Environ Earth Sci DOI 10.1007/s12665-014-3531-z
ORIGINAL ARTICLE
Evaluation of the groundwater quality with WQI (Water Quality Index) and multivariate analysis: a case study of the Tefenni plain (Burdur/Turkey) Simge Varol • Aysen Davraz
Received: 3 December 2013 / Accepted: 5 July 2014 Ó Springer-Verlag Berlin Heidelberg 2014
Abstract Groundwater is a vital source of water for domestic and agricultural activities in the Tefenni plain. Therefore, groundwater quality, seasonal variations and its suitability for drinking, irrigation and industrial usage were evaluated. In this study, 56 water samples were collected from springs, wells, and lake in dry and wet seasons. Ca– Mg–HCO3, Mg–Ca–HCO3, Na–CO3–Cl, and Na–HCO3– Cl water types are the dominant water types in the investigation area. Parameters, which are controlled to chemical variations of groundwater, were analyzed with R-mode factor and correlation analysis. According to R-mode factor analysis, total dissolved solids, Na, Cl, HCO3, and NH3 are the most important parameters. In addition, Water Quality Index (WQI) was applied to suitability for drinking purpose and to investigation of groundwater quality. Quality of groundwaters are suitable for drinkable both dry and wet season in study area. In terms of the irrigation and industrial usage, generally groundwater is suitable in dry season but is not suitable in wet season. Groundwater chemistry is affected with water–rock interaction and densely agricultural activities. Keywords Groundwater quality WQI Tefenni plain Hydrochemistry Groundwater
S. Varol (&) Water Institute, Suleyman Demirel University, Isparta, Turkey e-mail:
[email protected] A. Davraz Department of Geology Engineering, Suleyman Demirel University, Isparta, Turkey
Introduction Water is very vital for nature and can be a limiting resource to human and other living beings. Water of adequate quantity and quality is required to meet growing household, industrial, and agricultural needs (Azaza et al. 2011; Pazand et al. 2012). Depending on its usage and consumption, it can be a renewable or a nonrenewable resource. Therefore, water quality issues and its management options need to be given greater attention in the countries. Groundwater is the major source of water for domestic, agricultural, and industrial purposes in the world. It is estimated that approximately onethird of the world’s population use groundwater for drinking (Nickson et al. 2005). Among the various reasons, the most important are nonavailability of potable surface water and a general belief that groundwater is purer and safer than surface water due to the protective qualities of the soil cover (Mishra et al. 2005; Arumugam and Elangovan 2009). However, in recent years, due to the advent of industrial growth, large-scale application of synthetic fertilizers for agriculture production and use of pesticides and insecticides for production has caused serious concern regarding susceptibility of groundwater contamination. Changes in groundwater quality are due to rock–water interaction and oxydo-reduction reactions during the percolation of water through the aquifers. In addition to these processes, waterborne pathogens, toxic and nontoxic pollutants are the major water quality parameters that are transported from recharge area to discharge area through aquifers by groundwater motion (Kumar et al. 2009). Geochemical studies of groundwater provide a better understanding of possible changes in quality as development progress. Suitability of groundwater for domestic and irrigation purposes is determined by its groundwater geochemistry (Arumugam and Elangovan 2009).
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Environ Earth Sci
Fig. 1 Location, geological, and groundwater level (in May-2010) maps of the study area. (Varol 2011; Varol and Davraz 2014)
In the study area, groundwater is used as drinking and irrigation water. In addition, groundwater in the Tefenni plain, agriculture and livestock are the most important economic activities; therefore, a hydrogeochemical investigation was carried out to identify groundwater chemistry and its suitability for irrigation and drinking purposes. The objective of this study is to determine the groundwater quality of Tefenni plain, seasonal variation, and to delineate regions where groundwater is suitable or unsuitable for drinking, irrigation, and industrial purposes. Study area The Tefenni (Burdur) plain is located in the southwest of Turkey (Fig. 1) and it has a 1,527 km2 watershed area (Varol and Davraz 2014). Generally, climate of the plain and its vicinity are affected from Mediterranean climate zone in the middle Anatolian climate zone. The mean rainfall is 465.03 mm and the mean evapotranspiration are 326.74 mm in the study area (Varol 2011; Varol and Davraz 2014). Eren (Boz) stream is the most important surface flow in the study area and discharged to Burdur Lake, which is located out of the Tefenni plain. In addition, Karatas Lake is situated in the middle of the plain and irrigation water is supplied from this lake. In addition, Barutlusu and Pınarbas¸ ı springs with different hydrogeochemical properties are discharged from the Tefenni plain. These springs have been discharged from
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overthrust zone, which are developed between allochthonous Kızılcadag˘ ophiolite and Dutdere limestone. The discharge temperature of Barutlusu and Pınarbas¸ ı springs are 17.6 and 27 °C, respectively (Varol and Davraz 2014).
Materials and methods In this study, geological setting of the region investigated from utilizing the previous researches. According to hydrogeological properties of the lithological units, aquifer units described. In the research area, total 56 water samples from wells, springs, and lake waters were analyzed in July 2009 (dry period) and May 2010 (wet period) for the determination of their major chemical characteristics. Samples were stored in two polyethylene bottles. One of the bottles was acidified with suprapure HNO3 for determination of cations and another was kept unacidified for the anion analyses. The discharge temperature, pH, and electrical conductivity (EC) were measured in the field. The major chemical constituents were analyzed by inductively coupled plasma-mass spectrometer (ICP-MS) at the ACME Laboratory (Vancouver, Canada, an ISO 9002 accredited company). HCO3, CO3, Cl and SO4, NO3, NO2, NH3 were determined in the Chemical Laboratory of the State Hydraulic Works (SHW, Isparta/Turkey) (Table 1). The charge-balance error of the water samples is \5 %, which is within the limits of acceptability. In addition,
Environ Earth Sci Table 1 Showing of analytical methods in the study Type of samples
The analysis parameters
Method
Name of laboratory
Water Samples (Total 56)
Discharge Temperature (T °C), pH, and Electrical Conductivity (EC)
Insitu
Insitu
Ag, Al, As, Au, B, Be, Bi, Br, Ca, Cd, Ce, Cl, Co, Cr, Cs, Cu, Dy, Er, Eu, Fe, Ga, Gd, Ge, Hf, Hg, Ho, In, Ir, K, La, Li, Lu, Mg, Mn, Mo, Na, Nb, Nd, Ni, Os, P, Pb, Pd, Pr, Pt, Rb, Re, Rh, Ru,S, Sb, Sc, Se, Si, Sm, Sn, Sr, Ta, Tb, Te, Th, Ti, Tl, Tm, U, V, W, Y, Yb, Zn, Zr SO4
ICP Mass Spec. inductively coupled plasma-mass spectrophotometer
ACME Laboratory (Vancouver, Canada, an ISO 9002 accredited company)
Ion chromatography method
Chemical Laboratory of The State Hydraulic Works (SHW, Isparta/Turkey)
Cl
Titrimetric method
Chemical Laboratory of The State Hydraulic Works (SHW, Isparta/Turkey)
NO3, NO2, NH3
Ion chromatography method
Chemical Laboratory of The State Hydraulic Works (SHW, Isparta/Turkey)
HCO3, CO3
Titrimetric method
Chemical Laboratory of The State Hydraulic Works (SHW, Isparta/Turkey)
geology map was prepared using GIS-ArcView computer code. ArcGIS 9.3 software, Spatial Analyst extension, and inverse distance weight (IDW) interpolation methods were applied throughout research evaluations and the study area mapped to a UTM Zone 35, ED50 datum. Geology and hydrogeology The chemical quality of groundwater is related to both the lithology of the study area and the residence time of the water in contact with rock materials. Therefore, geological and hydrogeological properties of the study area were primarily determined. Autochthonous and allochthonous units are outcropped in the study area (Varol 2011; Varol and Davraz 2014; Fig. 1). Allochthonous units are composed from Marmaris peridotite and cumulate (Capan 1980), Kızılcadag ophiolitic melange and olistostrome (Poisson 1977), Orhaniye formation consist of dolomitic limestones and bedded chert member (Mes¸ hur et al. 1989), Dutdere limestone (S¸ enel et al. 1989; Bilgin et al. 1990), Kayalısırtı flysch unit (S¸ enel et al. 1989), So¨bu¨ce Yayla formation consist of orbitoidic sandstones (Poisson 1977), Mamatlar formation is composed from claystone, sandstone, and noduler limestone (Poisson 1977), Varsakyayla (Poisson 1977) and Karanasıflar formations are consist of limestone, claystone, sandstone, conglomerate (S¸ enel et al. 1989), Karabo¨g˘u¨rtlen formation consists of blocked flysch, Yavuz formation is composed from intermittent limestone levels in sandstone and claystone (Poisson 1977), Elmalı formation consists of turbiditic sandstones and shales
¨ nalan 1979). C¸ameli formation consists of claystone, (O sandstones, conglomerate, limestone (Erakman et al. 1982), alluvium, slope debris and cone of accumulations are also autochthonous units. The geological structure of the Tefenni plain developed as depending on tectonism. This region is located on the west side of the regional geological structure known as Isparta angle in the SW Turkey (Koc¸yig˘it et al. 2000). Fethiye-Burdur Fault zone is located between the Fethiye gulf and Burdur Lake in the study area. This zone has 300 km length. It is the most active fault system in the southwest Anatolia (Bozcu et al. 2007). The stratigraphic units within the study area have different hydrogeological characteristics. These units are grouped according to qualitatively as impermeable (aquifuge), semi-permeable (aquitard-1, 2), permeable-1 (granular aquifer), and permeable-2 (karstic aquifer). The units of similar hydrogeological characteristics are summarized in Table 2. Alluvium, which is the most important aquifer in the basin, has an area of approximately 174 km2 and entitled as Granular Aquifer. The well logs indicate that the thickness of the alluvium is ranged from 5 to 130 m in the Tefenni plains (Varol 2011; Varol and Davraz 2014). The groundwater of the study area occurs under unconfined conditions. The seasonal variation of groundwater level is controlled with natural factors, such as precipitation, evaporation, and runoff and artificial factors, such as withdrawing groundwater from wells and recharge with irrigation from Lake Karatas¸ . Rainfall is the main recharge source of groundwater in the basin. The
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Environ Earth Sci Table 2 Lithostratigraphic relations of the geologic units and hydrogeological properties Age
Formation
Lithology
Hydrogeological properties
Quaternary
Alluvium (Qal)
Gravel, sand, and mudstone
Permeable (Granular Aquifer)
Quaternary
Slope Alluvium (Qym)
Attached to the loose gravel, sand, and mudstone
Permeable (Granular Aquifer)
Pliocene (Neogene)
C¸ameli formation (Ply)
Conglomerate, sandstone, claystone, clayey limestone, marl, and conglomerate
Semipermeable (Aquitard-1)
L. Lutetian–E. Burdigalian (Eocene-Miocene)
Elmalı formation (Te)
Turbiditic sandstones and shales
Semipermeable (Aquitard-2)
L. Lutetian–Priabonian (Eocene)
Yavuz formation (Tey)
Flysch composed of sandstone, claystone, and siltstone
Semipermeable (Aquitard-1)
L. Lutetian–Priabonian (Eocene)
Varsakyayla formation (Tev)
Sandstone, conglomerate, and limestone
Semipermeable (Aquitard-1)
Montian–Thanetian (Paleocene)
Mamatlar formation (Tpm)
Conglomerate, Claystone, sandstone, conglomerate, nodular limestone, marl, and clayey limestone
Aptian–Albian (Cretaceous)
Marmaris peridotite (Kmo) Karabo¨g˘u¨rtlen formation (Kka) So¨bu¨ceyayla formation (Kso¨) Kızılcadag˘ ophiolites (Kkzm)
Peridotite, serpentinite, and serpentinized peridotite
Semipermeable (Aquitard-2) ˙Impermeable (Aquifuge)
L. Triassic-Cretaceous
Kayalısırtı units (JKks)
Pelletoidal limestone, red micrite, cherty micrite, and radiolarite
Permeable (Karstic Aquifer)
M.-L. Triassic
Dutdere Limestone (TrJd)
Recrystallized limestone
Permeable (Karstic Aquifer)
Jurassic-Cretaceous
Orhaniye formation (JKo)
Calciturbidite and cherty limestone
Semipermeable (Aquitard-2)
L. Cenoniyen (Cretaceous) L. Cenoniyen (Cretaceous) L. Cenoniyen (Cretaceous)
Sandstone, claystone, cherty limestone, and blocky flysch
Semipermeable (Aquitard-2)
Orbitoidic sandstone
Semipermeable (Aquitard-2) ˙Impermeable (Aquifuge)
Limestone, chert, diabase, and serpentinite blocks within ophiolitic matrix
mean seasonal groundwater levels fluctuations of the study area indicates that the maximum and minimum water level is in May (2009) and October (2010), respectively. The depth to groundwater table varies between 3.75 and 50.05 m at four periods (May 2009, October 2010). The general groundwater flow direction in the porous aquifer is to Burdur Lake which is located in the north of the plain both wet and dry periods (Varol and Davraz 2014; Fig. 1).
Results and discussion Hydrochemistry Understanding the groundwater quality is important, as it is the main factor determining its suitability for drinking, domestic, agricultural, and industrial purposes (Subramani et al. 2005). Physical and chemical parameters including
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statistical measures, such as minimum concentration, maximum concentration, mean concentration, and standard deviation are given in Table 3 for the dry and wet seasons. The chemical composition of the water samples (n = 28) in the study region shows a wide range. The electrical conductivity (EC) in the study region is varied from 5.27 to 3028.50 lS/cm with an average of 1117.40 lS/cm in dry season, from 5.85 to 2875.50 lS/cm with an average of 1221.13 lS/cm in wet season. The pH of the groundwater samples in the study area varies from 5.80 to 9.20 with an average value of 7.12, which indicates that the dissolved carbonates are predominantly in the HCO3- form (Adams et al. 2001) and indicating alkaline nature of the samples. The total dissolved solids (TDS) ranged from 191.54 to 1967.30 mg/l with a mean value of 513.60 mg/l in dry season and wet season. HCO3 is the most dominant ion, followed by Ca, SO4, Na, Mg, Cl, and K are the ions with the lowest concentration in the water of study area in dry season.
Environ Earth Sci Table 3 Descriptive statisticsfor concentrations of chemical constituents in dry and wet season samples
N (Sample number)
Dry season Min.
Wet season
Max.
Mean
SD
Min.
Max.
Mean
SD 552.50
EC
28
5.27
3028.50
1117.40
475.29
5.85
2875.50
1221.13
pH
28
5.80
9.20
7.12
0.68
7.20
10.40
7.96
0.60
TDS
28
191.54
1967.30
513.60
350.20
191.54
1967.30
513.60
350.20
Na
28
1.09
522.80
35.24
96.71
1.14
558.68
37.97
103.63
K
28
0.33
12.50
2.19
2.63
0.39
3.51
1.23
1.02
Ca
28
7.35
185.25
65.65
45.38
7.01
118.23
53.61
29.64
Mg
28
1.25
101.90
34.74
27.69
3.76
88.03
36.20
24.31
Cl
28
4.25
259.40
26.96
50.87
3.90
250.96
26.38
49.16
HCO3
28
39.50
1073.50
285.92
180.01
3.05
1295.60
345.73
219.31
CO3
28
0.00
51.00
3.07
11.49
0.00
32.40
3.47
9.32
SO4 NO3
28 28
1.00 0.00
230.00 165.63
39.89 17.31
49.05 31.14
5.76 0.25
191.15 35.60
38.35 4.06
50.17 6.75
NO2
28
0.00
0.20
0.016
0.04
0.00
0.01
0.00
0.00
NH3
28
0.00
3.91
0.187
0.73
0.00
3.50
0.14
0.65
Statistical analysis Correlation matrix The correlation analysis to establish the relationships between physicochemical characteristics of water samples, which can reveal the origin of solutes and the process that generated the observed water compositions (Azaza et al. 2011; Parizi and Samani 2013). The result of the correlation analysis is considered in the subsequent interpretation. A high correlation coefficient (near 1 or 1) means a good positive relationship between two variables and its value around zero means no relationship between them at a significant level of p \ 0.05. More precisely, it can be said that parameters showing r [ 0.7 are considered strongly correlated whereas r between 0.5 and 0.7 shows moderate correlation (Manish et al. 2006). The correlation matrices for EC, pH, TDS, NO3, NO2, NH3, and major ions were prepared and illustrated for both the dry and wet season (Tables 4a,b). EC shows high positive correlation with SO4 and NO3, and shows negative correlation with Na and NH3 in dry season, shows low and moderate positive correlation with Ca, Mg, K, SO4, and NO3 in wet season. In addition, EC shows negative correlation NO2 and NH3. pH shows high positive correlation with CO3 in dry and wet seasons. In addition, it shows negative correlation with K and Ca in dry season and with only Ca in wet season. TDS show high positive correlation with Na, HCO3, and NH3 in dry and wet season, show moderate positive correlation with Mg and SO4 in dry and wet season. In addition, it shows moderate positive correlation with K in wet season. For both the dry and wet seasons, some groups of species show moderate to strong
correlation (r [ 0.7), e.g., Na–Cl–HCO3–NO2–NH3, Mg– HCO3–SO4–NO3, Cl–HCO3–SO4–NO2–NH3, HCO3– NO2–NH3, SO4–NO3, and NO2–NH3. This situation is therefore be postulated that the concurrent increase/ decrease in the cations is the result mainly of dissolution/ precipitation reaction and concentration effects. Factor analysis Factor analysis is one of most important statistical methods for interpretation of hydrochemistry of groundwater (Subba Rao 2002). This method, a multivariate statistical method, yields the general relationship between measured chemical variables by showing multivariate patterns that may be help to classify the original data. It enables the geographical distribution of the resulting factors to be determined. The geological interpretation of factors yields insight into the main processes, which may govern the distribution of hydrochemical variables. Factor analysis can identify several pollution factors reasonably but the interpretation of these factors in terms of actual controlling sources and processes is highly subjective (Matalas and Reiher 1967; Bahar and Reza 2010; Edet et al. 2013). The aim of the factor analysis of the hydrogeochemical data is to explain the observed relationship in simple terms expressed as a new set of variables called factors (Usunoff and Guzma´nGuzma´n 1989; Singaraja et al. 2014, Vasanthavigar et al. 2013). Principal component analyses (PCA), which aim to load most of the total variance into one factor, are used in the present case, and factors were extracted through the principal extraction method (Mahlknecht et al. 2004; Srivastava and Ramanathan 2008). To limit the number of factors
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Environ Earth Sci Table 4 Pearson’s coorrelation matrix (r) and Sig. (2-tailed) (p) values (dry and wet season) a Dry season EC
pH
TDS
Na
K
Ca
Mg
Cl
HCO3
CO3
SO4
NO3
NO2
NH3
EC (r)
1
(p) pH (r) (p)
-0.05
1
0.78
TDS (r) (p)
0.01
0.05
0.93
0.79
1
20.38
0.27
0.84
0.04
0.16
0.00
0.10
20.40
0.14
0.06
0.61
0.03
0.45
0.74
Na (r) (p)
1
K (r) (p)
1
Ca (r)
0.22
20.67
0.04
-0.24
0.31
(p) Mg
0.25
0.00
0.81
0.20
0.10
(r) (p)
1
0.47
0.05
0.53
0.17
0.04
-0.19
0.01
0.78
0.00
0.38
0.83
0.32
1
-0.07
0.30
0.92
0.93
0.11
-0.14
0.34
0.69
0.11
0.00
0.00
0.57
0.47
0.07
Cl (r) (p)
1
HCO3 (r) (p)
-0.20
0.01
0.94
0.85
0.08
-0.05
0.50
0.84
0.30
0.96
0.00
0.00
0.68
0.76
0.00
0.00
1
-0.02
0.63
-0.15
-0.03
-0.19
-0.32
-0.02
0.02
-0.18
0.90
0.00
0.42
0.84
0.32
0.09
0.91
0.91
0.34
0.75
-0.02
0.47
0.08
0.16
0.19
0.62
0.37
0.23
-0.20
0.00
0.88
0.01
0.66
0.39
0.32
0.00
0.04
0.24
0.29
CO3 (r) (p)
1
SO4 (r) (p)
1
NO3 (r)
0.78
0.07
0.35
-0.01
0.03
0.23
0.42
0.29
0.10
-0.15
0.80
(p)
0.00
0.71
0.06
0.93
0.87
0.22
0.02
0.13
0.60
0.44
0.00
1
NO2 (r) (p)
-0.14 0.46
-0.01
0.36
0.44
0.04
-0.02
-0.06
0.39
0.39
-0.10
0.00
-0.06
0.95
0.05
0.01
0.81
0.91
0.74
0.03
0.03
0.61
0.99
0.74
1
NH3 (r) (p)
-0.41
0.24
0.83
0.98
0.05
-0.22
0.14
0.91
0.86
-0.06
0.03
-0.02
0.47
0.02
0.20
0.00
0.00
0.77
0.24
0.47
0.00
0.00
0.73
0.88
0.91
0.01
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1
Environ Earth Sci Table 4 continued b Wet season EC
pH
TDS
Na
K
Ca
Mg
Cl
HCO3
CO3
SO4
NO3
NO2
NH3
EC (r)
1
(p) pH (r) (p)
-0.34
1
0.06
TDS (r) (p)
0.13
-0.21
0.49
0.26
1
Na (r) (p)
-0.30
0.04
0.86
0.11
0.83
0.00
1
K 0.39
-0.25
0.67
0.52
0.03
0.20
0.00
0.00
(r)
0.53
-0.65
0.12
-0.20
0.30
(p) Mg
0.00
0.00
0.51
0.29
0.11
(r)
0.64
-0.14
0.55
0.23
0.39
-0.04
0.00
0.44
0.00
0.22
0.03
0.81
-0.12
0.12
0.88
0.94
0.56
-0.10
0.31
0.52
0.51
0.00
0.00
0.00
0.59
0.10
0.03
-0.31
0.95
0.86
0.61
0.08
0.52
0.81
0.86
0.10
0.00
0.00
0.00
0.65
0.00
0.00
-0.04
0.73
-0.16
-0.06
-0.02
-0.41
0.05
0.02
-0.25
0.81
0.00
0.38
0.76
0.89
0.02
0.79
0.89
0.19
(r) (p)
1
Ca
(p)
1
1
Cl (r) (p)
1
HCO3 (r) (p)
1
CO3 (r) (p)
1
SO4 0.70
-0.33
0.57
0.29
0.66
0.38
0.64
0.43
0.44
-0.18
0.00
0.07
0.00
0.12
0.00
0.04
0.00
0.02
0.01
0.35
NO3 (r)
0.67
-0.33
0.34
0.01
0.27
0.52
0.42
0.24
0.20
-0.20
0.64
(p)
0.00
0.08
0.07
0.93
0.15
0.00
0.02
0.21
0.29
0.28
0.00
(r) (p)
1
1
NO2 (r) (p)
-0.43
0.04
0.81
0.98
0.43
-0.20
0.15
0.89
0.84
-0.07
0.14
-0.09
0.02
0.82
0.00
0.00
0.02
0.20
0.43
0.00
0.00
0.71
0.45
0.63
1
-0.37
0.03
0.84
0.98
0.40
-0.21
0.18
0.92
0.86
-0.06
0.19
-0.01
0.99
0.04
0.85
0.00
0.00
0.01
0.28
0.33
0.00
0.00
0.72
0.31
0.95
0.00
NH3 (r) (p)
1
Bold values indicate moderate and strong correlations
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Environ Earth Sci Table 5 Results of the R-mode factor analysis on the chemical parameters from the study area (dry and wet season)
Component (dry season) Factor 1
Factor 3
Factor 1
Factor 2
Factor 3
EC
-0.33
0.88
-0.08
-0.29
0.92
pH
0.15
0.07
0.92
-0.02
-0.21
0.90
TDS
0.90
0.37
-0.10
0.88
0.39
-0.12
Na
0.97
-0.06
0.12
0.99
-0.00
0.05
K
-0.17
0.11
0.06
-0.55
0.53
0.55
-0.12
Ca
-0.15
0.11
-0.78
-0.16
0.39
-0.73
Mg
0.27
0.71
0.10
0.26
0.77
0.15
Cl
0.92
0.26
0.11
0.93
0.20
0.12
HCO3
0.93
0.14
-0.08
0.90
0.26
-0.20
CO3
-0.12
-0.04
0.73
-0.11
0.08
0.87
SO4 NO3
0.14 0.02
0.91 0.89
-0.15 -0.08
0.28 0.01
0.84 0.73
-0.19 -0.29
NO2
0.53
-0.15
-0.12
0.98
-0.14
0.04
NH3
0.97
-0.10
0.09
0.99
-0.07
0.04
Initial Eigenvalues
5.18
3.23
2.24
6.35
3.76
1.78
% of Variance
37.03
23.12
16.02
45.42
26.90
12.73
Cumulative %
37.03
60.15
76.18
45.42
72.33
85.06
to be extracted, only factors with eigenvalues higher than one were taken into consideration (Kaiser Normalization). Creating a distribution map of the factor scores in this way tests the usefulness of the results, and the factor scores are a measure of the statistical weight of each case on the extracted factors. The software provides a numerical value resulting from different variants as components and initial eigenvalues for each species (Table 5). With the help of linear combinations, an originally large number of variables can be reduced to a few factors. These factors can be interpreted in terms of new variables. There exist numerous solution methods and variations for determination of factors (Mahlknecht et al. 2004; Srivastava and Ramanathan 2008). Factor analyses allow for determination of basic independent dimensions of variables (Chidambaram and Ramanathan 2000). Weathering processes and anthropogenic inputs are the two main contributors for changing the geochemical composition of the groundwater (Chan 2001). Therefore, the variables for factor analysis were EC, pH, TDS, Na, K, Ca, Mg, Cl, HCO3, CO3, SO4, NO3, NO2, and NH3 for both dry and wet season in this study (Table 5). Three factors are extracted to the statistically represent the contributions influencing chemical composition of groundwater. R-mode factor analysis (Varimax Rotation with Kaiser Normalization) was carried out with the help of SPSS.15 software to extract the factors. In general, the factor will be related to the largest eigenvalue and will explain the greatest amount of variance in the data set. Factor analysis on the combined datasets provided three
123
Factor 2
Component (wet season)
factors with eigenvalue [1 that can explain approximately in dry and wet seasons 76.18 and 85.06 % of the data variability, respectively. Factor loadings are classified by Liu et al. (2003) as ‘‘strong’’, ‘‘moderate’’, and ‘‘weak’’ corresponding to absolute loading values of [0.75, 0.75–0.50, and 0.50–0.30, respectively. In this study, this classification was used to evaluation of data (Table 5). Factor 1 of the PCA matrix of groundwater around the Tefenni plain is characterized by the strong loading of TDS, Na, Cl, HCO3, NH3, and moderate loading of NO2 and K in dry season. In wet season, this factor has strong loading of TDS, Na, Cl, HCO3, NH3, NO2, and moderate loading of K. In this factor, accounts for 37.03 and 45.42 % of the total variance in dry and wet season, respectively (Table 5). Strong loads, TDS, Na, Cl, and HCO30 s that Factor 1 represents the natural hydrogeochemical evolution of groundwater by groundwater–rock interaction which can be explained by the dissolution of rocks and minerals in sediments by chemical weathering. Na, Cl, HCO3, and TDS have the high values in groundwater. The reason for this situation, clays in the C¸ameli formation and Dutdere limestones have a wide spread in the study area. The strong loading of HCO3 ions with alkali and alkaline earth metals supports the view of natural weathering sources. The HCO3 can come from the dissolution of carbonate minerals, from soil CO2 or from the bacterial degradation of organic contamination. Strong and moderate loads of NH3 and NO2 show that groundwater pollutants were involved from agricultural areas in the study area. Because the NO2 has no known lithologic source, and the nitrite in groundwater
Environ Earth Sci
mainly originates from the oxidation of ammonium resulting from the infiltration of effluents in which urea and ammonium prevail over other nitrogen compounds (Zilberbran et al. 2001). NO2 may be attributed to rain-fed agriculture, irrigated crops, and dairy activities (Feng et al. 2005; Andrade et al. 2008; Yammani et al. 2008). Factor 2 explains a significant proportion of 23.12 and 26.90 % of the total variance in dry and wet season, respectively, has strong positive loading on EC, SO4, and NO3 and has moderate positive loading on Mg in dry season. In wet season, this factor has strong positive loading on EC, SO4, and Mg and has moderate positive loading on K and NO3 and has weak positive loading on TDS and Ca (Table 5). Factor 2 represented the contribution of nonpoint source pollution from agriculture areas. In these areas, farmers use the nitrogenous fertilizer and the watercourses receive nitrate via groundwater leaching and runoff. The presence of NO3 could be attributed to the use of fertilizers in the study area (Tisdale and Nelson 1975). The existence of many ions and their compounds led to the high loadings of EC. The SO4 sources in groundwater and surface waters include: (1) atmospheric deposition (Wayland et al. 2003), (2) sulfate-bearing fertilizers, and (3) bacterial oxidation of sulfur compounds (Sidle et al. 2000). Accordingly, the sulfate might come from the breakdown of organic substances of weathered soils, leachable sulfate from fertilizers and other human influences like sulfuric salts in domestic wastewater (Craig and Anderson 1979; Bahar and Yamamuro 2008). Loading of Mg ion indicates that they may be derived from rock–water interaction processes in the study area. In areas where groundwater interacts with Dutdere dolomitic limestones, increased Mg ions. Factor 3 explains the largest proportion of 16.02 and 26.90 % of the total variance has strong positive loading on pH and has moderate positive loading on CO3 in dry and wet season, respectively. In addition, this factor has a negative loading on K and Ca in dry and wet season (Table 5). The CO3 has strong positive loadings for the factor 3 and indicates that this ion is derived from processes, such as weathering, carbonate and gypsum dissolution. Factor 3 indicates groundwater chemistry is controlled by the pH variation in the aquifer system. The positive loadings of pH values suggested that the major ion concentration is controlled by pH variations in the study area. Although pH is a controlling factor, factor1 and factor 2 have the stronger positive loadings than the factor 3. This shows that they derived from rock–water interaction. Hydrochemical types Hydrogeochemical types reflect the effects of chemical reactions occurring between the minerals within the
lithologic framework and groundwater. Hydrogeochemical diagrams are aimed at facilitating interpretation of evolutionary trends, particularly in groundwater systems, when they are interpreted in conjunction with distribution maps and hydrogeochemical types. An overall characterization of hydrogeochemical data can be possible by knowing the hydrogeochemical types of water, generally known as water type, using various plots, such as Durov (1948), trilinear Piper (1944), Schoeller (1965), and Chadha (1999) diagrams. The Piper diagram (Piper 1944) used because of the most widely used graphical form, in this study (Fig. 2). The diagram consists of two triangular fields and a central diamond-shaped field. In the two triangular fields, percentage meq/l values of major cations and anions are plotted separately and then projected onto the central field for the representation of overall characteristics of water. This plot reveals useful properties and relationships for large sample groups (Srivastava and Ramanathan 2008). Accordingly, in the study area Ca–Mg–HCO3, Mg–Ca– HCO3, Na–CO3–Cl, and Na–HCO3–Cl the dominant water types were observed in the Piper diagram due to water– rock interaction (Fig. 2). In the study area, limestone, dolomitic limestone, ophiolitic complex, and Neogene units are situated as intricate via tectonic activities. The natural system beneath the underground is complex, consisting of fine to coarse sediments deposited in a heterogeneous manner and different rock units are situated. Groundwater is actually a complex, generally dilute, chemical solution. The chemical composition is derived mainly from the dissolution of minerals in the soil and the rocks with which it is or has been in contact. Rainfall itself is a dilute chemical solution and contributes significant proportions of some constituents in groundwater (www. groundwateruk.org). Evaluation of groundwater quality In the study area, the assessment of groundwater quality was carried out to identify its suitability to drinking, irrigation, and industrial purposes. Water quality for drinking purposes Groundwater chemistry has been utilized as a tool to outlook water quality for drinking and irrigation purposes (Subba Rao 2006; Edmunds et al. 2002). Water Quality Index (WQI) is defined as a technique of rating that provides the composite influence of individual water quality parameters on the overall quality of water (Mitra and ASABE Member 1998) for human consumption. Water Quality Index is an important parameter for demarcating groundwater quality and its suitability for
123
Environ Earth Sci
Fig. 2 Piper diagrams in dry and wet season (Piper 1944)
drinking purposes (Tiwari and Mishra 1985; Singh 1992; Subba Rao 1997; Mishra and Patel 2001; Naik and Purohit 2001; Avvannavar and Shrihari 2008). The WQI has been widely used to characterize the usability of water resources for domestic purposes. The index is a weighted summation of the composition of water in the light of certain defined objectives. The objectives are usually related to national as well as the World Health Organization’s standards for each of the major parameters in drinking water (Yidana et al. 2010). In this study, WQI was computed at three steps, and results were compared and evaluated with WHO (2008) and TSI 266 (2005) (Table 6). In the first step, each of the 11 parameters (TDS, pH, HCO3, Cl, SO4, NO3, NO2, Ca, Mg, Na, and K) has been assigned a weight (wi) according to its relative importance in the overall quality of water for drinking purposes (Table 6). Much weight is assigned to parameters which have critical health effects and whose presence above certain critical concentration limits could limit the usability of the resource for domestic and drinking purposes (Yidana et al. 2010). The maximum weight of 5 has been assigned to the parameters like TDS, Cl, SO4, NO3, and NO2 due to their major importance in water quality assessment (Srinivasamoorthy et al. 2008). The minimum weight of 1 has been assigned to the parameter HCO3 due to the least importance in water quality assessment. Especially, TDS, which is one of the major quality parameters, denote mainly the various kinds of minerals present in water. To ascertain the suitability of groundwater of any purposes, it is essential to classify the groundwater depending upon their hydrochemical properties based on
123
Table 6 Relative weight of chemical parameters in study area Chemical parameters
WHO (2008) Standards
Turkısh Drinking Water Standard (TSI 266) (2005)
Weight (wi)
Relative weight (Wi)
Total dissolved solids (TDS)
500–1500
1500
5
0.093
pH HCO3(mg/l)
6.5–8.5 –
6.5–9.5 –
4 1
0.023 0.116
Cl- (mg/l)
250
250
5
0.023
SO42(mg/l)
250
250
5
0.116
NO3(mg/l)
50
50
5
0.116
NO2(mg/l)
3.0
0.50
5
0.116
NH3 (mg/l)
0
0
5
0.106
Ca?2 (mg/l)
300
200
3
0.116
Mg?2 (mg/l)
30
150
3
0.069
Na? (mg/l)
200
200
4
0.069
K? (mg/l)
–
12
2 P wi = 47
0.093 P wi = 1
their TDS values (Davis and DeWiest 1966; Freeze and Cherry 1979). High values of TDS in groundwater are generally not harmful to human beings but high concentration of these may affect persons who are suffering from
Environ Earth Sci
kidney and heart diseases. Water containing high solids may cause laxative or constipation effects (Yue et al. 2010). The concentration of Cl in groundwater is relatively high due to evaporation enriched irrigation return flow related to the water–rock interaction. The permissible limit of Cl for drinking water is specified as 250 mg/l max in the WHO (2008) and TSI 266 (2005). High concentrations of Cl in drinking water cause a salty taste and have a laxative effect (Bhardwaj and Singh 2010). Sulfate is one of the least toxic anions, even though dehydration is observed at high concentrations. WHO (2008) and TSI 266 (2005) suggested that highest desirable and maximum permissible limit of sulfate is 250 mg/l. If the limit of sulfate exceeds, it may cause gastrointestinal irritation and laxative effect at higher level (WHO 1993). As is known, nitrate pollution is the one of the most important causes of the groundwater pollution. Nitrogen compounds are present in groundwater in the form of nitrate (NO3) and nitrite (NO2) ions. Nitrite is more toxic to animal and human health than nitrate. Nitrates are extremely soluble in water and can move easily through soil into the drinking water supply (Saba et al. 2006). The fertilizers and domestic wastes are main sources of nitrogen-containing compounds and they are converted to nitrates in the soil. WHO (2008) prescribed maximum permissible concentration for nitrate as 50 mg/l. The consumption of water with high nitrate concentration causes blue babies or methemoglobinemia disease in infants, gastric carcinomas, abnormal pain, central nervous system birth defects, and diabetes (Vasanthavigar et al. 2010). Bicarbonate HCO3 is given the minimum weight of 1 as it plays an insignificant role in the water quality assessment. Other parameters like pH, calcium, magnesium, sodium, and potassium were assigned weight between 1 and 5 depending on their importance in water quality determination. In the second step, the relative weight (Wi) is computed from the following Eq. (1): Calculated relative weight (Wi) values of each parameter are given in Table 6. Wi ¼ wi =
n X
wi
ð1Þ
i¼1
where Wi is the relative weight, wi is the weight of each parameter, n is the number of parameters. In the third step, a quality rating scale (qi) for each parameter is assigned by dividing its concentration in each water sample by its respective standard according to the guidelines laid down in the WHO (2008) and TSI 266 (2005) and the result is multiplied by 100 (Eq. 2): qi ¼ ðCi=SiÞ 100
ð2Þ
where qi is the quality rating, Ci is the concentration of each chemical parameter in each water sample in
Table 7 According to the WQI type of water (Sahu and Sikdar 2008) Range
Type of water
\50
Excellent water
50–100.1
Good water
100–200.1
Poor water
200–300.1
Very poor water
[300
Water unsuitable for drinking purposes
milligrams per liter. Si is the drinking water standard for each chemical parameter in milligrams per liter according to the guidelines of the WHO (2008) and TSI 266 (2005). For computing the WQI, the SI is first determined for each chemical parameter, which is then used to determine the WQI as per the following Eqs. (3, 4) SIi ¼ Wi qi X WQI ¼ SIi
ð3Þ ð4Þ
where SIi is the subindex of i th parameter, qi is the rating based on concentration of i th parameter, n is the number of parameters. Water quality types were determined based on WQI. The computed WQI values ranges from 17.440 to 110.755 and 17.266 to 84.110 for dry season and wet season, respectively. The WQI range and type of water can be classified in Table 7. The chemical analysis of groundwater and the percent compliance with TSI 266 (2005) and WHO (2008) is represented in Table 7, which indicates that majority of the sample exceeds the permissible limit set by TSI 266 (2005) and WHO (2008). Calculation of WQI for individual samples is represented in Table 8. During dry season, 89.28 % of groundwater samples represent ‘‘excellent water’’, 7.14 % indicate ‘‘good water’’, and 3.57 % shows ‘‘poor water’’, during wet season 89.28 % of groundwater samples represent ‘‘excellent water’’ and 10.72 % indicate ‘‘good water’’. The dry season samples exhibit poor quality in greater percentage (3.57 %) when compared with wet season (0 %). This may be due to effective leaching of ions, over exploitation of groundwater, direct discharge of effluents, and agricultural impact (Sahu and Sikdar 2008). The WQI was tested with chloride and EC values, which are selected as pollution indicators. The observed high values of chloride and EC correspond to the same WQI, indicating the poor quality of groundwater in the study area. During dry season, bad quality water is observed in location S23. The same, EC of this water (S23) is the highest value. The high value of WQI has been found to closely related to the high values of TDS, chloride, sulfate, nitrite, and nitrate. Poor quality water area mainly locates in the northwestern part because of strong water–rock interaction with the C¸ameli formation.
123
Environ Earth Sci Table 8 Type of waters in dry and wet season in the study area (Tefenni Plain) Sample No.
Dry season P SI Type of water
Wet Season P SI Type of water
S1
20.53
Excellent water
18.94
Excellent water
S2
26.98
Excellent water
29.68
Excellent water
S3 S4
39.36 34.55
Excellent water Excellent water
30.60 33.54
Excellent water Excellent water
S5
31.33
Excellent water
32.80
Excellent water
S6
17.66
Excellent water
22.01
Excellent water
S7
33.31
Excellent water
31.56
Excellent water
S8
29.00
Excellent water
25.75
Excellent water
S9
33.51
Excellent water
28.89
Excellent water
S10
36.90
Excellent water
35.07
Excellent water
S11
24.44
Excellent water
22.96
Excellent water
S12
35.96
Excellent water
30.58
Excellent water
S13
17.44
Excellent water
18.77
Excellent water
S14
35.79
Excellent water
36.63
Excellent water
S15
30.55
Excellent water
30.75
Excellent water
S16
20.17
Excellent water
20.88
Excellent water
S17
23.63
Excellent water
22.42
Excellent water
S18 S19
24.61 33.03
Excellent water Excellent water
17.26 26.43
Excellent water Excellent water
S20
38.95
Excellent water
33.47
Excellent water
S21
29.37
Excellent water
28.54
Excellent water
S22
78.92
Good water
84.11
Good water
S23
110.75
Poor water
74.04
Good water
S24
44.11
Excellent water
48.18
Excellent water
S25
26.58
Excellent water
24.38
Excellent water
S26
26.21
Excellent water
25.95
Excellent water
S27
32.96
Excellent water
26.14
Excellent water
S28
60.93
Good water
59.42
Good water
Water quality for irrigation purposes An assessment of the suitability of groundwater for irrigation is based on an assessment of the content of sodium compared to the total cations in the system. High sodium waters are not suited for irrigation activities because the sodium ion engages in cation exchange processes which tend to affect the ability of soils to sustain crop productivity. The Na? ion adsorbs onto cation exchange sites, causing soil aggregates to disperse, thus reducing soil permeability (Tijani 1994; Yidana et al. 2010). Irrigational suitability of groundwater in the study area was evaluated by EC, SAR, RSC, USSL classification, Na %, and Wilcox diagram. Relation EC with Sodium adsorption ratio (SAR) The total content of soluble salts, such as Na to Ca and Mg and its relative proportion affect the suitability of groundwater
123
for irrigation. The EC and Na contents are important in classifying irrigation water. According to Richards (1954), the irrigation water is classified into four groups, such as low (EC B 250 lS/cm), medium (250–750 lS/cm), high (750–2250 lS/cm), and very high (2,250–5,000 lS/cm) salinity (Table 10). High EC in water leads to formation of saline soil, whereas high Na content in water causes alkaline soil. According to EC values, 3.57 % of groundwater samples are low, 7.14 % of groundwater samples are medium, 85.72 % of groundwater samples are high, and 3.57 % of groundwater samples are very high salinity for irrigation in dry season. Furthermore, 3.57 % of groundwater samples are low, 10.71 % groundwater samples are medium, 7.14 % of groundwater samples are high, and 78.58 % of groundwater samples are very high salinity for irrigation in wet season (Table 10). Features that generally need to be considered for evaluation of groundwater suitability for irrigation are salinity, sodium percentage, and SAR. The irrigation water containing a high proportion of sodium will increase the exchange of sodium content of the soil, affecting the soil permeability, and the texture makes the soil hard to plow and unsuitable for seedling emergence (Trivedy and Goel 1984; Nagarajan et al. 2010). SAR and EC reciprocally can be used to evaluate irrigation water quality. The SAR recommended by the salinity laboratory of the US Department of Agriculture (Wilcox 1955) is calculated using the Eq. (5): 1=2 SAR ¼ rNaþ = r Caþ2 þ rMgþ2 =2 : ð5Þ SAR values greater than 18 indicate groundwater is unsuitable for irrigation purposes (Sahinci 1991) (Table 9). In the study area, SAR values were ranging from 0.04 to 14.87 and 0.03–14.74 meq/l in both periods that are dry and wet season, respectively, indicating all of samples are suitable for irrigation purposes (Table 10). A more detailed analysis, however, with respect to the irrigation suitability of the groundwater, was made by plotting the data on the diagram of the US Salinity Laboratory of the Department of Agriculture (United States Salinity Laboratory (USSL) 1954) (Fig. 3). Overall, 92.86 % of samples fall in C3S1 and C2S1 fields, indicating medium- to high-salinity and low-alkalinity water,
Table 9 According to the SAR of the irrigation waters based on USSL classification in study area
SAR \10
Very good water
10–18
Good water
18–26
Poor water
[26
Very poor water
Environ Earth Sci Table 10 Summary of important hydrophysical parameters for groundwater in dry and wet season in study area. (SAR, RSC, and Na % expressed in meq/L)
Sample No.
EC
SAR
Dry season
Wet season
S1
1,359
S2
1,372.5
S3
1,372.5
1273.05
S4
1,215
1521
S5
976.5
1368
S6
1,066.5
S7 S8
RSC
Na %
Dry season
Wet season
Dry season
Wet season
747
0.08
0.07
-0.84
-0.13
3.29
3.28
1553.85
0.75
0.95
-1.58
0.36
20.19
25.74
0.74
0.53
-0.36
-0.15
18.95
15.03
0.20
0.19
2.72
-0.68
5.27
5.62
0.09
0.08
0.13
-0.23
2.56
2.29
771.75
2.09
2.23
1.21
-0.32
55.73
62.10
1,092 1,080
1185.75 1071
0.08 0.48
0.07 0.24
-0.58 1.51
-0.16 0.13
2.35 13.57
2.01 8.48
S9
1,048.5
1102.5
0.63
0.51
0.53
0.06
16.67
14.43
S10
1,147.5
1375.65
0.06
0.05
-2.03
-0.23
1.75
1.57
S11
1,044
834.75
0.46
0.40
0.28
-0.08
14.76
14.03
S12
1,035
1309.95
0.36
0.37
0.86
0.11
9.52
9.66
S13
639
606.6
0.09
0.07
0.68
-0.14
3.69
3.13
S14
1,102.5
1498.5
0.49
0.42
0.19
-0.13
11.89
10.60
S15
949.5
1258.65
0.66
0.54
0.81
-1.32
17.21
15.28
S16
787.5
832.5
0.30
0.26
0.74
-0.17
10.71
10.22
S17
841.5
821.7
0.44
0.50
-0.35
-0.01
15.05
17.60
S18
630
520.2
0.04
0.03
0.91
0.09
2.10
3.40
S19
900
1143.45
0.08
0.06
3.56
-0.2
3.02
1.69
S20
999
1417.5
0.23
0.17
1.75
-0.18
6.15
3.95
S21
1,170
1242.45
0.34
0.35
1.09
-0.48
9.90
9.35
S22 S23
5.27 3,028.5
5.85 2875.5
14.87 1.07
14.74 0.60
12.31 -2.7
-0.39 15.9
82.04 17.57
82.73 10.51
S24
1,219.5
1857.6
1.32
1.16
-0.27
-3.64
25.40
24.74
S25
1,107
980.1
0.12
0.94
1.97
-1.43
3.75
22.28
S26
1,372
1374.3
0.60
0.49
0.22
-0.13
17.41
14.45
S27
1,242
1252.35
0.30
0.35
1.81
0.04
9.46
11.16
S28
1,485
2390.4
1.20
1.10
-1.54
-0.44
20.92
19.31
this can be used for irrigation, where moderate amount of leaching occurs and moderate permeability with leaching soil. Besides, 3.57 % of samples fall in C1S2 field, indicating low salinity and medium sodium hazard. In addition, 3.57 % of samples fall in C4S1 field, indicating very high salinity and low sodium hazard, which restrict its suitability for irrigation which restrict its suitability for irrigation in dry season. Furthermore, in the study area, 89.29 % of samples fall in C3S1 and C2S1 fields, indicating medium- to high-salinity and low-alkalinity water, this can be used for irrigation. As in dry season, 3.57 % of samples fall in C1S2 field, and 7.14 % of samples fall in C4S1 field, indicating very high salinity and low sodium hazard, this limits its suitability for irrigation in wet season (Fig. 3). According to diagram, generally the groundwater of the study area except for S23 can be used for irrigation in two hydraulic periods, which are dry and wet season.
Dry season
Wet season
Residual sodium carbonate (RSC) RSC is an important parameter to evaluate the suitability of irrigation water (Siddiqui et al. 2005), calculated using the Eq. (6) þ2 RSC ¼ CO2 ð6Þ þ Mgþ2 : 3 þ HCO3 Ca Lloyd and Heathcote (1985) have classified irrigation water based on RSC as suitable (\1.25), marginal (1.25–2.5) and not suitable ([2.5). According to RSC values, 75.01 % of groundwater samples are suitable, 14.28 % of groundwater samples are marginal properties for irrigation and 10.71 % of samples (S4, S19, and S22) are not suitable for irrigation in dry season. In addition according to RSC values in wet season, 3.57 % of groundwater samples (S23) is not suitable and all other samples (96.43 %) are suitable for irrigation in the study area (Table 10). Sodium percentage (Na %) In all natural waters, percent of sodium content is a common parameter to assess its
123
Environ Earth Sci
Fig. 3 USSL classification of groundwater samples in dry and wet season
suitability for agricultural purposes (Wilcox 1948). Sodium combined with carbonate can lead to the formation of alkaline soils, while sodium combined with chloride forms saline soils. Both these soils do not help plant growth (Nagarajan et al. 2010). The sodium percentage (Na %) is calculated using the Eq. (7) given below. Na% ¼ ðNa þ KÞ 100 = Ca þ Mg þ Na þ K ð7Þ Sodium percentage of groundwater samples was calculated in the study area for dry and wet seasons (Table 10). These sample points are plotted against specific conductance in Wilcox diagram (Fig. 4) and summarized in Table 11. The Wilcox (1955) diagram relating sodium percentage and total concentration indicates that 14.28, 72.13, and 3.57 % of the groundwater samples fall in the fields of excellent to good, good to permissible, and unsuitable, respectively, for irrigation in dry season. Besides 17.85, 75.01, and 7.14 % of the groundwater samples fall in the study area of excellent to good, good to permissible, and doubtful to unsuitable, respectively, for irrigation in wet season (Table 12). Sodium concentration plays an important role in evaluating the groundwater quality for irrigation because sodium causes an increase in the hardness of soil as well as a reduction in its permeability (Tijani 1994; Nagarajan et al. 2010). The Na % was higher during dry season when compared with wet season, due to long residence time of water, dissolution of minerals from lithological
123
composition, and the addition of chemical fertilizers by the irrigation waters (Subba Rao et al. 2002; Qiyan and Baoping 2002; Vasanthavigar et al. 2010). Permeability index (PI) PI values also indicate that the groundwater is suitable for irrigation. It is defined as follows Eq. (6). h PI ¼ 100 ½Na + [HCO31=2 =2 =½Na þ ½Ca þ ½Mg ð6Þ All ions are expressed as meq/l in this equation (Ragunath 1987; Aghazadeh and Mogaddam 2010). WHO (1989) uses a criterion for assessing the suitability of water for irrigation based on permeability index. Accordingly, the PI is classified under Class I ([75 %), Class II (25–75 %), and class III (\25 %) orders. Class I and Class II waters are categorized as good for irrigation with 75 % or more of maximum permeability. Class III waters are unsuitable with 25 % of maximum permeability. The analytical data is plotted on the chart (Fig. 5). The PI ranges from 8.29 to 68.57 % in dry season which 0.05 % of the samples entering into Class III, ranges from 46.85 to 125.50 % in wet season which all except one of the samples entering into Class III of Doneen’s chart, only one sample entering into Class II of Doneen’s chart in wet season (Table 13) (Domenico and Schwartz 1990). Magnesium hazard (MH) Szaboles and Darab (1964) have proposed an MH for assessing the suitability of water
Environ Earth Sci
Fig. 4 According to the Wilcox (1955) diagram, irrigational suitability of groundwater in the study area
Table 11 Classification of groundwater (Wilcox) in study area
Dry season Classif.
Sample num.
Excellent to good
S5, S13, S16, S22,
Good to permissible
Wet season Per. of samples
Sample num.
4
14.28
S1, S6, S13, S18, S22
5
17.85
S1, S2, S3, S4, S6, S7, S8, S9, S10, S11, S12, S14, S15, S17, S18, S19, S20, S21, S24, S25, S26, S27, S28
23
72.13
S2, S3, S4, S5, S7, S8, S9, S10, S11, S12, S14, S15, S17, S19, S20, S21, S24, S25, S26, S27
21
75.01
Permissible to doubtful
–
–
–
Doubtful to unsuitable
–
–
–
2
7.14
Unsuitable Total
S23
quality for irrigation. Generally, Ca and Mg maintain a state of equilibrium in water, and they do not behave equally in the soil system. Magnesium damages soil structure, when water contains more Na and high saline. Normally, a high level of Mg is caused by exchangeable Na in irrigated soils. In equilibrium, more Mg can affect soil quality by rendering it alkaline. Thus, it affects crop yields. The MH is expressed in terms of magnesium ratio
Num. of samples
1
3.57 100
S23, S28
Num. of samples
Per. of samples
100
(MR). This is a ratio of Mg ion concentration to combination of Ca and Mg ions concentration, which is multiplied by 100 (Subba Rao et al. 2012) (Eq. 8). MR ¼ Mgþ2 = Caþ2 þ Mgþ2 100 ð8Þ where all ionic concentrations are expressed in milliequivalents per liter. If MR exceeds the value of 50, the water associated with such a value is considered harmful
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Environ Earth Sci Table 12 Irrigation water quality of groundwater based on sodium percentage in study area
%Na
Classif.
Dry season
Wet season
Sample num.
Num. of samples
Per. of samples
Sample num.
Num. of samples
Per. of samples
23
82.15
S1, S3, S4, S5, S7, S8, S9, S10, S11, S12, S13,S14, S15, S16, S17, S18, S19, S20, S21, S23, S26, S27, S28
23
82.15
S2, S24, S25
3
10.71
\20
Excellent
S1, S3, S4, S5, S7, S8, S9, S10, S11, S12, S13,S14, S15, S16, S17, S18, S19, S20, S21, S23, S25, S26, S27
20–40
Good
S2, S24, S28
3
10.71
40–60
Permissible
S6
1
3.57
60–80
Doubtful
[80
Unsuitable
S22
1
3.57
Total
and hence is unsuitable for irrigation, because it adversely affects the crop yields. In the present study area, the MR is varied from 2.86 to 23.68 in dry season and 9.59 to 94.38 in wet season (Table 13). All of the samples are suitable for irrigation in terms of MR in dry season. However, the MR exceeds the value of 50 in approximately 57.14 % of the groundwater samples in wet season, which are not suitable for irrigation. Water quality for industrial use The quality requirements for industrial water supplies range widely and almost every industrial unit has its own standards. Industries frequently suffer from the common undesirable effects of incrustation and corrosion, which are the chemical reactions caused by inferior water quality. The incrustation involves a deposition of undesired material of CaCO3 on surface of metals, while the corrosion is a chemical action on metals that results in the metal being eaten away. Therefore, the following water quality criteria have been adopted (Johnson 1983; Subba Rao et al. 2012) for deciding the incrusting and corrosive properties of the water in the present study area: a. b.
Water, with HCO3 more than 400 mg/l or SO4 more than 100 mg/l, may cause incrustation, and Water, with pH\7 or TDS more than 1,000 mg/l or Cl more than 500 mg/l, may cause corrosion
The content of HCO3 exceeds the limit of 400 mg/l in approximately 10.71 % of the groundwater samples in dry season and 25 % of the groundwater samples in wet season. The concentration of SO-2 4 is more than 100 mg/l in approximately 7.14 % of the groundwater samples in dry season and 10.71 % of the groundwater samples in wet season. Such groundwater quality can develop incrustation
123
100
S6
1
3.57
S22
1
3.57 100
on metal surfaces and hence is not recommend for industrial use. The groundwater is free from corrosion, as the pH is more than 7. However, highly mineralized water, with TDS more than 1,000 mg/l, is observed from approximately 7.14 % of the total water samples in dry and wet seasons. Therefore, S22 and S23 groundwater samples can cause corrosion on metal surfaces. In addition, the concentration of Cl is not exceeds the limit of 500 mg/l in the any water samples in the dry and wet seasons.
Conclusions Groundwater quality and its suitability for drinking and agricultural uses in the Tefenni plain were evaluated since groundwater which is a major source of water for domestic, agricultural, and industrial activities in the study area. In the study, total 56 water samples taken from wells, springs, and lake water were analyzed in dry period and wet period. The hydrogeochemical studies conducted in the groundwater of the Tefenni plain provides the following conclusion: –
The statistical analyses were applied for understanding the groundwater quality variations. A correlation analysis for EC, pH, TDS, NO3, NO2, NH3, and major ions is a bivariate method applied to describe the degree of relation between two hydrochemical parameters in dry and wet season. In the study area, some groups of species show moderate to strong correlation (r [ 0.7). The cause of this situation is postulated that the concurrent increase/decrease in the cations is the result mainly of dissolution/precipitation reaction and concentration effects.
Environ Earth Sci Table 13 Permeability index values and Magnesium hazard values (MR) of the groundwater in the study area
Fig. 5 Doneen classification of irrigation water based on permeability index (PI) (Doneen 1964)
–
–
–
The factor analysis was performed to evaluated seasonal variations in the chemical composition of groundwater. According to the factor analysis results, Factor 1 represents the natural hydrogeochemical evolution of groundwater by water–rock interaction. Strong and moderate loads of NH3 and NO2 show that groundwater pollutants were involved from agricultural areas in the study area. Factor 2 represented the contribution of nonpoint source pollution from agriculture areas. Factor 3 shows that they derived from rock–water interaction. Piper diagram were used to determine hydrogeochemical types of groundwater. Accordingly, in the study area Ca–Mg–HCO3, Mg–Ca–HCO3, Na–CO3–Cl, and Na–HCO3–Cl, the dominant water types were observed due to water–rock interaction in the study area. Water Quality Index was used to determine the groundwater quality and its suitability for drinking purposes. According to the WQI, during dry season, 89.28 % of groundwater samples represent ‘‘excellent water’’, 7.14 % indicate ‘‘good water’’, and 3.57 % shows ‘‘poor water’’; during wet season, 89.28 % of groundwater samples represent ‘‘excellent water’’ and 10.72 % indicate ‘‘good water’’. This situation was thought to be due to effective leaching of ions, over
Sample num.
PI (dry season)
S1
18.31
8,33
51.26
9,59
S2
26.14
5,96
62.45
63,43
S3 S4
48.05 29.41
7,12 12
63.94 52.20
39,48 27,28
S5
18.80
4,54
51.54
65,42
S6
28.61
23,68
62.44
42,44
S7
8.29
3,60
46.85
94,38
S8
11.99
15
61.25
35,53
S9
28.62
8,13
63.86
52,2
S10
15.84
3,19
50.99
92,46
S11
57.59
10,71
61.70
56,70
S12
54.05
6,95
58.00
67,40
S13
87.64
16,26
51.94
28,11
S14
76.15
5,92
54.93
54,71
S15
19.52
7,82
62.75
49,28
S16
20.95
12,98
57.89
34,28
S17
25.96
8,01
62.23
75,97
S18 S19
20.72 61.41
21,6 15,97
50.18 51.56
14,28 12,61
S20
68.57
7,80
53.60
38,36
S21
30.74
6,47
54.84
54,56
S22
53.76
5,10
125.50
85,01
S23
17.59
2,86
56.62
55,09
S24
36.45
5,18
65.04
64,54
S25
36.59
14,36
52.11
20,07
S26
29.32
8,18
62.87
51,79
S27
19.61
7,18
55.30
50,95
S28
22.84
9,57
60.19
62,99
–
MR (dry season)
PI (wet season)
MR (wet season)
exploitation of groundwater, direct discharge of effluents, and agricultural impact. Irrigational suitability of groundwater in the study area was evaluated by EC, SAR, RSC, PI, MR, USSL classification, Na %, and Wilcox diagram. According to the SAR values in both periods that are dry and wet season, respectively, indicating all of samples are suitable for irrigation purposes. According to RSC values, 10.71 % of samples are not suitable for irrigation in dry season. In addition, 3.57 % of groundwater samples are not suitable for irrigation in wet season in the study area. According to the Na % ratio, was higher in dry season when compared with wet season, due to long residence time of water, dissolution of minerals from lithological composition, and the addition of chemical fertilizers by the irrigation waters. According to the PI values, in the study area the groundwaters are suitable for irrigation in dry season,
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Environ Earth Sci
–
–
but not suitable for irrigation in wet season. Reason for the negative changes in groundwater quality is related to precipitation and agricultural applications in the study area. In addition, according to the MR values, the groundwaters are suitable for irrigation in dry season, but not suitable for irrigation in wet season in the study area. This situation has been related to the lithology, precipitation, and rock-water interaction in the study area. The groundwaters evaluated in terms of industrial purposes, corrosion on metal surfaces can cause only S22 and S23 water samples in the study area. The causes of the major ion chemistry changing in the groundwater are determined to be related to the three prevalent factors operating in this study. The major factors, which explain the various loadings, are precipitation, chemical weathering, and anthropogenic.
Acknowledgments This work was supported by the Research Fund of the Su¨leyman Demirel University. Project number: 1805-D-09. The support of the General Directorate of State Hydraulic Works (SHW) XVIII Regional Directorate, Isparta is gratefully acknowledged. In addition, the authors would also like to thank Birol Ozgul and Hudai Manga working in SHW.
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