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Sustain. Water Resour. Manag. DOI 10.1007/s40899-017-0087-6

ORIGINAL ARTICLE

Evaluation of groundwater quality of the Quaternary aquifer through multivariate statistical techniques at the southeastern part of the Nile Delta, Egypt

M. A. El‑Fakharany1 · N. M. Mansour1 · M. M. Yehia2 · M. Monem3 

Received: 19 January 2016 / Accepted: 30 January 2017 © Springer International Publishing Switzerland 2017

on the groundwater composition and suggests that they come mostly from surface sources.

Keywords Evaluation groundwater · Multivariate statistical techniques

Introduction

Surface water and groundwater pollution may be defined as the artistically induced degradation of natural water quality. Pollution can impair the use of water and can create haz-ards to public health through toxicity or the spread of dis-ease. Most pollution originated from the disposal of waste-water following the use of water for any of a wide variety of purposes. Thus, a large number of sources and causes can modify groundwater quality, ranging from septic tank to irrigated agriculture. In contrast with surface water pol-lution, subsurface pollution is difficult to detect, and is even more difficult to control, and may persist for decades. With the growing recognition of the importance of under-ground water resources, efforts are increasing to prevent, reduce, and eliminate groundwater pollution. The applica-tion of different multivariate statistical techniques such as descriptive statistics, correlations matrices and Hierarchical cluster analysis of water quality parameters of groundwater helps in the interpretation of complex data matrices to bet-ter understand the water quality and ecological status of the studied systems, allows the identification of possible factors sources that influence water systems and offers a valuable tool for reliable management of water resources, as well as rapid solution to pollution problems (Lee et al. 2001; Reg-hunath et al. 2002; Vega et al. 1998; Wunderlin et al. 2001). The area under investigation lies in the southeastern part of

Abstract This work aims to evaluate the spatial vari-ation of groundwater quality of the Quaternary aquifer at the southeastern part of the Nile Delta through multivariate statistical techniques. For this study, a total of 209 ground-water samples were collected from 144 shallow wells and 65 deep wells distributed over the study area and analyzed for major elements. Statistical analyses such as descriptive statistics, correlation and Hierarchical cluster analysis for water quality parameters were carried out. Results indicate that the TDS shows a strong positive correlation with Na+, Cl− and SO4

2−, while the Na+ shows a strong positive cor-relation with SO4

2− and Cl− in shallow groundwater sam-ples. In the deep groundwater samples, the TDS shows a strong positive correlation with Na+. The high degree of association between TDS with Na+, Cl− and SO4

2− indi-cates the anthropogenic activities among these are landfill waste sites, septic tanks, domestic and industrial effluents and intensive use of fertilizers. The results of HCA indi-cate that the numbers of clusters of groundwater samples in the study area are changed with depth, while the shallow groundwater samples falls into seven clusters and the deep groundwater samples falls into four clusters. Accordingly, it is certain that the factors characterizing the hydrogeo-chemical characters of groundwater in the study area are changed with depth. Increasing concentrations of K+, Na+, SO4

2−, Cl−, and NO3− in shallow wells toward the top of

the aquifer point to the influence of anthropogenic activities

* N. M. Mansour nehad.mansour@fsc.bu.edu.eg

1 Geology Department, Faculty of Science, Benha University, Banha, Egypt

2 National Water Research Center, Al Qaluibya, Egypt3 Research Institute for Groundwater, Al Qaluibya, Egypt

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the Nile Delta. It is bounded by 31°3′, 31°35′20″ longitudes E and 30°06′20″, 30°36′26″ latitudes N (Fig. 1).

Aim and methods of study

This work aims to evaluate the spatial variation of ground-water quality of the Quaternary aquifer at the southeastern part of the Nile Delta through multivariate statistical tech-niques. This would be achieved through the following:

Collection of samples

A total of 209 groundwater samples were collected from the wells, 144 shallow wells (at a depth of 10–25 m depth) and 65 deep wells (at a depth of 80–120 m depth). The col-lected samples were stored in cleaned and well dried brown polythene glass bottles (2.5 L), with necessary precautions (APHA 1995). These bottles were labeled with respect to the collecting points, date, and time to avoid any error between collection and analysis. All the sample collections were immediately preserved in an icebox and brought to the laboratory for determining the specific water quality parameters.

Sample analysis

The collected samples were analyzed for specific water quality parameters such as pH, electrical conductivity (EC), total dissolved solids (TDS), major cations [calcium (Ca2+), magnesium (Mg2+), sodium (Na+), and Potassium (K+)] and major anions [nitrates (NO3

−), chlorides (Cl−), sulfate (SO4

− 2) and bicarbonates (HCO3−)].

Statistical analyses

SPSS (statistical package social science), was used to per-form the statistical analyses, such as descriptive statistics (e.g. maximum, minimum, mean, and standard deviation), relationships among variables (e.g., correlation) and Hier-archical cluster analysis for water quality parameters. SPSS (statistical package social science), one of most widely used statistical software package, was used to perform the statistical analyses. The package covers a broad range of statistical procedures that provide descriptive statistics (e.g. maximum, minimum, average, statistical deviation), con-ducts analysis of variance and statistical tests, examines relationships among variables (e.g., correlation, regression) and displays graphical results (e.g. bar chart). Multivariate statistical methods have gained wide acceptance as data classifying tools in basin wide hydrochemical analysis. In

Fig. 1 Location map of the study area

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particular, the use of cluster analysis has enabled the deter-mination of the main sources of variation in a set of hydro-chemical data.

Results and discussions

Geologic and hydrogeologic setting

According to El Fayoumy (1968), Shata and El Fayoumy (1970), El Shazly et  al. (1975), El Diary (1980), RIGW (1989), the area east of the Nile Delta is essentially occu-pied by rock units belonging to the Tertiary and Quater-nary. Basaltic rocks belonging to Upper Oligocene age are exposed at Abu Zaabal Quarries, while Miocene and Pliocene sediments outcrop at the eastern portions (Fig. 2). The Pliocene clay is overlain by the Quaternary depos-its in the Nile Delta flood plain with a thickness of about 200 m.The Quaternary deposits covers the majority of the area, it is classified into two rock units; the upper unit is the Holocene Nile silt and clay, while the lower one is the Pleistocene sand and gravel intercalated with clay lenses that exposed at the eastern parts of the studied area. These sediments rest uncomfortably on the older rock units (Sal-louma 1983).

The Nile Delta (Quaternary) aquifer is one of the most important water resources in Egypt. It forms an immense and complex groundwater system. Diab et  al. (1984), Korany et  al. (1993), Taha et  al. (1997), Eweida et  al. (1999) and Yehia (2000); have discussed in detail the hydrogeology of eastern Nile Delta. The study area is mainly occupied by the Quaternary aquifer which discrimi-nated into two hydrogeological units; the upper unit is the

Holocene aquitard and the lower one is the Pleistocene aquifer. The Pleistocene aquifer system consists of sand and gravel with clay lenses, which overlain by a semi pervious Holocene aquitard (clay cap) and underlain by an imper-meable Pliocene aquiclude. The thickness of the Holocene aquitard ranges between 0 m at the eastern portions to 20 m at the northern part of the investigated area (Fig.  3). The Pleistocene aquifer has a variable thickness, at the eastern parts, the thickness ranges from 0 to nearly 50 m, while at the northwestern portions of the investigated area it may reach 400  m (Fig.  2). In the study area, the Quaternary aquifer is mainly recharged from the Nile River, subsurface flow from Damietta branch, Ismailia Canal and leakage from the net of irrigation canals and drains. The discharge takes place by pumping wells for the irrigation and domes-tic uses. The groundwater levels in the Quaternary aquifer slope generally from south to north (Fig. 2). Tracer experi-ments on direction of groundwater flow system approaches steady state conditions and direction from SW toward the NE of the study area (Mabrook et al. 1983).

Statistical analyses

Descriptive statistics help us to simplify large amount of data in a sensible way. Each descriptive statistic reduces lots of data into a simpler summary. There are two basic methods: numerical and graphical. Using the numerical approach one might compute statistics such as the mean and standard deviation. The mean is the arithmetic average of a set of scores. The arithmetic mean is obtained by tak-ing the sum of all the numbers in the set and dividing by the total number of scores in the set.

The standard deviation formula is similar to the variance formula. It is given by:

Fig. 2 Hydrogeological map of the study area (modified after RIGW 1991) Fig. 3 Thickness map of the Holocene aquitared (after RIGW 1982)

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𝜎 =

�

1

N

N∑

i=1

(xi − x̄)2

σ= standard deviation, xi = each value of dataset, x (with a bar over it) = the arithmetic mean of the data, N = the total number of data points, ∑ (xi − mean)2 = the sum of (xi − mean)2 for all data points.

The standard deviation (σ) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. A low standard deviation indicates that the data points tend to be close to the mean of the set, while a high standard deviation indicates that the data points are spread out over a wider range of values.

Multivariate statistical analyses, approach to investigate the statistical relationships among the dissolved constitu-ents and environmental parameters in water (Derver 1997). Statistical techniques, such as cluster analysis has been advocated as tools that can provide understanding of the geochemistry of the surface water and groundwater. This technique can provide an unbiased description of a distri-bution of samples, the distribution of their compositions. Maps of chemical variables can be created using statically tools by combining observations with interpolation models that can sometimes incorporate simple process relations (Pebesma and De-Kwaadsteniet 1997; Smith et  al. 1997). In the meantime, the use of these techniques for evaluating the water quality is established to be effective in detecting the most severe localities prohibited for drinking and irri-gation. Alternatively, it can be achievable to differentiate the studies localities into groups; each has its characteristic composition and geographic setting; taking into considera-tion the depiction of the various elements in water and the relation of these constituents to water uses in drinking and irrigation.

The correlation coefficient

The statistical relationship between the water quality parameters was examined through the analysis of the lin-ear correlation method. The correlation coefficient is a technique used to measure the degree of interrelation and association between two variables (Davis 1986). The quan-tity r, called the linear correlation coefficient, measures the strength and the direction of a linear relationship between two variables. The linear correlation coefficient is some-times referred to as the Pearson product moment correla-tion coefficient in honor of its developer Karl Pearson. The mathematical for formula computing r is:

r =n∑

xy−(∑

x)(∑

y)√

n(∑

x2)−(∑

x)2

√

n(∑

y2)−(∑

y)2

where n is the number of pairs of data.

The correlation coefficient ranges from −1 to +1. A correlation of +1 indicates a perfect positive relation-ship between two variables; as the value of one variable increase, the value of the other variable increases at the same rate. A correlation of −1 indicates that one variable changes inversely with relation to the other; as the value of one variable increases, the value of the other variable decreases at the same rate. Between the two extremes, is a spectrum of less than perfect relationships, including zero, which indicates that there is no relationship between the two variables. The terms, strong, moderate and weeks applied to r (correlation coefficient) values, refer to range >0.75, 0.75–0.5, and 0.5–0.3, respectively (Wang et  al. 2007).

Correlation investigation of groundwater The correlation matrices for the water quality parameters of groundwater samples (shallow and deep wells) are listed in Table 1. In shallow groundwater samples, the TDS shows a strong posi-tive correlation with Na+, Cl− and SO4

2−, while the Na+ shows a strong positive correlation with SO4

2− and Cl−. The high degree of association between the TDS with Na+, Cl− and SO4

2− indicates the anthropogenic activities among these are landfill waste sites, septic tanks, and domestic and industrial effluents. In deep groundwater samples, the TDS shows a strong positive correlation with Na+. The high degree of association between the TDS with Na+ indicates the anthropogenic activities such as discharge of sewage, which percolates and mixes with Groundwater (Raja and Venkatesan 2010).

Hierarchical cluster analysis

Cluster analysis comprises a series of multivariate meth-ods which are used to find true groups of data. In cluster-ing, the objects are grouped such that similar objects fall into the same class (Danielsson et  al. 1999). Hierarchical cluster analyses are the most widely applied techniques in the earth sciences and is used in this study. Hierarchi-cal clustering joins the most similar observations, and then successively the next most similar observations. The levels of similarity at which observations are merged are used to construct a dendrogram. Some measure of similarity must be computed between every pair of objects. In this study, a standardized m-space Euclidian distance (Davis 1986), dij is used:

dij =

�

∑m

k=1(Xik−Xjk)

2

m

where Xik denotes the kth variable measured on object i and Xjk is the kth variable measured on object j. A low

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distance shows the two objects are similar or ‘‘close together”, whereas a large distance indicates dissimilarity.

The purpose of cluster analysis is to identify groups or clusters of similar sites on the basis of similarity within a class and dissimilarities between different classes (Sparks 2000). In hierarchical cluster analysis the distance between samples is used as a measure of similarity (Vega et al. 1998). To classify the objects of the system into cat-egories or clusters based on their nearness or similarity (Panda et  al. 2006). Agglomerative hierarchical clusters are formed sequentially, by starting with the most similar pair of objects and forming higher cluster step by step.

Hierarchical cluster analysis of  shallow groundwater samples As Figs. 4, 5 and Table 2 illustrates, the study area can be divided into seven zones. However, clusters D, E, F and G involves only isolated samples, which can be considered as outlier due to the anomalous excess of SO4

2−, NO3− and K+ concentrations (clusters D and E), excess of TDS, SO4

2−, and Cl− concentrations (cluster G), as well as excess of TDS, SO4

2−, Cl− NO3−, Na+ and K+

concentrations (cluster F). Thus, it is appropriate to divide the study area into three zones.

Cluster “A” Includes 26 groundwater samples (18% of samples). This zone located along the Damietta Branch (Fig. 5), where the thicknesses of clay cap aquitard var-ies from 10 to 15 m, which can minimize anthropogenic degradation of water quality from surface. This zone

is mainly affected by the recharge from the Damietta Branch, where the TDS ranged from 204 to 634  mg/L with mean and standard deviations (SD) of 400  mg/L and 129, respectively (Table  2). This is confirmed by the following ion dominance; Na+  >  Ca2+  >  Mg2+ and HCO3

− > SO42− > Cl− (Fig. 6) and the hypothetical salt

combination are K Cl, Na Cl, Na2SO4, NaHCO3, Mg (HCO3)2, Ca (HCO3)2.

Cluster “B” Includes 39 samples (27% of samples). This zone located at the center and extended to the eastern portion of the study area (Fig.  5), where the thickness of clay cap aquitard varies from 5 to 10  m. Thus, this zone is affected by anthropogenic degradation of water qual-ity from the surface, where the TDS ranged from 436 to 1438  mg/L with mean and standard deviations (SD) of 711 mg/L and 234, respectively (Table 2). This is confirmed by the following ion dominance; Mg2+ > Na+ > Ca2+ and SO4

2−  >  HCO3−  >  Cl− (Fig.  6) and the hypothetical salt

combination are KCl, NaCl, Na2SO4, MgSO4, Mg (HCO3)2, Ca(HCO3)2. It is noticed that the existence of MgSO4 salts instead of NaHCO3 salts if compared with cluster C.

Besides, Chloride concentrations can be used as an indi-cator for the anthropogenic contamination of groundwater because it is conservative in most of the natural environ-ment and essentially originates from surface contamination sources (Bowser 1992; Andreasen and Fleck 1997; Low-rance et  al. 1997; Thunqvist 2004). Chloride concentra-tions ranged from 41 to 312 mg/L with mean and standard

Table 1 Correlation coefficient (r) for water quality parameters of groundwater samples (shallow and deep wells)

Mg2+ Ca2+ Na+ K+ Cl− HCO3− SO4

2− TDS

Samples of shallow wells Mg2+ 1 Ca2+ 0.061183 1 Na+ 0.212408 0.451346 1 K+ 0.215073 0.250518 0.260125 1 Cl− 0.411539 0.58734 0.794238 0.2855 1 HCO3

− 0.484704 0.189636 0.519145 0.156927 0.366444 1 SO4

2− 0.362546 0.58535 0.801725 0.304243 0.597134 0.304414 1 TDS 0.496134 0.636667 0.902688 0.35198 0.846749 0.567401 0.881569 1

Samples of deep wells Mg2+ 1 Ca2+ −0.42551 1 Na+ −0.10175 0.515457 1 K+ −0.09329 0.386247 0.427458 1 Cl− 0.088434 0.423267 0.657602 0.49543 1 HCO3

− −0.00511 0.006966 0.350793 −0.08128 −0.04161 1 SO4

2− 0.281347 0.529774 0.451525 0.298006 0.285657 −0.35983 1 TDS 0.185788 0.631742 0.879979 0.422765 0.629549 0.335304 0.663979 1

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Fig. 4 Dendrogram showing clustering of chemical analyses of shallow groundwater samples

D

E

A

B

G

F

C

Dendrogram using Ward LinkageRescaled Distance Cluster Combine0 5 10 15 20 25

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deviations (SD) of 119 mg/L and 68, respectively. Sulfate concentrations varied from 29 to 462 mg/L with mean and standard deviations of 191  mg/L and 81, respectively. As with chlorides, sulfates in groundwater are likely to origi-nate from the land surface mainly from fertilizers (MgSO4 and K2SO4).

Cluster “C” Includes 25 samples (17.3% of samples). This cluster is located on both sides of the Ismailia canal (Fig.  5), where the thickness of clay cap aquitard varies from 0 to 10 m. Thus, this zone is more affected by anthro-pogenic degradation of water quality from the surface, where the TDS ranged from 567 to 1801 mg/L with mean and standard deviations (SD) of 966 mg/L and 347, respec-tively (Table 2). This is confirmed by the ion dominance; Na+  >  Ca2+  >  Mg2+ and SO4

2−  >  HCO3−  >  Cl−(Fig.  6)

and the hypothetical salt combination are KCl, NaCl, Na2SO4, MgSO4, Mg(HCO3)2, Ca(HCO3)2 or KCl, NaCl, Na2SO4 Na(HCO3), Mg(HCO3)2, Ca(HCO3)2.Chloride concentrations ranged from 39 to 418  mg/L with mean and standard deviations (SD) of 146 mg/L and 92, respec-tively (Table  2). Sulfate concentrations varied from 27 to 615 mg/L with mean and standard deviations of 282 mg/L and 164, respectively. Nitrate concentrations ranged from 6 to 210 mg/L with mean and standard deviations (SD) of 60 mg/L and 76, respectively. Sodium concentrations var-ied from 82 to 480  mg/L with mean and standard devia-tions of 196 mg/L and 100, respectively. Potassium concen-trations varied from 1.1 to 72 mg/L with mean and standard

deviations of 16  mg/L and 19, respectively. High values of K+, Na+, SO4

2−, NO3− and Cl− indicate that this zone

is highly affected by anthropogenic degradation of water quality from surface.

Hierarchical cluster analysis of  deep groundwater sam-ples As Figs. 7, 8 and Table 3 illustrates, the study area can be divided into four zones. However, cluster D involves only isolated samples, which can be considered as outlier due to the anomalous excess of SO4

2− concentrations (the min, max., and mean values are 187, 325, and 242, ppm, respectively, with SD of ±44). Thus, it is appropriate to divide the study area into three zones.

Cluster “A” Includes 20 groundwater samples (30.8% of samples). This zone is mainly affected by the recharge from the Damietta branch (Fig. 8), where the TDS ranged from 236 to 818.79  mg/L with mean and standard devia-tions (SD) of 511  mg/L and 166, respectively (Table  3). This is confirmed by the following ion dominance; Na+ > Ca2+ > Mg2+ and HCO3− > SO4

2− > Cl− (Fig. 9) and the hypothetical salt combination are KCl, NaCl, Na2SO4, NaHCO3, Mg(HCO3)2, Ca(HCO3)2.

Cluster “B” Includes 17 samples (26.2% of samples). This zone located at the center and extended to the eastern portion of the study area (Fig.  8), where the thickness of clay cap aquitard varies from 0 to 10  m. Thus, this zone is more affected by anthropogenic degradation of water quality from surface, where the TDS ranged from 539 to 786  mg/L with mean and standard deviations (SD) of 628 mg/L and 30, respectively (Table 3). This is confirmed by the following ion dominance; Na+ > Ca2+ > Mg2+and SO4

2−  >  Cl−  >  HCO3− (Fig.  9) and the hypothetical salt

combination are KCl, NaCl, Na2SO4, MgSO4, CaSO4, and Ca(HCO3)2. It is noticed that the existence of MgSO4 and CaSO4 salts instead of NaHCO3 and Mg(HCO3)2 salts if compared with cluster B. Chloride concentrations ranged from 85 to 188  mg/L with mean and standard deviations (SD) of 114 mg/L and 30, respectively. Sulfate concentra-tions varied from 96 to 246 mg/L with mean and standard deviations of 175 mg/L and 38, respectively.

Cluster “C” Includes 16 samples (24.6% of sam-ples). This cluster is divided into two zones; one of them located at the north and is mainly affected by the recharge from the Damietta branch and the other is located on both sides of the Ismailia canal (Fig.  8) and mainly affected by the recharge from it, where the TDS ranged from 424 to 623  mg/L with mean and standard deviations (SD) of 519  mg/L and 166, respectively (Table  3). This is con-firmed by the ion dominance; Na+  >  Mg2+  >  Ca2+ and HCO3

−  >  SO42−  >  Cl− (Fig.  9) and the hypothetical

salt combination are KCl, NaCl, Na2SO4, MgSO4, Mg

Fig. 5 Spatial distribution of shallow groundwater identified by sam-ples cluster in study area HCA

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Table 2 Minimum, maximum, mean and standard deviation of water quality parameters for shallow groundwater samples

Descriptive statistic Mg2+ Ca2+ Na+ K+ Cl− HCO3− SO4

2− NO3− TDS

Cluster A Mean 19.80 34.39 85.54 3.71 51.88 221.03 91 3.5925 400.41 Standard deviation 7.77 11.28 34.02 2.11 25.21 78.34 34.82 1.4014 129.22 Minimum 2.88 13.40 30.00 1.00 21.00 86.62 34.50 1.91 204.53 Maximum 35.44 60.40 148.00 7.50 114.00 347.70 182 5.31 634.34

Cluster B Mean 57.95 39.56 Na 91 8.57 119 205.06 191 7.97 711.30 Standard deviation 21.07 22.31 35.24 11.71 68 74.67 81 6.9496 244.25 Minimum 28.80 3.60 37 1.00 41 81.74 29.07 2.67 436.07 Maximum 105.12 107.20 180 60.00 312 420.90 462 20 1423.00

Cluster C Mean 26.38 61.43 196.61 16 146 238.17 282 60 966.03 Standard deviation 12.47 25.78 100.34 19 92 73.65 164 76 347 Minimum 8.16 15.20 82.00 1.10 39 128.10 27 E 6 567.30 Maximum 57.60 116.00 480 72 418 390.40 615 210 1801

Cluster D Mean 26.19 59.71 72.00 8.45 53.50 143.87 212 577.62 Standard deviation 8.38 17.65 22.44 5.98 20.49 52.47 63.51 146.81 Minimum 12.96 27.20 40.00 1.00 27.00 57.34 111 353.33 Maximum 39.36 88.80 110.00 20.00 85.00 241.56 314 13.1 836.42

Cluster E Mean 29.72 37.53 95.45 5.24 67.36 230.91 124 21 590.07 Standard deviation 10.64 13.28 21.13 5.61 27.93 69.17 54.79 31.466 144.69 Minimum 12.48 25.60 65.00 1.50 29.00 132.98 14.44 4.35 371.58 Maximum 54.72 64.80 115.00 21 131.00 341.60 195 77 833.55

Cluster F Mean 23.30 101.16 148 9.27 241 167.72 195 11 885 Standard deviation 10.54 25.50 56.00 5.44 55.10 30.60 118.30 10 195.52 Minimum 2.88 68.00 64 2 179 112.24 42 4 625.77 Maximum 35.52 142.40 235 20 372 204.96 387 17.6 1169

Cluster G Mean 29.08 81.27 91.42 5.99 126 208.93 154 4.105 697 Standard deviation 16.98 16.91 32.40 3.19 38.55 79.48 65.94 5.3811 211.58 Minimum 7.20 66.40 51.00 1.85 75 95.16 43 0.3 492 Maximum 65.28 120.00 145.00 12 201 380.64 272 7.91 1038

Fig. 6 Mean concentration of major ions of the cluster group (shallow groundwater samples in study area)

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(HCO3)2, Ca (HCO3)2. Chloride concentrations ranged from 29 to 122  mg/L with mean and standard deviations (SD) of 80.7 mg/L and 28, respectively. Sulfate concentra-tions varied from 65 to 209 mg/L with mean and standard deviations of 124 mg/L and 37, respectively.

Conclusion

The Nile Delta (Quaternary) aquifer is one of the most important water resources in Egypt. The study area is mainly occupied by the Quaternary aquifer which discrimi-nated into two hydrogeological units; the upper unit is the Holocene aquitard and the lower one is the Pleistocene aquifer.

Statistical analyses such as, descriptive statistics, rela-tionships among variables (e.g., correlation) and Hierar-chical cluster analysis for water quality parameters were carried out. Results indicate that the TDS shows a strong positive correlation with Na2+, Cl− and SO4

2−, while the Na+ shows a strong positive correlation with SO4

2− and Cl− in shallow groundwater samples. In the deep ground-water samples, the TDS shows a strong positive correla-tion with Na. The high degree of association between the TDS with Na2−, Cl− and SO4

2− indicates the anthropogenic activities among these are landfill waste sites, septic tanks, and domestic and industrial effluents and intensive use of fertilizers.

The results of HCA indicate that the numbers of clus-ter of groundwater samples in the study area are changed with depth. The shallow groundwater samples falls into seven clusters, however, four of them involves only iso-lated samples, which can be considered as outlier due to the anomalous excess of TDS, Na+, K+, SO4

2−, Cl−, and NO3− concentrations. On the other hand, the deep ground-water samples falls into four clusters, however, one of them involves only isolated samples, which can be consid-ered as outlier due to the anomalous excess of SO4

2− con-centrations. Thus, it was appropriate to divide the study area into three major zones for both shallow and deep groundwater samples. The results show that there is a good correspondence between spatial location and the statistical groups as determined by the HCA. Accordingly, it is cer-tain that the factors characterizing the hydrogeochemical characters of groundwater in the study area are changed with depth. Increasing concentrations of K+, Na+, SO4

2−, Cl−, and NO3− toward the top of the aquifer point to the

Fig. 7 Dendrogram showing clustering of chemical analyses of deep groundwater samples

Fig. 8 Spatial distribution of deep groundwater

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influence of anthropogenic activities on the groundwater composition and suggests that they come mostly from sur-face sources.

Acknowledgements Authors would like to thank all who helped in the completion of this research and also the Benha University, Faculty of Science and also thank the anonymous reviewers have all the com-ments and suggestions.

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Table 3 Minimum, maximum, mean and standard deviation of water quality parameters for deep groundwater samples

Descriptive statistic Mg2+ Ca2+ Na+ K+ Cl− HCO3− SO4

2− NO3− TDS

Cluster A Mean 19.78 38.42 87.15 4.62 53.5 230.2 99.26 5.372 511 Standard deviation 6.584 13.09 32.39 3.35 20.73 67.71 41.80 1.3744 166.142 Minimum 10 17.2 34 1.7 16 112.48 24.35 3.96 236.71 Maximum 36.08 63.2 150 14 95 330.18 179.1 7.1 818.79

Cluster B Mean 17.98 60.11 107.07 8.17 114.3 145.2 175 628.06

Standard deviation 9.11 22.06 20.67 3.27 30.30 44.18 38 80.29 Minimum 5.76 19.20 69 3.00 85 93.94 96.10 539.60 Maximum 35.52 100 150 13.5 188 233 246.3 8.74 786.20

Cluster C Mean 42.78 22.9 68.88 4.8 80.69 173.55 124 5.2367 519 Standard deviation 10.003 10.23 15.12 2.207 28 29.692 37 2.6989 52.7 Minimum 29.76 5.6 42 1.8 29 136.64 65 2.42 424.6 Maximum 68.64 53.6 95 9.5 122 236.68 209 7.8 623

Cluster D Mean 30.91 54.00 81.30 8 74.60 103.94 242 4.410 595 Standard deviation 6.93 14.47 19.22 4.94 14.14 14.23 44 4.081 72.31 Minimum 17.76 26.40 60.00 5.00 43.00 81.74 186.9 0.200 480.37 Maximum 42.24 73.60 125.00 21 95.00 123.22 325 10 735.98

Fig. 9 Mean concentration of major ions of the cluster group identi-fied by Samples cluster in study area HCA (deep groundwater sam-ples in study area)

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