Research Journal of Environmental and Earth Sciences 4(7): 756-768, 2012
ISSN: 2041-0492
© Maxwell Scientific Organization, 2012
Submitted: June 07, 2012
Accepted: July 04, 2012
Published: July 25, 2012
Multivariate Statistical Approach to Geochemical Methods in Water Quality Factor
Identification; Application to the Shallow Aquifer System of the Yarmouk
Basin of North Jordan
Awni Batayneh and Taisser Zumlot
Department of Geology and Geophysics, King Saud University, P.O. Box 2455,
Riyadh 11451, Saudi Arabia
Abstract: The study of groundwater hydrogeochemistry of the sedimentary rock shallow aquifer system in the
Yarmouk Basin of north Jordan produced a large geochemical dataset. Groundwater samples were collected at 36
sites in October 2009 (dry season) and in May 2010 (wet season) over a 1426 km2 study area and analyzed for major
and minor ions. The large number of data can lead to difficulties in the integration, interpretation and representation
of the results. Two multivariate statistical methods, Hierarchical Cluster Analysis (HCA) and Principal Components
Analysis (PCA), were applied to a subgroup of the dataset to evaluate their usefulness to classify the groundwater
samples and to identify geochemical processes controlling groundwater geochemistry. This subgroup consisted of
36 samples and 28 parameters (Ca2+, Na+, Mg2+, K+, Cl-, HCO3-, NO3-, SO42-, Al, B, Ba2+, Be, Bi, Cd, Co, Cr, Cu,
Fe2+, Li, Mn2+, Ni, Pb, Sb, Se, Zn, P, Sr, V). Seven geochemically distinct clusters, C1-C7, resulted from the HCA.
Calcium and magnesium are the dominant ions in the groundwater of the basin (clusters C1, C5 and C7), while
bicarbonate is the most abundant of the anions (clusters C2 and C3). A total of five PCA components were extracted
for dry and wet seasons, where it accounts 68.6 and 72.6% of the total variance in the dataset, respectively. For dry
and wet season water samples characteristic loadings, two components were defined as the salinity and hardness
components, while the other components were related to more local and geologic effects.
Keywords: Hierarchical cluster analysis, hydrochemistry, multivariate techniques, north Jordan, principal
component analysis, Yarmouk Basin
quantitative approach for interpreting and representing
data concerning groundwater pollutants and
geochemistry. Steinhorst and Williams (1985), Olmez
et al. (1994), Farnham et al. (2003), Chen-Wuing et al.
(2003), Love et al. (2004), Cloutier et al. (2008) and
Farooq et al. (2010) adopted factor analysis technique
for groundwater contamination and geochemical
evolution and to identify rock water interaction
processes. Cluster analysis was used to interpret the
hydrochemical data based on factor scores (Suk and
Lee, 1999; Reghunath et al., 2002; Ji-Hoon et al., 2005;
Belkhiri et al., 2011). In addition, multivariate analysis
was used to interpret relationship among the chemical
variables and the processes involved and to show the
regional impact of human activities on groundwater
composition (Lawrence and Upchurch, 1982; Usunoff
and Guzma´n-Guzma, 1989; Razack and Dazy, 1990;
Briz-kishore and Murali, 1992; Melloul and Collin,
1992; Schot and Van der Wal, 1992; Subyani and Al
Ahmadi, 2010). Moreover, multivariate statistical
methods can also help understand groundwater flow in
complex aquifer systems (Farnham et al., 2000;
Stetzenbach et al., 2001).
INTRODUCTION
The composition of groundwater in arid and semi
arid regions is controlled by many processes, including
evapotranspiration, depositional environment and type
of interaction between water and aquifer and human
activities. Increase knowledge of geochemical process
could lead to better understanding that can contribute to
effective groundwater management and utilization.
Because of the complexities of the regional
conditions and hydrochemical processes and because of
large amounts of basic information regarding the
groundwater chemistry evaluation, advance techniques
are required to interpret observed relationship among
variables. The early study of groundwater
hydrochemistry is the use of different maps and
graphical representations to interpret the geochemical
data, such as: Piper diagram (Piper, 1944), Stiff pattern
diagram and scatter plots of chemical parameters.
Recently and with increasing number of chemical and
physical variables of groundwater, a wide range of
multivariate statistical techniques are now used. Cluster
analysis and factor analysis are frequently applied as a
Corresponding Author: Awni Batayneh, Department of Geology and Geophysics, King Saud University, P.O. Box 2455,
Riyadh 11451, Saudi Arabia, Tel.: +966-56-8086395
756
Res. J. Environ. Earth Sci., 4(7): 756-768, 2012
Fig. 1: Context of the study area: (a) Location map of north Jordan showing principal physiographic features (Batayneh, 2010),
(b) geology (Batayneh, 2012) and (c) drainage systems of the Yarmouk Basin (Ta’any et al., 2007)
The main objectives of this scientific paper was to
better identify the processes controlling the
geochemical evolution of groundwater in the study area
by using two well-proven multivariate methods to
analyze the geochemical data, Hierarchical Cluster
Analysis (HCA) and Principal Components Analysis
(PCA). Given the geological setting of the study area,
HCA and PCA may be able to help distinguish the role
of geological and hydrogeological contexts. A
methodological study was also applied in order to
assess the relative applicability and complementarity of
HCA and PCA in achieving the scientific objective,
compared to conventional geochemical grouping.
Context of the study area:
Geology and hydrogeology: Yarmouk Basin is a
transboundary basin shared between Jordan and Syria
with a total area of about 7240 km2. The basin is
located in north Jordan area between coordinates 32°20′
to 32°45′ N and longitudes 35°42′ to 36°23′ E, covering
an area of about 1,426 km2 (Fig. 1a). North Jordan area
is part of the Mediterranean semi-arid climate which is
characterized by limited precipitation occurring in the
winter and hot dry summer. The average annual rainfall
is 475 mm, mostly occurring between November and
April,peakinginJanuary(http://met.jometeo.gov.jo/acc_r
ain).
From a geological point of view, the outcroppings
rocks in the study area are of Upper Cretaceous to
Tertiary age formations (Makhlouf et al., 1996; Moh’d,
2000). The oldest is the Wadi Es-Sir Limestone (WSL)
formation of Turonian age. It is essentially composed of
limestone and mainly exposed on the southwest part of
the Yarmouk Basin (Fig. 1b). The overlying
sedimentary sequences are of Santonian to Eocene age
and includes in ascending order: Wadi Umm Ghudran
(WG), Amman Silicified Limestone (ASL), Muwaqqar
Chalk-Marl (MCM), Umm Rijam Chert-Limestone
(URC) and Wadi Shallala (WS) formations. These
comprise limestone, marl limestone, marl, chalk, chert
and phosphorite sedimentary rocks. Basaltic flows (BS
formation) of Oligocene age cover the eastern part of
757 Res. J. Environ. Earth Sci., 4(7): 756-768, 2012
the basin. Basalts are also found as small outcrops
scattered on the north and northwest of the Yarmouk
Basin (Fig. 1b).
According to the study of Hawi (1990),
Abderahman and Awad (2002), Abu-Jaber and Ismail
(2003), Ta’any et al. (2007) and Batayneh et al. (2008)
two systems of aquifer are present in north Jordan:
•
•
The shallow aquifer (namely B4/5 aquifer) presents
in the north and northwest of the Yarmouk Basin.
It is recharged either along the elevated areas of
Golan Heights and Ajlun Highlands, or due to local
surface water infiltration through the URC and WS
formations outcrops in the northern and
northwestern parts of the basin.
The second aquifer system (namely B2/A7 aquifer)
is the most regionally extensive aquifer and the
most important for water resources in north Jordan,
because of the extended outcrop catchment areas
located in the Ajlun Highlands and on the uplands
of the Hermon Mountains.
The drainage systems in the Yarmouk Basin are
shown in Fig. 1c. The map shows three distinctive
drainage patterns:
•
•
•
Areas in the north, northwest and eastern parts of
the basin are dominated by a NW drainage trend
A NE trend dominates areas in the southwest
The central part of the basin dominated by N-S
drainage trend
The water table contours (Fig. 1c) indicate that the
direction of groundwater flow is from south and east to
the north and northwest and strongly coincides with
topography and drainage patterns. Depth to water table
with reference to sea level varies from artesian flow in
the northwest area, to about 250 m below land surface
near the central part. Cross section A-A’ illustrates the
groundwater flow conceptual model and presents the
main hydrostratigraphic units (Fig. 1d).
Hydrochemistry: Ta’ any et al. (2007) and Batayneh
et al. (2008) identified groundwater types according to
majors cations (Ca2+, Na+, Mg2+, K+) and anions (Cl-,
HCO3-, NO3-, SO42-) and showed that the shallow
aquifer system of the Yarmouk Basin of north Jordan
has a highly variable groundwater geochemistry. The
concentration of the cations is of order (Ca2+ > Na+ >
Mg2+ > K+) while that for anions is (Cl- > HCO3- > NO3> SO42-). Statistical analyses on groundwater (Batayneh
et al., 2008) indicate positive correlation between the
following pairs of parameters: Cl- and Mg2+ (r = 0.49),
Cl- and K+ (r = 0.46), Cl- and Na+ (r = 0.74), SO42and
Mg2+ (r = 0.23) and
SO42- and K+ (r =
0.20). Water shows varying
chemical
facies
(Ca2+-SO42-, Mg2+- SO42-, Na+-SO42-), which relates to
the interaction with the geological formations of the
basin (carbonates, dolomite, marl, basalt and various
silicates) and evaporation. The dissolution of halite,
calcite, dolomite and gypsum explains part of the
observed Ca2+, Na+, Mg2+, Cl-, HCO3- and SO42-, but
other processes, such as cation exchange and
weathering of aluminosilicates also contribute to the
water composition.
METHODOLOGY
Hydrochemical
dataset:
The
regional
hydrogeochemical characterization of the north Jordan
shallow aquifer system was carried out in the 2004 and
2005 with the groundwater sampling of private,
municipal and observation wells and springs. Water
samples were collected twice in October 2006 (dry
season) and May 2007 (wet season). A total of 36 water
samples were collected from major springs of the
Yarmouk Basin in northern Jordan. The sample sites,
distributed over the northwest parts, cover the
permeable URC and BS hydrostratigraphic units. A
rigorous, but conventional and recognized, protocol was
used for samples collection and preservation and the
chemical analyses were performed in certified
laboratories using standard methods. In situ field
measurements were made on water samples for
hydrogen
ion
concentration
(pH),
Electrical
Conductivity (EC) and temperature. Groundwater
samples were analyzed for major, minor and trace
constituents for a total of 28 parameters, plus Total
Dissolved Solids (TDS). These water samples were
analyzed by using VISTA-MPX instrument with
Charge-Coupled Device (CCD) Inductively Coupled
Plasma-Optical Emission Spectrometry (ICP-OES) in
the laboratories of the Department of Chemistry,
Yarmouk University, Irbid, Jordan. The details of
analytical data of major and minor ions are given in
Table 1. In addition, some descriptive statistics such as
mean, median, standard deviation, coefficient of
variation and coefficient of skewness are also given.
Data preparation for the multivariate statistical
analysis: As mentioned, each sampling site is
characterized by a large number of chemical and
physical
variables,
making
the
regional
hydrogeochemical study a multivariate problem. The
multivariate statistical analysis is a quantitative and
758 Res. J. Environ. Earth Sci., 4(7): 756-768, 2012
Table 1: Descriptive statistics for the 36 groundwater samples (concentration in mg/L). Min: Minimum; Max: Maximum; STD: Standard
Deviation; CV: Coefficient of Variation
Range
------------------------------------Parameters
Min
Max
Mean
Median
STD
CV
Skewness
Dry season
Ca
26.8900
44.190
38.430
38.760
3.490
0.091
-1.094
Mg
4.4700
23.120
9.280
7.430
4.590
0.495
1.532
Na
11.8300
39.050
20.920
17.100
8.320
0.397
1.044
K
0.1300
63.540
6.910
2.020
13.880
2.010
3.049
Cl
5.2900
161.20
67.450
56.200
31.100
0.451
1.120
39.7500
90.140
68.100
69.780
12.700
0.187
-0.221
HCO3
8.1600
123.680
55.010
47.200
28.390
0.521
1.013
NO3
0.9400
55.980
24.100
21.470
15.580
0.676
0.375
SO4
Al
0.0070
0.365
0.057
0.025
0.077
1.352
2.699
B
0.0820
0.201
0.116
0.104
0.033
0.283
1.249
BA
0.0520
0.696
0.377
0.393
0.131
0.348
-0.256
Be
0.0010
0.001
0.001
0.001
0.001
0.252
1.781
Bi
0.0030
1.626
0.057
0.009
0.269
4.750
5.986
CD
0.0010
0.001
0.001
0.001
0.001
0.431
0.721
Co
0.0010
0.198
0.007
0.002
0.033
4.702
5.998
Cr
0.0010
0.006
0.003
0.003
0.001
0.468
0.528
Cu
0.0020
0.009
0.005
0.005
0.001
0.279
0.896
Fe
0.0010
0.292
0.041
0.006
0.075
1.826
2.431
Li
0.0020
0.012
0.004
0.003
0.002
0.581
1.938
Mn
0.0001
0.020
0.003
0.001
0.005
1.969
2.650
Ni
0.0030
0.062
0.010
0.007
0.010
1.007
4.476
Pb
0.0001
0.014
0.004
0.003
0.003
0.851
1.750
Sb
0.1410
1.153
0.576
0.530
0.262
0.455
0.492
Se
0.1100
0.054
0.022
0.021
0.008
0.370
1.827
Zn
0.002
1.354
0.049
0.007
0.224
4.547
5.960
P
0.004
0.170
0.033
0.019
0.040
1.218
2.709
Sr
0.200
1.110
0.415
0.31
0.220
0.531
1.758
V
10.84
81.88
27.59
21.12
16.39
0.594
1.818
Wet season
Ca
32.860
46.990
40.980
41.200
2.940
0.070
-0.320
Mg
4.670
23.210
9.480
7.890
4.620
0.490
1.500
Na
12.500
40.500
21.250
17.880
8.680
0.410
0.990
K
0.190
63.930
6.590
1.6600
13.880
2.110
3.110
Cl
5.320
165.570
68.740
56.720
31.130
0.453
1.165
40.260
90.280
67.300
70.150
12.710
0.189
-0.228
HCO3
8.680
125.860
54.340
47.120
28.470
0.524
1.016
NO3
0.960
56.640
23.060
21.600
15.560
0.674
0.378
SO4
Al
0.009
0.120
0.027
0.022
0.020
0.730
3.500
B
0.072
0.176
0.104
0.093
0.028
0.270
1.070
BA
0.055
0.709
0.376
0.379
0.156
0.361
-0.049
Be
0.001
0.005
0.001
0.001
0.001
1.052
4.567
Bi
0.001
0.062
0.009
0.004
0.012
1.256
3.157
CD
0.001
0.004
0.001
0.001
0.001
1.726
4.406
Co
0.001
0.005
0.001
0.001
0.001
0.595
3.795
Cr
0.001
0.007
0.003
0.002
0.002
0.571
1.014
Cu
0.003
0.010
0.005
0.004
0.001
0.273
2.448
Fe
0.001
0.092
0.016
0.006
0.025
1.547
1.971
Li
0.002
0.011
0.004
0.003
0.002
0.609
1.589
Mn
0.001
0.020
0.002
0.001
0.005
1.934
3.116
Ni
0.031
0.064
0.047
0.045
0.008
0.178
0.251
Pb
0.001
0.008
0.003
0.003
0.002
0.454
0.480
Sb
0.042
0.574
0.243
0.215
0.132
0.541
0.866
Se
0.007
0.047
0.019
0.017
0.008
0.404
1.748
Zn
0.002
0.454
0.030
0.006
0.086
2.915
4.298
P
0.006
0.092
0.028
0.022
0.019
0.681
1.812
Sr
0.223
1.231
0.455
0.332
0.259
0.570
1.859
V
10.74
72.26
25.00
19.19
13.87
0.555
1.888
independent approach allowing grouping of
groundwater samples and making correlation between
chemical parameters. In this study, two multivariate
methods were applied using SPSS 20 software package:
759 Res. J. Environ. Earth Sci., 4(7): 756-768, 2012
Fig. 2: Dendrogram for 36 groundwater samples (dry season samples), showing the division into seven clusters analysis in Qmode
the Hierarchical Cluster Analysis (HCA) and the
Principal Components Analysis (PCA). Description of
HCA and PCA techniques and the methodology used
for their application can be found in Meng and
Maynard (2001), Subba Rao et al. (2001), Cloutier
et al. (2008) and Kanade and Gaikwad (2011).
The multivariate statistical methods were applied
to a subgroup of the whole hydrogeochemical dataset
that consists of 36 groundwater samples and 28
parameters. These parameters include major
constituents Ca2+, Na+, Mg2+, K+, Cl-, HCO3-, NO3- and
SO42-, as well as minor and trace constituents Al, B,
Ba2+, Be, Bi, Cd, Co, Cr, Cu, Fe2+, Li, Mn2+, Ni, Pb, Sb,
Se, Zn, P, Sr and V. A certain number of parameters
were excluded from the multivariate statistical analysis
for the following reasons: parameters with additive
characteristics such as EC and TDS and parameters that
show small regional variation such as pH.
The frequency diagrams of most chemical
parameters do not follow a normal distribution;
therefore HCA and PCA are carried out for the
logarithmic transformation of data set because it is
closer to the normality condition which is required for
these analyses. Standardization was applied to the log
normal distribution to ensure that each variable is
weighted equally.
RESULTS OF MULTIVARIATE
STATISTICAL ANALYSIS
Hierarchical Cluster Analysis (HCA) for water
samples: The HCA is a data classification technique
that widely applied in Earth sciences (Davis, 1986) and
often used in the classification of hydrogeochemical
data (Steinhorst and Williams, 1985; Schot and Van der
Wal, 1992; Guler et al., 2002).
The main result of the HCA performed on the 36
groundwater samples is the dendrogram (Fig. 2 and 3).
In this study, the Euclidean distance is selected as the
distance measure. The sampling sites with the larger
760 Res. J. Environ. Earth Sci., 4(7): 756-768, 2012
Fig. 3: Dendrogram for 36 groundwater samples (wet season samples), showing the division into seven clusters analysis in Qmode
similarity are first grouped. Next, groups of samples are
joined with a linkage rule and the steps are repeated
until all observations have been classified. With this
geochemical dataset, Ward’s method was more
successful to form clusters that are more or less
homogenous and geochemically distinct from other
clusters, compared to other methods such as the
weighted pair-group average. Ward’s method is distinct
from other linkage rules because it uses an analysis of
variance approach to evaluate the distances between
clusters. Other studies used Ward’s method as linkage
rule in their cluster analysis (Adar et al., 1992; Schot
and Van der Wal, 1992). Guler et al. (2002) also found
that using the Euclidean distance as a distance measure
and Ward’s method as a linkage rule produced the most
distinctive groups.
Figure 2 shows the dendrogram of water samples
for dry season where it can be classified into seven
groups based on a visual observation of the
dendrogram, namely C1-C7. Observation of
dendrogram shows some indications of the level of
similarity between the seven clusters (Fig. 2). C1 and
C2 have the lower linkage distance between the defined
clusters, therefore, has the greater similarity between all
clusters. It can be expected that the geochemistry of
C1samples would have similarities with the ones of C2.
Clusters C3 and C5 have an elevated linkage distance
between the defined cluster. To describe the
characteristics of each cluster of sample, Table 2 shows
the median values of geochemistry data, including the
28 chemical elements. Samples from C1 cluster are
characterized by high concentrations of Cu and by low
concentrations of Na, SO4 and Cl in all clusters.
Samples from C2 are characterized by lowest
concentrations in HCO3, Ni and Zn of all clusters.
Samples from C3 are characterized by high
761 Res. J. Environ. Earth Sci., 4(7): 756-768, 2012
Table 2: Geochemical characteristics of each cluster for dry season (median concentration in mg/L, bold values: highest values; underlined
values: lowest values)
Parameters
C1
C2
C3
C4
C5
C6
C7
Ca
36.44000
38.04500
36.04500
41.59500
38.78000
41.96500
34.27500
K
0.62000
4.02500
1.05500
0.56000
2.60000
42.45500
3.08000
Mg
5.90000
7.14000
6.57500
8.96000
6.37000
13.32500
19.47500
Na
14.66000
18.59000
18.81000
16.47000
16.80000
34.82000
35.45500
70.15000
59.76000
80.21500
75.03000
59.78000
59.78000
79.91000
HCO3
4.80000
26.88000
30.24000
24.48000
7.68000
44.64000
36.00000
SO4
Cl
47.20000
55.45000
62.39500
53.00000
56.80000
117.35000
115.150000
54.56000
61.69000
40.30000
33.48000
44.64000
55.49000
77.81000
NO3
Al
0.02408
0.01741
0.02718
0.04045
0.25320
0.02359
0.01635
B
0.09768
0.11241
0.14347
0.08719
0.09168
0.16712
0.13070
Ba
0.43654
0.36859
0.35440
0.47488
0.41657
0.25000
0.15842
Be
0.00048
0.00051
0.00080
0.00058
0.00061
0.00039
0.00065
Bi
0.00962
0.01386
0.03332
0.00642
0.00866
0.01005
0.00484
Cd
0.00052
0.00067
0.00095
0.00042
0.00025
0.00053
0.00055
Co
0.00142
0.00144
0.00144
0.00153
0.00177
0.00143
0.00234
Cr
0.00262
0.00301
0.00319
0.00172
0.00386
0.00382
0.00140
Cu
0.00541
0.00453
0.00524
0.00435
0.00391
0.00515
0.00514
Fe
0.00433
0.00552
0.00705
0.02577
0.27668
0.00901
0.00514
Li
0.00254
0.00261
0.00295
0.00287
0.00311
0.00459
0.00897
Mn
0.00030
0.00039
0.00081
0.00076
0.01717
0.00047
0.00037
Ni
0.00641
0.00520
0.00704
0.00979
0.00758
0.01125
0.01510
Pb
0.00163
0.00292
0.01049
0.00251
0.00678
0.00335
0.00169
Sb
0.57412
0.41512
0.76269
0.71135
0.53434
0.77219
0.34885
Se
0.01559
0.02127
0.01848
0.02284
0.01854
0.02607
0.02171
Zn
0.00674
0.00548
0.70629
0.00883
0.01499
0.01225
0.00683
P
0.01704
0.01858
0.01438
0.01769
0.16655
0.02807
0.01901
Sr
0.26000
0.30500
0.25500
0.42000
0.31000
0.43500
0.86500
V
17.11000
19.94000
18.06500
25.19500
17.07000
39.44500
64.75000
Table 3: Geochemical characteristics of each cluster for wet season (median concentration in mg/L, bold values: highest values; underlined
values: lowest values)
Parameters
C1
C2
C3
C4
C5
C6
C7
Ca
39.09320
41.27470
39.04180
36.69810
44.84550
41.12420
42.41680
K
0.90066
1.97648
3.94878
1.63209
0.62200
1.38548
12.38270
Mg
5.71827
6.40644
8.26673
6.110490
9.63134
7.39187
13.15320
Na
13.5402
16.89610
25.80370
13.79060
17.70460
18.86610
35.04730
59.78000
79.91000
50.02000
70.15000
70.15000
79.91000
64.96500
HCO3
7.41000
39.36000
26.88000
21.12000
22.08000
6.58000
36.96000
SO4
Cl
47.50700
50.34330
76.93300
53.17950
53.88860
56.72480
114.86800
54.56000
43.40000
73.78000
42.78000
21.7000
47.74000
83.70000
NO3
Al
0.02369
0.02379
0.02721
0.01200
0.022190
0.06394
0.020950
B
0.07332
0.08958
0.11107
0.10286
0.08180
0.09360
0.13464
Ba
0.44112
0.41970
0.35889
0.33396
0.49329
0.37719
0.25876
Be
0.00052
0.00056
0.00056
0.00059
0.00039
0.00045
0.00076
Bi
0.00407
0.00691
0.00551
0.01629
0.00383
0.00189
0.01025
Cd
0.00016
0.00028
0.00017
0.00029
0.00013
0.00013
0.00053
Co
0.00113
0.00106
0.00066
0.00125
0.00115
0.00128
0.00162
Cr
0.00191
0.00286
0.00370
0.00268
0.00109
0.00166
0.00395
Cu
0.00395
0.00434
0.00421
0.00767
0.00410
0.00421
0.00480
Fe
0.00322
0.00303
0.00164
0.00834
0.00531
0.07740
0.00580
Li
0.00197
0.00239
0.00270
0.00315
0.00313
0.00245
0.00547
Mn
0.0004
0.00062
0.00020
0.00147
0.00055
0.01815
0.00101
Ni
0.04102
0.04034
0.04039
0.04881
0.05920
0.05190
0.04897
Pb
0.00249
0.00291
0.00113
0.00455
0.00345
0.00465
0.00296
Sb
0.19701
0.27525
0.17378
0.21379
0.39504
0.18597
0.15842
Se
0.01715
0.01283
0.01719
0.01518
0.01784
0.01875
0.02624
Zn
0.00472
0.00669
0.00573
0.27547
0.00863
0.00707
0.00587
P
0.01918
0.02378
0.01588
0.01608
0.02667
0.06478
0.02878
0.29511
0.33045
0.29093
0.30515
0.50260
0.42851
0.46360
Sr
V
15.5822
17.81290
19.63150
14.97330
23.78260
18.16750
34.9646
concentrations in HCO3, Be, Bi, Cd, Pb and Zn and by
the lowest concentrations for P and Sr of all clusters.
Samples from C4 are characterized by high
concentrations in Ba and by the lowest concentrations
for K, NO3 and B of all clusters. Samples from C5 are
characterized by high concentrations in Al, Cr, Fe, Mn
762 Res. J. Environ. Earth Sci., 4(7): 756-768, 2012
Fig. 4: Dendrogram for 28 variables from cluster analysis in R-mode for dry season groundwater samples
and P of all clusters. Samples from C6 are characterized
by high concentrations in Ca, K, SO4, Cl, B and Sb and
by the lowest concentrations for Be of all clusters.
Samples from C7 are characterized by high
concentrations in Mg, Na, NO3, Co, Li, Ni, Sr and V of
all clusters.
Figure 3 shows the dendrogram of water samples
for wet season where it can be classified into seven
groups, named C1-C7. Observation of dendrogram
shows some indications of the level of similarity
between the seven clusters (Fig. 3). Samples from C1
and C3 have the lower linkage distance between the
defined clusters, therefore, has the greater similarity
between all clusters. C2 and C4 are linked at a low
distance. Also C5 and C6 are linked at a low distance.
To describe the characteristics of each cluster of
sample, Table 3 shows the median values of
geochemistry data, including the 28 chemical elements
for wet season. Samples from C1 are characterized by
lowest concentrations for Mg, Na, Cl, B, Cu, Li and Zn
of all clusters. Samples from C2 are characterized by
high concentrations in HCO3 and SO4 and by the lowest
concentrations for Ni and Se of all clusters. Samples
from C3 are characterized by lowest concentrations for
HCO3, Fe, Mn, Ni, Pb, P and Sr of all clusters. Samples
from C4 are characterized by high concentrations in Bi,
Cu and Zn and by the lowest concentrations for Ca, Al
and V of all clusters. Samples from C5 are
characterized by high concentrations in Ca, Ba, Ni, Sb
and Sr and by the lowest concentrations for K, NO3, Be,
Co and Cr of all clusters. Samples from C6 are
characterized by high concentrations in Al, Fe, Mn, Pb
and P and by the lowest concentrations for SO4, Bi and
Cd of all clusters. Samples from C7 are characterized
by high concentrations in K, Mg, Na, Cl, NO3, B, Be,
Cd, Co, Cr, Li, Se and V and by the lowest
concentration for Ba and Sb of all clusters.
Hierarchical Cluster Analysis (HCA) for elements:
The dendrogram (cluster tree) for the elements for dry
season is shown in Fig. 4. The 28 elements for the dry
season can be classified into two major groups: from Bi
to P and from Mg to Se (Fig. 4). These elements can be
further classified into five groups: (i) Bi, Co and Ni; (ii)
Ca, Sb, Ba, HCO3, Cu, Be, Pb, Zn, Cd, Cr and NO3;
(iii) Fe, Mn, Al and P; (iv) Mg, V, Li and Sr; and (v)
763 Res. J. Environ. Earth Sci., 4(7): 756-768, 2012
Fig. 5: Dendrogram for 28 variables from cluster analysis in R-mode for wet season groundwater samples
Table 4: Principal component loadings from PCA after rotation for the maximum variance for dry season
Parameters
PC1
PC2
PC3
Ca
0.066
0.049
-0.500
K
0.780
-0.015
-0.230
Mg
0.783
0.065
0.097
Na
0.924
-0.045
0.232
HCO3
0.002
-0.054
-0.026
SO4
0.589
-0.135
-0.258
Cl
0.733
0.072
0.116
NO3
0.162
-0.305
-0.077
Al
-0.132
0.966
-0.023
B
0.803
-0.095
-0.022
Ba
-0.774
0.158
-0.174
Be
0.060
0.168
0.465
Bi
0.044
-0.091
0.933
Cd
0.178
-0.269
0.132
Co
0.039
-0.089
0.973
Cr
0.329
0.193
0.012
Cu
0.120
0.042
-0.108
Fe
-0.064
0.975
0.044
Li
0.665
0.070
0.161
Mn
0.013
0.905
0.084
Ni
0.205
0.057
0.906
Pb
-0.107
0.232
0.160
Sb
0.024
-0.042
-0.267
Se
0.545
-0.163
-0.193
Zn
-0.206
-0.096
0.021
P
-0.043
0.915
-0.056
Sr
0.566
0.117
0.044
V
0.763
0.035
0.068
% of Variance
21.987
14.191
12.423
Cumulative %
21.987
36.178
48.601
Bold values: loadings > 0.5
764 PC4
-0.142
-0.387
0.522
0.077
0.654
0.165
0.026
-0.579
-0.040
-0.249
0.061
0.262
-0.240
-0.108
-0.224
-0.722
0.197
0.134
0.621
0.265
0.030
-0.087
-0.035
-0.058
0.157
-0.148
0.710
0.552
12.131
60.732
PC5
-0.180
-0.010
-0.223
-0.030
0.239
0.209
-0.073
-0.030
-0.022
0.323
-0.010
0.276
0.089
0.684
0.055
0.341
0.068
-0.053
-0.204
-0.022
-0.033
0.737
0.021
-0.157
0.681
-0.011
-0.308
-0.221
7.834
68.566
Res. J. Environ. Earth Sci., 4(7): 756-768, 2012
Table 5: Groups of elements based on the principal components loading for dry season, 5 factors incorporate of the 68.566 variance
Eigen value
Major constituents
Trace constituents
Negative
6.156
K, Mg, Na, SO4, Cl
B, Li, Se, Sr, V
Ba
3.973
Al, Fe, Mn, P
3.478
Bi, Co, Ni
Li, Sr, V
Cr, NO3
3.397
Mg, HCO3
2.193
Cd, Pb, Zn
Table 6: Principal component loadings from PCA after rotation for the maximum variance for wet season
Parameters
PC1
PC2
PC3
Ca
0.211
-0.249
0.215
K
0.594
0.03
-0.665
Mg
0.918
-0.039
0.312
Na
0.918
-0.045
-0.213
0.118
-0.144
0.404
HCO3
0.481
0.177
-0.066
SO4
Cl
0.726
-0.052
-0.161
0.047
0.169
-0.588
NO3
Al
-0.153
-0.133
0.092
B
0.752
0.242
-0.464
Ba
-0.788
-0.088
0.237
Be
0.030
0.970
-0.095
Bi
0.088
0.739
-0.286
Cd
0.062
0.977
-0.061
Co
0.079
0.944
0.010
Cr
0.121
0.559
-0.673
Cu
-0.056
0.557
-0.068
Fe
-0.05
-0.052
0.177
Li
0.849
0.202
0.389
Mn
0.164
0.034
0.211
Ni
0.279
0.101
0.720
Pb
-0.025
0.536
0.261
Sb
-0.209
-0.019
0.392
Se
0.839
0.043
0.118
Zn
-0.02
0.052
-0.016
P
0.056
0.215
-0.121
Sr
0.774
-0.205
0.544
V
0.905
-0.048
0.313
% of Variance
25.364
16.526
12.116
Cumulative %
25.364
41.889
54.006
Bold values: loadings > 0.5.
Na, B, K, Cl, SO4 and Se. The lowest linkage distances
in the dendrogram of the 28 chemical parameters are
Bi, Co and Ni. Other chemical elements linked at low
distance include Fe, Mn, Al and P and Mg, V, Li and
Sr.
The dendrogram for the elements for wet season is
shown in (Fig. 5). The 28 elements can be classified
into two major groups: from Be to Ba and from Mg to
SO4 (Fig. 5). These elements can be further classified
into five groups: (i) Be, Cd, Co, Bi, Cr, NO3, Cu, Pb
and Zn; (ii) Al, Fe, Mn and P; (iii) Ca, Ni, HCO3, Sb
and Ba; (iv) Mg, V, Sr, Li, Se; and (v) Na, B, K, Cl and
SO4. The lowest linkage distances in the dendrogram of
the 28 chemical parameters are Be, Cd and Co. Other
chemical elements linked at low distance include Al,
Fe, Mn and P and Mg, V, Sr and Li.
Principle Component Analysis (PCA): Principal
component analysis is performed to reduce a large
number of variables into a smaller number and for
PC4
0.200
0.022
0.021
-0.010
0.111
-0.117
0.094
-0.269
0.769
-0.010
0.132
-0.020
-0.100
-0.035
0.088
-0.074
0.039
0.886
-0.044
0.860
0.017
0.327
0.003
0.067
-0.049
0.828
0.069
0.018
11.121
65.127
PC5
0.748
0.241
0.143
-0.01
0.191
0.243
-0.058
-0.052
0.223
-0.070
0.270
-0.058
0.115
-0.028
-0.073
-0.066
-0.222
-0.004
-0.015
-0.154
0.142
-0.456
0.543
-0.046
-0.729
0.123
0.143
0.166
7.451
72.578
further investigation of the relationships between the
elements. The Principal Component (PCs) with Eigen
values larger than 1 are extracted with the PC loadings
rotated for the maximum variance. A total of five PCs
are extracted for dry season, which account for 68.6 %
of the total variance. Table 4 shows the PC loadings for
dry season as well as their respective explained
variance. Loading, that represent the importance of the
variable for components, are bold for values greater
than 0.5. Based on the PC loadings, the 28 elements can
be grouped into five PCs groups (Table 5): (i) (K, Mg,
Na, SO4, Cl, B, Li, Se, Sr and V) and (Ba); (ii) (Al, Fe
Mn and P); (iii) (Bi, Co and Ni); (iv) (Mg, HCO3) and
(Cr, NO3); and (v) (Cd, Pb and Zn). Factor (1) accounts
for 21.98 % of the total variance and contains a high
positive loading of K, Mg, Na, SO4, Cl, B, Li, Se, Sr
and V and Ba with a negative loading. Factor (2)
accounts for 14.19 % of the total variance and contains
a high positive loading of Al, Fe Mn and P. Factor (3)
accounts for 12.42% of the total variance and contains a
765 Res. J. Environ. Earth Sci., 4(7): 756-768, 2012
Table 7: Groups of elements based on the principal components loading for wet season, 5 factors incorporate of the72.578 variance
Eigen value
Major constituents
Trace constituents
Negative
7.102
K, Mg, Na, Cl
B, Li, Se, Sr, V
Ba
4.627
Bi, Cd, Co, Cr, Cu, Pb
3.393
Sr
K, NO3, Cr
3.114
Al, Fe, P
2.086
Ca
Sb
Zn
high positive loading of Bi, Co and Ni. Factor (4)
accounts for 12.13% of the total variance and contains a
high positive loading of Mg and HCO3 and NO3 and Cr
with a negative loading. Factor (5) accounts for 7.83%
of the total variance and contains a high positive
loading of Cd, Pb and Zn.
Also a total of five PCs are extracted for wet
season, which account for 72.6 % of the total variance.
Table 6 shows the PC loadings for wet season as well
as their respective explained variance. Loading, that
represent the importance of the variable for
components, are bold for values greater than 0.5. Based
on the PC loadings, the 28 elements can be grouped
into five PCs (Table 7): (i) (K, Mg, Na, Cl, Li, Se, Sr
and V) and (Ba); (ii) (Bi, Cd, Co, Cr, Cu and Pb); (iii)
(Sr) and (K, NO3 and Cr); (iv) (Al, Fe and P); and (v)
(Ca and Sb) and (Zn). Factor (1) accounts for 25.364 %
of the total variance and contains a high positive
loading of K, Mg, Na, Cl, Li, Se, Sr and V and Ba with
a negative loading. Factor (2) accounts for 16.526 % of
the total variance and contains a high positive loading
of Bi, Cd, Co, Cr, Cu and Pb. Factor (3) accounts for
12.116% of the total variance and contains a high
positive loading of Sr and a negative loading of K, NO3
and Cr. Factor (4) accounts for 11.121% of the total
variance and contains a high positive loading of Al, Fe
and P. Factor (5) accounts for 7.451% of the total
variance and contains a high positive loading of Ca and
Sb and with a negative loading of Zn.
CONCLUSION
The interpretation of the geochemical evolution of
groundwater in the study area takes into account
previous work that identified the origin of groundwater
(Ta’any et al., 2007; Batayneh et al., 2008) and
recognized the on-going geochemical processes that
influenced groundwater geochemistry in this aquifer
system. To further refine our geochemical
interpretation, we applied well-proven multivariate
statistical methods (HCA and PCA), which are wellsuited here because of highly variable groundwater
geochemistry influenced by a variety of geological and
hydrogeological factors.
The HCA classified the 36 groundwater samples
into seven geochemically distinct clusters (C1–C7).
Calcium and magnesium are the dominant ions in the
groundwater of this basin (clusters C1, C5 and C7),
while bicarbonate is the most abundant of the anions
(clusters C2 and C3). A total of five PCA components
are extracted for dry and wet seasons, which account
for 68.6 % and 72.6 % of the total variance of the
dataset, respectively. Component 1 is characterized by
highly positive loadings in K+, Mg2+, Na+, SO42-, Cl-, B,
Li, Se, Sr and V for both seasons and is related to
groundwater solute diffusion from the clay members
and is defined as the salinity component. Enrichment of
Na+ and Cl- is also possible, related to urban
wastewaters and high rate of evapotranspiration. High
loadings for K+, represents the contribution of
agricultural activities and weathering of K-feldspar
from surrounding geology. Mg2+ with SO42- highly
positive loading suggest the importance of dissolution
of carbonate rocks in this aquifer system and is defined
as the hardness component. Also high SO42- is related to
the long-history of evaporation process and also the
effect of industrial pollution.
Component 2 show high loading of Al, Fe2+, Mn2+
and P for dry season and show high loading of Bi, Cd,
Co, Cr, Cu and Pb for wet season. Highly positive
loadings for both Fe2+ and Mn2+ indicate a similar
geochemical behavior for these parameters. The sources
for these elements could be the dissolution of Fe-oxides
and Mn-oxides and the oxidation of sulfide minerals.
Phosphates primary come from shale and limestone and
also from animal remains. The source for Al could be
the dissolution of organic matter of black shale. Highly
positive loadings of Bi, Cd, Co, Cr, Cu and Pb elements
in the wet season may be related to anthropogenic
activities such as agriculture, industry and urban life in
the study area.
Component 3 show high negative loading of K+,
NO3 and Cr and high positive loading of Sr for wet
season. High loading of NO3- is related to pollution,
where NO3- has no known lithologic source, which is
attributed to the urban wastewaters and agricultural
practices involving chemical (nitrogenous) fertilizer
applications. The high K+ suggests pollution from
application of potash fertilizers to agricultural lands.
ACKNOWLEDGMENT
The authors thank the Chemistry Department of
Yarmouk University, Irbid, Jordan for their help with
766 Res. J. Environ. Earth Sci., 4(7): 756-768, 2012
the chemical analysis, namely Prof. I. Al-Momani. The
author also wishes to thank
Dr. R. Jaradat, Dr. M. Awawdeh and Dr. A.
Rawashdeh for their aid in data acquisition.
REFERENCES
Abderahman, N. and M. Awad, 2002. Hydrochemical
characteristics of the major springs in the Maqarin
dam site on the Yarmouk River (Northern Jordan).
Env. Geo., 9(3): 127-138.
Abu-Jaber, N. and M. Ismail, 2003. Hydrogeochemical
modeling of the shallow ground water in the
Northern Jordan Valley. Environ. Geol., 44(4):
391-399.
Adar, E.M., E. Rosenthal, A.S. Issar and O. Batelaan,
1992. Quantitative assessment of the flow pattern
in the Southern Arava Valley (Israel) by
environmental tracers and a mixing cell model. J.
Hydrol., 136(1-4): 333-352.
Batayneh, A.T., 2010. Heavy metals in water springs of
the Yarmouk Basin, North Jordan and their
potentiality in health risk assessment. Int. J. Phys.
Sci., 5(7): 997-1003.
Batayneh, A., 2012. Toxic (aluminum, beryllium,
boron, chromium and zinc) in ground water: Health
risk assessment. Int. J. Env. Sci. Technol., 9(1):
153-162.
Batayneh, A.T., I.F. Al-Momani, R.A. Jaradat, M.M.
Awawdeh, A.M.M. Rawashdeh and R.A. Ta'any,
2008. Weathering processes effects on the
chemistry of the main springs of the Yarmouk
Basin North Jordan. J. Env. Hydrol., 16(20).
Belkhiri, L., A. Boudoukha and L. Mouni, 2011. A
multivariate statistical analysis of groundwater
chemistry data. Int. J. Env. Res., 5(2): 537-544.
Briz-kishore, B.H. and G. Murali, 1992. Factor analysis
for revealing hydrochemical characteristics of a
watershed. Environ. Geol., 19(1): 3-9.
Chen-Wuing, L., L. Kao-Hung and K. Yi-Ming, 2003.
Application of factor analysis in the assessment of
ground water quality in a black foot disease area in
Taiwan. Sci. Total. Environ., 313(1-3): 77-89.
Cloutier, V., R. Lefebvre, R. Therrien and M.M.
Savard, 2008. Multivariate statistical analysis of
geochemical data as indicative of the
hydrogeochemical evolution of ground water in a
sedimentary rock aquifer system. J. Hydrol., 353(34): 294-313.
Davis, J., 1986. Statistics and Data Analysis in
Geology. 2nd Edn., Wiley, New York, pp: 646,
ISBN: 0471080799.
Farooq, M., M. Abdul Malik, A. Hussain and H.N.
Abbasi, 2010. Multivariate statistical approach for
the assessment of salinity in the periphery of
Karachi, Pakistan. World Appl. Sci. J., 11(4): 379387.
Farnham, I.M., K.H. Johannesson, A.K. Singh, V.F.
Hodge and K.J. Stetzenbach, 2003. Factor
analytical approaches for evaluating ground water
trace element chemistry data. Anal. Chim. Act.,
490(1-2): 123-138.
Farnham, I.M., K.J. Stetzenbach, A.K. Singh and K.H.
Johannesson, 2000. Deciphering groundwater flow
systems in oasis valley, nevada, using trace
element chemistry, multivariate statistics and
geographical information system. Math. Geol.,
32(8): 943-968.
Guler, C., G. Thyne, J. McCray and K. Turner, 2002.
Evaluation of graphical and multivariate statistical
methods for classification of water chemistry data.
Hydrogeol. J., 10(4): 455-474.
Hawi, M., 1990. Hydrogeology and Ground Water
Flow System in the Area between Wadi El Yabis
and Yarmouk River/Jordan Valley area. M.Sc.
Thesis, University of Jordan, Retrieved from:
http://met.jometeo.gov.jo/acc-rain.
Kanade, S. and V.B. Gaikwad, 2011. A multivariate
statistical analysis of bore well chemistry dataNashik and Niphad Taluka of Maharashtra, India.
Univer. J. Env. Res. Technol., 1(2): 193-202.
Ji-Hoon, K., K. Rak-Hyeon, J. Lee, T. Cheong, B. Yum
and H. Chang, 2005. Multivariate statistical
analysis to identify the major factors governing
groundwater quality in the coastal area of Kimje,
South Korea. Hydrol. Process., 19(6): 1261-1276.
Lawrence, F. and S.B. Upchurch, 1982. Identification
of recharge areas using geochemical factor
analysis. Ground Water, 20(6): 680-687.
Love, D., D. Hallbauer, A. Amos and R. Hranova,
2004. Factor analysis as a tool in ground water
quality management: Two Southern African case
studies. Phys. Chem. Earth, 29(15-18): 1135-1143.
Makhlouf, I., H. Abu-Azzam and A. Al-Hiayri, 1996.
Surface
and
Subsurface
Lithostratigraphic
Relationships of the Cretaceous Ajlun Group in
Jordan. Hashemite Kingdom of Jordan, Amman,
pp: 96.
Melloul, A. and M. Collin, 1992. The principal
component statistical method as a complementary
approach to geochemical methods in water quality
factor identification: Application to coastal plain
aquifer of Israel. J. Hydrol., 140(1-4): 49-73.
Meng, S.X. and J.B. Maynard, 2001. Use of statistical
analysis to formulate conceptual models of
geochemical behavior: Water chemical data from
the Botucatu aquifer in Sao Paulo state. Brazil. J.
Hydrol., 250(1-4): 78-97.
767 Res. J. Environ. Earth Sci., 4(7): 756-768, 2012
Moh’d, B., 2000. The Geology of Irbid and Ash Shuna
Ash Shamaliyya (Waqqas): Map Sheets No. 3154II and 3154-III. Hashemite Kingdom of Jordan,
Amman, pp: 67.
Olmez, I., J.W. Beal and J.F. Villaume, 1994. New
approach to understanding multiple-source ground
water contamination: Factor analysis and chemical
mass balances. Water Res., 28(5): 1095-1101.
Piper, A., 1944. A graphic procedure in the
geochemical interpretation of water-analysis.
Trans. Geophy. Union., 25: 914-923.
Razack, M. and J. Dazy, 1990. Hydrogeochemical
characterization of ground water mixing in
sedimentary and metamorphic reservoirs with
combined use of Pipers principle and factor
analysis. J. Hydrol., 114: 371-393.
Reghunath, R., T.R.S. Murthy and B.R. Raghavan,
2002. The utility of multivariate statistical
techniques in hydrogeochemical studies: An
example from Karnataka, India. Water Res.,
36(10): 2437-2442.
Schot, P.P. and J. Van der Wal, 1992. Human impact on
regional ground water composition through
intervention in natural flow patterns and changes in
land use. J. Hydrol., 134: 297-313.
Steinhorst, R. and R. Williams, 1985. Discrimination of
groundwater sources using cluster analysis,
MANOVA, canonical analysis and discriminant
analysis. Water Resour. Res., 21(8): 1149-1156.
Stetzenbach, K., V. Hodge, C. Guo, I. Farnham and K.
Johannesson, 2001. Geochemical and statistical
evidence of deep carbonate groundwater with in
averlying volcanic rock aquifers/aquitards of
southern Neveda, USA. J. Hydrol., 243: 254-271.
Subba Rao, N., J.P. Rao, D.J. Devadas, K.S. Rao and C.
Krishna, 2001. Multivariate analysis for identifying
the governing factors of ground water quality. J.
Env. Hydrol., 9: 1-9.
Subyani, A. and M. Al Ahmadi, 2010. Multivariate
statistical analysis of ground water quality in Wadi
Ranyah, Saudi Arabia. Earth Sci., 21: 29-46.
Suk, H. and L. Kang-Kun, 1999. Characterization of a
ground water hydrochemical system through
multivariate analysis: Clustering into ground water
zones. Ground Water, 37(3): 358-366.
Ta’any, R., A. Batayneh and R. Jaradat, 2007.
Evaluation of ground water quality in the Yarmouk
Basin, North Jordan. J. Env. Hydrol., 15: 28.
Usunoff, E.J. and A. Guzma´n-Guzma, 1989.
Multivariate analysis in hydrochemistry: An
example of the use of factor and correspondence
analyses. Ground Water, 27(1): 27-34.
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