Environ Earth Sci DOI 10.1007/s12665-015-4664-4
ORIGINAL ARTICLE
Assessment of hydrochemical evolution of groundwater and its suitability for drinking and irrigation purposes in Al-Khazir Gomal Basin, Northern Iraq Hussein Jassas1,2 • Broder Merkel1
Received: 11 March 2015 / Accepted: 14 June 2015 Ó Springer-Verlag Berlin Heidelberg 2015
Abstract This study evaluates the groundwater suitability for drinking and agricultural purposes and assesses the hydrochemical evolution in Al-Khazir Gomal Basin, north of Iraq. Sixty groundwater samples and 10 river water samples were collected in the dry season (October) and wet season (April). The samples were analyzed to determine major and some minor ions, trace elements, and physicochemical properties. All surface and groundwater samples are considered as fresh water (TDS \ 794 mg/L) and slightly vary in chemical composition. The abundance of the major ions is as follows: Ca2? [ Mg2? [ Na? [ K? = HCO3- [ SO42- [ NO3- [ Cl-. Interpretation of analytical data showed predominance, the water type of Ca–HCO3 and Ca–Mg–HCO3 indicated young and renewable groundwater. Total dissolved solid, total hardness, major ions, and trace elements are all within permissible limits of the potable and domestic purposes according to the European and WHO standards. The parameters of irrigation suitability showed that all of the samples are fit for irrigation purpose. Moreover, cluster and factor analyses were applied to the large data set (70 samples and 25 variables) to unravel the hidden relationships between the parameters, and to reveal the main factors affecting the water quality. The samples collected from the same well during the wet and dry season were clustered together indicating that the seasonal variability is & Hussein Jassas
[email protected] Broder Merkel
[email protected] 1
Hydrogeological Institute, TU Bergakademie Freiberg, Gustav-Zeuner-Str. 12, 09599 Freiberg, Germany
2
Iraq Geological Survey, Baghdad, Iraq
negligible. Factor analysis showed that the rainfall leaching processes (recharge), carbonate minerals dissolution, aluminosilicate weathering, and ionic exchange are the dominant factors involved in controlling the groundwater chemical composition. Keywords Groundwater quality Suitability Cluster analysis Factor analysis Al-khazir Gomal Iraq
Introduction Water quality affects human health and agricultural production to a high extent. Thus, knowledge of hydrochemical characteristics is vital to understand water suitability for various purposes (Cicchella et al. 2010). Groundwater quality in an area is a function of physical and chemical parameters that are greatly influenced by geological formations, climate, and topography (Helena et al. 2000; Subramani et al. 2005; Balasubramanian et al. 2015). In addition, human activities such as industrialization, agriculture, mining, and urbanization produce effluents affect groundwater quality (Dinelli et al. 2010; Singh et al. 2012; Oyarzu´n et al. 2015). Normally, groundwater contamination occurs gradually with little impact in the initial period of deterioration, but if it is not controlled at the right time, this water may not be suitable for any purpose for a long time (Rao et al. 1997). In recognition of this, the importance of regular monitoring of groundwater is underlined, especially when there are significant changes in industrial, agricultural, and urbanization activities. The phenomenal human population growth has intensified pressure on the natural resources to produce adequate food and raw materials to meet the proportional demand (Smil 1999; Ga´rfias et al. 2010; Touhari et al. 2015). World
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population growth rate is about 1.17 % (United Nations 2013), while in Kurdistan region north of Iraq, the population growth rate is about 2.7 % per annum (Stevanovic and Iurkiewicz 2009). The increase in population is conjugated with the improvement in live standards and the increase in water consumption per capita (Maran and Stevanovic 2008). Population growth subsequently requires improving the agricultural production, which in turn needs to apply different fertilizers and to increase groundwater exploitation. In Al-Khazir Gomal Basin, groundwater is considered the main source of water supply. Thus, monitoring and evaluating the groundwater quality is an important issue to insure the balance between requirements of life and environmental and human health concerns. The early studies of groundwater evaluation utilized two-dimensional graphical representations. However, the Piper and Stiff (diagrams) conventional classification techniques consider only selected major ions in determining the groundwater type. Therefore, advance procedure must be used to explain and interpret the complex hydrochemical processes that occur in aquifers. Multivariate statistical techniques, for instance, are used nowadays to overcome the limitations of graphical techniques by simplifying the large data sets and providing meaningful insight into underlying structure of the variables (Laaksoharju et al. 1999). The principle component analysis (PCA), factor analysis (FA), and cluster analysis (CA) techniques have been utilized widely with significant success as a tool in groundwater quality studies (Gu¨ler et al. 2002; Farnham et al. 2003; Chen et al. 2007; Belkhiri et al. 2010). These techniques are used to assess groundwater composition, and to evaluate the spatial and temporal distribution of the potential pollutants (Ashley and Lloyd 1978; Singh et al. 2004). The aforementioned methods are implemented in this study to assess the hydrochemical evolution of the groundwater in Al-khazir Gomal Basin. There is no systematic or scientifically documented study on the groundwater quality in this basin so far; apart from a few unpublished available reports from the Iraqi Geological Survey (Al-Sam et al. 1978; Al-Sam and Hanna 1981; AlBasrawi 2006; Al-Jiburi 2007). These reports previously evaluated the groundwater, and analyzed only the major ions. The present study was thus carried out to evaluate groundwater quality in Al-khazir Gomal Basin depending on major ions and trace elements using different graphical and statistical approaches. The main goals of this study are evaluating the suitability of groundwater for drinking and agricultural uses, investigating the main dominant factors affecting the water quality, and assessment of the seasonal variation of groundwater composition.
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Study area Al-khazir Gomal River Basin is located in the north of Iraq and to the north-eastern of THE Mosul city between a latitude of 36°520 3300 /36°220 0000 and a longitude of 43°440 2500 /43°140 0000 with an approximate area of about 3185 km2 (Fig. 1). The elevation ranges from 216 m (a.s.l.) at the lowest point of the watershed outlet to 2165 m (a.s.l.) at the Aqra mountains in the far northern parts of the basin [as shown in Fig. 3 (Jassas and Merkel 2015)]. The fan-shaped basin is characterized by a large geological heterogeneity in surface and subsurface and it is completely bounded by water divides, separating it from the adjacent basins. Al-Khazir River and its tributary Gomal River traverse the basin from north to south and conjugate near Bardarash Mountain, and then drain to the Greater Zab River which in turn flows into the Tigris River. The stratigraphical sequence from the oldest to youngest geological formation and cross-sections is illustrated in Fig. 2. According to the geological and structural settings, the basin is divided into three regions (Al-Sam and Hanna 1981). The first region is represented by mountainous area which consists mainly of Paleogene carbonate sediments (Pilaspi Aquifer System) and Cretaceous sediments (AqraBekhme and Qamchuqa Aquifer Systems). In terms of hydrogeology, this region has only little significance, because it has small intergranular and fractured aquifers restricted on the longitudinal and narrow synclines. The second region is a vast plain laying on a broad syncline spanning between Aqre and Bardarash–Maqlub mountains, covering an area of about 1224 km2. The intergranular aquifer in this region consists of thick alluvial sediments (gravel, sand, and silt) of Quaternary and Neogene ages; groundwater occurs both under unconfined and confined conditions. The third region comprises the catchment of the lower reach of Al-Khazir River forming a hilly plain to the south of Bardarash Mountain. The hydrogeological conditions in both third and second regions are the same, but the quantity and quality of groundwater are less in the third region; this is because it is structurally separated from the high recharge area in the second region. Transmissivity varies from 100 m2/d at the edge of the basin to more than 7000 m2/d in the middle part of the basin (Al-Sam and Hanna 1981). The general direction of groundwater flow is from north to south and the regional groundwater recharge estimated in the basin is about 119 mm/y (Jassas and Merkel 2014). The area is characterized by a continental climate (Mediterranean type), cold and rainy in winter and hot and dry in summer. The average annual temperature, precipitation, and potential evapotranspiration are 20 °C, 650 mm,
Environ Earth Sci
Fig. 1 Location map of the study area with shaded relief image showing the three regions
and 1500 mm, respectively. Nearly 70 % of the annual rainfall occurs in winter (November to February) and 27 % during spring (March to May), while summer season (June to September) is characterized by rapid rise in temperature, low humidity, and almost no rainfall. The relative humidity ranges between 76.6 % in January and 20.8 % in August with an annual average of around 46 %. The predominant wind direction is from north-west with an average speed of 1.54 m/s. According to the aridity index (UNEP 1992), the study area is classified as a semi-arid to dry sub-humid region. This study will focus mainly on the second region due to its importance and the availability of the data.
Methodology Sampling and chemical analysis A total of 60 groundwater samples and 10 river samples were collected from Al-khazir Gomal Basin over two seasons, wet season (April 2012) and dry season (October 2012), as shown in Fig. 3. Garmin eTrex 20 GPS device was used for field data collection, which generally showed a spatial accuracy of ±4 m. Groundwater samples were collected after pumping until Electrical Conductivity (EC),
temperature, and pH showed constant values. Samples for anions were filtered through cellulose acetate filters (0.2 lm pore size) into clean and pre-rinsed 50 ml polyethylene bottles. For cations and trace element analyses, samples were filtered and acidified with 2–3 drops of halfconcentrated nitric acid, HNO3. Samples for analyzing inorganic and organic carbon were collected in 100 ml glass bottles. All these samples were stored in a cool box at a temperature of less than 4 °C and transported to the water lab of the Hydrogeology Department of TU Bergakademie Freiberg in Germany. Water temperature, pH, EC, and redox potential (Eh) were all measured in the field with a portable multi-parameter water analyzer (WTWMulti 3430) device. Total Dissolved Solid (TDS) was calculated simply by adding the individual ions and silicium together. Trace element concentrations of B3?, Al3?, V2?, Cr2?, Cd2?, Co2?, Fe3?, Mn2?, Zn2?, As3?, Br-, Rb?, Sr2?, Sb3?, Ba2?, Pb2?, U3?, and Si4? were determined by Inductively Coupled Plasma Mass Spectrometry (Thermo Scientific Element XSERIES 2 ICP-MS). The helium collision gas mode was used to measure Al3?, V2?, Cr2?, Cd2?, Co2?, Fe3?, Mn2?, Zn2?, and As3? with precision (RSD %) of 14.26, 1.32, 1.79, 8.15, 6.76, 3.88, 7.54, 1.00, and 9.19, respectively. On the other hand, measuring B3?,
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Environ Earth Sci Fig. 2 Geological map and cross-sectional views A–A0 and B–B0 (after Jassas and Merkel 2015)
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Environ Earth Sci Fig. 3 Topographic map with groundwater flow direction and sampling sites (modified after Jassas and Merkel 2015)
Br-, Rb?, Sr2?, Sb3?, Ba?2, Pb?2, and U3? with precision 1.07, 9.19, 0.89, 1.09, 0.37, 1.65, 2.63, and 0.40, respectively was done without collision gas determination mode. Anions (Cl-, NO32-, SO42-, and F-) and cations (Na?, Ca?2, Mg?2, and K?) were analyzed by the Ion Chromatography (IC, Metrohm-881-Compact). The Total Inorganic Carbon (TIC) and Total Organic Carbon (TOC) were determined with LiquiTOC (Elementary Analysensysteme GmbH). The HCO3 was calculated from TIC depending on pH. PHREEQC version 2.18.0.0 (Parkhurst and Appelo 1999) was used with the WATE4QF database, to compute percent error of analysis, saturation indices of minerals, and partial pressure of CO2. The degree of hardness is important for esthetic, economic, and operational considerations. Therefore, the total hardness (TH) was calculated using the formula given by Hem (1989)
TH ðas CaCo3 Þ
mg ¼ ðCa2þ þ Mg2þ Þ 50; L
ð1Þ
where Ca2? and Mg2? concentrations are represented in meq/L. The Sodium Adsorption Ratio (SAR) was calculated by the following equation given by Richards (1954) as follows: Na SAR ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; ðCa þ MgÞ=2
ð2Þ
where concentrations are in meq/L. Sodium percentage (Na %) was calculated by the following equation (Todd 1980): Na % ¼
ðNa þ KÞ 100 ; Ca þ Mg þ Na þ K
ð3Þ
where all the concentrations are in meq/L.
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A trilinear diagram (Piper 1944) was used to define the hydrochemical facies with help of AquaChem 2011.1 software. The suitability of the groundwater for drinking was evaluated by comparing the values of different water quality parameters with those of the World Health Organization (WHO 2006) and European Standards guidelines values for drinking water (European Communities 1998). Suitability of the surface and groundwater for irrigation was evaluated based on SAR (Richards 1954), EC, and Na % (Ragunath 1987). ArcMap 10 was used as geographic information system for mapping purpose.
deviation of the parameter (Liu et al. 2003). A certain number of parameters were excluded from the multivariate statistical analysis because some of them such as EC and TDS are inter-correlated with each other and others (e.g., Ca2?, HCO3-). Other parameters were excluded due to the fact that more than 40 % of them were below the detection limit. e.g., Co, Cd, Fe and others were excluded because they show small spatial variation, e.g., Eh, Temperature, TOC. For multivariate statistical analysis under detection limit values were replaced by 0.5 times the lower detection limit.
Multivariate statistical methods
Results and discussion Multivariate statistical methods do not indicate directly any cause and effect relationships; however, they offer information from which such relationships might be inferred (Monjerezi et al. 2011). Thus, they have been used extensively in evaluating hydrochemical data, classifying groundwater, discovering the major mechanisms influencing groundwater chemistry, and determining the contamination sources. In this study, 25 chemical variables (EC, pH, Ca2?, Mg2?, Na?, K?, HCO3-, Cl-, NO3-, SO42-, SiO2, B3?, Al3?, V2?, Cr2?, Mn2?, Zn2?, As3?, Br-, Rb?, Sr2?, Sb3?, Ba2?, Pb2?, and U3?) were used as inputs for Q-mode hierarchical cluster analysis (HCA) and factor analysis (FA) with the help of SPSS version 16.0. Hierarchical clustering is the most regular approach in which clusters are formed sequentially, by starting with the most similar pair of objects, and forming higher clusters step by step. In this study, Q-mode Hierarchical CA was performed on the normalized data set by means of the Ward’s method, using square Euclidean distances as a measure of similarity. However, cluster analysis does not provide information whether clusters found are statistically significant. Thus, it is highly recommended to perform a variance analysis or a Kruskal–Wallis Test if data are not normal distributed. Factor analysis approach is applied widely in hydrochemical assessments to explain the underlying structure of the data by performing it on correlation matrix of rearranged data. In this study, the method of Principle Component Analysis (PCA) was used to extract significant principle components (PCs). For further reduction of the variables contribution with minor significance, these PCs were subjected to varimax rotation. The aim of varimax rotation is that all variables have a high loading only on a single factor and low loadings on the remaining factors. According to the criteria proposed by Kaiser (1958), only factors with eigenvalues that exceed one are retained. Prior to applying the multivariate analysis, the data set are routinely stanthrough z-scale transformation by subtracting the mean value and dividing by the standard
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Groundwater chemistry The results of basic statistics of chemical analyses and field measurements for 70 water samples are summarized in Table 1. The charge balance between cations and anions, which is calculated by PHREEQC software was within acceptable limits ranging from 4.9 to 1.8 %. The water samples had average temperatures during sampling of around 21 C°, reflecting the average yearly temperature of the study area. The pH of the groundwater ranges between 7.10 and 8.33, with an average of 7.6, indicating that the dissolved carbonates are predominantly in the form of HCO3-. The higher calculated partial pressure of carbon dioxide (PCO2) with which water equilibrated in the infiltrating water was in the range of 10-2.63–10-1.40 atm compared to atmospheric air (10-3.5 atm) suggests that the waters have gained CO2 from root respiration and/or organic matter decay (Langmuir 1997). The redox potential values are in the range of 308–582 mV with an average of 354 mV (Table 1) and thus moderately reduced. Chemical composition of groundwater showed no significant seasonal variation. The groundwater in the basin is fresh (TDS \ 794 mg/L) and the salinity increases southward and westward (Fig. 7). The dominant cation is Ca2?, which has a mean concentration of 59.1 mg/l, followed by Mg2?, Na?, and then K? with an average concentration of 18.3 mg/L, 12.7 mg/L, 0.92 mg/L, respectively. The major anion is HCO3 with mean concentration 270 mg/L, followed by SO42-, NO3-, Cl-, and F- with an average concentration of 21.2 mg/L, 12.1 mg/L, 5.7 mg/L, and 0.19 mg/L, respectively. Figure 4 shows EC and the spatial distribution of the main cations (Ca2?, Mg2?, and Na?) and main anions (HCO3- and SO42-). In spite of applying fertilizers in the area such as phosphoric and urea fertilizers, and absence of sewage treatment plants, the NH4? and PO43- were in low concentration with an average of 0.07 mg/L and 0.047 mg/L, respectively. This can be interpreted by the thick unsaturated zone (19 m in average)
Environ Earth Sci Table 1 Basic statistics of chemical data of 70 water samples collected from 35 sites in dry and wet seasons (60 groundwater samples and 10 river water samples)
Parameter measured
Minimum
Maximum
Mean
Median
Std. deviation
Temperature (C°)
18
23
20.7
21
2
pH
7.10
8.33
7.63
7.60
0.27
Redox potential (mV)
308
582
354
346
46.6
EC (ls/cm)
272
853
466
447
135
TDS (mg/L)
246
794
400
391
119
SAR
0.04
2.74
0.50
0.29
0.55
Na %
4.77
73.5
28.3
23.2
17.0
Total hardness (mg/L CaCO3)
36.2
97.9
57.0
52.2
16.7
Na (mg/L)
1.37
71.8
12.7
5.7
14.8
K (mg/L)
0.52
2.09
0.92
0.89
0.46 15.1
Ca (mg/L)
35.7
102.1
59.1
55.6
Mg (mg/L)
4.09
48.19
18.34
18.30
9.23
NH3 (mg/L)
0.01
0.30
0.07
0.03
0.10
Cl (mg/L)
1.03
33.9
5.74
3.87
6.54
F (mg/L) NO3 (mg/L)
0.06 0.58
0.39 36.4
0.19 12.1
0.17 10.8
0.08 6.8
SO4 (mg/L)
1.83
100
21.2
12.9
22.3
HCO3 (mg/L)
152
477
270
252
81.6
SiO2 (mg/L)
11.1
27.6
19.9
20.0
3.94
Log10 (PCO2) atm
-2.63
-1.40
-2.17
-2.21
0.31
PO4 (mg/L)
0.016
0.072
0.047
0.047
0.013
Total organic carbon (mg/L)
0.14
1.33
0.43
0.32
0.38
B (lg/L)
7.19
169
36.8
26.3
34.6
Al (lg/L)
\1
12.09
1.94
1.55
1.53
V (lg/L)
2.66
20.8
6.98
6.09
3.58
Cr (lg/L)
0.12
34.7
3.47
1.42
6.31
Cd (lg/L)
\0.01
0.191
0.007
0.001
0.030
Co (lg/L)
\0.01
0.13
0.027
0.011
0.036
Mn (lg/L)
\0.05
26.0
0.91
0.11
3.22
Fe (lg/L)
\1
2.55
0.70
0.50
0.47
Zn (lg/L) As (lg/L)
\1 0.24
1076 2.18
86.4 0.59
57.0 0.50
136 0.35
Br (lg/L)
13.39
161
43.4
35.9
31.91
Rb (lg/L)
0.13
1.04
0.40
0.36
0.18
Sr (lg/L)
133
1950
672
574
434
Mo (lg/L)
0.39
4.96
1.54
1.26
0.96
Sb (lg/L)
0.01
0.68
0.21
0.10
0.22
Ba (lg/L)
24.66
714
151
123
145
Pb (lg/L)
\0.01
4.12
0.12
0.03
0.51
U (lg/L)
0.18
4.04
1.15
0.88
0.99
Values less than (\) (example: \0.01) indicate detection limit (DL) used for that particular parameter
and dilution by significant groundwater recharge. The concentration of SiO2, which reflects the weathering of aluminosilicate minerals in the aquifers ranged from 11.1 to 27.6 mg/L with an average of 19.9 mg/L. Saturation index (SI) gives an insight to the processes controlling groundwater chemistry by predicting the possible reactivity of the subsurface mineralogy (Deutsch
1997). Saturation index of calcite varies between 0.06 and 0.38 with 92 % of the samples being slightly oversaturated with respect to calcite. Dolomite SI values are in the range of -0.49–0.39 with 66 % being oversaturated. All groundwater samples reached quartz saturation (SI in the range of 0.16–0.42). Gypsum and halite were under saturation and the SI values vary between -3.3 and -1.8 and
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Environ Earth Sci Fig. 4 The spatial distribution of the electrical conductivity (EC ls/cm) and the main cations (Ca, Mg, Na) and anions (HCO3 and SO4) all of them in mg/l
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Environ Earth Sci
-10.15 to -7.19, respectively; this suggests that halite and sulfate mineral phases are either absent in the host rocks or only in minor quantities distributed. Therefore, sources of low concentration of Cl- and SO-2 in groundwater are 4 expected to be from meteoric water, applying the fertilizer, organic material degradation, and some dumping sites. As shown in Fig. 2, the host rock in the study area consists mainly of gravel, sand, silt, and clay, in addition to the carbonate mineral as cementing material (Al-Sam and Hanna 1981). Therefore, the main expected mechanisms contributing to the chemical evolution of the groundwater are the preferential chemical weathering of the carbonate cement and geochemical weathering of aluminosilicate minerals (e.g., albite, kaolinite). In addition to the aluminosilicate weathering, the meteoric NaCl is another source of dissolved Na? and Cl- ions in freshwater samples, and more Na? might sourced from cation exchange with dissolved Ca2? and Mg2?. The relative low concentration of the SO42- and Cl- refers to the absence of evaporates (e.g., gypsum and halite). The aforementioned hypothesis was supported by the dominance of alkaline earth elements (Ca2? and Mg2?) and acidic anion (HCO3-) and relatively high concentration of silicon dioxide (SiO2). The classification of groundwater was studied by plotting the concentrations of major cations and anions in the Piper diagram (Piper 1944) (Fig. 5). The Trilinear plots showed that among anions and cations, HCO3- and Ca2?
have a clear dominance and most of the samples in the second region are classified as Ca–Mg-HCO3 and CaHCO3 type, which suggests renewable young water. The evolution of groundwater in the third region to Ca–Mg– Na–HCO3 type might be attributed to cation exchange which reflects a longer contact time (residence time) of groundwater and the relatively low recharge in the third region. The water type in sample 7 (Na–Ca–Mg–HCO3– SO4) needs further investigation to explain the possible evolution. However, the leakage of deep groundwater could be the reason of the sharp change in the chemical facies. In general, the piper diagram reflects no significant change in hydrochemical facies during wet and dry seasons. The trace element concentrations in all samples are within naturally acceptable limits (Table 1). Low concentrations of trace elements in the basin are a result of the absence of geogenic source or significant industrial activities and the rather low population density (*40000 and most of them live in small villages). In addition, the renewable groundwater and low residence time is another reason of the low concentration. Thus, the present data set will be useful as a reference source for trace element pollution monitoring in the study area. Multivariate statistical analysis results Correlation matrix
Fig. 5 Piper diagram showing no significant change in the chemical facies of the two seasons, the red circles represent wet season samples (spring) and the black circles represent dry season samples (autumn)
Table 2 shows the results of correlation analysis of major ions and trace elements in the study area. The correlation matrix illustrates that EC shows positive correlation (at p \ 0.001) with Na?, Mg2?, Ca2?, Cl-, SO42-, HCO3-, B, Br, Sr, and U. In particular, the pairs of Na?–Cl-, Na?– SO42-, Na?–Mg2?, HCO3-–(Ca2?, Mg2?), B–Na?, B– Mg2?, B–Cl-, B–Br, B–Sr show a significant correlation (r [ 0.5 at p \ 0.001) with EC and among themselves indicating that such ions are derived from the same source. The weak correlation between SO42- and Ca2? is an indicator that the presence of gypsum as a source for these ions can be ruled out. The relationship between HCO3and the alkali earth metals (Ca2? and Mg2?) is expected consequence of carbonate dissolution. Ionic exchange between Ca2? and Na? is suggested to be the reason for the negative correlation between them (r = -0.23 with p \ 0.05). On the other hand, the SiO2 is significantly correlated with Na? and K? (r = 0.66 and 0.53, respectively at p \ 0.001). These correlations suggest a common source, i.e., weathering of aluminosilicate minerals, which is present in this kind of lithology (e.g., albite, kaolinite, and feldspars). Some of the trace elements were significantly correlated (at p \ 0.001); As with Cr, V, U, Rb, and Sr as well as U
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123
0.84
0.31
0.46
0.85
0.71
0.07
0.61
Ca
Mg
Cl
NO3
SO4
HCO3
1.00
0.55
K
0.66
0.30
0.67
-0.05
0.46
0.32
0.22
-0.20
0.25
0.75
0.26
0.71
0.18
-0.15
-0.17
0.76
SiO2
B
Al
V
Cr
Mn
Zn
As
Br
Rb
Sr
Sb
Ba
Pb
U
0.40
-0.24
0.25
0.10
0.44
0.58
0.53
0.31
-0.18
0.24
0.26
0.28
-0.23
0.21
0.11
-0.10
-0.03
-0.07
0.00
-0.42
0.03
-0.19
-0.34
0.36
-0.22
-0.15
-0.12
-0.11
-0.26
0.68
0.10
-0.12
0.07
0.29
1.00
Ca
0.84
-0.17
-0.06
0.26
0.78
0.25
0.51
0.28
-0.11
0.03
0.29
0.52
-0.18
0.69
0.397
0.83
0.56
0.04
0.64
1.00
Mg
0.77
-0.05
-0.07
0.19
0.72
0.42
0.70
0.51
0.02
0.00
0.68
0.69
0.06
0.75
0.46
0.43
0.73
0.34
1.00
Cl
Bold values are significant at the level of p \ 0.001
0.81
-0.02
-0.17
0.34
0.76
0.48
0.53
0.59
0.02
-0.12
0.80
0.82
0.03
0.86
0.27
0.31
0.82
0.53
0.45
0.03
0.44
0.48
0.06
1.00
K
0.72
0.59
-0.23
0.30
1.00
0.54
Na
Na
EC
EC
Var.
0.22
0.15
0.32
-0.36
0.30
0.65
-0.10
-0.16
0.18
0.55
0.26
0.50
0.13 0.02
0.60
-0.09
0.28
0.67
0.61
-0.04
0.63
0.26
0.27
1.00
SO4
0.14
0.31
-0.18
0.11
0.26
0.23
0.35
0.23
-0.03
0.04
1.00
NO3
0.67
-0.16
-0.10
0.21
0.60
0.02
0.40
0.02
-0.28
0.11
0.06
0.29
-0.16
0.47
0.24
1.00
HCO3
Table 2 Correlation coefficient matrix of water quality parameters (n = 70)
0.51
-0.05
-0.10
0.28
0.67
0.56
0.29
0.23
-0.25
-0.23
0.42
0.37
-0.08
0.52
1.00
SiO2
0.86
-0.05
-0.13
0.23
0.84
0.37
0.62
0.45
0.08
-0.07
0.56
0.70
0.07
1.00
B
-0.10
0.70
-0.30
-0.15
0.03
0.09
0.19
0.24
0.20
0.06
-0.02
-0.16
1.00
Al
0.73
-0.10
0.09
0.34
0.57
0.45
0.45
0.61
0.25
-0.18
0.78
1.00
V
0.55
0.03
-0.16
0.18
0.50
0.50
0.47
0.75
0.04
-0.13
1.00
Cr
0.02
-0.02
-0.08
-0.12
-0.07
-0.05
0.17
0.06
-0.09
1.00
Mn
-0.03
0.28
0.38
-0.27
-0.09
0.21
0.02
0.14
1.00
Zn
0.52
0.44
-0.06
0.12
0.47
0.54
0.37
1.00
As
0.60
-0.06
-0.13
0.06
0.65
0.61
1.00
Br
0.40
0.09
0.12
0.17
0.53
1.00
Rb
0.91
-0.09
-0.08
0.10
1.00
Sr
0.28
-0.08
-0.09
1.00
Sb
-0.07
-0.17
1.00
Ba
-0.11
1.00
Pb
1.00
U
Environ Earth Sci
Environ Earth Sci
that the variables Cr, Na, V, SO42-, Cl, As, and B have high to moderate positive loadings on factor 1, whereas HCO3-, Mg, EC, U, pH, Ca, and Sr have high to moderate positive loadings on factor 2. In factor 3, Rb, K, SiO2, and Br have high to moderate positive loading, while Sb has negative loading. For factor 4, the most contribution is given by Ba, Zn, and NO3. The factor loading of Pb and Al shows high positive values in factor 5. Finally, in factor 6 Mn has negative loading. The results of factor analysis refer to six different processes involved in controlling the chemical composition of groundwater in the area. According to the cumulative variance (63.6 %), the first three factors are expected to have a significant effect on water chemistry. Evaluation of the Factor 1 (recharge water factor) loadings showed that most of the trace elements with high positive loadings typically occurred as soluble oxyanions in oxic alkaline condition. Chloride, sodium, and sulfate which have high loading in this factor are expected to source from
with Rb, V, Cr, B, Sr. The significant correlations of these elements and the low concentration (lower than natural background concentration) suggest their natural sources in groundwater. Factor analysis Factor analysis, in general is used simply as a numerical method of discovering those variables which are more important than the others for representing parameter variations affording data reduction with minimum loss of original information (Matalas and Reiher 1967; Ashley and Lloyd 1978; Vega et al. 1998). Six factors were extracted using principle component analysis method with varimax rotation (Table 3). These factors (with eigenvalue [1) explain about 82.97 % of total sample variance. The variance explanation of the factors is 39.2 % for factor 1, 15.5 % for factor 2, 8.7 % for factor 3, 7.8 % for factor 4, 6.9 % for factor 5, and 4.9 % for factor 6. Table 3 shows Table 3 R-mode rotated factor loading matrix (extraction method: principle component analysis and the rotation method is varimax with Kaiser Normalization)
Variable
Factor 1
Cr
0.91
Na
0.87
V
0.84
SO4
0.81
Cl
0.74
As
0.73
B
0.70
Factor 2
HCO3
0.96
Mg
0.83
EC
0.83
U
Factor 3
Factor 4
Factor 5
0.66
pH Ca
-0.65 0.63
Sr
0.61
Rb
0.84
K
0.79
SiO2
0.56
Br
0.53
Pb
0.90
Al
0.89
Mn
-0.73
Ba
0.80
Zn
0.78
NO3
0.70
Sb Initial eigenvalue Percentage of variance Cumulative % of variance
Factor 6
-0.78 9.02
4.23
2.00
1.79
1.57
1.12
39.24 39.24
15.5 54.74
8.70 63.44
7.78 71.22
6.85 78.07
4.90 82.97
Absolute loading values \0.5 are suppressed
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rainwater. These indicators, in addition to the oxidation conditions, imply that this factor is controlled by recharged rainfall which dissolves these elements from soil into groundwater in a process knowing as leaching. The high loading of EC, HCO3-, Mg?, and Ca? in Factor 2 (carbonate dissolution factor) indicates that carbonate dissolution explains this factor. The high loading of U and Sr on this factor suggests that these trace elements are associated with the carbonate. Factor 3 (aluminosilicate weathering factor) loads on K? and moderate on SiO2 which can be explained by aluminosilicate weathering. The positive loading of Rb on this factor implies that the source of this element is the rubicline tectosilicate mineral (Rb, K) [AlSi3O8]. There is no explanation so far for the negative loading of Sb in this factor. Normally, NO3- ion is known to have no lithological source and its occurrence in groundwater is due to atmospheric or pollution sources (Jeong 2001). The soil with high leaching capacity (as the case study) can sometime lack zinc altogether (Alloway 2008). Thus it is expected to apply zinc fertilizer in the study area. Consequently, the high positive loading of Ba, Zn, and NO3 in factor 4 (fertilizer and sewage factor) might be attributed to apply zinc containing nitrate fertilizer and sewage water percolation. There are no natural or anthropogenic sources of aluminum and lead in the study area; thus, the high positive loading of Al and Pb on factor 5 cannot be explained. Manganese is generally more soluble at low pH and oxygen-depleted groundwater. On the other hand, it precipitates as Mn(IV) in alkaline and more oxidizing water (Hem 1989). Therefore, the negative loading of Mn (-0.73) on factor 6 suggests oxic and alkaline condition. Cluster analysis Q-mode hierarchical cluster analysis is an unsupervised pattern recognition technique, which allows grouping water samples according to their chemical composition based on their nearness or similarity without making a prior assumption about the data (Ashley and Lloyd 1978; Vega et al. 1998). Many researchers used this technique successfully to classify water samples (Williams 1982; Farnham et al. 2000; Chen et al. 2007; Cloutier et al. 2008; Belkhiri et al. 2010; Monjerezi et al. 2011). Hierarchical cluster analysis results were given as a dendrogram (Fig. 6). As shown in this figure, the samples collected from the same site during different season were clustered together. There are three groups of clusters obtained. The first cluster which characterized by relatively low EC values and Ca-HCO3 water typerepresents most of the confined aquifer samples (16, 17, 23, and 31) and the unconfined aquifer samples near the recharge areas (1, 2, 10, 12, 13, and 25). Other samples in the second region (4,
123
Rescaled Distance Cluster Combine C A S E 0 5 10 15 20 25 Label +---------+---------+---------+---------+---------+ A16 ─┐ S16 ─┤ A17 ─┤ S17 ─┤ A15 ─┤ S15 ─┤ A12 ─┤ A13 ─┤ S13 ─┤ A23 ─┤ S23 ─┼─┐ A31 ─┤ │ S31 ─┤ │ S12 ─┤ │ A25 ─┤ │ S25 ─┘ ├─────────────────────────┐ A2 ─┐ │ │ S2 ─┤ │ │ S10 ─┤ │ │ A1 ─┤ │ │ S1 ─┼─┘ │ A3 ─┤ ├───────────────────┐ S3 ─┤ │ │ A10 ─┘ │ │ A21 ─┐ │ │ S21 ─┼───────┐ │ │ │ │ │ S18 ─┤ S11 ─┘ │ │ │ A32 ─┐ ├───────────────────┘ │ S32 ─┼─────┐ │ │ A11 ─┘ │ │ │ A36 ─┐ │ │ │ S36 ─┤ ├─┘ │ S19 ─┼───┐ │ │ A19 ─┘ │ │ │ A20 ─┐ │ │ │ S20 ─┼─┐ ├─┘ │ A30 ─┤ │ │ │ S30 ─┤ │ │ │ A33 ─┤ │ │ │ S33 ─┘ ├─┘ │ A14 ─┐ │ │ S14 ─┼─┤ │ A6 ─┤ │ │ S6 ─┘ │ │ A26 ─┐ │ │ S26 ─┤ │ │ A37 ─┤ │ │ S37 ─┤ │ │ A4 ─┼─┘ │ S4 ─┤ │ A5 ─┤ │ S5 ─┤ │ S34 ─┤ │ A24 ─┤ │ S24 ─┤ │ A18 ─┤ │ A34 ─┘ │ A22 ─┬─┐ │ S22 ─┘ ├───────────┐ │ A8 ─┬─┘ │ │ S8 ─┘ ├─────────────────────────────────┘ A7 ─┬───────┐ │ S7 ─┘ ├─────┘ A27 ─┬─┐ │ S27 ─┘ ├─────┘ A28 ─┐ │ S28 ─┼─┘ A9 ─┤ S9 ─┘
Fig. 6 Dendrogram of the hierarchical cluster analysis using the Ward method, A and S letter denoted autumn and spring seasons, respectively
5, 18, 20, 21, 24, 26, 33, 34, and 37) and the river samples (6, 14, 19 and 36) which have Ca–Mg–HCO3 water type are grouped in the second cluster. This suggests a hydraulic connection between ground and surface water in this area.
Environ Earth Sci Table 4 Allowable limits of drinking water prescribed by WHO (2006) and European Standards (1998)
Water quality parameter
WHO (2006) Maximum allowable limit
EU standards 1998 Maximum allowable limit
Samples exceeding allowable limits
Percentage of samples exceeding allowable limits
pH
6.5–9.2
6.5–9.5
Nil
–
TDS (mg/L)
1000
1500
Nil
–
TH (mg/L)
500
N.S
Nil
–
Na? (mg/L)
200
200
Nil
–
Ca2? (mg/L)
200
N.S
Nil
–
Mg2? (mg/L)
150
N.S
Nil
–
K? (mg/L) NH3 (mg/L)
12 0.3
N.S 0.5
Nil Nil
– –
Cl- (mg/L)
250
250
Nil
–
F (mg/L)
1.5
1.5
Nil
–
NO3-
50
50
Nil
–
SO42- (mg/L)
250
250
Nil
–
Al (mg/L)
0.2
0.2
Nil
–
V (mg/L)
N.S
N.S
–
Sb (mg/L)
0.005
0.005
Nil
–
AS (mg/L)
0.01
0.01
Nil
–
Ba (mg/L)
0.3
N.S
Nil
–
B (mg/L)
0.5
1.0
Nil
–
Br (mg/L)
N.S
N.S
–
–
Zn (mg/L)
3.0
3.0
Nil
–
Cd (mg/L)
0.003
0.005
Nil
–
Cr (mg/L) Ni (mg/L)
0.05 0.02
0.05 0.02
Nil Nil
– –
Pb (mg/L)
0.01
0.01
Nil
–
Mn (mg/L)
0.5
0.05
Nil
–
Fe (mg/L)
0.3
0.2
Nil
–
Co (mg/L)
N.S
N.S
–
Sr (mg/L)
N.S
N.S
–
Mo (mg/L)
0.07
N.S
Nil
–
U (mg/L)
0.015
N.S
Nil
–
N.S no standard guide value available Table 5 Water classification based on TH (Sawyer and McMcartly 1967)
Total hardness (TH) as CaCO3 (mg/L)
Type of water
Sample numbers
Number of samples
Percentage of samples
\ 75
Soft
All the rest samples
30
86
75–150
Moderately high
8, 11, 22, 32, 40
5
14
150–300
Hard
nil
–
–
[ 300
Very hard
nil
–
–
TH represents the average values of the two seasons
The samples in the third region (7, 8, and 9) and the samples which locate in the western part of the second region (22, 27, and 28) are grouped in the third cluster. The similarity between these two groups of samples which locate in two different regions and have relatively high EC, SO42-, Cl-, Na?, and Mg2? values could be explained by the host rock similarity and/or they are
effected by set of faults causing deep groundwater leakage. Drinking water quality The water quality parameters of the ground and river water samples were compared with the standard guideline values
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Environ Earth Sci b Fig. 7 The spatial distribution of the total dissolved solid (TDS), the
total hardness (TH), and sodium absorption ratio (SAR)
recommended by the World Health Organization (WHO 2006) and the standards of the European Communities (1998). The chemical analyses results show that the surface and groundwater in the study area are suitable for drinking and domestic purposes (Table 4). Fluoride concentration is in the range of 0.06–0.39 mg/L with an average of 0.19 mg/L. Deficiency or low concentration of fluoride (\0.5 mg/l; therefore, low intake dose) could lead to dental caries (Apambire et al. 1997). Therefore, it could be recommended to add fluoride to the distributed drinking water. The classification of groundwater based on total hardness [(Sawyer and McMcartly 1967) Table 5] shows that 86 % of the water samples fall in the soft category (TH \ 75 mg/l) and 14 % in the moderately high (TH \ 150 mg/l). In all water samples, the alkaline earth metals exceed the alkalis metals (Ca ? Mg [ Na ? K) and also the weak acid exceeds strong acids (HCO3 [ SO4 ? Cl). Therefore, such water has only temporary hardness according to Handa’s classification (Handa 1964). As shown in Fig. 7, the TH increases toward north-west with the change of the intergranular aquifer to fractured aquifer (carbonate aquifer). The low hardness in addition to other chemical factors e.g., low pH and low alkalinity can lead to corrosion of pipes. Consequently, certain trace elements such as Pb, Cu, Zn, and Cd may be present in the distributed water (Hudson and Gilcreas 1976; Zacheus and Martikainen 1997). However, as the SI of calcite in most samples was slightly above zero, the possibility of corrosion can be excluded. In contrast, hard water can clog up the water distribution pipes and cause problem in equipment that handles water by forming calcium carbonate scales, especially when the water temperature exceed 100 °C (de Moel et al. 2013). The maximum allowable limit of TH for drinking purpose by WHO is 500 mg/L and the most desirable limit is 100 mg/L (Table 4). All the water samples were classified as fresh water (TDS \ 1000) according to Freeze and Cherry (1979), where the TDS in the study area varies in the range of 246–794 mg/l with an average of 400 mg/L. Irrigation water quality The suitability of groundwater for irrigation purpose is evaluated according to the effects of water mineral constituents on both the plant and soil (Bahar and Reza 2010). The total salt concentration expressed by EC and relative proportions of Na? as expressed by %Na and SAR play a decisive role in verifying water suitability for irrigation. Excessive amount of salt in irrigation water causes an
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Environ Earth Sci Table 6 Suitability of water for irrigation based on SAR (Richards 1954), EC and Na % (Ragunath 1987)
Quality parameter
Range
SAR
\10 18-Oct
EC (ls/cm)
Na %
Classification
Number of samples
Percentage of samples
Excellent
70
100
Good
–
18–26
Doubtful
–
[26
Unsuitable
–
\250
Excellent
2
2.8
250–750
Good
68
97.2
750–2000
Permissible
– –
2000–3000
Doubtful
[3000
Unsuitable
–
\20
Excellent
32
45.7
20–40
Good
24
34.2
40–60
Permissible
14
20.1
60–80
Doubtful
–
–
[80
Unsuitable
–
–
The quality parameters represent the average values of the two seasons
Fig. 8 Irrigational suitability of water samples during dry (autumn) and wet (spring) seasons on the Wilcox diagram
increase in soil solution osmotic pressure (Thorne and Peterson 1954), and affects the chemical and physical properties of the plants and soil. High concentration of Na? in water increases its tendency to be absorbed by clay minerals by replacing Mg2? and Ca2? (Hem 1989). This process will cause swelling clay minerals, and consequently make the soil hard and reduce its permeability (Tijani 1994). The SAR increases gradually from north to south (Fig. 7) and its values range from 0.04 to 2.74 with an average of 0.5 (Table 1). According to Richards (1954), all samples are classified as excellent suited (Table 6), and the
alkali hazard is ruled out. The Na % indicates that all water samples are suitable for irrigation, and 45.7, 34.2, and 20.1 % of them are classified under excellent, good, and permissible category, respectively [according to Ragunath classification (1987)] (Table 6). The classification of water samples with respect to the EC (Ragunath 1987) shows that the majority of the samples (97 %) are classified as good. Plotting the water samples in a Wilcox diagram (Fig. 8) shows that all samples are in the low sodium hazard and medium salinity hazard zone; an exception is sample 7 which is located in the high salinity hazard category. However, the salinity hazard is ruled out due to the high
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Environ Earth Sci
infiltration capacity of the soil, which consists mainly of silt, sand, and gravel.
Conclusion Groundwater quality in the Al-Khazir Gomal Basin is mainly influenced by rock water interactions and there is no significant anthropogenic effect. All ground and surface water are freshwaters and the predominant hydrochemical facies in the second region are Ca–HCO3 and Ca–Mg–HCO3, and evolves downstream (third region) into Ca–Mg–Na–HCO3 facies. Quality assessment shows that all ground and surface waters are suitable for potable and domestic purposes. Based on the SAR classification, all water samples are considered suitable for irrigation and the salinization hazardous possibility is excluded, especially due to the high infiltration capacity of the soil. The multivariate statistical analysis revealed that the seasonal variation is not that much. In addition, the chemical content of groundwater is mainly controlled by rainfall, dissolution of carbonate minerals, aluminosilicate weathering, and cation exchange. The source of the low concentrations of the trace elements in groundwater is a natural geogenic, mostly leaching from basement lithology. Thus, the significant anthropogenic effects can be excluded and the basin is considered as a pristine basin so far. Data from this study will not only help to improve the understanding of the factors that control groundwater quality, but it will also help to determine background level of major ions and trace elements in the study area. Thus, it will contribute to the sustainable management of groundwater resources, where significant anthropogenic inputs are expected from urban expansion and agricultural/industrial activities planned by Kurdistan Region Government. Acknowledgments This work was supported by the German Academic Exchange Service (DAAD), the Geological Survey of Iraq (GEOSURV), and the Iraqi Ministry of Higher Education and Scientific Research. Also, the authors want to sincerely thank the staff of the laboratories of the Hydrogeology Department of TU Bergakademie Freiberg, Germany (Frau Schlothmann, Dr. Kummer, and Herr Peter) for assisting in conducting the hydrochemical analyses.
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