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International Journal of Scientific Research in Environmental Sciences, 2(10), pp. 346-360, 2014
Available online at http://www.ijsrpub.com/ijsres
ISSN: 2322-4983; ©2014 IJSRPUB
http://dx.doi.org/10.12983/ijsres-2014-p0346-0360
Full Length Research Paper
Groundwater Quality Assessment in Haraz Alluvial Fan, Iran
Atikeh Afzali*, Kaka Shahedi, Mahmoud Habib Nezhad Roshan, Karim Solaimani, Ghorban Vahabzadeh
Sari Agricultural Sciences and Natural Resources University, Watershed Management Department, Sari, Iran,
* Corresponding author: E-mail: [email protected], +989113006807
Received 13 July 2014; Accepted 24 August 2014
Abstract. Evaluation of groundwater quality is an important issue to assure from its safe and stable use. However, describing
quality conditions is generally difficult considering spatial variability of pollutants and a wide range of indicators (biological,
physical and chemical substances) which can be measured. In this research, groundwater quality of Haraz alluvial fan located
in southern part of Caspian Sea has been investigated using Piper, Scholler, Wilcox and GQI methods. Piper diagram (a
graphical representation of a water sample chemistry) results showed that groundwater type and facies of Calcium bicarbonate
in 29 wells and sodium bicarbonate in 2 wells. Scholler diagram shows moderate to acceptable quality of water and with
Wilcox method it has been determined that all water samples (100%) are in C3S1 Class that Indicate high water quality.
Investigation of water samples with GQI method also showed that the study area in terms of the indicator is in range of
moderate to good quality (71.83 – 82.26).
Keywords: Groundwater quality, Piper method, Scholler method, Wilcox method, GQI
(Babiker et al., 2007). Aquifers face with different
risks such as declining their levels, reduced recharge
due to lack of rainfall and normal and abnormal
pollutants, so groundwater quality monitoring is
extremely important (Mohamadi et al., 2011).
Chemical composition of groundwater is measured
to determine its suitability as a source for human use
and animal consumption, irrigation, industrial
purposes and the others. Water quality refers to the
physical, chemical, and biological characteristics that
are required for water uses. Therefore, water quality
monitoring is important because clean water is
essential for human health and integrity of aquatic
ecosystems (Hiyama, 2010). Also quality evaluation
will be clear vision to specialists and managers of
groundwater quality trends and risk of contamination
of water resources (Nakhaei et al., 2009).
There are several methods to determine water
quality, the most common method to assess water
quality for drinking purpose, is Scholler diagram, that
provides possibility of water study at a certain point in
the area, but, the spatial variability of groundwater
quality cannot be evaluated by this diagram, For this
reason, Babiker et al. (2007) has introduced
Groundwater Quality Index (GQI), which is used to
evaluate spatial variability of groundwater quality
(Rahmani et al., 2011).
1. INTRODUCTION
Increasing population growth and rising living
standards in many countries necessitate higher quality
water resources for various uses as agriculture,
industry and drinking (Rahmani, 2010). In this way
groundwater resources have been considered as
valuable reserves and infrastructure developing
countries, also tried to understand the capabilities of
these resources and their usage can be found
(Mohamahi et al., 2011). Groundwater is almost
globally important for human consumption as well as
for the support of habitat and for maintaining the
river's base-flow. It is usually of excellent quality.
Being naturally filtered in their passage through the
ground, they are usually clear, colorless, and free from
microbial contamination and require minimal
treatment (Babiker et al., 2007). Water quality with
respect to path length and abundance of soluble
ingredient can be very different in various regions
(Mahdavi, 2011).
A groundwater threat is now posed by an everincreasing number of soluble chemicals from urban
and industrial activities and from modern agricultural
practices. Nevertheless, landslides, fires and other
surface processes that increase or decrease infiltration
or that expose or blanket rock and soil surfaces
interacting with downward-moving surface water,
may also affect the quality of shallow groundwater
346
Afzali et al.
In assessing of water quality for agriculture,
Wilcox method is a common method, (Devi Oinam et
al., 2012).
Piper diagram indicate chemical characteristics of
water in terms of relative concentrations of the major
cations and anions. Through its application, it is
possible to determine type and frequency of
components of the solution.
Groundwater chemistry patterns in the phreatic
aquifer of the central Belgian coastal plain using Piper
diagram were determined. In the region, main
processes determining general water quality are
Cations exchange, carbonate mineral dissolution and
oxidation of organic matter (Vandenbohede and
Lebbe, 2012). In Geochemical and analysis evaluation
of groundwater in Imphal and Thoubal district of
Manipur, India, Wilcox plot and USSL diagrams were
used to study spatial and temporal variability in
groundwater quality. The study reflected the overall
suitability of groundwater for human use (Devi Oinam
et al., 2012). Hydro-geochemistry of groundwater
with Using 25 water samples analysis with Piper
diagram in parts of the Ayensu Basin of Ghana
indicated that dominant composition of water was NaCL and Na-HCO3-CL (Zakaria et al., 2012). In
examining the origin and evolution of brine of Iran
Mighan Playa using Piper and Stiff diagrams, which
water inlet type was Na-(Ca)-(Mg) SO4-Cl-(CO3) and,
during Geochemical evolution and evaporation,
mineral deposition of brine have become Na-Cl-SO4
type (Abdi and Rahimpoor Bonab, 2010). Aquifers
quality for drinking purpose in Dargaz, Iran, was
assessed by using GQI index and Scholler method.
GQI index changed between 66 to 86 and indicated
moderate to good groundwater quality for drinking.
But with using Scholler method, water samples are in
good to very unpleasant category (Sayyad et al.,
2011). Hydro geochemical study of water in Chegart
mine using graphical and statistical analysis showed
that using conventional graphical methods such as
Piper and Stiff diagrams for classification of water
samples, is efficient, especially in the case of few
data, (Eslamzadeh and Morshedi, 2011). Groundwater
quality determined using GQI index in Nasuno basin,
Japan showed good water quality. In this study, the
GQI value was more than 90 (Babiker et al., 2007).
For groundwater vulnerability assessment using GQI
index in Nasuno, Japan, Hiyama (2010) concluded
that water quality was good and GQI index was 83.
In the study area, due to the proximity to the
Caspian Sea, close to the surface of the groundwater
level, doing agricultural operations and the suitability
of soil, groundwater quality degradation is more
likely. Thus groundwater quality was evaluated using
Piper, Wilcox, Scholler, and GQI methods. Evaluation
of groundwater quality in combination with the
above-mentioned methods were commonly accepted
in the world for determining water quality for
agriculture and drinking uses, give more
comprehensive information of groundwater quality in
Haraz alluvial fan and provide condition for better
management of this valuable resource and sustainable
usage is possible.
2. MATERIAL AND METHODS
2.1. Study area
Haraz alluvial fan located between Caspian Sea in
northern part and Alborz Mountain from southern part
and surrounds Amol and Mahmoodabad cities. This
region was located between 52° 19′ 5′′ E and 52° 35′ 9′′
E Longitudes and 36° 24′ 5′′N and 36° 39′ 40′′ N
latitudes and latitudes. Haraz River is the main river
of this area. The study area in terms of geology
contains Sandy coastline (QT2C) and agricultural
Clay of covered areas (QC) (Fig 1).
Chemical analysis of water samples provides much
information which are useful for many practical
problems such as study of mixing of waters from
different sources, groundwater quality condition,
effect of different structures on water quality,
investigation of origin of salinity.
In this research, 31 wells with complete records in
terms of Electrical conductivity (EC), Total dissolved
solids (T.D.S), logarithm of the hydrogen ion activity
(Ph), Calcium (Ca), Magnesium (Mg), Sodium (Na),
Potassium (K), Bicarbonate (HCO3), Chlorine (CL),
Carbonate (CO3), and Sulfate (SO4 ) during 1998 until
2006 were used and their data converted to mg/l
(Table 1). Then data were analyzed in Excel. Excel is
capable of just analyzing of 23 wells data and draws
the diagrams. Thus, in this project at the first stage, 20
wells data with the name of 1 were given to the
software and in the next stage data of 11 remained
wells with the name of 2 for data analyzing and graph
drawing entered in the software. These two names (1,
2) are visible on the top of the diagram. GQI method
was performed in ARCGIS.
2.2.
HYDROCHEMISTRY
GRAPHICAL
METHODS FOR CATEGORICAL DATA
2.2.1. Piper method
Piper diagram is made of combination of three
different fields that implements anions and cations
percentage in triangle fields and their combined
condition in rhombus Square. Percentages are
calculated in terms of equivalent in millions of main
ions.
347
adsorption ratio) and each of them are divided to four
parts that totally results in the emergence of 16 water
quality groups. In Wilcox diagram S is indicator of
Sodium - adsorption ratio and C is representative of
Electrical Conductivity. The larger are the indices; the
worse is condition of water quality.
2.2.2. Wilcox method
Today, the most common method for water
classification in terms of agriculture is Wilcox
(Mahdavi, 2011). In this classification, two factors
considered (The Electrical Conductivity and Sodium -
Fig. 1: Study area with location of wells, city and regional geology
Cations, Total Dry Residue and Total Hardness of
water resources (S ay ad et a l ., 2 0 1 1 ) . The Total
Dissolved Solids (TDS) is an effective parameter in
the taste of drinking water. The water that has TDS
lower than 500 mg, in terms of drinking standards, is
considered as very good water. TDS between 500 and
1000 is favorable and TDS value from 1000 to 1500 is
allowed for drinking but above 1500 mg is not
suitable (Dindarlou et al., 2006).
2.2.3. Scholler method
Scholler diagram is a graphical method for drinking
water quality classification. In this diagram, studied
waters are divided into 6 groups including good,
acceptable,
average,
inappropriate,
generally
unpleasant and not-potable. The most important water
quality parameters for classification in terms of
drinking suitability using Scholler diagram are the
main water-soluble salts including Anions and
348
Afzali et al.
Table 1: The parameters used to assess water quality
number
Well Name
EC
T.D.S
pH
Ca2+
Mg2+
Na+
K+
HCO3
CO32
Cl-
SO42-
465.6
353.2
392.0
357.2
453.6
364.6
436.6
481.2
378.8
394.9
401.1
348.0
352.8
415.1
376.6
397.5
424.8
499.2
333.4
447.5
492.0
330.4
417.4
476.8
472.9
510.2
430.4
520.1
521.7
394.3
511.8
0.000
0.000
0.000
0.000
0.005
0.006
0.011
0.022
0.000
0.005
0.000
0.026
0.000
0.000
0.000
0.395
0.000
0.011
0.000
0.000
0.000
0.032
0.000
0.006
0.000
0.000
0.000
0.000
0.000
0.000
0.000
64.1
32.7
68.2
23.9
66.0
23.9
67.5
61.9
40.4
31.8
34.6
25.0
28.6
29.7
39.6
32.3
28.8
57.2
31.2
58.3
58.4
65.6
39.6
94.1
62.8
82.6
83.4
142.6
241.2
87.6
71.9
95.5
70.7
92.2
65.1
118.7
49.3
126.3
140.8
109.6
68.0
107.5
55.6
79.3
84.1
86.1
90.7
83.6
142.4
66.5
157.9
131.3
107.6
94.2
147.7
169.3
154.1
132.0
146.0
205.5
69.5
140.7
-
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Kharabmahale
Hoseynabad
Khardon kola amol
Aghozbin
Kolodeh
Kasemdeh
Yamchi
Kola safa
Police rah amol
Skandeh
Rodbar
No kola
Sharm kola
Balamirdeh
Pasha kola
Heshtelpaeen
Afrasara
Eshkar kola
Sorkh rood
Darvishkheyamol
Spi kola
Marzango
Bonehkenar
Shariat kola
Ghiyas kola
Form
Karon
Talikran
Roz kenar
Darya kenar
Rodbast
1182.7
811.7
1037.3
790.4
1218.9
764.8
1201.8
1304.3
1005.6
885.1
995.3
757.8
815.6
952.9
901.1
934.9
975.3
1327.0
766.2
1271.1
1269.8
956.5
1008.9
1414.3
1362.9
1444.5
1277.1
1617.6
2042.0
1049.5
1368.1
745.9
541.1
673.4
521.9
775.7
511.8
759.4
819.3
649.6
583.8
644.5
506.3
538.2
619.4
595.7
612.9
632.2
832.3
508.0
804.2
810.2
633.6
649.6
897.0
859.9
902.1
803.4
1014.5
1305.2
686.5
870.3
7.3
7.4
7.3
7.2
7.3
7.3
7.3
7.3
7.3
7.3
7.4
7.5
7.4
7.3
7.4
7.2
7.3
7.2
7.4
7.2
6.9
7.5
7.3
7.2
7.3
7.4
7.3
7.2
7.2
7.4
7.4
96.1
73.8
83.8
78.9
102.3
75.6
103.3
112.3
92.8
81.1
82.8
74.1
82.6
88.5
86.1
92.1
93.1
115.3
79.6
109.6
111.8
75.1
92.8
101.1
123.1
117.4
103.2
114.0
119.8
87.5
111.8
38.0
29.0
33.0
26.2
39.2
26.3
37.6
40.9
36.1
31.1
31.6
27.6
28.8
33.9
28.4
33.0
32.0
39.8
26.2
40.6
42.4
27.9
31.8
42.3
42.3
43.8
36.2
42.6
45.3
32.7
40.2
76.8
47.2
76.9
40.3
76.9
39.6
75.7
81.0
44.6
49.4
71.0
37.0
39.3
49.0
54.0
45.0
50.6
82.8
36.6
77.1
74.8
77.8
59.0
113.3
76.9
98.4
92.4
145.5
206.7
75.9
100.0
1.4
1.0
1.3
1.3
1.5
1.0
1.6
1.6
1.3
1.2
1.3
1.0
1.0
1.3
1.2
1.2
1.3
1.8
1.0
1.7
1.9
1.3
1.3
2.1
1.7
2.4
1.7
2.1
2.5
1.3
1.7
-
EC (µSiemens/cm), T.D.S(mg/l), Ca(mg/l), Mg(mg/l), Na(mg/l), K(mg/l), HCO3(mg/l), CO3(mg/l), Cl(mg/l), SO4(mg/l).
Each data is annual average of per Parameter in each well.
Fig. 2: Piper Diagram (1)
Fig. 3: Piper Diagram (2)
349
Table 2: Types and facies in Piper method
Abbreviation
W1
Location of
sampling
Kharab mahale
Concentration of
anions
HCO3 > SO4 > Cl
Concentration of
cations
Ca > Na+K > Mg
Type of
water
Bicarbonate
Water
facies
Calcic
W2
Hoseyn abad
HCO3 > SO4 > Cl
Ca > Mg > Na+K
Bicarbonate
Calcic
W3
Khardon kola amol
HCO3 > SO4 > Cl
Ca > Na+K > Mg
Bicarbonate
Calcic
W4
Aghozbin
HCO3 > SO4 > Cl
Ca > Mg > Na+K
Bicarbonate
Calcic
W5
Kolodeh
HCO3 > SO4 > Cl
Ca > Na+K > Mg
Bicarbonate
Calcic
W6
Kasemdeh
HCO3 > SO4 > Cl
Ca > Mg > Na+K
Bicarbonate
Calcic
W7
Yamchi
HCO3 > SO4 > Cl
Ca > Na+K > Mg
Bicarbonate
Calcic
W8
Kola safa
HCO3 > SO4 > Cl
Ca > Na+K > Mg
Bicarbonate
Calcic
W9
Police rah amol
HCO3 > SO4 > Cl
Ca > Mg > Na+K
Bicarbonate
Calcic
W10
Skandeh
HCO3 > SO4 > Cl
Ca > Mg > Na+K
Bicarbonate
Calcic
W11
Rodbar
HCO3 > SO4 > Cl
Ca > Na+K > Mg
Bicarbonate
Calcic
W12
No kola
HCO3 > SO4 > Cl
Ca > Mg > Na+K
Bicarbonate
Calcic
W13
Sharm kola
HCO3 > SO4 > Cl
Ca > Mg > Na+K
Bicarbonate
Calcic
W14
Bala mir deh
HCO3 > SO4 > Cl
Ca > Mg > Na+K
Bicarbonate
Calcic
W15
Pasha kola
HCO3 > SO4 > Cl
Ca > Na+K > Mg
Bicarbonate
Calcic
W16
Heshtel paeen
HCO3 > SO4 > Cl
Ca > Mg > Na+K
Bicarbonate
Calcic
W17
Afra sara
HCO3 > SO4 > Cl
Ca > Mg > Na+K
Bicarbonate
Calcic
W18
Eshkar kola
HCO3 > SO4 > Cl
Ca > Na+K > Mg
Bicarbonate
Calcic
W19
Sorkh rood
HCO3 > SO4 > Cl
Ca > Mg > Na+K
Bicarbonate
Calcic
W20
Darvish khey amol
HCO3 > SO4 > Cl
Ca > Na+K > Mg
Bicarbonate
Calcic
W21
Spi kola
HCO3 > SO4 > Cl
Ca > Mg > Na+K
Bicarbonate
Calcic
W22
Marzango
HCO3 > SO4 > Cl
Ca > Na+K > Mg
Bicarbonate
Calcic
W23
Boneh kenar
HCO3 > SO4 > Cl
Ca > Mg > Na+K
Bicarbonate
Calcic
W24
Shariat kola
HCO3 > SO4 > Cl
Ca > Na+K > Mg
Bicarbonate
Calcic
W25
Ghiyas kola
HCO3 > SO4 > Cl
Ca > Mg > Na+K
Bicarbonate
Calcic
W26
Form
HCO3 > SO4 > Cl
Ca > Na+K > Mg
Bicarbonate
Calcic
W27
Karon
HCO3 > SO4 > Cl
Ca > Na+K > Mg
Bicarbonate
Calcic
W28
Talikran
HCO3 > Cl > SO4
Na+K > Ca > Mg
Bicarbonate
Sodic
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Calcic
Bicarbonate
Sodic Bicarbonate
W29
Roz kenar
HCO3 > Cl > SO4
Na+K > Ca > Mg
Bicarbonate
Sodic
Sodic Bicarbonate
W30
Darya kenar
HCO3 > Cl > SO4
Ca > Na+K > Mg
Bicarbonate
Calcic
W31
Rodbast
HCO3 > SO4 > Cl
Ca > Na+K > Mg
Bicarbonate
Calcic
Calcic
Bicarbonate
Calcic
Bicarbonate
350
Type & facies
Afzali et al.
Fig. 4: Wilcox Diagram (1)
Fig. 5: Wilcox Diagram (2)
Fig. 6: Scholler Diagram (1)
Fig. 7: Scholler Diagram (2)
For this purpose, At first step, we provided related
parameter concentration raster map in ARCGIS with
Kriging interpolation of point data and then for having
one common scale with the use of the following
formula, concentrations of each pixel (C) in raster
maps that have been created in the last step, make a
connection with WHO standard of that parameter
(C(WHO)).
Ci-new = Ci – C(WHO)i / Ci + C(WHO)i
(1)
The results of these unifications are six new maps
with value range from -1 and 1.Concentrations in
these maps are graded between 1 and 10 until graded
map of each parameter was obtained. In these maps, 1
is indicator of good quality of groundwater and 10 is
2.2.4. Groundwater quality index (GQI)
GQI index provides a method for summarizing of
water quality condition that can be obviously notified
to different researchers and also can help to
understand whether total quality of groundwater
components are regarded as a potential threat for
different application of water. In this section, we used
GIS to calculate GQI value, such as other mentioned
diagrams, we used chemical analysis result of 31
samples. In GQI method, six chemical parameters
(T.D.S, Ca, Mg, Na, CL, and SO4) that have high
frequencies in groundwater and are important for
human health are compared with WHO standards.
351
indicator of destruction of groundwater quality.
Indeed, in this unit conversion,-1 in the previous step
map should be converted to 1, 0 to 5 and 1 to 10 in the
graded map. For this purpose, we use the following
Polynomial function for conversion of each pixel of
the previous map (C) to new value (R) )Fig 9, 10, 11,
12, 13, 14) (Babiker et al., 2007).
R=0.5×C2+4.5×C+5
(2)
For the creation of a map that is representative of
all six chemical parameters and showing quantity
condition of groundwater quality compared with
WHO standard, application of GQI index and related
layers parameters were combined (Fig 8).
GQI = 100 – ((r1 w1 + r2 w2+...+rnwn) /N) (3)
W = mean r + 2
(4)
Where r is the maps that obtained from previous
stage and N is final numbers of parameters. For
calculating GQI from various parameters, weight
average is taken. Parameters with higher value (The
difference with the standard) have higher weight and
as a result their Influence is more significant (Hiyama,
2010).
Fig. 8: GQI map
352
Afzali et al.
Fig. 9: Ca r map
3. RESULTS AND DISCUSSIONS
3.2. Wilcox method
3.1. Piper diagram
The results of Wilcox method indicated that 100% of
the wells were located in C3 S1Class, and all
irrigation wells were salty and their applications for
agricultural purpose are permitted (Table 3, 4, Fig 4,
5).
Data analysis using Piper diagram from 31 wells
indicated that 29 wells had calcic bicarbonate and two
wells had sodic bicarbonate facies. The dominant
anions wereHCO3 > SO4 > Cl (Right triangle), and the
dominant cations were Ca >Na+K> Mg (Left triangle)
(Table 2, Fig 2,3).
353
Table 3: Water quality for agriculture of the Wilcox method
Abbreviation
W1
W2
W3
W4
W5
W6
W7
W8
W9
W10
W11
W12
W13
W14
W15
W16
W17
W18
W19
W20
W21
W22
W23
W24
W25
W26
W27
W28
W29
W30
W31
sampling Location
Kharab mahale
Hoseyn abad
Khardon kola amol
Aghozbin
Kolodeh
Kasemdeh
Yamchi
Kola safa
Police rah amol
Skandeh
Rodbar
No kola
Sharm kola
Bala mir deh
Pasha kola
Heshtel paeen
Afra sara
Eshkar kola
Sorkh rood
Darvish khey amol
Spi kola
Marzango
Boneh kenar
Shariat kola
Ghiyas kola
Form
Karon
Talikran
Roz kenar
Darya kenar
Rodbast
C4
S4
0
S3
0
S2
0
SAR
1.67
1.17
1.79
1
1.63
1
1.62
1.66
0.99
1.18
1.68
0.93
0.95
1.12
1.29
1.02
1.15
1.69
0.91
1.59
1.52
1.94
1.34
2.38
1.52
1.96
1.99
2.94
4.07
1.75
2.06
EC
1182.67
811.684
1037.26
790.389
1218.89
764.833
1201.79
1304.28
1005.58
885.105
995.3
757.842
815.579
952.895
901.105
934.895
975.263
1327
766.2
1271.11
1269.75
956.474
1008.89
1414.33
1362.89
1444.53
1277.06
1617.63
2041.95
1049.53
1368.05
Water Class
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
C3-S1
Water quality for agriculture
Salty - usable for agriculture
Table 4: Percentage of each Wilcox classification class for agricultural purposes
C3
C2
C1
S1
S4
S3
S2
S1
S4
S3
S2
S1
S4
S3
S2
0
0
0
0
100
0
0
0
0
0
0
0
Classification of
water quality for
drinking
Good
Acceptable
Average
Inappropriate
Completely
inappropriate
Non-potable
Table 5: Water quality classification with Scholler method
Na
CL
SO4
TH
TDS
>115
155 - 230
230 - 460
460 - 920
920 - 1840
>177.5
177.5 - 350
350 – 710
710 - 1420
1420 - 2840
>144
144 - 288
288 - 576
576 - 1152
1152 - 2340
>250
250 - 500
500 - 1000
1000 - 2000
2000 - 4000
>500
500 – 1000
1000 – 2000
2000 – 4000
4000 – 8000
<1840
<2840
<2340
<4000
<8000
S1
0
GQI
<80
60 - 80
>60
3.3. Scholler method
3.4. Groundwater quality index (GQI)
Schuler diagram results showed acceptable quality for
drinking water (Fig 6,7).
GQI were as follows the maps r (formula 1, 2) and
weight w (formula 4) in the study:
) (
)
((
(
) (
)
(
) (
) )
Groundwater quality based on the index is in average
rank (table 5).
354
Afzali et al.
Fig. 10: CL r map
results show Na-SO4-Cl type and affecting Factor for
final solution are: Precipitation, Evaporation and
Reaction of meteoric waters with rocks in the basin.
Nakhaei et al. (2009), determined type of groundwater
quality and qualitative evolution by using Piper
diagram, and concluded dominant type of Torbat
Heydariye plain of Iran is was sodium chloride and in
some areas was sodic sulphate and dominant hydro
chemical type of plain is functions of lithology,
dissolution strength, and flow pattern. Zakaria et al.
(2012) in their research used Piper diagrams to
determine water quality; they stated that dominant
type of water is in effect of soluble salts in soil layers
and the decomposition of organic material.
Studying by Wilcox diagram suggested that 100%
of data are in C3S1 class that was indicator of water
with moderate quality and application of this water
was suggested in irrigation of coarse lands with good
drainage.
4. CONCLUSIONS
Many researchers have studied the groundwater
quality, and each of them have used different methods
for research (i.e., Ab d i et al., 2010, Babiker et al.,
2007, Devi Oinam et al., 2012, Din d arlo et al .,
2006, Moha mad i et al., 2 0 1 1 ) , nevertheless,
studies in which different methods are used to assess
water quality, can provide more comprehensive view
of the future management of groundwater. In this
study, groundwater quality was assessed in Haraz
alluvial fan with different methods. As previously
mentioned, by use of Piper diagram water type and its
components can be easily realized. Results of this
diagram represent type and facies of Calcium
bicarbonate in 29 wells and sodium bicarbonate in 2
wells.
Abdi and Rahimpoor bonab (2010), determined
type of Iran's Mighan Playa by Piper diagram. Their
355
Scholler diagram is a traditional method that
parameters are separately evaluated, final quality is
determined by the worst quality and its parameters are
fixed. In this study, Scholler diagram showed
acceptable water quality in most wells that could be
used as drinking water. Water quality with GQI values
between 71.8 and 82.3 is acceptable (Table 3). The
number and type of parameters in this method is
completely optionaland this enables the researcher to
suggest that qualitative changes in accordance with
the needs and problems of each region. Babiker et al
in 2007 expressed GQI index in Nasuno basin, Japan
was more than 90. Hiyama (2010) was concluded GQI
in Nasuno, Japan 83. Sayyad et al. (2011) results show
that GQI index changed between 66 to 86.
Fig. 11: Mg r map
With regard to presented results, we can state that
almost all the methods represented acceptable
groundwater quality of studied area for drinking and
agriculture. It can be due to abundant rainfall, low
evaporation, and the region being as alluvial fan,
which causes water transfer from upstream the Alborz
Mountains to downstream through Haraz River.
In evaluation of groundwater, the goal is water
quality investigation and planning for sustainable
application of these sources. With regard to relatively
acceptable quality of this area, appropriate application
of this vital resource is suggested. This research has
been done with using data from 9-year period.
However, due to the vicinity and similarly of different
wells data, the results of this research can be the same
with the result of groundwater analyzed in each year
of this period. Also, the study was not done in recent
years, was due to the lack of reliable data.
356
Afzali et al.
Fig. 12: Na r map
Department at Sari that helped us to learn how use
GIS to draw maps and Mr Adel Khashave and Dr
Mehran Nor Mohamad Por Omran that helped us for
editing this paper.
ACKNOWLEDGEMENTS
We appreciate the assistance of Mr Morteza Shabani
from GIS labratory in Watershed Management
357
Fig. 13: TDS r map
Abas. Medical Sciences Journal of Hormozgan,
10(1): 57-65.
Eslamzaded M, Morshedi A (2011). Study of
hydrochemistrysamples of water in Choghart
mine using graphical and statistical. Journal of
Land and Resources of Lahijan Branch, 4(2),
Summer.
Hiyama T( 2010). Evaluation of groundwater
vulnerability (and sustainability).
20th
UNESCOIHP training course DOI 10.
1007/S11269-006-9059-6.
Mahdavi M ( 2011). Applied Hydrology. in: Tehran
University 9th ed. Tehran, Iran.
Mohamadi M, Mohamadi Ghale Ney M, Ebrahimi K
(2011). Temporal and spatial variation of
groundwater quality in Qazvin. Journal of Iran
Water Researc, 5(8): 41-52.
Nakhaee M, Vadiati M, Saberi Nasr A (2009). Hydro
geochemistry Evolution of Torbate Heydariye
REFRENCES
Abdi
L,
Rahimpoor
Bonab
H
(2010).
HydrochemistryOrigin and brine evolution of
Mighan Playa of Arak (Iran). Stratigraphy and
sedimentology studies. Years Twenty-six,
Serial 38, Number One.
Babiker S, Mohamed MAA, Hiyama T (2007).
Assessing groundwater quality using GIS.
Water resource Manage. Springer science +
Business Media B.V.21:699-715.
Devi Oinam J, Ramanathan AL, Singh G (2012).
Geochemical and statistical evaluation of
groundwater in Imphal and Thoubal district of
Manipur, India. Journal of Asian Earth
Sciences, 48: 136–149.
Dindarlo K, Alipoor V, Farshidfar GhR (2006).
Chemical quality of drinking water in Bandar
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plain. Fifteenth Meeting of Geological Society
of Iran.
Rahmani A (2010). Study of groundwater quality
changes trend(case study: Qaemshahr – Joybar,
Mazandaranprovince). MS Thesis, Natural
Resources Faculty of Sari. 69-93.
Rahmani Gh R, Chitsazan M, Zaresefat M, Kalantari
N (2011). Evaluation of groundwater quality for
drinking with GQI in Ize – Piyon plain. Fourth
International Conference on Water Resources
Management of Iran.
Sayyad H, Mohamadzaheh H, Velayati S (2011).
Assessment of water quality in Dargaz aquifer
for drinking with using Scholler diagram and
GQI index. Proceedings of the Thirtieth
Meeting of Geological Sciences. Iran.
Vandenbohede A, Lebbe L (2012). Groundwater
chemistry patterns in the phreatic aquifer of the
central Belgian coastal plain. Applied
Geochemistry, 27: 22–36.
Zakaria N, Akiti TT, Osae Sh, Dickson A, Ganyaglo
SY, Hanson JEK, Ayanu G (2012). Hydro
geochemistry of groundwater in parts of the
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Fig. 14: SO4 r map
359
Atikeh Afzali is currently PhD student in the field of watershed management in the department of
natural resources at Sari University of Agricultural Sciences and Natural Resources, Iran. She received
her associate degree from Ferdowsi Mashhad University in 2001 in Range and Watershed
Management. She obtained degree in Bachelor in Restoration of desert from Yazd University of Iran
(2005) and Master degree in Tehran University of Iran (2008). She is teaching in some branch of
Payame Noor University since 2009 in Natural Resources Faculty.
Kaka Shahedi earned his phd degree in hydrology and quantitative water management from
Wageningen University and Research center, the Netherlands, in 2008. He is currently assistant
professor in Watershed Management Department, Sari University of Agricultural Science and Natural
Resources University, Iran.
E-mail: [email protected]
Mahmoud Habibnejad is Professor at the Sari Agricultural Sciences and Natural Resources University
(SANRU), Watershed Management Department. His research area is watershed hydrology. He received
his Ph.D in hydrology from University of Provenc, France in 1994. He is chief editor for Journal of
Watershed Management Research published by SANRU.
Karim Solaimani is currently Professor and the head of RS and GIS lab in Sari Agricultural Sciences and
Natural Resources University, Sari, Iran, where he serves as a faculty member since 1997. He received
his Ph.D. in Remote Sensing and Environmental Sciences from Glasgow University, U.K. in 1997.
Gorban Vahabzadeh earned his PhD degree in Economic Geology from Shahid Beheshti University of
Tehran, Iran, in 2007. He is currently assistant professor in Watershed Management Department, Sari
University of Agricultural Science and Natural Resources University, Iran
360

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