INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES
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1. Introduction
Drinking water quality is strongly influenced by the quality of the corresponding “parent”
natural water from which drinking water derives. Groundwater quality in a region is largely
determined by both the natural processes (lithology, groundwater velocity, quality of
recharge waters, and interaction with other types of water aquifers) and anthropogenic
activities (agriculture, industry, urban development, and increasing exploitation of water
resources) (Helena et al., 2000).
In recent decades, there has been a rapid increase in nonpoint sources of groundwater
pollution i.e. pollution, entering the system over a wide area. Martinez and Albiac (2004)
noted that non-point source pollution from agriculture has increased significantly in several
parts of the state. Agriculture is now considered to be the dominant source of non-point
pollution of groundwater (Jones, 1997). Many different sources and processes can be
responsible for the contaminants polluting the groundwater. The commonly found
contaminants in groundwater due to agriculture are nitrate, chloride, sodium, calcium,
magnesium, ammonia, phosphate and trace elements (George et al., 1987; Burkhart and
Kolpin, 1992; Fetter, 1992; Spalding and Exner, 1993; Beke et al., 1993). Detailed
hydrochemical research is needed to evaluate the different processes and mechanisms
involved in polluting water (Helena et al., 1999).
The conventional method like piper and stiff are used to determine the quality of groundwater
in minor extent. In recent years, the limitations of the use of conventional methods and
increasing number of chemical parameters, a wide range of statistical techniques are now
required for proper analysis of data (Ashley and Lloyd, 1978; Dalton and Upschurch, 1978;
Usunoff and Guzman, 1989; Olmez et al., 1994; Voudouris et al. 2000; Guler et al., 2002;
Reghunath et al, 2002; Hussein, 2004; Mahlknecht et al., 2004). In this view, the Multivariate
Statistical analysis technique is very useful tool for interpret the water quality parameters.
Submitted on August 2012 published on September 2012
285
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The purpose of the present study is to evaluate the similarities and dissimilarities with
specific process that control the quality of groundwater throughout the Principal Component
Analysis (PCA).
2. Study area
The study area is located at Tamil Nadu, India. The study area is bounded on the north by
latitude (Fig. 1). The area forming part of Indian peninsular gneissic complex, with chief rock
types of augen gneiss, quartzite and Hornblende Biotite Gneiss (HBG) and miopliocene
sandstone and clay. The sedimentary formation represented by sandstones and clay (fig 2)
are major in extent. The gneissic rock exhibit secondary porosity developed by fracture and
joints. From the groundwater point of view, the Precambrian rocks and quaternary sediments
are referred to as crystalline rocks and unconsolidated sediments, respectively; the former
have fracture porosity, while the latter have pore porosity. Weathering process is very
common in this area. The weathered zone corresponds to unconfined conditions and the
fractured zone corresponds to semi – confined to confined conditions to support the
groundwater. The study area, experiences relatively less than rainfall (av. 70–100 mm/yr)
than the average annual rainfall of the state. This rainfall occurs primarily in the North East
monsoon (90%) and small amount received from South East Monsoon season.
Figure 1: Study area
3. Methodology
3.1 Sampling
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The 31 representative groundwater samples were collected from dug wells during the pre
monsoon season of 2007. The samples were taken in pre – washed IL polythelene bottles
filling it to the capacity. Sampling, preservation, and transportation of the water samples to
the laboratory were conducted by the standard methods reported by APHA (1998) and
Richards (1954). The samples were analyzed for 17 parameters.
3.2 Statistical procedure
3.2.1 Principal Component Analysis (PCA)
Multivariate techniques can help to simplify and organize large data sets and to make useful
generalizations that can lead to meaningful insight (Laaksoharju et al 1999). Cluster and
factor analyses are efficient ways of displaying complex relationships among many objects
(Davis 1986). In general, the PCA gives large set of principal components to explain the
relationship among the chemical variables. The computer package Statistical Packages like
for Social Sciences (SPSS) and STATISTICA 6 have been used to carry out the analysis. The
data have been standardized by using standard statistical procedures. The interpretation of the
principal components can be simplified, using the certain rotational procedures. In the
present study, the varimax normalised rotation of Kaiser (1958) was applied to obtain a
simple structure with scores. The scores are obtained called factor scores, which show the
intensity of chemical process (factor) described by each principal component (Dalton and
Upchurch, 1978).
Regionally distributed and lithologically controlled variables are extracted first from the
principal component scores and the more local pathway/origin controlled variables are then
identified (Lawrence and Upchurch, 1982). Three sets of plots were made from the PCA
results. (1) Scatter plots between the component loadings of the three selected principal
component scores of Kaiser varimax normalized rotation and (iii) sample – wise areal
distribution of the principal component scores on the map area.
4. Results and discussion
Table 1: Physico – chemical characteristics of ground waters of study area in pre onsoon
season – 2007
S.
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Location
Maaplampatti
Kil Pungudi
Eluvankottai
Devakottai
Aravayal
Kalluppatti
Ogampatti
Sevarakottai
Perichchikovil
Tiruppattur
Kongaratti
Alangudi
Variyangaval
Devakottai Rasta
Iluppaikkudi
EC
1900
1750
1280
2500
2200
1500
4300
1410
650
1220
1530
1470
1970
910
1100
TDS
241.62
3951.97
720.11
519.64
817.59
711.92
1163.51
3034.37
784.00
1448.79
1367.00
308.99
1005.34
638.41
241.62
pH
7.87
7.72
8.47
7.26
8.35
8.70
8.60
7.30
7.42
8.05
8.50
8.69
7.90
8.21
8.90
K.Ratio
0.43
0.78
1.80
2.08
0.58
2.31
1.91
2.19
1.14
0.88
0.97
0.59
1.36
1.90
0.43
SSP
45.24
45.49
66.15
71.43
41.29
72.57
67.62
69.29
54.00
47.39
50.20
50.73
57.89
66.39
45.24
RSC
0
0
0.90
0.30
0
2.40
0
0
2.50
0
0.70
0
0.30
3.40
0
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SAR
0.93
6.88
5.33
4.74
2.48
6.12
7.03
12.87
4.22
4.70
4.88
1.38
5.43
5.36
0.93
Mg
hazards
30.43
56.81
25.00
30.77
42.86
40.00
73.53
86.05
44.93
36.11
33.86
40.74
60.00
30.00
30.43
Na%
45.24
45.49
66.15
71.43
41.29
72.57
67.62
69.29
54.00
47.39
50.20
50.73
57.89
66.39
45.24
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16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Karaikudi
Managiri
Ilangudi
Siruvayal
Sennalkudi
Avadapoigai
Kunnakudi
Kottaiyur
Puduvayal
Karnakkudi
Sirugappatti
Ponnambalam
Pallattur
Chokkanpatti
Muttuppattanam
Kanadukattan
1370
840
2100
830
2200
1800
2400
430
1880
1300
1740
420
670
1200
500
980
1689.29
286.73
241.94
1349.53
1717.09
352.60
2368.47
1038.82
801.59
959.56
520.65
158.61
280.92
983.99
548.09
2511.26
7.40
8.56
8.74
8.70
8.00
6.90
7.30
8.65
8.84
9.16
8.46
9.71
8.10
7.90
9.09
8.63
0.10
0.50
0.45
0.65
0.85
0.26
0.56
1.00
0.43
0.52
0.71
0.53
3.29
0.73
0.34
0.67
10.94
41.18
39.53
39.77
46.88
27.34
36.56
51.05
31.62
36.21
44.79
48.28
78.00
43.09
27.08
40.42
0
0
0
0
0
0
0
0
0
0
0
0.70
0.90
0
0
0
0.65
1.22
1.03
3.62
4.97
0.73
4.30
4.31
1.63
2.43
2.30
0.92
4.88
3.39
1.26
5.03
57.89
30.00
11.54
51.92
28.82
55.26
63.23
35.48
29.17
11.71
41.51
33.33
18.18
58.88
25.71
64.34
10.94
41.18
39.53
39.77
46.88
27.34
36.56
51.05
31.62
36.21
44.79
48.28
78.00
43.09
27.08
40.42
Unit: Concentration in epm except pH: EC (uS Cm-1) RSC and SAR (meq l-1).
Table 2: Physico – chemical characteristics of ground waters of study area in pre monsoon
season – 2007
S. No
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
Location
Maaplampatti
Kil Pungudi
Eluvankottai
Devakottai
Aravayal
Kalluppatti
Ogampatti
Sevarakottai
Perichchikovil
Tiruppattur
Kongaratti
Alangudi
Variyangaval
Devakottai Rasta
Iluppaikkudi
Karaikudi
Managiri
Ilangudi
Siruvayal
Sennalkudi
Avadapoigai
Kunnakudi
Kottaiyur
Puduvayal
Karnakkudi
Sirugappatti
Ponnambalam
Pallattur
Chokkanpatti
Ca
32.1
336.7
66.1
36.1
104.2
42.1
36.1
48.1
76.2
184.4
168.3
32.1
64.1
56.1
32.1
192.4
42.1
46.1
150.3
242.5
34.1
214.4
120.2
102.2
196.4
62.1
20.0
18.0
88.2
Mg
8.5
268.7
13.4
9.7
47.4
17.0
60.8
180.0
37.7
63.2
52.3
13.4
58.4
14.6
8.5
160.5
10.9
3.6
98.5
59.6
25.5
223.7
40.1
25.5
15.8
26.8
6.1
2.4
76.6
Na
23.0
698.3
181.7
124.2
121.9
186.3
298.3
868.0
180.3
289.8
282.9
36.8
249.8
174.3
23.0
50.6
34.5
27.1
232.3
333.5
23.0
377.2
213.7
71.3
131.6
86.0
18.4
83.3
180.6
K
35.2
82.1
27.4
43.0
43.0
45.4
48.1
41.4
10.2
14.5
19.6
46.1
5.5
12.5
35.2
23.5
23.5
20.3
7.8
19.6
16.8
14.5
16.0
9.0
22.7
21.9
23.5
10.9
9.8
HCO3
42.7
250.2
311.2
134.2
494.3
317.3
244.1
951.9
555.3
756.6
805.5
134.2
506.5
433.2
42.7
689.5
109.8
67.1
616.3
903.1
128.1
829.9
256.3
311.2
244.1
109.8
115.9
85.4
390.5
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Co3
12.0
12.0
6.0
21.0
0.0
21.0
12.0
12.0
9.0
12.0
6.0
12.0
0.0
9.0
12.0
9.6
12.0
9.0
9.0
6.0
6.0
15.0
9.0
12.0
9.0
36.0
9.0
18.0
12.0
So4
0.0
240.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Cl
109.9
2191.4
273.0
219.9
258.9
244.7
588.6
1418.4
198.6
514.2
443.3
102.8
379.4
159.6
109.9
914.9
109.9
102.8
549.6
613.5
184.4
1117.0
514.2
429.1
464.5
234.0
24.8
106.4
425.5
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30
31
Muttuppattanam
Kanadukattan
104.2
204.4
21.9
223.7
54.3
437.7
9.4
14.5
79.3
756.6
0.0
12.0
96.1
0.0
223.4
1248.2
Note: All values are in ppm
The concentration of sodium, chloride, calcium and bicarbonate content also show a
significant difference between the medium and maximum values. The mean values are near
the quarter values of the maximum values. It suggests that local contamination inputs to the
groundwater system. The wide range of bicarbonate content, from 42.7 to 951.9 ppm (Table
3) is the result of the lateral geological variations of the layers conferring to the groundwater
different concentration. Box plot of the chemical concentration show that bicarbonate,
calcium, chloride and TDS have the largest dispersions (Figure 2). The enrichment of
chloride and TDS from values of 24.8 to 2191.4 is present in the groundwater located eastern
side of the study area. The increase in the salt concentration could be associated to different
mechanisms like water rock interaction processes.
Table 3: Mean and standard deviation of the groundwater sample of pre monsoon season
Parameters
Ca
Mg
Na
K
HCO3
CO3
SO4
Cl
EC
TDS
pH
K.Ratio
SSP
RSC
SAR
Mg Hazard
Na %
Mean
101.7
60.5
196.6
24.9
376.6
11.0
10.8
466.9
1495.2
1056.9
8.3
1.0
48.2
-3.5
3.7
41.2
48.2
Minimum
18.0
2.4
18.4
5.5
42.7
0.0
0.0
24.8
420.0
158.6
6.9
0.1
10.9
-34.4
0.7
11.5
10.9
Maximum
336.7
268.7
868.0
82.1
951.9
36.0
240.2
2191.4
4300.0
3952.0
9.7
3.3
78.0
3.4
12.9
86.0
78.0
Std.Dev.
79.8
72.9
195.3
16.7
286.8
6.8
45.9
472.4
783.0
891.0
0.7
0.7
15.1
7.3
2.6
17.7
15.1
Unit: Concentration in epm except pH: EC (uS Cm-1) RSC and SAR (meq l-1).
4.1 Principal component analysis
In order to evaluate the most significant parameters in water quality assessment, a sensitivity
analysis was performed using FA/PCA methodology. FA/PCA was applied to the all variable
in order to compare the compositional patterns between the analyzed water samples and to
identify the factors that influence each one. PCA was performed on the physical–chemical
data from the study area using STATISTIC 6 code (Statsoft Inc, 2001). Three components
with eigenvalues over 1 were obtained (Table 4 and Fig. 3). The eigenvalues determine the
number of principal components that are necessary. This number depends on the amount of
variation in the data.
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Box & Whisker Plot
3000
2500
2000
1500
1000
500
0
-500
Mean
±SD
±1.96*SD
-1000
Ca
Mg
Na
K
HCO3 CO3
SO4
Cl
TDS
Figure 2: Box and Whisker plot for chemical parameters of groundwater sample of pre
monsoon season
Factor Loadings, PC 1 vs. PC 2 vs. PC 3
Rotation: Varimax Normalized
Extraction: Principal components
K
SO 4
SAR
Na
SSP
%
K.Ratio
CO 3 EC
pH
Cl
Na TDS
Mg
Ca
Mg Hazard
HCO 3
RSC
Figure 3: PCA Loading from the study area
We assume that in this study one principal component is enough to represent the data in a
sufficiently accurate way. According to the eigenvalue-one criterion, three PCs in the study
area were selected, which explain 71.32% of the total variance in the water quality data sets
(Table 4). This analysis indicates that the most relevant variables defining water quality are
related to water dissolved salts (EC, Ca, Na, K, Cl, SO4, HCO3).
PC 1 accounts for 36% of the total variance and appears to characterize the water–rock
interaction processes. The aquifer matrix dissolution induces high loadings in Ca, Mg, Na, Cl
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and HCO3. This represents the groundwater evolution and the importance of water–rock
interaction processes. It emphasizes that groundwater, with a higher residence time, has
higher values in bicarbonate, calcium and magnesium, related with the lithology of the
aquifer (reference). PC 2 represents 24% of the total variance and shows high loadings for
SSP, K Ratio, SAR and Na %. PC 3 is dominated by high loadings in K and SO 4. This fact
might be the result of agricultural contamination by the use of pesticides.
Table 4: Results of PCA in the study area
Parameters
Ca
Mg
Na
K
HCO3
CO3
SO4
Cl
EC
TDS
pH
K.Ratio
SSP
RSC
SAR
Mg Hazard
Na %
Eigenvalue
% Total Variance
Cumulative
PC 1
0.659
0.889
0.896
0.120
0.830
-0.117
0.284
0.873
0.129
0.938
-0.497
0.096
-0.008
-0.052
0.701
0.718
-0.008
6.17
36.28
36.28
PC 2
-0.492
-0.257
0.219
0.111
0.024
0.290
-0.276
-0.189
0.099
-0.124
-0.134
0.936
0.963
0.602
0.648
0.046
0.963
4.15
24.39
60.66
PC 3
0.132
0.228
0.263
0.881
-0.431
0.331
0.691
0.403
0.386
0.289
0.037
0.087
0.171
-0.258
0.157
0.083
0.171
1.81
10.65
71.31
4.2 Pricipal component loadings
Scatter plot of the pricipal component loading between PC 1 and PC 2, PC 1 and PC 3, and
PC 2 and PC 3 are illustrated in Fig. 4 to 6. This figure can be read for the variation from
positive to negative loading of each principal component and also the scatter between the
principal components of the different pairs.
4.2.1 Principal Compontent – 1
The TDS, Na, Ca, Mg, Cl, HCO3, SAR and Mg hazards plotting close to one other in the
positive region of PC – 1 in fig 4 & 6.PC 1 is taken as salinity factor. Salinity is directly
controlled by the concentration of Na and Cl ions, which is derived from the interaction of
water and rock, and gets enriched in the groudnwater due to their higher solubility
(Reference). Accoring to the classification of WHO (1991) water quality standard 12
groudnwater samples are of brackish type (TDS >1000 ppm) and remaining samples shows
the fresh water environment. The samples located in the areas of unconsolidated sediemnts
are shows the fresh water and others are in the areas of crystalline rocks. This difference
clearly suggests the ligthological control over the TDS concentration and other major ion
concentrations.
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Factor Loadings, PC 1 vs. PC 2
Rotation: Varimax Normalized
Extraction: Principal components
1.2
Na
SSP
% K.Ratio
1.0
0.8
PC 2
SAR
RSC
0.6
0.4
CO 3
Na
0.2
K
EC
Mg HazardHCO 3
0.0
TDS
Cl
Mg
pH
-0.2
SO 4
-0.4
Ca
-0.6
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
PC 1
Figure 4: Scatter plot of principal component loading for (a) PC – 1 vs PC – 2
Factor Loadings, PC 1 vs. PC 3
Rotation: Varimax Normalized
Extraction: Principal components
1.0
K
0.8
SO 4
0.6
PC 3
0.4
Cl
EC
CO 3
NaTDS
Mg
Na
SSP
%
0.2
CaSAR
Mg Hazard
K.Ratio
pH
0.0
-0.2
RSC
HCO 3
-0.4
-0.6
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
PC 1
Figure 5: Scatter plot of principal component loading for (a) PC – 1 vs PC - 3
4.2.2 Principal Component – 2
Principal component – 2 has higher positive loading of SSP, Na %,K ratio, SAR and RSC
(Table 4) and their close plotting indicates that their relationship comes under the single
process. This indicate the indirect effect of the agriculture activities. Residential and
agricultural activities appear to play an important role in the study area. Since there is no
lithological impact on the pollution activity, PC – 2 is considered as a non – lithologically
controlled factor.
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Factor Loadings, PC 2 vs. PC 3
Rotation: Varimax normalized
Extraction: Principal components
1.0
K
0.8
SO 4
0.6
Cl
0.4
EC
PC 3
TDS
0.2
CO 3
Na
Mg
Na
SSP
%
K.Ratio
SAR
Ca
pH
Mg Hazard
0.0
-0.2
RSC
HCO 3
-0.4
-0.6
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
PC 2
Figure 6: Scatter plot of principal component loading for (a) PC – 2 vs PC - 3
4.2.3 Principal Component - 3
PC – 3 has a higher positive loading of K and SO4, which is apparent as isolated one in fig.
This suggests that the K and SO4 act as independent variables, which represents the pollution
effect on the groundwater quality of this region. In figs. 4 and 5 K and SO4 falls on high
positive region of PC – 3. The concentration of K exceeds 10 ppm in the 96% of the total
groundwater samples in the study area. The higher concentration of K has lithological sources
and represents dissolution felspathic group minerals like albite and anorhthite.
4.2.4 Principal Component Scores
To identify the influence of relation of processes (factors) on the specific field setting of the
samples, principal component scores are plotted with respect to the samples in plots of PC – 1
vs. PC – 2, PC – 1 vs. PC – 3 and PC – 2 vs. PC – 3 (Table 5 and Figs. 7 to 9).
Table 5: Results of principal component scores
S.No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
PC – 1
-1.060
1.949
-0.340
-0.649
-0.164
-0.414
0.108
2.571
0.275
0.637
0.551
-1.036
0.503
-0.101
-1.208
0.835
PC – 2
-0.325
-1.022
1.041
1.403
-0.519
1.892
1.198
1.704
0.779
-0.116
0.106
-0.120
0.561
1.608
-0.402
-1.887
PC - 3
0.537
3.981
-0.088
1.135
0.088
0.591
1.557
0.132
-1.419
-0.720
-0.787
0.745
-0.925
-1.113
0.478
-0.733
S.No
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
PC – 1
-0.990
-1.211
0.534
0.899
-0.429
1.708
-0.090
-0.501
-0.529
-0.877
-1.232
-0.800
0.224
-0.794
1.630
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Volume 3 Issue 1, 2012
PC – 2
-0.483
-0.594
-0.553
-0.233
-0.931
-0.681
-0.013
-0.890
-0.873
-0.046
-0.330
1.997
-0.255
-1.404
-0.612
PC - 3
-0.030
0.194
-0.914
-0.627
-0.502
-0.439
-0.322
-0.318
0.043
0.790
-0.051
-0.100
-0.603
-0.049
-0.530
293
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An analysis of the above table shows that only 11 samples (7, 9, 10, 11, 13, 16, 19, 20, 22, 29
and 31) have positive principal component scores in PC – 1, PC – 2, PC – 3 (Table 6). These
11 samples are possibly influenced by the factors of lithological influences, but not uniformly,
because the positive scores vary among them.
Figure 7: A score plot of Principal Component for PC – 1 vs. PC - 2
Figure 8: A score plot of Principal Component for PC – 1 vs. PC - 3
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Figure 9: A score plot of Principal Component for PC – 2 vs. PC – 3
Table 6: Distribution of groundwater samples in positive principal component scores
Positive Principal Component Scores
PC – 1
PC – 2
13 samples)
(10 samples)
2, 7, 8, 9, 10, 11, 13, 16,
3, 4, 6, 7, 8, 9,
19, 20, 22, 29, 31
11, 13, 14, 28
PC – 1 vs. PC – 2
PC – 1 vs. PC – 3
(12 Samples)
(10 samples)
2, 7, 9, 10, 11, 13,16, 19, 20, 22, 1, 7, 10, 11, 16, 19, 20, 22,
29, 31
29, 31
PC – 1 vs. PC – 2, PC – 1 vs. PC – 3, PC – 2 vs. PC – 3
(11 Samples)
7, 9, 10, 11, 13, 16, 19, 20, 22, 29, 31
PC – 3
(12 Samples)
1, 2, 4, 5, 6, 7, 8, 12,
15, 18, 25, 26
PC – 2 vs. PC – 3
(10 samples)
3, 4, 6, 7, 8, 9, 11, 13, 14, 28
5. Conclusion
The PCA of 13 chemical variables in the groundwater samples taken as a complementary
solution to hydrogeochemcial study led to the identification of three principal components.
The results obtained by a scientific evaluation groundwater quality data can be addition by
using PCA which leads to the interpretation that the water–rock interaction process is the
major mechanism responsible for the groundwater salinity. These results are described as
lithologically and non – lithologically controlled factor.
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