. El-Sorogy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYDACdhhDgsHwAQPDASK0MCO0GBuQrMVMgigt/MzsDx9XVNQm9s9u3lbNU3NHjp+B+eGjG3i0SDbzGBueOXM8ccadY2W3eY49M5ZsYDM2zsGjxeAwD5tkY9uxxIYbOWa3edgOJ244wMMmjU+L/WH2Z2At84Fainn+EaHFgJnBDKilJnEDUAszbxsRWiQOA/3ScOaA8cY7x4ol5/YdNpZsJuAX/vb2hw8bKupk591u3vjhzbfDcvzszQ8f49MCBYcdG4AkEw+IzYxfKQzU2YNIxh/EqR4Fo2AUjIIRBgBYulFTltshZQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-0283-1433","institution":"King Saud University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Abdelbaset","middleName":"S.","lastName":"El-Sorogy","suffix":""}],"badges":[],"createdAt":"2022-07-26 20:37:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1899157/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1899157/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":25509322,"identity":"9541ed9a-edd3-4d75-81ca-3bf5aa25af13","added_by":"auto","created_at":"2022-08-22 18:23:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2808539,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the groundwater samples at Harrat Khaybar, western Saudi Arabia.\u003c/p\u003e","description":"","filename":"Figures1.png","url":"https://assets-eu.researchsquare.com/files/rs-1899157/v1/fec58941d4e628fbce2a8b05.png"},{"id":25509319,"identity":"9347479d-8fa0-4078-959a-89437a2e3398","added_by":"auto","created_at":"2022-08-22 18:23:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1766335,"visible":true,"origin":"","legend":"\u003cp\u003eGeologic map of Harrat Khaybar, western Saudi Arabia.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figures2.png","url":"https://assets-eu.researchsquare.com/files/rs-1899157/v1/8ce8d4d33835f70d38679d08.png"},{"id":25509318,"identity":"0a1e09d7-09c2-4e7e-ac1a-8591d35f923a","added_by":"auto","created_at":"2022-08-22 18:23:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":353807,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of the groundwater facies using Piper diagram.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figures3.png","url":"https://assets-eu.researchsquare.com/files/rs-1899157/v1/6618676d29261ff35e5ed043.png"},{"id":25509325,"identity":"79401714-92da-44fb-8ad4-d0a91ddd54b0","added_by":"auto","created_at":"2022-08-22 18:23:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1316812,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of HMs in the groundwater samples at Harrat Khaybar.\u003c/p\u003e","description":"","filename":"Figures4.png","url":"https://assets-eu.researchsquare.com/files/rs-1899157/v1/cbc84a551f1389928bece5a4.png"},{"id":25509737,"identity":"6c0b7011-9c85-4cd9-8526-bb7c1592621c","added_by":"auto","created_at":"2022-08-22 18:28:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":121709,"visible":true,"origin":"","legend":"\u003cp\u003eQ-mode HCA for the groundwater samples.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figures5.png","url":"https://assets-eu.researchsquare.com/files/rs-1899157/v1/116e6e1c74878f1b4e622be0.png"},{"id":25509323,"identity":"529feee7-2d5c-4113-aea8-781ddefc0686","added_by":"auto","created_at":"2022-08-22 18:23:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":180194,"visible":true,"origin":"","legend":"\u003cp\u003eR-mode HCA dendrogram for the hydrochemical parameters in groundwater samples.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figures6.png","url":"https://assets-eu.researchsquare.com/files/rs-1899157/v1/04f4653fc72f93880c8d21a0.png"},{"id":27348582,"identity":"235a4424-43ef-4e96-a576-03ea1705477d","added_by":"auto","created_at":"2022-10-05 02:27:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4596557,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1899157/v1/4650bd74-5929-4702-9e96-ab211f7ee565.pdf"},{"id":25510344,"identity":"64f83caa-0053-47c5-83c6-954137c8e642","added_by":"auto","created_at":"2022-08-22 18:33:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20685,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-1899157/v1/c84995ca4babb68eb68ff73e.docx"},{"id":25510343,"identity":"c2366ecf-ba18-4e41-a425-13d31bc3243a","added_by":"auto","created_at":"2022-08-22 18:33:32","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":43619,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-1899157/v1/d42f4ef05c7ef8f00a4ecc92.docx"}],"financialInterests":"","formattedTitle":"Groundwater quality assessment in Harrat Khaybar, western Saudi Arabia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGroundwater is the main source of water used for drinking, agriculture, industrial, and domestic uses (Delgado et al. 2010; Li et al. 2013; El Maghraby 2015; Singh et al. 2020; Mallick et al. 2021). The quality and quantity of groundwater, which is an important natural resource must be evaluated and monitored to ensure access to water of good quality especially in areas with urban development (Khan et al. 2020; Alghamdi et al. 2020). Anthropogenic activities close to boreholes and shallow hand dug wells such as domestic practices (waste disposal and poor sanitation), agriculture, mining, industrialization, and urbanization deteriorate the groundwater system (Salifu et al. 2015; Ashehri et al. 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverexploitation of groundwater resources due to intense agricultural and industrial activities and population growth have caused an extensive declining of the groundwater level and putting these resources at a greater risk of contamination (Jiang and Yun 2010; Tayfur et al. 2008; Aghazadeh and Moghaddam 2010; Nagarajan et al. 2010). The climate, rock weathering, and evapotranspiration were the natural geochemical characteristics affecting groundwater quality, while, sewage disposal, agriculture and industrial wastes were the anthropogenic ones (Singh and Chandel 2006; Nisi et al. 2008; Jiang and Yan 2010).\u003c/p\u003e\n\u003cp\u003eGlobally, Saudi Arabia is one of the driest regions with scarce water resources and is considered to be the largest country on the planet without perennial streams or lakes (Al-Harbi et al. 2009; Mallick et al. 2021). Groundwater is a valuable source of water in Saudi Arabia. Saudi Arabia depends largely on the desalination of seawater (Red Sea and Arabia Gulf) and groundwater for drinking, irrigation, and industry purposes (Saud and Abdullah 2009). The shallow groundwater aquifers near the major cities in Saudi Arabia are becoming polluted due to agriculture and domestic sewerage and industrial effluent discharge (Mallick et al. 2021). Various agricultural farms around Al-Madinah and Khaybar cities conduct important agricultural activities. Groundwater is originated from the fractured basement and shallow alluvial aquifer, and from deep aquifers (Abderrahman and Al-Harazin 2008; Al-Shaibani 2008).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The groundwater in western Saudi Arabia is subjected to many hydrogeological and hydrogeochemical studies (e.g., Matsah and Hossain, 1993; Al Harbi et al., 2006; Al-Shaibani et al., 2007; Khashogji and El Maghraby, 2013; Shraim et al., 2013; ElMaghraby et al., 2013; El Maghraby, 2015; Sonbul, 2016; Alghamdi et al., 2020; Khan et al., 2020; Alshehri et al., 2021; Mallick et al., 2021; Alfaifi et al., 2021). The source of renewable groundwater in Khaybar region is a shallow aquifer made up of the weathered basement, sands and gravels, and fractured basalts with free top surface and minor confined zones. This shallow aquifer is becoming polluted through agriculture, domestic, and industrial discharges. Since the groundwater is sometimes used for irrigation, drinking, and cooking without pretreatment, the continuous evaluation of water quality is an important health issue. Therefore, the main objectives of the present work are to evaluate the status of the overall pollution level of the groundwater in the Harrat Khaybar, western Saudi Arabia with respect to physicochemical properties and to document the possible sources of HM contamination using pollution indices and multivariate analyses. The outcomes of this work provide essential information on the suitability of the water source for different uses and its results can be used by decision-makers as a guide for managing the aquifer in the study area.\u003c/p\u003e"},{"header":"Material And Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003eGeology of the study area\u003c/h2\u003e\n \u003cp\u003eHarrat Khaybar is located north of Medina in western Saudi Arabia, at 25\u0026deg;44ʹ04\u0026Prime; N and 39\u0026deg;58ʹ51\u0026Prime; E, and covers approximately 14,000 km\u003csup\u003e2\u003c/sup\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). It is a Cenozoic lava field which is mainly composed of basaltic lava flows and created during the formation of Red Sea (Pint \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Sonbul \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Alhejji \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Geologically, the following rock units were described from Harrat Khaybar (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e): Al Ays volcanic and sedimentary group, the Khanzirah complex, Hamra Badi\u0026mdash;partly covered by lower Paleozoic sandstone and Cenozoic flood basalt\u0026mdash;Cambrian-Ordovician thick-bedded and pink weathering Saq sandstone, Cenozoic Harrat Khayber and Tertiary boulder conglomerates and fissile shales, unconsolidated Quaternary deposits of wadi alluvium, eolian sand, and sabkhah deposits (Kemp \u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e; Pellaton \u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e; Fairer \u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e; Johnson \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Sonbul \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eHarrat Khaybar differs from all other harrats in Saudi Arabia because of the presence of white felsic rocks present as tuff rings and domes with pyroclastic aprons (Sonbul \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). On the eastern edge of Harrat Khaybar, there are many villages and small towns, such as Al-Nakheel, Al-Hanaquiyah, Al-Huwait, Al-Hayit, and Ash-Shamly, while Khaybar and Al-Ashash lie on its western edge. The climate in Harrat Khaybar varies between wet periods during the Pliocene and some parts of the Pleistocene to dry-arid conditions during the Holocene and desert at present (Peel et al. 2007; Parker et al. 2010; Sulieman et al. 2021). The Harrat Khayber is considered as an arid region with high temperatures throughout the whole year with high evaporation and relatively less infiltration rates. The major source of any natural water storage is rainfall. The annual rainfall varied from year to year with high percentage in the winter and spring seasons. The mean of precipitation is less than 13 mm rainfall per year.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch2\u003eSampling, Analytical, And Multivariate Analyses\u003c/h2\u003e\n\u003cp\u003eA total of 68 groundwater samples were sampled\u0026mdash;from 4.2\u0026ndash;130 m depth dugwells and boreholes in Harrat Khaybar, western Saudi Arabia (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The investigated groundwater is almost used for irrigation through dug wells and boreholes in farms, as well as livestock, domestic, drinking, and industrial benefits. The major source of domestic water is desalinated water that is pumped from Yanbu Power and Desalination plant at the Red Sea coast.\u003c/p\u003e\n\u003cp\u003eData were obtained from the Saudi Ministry of Water and Electricity reports (MoWE 2015), including hydrogeochemical parameters (pH, EC, and TDS), the ions (SiO\u003csub\u003e2\u003c/sub\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, F\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, and Na\u003csup\u003e+\u003c/sup\u003e), and HMs (Hg, Al, Sb, Cu, Cr, B, Pb, Ni, Se, Cd, As, and Zn). The EC and pH were determined in the field using a portable EC/pH meter (Hanna HI 9811-5). Mg\u003csup\u003e2+\u003c/sup\u003e and Ca\u003csup\u003e2+\u003c/sup\u003e were determined using the titration method with ethylenediaminetetraacetic acid. K\u003csup\u003e+\u003c/sup\u003e and Na\u003csup\u003e+\u003c/sup\u003e were determined by a flame photometer (Corning 400). HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e was determined using acid titration. NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and B were established utilizing phenoldisulfonic acid and azomethine-H, respectively. Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e was determined by using silver nitrate titration. SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e was estimated using a turbidity procedure. F\u003csup\u003e\u0026minus;\u003c/sup\u003e was determined by using a fluoride selective electrode. HMs were determines using Inductively Coupled Plasma-Mass Spectrometer (ICP-MS). Supplementary Table 1 shows the coordinates of groundwater wells, hydrogeochemical parameters, major anions, major cations, and HMs.\u003c/p\u003e\n\u003cp\u003eThe Piper plot is prepared to determine the groundwater facies. The principal component analysis (PCA), hierarchical cluster analysis, Q and R-modes (HCA), and correlation analysis (CA) were used as multivariate analyses in combination with hydrogeochemical analysis to identify groundwater hydrochemical characteristics and hydrogeochemical evolution processes and for understanding groundwater quality (Ayed et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Heydarirad et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Patil et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003ch2\u003ePollution Indices And Criteria\u003c/h2\u003e\n\u003cp\u003eThe GWQI, HPI, and MI are used as pollution indices to document water quality, while SAR, %Na, KR, and MR are used as criteria to identify the characteristics of water used for irrigation. The following are the procedures and classification of these indices and criteria:\u003c/p\u003e\n\u003ch2\u003eGroundwater Quality Index (Gwqi)\u003c/h2\u003e\n\u003cp\u003eEach of the 11 parameters has been assigned a weight (wi) according to its relative importance vis-\u0026agrave;-vis the overall quality of drinking water as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The relative weight (Wi) is computed from the following equation:\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe minimum, maximum, averages, standard deviation and the maximum allowable concentration of the measured physical and chemical parameters.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC (\u0026micro;S/cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3109.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u0026ndash;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTDS (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2018.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTH (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3167.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e758.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e718.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e746.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e711.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e127.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2244.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e427.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e454.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e( mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3700.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e635.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e842.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCO3\u003csup\u003e\u0026minus;\u003c/sup\u003e( mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e876.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e361.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e174.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO3\u003csup\u003e\u0026minus;\u003c/sup\u003e( mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO4\u003csup\u003e2\u0026minus;\u003c/sup\u003e( mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2200.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e518.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e555.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003csup\u003e\u0026minus;\u003c/sup\u003e( mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1888.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e454.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e382.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAl (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCr (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMn (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e735.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNi (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCu (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZn (\u0026micro;g/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAs (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSe (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCd (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSb (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBa (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e311.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePb (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eU (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eW\u003csub\u003ei\u003c/sub\u003e = w\u003csub\u003ei\u003c/sub\u003e/\u0026Sigma; w\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003ewhere W\u003csub\u003ei\u003c/sub\u003e is the relative weight and w\u003csub\u003ei\u003c/sub\u003e is the weight of each parameter.\u003c/p\u003e\n\u003cp\u003eThe quality rating scale (q\u003csub\u003ei\u003c/sub\u003e) for each parameter is calculated by dividing the parameter concentration in each water sample by its respective standard (WHO 2011) multiplied by 100:\u003c/p\u003e\n\u003cp\u003eq\u003csub\u003ei\u003c/sub\u003e = (C\u003csub\u003ei\u003c/sub\u003e/S\u003csub\u003ei\u003c/sub\u003e) \u0026times; 100\u003c/p\u003e\n\u003cp\u003ewhere q\u003csub\u003ei\u003c/sub\u003e is the quality rating score, C\u003csub\u003ei\u003c/sub\u003e is the concentration of each chemical parameter in each water sample in mg/L, and Si is the WHO (2011) standard for each chemical parameter. Finally, the W\u003csub\u003ei\u003c/sub\u003e and qi are used to calculate the SI\u003csub\u003ei\u003c/sub\u003e for each chemical parameter, and then the GWQI is calculated from the following equation (Bodrud-Doza et al. 2016):\u003c/p\u003e\n\u003cp\u003eSI\u003csub\u003ei\u003c/sub\u003e = W\u003csub\u003ei\u003c/sub\u003e \u0026times; q\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eGWQI\u0026thinsp;=\u0026thinsp;\u0026Sigma;SI\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003ewhere SI\u003csub\u003ei\u003c/sub\u003e is the sub index of each parameter and qi is the rating based on concentration of each parameter. The computed GWQI values are classified into five categories (Ramakrishnalah et al. 2009; Ketata-Rokbani 2011; Aly et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e): GWQI\u0026thinsp;\u0026lt;\u0026thinsp;50 (excellent water), GWQI\u0026thinsp;=\u0026thinsp;50\u0026ndash;100.1 (good water), GWQI\u0026thinsp;=\u0026thinsp;100\u0026ndash;200.1 (poor water), GWQI\u0026thinsp;=\u0026thinsp;200\u0026ndash;300.1 (Very poor water), and GWQI\u0026thinsp;\u0026gt;\u0026thinsp;300 (Unsuitable for drinking purposes).\u003c/p\u003e\n\u003ch2\u003eHeavy Metal Pollution Index (Hpi)\u003c/h2\u003e\n\u003cp\u003eThe HPI index is calculated as follows (Mohan et al. \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e):\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eW\u003c/em\u003e \u003csub\u003ei\u003c/sub\u003e = 1/MAC\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the relative weight of each parameter and MAC is the maximum allowable concentration in drinking water.\u003c/p\u003e\n\u003cp\u003eAn individual \u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is computed for each parameter using the following equation:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQ\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003ei\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026Sigma; (M\u003csub\u003ei\u003c/sub\u003e - I\u003csub\u003ei\u003c/sub\u003e/S\u003csub\u003ei\u003c/sub\u003e \u0026ndash; M\u003csub\u003ei\u003c/sub\u003e) \u0026times; 100\u003c/p\u003e\n\u003cp\u003ewhere M\u003csub\u003ei\u003c/sub\u003e is the monitored value of HM in the water sample, I\u003csub\u003ei\u003c/sub\u003e is the ideal value of the parameter, and S\u003csub\u003ei\u003c/sub\u003e is the standard value of the parameter. the overall index is computed using the following equation:\u003c/p\u003e\n\u003cp\u003eHPI = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eQ\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e/\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eBased on the HPI, the groundwater quality is classified into three categories (Mohan et al. \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e; Bodrud-Doza et al. 2016): HPI\u0026thinsp;\u0026lt;\u0026thinsp;45 (low pollution), HPI\u0026thinsp;=\u0026thinsp;45\u0026ndash;90 (medium pollution), and HPI\u0026thinsp;\u0026gt;\u0026thinsp;90 (high pollution).\u003c/p\u003e\n\u003ch2\u003eMetal Index (Mi)\u003c/h2\u003e\n\u003cp\u003eThis index can be expressed by the following equation (Islam et al. 2017):\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003eMI =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum Ci/MAC\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e\n \u003cp\u003ewhere MI is the metal index, C is the concentration of each element in the solution, and MAC is the maximum allowed concentration of each element. MI is classified into six categories (Siegel 2002): MI\u0026thinsp;\u0026lt;\u0026thinsp;0.3 (very pure), MI\u0026thinsp;=\u0026thinsp;0.3\u0026ndash;1.0 (pure), MI\u0026thinsp;=\u0026thinsp;1.0\u0026ndash;2.0 (slightly affected), MI\u0026thinsp;=\u0026thinsp;2.0\u0026ndash;4.0 (moderately affected), MI\u0026thinsp;=\u0026thinsp;4.0\u0026ndash;6.0 (strongly affected), and MI\u0026thinsp;\u0026gt;\u0026thinsp;6.0 (seriously affected).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch2\u003eSodium Adsorption Ratio (Sar)\u003c/h2\u003e\n\u003cp\u003eIt is used to indicate the degree of hazard that irrigation water sodium causes to soil or plants (Karanth \u003cspan class=\"CitationRef\"\u003e1987\u003c/span\u003e; Ghouili et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSAR\u0026thinsp;=\u0026thinsp;Na+/(\u0026radic; Ca\u003csup\u003e2+\u003c/sup\u003e + Mg\u003csup\u003e2+\u003c/sup\u003e)/2\u003c/p\u003e\n\u003cp\u003eAll concentrations are stated in meq/L. The ratio classifies groundwater quality into four groups (Richards 1954): SAR\u0026thinsp;\u0026lt;\u0026thinsp;10 (excellent), SAR\u0026thinsp;=\u0026thinsp;10\u0026ndash;18 (good), SAR\u0026thinsp;=\u0026thinsp;18\u0026ndash;26 (doubtful), and SAR\u0026thinsp;\u0026gt;\u0026thinsp;26 (unsuitable).\u003c/p\u003e\n\u003ch2\u003eSodium Percentage (%na)\u003c/h2\u003e\n\u003cp\u003eIt is used to evaluate the degree of sodium damage (Kumar et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eNa% = Na\u003csup\u003e+\u003c/sup\u003e/ (Na\u003csup\u003e+\u003c/sup\u003e+ K\u003csup\u003e+\u003c/sup\u003e+ Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e) \u0026times; 100\u003c/p\u003e\n\u003cp\u003eAll the values are expressed in meq/L. It classifies groundwater quality into five groups (Saha et al. 2017): Na% \u0026lt; 20% (excellent), Na% = 20\u0026ndash;40% (good), Na% = 40\u0026ndash;60% (permissible), Na% = 60\u0026ndash;80% (doubtful), and Na% \u0026gt; 80% (unsuitable).\u003c/p\u003e\n\u003ch2\u003eKelly\u0026rsquo;s Ratio (Kr)\u003c/h2\u003e\n\u003cp\u003eIt evaluates the suitability of water for irrigation by examining the balance between sodium ions, calcium ions, and magnesium ions (Zhang et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eKR\u0026thinsp;=\u0026thinsp;Na\u003csup\u003e+\u003c/sup\u003e/(Ca\u003csup\u003e2+\u003c/sup\u003e + Mg\u003csup\u003e2+\u003c/sup\u003e)\u003c/p\u003e\n\u003cp\u003eAll the values are expressed in meq/L. The ratio classifies groundwater quality into two groups: KR\u0026thinsp;\u0026lt;\u0026thinsp;1 (safe) and KR\u0026thinsp;\u0026gt;\u0026thinsp;1 (unsafe).\u003c/p\u003e\n\u003ch2\u003eMagnesium Ratio (Mr)\u003c/h2\u003e\n\u003cp\u003eIt is one of the important criteria which is used to assess the suitability of irrigation water (Szabolcs \u003cspan class=\"CitationRef\"\u003e1964\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eMR\u0026thinsp;=\u0026thinsp;Mg\u003csup\u003e2+\u003c/sup\u003e/ (Ca\u003csup\u003e2+\u003c/sup\u003e + Mg\u003csup\u003e2+\u003c/sup\u003e) \u0026times; 100\u003c/p\u003e\n\u003cp\u003eIt is proposed by Raghunath (1987) and classifies groundwater quality into two groups: MR ˂ 50% (suitable) and MR\u0026thinsp;\u0026gt;\u0026thinsp;50% (unsuitable).\u003c/p\u003e"},{"header":"Results And Discussion","content":"\u003cdiv class=\"Section2\" id=\"Sec15\"\u003e\n \u003ch2\u003eGroundwater chemistry\u003c/h2\u003e\n \u003cp\u003eThe hydrochemistry of groundwater is influenced by different factors, such as geology and hydrogeology of the study area, chemical weathering, and human activities (Li et al. 2017; Wu et al. 2019). The pH of the groundwater varies from 6.54 to 8.07 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), implying slightly acidic to slightly basic waters, and fall within the standards prescribed for drinking water (WHO 2014). The 8.00 values reflect the possible silicate mineral and carbonate minerals dissolution, which is accompanied by high HCO\u003csub\u003e3\u003c/sub\u003e contents in the groundwater (Appelo and Postma \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Maghraby \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). TDS varied from 225\u0026ndash;8340, with an average of 2165 mg/L, indicating values greater than the acceptable limits of WHO (2005, 1000 mg/L). According to Freeze and Cherry (\u003cspan class=\"CitationRef\"\u003e1979\u003c/span\u003e), 24 groundwater samples (35.29%) fall under the freshwater category (e.g., samples 14, 15, 46, 52, 63, 64, 65, and 68) with TDS less than 1000 mg/L, and 44 samples (64.71%) fall under the brackish to saline water category (e.g., samples 3, 4, 19, 34, 40, 41, and 54) with TDS greater than 1000 mg/L. Na\u003csup\u003e+\u003c/sup\u003e was the most abundant cations (average of 427.90 mg/L), followed by Ca\u003csup\u003e2+\u003c/sup\u003e (average of 150.95 mg/L), Mg\u003csup\u003e2+\u003c/sup\u003e (average of 99.86 mg/L), and K\u003csup\u003e+\u003c/sup\u003e (average of 12.42 mg/L), and B\u003csup\u003e3+\u003c/sup\u003e (average of 0.45 mg/L). Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e was the most abundant anions (average of 635.74 mg/L), followed by SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e (average of 518.34 mg/L), HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (average of 361 mg/L), NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (average of 67.87 mg/L), and F\u003csup\u003e\u0026minus;\u003c/sup\u003e (average of 0.53 \u0026micro;g/L).\u003c/p\u003e\n \u003cp\u003eTable 1 The minimum, maximum, averages, standard deviation and the maximum allowable concentration of the measured physical and chemical parameters.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe quality of groundwater in its natural state indicates the hydrogeochemical nature of groundwater with respect to aquifers (Maghraby \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the hydrogeochemical facies and groundwater types. The triangle diagram of cations shows that sodium and potassium are the leading cations in 52.94% of the groundwater samples, 32.35% fall within the no dominant type, and 14.71% of samples have cations that are dominated by calcium and magnesium. On the anions plot, 70.59% of groundwater samples fall under the sulphate and chloride type, which may be due to the effect of halite dissolution and human activities, while the remaining 29.41% are of the bicarbonate type, indicating a leading role for bicarbonate in groundwater. The investigated groundwater is characterized by the dominance of the alkalines (sodium and potassium) over the alkaline earth elements (calcium and magnesium), and the strong acids (chloride and sulphate) and the nearly balance of the weak acids (bicarbonate). The diamond diagram shows that 32 samples (47.10%) represent the (Na-K)-(SO\u003csub\u003e4\u003c/sub\u003e-Cl) type, 16 samples (23.51%) account (Ca-Mg)-(CO\u003csub\u003e3\u003c/sub\u003e-HCO\u003csub\u003e3\u003c/sub\u003e) type, 16 samples (23.51%) represent (Ca-Mg)-(SO\u003csub\u003e4\u003c/sub\u003e-Cl) type, and 4 samples (5.88%) represent (Na-K)-(CO\u003csub\u003e3\u003c/sub\u003e-HCO\u003csub\u003e3\u003c/sub\u003e) type (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). These water types indicated that the geological composition in the area was mainly gypsum, anhydrite, and halite. The 24 fresh water samples (e.g., samples 14\u0026ndash;17, 46, 51,52, 63, 65, 67, 68) showed the lowest values of TDS and Na\u003csup\u003e+\u003c/sup\u003e, and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e (sample 15), Mg\u003csup\u003e2+\u003c/sup\u003e (sample 52), K\u003csup\u003e+\u003c/sup\u003e and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e (sample 14), F\u003csup\u003e\u0026minus;\u003c/sup\u003e (sample 67), and B\u003csup\u003e3+\u003c/sup\u003e (sample 46).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch2\u003eSuitability Of Groundwater For Drinking\u003c/h2\u003e\n\u003cp\u003eThe average values of Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e, and Mg\u003csup\u003e2+\u003c/sup\u003e were greater than the permissible limit for drinking water (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), especially in most Na-K-SO\u003csub\u003e4\u003c/sub\u003e-Cl and Ca-Mg-SO\u003csub\u003e4\u003c/sub\u003e-Cl water sample types, indicating ion exchange reactions and dissolution of carbonates, evaporites, and silicates (Li et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003ea). Moreover, some of these ions might originate anthropogenically. High Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e may originate from wastewater, domestic effluents, and the metal industry (Reimann and Caritat \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e; Pitt et al. \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e ions can be used as an effective indicator of pollution from fertilizers and sewage. High HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e concentration results from leaky industrial and domestic sewage (Canter, \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e). SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e may be derived from industrial effluents and phosphate fertilizers (Subbarao et al. \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e; Alghamdi et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWater samples from wells 1 and 13 showed F\u003csup\u003e\u0026minus;\u003c/sup\u003e levels that were greater than the permissible limit in drinking water (1.5 mg/L), implying extensive use of phosphatic fertilizers in agricultural areas and leaching of F\u003csup\u003e\u0026minus;\u003c/sup\u003e-rich minerals (Aswathanarayana et al. \u003cspan class=\"CitationRef\"\u003e1985\u003c/span\u003e; Dissanayake and Chandrajith \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Fluoride can also come from runoff and infiltration of chemical fertilizers in agricultural areas, septic and sewage treatment system discharges, and from waste from industrial sources (Smedley et al. \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Edmunds and Smedley, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Notably, 54.41% and 48.53% of the water samples had SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e concentrations greater than the permissible limit (250 mg/L), respectively. Further, 91.18% and 67.65% of the water samples had HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and Na\u003csup\u003e+\u003c/sup\u003e concentrations greater than the permissible limit (200 mg/L), respectively. Furthermore, 70.59%, 55.88%, and 45.59% had Mg\u003csup\u003e2+\u003c/sup\u003e, Ca\u003csup\u003e+\u003c/sup\u003e and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e concentrations greater than the permissible limit (30, 75, 50 mg/L), respectively.\u003c/p\u003e\n\u003cp\u003eGWQI is a mathematical application to transfer large amounts of water quality-related data into a single number, which indicates the suitability of water for drinking purposes (Patel and Vadodaria \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sahu and Sikdar \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e, Alfaifi et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Alshehri et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). It ranged from 24.25 in sample 15 to 637.20 in sample 40 (Supplementary Table\u0026nbsp;2). Based on the calculated values of the GWQI, 13 of the water samples (19.12%) and 16 samples (23.53%) fell under excellent water and good water, respectively (fresh water category). 24 samples (35.29%) fell under poor water, 4 samples (5.88%) fell under very poor water, and the remaining 11 samples (16.18%) were categorized as unsuitable for drinking purposes. The excellent quality samples (samples 14\u0026ndash;17, 46, 51\u0026ndash;53, 63\u0026ndash;65, 67, 68) showed the lowest values of EC, TDS, Na\u003csup\u003e+\u003c/sup\u003e, and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e (sample 15), Mg\u003csup\u003e2+\u003c/sup\u003e (sample 52), K\u003csup\u003e+\u003c/sup\u003e and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e (sample 14), F\u003csup\u003e\u0026minus;\u003c/sup\u003e (sample 67), and B\u003csup\u003e3+\u003c/sup\u003e (sample 46). In the other hand, the wells of unsuitable water for drinking purposes (samples 3, 4, 19, 34, 35, 39\u0026ndash;41, 54, 61, and 62) are mainly due to the highly dissolved soluble ions and characterized by the highest values of EC, TDS, Na\u003csup\u003e+\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e (sample 40), TH (sample 62), Ca\u003csup\u003e2+\u003c/sup\u003e (sample 61), Mg\u003csup\u003e2+\u003c/sup\u003e (sample 34), K\u003csup\u003e+\u003c/sup\u003e (sample 39), NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (sample 54), SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e and B\u003csup\u003e3+\u003c/sup\u003e (sample 4), and F\u003csup\u003e\u0026minus;\u003c/sup\u003e (sample 13).\u003c/p\u003e\n\u003cp\u003eBa was the abundant HMs (average 23.76 \u0026micro;g/l), followed by Se (average 14.27 \u0026micro;g/l), Cr (average 12.90 \u0026micro;g/l), Mn (average 11.68 \u0026micro;g/l), Zn (average 7.51 \u0026micro;g/l), Al (average 6.47 \u0026micro;g/l), U (average 4.40 \u0026micro;g/l), Ni (average 4.39 \u0026micro;g/l), As (average 2.84 \u0026micro;g/l), Cu (average 1.61 \u0026micro;g/l), Pb (average 0.40 \u0026micro;g/l), Cd (average 0.11 \u0026micro;g/l), and Sb (average 0.11 \u0026micro;g/l). The average values of these HMs were less than the permissible limit of WHO standards for drinking water (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrated the spatial distribution of HMs in the groundwater samples. Chromium levels exceeded the permissible limit of WHO standards (50 \u0026micro;g/L) in seven water samples 31, 32, 36, 37, and 43\u0026ndash;45 (53.79, 56.39, 50.91, 57.28, 61.69, 78.93, 69.13 \u0026micro;g/L, respectively) in the east central part of the study area, which is covered by basalt and andesite. Selenium levels exceeded the permissible limit (40 \u0026micro;g/L) in six water samples 23, 25\u0026ndash;27, 40 and 54 (110.72, 48.68, 44.69, 41.11, 40.21, 50.06 \u0026micro;g/L, respectively) in the central part of the study area, which is covered by basalt and andesite. Arsenic levels exceeded the permissible limit (10 \u0026micro;g/L) in the water samples 10 (25.39 \u0026micro;g/L), in the southeastern side of the study area, which is covered by Haliban Formation, and samples 40 and 41 (13.89, 10.21 \u0026micro;g/L, respectively) in the central part of the study area which is covered by basalt and andesite. Zinc levels exceeded the permissible limit (50 \u0026micro;g/L) in water samples 29 and 50 (123.31, 101.02 \u0026micro;g/L, respectively) in the central eastern side of the study area, which is covered by basalt and andesite (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Lead levels exceeded the permissible limits (10 \u0026micro;g/L) in water sample 36 (18.97 \u0026micro;g/L) in the central eastern side of the study area, which is covered by basalt and andesite. Leakage of industrial wastewater might be the main point source of the pollution with Cr, Se, As, Zn, and Pb in some groundwater wells.\u003c/p\u003e\n\u003cp\u003eHPI is a powerful tool for ranking the composite influence of individual HMs on overall water quality (Rizwan et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rezaei et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e: Alfaifi et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). HPI values varied from 2.17 in sample 65 to 43.55 in sample 10, with an average of 10.07 (Supplementary Table\u0026nbsp;2). Accordingly, all water samples fell within the low pollution category (HPI\u0026thinsp;\u0026lt;\u0026thinsp;45). This is due to the fact that the average values of HMs were less than the permissible limit of WHO standards for drinking water. The higher levels of HPI in same water samples, e.g., 10, 36, and 40 (43.55, 41.37, and 28.67, respectively) contribute to the exceeding of the As value (sample 10), Cr and Pb values (sample 36), and As and Se values (sample 4) in comparison with MAC values of drinking water.\u003c/p\u003e\n\u003cp\u003eMI helps evaluate the overall quality of drinking water quickly and takes into account the possible additive effects of HMs on human health (Enaam Abdullah \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rezaei et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). MI values varied from 0.22 in sample 65 to 16.38 in sample 44, with an average of 3.83 (Supplementary Table\u0026nbsp;2). Based on the calculated values of MI, two water samples (46 and 65) fell within very pure category, 19 samples as pure, 14 samples as slightly affected, 13 samples as moderately affected, 7 samples as strongly affected, and 13 samples as seriously affected. The high concentrations of Se in samples 23, Cr in samples 30 and 32 and in samples 36, 37, and 43\u0026ndash;45, and Pb in sample 36 were the reasons for the higher values of MI in these samples.\u003c/p\u003e\n\u003ch2\u003eSuitability Of Groundwater For Irrigation\u003c/h2\u003e\n\u003cp\u003eIrrigation and drainage have often been associated with a loss of water quality caused by salt, pesticides and fertilizer runoff, and leaching (Mateo-Sagasta et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Since Khaybar city is characterized by various agricultural farms produce\u0026mdash;vegetables, dates, and alfalfa\u0026mdash;it is important to evaluate the quality and suitability of groundwater for agricultural usage. Supplementary Table\u0026nbsp;2 presented the results and classification of EC, %Na, SAR, KR, and MR in the present study. The acceptable limit of pH for irrigation water is between 6.5 and 8.4 (Ayers and Westcot 1985). pH varied from 6.54 to 8.07, and accordingly, all water samples fell within the acceptable limit. EC is a good measurement of salinity hazard to crops as it reflects the TDS in groundwater (Dumaru et al. 2021). EC ranged between 347 to 12870 \u0026micro;S/cm, with an average value of 3333 \u0026micro;S/cm. According to the classification by Ayers and Westcot (1985), 8 water samples showed no degree of restriction for irrigation purpose, 34 samples showed slight to moderate restriction, and 26 samples showed severe restriction, particularly those of the Na-K-SO\u003csub\u003e4\u003c/sub\u003e-Cl and Ca-Mg-SO\u003csub\u003e4\u003c/sub\u003e-Cl types.\u003c/p\u003e\n\u003cp\u003eThe % Na values, which indicated that the soluble sodium content varied from 9.52 to 85.66, with an of average 50.55. Water quality classification based on % Na showed that 51 of the groundwater samples (75%) were suitable for irrigation, (3 samples were of excellent quality, 11 samples were of good quality, 37 samples belonged to the permissible category), and 17 samples (25%) were doubtful and unsuitable for irrigation (Supplementary Table\u0026nbsp;2). The doubtful and unsuitable groundwater samples for irrigation, e.g., samples 3, 4, 27, 31, and 40 showed higher levels of Na\u003csup\u003e+\u003c/sup\u003e. Long-term use of water for irrigation with excessive sodium will destroy the soil structure and permeability, leading to soil compaction and reduction of crop yields (Salifu et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Marghade et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Based on SAR results, 85.3% (58 samples) were categorized as excellent for irrigation, 9 samples as good for irrigation, and only 1 (sample 53) as doubtful for irrigation, which of the Na-K-SO\u003csub\u003e4\u003c/sub\u003e-Cl type. The KR is based on the ratio of the concentration of sodium to calcium and magnesium (Kale et al. 2021). It ranged from 0.11 to 6.50, with an average of 1.56 (Supplementary Table\u0026nbsp;2). 23 of the groundwater samples (33.8%) are categorized under safe for irrigation (KR ˂ 1), while 45 water samples (66.2%) fell within unsafe for irrigation (KR\u0026thinsp;\u0026gt;\u0026thinsp;1). Increasing magnesium content in groundwater results in the alkaline nature of the soil and thereby reduces the crop yield (Kumar et al. 2007; Dumaru et al. 2021). MR varied from 16.65 to 84.62%, with an average of 48.07%, suggesting that 37 of the water samples (54.41%) are suitable for irrigation (MR ˂ 50%) and 31 samples (45.59%) are not suitable for irrigation. The unsuitable water samples for irrigation, e.g., samples 12, 25\u0026ndash;27, 40\u0026ndash;42, 54, and 60 showed higher concentrations of Mg\u003csup\u003e2+\u003c/sup\u003e (Supplementary Table 1).\u003c/p\u003e\n\u003ch2\u003eMultivariate Analysis And Possible Sources Of Contamination\u003c/h2\u003e\n\u003cp\u003eThe most common multivariate statistical techniques used to identify hydrogeochemical processes and solute sources and for interpretation of datasets are HCA, PCA, and correlation analysis CA (Yidana et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Khan et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). HCA is an effective tool to divide water samples into different clusters based on groundwater chemistry data (Belkhiri et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). Q mode HCA categorizes the 68 groundwater samples into three clusters, mainly based on TDS and ion levels (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Cluster 1 includes 7 samples (3, 4, 19, 34, 40, 41, and 54), which account for higher levels of TDS (ranged from 5490 to 8340 mg/L, with an average of 6950 mg/L), and the highest values of EC, Na\u003csup\u003e+\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, and Ni (sample 40), Mg\u003csup\u003e2+\u003c/sup\u003e, Sb, and Ba (sample 34), NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (sample 54), SO4\u003csup\u003e2\u0026minus;\u003c/sup\u003e and B (sample 4), and U (sample 19). Increasing NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e might be related to the intensive use of fertilizers and pesticides (Alshahri and El‑Taher 2018). All groundwater samples of cluster 1 belong to Na-K-SO\u003csub\u003e4\u003c/sub\u003e-Cl type, except sample 34, which belongs to Ca-Mg-SO\u003csub\u003e4\u003c/sub\u003e-Cl type. Cluster 2 includes 8 samples (26, 27, 35, 39, 42, 55, 61, and 62), which have medium TDS levels in the study area (ranged from 3250 to 5250 mg/L, with an average of 4018.75 mg/L). Samples 35, 42, 55, 61, and 62 belong to Ca-Mg-SO\u003csub\u003e4\u003c/sub\u003e-Cl type, while samples 26, 27, and 39 belong to Na-K-SO\u003csub\u003e4\u003c/sub\u003e-Cl type. Cluster 3 includes the remaining 53 samples, accounting the lower values of TDS (ranged from 225 to 2930 mg/L, with an average of 1264.64 mg/L), and the lowest levels of EC, TDS, and Na\u003csup\u003e+\u003c/sup\u003e (sample 15), pH, HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, and U (sample 66), TH (sample 30), Ca\u003csup\u003e2+\u003c/sup\u003e (sample 29), Mg\u003csup\u003e2+\u003c/sup\u003e (sample 52), K\u003csup\u003e+\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, and Zn (sample 14), NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (sample 31), F (sample 6), B (sample 46), Cr (sample 2), Mn (sample 43), Ni (sample 22), Cu (sample 11), As (sample 65), Se (sample 67), Ba (sample 44). 23 samples of the cluster 3 belong to Na-K-SO\u003csub\u003e4\u003c/sub\u003e-Cl type, 16 samples to Ca-Mg-CO\u003csub\u003e3\u003c/sub\u003e-HCO\u003csub\u003e3\u003c/sub\u003e type, 10 samples to Ca-Mg-SO\u003csub\u003e4\u003c/sub\u003e-Cl type and 4 samples to Na-K-CO\u003csub\u003e3\u003c/sub\u003e-HCO\u003csub\u003e3\u003c/sub\u003e type. 24 groundwater samples of cluster 3 were of freshwater category (TDS less than 1000 mg/L). R mode HCA is used to classify the parameters into groups based on the similarity of each other (Banoeng-Yakubo et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). It classifies the hydrogeochemical parameters into two clusters (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The first cluster includes EC and TDS, while the second one accounts the remaining hydrogeochemical parameters and HMs.\u003c/p\u003e\n\u003cp\u003ePearson correlation is a technique used to identify similar sources of major ions and HMs with good correlation (Fisher and Mullican \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e; Yin et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The correlation coefficient (r)\u0026thinsp;\u0026lt;\u0026thinsp;0.5 indicates weak correlation, r\u0026thinsp;=\u0026thinsp;0.5 to 0.7 indicates moderate correlation, and r\u0026thinsp;\u0026gt;\u0026thinsp;0.7 indicates strong correlation (Oinam et al. 2012). Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e showed strong and moderate correlations between EC and TDS, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, B, Ni, Cu (r\u0026thinsp;=\u0026thinsp;1.00, 0.75, 0.85, 0.94, 0.98, 0.55, 0.93, 0.70, 0.77, and 0.66, respectively), which indicates a similar origin related to rock-water interaction and evaporation (Khan et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Ca\u003csup\u003e2+\u003c/sup\u003e showed moderate and strong correlations with Mg\u003csup\u003e2+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, Ni, and Cu (r\u0026thinsp;=\u0026thinsp;0.64, 0.55, 0.78, 0.69, 0.70, and 0.56, respectively), reflecting the rock\u0026ndash;water interaction is possible source of these ions in groundwater (Li et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003ea; Zhang et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003eb; Wu 2020). SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e is strongly and moderately correlated with Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (r\u0026thinsp;=\u0026thinsp;0.69, 0.78, 0.89, 0.86, and 0.50, respectively), indicating the possibility of dissolution of halite, gypsum, sulfur-bearing minerals, as well as agricultural and industrial wastewater (Jalali \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhang et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, Ni and Cu are strongly and moderately correlated with Ca\u003csup\u003e2+\u003c/sup\u003e, TH, Mg\u003csup\u003e2+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e. Moreover, Ba is moderately correlated with Mg, Ni, and Sb. Moreover, U is moderately correlated with SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e and B, suggesting that soluble sulphate minerals and excessive use of phosphate-containing fertilizers are sources of these metals (Sharma and Singh 2016; Kale et al. 2021).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;Principal component analysis (PCA) divides the hydrochemical parameters according to the relationship between the different variables and identifies the factors that control the chemistry of the groundwater (Cloutier et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Cortes et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wen et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nine principal components, accounting for 34.56%, 9.15%, 7.80%, 6.65%, 5.51%, 5.41%, 4.74%, 4.01%, and 3.83% of the total variance, were extracted with eigenvalues greater than 1 (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). PC1 is the main factor affecting the hydrochemistry of the groundwater in the study area and showing high positive loading of EC, TDS, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, B, Ni, Cu, and As, reflecting a natural process of the dissolution of rocks in the study area and the increase in groundwater salinity (Rezaei et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kim et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wu 2020; Alshehri et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, the high positive loading with NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e indicates an anthropogenic factor from the agricultural activity (Li et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Alfaifi et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). PC3 shows high loading for Pb and Cd, implying soil leaching from usage of fertilizers and pesticides (Kukrer and Mutlu \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wen et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Alharbi et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). PC5 and PC8 showed high loading for Zn and Mn, respectively, which might originate from mixed anthropogenic and natural factors (Nour et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Al-Hashim et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrincipal component loadings and explained variance of the analyzed parameters with varimax normalized rotation.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003eComponent\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.984\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.546\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.984\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.781\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.855\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.911\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.549\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.965\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.554\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n 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align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.678\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.608\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.819\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n 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\u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.566\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.555\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.876\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.696\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.343\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% of Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCumulative %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this work, GWQI, HPI, MI, SAR, % Na, KR, and MR were used as pollution indices and criteria to evaluate the suitability of groundwater for drinking and irrigation purposes in Harrat Khaybar, western Saudi Arabia. Hydrogeochemical methods and multivariate statistical methods are employed to identify the hydrochemistry characteristics and controlling mechanisms of the groundwater in the study area. The findings were the following:\u003c/p\u003e\n\u003cp\u003e1. The average concentration of the ions and HMs was in the following descending order: Cl\u003csup\u003e\u0026ndash;\u003c/sup\u003e \u0026gt; SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026ndash;\u003c/sup\u003e \u0026gt; Na\u003csup\u003e+\u003c/sup\u003e \u0026gt; HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e \u0026gt; Ca\u003csup\u003e2+\u003c/sup\u003e \u0026gt; Mg\u003csup\u003e2+\u003c/sup\u003e \u0026gt; NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e \u0026gt; K\u003csup\u003e+\u003c/sup\u003e \u0026gt; F\u003csup\u003e\u0026ndash;\u003c/sup\u003e \u0026gt; B\u003csup\u003e3+\u003c/sup\u003e \u0026gt; Ba \u0026gt; Se \u0026gt; Cr \u0026gt; Mn \u0026gt; Zn \u0026gt; Al \u0026gt; U \u0026gt; Ni \u0026gt; As \u0026gt; Cu \u0026gt; Pb \u0026gt; Cd \u0026gt; Sb. Average values Cl\u003csup\u003e\u0026ndash;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026ndash;\u003c/sup\u003e, HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, and TDS were greater than the permissible limit for drinking water while values of Cr, Se, As, Zn, and Pb were greater than the permissible limit in some individual samples.\u003c/p\u003e\n\u003cp\u003e2. On the whole, 3\u003cspan dir=\"RTL\"\u003e5\u003c/span\u003e.29% of the groundwater samples\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003efell under the freshwater category and 64.71% fell under the brackish to saline water category. The freshwater category includes all samples of the Ca-Mg-CO\u003csub\u003e3\u003c/sub\u003e-HCO\u003csub\u003e3\u003c/sub\u003e and Na-K-CO\u003csub\u003e3\u003c/sub\u003e-HCO\u003csub\u003e3\u003c/sub\u003e types, except one sample while most samples of the Ca-Mg-SO\u003csub\u003e4\u003c/sub\u003e-Cl the Na-K-SO\u003csub\u003e4\u003c/sub\u003e-Cl types were in the brackish to saline category.\u003c/p\u003e\n\u003cp\u003e3. GWQI indicated that 42.65% of the groundwater samples\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003efell under excellent and good water for drinking purposes, and 57.15% fell under poor, very poor water, and unsuitable for drinking. MI results indicated that 51.47% samples fell within very pure, pure, and slightly affected categories and 48.53% fell under moderately affected, strongly affected, and seriously affected categories. The irrigation criterion revealed that more than half of the groundwater wells were suitable for irrigation.\u003c/p\u003e\n\u003cp\u003e4. Multivariate statistical methods revealed that the dissolution of rock forming minerals as well as domestic, agricultural, and industrial effluents are the factors that control the geochemistry of groundwater and HM pollution in some wells in the study area.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe authors extend their appreciation to the Abdullah Alrushaid Chair for Earth Science Remote Sensing Research for funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read the manuscript and agreed to publish the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFA and AS. designed the study and was responsible for the data collection as well as analysis of the data and wrote the initial draft; FA and AM were responsible for the data analysis, data curation and modeling as well as editing of the initial draft and supervised the project; BP helped in the data preparation as well as provided technical support; AR provided software guidance, helped in validation as well as reviewed the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch has been funded through Abdullah Alrushaid Chair for Earth Science Remote Sensing Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest has been reported among the authors on any issue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eWe wish to confirm that there are no known conflicts of interest associated with this publication, and there has been no significant financial support for this work that could have influenced its outcome.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eAbderrahmanWA, Al-Harazin IM (2008) Assessment of climate changes on water resources in the Kingdom of Saudi Arabia, GCC Environment and Sustainable Development Symposium, 28\u0026ndash;30\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAghazadeh N, Moghaddam AA (2010) Investigation of hydrochemical characteristics of groundwater in the Harzandat aquifer, Northwest of Iran. 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Water 10:80\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Quality evaluation, Groundwater contamination, Harrat Khaybar, Saudi Arabia","lastPublishedDoi":"10.21203/rs.3.rs-1899157/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1899157/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEvaluating the suitability of groundwater for human use is essential for water supply and health in arid and semi-arid regions. In this work, a total of 68 groundwater samples were collected from Harrat Khaybar, western Saudi Arabia to evaluate the suitability of groundwater for drinking and irrigation purposes and to document the controlling mechanisms using pollution indices and multivariate statistical methods. The results showed that the average values of the ions Cl\u003csup\u003e–\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2–\u003c/sup\u003e, HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e–\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e–\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, and total dissolved solids (TDS) were greater than the permissible limit for drinking water while the average values of heavy metals (HMs) were less than the permissible limit, with exceeding limits of Cr, Se, As, Zn, and Pb in some individual samples. Piper diagram indicated that 47.10% of the water samples are of Na-K-SO\u003csub\u003e4\u003c/sub\u003e-Cl type, 23.51% of Ca-Mg-CO\u003csub\u003e3\u003c/sub\u003e-HCO\u003csub\u003e3\u003c/sub\u003e type, 23.51% of Ca-Mg-SO\u003csub\u003e4\u003c/sub\u003e-Cl type, and 5.88% of Na-K-CO\u003csub\u003e3\u003c/sub\u003e-HCO\u003csub\u003e3\u003c/sub\u003e type. Based on the groundwater quality index (GWQI), 29 of the groundwater samples were categorized as excellent and good water for drinking purposes, while 29 samples fell under poor, very poor water, and unsuitable for drinking. Additionally, results of heavy metal pollution index (HPI) indicated that all water samples fell within the low pollution category, while the metal index (MI) results indicated that 35 samples fell within very pure, pure, and slightly affected categories, while 33 samples fell in the moderately, strongly, and seriously affected categories. Results of electrical conductivity (EC), sodium adsorption ratio (SAR), sodium percentage (%Na), Kelly’s ratio (KR), and magnesium ratio (MR) revealed that 33.82–98.5 % of the water samples are suitable for irrigation depending on the parameter type. Ions exchange reactions and dissolution of carbonates, evaporites, and silicates, as well as industrial and domestic effluents and intensive use of fertilizers and pesticides were the natural and athropogenic factors controlling the groundwater geochemistry in the study area and HM pollution in some wells.\u003c/p\u003e","manuscriptTitle":"Groundwater quality assessment in Harrat Khaybar, western Saudi Arabia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-22 18:23:30","doi":"10.21203/rs.3.rs-1899157/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d3f75c93-db64-4991-bcee-4c8b352a25b9","owner":[],"postedDate":"August 22nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-10-05T02:27:32+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-22 18:23:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1899157","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1899157","identity":"rs-1899157","version":["v1"]},"buildId":"re_ckhLnmML6MCF96OHNJ","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}