Detection of Groundwater Quality Changes in Minia Governorate, West Nile River

Abstract

The need for freshwater supplies is increasingly rising according to the increase in the inhabitants’ expansion and economic growth. Available water resources are reduced by pollution and overpumping. This research’s prime objective is to study changes in the water quality of the Pleistocene aquifer in Minia Governorate. Historical hydro-chemical data of the groundwater in two years 2009 and 2019 were used to study the changes in the groundwater quality of the Pleistocene aquifer under the impact of the recharge and discharge processes. The Nile River, and the Al-Ibrahimia and Bahr Youssef Canals are considered the main sources of aquifer recharge. Collected data from 53 groundwater wells in the Pleistocene aquifer were used to calculate the sodium adsorption ratio (SAR), sodium percentage (Na%), Kelly index (KI), Soluble Sodium Percentage (SSP), magnesium ratio (MR%), permeability index (PI) and chloro-alkaline index (CAI). These data were used to evaluate and detect the quality and changes in groundwater through the years 2009 and 2019 using spatial mapping in the geographic information system (GIS). The values of SAR, KI and Na% varied between 0.06–1.22, 0.02–0.57 meq/L and 3.7–37.63%, respectively, in the year 2009, but these values changed to 0.4–0.75, 0.16–0.28 meq/L and 15.07–23.44% in the year 2019. The calculated MR and PI values indicate that 100% of the groundwater samples were in the “suitable” category. The calculated SSP reflects no changes in groundwater alkalinity between the years 2009 and 2019. The hydro-chemical analysis of the studied groundwater (G.W.) samples shows high pollution levels caused by Pb and Fe in some parts of the study area. Pb was found to be >40 µg/L in the middle parts, whereas Fe was found with high levels in 27% of the studied groundwater samples. The localities of these samples were affected by pollution from the industrial wastewater from the sugar factory of Abou-Qarqas city (e.g., El-Moheet drain), the fertilizer leaching process and pesticides seeping into groundwater from soils and agricultural wastewater.

1. Introduction

Water quality is one of the critical factors directly concerned with the health of humans and other living beings [1]. It is governed by both natural processes and anthropogenetic effects [2]. For sustainable development, monitoring of water quality is an important means to provide critical data for water management [3] as it is a critical issue in many countries. Groundwater quality in a region is highly dependent on the quality and scope of the industrial, agricultural and other human-related activities [4].

Irrigation water quality is generally defined in terms of total dissolved solids, major anions and cations. However, whether water quality is good or bad is not merely reflected in terms of physical and chemical parameters [5].

Water quality for irrigation is defined by the concentration and type of dissolved solids and salts [6]. Irrigation water quality information holds critical importance for understanding the changes in product quality and the required modifications in water management [7].

Irrigation water quality should be continuously monitored for sustainable development in agricultural areas. Industrialization growth led to the release of hazardous industrial wastewater into the environment. Consequently, ground and surface water sources were affected. Therefore, it is necessary to evaluate water resources’ quality in terms of chemical and biological pollution [8,9,10]. In Egypt, the change in the Nile River’s water quality is the main problem and is a serious and continuous concern due to the expansion of agricultural and industrial activities in addition to the effect of agricultural return, and municipal, industrial and river ships’ wastewaters [11,12]. The continued water deficiency in Egypt and the probability of supplemental deficits in Nile River water, especially after the countries of the Nile Basin began to construct new dams, is directing the efforts of the Egyptian government to find solutions for this problem by identifying additional new water resources [13,14,15,16]. Responding to government endeavors, many huge projects have been launched to fill the gap between the lack of water resources and the rapidly increasing population. The most important of these projects at present is a new project for the reclamation of 1.5 million acres, recently launched by the Egyptian government. Minia Governorate can be considered a part of this huge project. The desert area to the west of Minia is the largest area to be subjected to reclamation during the past ten years depending mainly on groundwater extraction from the groundwater aquifer. In this research, groundwater quality estimation was introduced by using different parameters that define water quality. The Soluble Sodium Percentage (SSP), sodium adsorption ratio (SAR) and some quality indexes were primarily calculated to evaluate groundwater (G.W.) quality for irrigation purposes. Then change detection in the Quaternary aquifer’s water quality through the years 2009 and 2019 in Minia Governorate was calculated using historical hydro-chemical data and GIS spatial mapping.

The evaluation of water quality has a vital rule in the planning, design as well as operation of irrigation systems, especially in arid and semi-arid regions [17,18]. Water quality monitoring methods and degradation through different sources have been widely considered [19]. The monitoring procedure includes activities and programs that check the water resources’ quality and the performance of the system of pollution reduction [20]. As irrigation uses more than two-thirds of the available freshwater resources in the world, agriculture is considered the major consumer of water [21]. For sustainable development in agricultural areas, irrigation water quality should be continuously monitored. Some approaches have been applied for the evaluation of the irrigation water effect on soils and plants. Numerous researchers have used hydro-chemical indices such as SAR, Na%, KI, MR, PI and the Index of Base Change, the chloro-alkaline index (CAI) and the Hydro-chemical Facies Evolution Diagram [22,23,24,25,26,27]. Instead of using a single parameter, for better results, the combined use of the chemical analyses of all ions is expected to be a better solution [28]. Many uses of water quality data cannot be met with an index. An index is useful for purposes of comparative and general questions (what places have poor water quality?) [29,30].

In this research, SAR, KI, Na%, PI, MR and CAI were primarily calculated to evaluate surface water quality for irrigation purposes.

2. Study Area

Minia Governorate is a part of the Middle East and North Africa (MENA) region, one of the world’s most water scarce regions. In the last few years, some countries in this region have faced a crisis of water and there is an expectancy that the water situation will deteriorate from bad to worse [31]. In this regard, Minia Governorate has characteristics similar to most of the governorates in MENA region countries such as Morocco, Algeria, Tunisia, Libya, Mauritania, Sudan, Lebanon, Jordan, Iraq, Syria, Saudi Arabia, Kuwait, Bahrain, Qatar, the United Arab Emirates, Oman and Yemen, which suffer from water shortages [32,33]. Therefore, the methodology adopted in this study can be applied to similar areas in MENA regions. Furthermore, the methodology can be applied to Assiut, Suhag, Qena and other governorates in Egypt. It was chosen to represent several governorates in the MENA region and Egypt on the Nile River main valley and its Delta. The nature of water resources in Minia Governorate is characterized by the diversity of its water resources, including surface, groundwater and recycled agricultural drainage water [34]. Consequently, groundwater quality in the study area is negatively impacted as the aquifer is continuously renewed from the theses sources and mainly from the Nile waters [34,35,36].

Minia Governorate covers about 56,587 km² and is in central Egypt, north of Upper Egypt, on the western bank of the Nile, Egypt. It extends between latitudes 27°35′32.39″ and 28°46′44.04″ N and longitudes 28°27′51.90″ and 32°34′0.31″ E and its boundaries are as follows: Bani Souef Governorate in the north, Assuit and Al-Wadi Al-Gadid Governorates in the south, Eastern desert in the east and Al-Giza Governorate in the West (Figure 1). The Governorate is divided administratively into nine cities: El-Edwa, Maghagha, Beni-Mazar, Matai, Samalut, El-Minia city, Abou-Qarqas, Mallawi, and Der Mawas, from north to south, respectively, as shown in Figure 1.

The study area location is a part of the Minia Governorate. It is located between latitudes 27°35′32.39″ N and 28°45′44.04″ N and longitudes 30°30′51.90″ E and 31°10′0.31″ E (Figure 1). The freshwater canals network in the study area has about 854 canals. Egypt depends by about 97% on Nile River water. The Nile River covers the entire length of the Minia Governorate (approximately 148 km), and the city is on its banks. The Governorate extends on the axis of the Nile River from Km. 612 to Km. 760 north. Assiut Barrages on the Nile are the controlling constructions of the waters of Minia Governorate and the governorates following it that are irrigated from Al-Ibrahimia canal and its branches [36]. The Governorate is fed the surface waters from the Nile River in front of Assiut Barrages through Al-Ibrahimia canal. Al-Ibrahimia canal carries water from the front of Assiut Barrages to Dairoot center in the north, from which Bahr Youssef branches out to feed the governorate from the west.

3. Geological and Hydrogeological Setting

An arid climate characterizes the study area. A maximum monthly mean temperature of 28.7 °C is recorded in July, while a minimum of 11.8 °C is recorded in January. The mean relative humidity in winter and at the end of autumn reaches 68.8%, while it drops to approximately 39.2% during summer [36,37,38]. The maximum evaporation rate is 14.85 mm/month during June, and the minimum reaches 3.54 mm/month through December [36,37]. Hydrogeologically, the Quaternary aquifer is the main aquifer in the area. It is a semi-confined aquifer that is composed of sands and gravel [32,38]. This aquifer lies directly above the Pliocene clay. This clay acts as the impermeable layer of the aquifer (aquiclude) and the fissured Eocene limestone (Figure 2a) [38,39,40,41,42] and the hydrogeological cross-section (A-A) (East–West) in Figure 2a is shown in Figure S1 in the Supplementary Materials.

It is covered by sedimentary rocks that range in age from the Middle Eocene to Pleistocene; the lithology of the study area formation consists of a thick sequence of Middle Eocene fossiliferous limestone. It has some layers of sandy, chalky and marly intercalation; Pliocene deposits are exposed in the northwestern part. Plio-Pleistocene deposits constitute the earliest types of deposits laid by the Nile and form pre-Nile terraces. Pleistocene deposits are composed of massive cross-bedded fluvial sand with gravel and clay interpolate that acts as the main water-bearing formation in the study area; more about the study area lithology is discussed in [35,38,40]. Generally, this aquifer thickness decreases in the direction of the Eocene plateau and is connected hydraulically “with the underlying aquifer (Eocene) through the faults” [15,43]. The Nile River and irrigation canals are the main sources of recharge to the Quaternary aquifer. The groundwater levels vary between 29.4 m and 43 m in the northern part and southern part of the study area, respectively, while the depth of the groundwater (water table) in the Quaternary aquifer varies between 0.9 m and 8 m [35,36,38]. Figure 2b indicates that the groundwater levels decline from the southwest to the northeast direction and have a variable hydraulic gradient.

Abdel Moneim [44] concluded that the total amount of the Quaternary aquifer abstraction is higher than the leakage from the Nile River and adjacent areas and the recharge water from irrigation. This led to an annual decrease in the groundwater storage in the study area. The ratio between the recharge rate and extraction rate concerning the Quaternary aquifer should be kept constant to sustain the aquifer’s performance. In fact, the hydraulic potentials of the surface water in the irrigation and drainage system affects the levels of the groundwater in the aquifer [44]. The main direction of the groundwater flow in the study area is in the same direction as the Nile water flow, from the southwest to the northeast [35,36,38].

4. Methodology

The locations of the canal network and groundwater well in the study area were spatially digitized in ArcGIS, 10.3 software [27] based on Google earth and Landsat images. We tracked the concentrations of the major cations (Na⁺, Ca⁺², Mg⁺², K⁺), major anions (HCO₃⁻, Cl⁻ and SO₄⁻²) in addition to some of trace elements. The groundwater samples were taken by means of well pumps after a pumping period of at least 1.5 h. Prerinsed polypropylene bottles were filled with the samples, sealed tightly [38,45]. The reviewed parameters’ values in the study area were obtained and spatial thematic maps were constructed for the selected seven criteria by using GIS Kriging interpolation method [10] inside Spatial Analyst Tool [37,38]. The values of the parameters were interpolated from the studies listed in

Table 1. The reviewed research for groundwater quality change detection for years 2009, 2019.

Author	Study Area	Objectives	Methodology	Parameters	Results
Ismail, et al. [38]	Along the western bank of the Nile Valley facing El-Minia Governorate and lies between latitudes 27°35 and 28°45′ N and longitudes 30°30′ and 31°10′ E	Investigate the water quality and determine the hydrogeochemical evolution and evaluation in the Quaternary aquifer west of the Nile River, El-Minia	Statistical and hydrogeochemical modeling by measuring the parameters for the collected samples from the Nile River, Ibrahimia canal, Pleistocene aquifer and El-Moheet drain	TDS, major ions and trace elements and saturation indices distribution of the Pleistocene aquifer	The Nile River recharges the Pleistocene aquifer with depth at the hydrogeologically contacted areas. Nearly the recharge area. TDS are lower (<500 ppm). Some groundwater needs remediation technology before use. The groundwater pumping management should also be considered.
El Kashouty [45]	Along the western bank of the Nile Valley facing El-Minia Governorate and lies between latitudes 27°35 and 28°45′ N and longitudes 30°30′ and 31°10′ E	Assessing the hydrogeochemical characteristics of the groundwater in the Quaternary aquifer, El-Minia, Egypt	Collected groundwater samples, then analyzed using an integration of geochemical modeling and geo-statistical techniques. Charge Balance Error (CBE) was computed to validate the quality of water analyses. The analysis was performed by using the UNISTAT software.	pH, EC, TDS, major ions, and heavy metals	All samples classified as Ca-HCO3 water type and calcite precipitate. 49% of the collected groundwater samples exhibit high values of Mn, Ni, Co, As, Hg, Cr, Cd, and Se. These high values could be explained by the leaching of El-Moheet drain

. in addition to the data collected from unpublished report and studies we get from Egyptian Ministry of Water Resources and Irrigation. These factors are important for determination of groundwater quality [17,18,37,46], the calculated statistics of groundwater quality parameters are shown in Table 2 and their classification according to FAO guidelines for interpretation of water quality for irrigation is shown in Table 3. The geodatabase for these samples was constructed and linked to the digital point data in Shapefile format. SAR, SSP%, KI, Na%, MR% PI and CAI were calculated, these indicators are of high importance in determining water quality for various uses of drinking, irrigation, etc. Therefore, they are considered critical indicators and were calculated to detect and observe pollution in inhabited places that directly impact water pollution [23,24,26,47,48]. Water quality estimation as well as the trace elements was thus introduced by using these different parameters that define water quality to determine and detect the suitability and changes in area’s groundwater quality for irrigation, then the indices were calculated statistically, Table 4 and Table 5. Water quality of the area was categorized in terms of SAR, KI, SSP%, Na%, MR, PI and CAI parameters as well as trace elements on the basis of the standard value intervals given in (Table 6 and Table 7) and previous irrigation water classifications [49,50,51,52,53]. The historical records of the hydro-chemical parameters for the years 2009 and 2019 [53] were used through the present work to detect the changes in water quality (Figure 3). Coordinates of the studied wells are shown in Table S1 in the Supplementary Materials. The overall flowchart of the used methodology is described in Figure 4.

5. Results and Discussions

5.1. Groundwater Quality

By tracking the results of previous studies in the study area in the years 2009 and 2019, it was found that there was a change in groundwater quality, major cations (sodium Na⁺, potassium K⁺, magnesium Mg²⁺, calcium Ca²⁺) and major anions (chlorine Cl⁻, bicarbonate HCO₃⁻, sulfate SO₄²⁻) in groundwater quality. From the calculated indices for groundwater quality in the study area, we found that the concentrations of major cations and major anions were mostly still within the accepted ranges through the studied years 2009 and 2019, which reflects the continuous connection and recharge from the Nile and surface water.

The average potassium in the groundwater in the region was found to be 0.07 meq/L in 2009, which rose in 2019 with an average value of 0.08 meq/L. The activities of agricultural may be the main reason for the increases in the content of potassium in groundwater [54,55]. In 2009, the average value of calcium ion was 1.81 meq/L with a maximum value of 2.8 meq/L that rose to 2.05 meq/L with a maximum value of 3.00 meq/L in 2019; this may be due to the weathering of limestone rocks as they are calcium-bearing rocks [38,56]. The average value of magnesium was 1.35 meq/L in 2009, which rose to the average value of 1.63 meq/L in 2019; excess calcium and magnesium may be produced by silicate weathering or gypsum, magnesium and chloride dissolution [18]. Bicarbonates represent the dominant anion in the study area followed by chloride and sulfates. The highest concentration of sulfates (0.33 meq/L) was observed in 2009 and rose to 0.42 meq/L in 2019. High sulfate content may be due to human activities, the breakdown of the organic substances of weathered soils, and fertilizer and sulfate leaching [57,58]. Sulfate is generally assumed to be the result of the “direct dissolution of gypsum (or anhydrite) or the neutralization of acid waters by limestone or dolomite” [18]. The values of sulfate were still within the desirable limits. Chloride content was within the permissible limits in both years, 2009 and 2019 (<250 mg/L). On the other hand, Ca was found to be the dominant cation in the study area followed by Mg, Na and then K; the table below shows the statistics of water quality parameters and their percent of change during the period from 2009 to 2019 in the study area, Table 2.

Table 2. Calculated statistics of groundwater quality parameters for the years 2009 and 2019.

Parameter	Year 2009							Year 2019
	Units in meq/L							Units in meq/L
	Na	K	Ca	Mg	Cl	HCO3	SO4	Na	K	Ca	Mg	Cl	HCO3	SO4
Maximum (MAX)	1.74	0.11	2.80	2.08	1.13	5.41	0.33	1.13	0.15	3.00	2.08	1.27	5.25	0.42
Minimum	0.78	0.04	1.00	0.17	0.39	1.78	0.04	0.43	0.06	1.35	1.00	0.45	2.62	0.00
Average	1.18	0.07	1.81	1.35	0.71	3.63	0.18	0.85	0.08	2.05	1.63	0.81	4.02	0.2
Standard deviation	0.25	0.02	0.48	0.49	0.21	0.95	0.06	0.13	0.02	0.34	0.27	0.15	0.53	0.07
% of change in water quality parameters during the period from 2009 to 2019
Parameter	Na		K		Ca		Mg		Cl		HCO3		SO4
% of change	−27.97%		14.28%		13.26%		20.74%		14.08%		10.74%		11.11%

Table 2. Calculated statistics of groundwater quality parameters for the years 2009 and 2019.

Parameter	Year 2009							Year 2019
	Units in meq/L							Units in meq/L
	Na	K	Ca	Mg	Cl	HCO3	SO4	Na	K	Ca	Mg	Cl	HCO3	SO4
Maximum (MAX)	1.74	0.11	2.80	2.08	1.13	5.41	0.33	1.13	0.15	3.00	2.08	1.27	5.25	0.42
Minimum	0.78	0.04	1.00	0.17	0.39	1.78	0.04	0.43	0.06	1.35	1.00	0.45	2.62	0.00
Average	1.18	0.07	1.81	1.35	0.71	3.63	0.18	0.85	0.08	2.05	1.63	0.81	4.02	0.2
Standard deviation	0.25	0.02	0.48	0.49	0.21	0.95	0.06	0.13	0.02	0.34	0.27	0.15	0.53	0.07
% of change in water quality parameters during the period from 2009 to 2019
Parameter	Na		K		Ca		Mg		Cl		HCO3		SO4
% of change	−27.97%		14.28%		13.26%		20.74%		14.08%		10.74%		11.11%

5.1.1. Total Dissolved Solids (TDS)

TDS is one of the vital water quality determination factors, for the purposes of both drinking and irrigation, in most irrigation situations. Salinity level is the primary water quality factor, where salts affect both the yield of crop and the soil structure [59,60]. In this study, maximum salinity values varied between 750 mg/L in 2009 and decreased to 580 mg/L in 2019, reflecting the recharging from surface water. The concentration of TDS is more than the desirable limit of 500 mg/L [61,62] in northwest and south groundwater samples. Abou Heleika et al. [39] and Ibrahem [63] concluded that TDS concentration ranged from 355.3 to 908.8 mg/L in 2018, which agrees to a large extent with the present TDS values. The spatial distribution map of TDS classifies the study area into five classes, and there is a change in the TDS range in the whole of the study area, Figure 5a.

5.1.2. Major Ions

Sodium concentration values were around 18 and 40 mg/L during the year 2009 (≈0.78 meqL⁻¹ and 0.60 meqL⁻¹, $\left[\mathrm{Na} \left({\mathrm{meqL}}^{-1}\right)=\frac{\mathrm{Na} \left(\mathrm{mg}/\mathrm{L}\right)}{23}\right]$, where the largest concentration was 1.13 meqL⁻¹ during year 2019. The spatial distribution map of Na classifies the study area into five classes from <15 and >31 mg/L, and there is a change in the Na range in the whole of the study area, Figure 5b.

Calcium (Ca) concentration values varied between 20 and 56 mg/L through 2009, where during 2019, the concentrations rose to vary between 27 and 60 mg/L. The loss of soil calcium might be due to the strategy of fertilization and gypsum application, which increases the concentration of calcium in the well’s water [64]. The spatial distribution map of Ca classifies the study area into five classes from <30 and >45 mg/L, and there is a change in the Ca range in the middle and southern parts of the study area Figure 6a.

Bicarbonate ions’ concentration ranged between 1.87 and 5.41 meqL⁻¹ in 2009 and changed to range between 2.62 and 5.25 meqL⁻¹ in 2019. These values indicate that there is an increasing problem. However, the bicarbonate values of water show their ability to precipitate Ca²⁺ in the soil. The HCO₃ concentrations in groundwater are mainly sourced from the dissolution of carbonic acid in the aquifers and carbonate weathering [37,65]. The spatial distribution map of HCO₃ classifies the study area into five classes from <170 and >260 mg/L, and there is a change in the HCO₃ range in the north and south parts of the study area, Figure 6b.

Magnesium ions’ concentration ranged from 0.17 to 2.08 meqL⁻¹ in groundwater samples collected during 2009 and rose to range between 1.00 and 2.08 meqL⁻¹ in 2019. The spatial distribution map of Mg classifies the study area into five classes from <12 and >21 ppm, and there is a change in the Mg range in the middle and southern parts of the study area, Figure 7a.

Potassium concentration recorded maximum values of 0.11 meqL⁻¹ in 2009 and these rose to 0.15 meqL⁻¹ in 2019. Potassium may be subject to fixation by clay colloids in clayey soils and losses by the leaching of soils. The spatial distribution map of Ca classifies the study area into five classes from <2.5 and >3.1 ppm, and there is a change in the K range in the whole of the study area, Figure 7b.

Sulfate concentration had a maximum value of 0.33 meqL⁻¹ in 2009 which rose to an average maximum of 0.42 meqL⁻¹ in 2019. The SO₄²⁻ concentrations in the current groundwater are presented in Table 2. The spatial distribution map of SO₄²⁻ classifies the study area into five classes from <7 and >12 ppm, Figure 8a. According to Table 2, Ca⁺² > SO₄⁻², indicating a calcite/dolomite source [18,66].

Chloride concentration had a maximum value of 40 mg/L in 2009 which rose to a maximum of 45 mg/L in 2019. According to the water classification of [61,67], the chloride content in the current groundwater indicates the “No problem” class, which has values less than 4 as presented in Table 3. The spatial distribution map of Cl classifies the study area into five classes from <21 and >30 mg/L, Figure 8b.

Table 3. The FAO guidelines for interpretation of water quality for irrigation according to [61].

Irrigation Problems	Degree of Problem
	No Problem	Increasing Problem	Severe Problem
Specific ion toxicity (affects sensitive crops):
Chloride (meq/L)	<4	4–10	>10
Miscellaneous effects (affects susceptible crops):
HCO3 (meq/L) (overhead sprinkling)	<1.5	1.5–8.5	<8.5

Table 3. The FAO guidelines for interpretation of water quality for irrigation according to [61].

Irrigation Problems	Degree of Problem
	No Problem	Increasing Problem	Severe Problem
Specific ion toxicity (affects sensitive crops):
Chloride (meq/L)	<4	4–10	>10
Miscellaneous effects (affects susceptible crops):
HCO3 (meq/L) (overhead sprinkling)	<1.5	1.5–8.5	<8.5

5.2. Sodicity Hazard (Sodium Percentage, Na%)

Sodium concentration is an important parameter used for the classification of irrigation water quality [68]. The level presence of sodium in water is important as it influences the minerals of clay and erosion and drainage problems as well as the potential for distributing structural stability. Water sodicity is usually described in terms of the relative proportion of sodium cation compared with the divalent cations (i.e., magnesium and calcium).

The amount of sodium in irrigation water is generally defined as Na%. Sodium concentration in water induces the exchange of Ca²⁺ and Mg²⁺ ions. In turn, this exchange process reduces permeability in soil, which causes poor internal drainage [69]. Consequently, sodium is regarded as an important ion, for its reactivity with soil, its classification of irrigation water and because it reduces permeability [70,71]. Na% is used to determine the quality of water for agricultural purposes. Irrigation water with high Na% causes growth retardation in plants [72]. The author of [73] proposed a classification based on sodium percentage, Na%, via the calculation of the relative proportion of all cations available in water (Equation (1)) [71].

$$\mathrm{Na}\%=\frac{\mathrm{Na}+\mathrm{K}}{\mathrm{C}\mathrm{a}+\mathrm{Mg}+\mathrm{Na}+\mathrm{K}}\times 100$$

(1)

where Na, K, Ca, and Mg are sodium, potassium, calcium and magnesium concentrations, respectively (all ionic concentrations expressed in meq/L). From [71], the calculations of Na% showed that their values varied between 3.7 and 37.63 meq/L during the year 2009, whereas they varied between 15.07 and 23.44 meq/L in the year 2019, Table 5 and Table 6, and Figure 9a. As indicated by the calculated Na% values in 2009, 96.23% of the samples received were in the “good” category, and 3.77% were in the “excellent” category, Table 6. On the other hand, through 2019, the calculated Na% values showed 37.7% of the samples were in the “excellent” category, and 62.3% of the samples were in the “good” category. Abou Heleika et al. [39] also came to a conclusion that approximated these values during 2018, where Na% ranged between 20 and 40, similar to our values.

5.3. Soluble Sodium Percentage (SSP)

Sodium percent is a vital factor for studying sodium hazards. It is also used for judging the quality of water for purposes of agriculture. A high sodium percentage in water that is used for irrigation purposes might reduce the permeability of soil and affect the growth of plants. Todd and Mays [72] suggested a formula for calculating the Soluble Sodium Percentage (SSP) for groundwater and that is [74]:

$$\mathrm{SSP}=\frac{\mathrm{Na}}{{\displaystyle \sum }\mathrm{Cations}}\times 100$$

(2)

where Na is the sodium and cations is the (Na⁺, Ca⁺², Mg⁺², K⁺) concentrations (all ionic concentrations expressed in meq/L). The calculated Soluble Sodium Percentage values of groundwater in the study area varied between 20.99% and 36.99% during the year 2009, whereas they varied between 13.52% and 19.97% during the year 2019, indicating that 100% of the groundwater in the two years was in the category “suitable”, Figure 9b and Table 5.

5.4. Sodium Adsorption Ratio (SAR)

This parameter was first proposed by [74,75,76], and it is used to evaluate the Na ions’ tendency for adsorption on soil, and the dissolved cation levels’ tendency to enter into cation exchange regions in soil. High sodium concentrations affect the permeability of the soil and directly affect the total salinity of water [77]. Such concentrations can have toxic effects on delicate products [78]. The detection of salinity hazards depends on electrical conductivity measurements. The concept of SAR is used to detect a probable sodium hazard [79,80]. SAR is calculated by using Equation (3) [76], and Figure 10a shows the variations in its values in the study area.

If there is a high sodium (Na⁺) content in irrigation water, it can displace exchangeable Ca²⁺ and Mg²⁺ from the clay minerals of the soil, followed by the replacement of these cations by sodium. Sodium-saturated soil peptizes and misses their permeability, thus decreasing their fertility and suitability for cultivation [81,82], a situation that eventually damages the structure of soil, and leads to permeability, which ultimately affects the fertility status of the soil and crop yields are reduced [83].

Some methods have been proposed to express sodium hazard. SAR is the parameter that has come into wide use for the prediction of sodium hazard. It was proposed by [79], which classified irrigation water on the basis of SAR values into four classes (S1 < 10, S2 ranges between 10–18, S3 ranges between 18–26, S4 > 26). The SAR of water is calculated as follows [75,77]:

$$\mathrm{SAR}=\frac{\mathrm{Na}}{\sqrt{\frac{\mathrm{Ca}+\mathrm{Mg}}{2}}}$$

(3)

The sodium adsorption ratio of groundwater in this study was almost less than 10 and fell under the category of S1 indicating low alkali hazards according to [79]. Additionally, Sallam et al. [84] concluded that the same study area is in a low SAR hazard category.

According to [80]’s classification based on SAR values (Table 5), all calculated values of SAR in the study area were found to be in the category “Excellent” for irrigation during both years 2009 and 2019, and hence no alkali hazard is estimated for crops. The statistics of the calculated SAR are shown in the table below, Table 4, and Figure 10a.

Table 4. Statistics of calculated indices for groundwater quality and their percent of change during the period from the year 2009 to the year 2019.

Parameter	Year 2009						Year 2019
	Calculated Indices						Calculated Indices
	SSP	SAR	KI	Na%	PI	MR	SSP	SAR	KI	Na%	PI	MR
Maximum	36.99	1.22	0.60	38.74	95.04	48.94	19.97	0.75	0.26	22.04	73.95	50.00
Minimum	20.99	0.69	0.27	22.35	61.22	6.62	13.52	0.40	0.16	15.07	53.00	36.84
Average	27.34	0.95	0.39	29.02	72.93	41.99	18.38	0.62	0.23	20.09	63.46	44.22
Standard deviation	3.51	0.11	0.07	3.70	9.47	6.58	1.46	0.07	0.02	1.52	4.60	3.35
% of change in water quality indicators during the period from 2009 to 2019
Indicator	SSP		SAR		KI		Na%		PI		MR
% of change	−32.77%		−34.74%		−41.025%		−30.77%		−12.98%		5.31%

SSP and KI in meq/L; Na%, SAR and MR are ratio; and PI is unitless.

Table 4. Statistics of calculated indices for groundwater quality and their percent of change during the period from the year 2009 to the year 2019.

Parameter	Year 2009						Year 2019
	Calculated Indices						Calculated Indices
	SSP	SAR	KI	Na%	PI	MR	SSP	SAR	KI	Na%	PI	MR
Maximum	36.99	1.22	0.60	38.74	95.04	48.94	19.97	0.75	0.26	22.04	73.95	50.00
Minimum	20.99	0.69	0.27	22.35	61.22	6.62	13.52	0.40	0.16	15.07	53.00	36.84
Average	27.34	0.95	0.39	29.02	72.93	41.99	18.38	0.62	0.23	20.09	63.46	44.22
Standard deviation	3.51	0.11	0.07	3.70	9.47	6.58	1.46	0.07	0.02	1.52	4.60	3.35
% of change in water quality indicators during the period from 2009 to 2019
Indicator	SSP		SAR		KI		Na%		PI		MR
% of change	−32.77%		−34.74%		−41.025%		−30.77%		−12.98%		5.31%

SSP and KI in meq/L; Na%, SAR and MR are ratio; and PI is unitless.

5.5. Magnesium Ratio (MR)

If the MR ratio is larger than 50%, water can be classified as suitable for the purpose of irrigation, based on the magnesium ratio [83]. It is expressed as the following:

$$\mathrm{MR}=\frac{{\mathrm{M}}_{\mathrm{g}}^{+2}}{{\mathrm{Ca}}^{+2}+{\mathrm{M}}_{\mathrm{g}}^{+2}}\times 100$$

(4)

In general, in most waters, Mg and Ca are present in equilibrium. The soil quality is affected adversely when the magnesium content is high in water, resulting in the soil’s alkaline nature and thus reducing crop yield [29,83]. Based on MR calculations, all the groundwater samples during both years were suitable, Table 6 and Figure 10b.

5.6. Kelly Index (KI)

Na, Mg and Ca concentrations in the water represent alkali hazards [83]. For calculation of the KI parameter, Na concentration is measured against Ca and Mg, and in most waters, Ca and Mg preserve their equilibrium state. KI is calculated using Equation (5) [85,86].

$$\mathrm{KI}=\frac{\mathrm{Na}}{\mathrm{Ca}+\mathrm{Mg}}$$

(5)

In 2009, the calculated KI values varied between 0.02 and 0.87 meq/L, and varied between 0.16 and 0.28 in 2019, Table 3. According to the calculated KI values, all samples were in the “suitable” class indicating no detected changes, Figure 11a and Table 6.

5.7. Permeability Index (PI)

This index was developed by [82] to determine the suitability of water for irrigation and categorizes waters as Class 1 (PI > 75%), Class 2 (25% < PI < 75%) and Class 3 (PI < 25%). Class 1 and Class 2 waters are categorized as “good” and “suitable” with their higher maximum permeability [86]. The permeability of soil is affected by the amount of Na, Mg, Ca and HCO₃ ions in soil. PI is calculated using Equation (6) [86]

$$\mathrm{PI}=\frac{\mathrm{Na}+\sqrt{{\mathrm{HCO}}_3}}{\mathrm{Ca}+\mathrm{Mg}+\mathrm{Na}}$$

(6)

The calculated PI values indicate that 26.41% of the water samples were in the “Good” category and 73.59% were in the “suitable” category through year 2009 but changed to be 100% of the water samples in the “suitable” category in 2019, Figure 11b and Table 6.

5.8. Index of Base Exchange

During its travel to the subsurface, groundwater undergoes changes in the chemical composition, and ion exchange is one of the essential processes used to evaluate these changes [85,87]. Ion exchange that occurs between the groundwater and its host environment is indicated by the chloro-alkaline index, CAI, which was suggested by [88,89]. The ion exchange index is estimated by the following equation (Equation (7)):

$$\mathrm{Chloro} \mathrm{Alkaline} \mathrm{Index} \left(\mathrm{CAI}\right)=\frac{\mathrm{Cl}-\left(\mathrm{Na}+\mathrm{k}\right)}{\mathrm{Cl}}$$

(7)

When the index is positive, it means there is ion exchange of K⁺ and Na⁺ from the water with calcium and magnesium in the rock in this case; the exchange is known as “direct exchange”. If the index is found to be negative, it means a reverse exchange, and then the exchange is known as “indirect exchange”. The CAI was calculated for the two years and revealed that 100% of the calculated index indicated a negative ratio, and, consequently, the dominance of the reverse ion exchange, “indirect exchange”, as shown in Table 5.

Table 5. Calculated indices for groundwater samples during the years 2009 and 2019.

S. No.	Year 2009							Year 2019
	Calculated Indices							Calculated Indices
	SSP	SAR	KI	Na%	PI	MR	CAI	SSP	SAR	KI	Na%	PI	MR	CAI
1	25.20	0.85	0.34	26.75	69.68	43.96	−0.64	19.40	0.70	0.25	21.09	61.26	47.13	−0.21
2	24.03	0.84	0.32	25.56	67.43	42.08	−0.58	18.91	0.67	0.24	20.58	61.30	47.72	−0.16
3	22.27	0.71	0.29	23.59	68.62	44.69	−0.26	19.03	0.68	0.24	20.76	61.16	46.03	−0.09
4	23.65	0.91	0.32	25.05	63.46	44.35	−0.58	17.84	0.62	0.22	19.63	61.30	42.55	0.15
5	22.54	0.06	0.02	3.70	50.70	45.45	−0.54	19.25	0.70	0.24	21.42	59.50	44.90	0.06
6	36.99	0.41	0.22	19.74	82.39	41.67	−1.38	18.38	0.68	0.23	20.03	57.28	45.98	−0.33
7	23.19	0.65	0.25	20.95	63.47	44.81	−0.58	18.72	0.66	0.24	20.40	62.49	47.21	−0.10
8	33.42	1.16	0.52	35.52	87.43	6.62	−0.41	19.43	0.66	0.25	21.43	65.52	47.39	0.00
9	28.89	1.04	0.42	30.60	73.34	42.55	−0.96	18.33	0.65	0.23	20.03	60.32	46.03	−0.26
10	24.55	1.01	0.33	26.33	61.90	41.97	−0.61	19.27	0.75	0.25	21.85	58.60	45.45	−0.01
11	25.21	0.75	0.34	26.77	77.66	41.67	−0.83	15.04	0.55	0.18	16.58	56.27	40.00	−0.04
12	28.49	1.00	0.41	30.17	72.41	44.69	−0.91	18.57	0.65	0.23	19.98	60.94	46.61	−0.25
13	27.57	1.01	0.39	29.19	70.28	44.78	−0.75	19.03	0.69	0.24	20.69	58.38	46.00	−0.43
14	29.83	0.85	0.43	31.40	85.28	39.47	−0.93	13.52	0.50	0.16	15.07	53.92	38.98	0.09
15	25.40	0.88	0.35	26.61	69.29	43.37	−0.62	19.11	0.71	0.24	20.33	58.93	45.98	−0.41
16	32.76	1.14	0.50	34.69	84.21	19.23	−0.75	19.00	0.71	0.24	20.78	57.26	45.98	−0.45
17	30.03	0.88	0.44	32.07	85.39	46.61	−1.20	17.01	0.60	0.21	18.47	59.04	40.00	−0.05
18	30.14	0.90	0.45	32.34	83.91	48.78	−0.93	18.16	0.61	0.23	19.74	62.58	41.10	−0.14
19	28.64	0.93	0.41	30.47	77.07	42.76	−0.79	18.37	0.66	0.23	19.65	59.54	43.82	−0.21
20	28.50	0.90	0.41	30.18	79.46	40.82	−1.09	14.17	0.53	0.17	15.67	53.00	38.98	−0.01
21	27.08	0.89	0.38	29.07	74.48	39.63	−0.81	19.97	0.73	0.26	21.84	63.61	44.90	−0.19
22	22.20	0.84	0.29	23.70	61.86	43.82	−0.40	19.92	0.67	0.25	21.74	67.52	48.78	−0.30
23	20.99	0.69	0.27	22.35	67.22	44.04	−0.64	19.56	0.66	0.25	22.04	67.66	48.08	−0.02
24	25.84	0.95	0.36	27.26	67.01	42.25	−1.05	19.42	0.60	0.25	21.17	69.39	41.67	−0.02
25	25.31	0.95	0.35	26.90	66.67	44.25	−0.97	17.78	0.60	0.22	19.27	62.29	40.00	−0.14
26	26.43	0.95	0.37	27.99	70.23	42.71	−0.83	19.68	0.64	0.25	21.39	67.08	45.45	−0.18
27	25.84	0.96	0.36	27.36	67.45	45.45	−0.75	19.92	0.67	0.25	21.74	67.52	48.78	−0.30
28	25.71	0.99	0.35	27.32	66.37	46.61	−0.64	19.24	0.63	0.24	20.80	65.49	44.12	−0.22
29	34.72	0.98	0.55	36.77	91.18	36.84	−1.34	19.23	0.63	0.24	20.84	64.99	44.12	−0.18
30	35.44	1.04	0.57	37.63	95.04	34.65	−1.40	17.41	0.64	0.22	19.32	58.89	46.99	−0.26
31	29.35	1.22	0.43	31.08	68.40	44.90	−1.18	17.78	0.52	0.22	19.66	72.03	45.45	0.00
32	23.28	0.90	0.31	24.74	62.01	45.45	−0.41	18.86	0.61	0.24	20.47	67.75	37.88	−0.16
33	31.24	0.88	0.47	33.18	88.81	37.74	−1.22	19.07	0.61	0.24	20.82	66.50	38.46	−0.17
34	30.80	0.89	0.46	32.71	85.71	39.47	−1.05	17.77	0.60	0.22	19.32	62.29	40.00	−0.23
35	24.45	0.96	0.33	25.89	63.35	45.45	−0.45	16.61	0.53	0.20	18.44	63.10	41.46	0.02
36	23.29	0.77	0.31	24.92	69.17	43.24	−0.58	18.81	0.62	0.24	20.32	64.47	42.86	−0.13
37	26.84	1.11	0.38	28.51	64.25	45.45	−0.68	18.10	0.61	0.23	19.67	62.99	45.45	−0.18
38	31.90	0.92	0.48	33.97	87.89	41.67	−1.05	17.49	0.57	0.22	19.13	63.48	41.46	−0.10
39	27.25	1.00	0.38	28.97	70.24	44.12	−0.89	19.22	0.63	0.24	20.89	64.28	44.12	−0.27
40	29.89	0.87	0.44	31.83	85.45	42.02	−1.19	17.26	0.53	0.21	18.82	66.23	46.20	−0.26
41	31.25	0.97	0.47	33.33	82.13	39.06	−1.52	19.23	0.63	0.24	20.84	64.99	44.12	−0.14
42	28.25	1.03	0.40	29.97	71.82	41.24	−0.89	19.15	0.63	0.24	20.81	66.22	41.46	−0.23
43	25.86	1.11	0.36	27.39	61.38	42.66	−0.63	19.70	0.69	0.25	21.39	63.71	50.22	−0.12
44	25.89	0.88	0.36	27.35	70.91	40.98	−0.77	19.86	0.68	0.28	23.44	72.23	48.57	−0.06
45	27.60	0.99	0.39	29.23	70.79	41.24	−0.69	17.97	0.61	0.22	19.53	62.08	40.54	−0.10
46	25.56	0.96	0.35	27.17	66.30	44.84	−0.64	19.50	0.62	0.25	21.10	68.98	36.84	−0.25
47	25.21	0.98	0.34	26.93	64.00	45.45	−0.47	18.05	0.54	0.22	19.75	69.33	43.10	−0.27
48	24.73	1.00	0.34	26.40	62.24	45.45	−0.47	19.91	0.70	0.25	21.61	63.11	50.88	−0.15
49	25.89	0.88	0.36	27.35	70.91	40.98	−0.77	18.63	0.64	0.23	20.01	61.08	49.11	−0.18
50	26.97	1.06	0.38	28.56	64.62	48.94	−1.06	15.30	0.40	0.19	17.29	73.95	42.55	−0.03
51	29.16	0.81	0.42	31.07	87.48	40.54	−1.11	15.29	0.40	0.19	17.36	73.77	42.55	−0.10
52	27.27	1.08	0.38	28.92	67.32	48.32	−1.05	18.87	0.63	0.24	20.45	63.04	45.45	−0.14
53	26.07	1.09	0.36	27.69	62.77	45.45	−0.68	19.22	0.63	0.24	20.89	65.49	44.12	−0.10

SSP and KI in meq/L; Na%, SAR and MR are ratio; and CAI and PI are unitless.

Table 5. Calculated indices for groundwater samples during the years 2009 and 2019.

S. No.	Year 2009							Year 2019
	Calculated Indices							Calculated Indices
	SSP	SAR	KI	Na%	PI	MR	CAI	SSP	SAR	KI	Na%	PI	MR	CAI
1	25.20	0.85	0.34	26.75	69.68	43.96	−0.64	19.40	0.70	0.25	21.09	61.26	47.13	−0.21
2	24.03	0.84	0.32	25.56	67.43	42.08	−0.58	18.91	0.67	0.24	20.58	61.30	47.72	−0.16
3	22.27	0.71	0.29	23.59	68.62	44.69	−0.26	19.03	0.68	0.24	20.76	61.16	46.03	−0.09
4	23.65	0.91	0.32	25.05	63.46	44.35	−0.58	17.84	0.62	0.22	19.63	61.30	42.55	0.15
5	22.54	0.06	0.02	3.70	50.70	45.45	−0.54	19.25	0.70	0.24	21.42	59.50	44.90	0.06
6	36.99	0.41	0.22	19.74	82.39	41.67	−1.38	18.38	0.68	0.23	20.03	57.28	45.98	−0.33
7	23.19	0.65	0.25	20.95	63.47	44.81	−0.58	18.72	0.66	0.24	20.40	62.49	47.21	−0.10
8	33.42	1.16	0.52	35.52	87.43	6.62	−0.41	19.43	0.66	0.25	21.43	65.52	47.39	0.00
9	28.89	1.04	0.42	30.60	73.34	42.55	−0.96	18.33	0.65	0.23	20.03	60.32	46.03	−0.26
10	24.55	1.01	0.33	26.33	61.90	41.97	−0.61	19.27	0.75	0.25	21.85	58.60	45.45	−0.01
11	25.21	0.75	0.34	26.77	77.66	41.67	−0.83	15.04	0.55	0.18	16.58	56.27	40.00	−0.04
12	28.49	1.00	0.41	30.17	72.41	44.69	−0.91	18.57	0.65	0.23	19.98	60.94	46.61	−0.25
13	27.57	1.01	0.39	29.19	70.28	44.78	−0.75	19.03	0.69	0.24	20.69	58.38	46.00	−0.43
14	29.83	0.85	0.43	31.40	85.28	39.47	−0.93	13.52	0.50	0.16	15.07	53.92	38.98	0.09
15	25.40	0.88	0.35	26.61	69.29	43.37	−0.62	19.11	0.71	0.24	20.33	58.93	45.98	−0.41
16	32.76	1.14	0.50	34.69	84.21	19.23	−0.75	19.00	0.71	0.24	20.78	57.26	45.98	−0.45
17	30.03	0.88	0.44	32.07	85.39	46.61	−1.20	17.01	0.60	0.21	18.47	59.04	40.00	−0.05
18	30.14	0.90	0.45	32.34	83.91	48.78	−0.93	18.16	0.61	0.23	19.74	62.58	41.10	−0.14
19	28.64	0.93	0.41	30.47	77.07	42.76	−0.79	18.37	0.66	0.23	19.65	59.54	43.82	−0.21
20	28.50	0.90	0.41	30.18	79.46	40.82	−1.09	14.17	0.53	0.17	15.67	53.00	38.98	−0.01
21	27.08	0.89	0.38	29.07	74.48	39.63	−0.81	19.97	0.73	0.26	21.84	63.61	44.90	−0.19
22	22.20	0.84	0.29	23.70	61.86	43.82	−0.40	19.92	0.67	0.25	21.74	67.52	48.78	−0.30
23	20.99	0.69	0.27	22.35	67.22	44.04	−0.64	19.56	0.66	0.25	22.04	67.66	48.08	−0.02
24	25.84	0.95	0.36	27.26	67.01	42.25	−1.05	19.42	0.60	0.25	21.17	69.39	41.67	−0.02
25	25.31	0.95	0.35	26.90	66.67	44.25	−0.97	17.78	0.60	0.22	19.27	62.29	40.00	−0.14
26	26.43	0.95	0.37	27.99	70.23	42.71	−0.83	19.68	0.64	0.25	21.39	67.08	45.45	−0.18
27	25.84	0.96	0.36	27.36	67.45	45.45	−0.75	19.92	0.67	0.25	21.74	67.52	48.78	−0.30
28	25.71	0.99	0.35	27.32	66.37	46.61	−0.64	19.24	0.63	0.24	20.80	65.49	44.12	−0.22
29	34.72	0.98	0.55	36.77	91.18	36.84	−1.34	19.23	0.63	0.24	20.84	64.99	44.12	−0.18
30	35.44	1.04	0.57	37.63	95.04	34.65	−1.40	17.41	0.64	0.22	19.32	58.89	46.99	−0.26
31	29.35	1.22	0.43	31.08	68.40	44.90	−1.18	17.78	0.52	0.22	19.66	72.03	45.45	0.00
32	23.28	0.90	0.31	24.74	62.01	45.45	−0.41	18.86	0.61	0.24	20.47	67.75	37.88	−0.16
33	31.24	0.88	0.47	33.18	88.81	37.74	−1.22	19.07	0.61	0.24	20.82	66.50	38.46	−0.17
34	30.80	0.89	0.46	32.71	85.71	39.47	−1.05	17.77	0.60	0.22	19.32	62.29	40.00	−0.23
35	24.45	0.96	0.33	25.89	63.35	45.45	−0.45	16.61	0.53	0.20	18.44	63.10	41.46	0.02
36	23.29	0.77	0.31	24.92	69.17	43.24	−0.58	18.81	0.62	0.24	20.32	64.47	42.86	−0.13
37	26.84	1.11	0.38	28.51	64.25	45.45	−0.68	18.10	0.61	0.23	19.67	62.99	45.45	−0.18
38	31.90	0.92	0.48	33.97	87.89	41.67	−1.05	17.49	0.57	0.22	19.13	63.48	41.46	−0.10
39	27.25	1.00	0.38	28.97	70.24	44.12	−0.89	19.22	0.63	0.24	20.89	64.28	44.12	−0.27
40	29.89	0.87	0.44	31.83	85.45	42.02	−1.19	17.26	0.53	0.21	18.82	66.23	46.20	−0.26
41	31.25	0.97	0.47	33.33	82.13	39.06	−1.52	19.23	0.63	0.24	20.84	64.99	44.12	−0.14
42	28.25	1.03	0.40	29.97	71.82	41.24	−0.89	19.15	0.63	0.24	20.81	66.22	41.46	−0.23
43	25.86	1.11	0.36	27.39	61.38	42.66	−0.63	19.70	0.69	0.25	21.39	63.71	50.22	−0.12
44	25.89	0.88	0.36	27.35	70.91	40.98	−0.77	19.86	0.68	0.28	23.44	72.23	48.57	−0.06
45	27.60	0.99	0.39	29.23	70.79	41.24	−0.69	17.97	0.61	0.22	19.53	62.08	40.54	−0.10
46	25.56	0.96	0.35	27.17	66.30	44.84	−0.64	19.50	0.62	0.25	21.10	68.98	36.84	−0.25
47	25.21	0.98	0.34	26.93	64.00	45.45	−0.47	18.05	0.54	0.22	19.75	69.33	43.10	−0.27
48	24.73	1.00	0.34	26.40	62.24	45.45	−0.47	19.91	0.70	0.25	21.61	63.11	50.88	−0.15
49	25.89	0.88	0.36	27.35	70.91	40.98	−0.77	18.63	0.64	0.23	20.01	61.08	49.11	−0.18
50	26.97	1.06	0.38	28.56	64.62	48.94	−1.06	15.30	0.40	0.19	17.29	73.95	42.55	−0.03
51	29.16	0.81	0.42	31.07	87.48	40.54	−1.11	15.29	0.40	0.19	17.36	73.77	42.55	−0.10
52	27.27	1.08	0.38	28.92	67.32	48.32	−1.05	18.87	0.63	0.24	20.45	63.04	45.45	−0.14
53	26.07	1.09	0.36	27.69	62.77	45.45	−0.68	19.22	0.63	0.24	20.89	65.49	44.12	−0.10

SSP and KI in meq/L; Na%, SAR and MR are ratio; and CAI and PI are unitless.

Table 6. Classification of groundwater of the study area for irrigation purposes.

Parameters	Value Range	Water Classification	Number of Samples
			Year 2009	Year 2019
Alkalinity hazard SAR [78]	<10	Excellent	53	53
	10–18	Good	0	0
	18–26	Doubtful	0	0
	>26	Unsuitable	0	0
Kelly index KI [86]	<1	suitable	53	53
	>1	Unsuitable	0	0
Sodium percentage Na% [48]	<20	Excellent	2	20
	20–40	Good	51	33
	40–60	Permissible	0	0
	60–80	Doubtful	0	0
	>80	Unsuitable	0	0
Permeability index PI [89]	>5%	Good	14	0
	25–75%	Suitable	39	53
	<25%	Unsuitable	0	0
Soluble Sodium Percent SSP [74]	<50%	Suitable	53	53
	>50%	Unsuitable	0	0
Magnesium ratio (MR) [83]	<50%	Suitable	53	53
	>50%	Unsuitable	0	0

SSP and KI in meq/L; Na%, SAR and MR are ratio; and PI is unitless.

Table 6. Classification of groundwater of the study area for irrigation purposes.

Parameters	Value Range	Water Classification	Number of Samples
			Year 2009	Year 2019
Alkalinity hazard SAR [78]	<10	Excellent	53	53
	10–18	Good	0	0
	18–26	Doubtful	0	0
	>26	Unsuitable	0	0
Kelly index KI [86]	<1	suitable	53	53
	>1	Unsuitable	0	0
Sodium percentage Na% [48]	<20	Excellent	2	20
	20–40	Good	51	33
	40–60	Permissible	0	0
	60–80	Doubtful	0	0
	>80	Unsuitable	0	0
Permeability index PI [89]	>5%	Good	14	0
	25–75%	Suitable	39	53
	<25%	Unsuitable	0	0
Soluble Sodium Percent SSP [74]	<50%	Suitable	53	53
	>50%	Unsuitable	0	0
Magnesium ratio (MR) [83]	<50%	Suitable	53	53
	>50%	Unsuitable	0	0

SSP and KI in meq/L; Na%, SAR and MR are ratio; and PI is unitless.

5.9. Heavy Metals

An additional very important indicator for water quality evaluation is the monitoring of heavy metals concentration. In this study, we reviewed twelve kinds of heavy metals in the groundwater in the study area, as they have harmful effects on human health if there are extra concentrations in irrigation from such water. For example, when receiving cadmium (Cd) and lead (Pb) amounts from the organism, most of them concentrate on liver and kidneys, in a low discharge. It has been evidenced that both elements may remain in the body for several years. Cd is cancer-causing if a high content of it is in the organism. The concentrations of these harmful substances in groundwater are affecting both human health and the quality of the environment. Moreover, if aluminum (Al) precipitates in the aquifer in a crystalline form (gibbsite), a problem can quickly occur when the drinking water’s acidity is increased, especially from acid rain. In this case, aluminum may penetrate the sources of drinking water and cause contamination [45].

The contamination from metallurgic industries related to heavy metals production such as cadmium, chrome, manganese and iron is a major problem because of their ability to accumulate in living tissues.

“Iron is the second most abundant metal in the earth’s crust [86]. Maximum and minimum values of iron” and other heavy metals in the study area are presented in Table 7. About 27% of the studied groundwater samples have values of iron concentration above 0.3 mg/L. It was found that the average value of arsenic concentration was 19.42 µg/L. The observed levels of arsenic in the study area may be according to its evaporative environment as the climate of this region is arid, which can lead to more water evaporation loss than its gain by rainfall [82]. The arsenic pollutant is also strongly related with high iron concentrations, phosphate, “ammonium ions, and anthropogenic activities such as excessive groundwater withdrawal for agricultural irrigation” [86]. Extreme use of arsenical pesticides on crops may be a basis for arsenic pollutants due to the fertilizer leaching process and pesticides seeping into groundwater from soils. The main source of metal pollutants in water is the use of pesticides in the form of lead arsenate, calcium arsenate, arsenic acid and sodium arsenate [86].

Generally, groundwater was considered within the limits recommended for “Lead” Pb (<0.01 mg/L) and mercury, but nickel had a maximum value of 0.095, and this value is outside of the desirable limit (<0.07). Aluminum values ranged between 12 and 460 µg/L, selenium values ranged between 2 and 98 µg/L, silicon dioxide had a maximum value of 7 mg/L with an average of 3.6 mg/L, whereas phosphate ion values ranged between 0 and 70 µg/L, Table 7.

Table 7. Statistics of heavy metal values for groundwater in the study area.

No.	Fe (mg/L)	Mn (mg/L)	SiO2 (mg/L)	Pb “µg/L”	Cd “µg/L”	Cr “µg/L”	As “µg/L”	Hg “µg/L”	Ni “µg/L”	Al “µg/L”	Se “µg/L”	PO4 “µg/L”
Maximum	0.80	1.50	7.00	95.00	76.00	96.00	85.00	8.50	95.00	460.00	265.00	170.00
Minimum	0.00	0.00	0.00	1.00	0.10	0.00	1.00	0.10	0.00	12.00	2.00	0.00
Average	0.26	0.45	3.60	13.87	8.08	30.83	19.42	2.12	23.04	110.28	32.79	17.36
Standard deviation	0.16	0.35	1.35	20.31	14.77	26.47	24.13	2.56	27.65	133.80	50.00	31.14

Approximately half of the analyzed groundwater samples showed high values of Mn, As, Co, Cr, Ni, Hg, Cd and Se. These high values could be due to industrial water leaching from El-Moheet drain where Abou Heleika et al. [39]’s results almost agreed with the results of the present study for the same study area during the year 2018. However, about 50% of the groundwater samples had values below the permissible guideline limits of Egyptian standards; Figure 12, Figure 13, Figure 14 and Figure 15 show the distribution of heavy metals in the study area.

Table 7. Statistics of heavy metal values for groundwater in the study area.

No.	Fe (mg/L)	Mn (mg/L)	SiO2 (mg/L)	Pb “µg/L”	Cd “µg/L”	Cr “µg/L”	As “µg/L”	Hg “µg/L”	Ni “µg/L”	Al “µg/L”	Se “µg/L”	PO4 “µg/L”
Maximum	0.80	1.50	7.00	95.00	76.00	96.00	85.00	8.50	95.00	460.00	265.00	170.00
Minimum	0.00	0.00	0.00	1.00	0.10	0.00	1.00	0.10	0.00	12.00	2.00	0.00
Average	0.26	0.45	3.60	13.87	8.08	30.83	19.42	2.12	23.04	110.28	32.79	17.36
Standard deviation	0.16	0.35	1.35	20.31	14.77	26.47	24.13	2.56	27.65	133.80	50.00	31.14

Approximately half of the analyzed groundwater samples showed high values of Mn, As, Co, Cr, Ni, Hg, Cd and Se. These high values could be due to industrial water leaching from El-Moheet drain where Abou Heleika et al. [39]’s results almost agreed with the results of the present study for the same study area during the year 2018. However, about 50% of the groundwater samples had values below the permissible guideline limits of Egyptian standards; Figure 12, Figure 13, Figure 14 and Figure 15 show the distribution of heavy metals in the study area.

6. Conclusions

The current study on detecting the groundwater quality in the Minia Region along the West Nile River indicated that:

• Groundwater quality indicators/parameters are spatially variable from one site to another. We believe that integrating these parameters in a systematic procedure improves the awareness of the variation in the groundwater quality in the study area.
• From the chemical analysis of the groundwater, it was found that the major ions descend as follows: HCO₃ > Cl > SO₄ and Ca > Mg > Na > K for anions and cations, respectively.
• Most samples were found to be “suitable” for irrigation purposes based on the values of SAR, Na%, KI, SSP, MR%, PI and CAI.
• The calculated indices showed that the region’s groundwater lies almost into the good class for major ions and can be used for irrigation, but for trace elements it needs more attention and more studies to restrict values with respect to these metals.
• Seepage of wastewater from the Minia sewage treatment plant and network, industrial wastewater from a sugar factory of Abo-Qarqas city (e.g., El-Moheet drain), the fertilizer leaching process and pesticides seeping into groundwater from soils are the main sources of heavy metals traces.
• It is recommended that the needed measures are taken by the concerning authorities (Egyptian Environmental Agency Authority) to reduce the heavy metals traces that can be found in the groundwater in the Minia region, probably by applying the related Egyptian law no. 147/2021 and its detailed bylaw to any violations.