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© IWA Publishing 2019 Water Supply
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in press
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2019
Groundwater contamination characterization using
multivariate statistical analysis and geostatistical method
Akshay Kumar Chaudhry, Kamal Kumar and Mohd. Afaq Alam
ABSTRACT
The aim of the present study is to identify sources of groundwater contamination in Rupnagar
district, Punjab, using an integrated approach of exploratory factor analysis (EFA) and ordinary kriging
(OK). For this, a 13 physico-chemical parameter data set at 14 sampling locations for a period of over
25 years was assessed. The correlation was statistically examined amongst parameters. A five-factor
model is proposed which explains over 89.11% of total groundwater quality variation. Three semivariogram models, namely exponential, Gaussian, and spherical, fitted well for the data set and are
Akshay Kumar Chaudhry (corresponding author)
Kamal Kumar
Mohd. Afaq Alam
Department of Civil Engineering,
Punjab Engineering College (deemed to be
University),
Chandigarh,
India
E-mail: akki016@gmail.com
cross-validated using predictive statistics. Spatial variability maps of all the parameters and factor
scores are generated and are in good agreement with each other. The variation seen in groundwater
quality is mainly due to various hydrogeochemical, anthropogenic, and geogenic processes occurring
in the region. Thus, this study indicated that there is need to treat the industrial and municipal
wastewater before discharging it (directly/indirectly) into nearby streams and pits and to encourage
sustainable agricultural practices to prevent adverse health effects and minimize further
environmental degradation in the study region.
Key words
| correlation, exploratory factor analysis, ordinary kriging, physico-chemical parameters,
semi-variogram
INTRODUCTION
In a semi-arid country like India, groundwater is limited
huge amount of spatial data, which helps in assessing the
by quality rather than quantity. It has become an essential
water quality, its potability and planning sustainable man-
commodity and is the most threatened resource nowadays
agement of groundwater resources. The primary and most
due to its overexploitation by rapidly growing urbanization
important tool for handling such a type of data is the geo-
and industrialization. Groundwater once polluted stays in
graphical information system (GIS) and multivariate
an unusable condition for quite a long time or even hun-
statistical analysis.
dreds of years. So the issues related to groundwater
Multivariate statistical analysis is an unbiased data
contamination are a huge problem that has caught the atten-
reduction technique which involves the handling and
tion of social activists and researchers all around the world.
interpretation of hydrochemical parameters by pointing out
The study of physico-chemical parameters indicates the
significant interrelationship amongst the parameters used
diversity of the groundwater and orientation of the likely
(Wenning & Erickson ). It also acts as a valuable tool
hydrochemical processes that take place throughout the
for the evaluation of spatio-temporal variations and interpret-
aquifer (Sánchez-Martos et al. ). Thus, timely assess-
ation of complex water-quality data sets, apportionment of
ment of physico-chemical parameters involves handling a
contaminant sources (natural or anthropogenic), and the
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A. K. Chaudhry et al.
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Characterization of groundwater contamination using EFA and OK
Water Supply
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2019
design of a monitoring network for the effective management
Goovaerts ; Kitanidis ; Webster & Oliver ;
of water resources as well as for finding practical solutions to
Chang ).
contamination problems (Machiwal & Jha ). Several
Many past studies have analysed groundwater chemistry
studies have been conducted over recent years using different
to identify the cause of groundwater contamination
multivariate statistical techniques, including factor analysis
by applying the multivariate statistical technique and
(FA) and principal component analysis (PCA). All the studies
geostatistical modeling technique in segregation. To date,
showed that the FA and PCA methods are important
few studies are reported where the multivariate statistical
tools to determine underlying relationships between water
technique is integrated with the GIS-based geostatistical
quality parameters and identify sources of groundwater
modeling technique (Sánchez-Martos et al. ; Kolsi et al.
contamination. They appear to be different varieties of the
; Machiwal & Jha ). Singh et al. () illustrated the
same analysis rather than two different methods. However,
usefulness of the multivariate statistical technique integrated
there is a fundamental difference between them that has an
with GIS-based deterministic modeling technique for the
enormous effect on how to use them. PCA is a data reduction
interpretation and assessment of water-quality variations.
technique that explains variance in the data while reducing
Most of these previous studies mainly focused on the
the number of parameters to a few uncorrelated components.
interpretation and assessment of water-quality variations at
In contrast, the aim of FA is to help identify underlying
a specific sampling location using short-term data sets. But
factors that are accountable for the correlation amongst
in the present study, the groundwater quality of the study
the parameters used. Thus, both methods enable the
area was evaluated using long-term data sets (1990–2015) of
identification of groups of parameters or individuals (Wu &
physico-chemical parameters, which will effectively raise
Kuo ). Detailed explanations of these techniques are
the efficiency and reliability of the results obtained. This
enumerated in the literature (Saager & Esselaar ;
will be a valuable reference for managing groundwater con-
Ashley & Lloyd ; Zhang et al. ; Thuong et al. ;
tamination by revealing the primary factors that affect water
Kumarasamy et al. ), henceforth to avoid the unnecessary
quality and understanding the geochemistry of the aquifer.
length of this paper they are not discussed here.
GIS-based geostatistical techniques help in creating surfaces incorporating the statistical properties of the measured
MATERIALS AND METHODOLOGY
data. Many methods are associated with geostatistics, but
they are all in the kriging family. Ordinary, simple, universal,
Study area and data procurement
probability, indicator, and disjunctive kriging are some
of the geostatistical techniques available (ESRI ).
Rupnagar
0
district,
Punjab
(76 160 26″E–76 430 21″E,
0
These kriging methods produce not only prediction surfaces
30 44 21″N–31 25 53″N) is a part of the Satluj River
but also error surfaces, thus indicating how good the predic-
Basin and is located in the eastern part of the Punjab
tions are. It is also considered as an important tool for
State. It covers an area of 1,414 km2 (Figure 1). Agriculture
autocorrelation between sampling locations (Clark ;
is an important source for the economy in the state covering
Trangmar et al. ). It also helps in analysing the
almost 55% of the area (Central Groundwater Board
spatio-temporal variation of the physico-chemical par-
(CGWB) ). The river Satluj is the chief source of water
ameters.
these
in the area. It is the longest river in the Punjab region. The
parameters is determined by variographic analysis (i.e. cal-
climate here is semi-arid, with warm summers and cold win-
culating experimental and theoretical semi-variograms)
ters. The district gets its rainfall through the south-west
and by mapping these parameters using a geostatistical
monsoon which contributes about 78% of the total rainfall.
method (i.e. ordinary kriging (OK) in this study) (Sánchez-
The general direction of groundwater in the northern part of
Martos et al. ). Detailed explanations of different geosta-
the district is towards the south and south-easterly direction
For
this,
the
spatial
variability
of
tistical methods are enumerated in standard textbooks
whereas in the south-eastern part of the district the flow is in
on geostatistics (i.e. Clark ; Isaaks & Srivastava ;
the south and south-westerly direction (CGWB ).
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A. K. Chaudhry et al.
Figure 1
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Characterization of groundwater contamination using EFA and OK
Water Supply
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2019
Location map of study area showing sampling locations.
CGWB is a national agency working under the Minis-
() for the various parameters that are analysed are
try of Water Resources, Government of India. It monitors
listed in Table 1. From the descriptive statistics result it
and analyses data related to physico-chemical parameters
was apparent that the concentrations of NO3, TH, Mg2þ
of groundwater resources in the country in their chemical
and F well exceeded the acceptable limits of 45 mg/l,
laboratory using standard methods for the examination of
200 mg/l, 30 mg/l and 1 mg/l respectively. Geographic
water and wastewater as given in American Public Health
coordinates of each sampling location were linked to the
Association (APHA) () and Bureau of Indian Stan-
quality data of various parameters using ArcGIS 10.4
dards (BIS) IS:  (). Thirteen parameters were
Software.
selected that have continuity in their data set for a
period of over 25 years (1990–2015) for the 14 sampling
Exploratory factor analysis (EFA)
locations that the study area covers. These parameters
are pH, Electrical Conductivity (EC), Chloride (Cl),
Time and again a researcher is unclear if parameters have
2
Nitrate (NO
3 ), Sulphate (SO4 ), Total Hardness (TH),
a noticeable pattern amongst them or not. In order to deter-
Potassium (Kþ), Sodium (Naþ), Calcium (Ca2þ), Silica
mine this, factor analysis can be done in an exploratory way
(SiO2), Magnesium (Mg2þ), Bicarbonate (HCO
3 ), and Flu-
to determine patterns amongst the parameters used. It is a
oride (F). All concentrations (except pH) are in mg/l,
statistical method used to transform the correlation amongst
and EC is in µS/cm at 25 C. The sampling locations
the observed parameter data set to a much smaller number
from which the data have been taken include various
of parameters called factors that account for better common
dug wells and borewells in the study region. The descrip-
variance. These factors contain all the important infor-
tive statistics and acceptable limits as per BIS IS: 
mation
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regarding
the
interrelationship
amongst
the
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Table 1
A. K. Chaudhry et al.
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Characterization of groundwater contamination using EFA and OK
Descriptive statistical analysis of physico-chemical parameters used in the study
Physicochemical
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2019
another and of the error terms, such that M(ri) ¼ 0 and
Var(ri) ¼ 1.
Std.
BIS acceptable
limits (as per
parameters
Min.
Max.
Mean
dev.
IS: 10500)
raw data to make the data dimensionless and remove
pH
7.61
7.88
7.74
0.08
6.5–8.5
the influence of different units of measurement. Thus, the
EC
439.38
1,159.46
710.29
197.99
–
z-scale transformation was performed to standardize the
Cl
EFA follows the following procedure: (a) Normalize the
20.25
123.15
48.10
28.46
250
raw data. (b) Parameters are checked for sampling adequacy
NO
3
2.98
78.76
27.32
23.29
45
by performing these tests, namely, the Kaiser–Meyer–Olkin
SO2
4
9.77
153.38
51.72
36.15
200
(KMO) test and Bartlett’s test of sphericity (BTS). So in
TH
183.92
343.92
251.65
41.46
200
the data set used, the KMO test value (should be 0.5)
Kþ
1.91
39.98
11.53
10.51
–
came out to be 0.627, indicating the sample is adequate
Naþ
11.78
205.92
63.47
44.73
–
and the data is suited for EFA. As seen from BTS, the chi-
2þ
33.81
67.15
53.50
8.21
75
square value (χ 2) of the correlation matrix came out to be
SiO2
22.00
28.75
25.71
2.07
–
170.814. This value is greater than critical χ 2 ¼ 99.617
Mg2þ
13.34
46.85
28.34
10.62
30
(P ¼ 0.05 and 78 DOF), indicating correlation amongst the
HCO
3
193.50
376.23
298.25
60.34
–
parameters and allowing us to explain the variability in
F
0.12
4.90
0.77
1.24
1
the data set with a lesser number of parameters (or simply
Ca
factors). (c) Obtain Pearson’s correlation matrix between n
Units in mg/l except for pH and EC (µS).
variables. It tells about the inter-parameter relationship
between each parameter by giving the initial factor loadings
original data set. EFA assumes that the common variance or
which fit the observed ones as closely as possible (Table 2).
communality in the observed parameter is due to the pres-
(d) Find out the number of factors to be extracted by calcu-
ence of one or more latent factors (known as factors)
lating eigenvalues and eigenvectors of the correlation
which may have influence on these observed parameters.
matrix. For this the Kaiser criterion is followed, which
Thus, the contribution of latent factors to each different vari-
retains only those factors whose eigenvalues exceed 1
able helps in classifying each individual parameter. The
(Kaiser ) and discards those factors having eigenvalues
most commonly used EFA model equation is:
lower than 1 (Kim & Mueller ; Basilevsky ), thus
decreasing the dimensionality of the original data space by
A ¼xy þ r
2
X11 X12
6X
6 21 X22
¼6
4 Xn1 Xn2
32
3
2
means of EFA. (e) If the factor loadings which are extracted
3
Y1
R1
X1m
6Y 7 6R 7
X2m 7
76 2 7 6 2 7
76
7þ6
7, m n
54 5 4 5
Ym
Rm
Xnm
in the third step do not provide any reasonable explanation
(1)
of the results, go for rotation. For this, varimax rotation as
proposed by Kaiser () is used in the present study. In varimax rotation, only those factors are detected which are
related to some variables, as opposed to quartimax rotation,
where A ¼ (A1, A2, …, An) is the observation with n vari-
which detects factors influencing all the variables. Hence-
ables, and y ¼ (Y1, Y2, …, Ym) is the factor matrix for the
forth, post rotation only those parameters are considered
m number of factors, x ¼ (X11, X12, …, Xnm) is the factor
and retained for analysis whose factor loadings are strong
weight matrix, and r ¼ (R1, R2, …, Rm) is the residual
so that sufficient variance from the variable is extracted by
errors (Kim & Seo ). The equation is derived based on
factor (Table 3). Liu et al. () categorized factor loadings
two assumptions. First, the error terms do not depend on
as ‘strong’, ‘moderate’, and ‘weak’ analogous to loading
each other, such that M(ri) ¼ 0 and Var(ri) ¼ μi, where M
values of ‘ > 0.75’, ‘0.75–0.50’, and ‘0.50–0.40’ respectively.
denotes the variable mean value for all the observed data
The STATISTICA 10.0 Software package was used to
set, Var and μi are the variance and specificity respectively.
carry out all the aforementioned processes using the princi-
Second, the common factors Yi are independent of one
pal component method of factor analysis (or simply PCM).
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Corrected Proof
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A. K. Chaudhry et al.
Table 2
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Characterization of groundwater contamination using EFA and OK
Water Supply
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Correlation matrix of the physico-chemical parameters used in the study
pH
pH
EC
Cl
SO24
NO3
TH
Kþ
Naþ
Ca2þ
SiO2
Mg2þ
HCO3
F
0.96
EC
0.31
0.99
Cl
0.25
0.91**
0.99
NO
3
0.10
0.01
0.02
0.97
SO2
4
0.35
0.88**
0.83**
0.04
0.99
TH
0.29
0.57*
0.49
0.24
0.36
0.99
Kþ
0.10
0.42
0.05
0.12
0.32
0.17
0.98
þ
Na
0.30
0.88**
0.87**
0.21
0.85**
0.17
0.22
0.99
Ca2þ
0.09
0.32
0.38
0.57*
0.44
0.45
0.05
0.67**
0.99
SiO2
0.47
0.10
0.12
0.06
0.09
0.41
0.18
0.01
0.18
0.91
Mg2þ
0.35
0.83**
0.76**
0.02
0.78**
0.77**
0.19
0.63**
0.15
0.26
0.99
HCO
3
0.11
0.55*
0.29
0.29
0.24
0.36
0.53*
0.45
0.14
0.18
0.40
0.97
F
0.06
0.20
0.33
0.16
0.07
0.45
0.20
0.05
0.10
0.25
0.28
0.10
0.87
**Correlation is significant at 0.01 level (2-tailed).
*Correlation is significant at 0.05 level (2-tailed).
Table 3
|
utilized for mapping spatial variations using spatial corre-
Varimax rotated factor loadings
lations between the sampled points (Lee et al. ). As
Parameters
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5
compared with other geostatistical methods, the OK interp-
pH
0.295 0.131 0.146
0.036 0.823
olation method is moderately fast and accurate and is better
EC
0.906
0.005
0.361
Cl
0.930
0.056 0.260
NO
3
0.038
0.899
SO2
4
0.960
0.009 0.063 0.106
TH
0.381
0.456
0.656
Kþ
0.184
0.198
0.255 0.849
0.336 0.097 0.148
þ
Na
0.898
Ca2þ
0.468 0.773
0.154
0.093
than deterministic methods (i.e. inverse distance weighted,
0.020 0.067
spline interpolation method, etc.) in measuring prediction
0.156 0.106 0.036
error with variance (Chang ; Environmental Science
0.286
0.014
Research Institute (ESRI) ). Oliver & Webster ()
0.273
and Stein () stated kriging to be a multistep process,
0.110
which includes exploratory statistical analysis of data,
0.094
semi-variogram modeling, and spatial variability map cre-
0.294
0.115
0.137
ation. Predictions at each sampling location in the study
SiO2
0.053 0.002
0.323
0.009 0.850
area are done based on the semi-variogram and on the
Mg2þ
0.789
0.123
0.358
0.200
0.221
arrangement of spatially measured values that are close to
HCO
3
0.218
0.289 0.208
0.839
0.151
each other. The only limitation lies in the case of outliers
F
0.119
0.096 0.861
0.120 0.041
Eigenvalues
4.586
1.895
1.751
1.749
1.602
and nonstationary data (Weise ).
Cross-validation based on predictive statistics was car-
% Explained variance 35.280 14.579 13.456 13.470 12.325
ried out to determine the best-fit semi-variogram models
% Cumulative
variancea
(Stein ; Gorai & Kumar ; ESRI ; Chaudhry
35.280 49.860 63.315 76.785 89.110
a
the percentage of variance accounted for by the first n components.
Geostatistical technique used, cross-validation, and
goodness of fit
et al. ). For this, a comparison between the average standard error (ASE), mean error (ME), mean square error
(MSE), root mean square error (RMSE), and root mean
square standardized error (RMSSE) values was done
(Table 4). As per ESRI (), best-fit models are those
OK is one of the geostatistical methods used for spatial
which attain RMSSE close to unity and result in the mini-
interpolation and includes autocorrelation. It can also be
mum values of ASE, ME, MSE, and RMSE when
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Corrected Proof
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A. K. Chaudhry et al.
Table 4
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Characterization of groundwater contamination using EFA and OK
Water Supply
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2019
Best-fit models used for physico-chemical parameters and factor scores
Parameters/Factor scores
Model used
ME
MSE
RMSSE
RMSE
ASE
pH
Spherical
0.003
0.024
0.998
0.085
0.086
EC
Exponential
1.592
0.008
0.991
209.634
209.929
Cl
Exponential
0.480
0.090
0.998
29.591
29.891
NO
3
Spherical
1.398
0.030
0.995
21.318
22.793
SO2
4
Gaussian
0.856
0.055
0.990
35.860
38.089
TH
Gaussian
1.690
0.037
0.998
45.244
45.293
Kþ
Exponential
0.378
0.201
0.984
12.297
14.900
Naþ
Exponential
0.561
0.081
0.998
57.628
58.531
Ca2þ
Spherical
0.228
0.023
0.996
9.206
9.409
SiO2
Exponential
0.077
0.034
0.992
2.158
2.196
Mg2þ
Spherical
0.158
0.015
0.990
11.422
11.612
HCO
3
Spherical
1.102
0.027
0.999
66.524
67.263
F
Spherical
0.091
0.137
0.995
1.348
1.351
Factor score 1
Gaussian
0.026
0.020
0.982
1.002
1.030
Factor score 2
Exponential
0.011
0.005
0.983
0.972
0.984
Factor score 3
Exponential
0.010
0.011
0.995
1.077
1.098
Factor score 4
Gaussian
0.022
0.018
0.999
1.073
1.088
Factor score 5
Exponential
0.026
0.024
0.991
1.039
1.049
Table 5
|
Variographic analysis of physico-chemical parameters and factor scores
methods are equal, then the MSE value is considered for
Parameters/Factor
scores
Model used
Nugget, No
Sill, So
Range, r
pH
Spherical
0.005
0.008
33,498
EC
Exponential 29,201.000 42,201.000 15,558
Cl
Exponential 0.135
0.343
16,317
NO
3
Spherical
0.157
0.697
17,173
SO2
4
Gaussian
0.292
0.511
42,031
TH
Gaussian
1,777.600
2,196.600
62,185
Kþ
Exponential 0.259
0.850
16,558
Naþ
Exponential 0.316
0.486
15,558
Spherical
98.990
50,985
Ca
2þ
68.990
SiO2
Exponential 2.016
4.625
14,958
Mg2þ
Spherical
106.110
154.937
58,985
HCO
3
Spherical
3,554.800
4,004.800
0.366
F
compared with other models. If the RMSE values of two
determining the best-fit method (Hooshmand et al. ).
Johnston et al. () stated that, on examining the cross-validation results if the ASE value is close to the RMSE value,
then the prediction standard errors are appropriate and
one can adopt that model. Thus, three semi-variogram
models are used in this study for physico-chemical parameters and factor scores (Table 5):
Spherical Model: γ(h) ¼ No þ Po
!
3h 1 h 3
2r 2 r
h
Exponential Model: γ(h) ¼ No þ Po 1 Exp r
(2)
(3)
Spherical
1.551
1.651
0.376
Factor score 1
Gaussian
0.912
1.151
5.282
Factor score 2
Exponential 0.709
0.904
2.296
Factor score 3
Exponential 0.987
1.137
3.533
Factor score 4
Gaussian
0.999
1.397
5.282
where No is the nugget, Po is the partial sill, h is the lag
Factor score 5
Exponential 0.910
1.040
3.788
distance (m), and r is the range (m).
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Gaussian Model: γ(h) ¼ No þ Po
!
h 2
1 Exp r
(4)
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A. K. Chaudhry et al.
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Characterization of groundwater contamination using EFA and OK
RESULTS AND DISCUSSION
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breakdown of animal and human waste (Guo & Wang
). Langmuir () stated that bicarbonate enters the
Five factors which explain 89.11% of total groundwater
groundwater system as a result of uptake of carbon dioxide
quality variations are recognized. Parameters having strong
(CO2) both from soil zone gases and direct atmospheric
factor loadings are considered and retained for analysis.
input. It may also be attributed to carbonate dissolution in
Factor 1 explains 35.28% of the total variance and
the region. Factor 5 has a total variance of 12.33% with
shows strong positive loadings for
SO2
4 ,
2þ
EC, Mg , Cl ,
the strong negative loading of pH and positive loading of
and Naþ. Thus, factor 1 is the result of various hydrogeo-
SiO2. Freeze & Cherry () and Hounslow () stated
chemical processes occurring in the study area such as
that the rise in pH concentration in water is mostly due to
mineralization of the geological component of soils, lack
the interaction between atmospheric and biogenic CO2
of geological control, dust deposition, and solubilization in
which enter the surface water through infiltration and con-
the aquifer medium (Guo & Wang ). High loadings of
tribute to the alkalinity of the water and also react with
cations such as Naþ and Mg2þ in the groundwater can
alumina-silicates including kaolin and other clay minerals,
also be attributed to the ion-exchange process and dissol-
resulting in release of Caþ and Mg2þ cations, whereas the
ution of minerals (Davis & DeWiest ). Thus, factor 1
strong loading of SiO2 is due to dissolution of minerals
can be termed the hydrogeochemical factor. Factor 2
(Freeze & Cherry ). Thus, factors 4 and 5 are mainly
explains 14.57% of the total variance and shows strong
the result of various natural processes occurring in
þ
positive loadings for NO
3 and Ca . High loading of NO3
the region and can be termed the geogenic contamination
is attributed to long-term use of fertilizers, agricultural
factor.
runoff, animal waste, crop residues, and industrial waste discharge containing nitrogen (Fernandes et al. ), whereas
Geostatistical modeling assessment of factor scores
high loading of Caþ is attributed to andesine conversion to
kaolinite (Singh et al. ). Thus, factor 2 is mainly the
On the basis of cross-validation results (Table 4), Gaussian
result of various anthropogenic processes occurring in the
and exponential models are used to assess the spatial distri-
region and can be termed the anthropogenic contamination
bution of factor scores due to the slightly better performance
factor. Factor 3 has a total variance of 13.45% with a strong
of these models in most cases.
loading of F. It can be inferred that fluoride in water is due
The results interpreted from spatial variability maps of
to the weathering of rocks. Since there is no natural source
factor scores are in good agreement with the results of
of fluoride in the study area it may also be due to wastewater
spatial variability maps of the physico-chemical par-
pollution from industrial and domestic sources, due to
ameters (Figure 2(a)–2(r)). On associating both the
agricultural sources like long-term usage of manure and
maps, it can be clearly seen that areas with high factor
extreme irrigation practices in the study area and improper
scores match fairly to areas with poor quality groundwater
implementation of water fluoridation in the sewage treat-
(i.e. high values) and low factor score areas match well
ment plant. The vast distance between the groundwater
with good quality groundwater areas (i.e. low values).
residence time and recharge area alongside Caþ inadequate
These results indicate and confirm that multivariate stat-
type groundwater is also a major cause of fluoride contami-
istical analyses can be used with GIS to successfully
nation in groundwater (Singh et al. ). Thus, factor 3 can
identify groundwater contamination sources in the study
be termed the fluoride contamination factor. Factor 4
region.
explains 13.47% of the total variance and shows strong positive loadings for Kþ and HCO
3 . From this factor, it can be
interpreted that there may be a strong impact of limestone
CONCLUSION
with gypsum in the rocks of the recharge area. This suggests
that Kþ originates as a result of natural rock–water inter-
This study highlights an integrated approach involving EFA
action. It may also be attributed to fertilizer use and the
and OK. The results interpreted from spatial variability
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Corrected Proof
8
A. K. Chaudhry et al.
Figure 2
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Characterization of groundwater contamination using EFA and OK
(a)–(r) Spatial variability maps of physico-chemical parameters and factor scores. (Continued).
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Corrected Proof
9
A. K. Chaudhry et al.
Figure 2
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Characterization of groundwater contamination using EFA and OK
Continued.
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Corrected Proof
10
Figure 2
A. K. Chaudhry et al.
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Characterization of groundwater contamination using EFA and OK
Continued.
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Corrected Proof
11
Figure 2
A. K. Chaudhry et al.
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Characterization of groundwater contamination using EFA and OK
Continued.
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Corrected Proof
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Figure 2
A. K. Chaudhry et al.
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Characterization of groundwater contamination using EFA and OK
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Continued.
maps of factor scores were in good agreement with the
programs along with regular hydrochemical analysis
results of physico-chemical parameters. The EFA technique
should be adopted in the region.
helped in identifying the factors causing the degradation of
groundwater quality. Results indicated the occurrence of
groundwater contamination by hydrogeochemical, anthropogenic, and geogenic processes. Based on the results,
ACKNOWLEDGEMENTS
factors 1, 2, 3, and 4 & 5 are termed the hydrogeochemical
factor, anthropogenic contamination factor, fluoride con-
We would like to express our thanks to CGWB, Chandigarh,
tamination factor and geogenic contamination factor
Ministry of Water Resources, Government of India, for their
respectively. Therefore, the water quality of the study area
cooperation and for providing the physico-chemical data
is mainly of mixed physical and chemical characteristics
needed for this research. Comments/suggestions provided
and is controlled mainly by non-agricultural sources like
by an anonymous reviewers helped in improving the
domestic and industrial effluent discharge, septic tanks,
manuscript. Authors are thankful to them also.
and agricultural sources like fertilizers, crop residues,
animal waste, etc. This calls for an urgent need to develop
appropriate strategies to reduce the effects of groundwater
contamination. Steps should be taken to stop practicing
anthropogenic activities in the water fields as well as in
the city to check further degradation of groundwater in
the region. Continuous groundwater quality-monitoring
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