Introduction

Recently, Iraq was considered one of the arid or semi-arid territories. Water scarcity represented a serious challenge facing any activity that depends on water abundance. This was a result of water resources administration policies of surrounding countries; that is, dam construction and rivers diverting in addition to an emerging problem of water quality deterioration (Ismail et al. 2019; Yürekli et al. 2021). Furthermore, the salinity levels of rivers and lakes were remarkably increased to an extent preventing water treatment plants (WTPs) from dealing with such problems. This returned to the fact that the developing countries are still using traditional techniques that were not designed mainly for desalination (Mahmood et al. 2019).

On the other hand, drought is a serious problem in Iraq, particularly in the southern and central regions of the country. The country has been suffering from a severe water crisis for many years, which has been exacerbated by the effects of climate change (Hamed et al. 2023). Drought and groundwater are closely related in Iraq. The country has been suffering from a severe water crisis for many years, and this has led to a depletion of groundwater resources. The drought has had a significant impact on the groundwater levels in the country. The levels of groundwater have decreased significantly, and many areas have been left without water. This has had a devastating impact on the region’ s agriculture, which is heavily dependent on irrigation (Al-Quraishi et al. 2021; Hasan and Abdullah 2023; Jasim and Awchi 2020).

As a result of those problems, more wetlands turned into deserts with a significant decrease in agricultural fields (Zowain and Ismail 2015). However, the scope of solutions focused on the use of groundwater for various use such as irrigation, which deepened the problem as shallow wells were dug randomly and unprofessionally (Ismail et al. 2020). Alongside with the mentioned reasons, many other ones were interfered and deteriorating groundwater quality. This includes climate change, rising evaporation levels, and unprofessional management of water resources in the entire country of Iraq (Al-Ahmadi 2013; Ghalib 2017; Adimalla et al. 2018; Ismail et al. 2018).

This work deals with assessing the groundwater quality in Tikrit County, located exactly in Salah Al-Din province. The importance of this area comes from the fact that most its population depends on the activity of agriculture. For wider regard, the nature of geochemical properties of groundwater quality in Tikrit area and its suitability for irrigation and human direct consumption should be clearer (Nagarajan et al. 2010; Rao et al. 2012; Jasrotia et al. 2018; Kawo and Karuppannan 2018). In order to complete such job, many tests could be followed. Water ions examination, the techniques of advanced statistics; a case in point, cluster analysis to give more information of water quality in the studied region. Moreover, using multivariate statistical model has been proven in many previous researches for its successful handling of such data over many years (Wunderlin et al. 2001; Simeonova et al. 2003; Shrestha and Kazama 2007; Belkhiri et al. 2010; Ismail et al. 2014, 2015, 2020; Abed et al. 2021).

The goal of this study is to explore the groundwater quality and usability in Tikrit area for irrigation and drinking using WQI and geochemical indicators in addition to studying hydrochemical characteristics for a better understanding of groundwater and the complete geochemical process rolling water chemistry indicators in Tikrit.

Materials and methods

Study area

The study area is located in Tikrit, the administrative capital of Salah Al-Din province. This county covers 3539 Km2 of land (Fig. 1). Its geographical location is between a latitude of 36° 0′ 0″ and 34° 16′ 0″ North with a longitude of 43° 0′ 0″ and 44° 0′ 0″ East. It is occupied by 370,000 people (MOP 2007). The general climate of this area tends to be arid to semi-arid. The rainfall averaged to be 14.5 mm (MOT 2010). The air temperature varies from a minimum of 4.6 °C in winter (Dec. to Feb.) to a maximum of 48 °C in summer (Jun. to Aug.) (MOT 2010). The wind is breezing northwesterly most times with an average speed that varies from 1.7 to 3.5 km/h. Humidity; in general, as any of such lands cannot reach saturation. It may reach 68% in its maxima during winter and down to 23% during summer (MOT 2010).

Fig. 1
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Study area illustration

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Figure 2 illustrates geological map of the area under consideration. Quaternary (Holocene and Pleistocene) deposit represents the main geological composition in the studied land. It consists mainly of sand dunes, sand sheets, sediments of both valley fill and flood plains, Gypcrete (mainly in a form of gypsiferous as well as Al-Fatha sediments of alluvial fan). Injana formation consists mainly of a sandstone sequence, claystone, and siltstone layers (Jassim and Goff 2006). The aquifer of the studied area consists mainly of Injana formation and Quaternary deposits. The upper secondary layers of groundwater aquifer consists also from Mukdadiya formation which is located between Hamrin hills in the northeast and Tigris River in the west direction. The Outer Platform of Mesopotamia, which is a part of the Arabian Plate, represents the hydrological zones under study (Sissakian and Fouad 2015).

Fig. 2
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Geological nature of area under study

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Samples collection and analyzing

Twenty-seven samples were gathered using polypropylene bottles of one liter in volume each. The collection was during March 2020 from shallow tube wells. A continuous pumping was implemented for about ten minutes before taking any sample. Samples locations along the area of study are illustrated in Fig. 1. The parameters of water quality that are taken into consideration were TDS, pH, EC, as well as the following elements K+, Mg2+, Ca2+, Na+ in addition to the radicals of Chloride, Bicarbonate, Carbonate, Nitrate, and Sulfate. The followed analysis procedures were according to some American health institutes (APHA 2005). Some indicators such as EC and pH were tested in situ by portable equipment. The oven of controlled temperature was used to determine TDS. EDTA titration method was used to measure Magnesium (Mg2+) and Calcium (Ca2+). Flame emission photometry was used to estimate Potassium (K+) and Sodium (Na+) concentrations. Chloride ion (Cl−) level was measured by the silver nitrate method. The spectrophotometric method was used to determine Nitrate-Nitrogen (NO3−) and Sulfate (SO42−) levels. Volumetric titration with HCl was used to determine Carbonate (CO32−) and Bicarbonate (HCO3−) concentrations.

Cluster analysis

The method used for evaluating the studied spatial variations along the samples of groundwater sites was cluster analysis which is abbreviated as (CA) and is known a set of statistical multivariate methods. Using such a strategy could present a good explanation for the relationship among the groundwater variables (Ismail and Robescu 2019a). Ward’ s technique of linkage was used to obtain the desired hierarchical clustering. Also, the square Euclidean distance method was followed to determine the distance of similarity and briefed using a dendrogram. In multidimensional space, the geometrical distance is described by the following equation (Singh et al. 2008):

$${\text{Distance}}\;\left( {X{,}Y} \right) = \left\{ {\sum {i\left( {Xi - Yi} \right)^{2} } } \right\}^{1/2}$$
(1)

The variables of water quality are required by CA in order to comply with normal distribution. Normality distribution was checked using the test of Shapiro–Wilk. All the observed parameters of water quality are fit to normal distribution excluding TDS, EC, NO3−, SO42−, and K+. As a result, in order to transfer the original figures of non-normal distribution indicators, the form of x′ = log10(x) was used. Upon that, it was discovered that all the studied parameters were normalized excluding NO3−. IBM-SPSS 25 for windows was used to perform the relevant hierarchical CA (Ismail et al. 2018).

Results and discussion

The groundwater hydrochemistry and its general characteristics

Table 1 summarizes the descriptive statistic of the chemical composition of the groundwater in the study area. The values of pH of the area under consideration ranged from 7.11 to 8.18, the mean value estimated to be 7.35, which gives a direct indication of the slight alkalinity of the groundwater.

Table 1 Descriptive statistic of the chemical composition of the groundwater in the study area, n = 27
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The registered mean value for TDS as well as EC was found to be 1250 mg/L and 1925 μS/cm respectively. Standard deviation values were 778.24 mg/L and 1120.04 μS/cm with the same respect (Table 1). The high evaporation level may be the reason behind those high values as the study area was described as semi-arid. In the near district of Balad, Ismail et al. (2018) noticed the same high figures. For the cations of K+, Na+, Mg2+, and Ca2+ the mean values were 17.89, 355.44, 100.29, and 197.51 mg/L and the associated standard deviation were 25.90, 135.01, 37.73, and 77.53 mg/L respectively. The spatial variation of TDS and EC in addition to major ions concentrations is illustrated in Fig. 3.

Fig. 3
figure 3figure 3

EC, TDS as well as major ions spatial variation of the area under study

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The anions NO3−, HCO3−, SO42−, and Cl− were 3.35, 261.14, 773.81, and 479.40 mg/L. Their standard deviation had the values of 2.4, 126.34, 268.44, and 154.79 mg/L respectively. The carbonate (CO32−) ion concentration held the value of zero in all samples of groundwater. HCO3− can be introduced into the water naturally mainly by CO2 dissolving carbonate-containing minerals or be a result of the combination of CO2 with rainwater (Ismail et al. 2018, 2020).

The indices of Chloro-Alkaline such as CAI1 and CAI2 are used to investigate the ion exchange reaction (Schoeller 1965). The following formulas were used to calculate those indices:

$${\text{CAI}}1 = \left\{ {{\text{Cl}}^{ - } - ({\text{Na}}^{ + } + {\text{K}}^{ + } )} \right\}/{\text{Cl}}^{ - }$$
(2)
$${\text{CAI}}2 = \left\{ {{\text{Cl}}^{ - } - ({\text{Na}}^{ + } + {\text{K}}^{ + } )} \right\}/\left( {{\text{SO}}_{4} ^{{2 - }} + {\text{HCO}}_{3} ^{ - } + {\text{CO}}_{3} ^{{2 - }} + {\text{NO}}_{3} ^{ - } } \right)$$
(3)

where all values are expressed in milliequivalent per liter. Positive values of CAI1 and CAI2 indicate that Na+ and K+ ions are exchanged with Mg2+ and Ca2+ ions in water. Negative values of these indices indicate that Mg2+ and Ca2+ ions are exchanged with Na+ and K+ from rocks. The values of Schoeller indices for the studies samples in Tikrit showed negative values for 16 samples which indicates that Mg2+ and Ca2+ ions are exchanged with Na+ and K+. The other 11 samples had positive values which changed to Na+ and K+ in water exchanges with Mg2+ and Ca2+ on the rock surface. Calculations for the indices of CAI1 and CAI2 were implemented by Ismail et al. (2018) in Balad, Southern Salah Al-Din province (Fig. 1). It was concluded that 46% of the samples were with negative values of Schoeller indices. Balad area showed a relatively high levels of Na+

The values of hydrochemical evolution of the groundwater were determined using the Piper diagram (Piper 1944) which was implemented using the software of AquaChem (Fig. 4). This software gave the indication that most of the studied groundwater samples fall under three classifications: Ca–Cl, Na–Cl, and Mixed Ca–Mg–Cl type. Those types suggest that surface water is the source of high-salinity mixing water, and the reason behind the remarkable effects of the dispersed evaporates of the aquifer matrix (Ismail et al. 2020). Also, the outcome of Piper diagram complies with the obtained results by Ismail et al. (2019).

Fig. 4
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Piper plot

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Gibbs ratio was used to investigate the controlling mechanism of groundwater chemistry (Gibbs 1970), which could be applied to investigate the most effective sources that indicate the constitutive chemical dissolution (Fig. 5). From this plot, it can be concluded that all the samples of groundwater under consideration were categorized under the zone of evaporation dominance. Also, it is observed that the dominant driving force that controls water chemistry is the evaporation process.

Fig. 5
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Gibbs plot

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In the current study, a bivariate plot was used to investigate the hydrogeochemical processes. The assessment of carbonate and silicate weathering share was implemented by drawing Total Cations Concentration (TCC) against (Na+ + K+) and (Ca2+ + Mg2+) as illustrated in Fig. 6a, b (Kumar et al. 2008; Das et al. 2016, 2017). The obtained observation was that around 50% of the studied samples were below the 1:2 line. This was a direct indication of the abundance of Ca2+ and Mg2+ as a result of carbonate minerals and reverse ion exchange. The other samples lay along the mentioned line, which shows silicate-weathering dominance. The same behavior was observed by other researchers in Jorhat district in Assam, India as well as Tarmiah district in Iraq (Das et al. 2015; Ismail et al. 2020).

Fig. 6
figure 6figure 6

the groundwater hydrochemistry represented in Scatter plots and the spatial trends of carbonate weathering in Tikrit area. a TCC versus ions of Ca2+ and Mg2+, b TCC versus ions of Na+  + K+, c Na+:Cl− ratio in moles versus EC, d Na+ versus Cl−, e NO3− versus K+, f NO3 versus Cl−, g Ca2+/Mg2+ versus Sample No., h (Ca2+  + Mg2+) versus (SO42−  + HCO3−), i HCO3− versus Ca2+, j SO42− versus Ca

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EC was plotted versus Na+/Cl− molar ratio as shown in Fig. 6c. If this ratio runs near unity, this indicates halite dissolution (Na+ and Cl− will be equally released due to the process). If the above ratio exceeds unity; i.e. higher Na+, an indication that the dominance of silicate weather is obtained. On the other hand, a ratio under unity (higher Cl−) indicates the dominance of the evaporation step associated with the ion exchange of Ca+ reversibly with Na+. Also, the evaporation process could be dominant to an extent that controlling the chemistry of water in case the ratio of Na+/Cl− still unchangeable with increasing EC (Kumar et al. 2006). Figure 6c shows that about 50% of the studied groundwater samples had mostly fixed Na+/Cl− proportion with an increase of EC. In this work, it was concluded that both silicate weathering and evaporation have equal dominance over the study area (Fig. 6d).

As it is shown in Fig. 6e, no relation between NO3− and K+ was observed. The concentrations of NO3− registered very low levels compared to K+. This gave the conclusion that the source of K+ may be the silicate weather, while anthropogenic activities were the source of NO3− (Das et al. 2017). Figure 6f showed a positive relationship between NO3− and Cl−; the indication of this may be that both ions have the same source; i.e. the anthropogenic. Ismail et al. (2020) got the same behavior.

In Fig. 6g the molar ratio of Ca2+/Mg2+ in the samples of groundwater was shown. The first impression indicates the presence of dolomite dissolution and calcite (Kumar et al. 2006). A ratio near unity means dolomite dissolution, while a ratio of Ca2+/Mg2+ ranging from 1 to 2 indicates higher levels of calcite dissolution. A ratio above 2 gave the conclusion that dissolution and silicate weathering is the major process. It was observed that nearly 63% of samples ranged between 1 and 2 in Ca2+/Mg2+ ratio. This indicates that silicate weathering is less dominant than calcite dissolution within the studied area (Das et al. 2016). Figure 6h represents SO42− + HCO3− versus Ca2+ + Mg2+ plot. Around 60% of the studied samples were below the 1:1 line. Because of that, it can be concluded that reverse ion exchange had a slight increase more than the common values.

When plotting Ca2+ versus HCO3−, it appears clear that the majority of samples were under the equal line (Fig. 6i). From this, it is clear that carbonate mineral “calcite” weathering interferes with the release of Ca+2 into the water instead of carbonic acid “silicate” weathering. In addition, when plotting Ca2+ versus SO42−, we obtain that there is an excess amount of SO42− ion over the ion of Ca2+ (Fig. 6j). Moreover, the plot shows that the possible source of Ca2+ ion came from is anhydrite or may be gypsum dissolution, rather than the calcite weathering (Mgbenu and Egbueri 2019).

Groundwater usability

The usability of groundwater was explored using the Water Quality Index (WQI) technique, which is a well-known method that is frequently dealt with for assessing the suitability of groundwater (Vasanthavigar et al. 2010; Rajankar et al. 2011; Nagaraju et al. 2016). WQI is working with a scale between 0 and 100, higher number means better quality and vice versa (Massoud 2012). In addition to drinking water, it can evaluate water quality for various uses such as irrigation and industry (Abed et al. 2021). In works of literature, it could be found many WQI models, which can be frequently in review articles (Lumb et al. 2011; Sutadian et al. 2016). In the current study, the selected index was Bascaron Water Quality Index (BWQI) (Bascaron 1979). The choice was because of its wide usage globally (Pesce and Wunderlin 2000; Debels et al. 2005; Kannel et al. 2007; Massoud 2012; Ismail and Robescu 2019b). For multiple samples, this index can be identified mathematically as:

$$\mathrm{WQI}= \frac{\sum_{i=1}^{n}{C}_{i}{P}_{i}}{\sum_{i=1}^{n}{P}_{i}}$$
(4)

where n = The sum of variables. Ci = value of the factor i after the appropriate normalization. Pi = proportional weight associated with each factor which has the range of 1–4 depending to its impact on water usability.

In addition to reason above, BWQI was chosen because of its ability to include many variables of water usability for implementing the necessary calculation of the general index after specifying the normalization factors in addition to their weights. Some parameters of water quality are summarized in Table 2 which are normalized and weighted in previous research (Pesce and Wunderlin 2000; Kannel et al. 2007; Massoud 2012). 8 water quality factors were evaluated in this work; that is, NO3, SO4, Cl, Mg, Ca, TDS, EC, and pH. HCO3, Na, and K were excluded in the above calculations because they have no normalized and weighted parameters. This WQI was widely applied in the literature and has the advantage to include many water quality parameters for implementing the necessary calculation of the general index after specifying the normalization factors in addition to their weights. The normalization parameters as well as their relative participation listed in Table 2 were utilized for the chosen factors to get the final BWQI.

Table 2 Water usability factors which was normalized and weighted previously
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The following categories were adopted in order to classify water quality:

  • Excellent: 90–100

  • Good: 71–90

  • Medium: 51–70

  • Bad: 26–50

  • Very bad: 0–25

Table 3 described the calculated sub-indices as well as the final values and categories of the studied water usability, which is found to be medium or bad. Figure 7 illustrates the spatial variation of the studied BWQI.

Table 3 The sub-index values along with relative weight, WQI and categorization values of BWQI index
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Fig. 7
figure 7

The spatial variation of BWQI in the study area

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Quality of groundwater for irrigation

Multiple geochemical factors were evaluated in order to estimate the usability of the groundwater in irrigation. That is, Na percentage, Sodium Adsorption Ratio (SAR), US Salinity Diagram, and Mg Hazards (MH). In addition, EC evaluated the usability of the groundwater under consideration for use in irrigation. MH, %Na, and SAR can be estimated in the following equations:

$$\mathrm{SAR}=\frac{{\mathrm{Na}}^{+}}{\sqrt{\frac{{\mathrm{Ca}}^{2+}+{\mathrm{Mg}}^{2+}}{2}}}$$
(5)
$$\mathrm{Na\%}=\left[\frac{{\mathrm{Na}}^{+}+{\mathrm{K}}^{+}}{{\mathrm{Na}}^{+}+{\mathrm{K}}^{+}+{\mathrm{Ca}}^{2+}+{\mathrm{Mg}}^{2+}}\right]\times 100$$
(6)
$$\mathrm{MH}=\left[\frac{{\mathrm{Mg}}^{2+}}{{\mathrm{Ca}}^{2+}+{\mathrm{Mg}}^{2+}}\right]\times 100$$
(7)

where all values are expressed in milliequivalent per liter.

The spatial distribution of groundwater usability for irrigation along the area under consideration was shown in Fig. 8. It is obvious that EC has a remarkable impact on defining the level of usability of groundwater in irrigation. According to the obtained results of this parameter, most samples were not suitable for the use of irrigation. On the contrary, MH, %Na, and SAR were not of that unsuitability level. That is, for MH, which is proposed by Szaboles and Darab (1964), Fig. 8 described the majority of samples as suitable for the use of irrigation. %Na results gave the indication that the studied samples were categorized from permissible to good. The US salinity diagram, which is suggested by USSL (1954), and SAR were drawn against EC using AquaChem software as shown in Fig. 9. It was seen that, except for a single sample, all of them lay in C4S1, C4S2, and C4S3 zones which indicates the poor usability of such water for irrigation. The mentioned single excepted sample fell in C3S1 zone and is considered fair for irrigation. The previous works claimed that groundwater is unusable for irrigation in both southern and northern Baghdad, south of Salah Al-Din, and north-eastern Iran (Ghalib 2017; Ismail et al. 2018, 2020).

Fig. 8
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Groundwater usability for irrigation in the area under study based on EC, SAR, MH, and Na percentage

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Fig. 9
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USSL classification of groundwater samples

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Multivariate analysis

Hierarchical CA was utilized to estimate the spatial variations along the sites under study and shown as a dendrogram (Fig. 10). Resulted samples were categorized into Group A and Group B. Group A; in turn, was classified into two groups (A1 and A2). Group A1 included 59% of the samples that were linked to comparatively low pollution sites. Group A2 included 34% of samples and was linked to comparatively moderate pollution sites. Group B represented 7% of the samples and was linked to sites with relatively high pollution. Samples were spaced in each group at its place (Fig. 10). This gives the conclusion that the anthropogenic behaviour in the study area may directly affect groundwater quality.

Fig. 10
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Spatial clustering diagram for samples of groundwater under the study area

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Conclusions

Generally, this work revealed that the area under consideration facing a serious pollution threat. Hydrochemistry of the studied groundwater samples obtained from Piper plot gave 3 types of water which led to conclude that water of high salinity mixture was produced as a result of evaporation and surface water. In Gibbs ratio, most groundwater samples are found in the evaporation dominance zone. This gave the conclusion that groundwater chemistry is extremely affected by evaporation. A percentage of 59% of the studied samples recorded negative values of both CAI1 and CAI2 in ion-exchange reactions. The conclusion that is Mg2+ and Ca2+ exchange are reciprocated with K+ and Na+ ions from rock. The other samples percentage; i.e. 41%, recorded positive values which indicates a direct exchange reaction of base (cation–anion). Anthropogenic activities, carbonate weathering, silicate, and evaporation affect the groundwater hydrogeochemistry. The silicate weathering and evaporation registered the highest influence on the quality of groundwater. Regarding BWQI, the water quality categorization in all samples recorded medium to bad for drinkability. According to the diagram of US salinity, the conclusion was the unsuitability of the studied samples for irrigation. For pollution levels, 3 different of them were observed in hierarchical CA; that is, low, moderate, and high polluted sites. At every site, spaced and studied samples were included in each clustered group.