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A Multivariate Statistical Analysis of Bore Well Chemistry Data - Nashik and

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Pearson correlation matrix, Hierarchical cluster and principal component analysis (PCA) were simultaneously applied to groundwater hydro chemical data of 31 bore well locations in post monsoon 2007 from Nashik and Nipahd taluka. Using the Kaiser criterion, principle component (PC) was extracted from the data and rotated using varimax normalization, for 31 locations. The combined use of both technique resulted in more reliable interpretation of the hydrochemistry. From the analysis, concentration of total dissolved solids (TDS), electrical conductivity (EC), total hardness (TH), calcium (Ca), magnesium (Mg), sodium ( Na), chloride (Cl), bicarbonate (HCO3) at most of the sampling stations in agricultural area having higher values. Computational analysis of data set of hydro chemical constituents in the groundwater suggests that the aquifer is mainly controlled by Cl, Na, EC and TDS; there is strong positive relationship between TDS - EC, TDS- Cl, TDS - TH and TDS - Na. It indicates that, there is strong evidence of anthropogenic activities on major ions present in the groundwater and weathering of sodium, potassium minerals in the study area. The high Na and Cl contents detected in certain samples may suggest the dissolution of chloride salts. The dissolution of halite in water release equal concentrations of Na and Cl into the solution
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Content Preview
Universal Journal of Environmental Research and Technology
All Rights Reserved Euresian Publications (c) 2011 eISSN 2249 0256
Available Online at: www.environmentaljournal.org
Volume 1, Issue 2: 193-202



Open Access





Research Article

A Multivariate Statistical Analysis of Bore Well Chemistry Data - Nashik and
Niphad Taluka of Maharashtra, India

Kanade S. B. and Gaikwad V. B.

1Post Graduate Department of Environmental Science, KTHM College, Shivajinagar,
Gangapur Road, Nashik-422 002. Maharashtra, India.
Corresponding author: shaileshsk47@rediffmail.com, vbgaikwad@kthmcollege.com

Abstract:
Pearson correlation matrix, Hierarchical cluster and principal component analysis (PCA) were simultaneously
applied to groundwater hydro chemical data of 31 bore well locations in post monsoon 2007 from Nashik
and Nipahd taluka. Using the Kaiser criterion, principle component (PC) was extracted from the data and
rotated using varimax normalization, for 31 locations. The combined use of both technique resulted in more
reliable interpretation of the hydrochemistry. From the analysis, concentration of total dissolved solids
(TDS), electrical conductivity (EC), total hardness (TH), calcium (Ca), magnesium (Mg), sodium ( Na), chloride
(Cl), bicarbonate (HCO3) at most of the sampling stations in agricultural area having higher values.
Computational analysis of data set of hydro chemical constituents in the groundwater suggests that the
aquifer is mainly controlled by Cl, Na, EC and TDS; there is strong positive relationship between TDS - EC,
TDS- Cl, TDS - TH and TDS - Na. It indicates that, there is strong evidence of anthropogenic activities on major
ions present in the groundwater and weathering of sodium, potassium minerals in the study area. The high
Na and Cl contents detected in certain samples may suggest the dissolution of chloride salts. The dissolution
of halite in water release equal concentrations of Na and Cl into the solution.

Keywords: Dendrogram, Groundwater, Hydro chemical Data, Hierarchical Cluster, Multivariate Analysis,
Principal Component analysis.

1. Introduction
urbanization and agricultural expansion pose high
The early study of the characterization of
pressure on groundwater resources (Yadina et al.,
groundwater
facies
utilized
graphical
2008), therefore it is necessary to monitor and
representations of the major compositions of
evaluate the groundwater quality. The effect of
groundwater.
These
classical
classification
discharge of tannery effluents in the Palar river
techniques such as Stiff and Piper diagrams only
basin was studied using factor analysis and
consider selected major water constituents in
geostatistics (Sajil Kumar et al., 2011).

determining the groundwater type (Hem, 1989).
In present study, at certain places various
Multivariate statistical techniques were used to
parameters exceed the Indian standards of
interpret complex data matrices to better
potable water. Groundwater quality of 31 water
understand the water quality and ecological status
samples of bore well has been statistically
of the studied system and suggested that, the
researched by using multivariate technique. In this
extent of salinity appears to be as a function of
principal component analysis (PCA), cluster
magnesium salts rather them calcium salt (Farooq
analysis and parameters correlation analysis are
et al., 2010). The ground water quality depends
used to categorize the spatial variation. These
not only on natural factors such as the lithology of
methods are also giving a better understanding of
the aquifer, the quality of recharge water and the
the physical and chemical properties of the
type of interaction between water and aquifer, but
groundwater system (Subba rao et al., 2001;
also on human activities, which can alter these
Subyani and Masoud, 2009; Chenini and Khemiri,
groundwater systems either by polluting them or
2009 and Belkhiri et al., 2010). Groundwater is the
by changing the hydrological cycle (Abdulmuhsin
main source of potable water of Nashik and
and Abdul Baqi, 2010). These multivariate
Niphad taluka. The main source for groundwater
statistical analysis methods were used with a
contamination is from industrial, domestic and
remarkable success in evaluation of trace
agricultural waste. The industrial waste is directly
elements in groundwater (Kouping et al., 2006).
added in the Nasardi River, which is flowing in
The groundwater quality is depleting rapidly with
industrial area. Domestic waste from effluent
change
in
human
life
style
i.e.massive
treatment plant (ETP) at Tapovan, added in
industrialization,
urbanization
and
more
Godavari River, which flowing through irrigated
agricultural activities. The rapid industrialization,
area.
193
Kanade and Gaikwad

Universal Journal of Environmental Research and Technology
2. Study Area

The study area lies between at latitude 190 33' and
trap. As regards the soil of Niphad taluka is filled
250 53' North and longitude 730 16' and 750 6' East
with disintegrated basalts of various shades from
in Northern Maharashtra and covers total area of
gray to black. This is favorable for the grapes,
476 sq.km. Agriculture is main occupation of the
onions, vegetables, flowers and sugarcane. The
people of the area. The drainage on the whole
black soil contains high alumina and carbonates of
area is fine to medium the amount of
calcium and magnesium with variable amount of
precipitation,
permeability,
topography
and
potash, low nitrogen and phosphorus (Santech
structure in the area. The structure and lithology
system). In the developing country like India, the
have played major role in the evolution of the
drinking water supply is carried out through
topography and drainage pattern in the area.
surface as well as groundwater. Niphad gets water
Average rainfall is approximately 650mm, most of
supply mainly from Groundwater that is dug wells
which is during period June-September (Santech
and bore wells. These water sources are used for
system). The whole taluka is covered by Deccan
drinking,
irrigation
and
industrial
purpose.

Location of study area is shown in figure a





Figure a: Location map of Nashik and Niphad Taluka of Maharashtra, India
3. Methodology

The study area was divided in several grids and
physical
and
chemical
parameters
were
representative groundwater samples were taken
determined (APHA, 2005). The measurement of
from each grid. The grids were of 10 x 10 km2 and
temperature, conductivity, pH and total dissolved
samples were collected from each grid. While
solids were taken in the field, immediately after
selecting each sampling station, drainage pattern
the collection of samples using portable water
and type of activity present in the specific area
quality analyzer (Elico PE-136). Chloride, hardness,
were considered. All underground water samples
calcium, total alkalinity were analyzed by
are collected from bore wells in different parts of
titrimetry. Sodium and potassium were estimated
the study area during post-monsoon 2007 season
by flame photometer (Elico). Nitrate, fluoride, and
and analyzed for their chemistry. The containers
iron
were
done
by
the
UV
2201
used for sample collection are polythene
spectrophotometer (Systronics). Turbidity and
containers of capacity two liters. These containers
sulphate
were
determined
by
were thoroughly cleaned, washed and rinsed
nepheloturbiditimeter. Standard procedures used
before collection. During the present study
in groundwater analysis is given in Table 1 and

location of sampling stations were given in Table 2
194
Kanade and Gaikwad

Universal Journal of Environmental Research and Technology
Table 1: Unit, Method of Analysis of Each Parameter

Sr.
Parameter
Unit
Method
No
1
Temperature
0C
Thermometer
2
pH
-
Potentiometric
3
EC
S/cm
Potentiometric
4
TDS
mg/l
Potentiometric
Flame Emission
5
Na
mg/l
Photometric
Flame Emission
6
K
mg/l
Photometric
7
TH
mg/l
EDTA Titrimetric
8
Ca
mg/l
EDTA Titrimetric
Calculation from TH
9
Mg
mg/l
and Ca
10
Total alkalinity
mg/l
Titrimetric
Argentometric
11
Cl
mg/l
Titration
12
NO3
mg/l
Spectrophotometric
13
F
mg/l
Spectrophotometric
14
Fe
mg/l
Spectrophotometric
15
SO4
mg/l
Nephelometric
16
Turbidity
NTU
Nephelometric


Table 2: Location of Sampling Stations

Stn.
Stn.
Location
Land Use
Location
Land Use
No
No
1
Satpurgaon
Industrial
17
Eklahare
Urban
2
Satpur
Industrial
18
Eklahare
Urban
3
Satpur
Industrial
19
Eklahare
Urban
4
Ambad
Industrial
20
Lakhalgaon
Urban
5
Ambad
Industrial
21
Chehadi.Khurd
Agricultural
6
Ambad
Industrial
22
Chitegaon phata
Agricultural
7
Ambadgaon
Industrial
23
Shimpi takli
Agricultural
8
Untawadi
Industrial
24
Chandori
Agricultural
9
Kailasnagar
Urban
25
Chandori
Agricultural
Kasbesukene
10
Dasak
Urban
26
Agricultural
Phata
11
Madsangavi
Urban
27
Kherwadi
Agricultural
12
Madsangavi
Urban
28
Kherwadigaon
Agricultural
13
Madsangavi
Urban
29
Ozarshivar
Agricultural
14
Shilapur
Urban
30
Ozarshivar
Agricultural
15
Azadnagar
Urban
31
Oane
Agricultural
16
Odha
Urban
Total = 31 sampling sites



4. Multivariate Analysis

variables to a new set of variables, which are
Multivariate statistical approaches allow driving
uncorrelated and arrange in decreasing order of
hidden information from the data set about their
importance to simplify the problem. Principal
possible influences of the environment on water
components
analysis
was
performed
on
quality. Multivariate analysis was performed on
correlation matrix of the raw data in which a water
matrix of hydro-geochemical data. The statistical
sample is described by sixteen physical and
analysis was performed using SYSTAT 13 software
chemical parameters. In recent times, multivariate
package. PCA aims to transform the observed
statistical methods have been applied widely to
195
Kanade and Gaikwad

Universal Journal of Environmental Research and Technology
investigate
environmental
phenomenon
local government and industries to plan the use
(Gajendran and Thamrai, 2008) the combined use
and protection of groundwater resources (Liu et
of principle component analysis (PCA) and cluster
al., 2008). Not only groundwater but also analyses
analysis enabled the classification of groundwater
of the quality of surface water were done by
samples into distinct groups on the basis of their
multivariate techniques (Singh et al., 2008).
hydro
chemical
characteristics.
Multivariate

statistical tools have been used to study and
5. Results and Discussion
classify different sediment types (Huisman, and
The descriptive statistics of the analyzed
Kiden, 1998) , and hydro geochemical processes
groundwater quality parameters are depicted in
(Cameron., 1996) , used cluster analysis and PCA to
Table 3. Values of electrical conductivity (EC), total
identify the temporal and spatial variations of
dissolved solids (TDS), sodium (Na), chloride (Cl),
water chemistry in New York. Multivariate
total hardness (TH), calcium (Ca), magnesium
statistical techniques, cluster and principal
(Mg), Total alkalinity (T.alk) are increases as
component analysis were applied to the data on
Industrial < Urban < Agriculture. It is interesting to
groundwater quality of Ain Azel plain (Algeria), to
note that higher values of most of the parameters
extract principal factors corresponding to the
are found in agricultural area, while lower values
different
sources
of
variation
in
the
of parameters are observed in industrial area. This
hydrochemistry, with the objective of defining the
shows clear impact of irrigation activities in study
main controls on the hydrochemistry at the plain
area on groundwater.
scale
(Belkhiri
et
al.,
2010).
A
grouped

characteristic of the groundwater quality helps the

Table 3: Basic Statistics of Groundwater in Post Monsoon 2007
Sr.
No. Sampling
Standard
Standard
Parameters
Minimum
Maximum
Mean
Variance
No.
Sites
Error
Deviation
1
Temp.
31
18.7
34.4
25.6
0.5
2.9
8.3
2
pH
31
7.14
8.4
7.6
0.0
0.3
0.1
3
EC
31
551.0
3,059.0
1,386.4
115.7
644.2
415,018.4
4
TDS
31
358.2
1,988.4
901.1
75.2
418.7
175,345.3
5
Na
31
15.6
310.0
122.2
16.3
90.8
8,243.5
6
K
31
0.1
40.8
3.5
1.8
9.9
98.3
7
TH
31
189.9
896.4
411.5
27.8
155.1
24,049.7
8
Ca
31
22.4
286.8
90.4
9.9
54.9
3,014.3
9
Mg
31
14.7
109.4
44.4
4.5
25.2
634.5
10
Total alkalinity
31
154.0
642.4
348.0
21.5
119.6
14,304.9
11
Cl
31
22.0
586.9
185.0
25.6
142.5
20,316.2
12
NO3
31
5.2
183.1
40.6
6.8
38.0
1,446.7
13
F
31
0.1
0.8
0.3
0.0
0.2
0.0
14
Fe
31
0.0
0.5
0.2
0.0
0.1
0.0
15
SO4
31
16.8
152.5
72.2
6.3
34.9
1,221.2
16
Turbidity
31
0.1
9.0
1.1
0.3
1.8
3.1





5.1 Pearson Correlation
1.0), moderate (r = 0.6 to 0.8) and low (r = 0.5 to
Correlation coefficient is commonly used to
0.6) correlation between selected variables was
measure and establish the relationship between
found out. To know this objective r- values
two variables. It is a simplified statistical tool to
correlation probability value were calculated. To
show the degree of dependency of one variable to
nullify the effect of missing data, pair wise missing
the other (Belkhiri et al., 2010). From Sixteen
data deletion technique has been adopted. The
variables have been considered in analysis and a
generated simple matrix plots are presented from
curve fit procedure for linear mode has been
figure b to d. The calculated correlation table and
adopted to find out the possible correlation
correlation r- value are complied in Table 4.
between selected parameters. The scattering data

in the block is an indication of inadequate

relationship between selected variables in the

study area. From matrix plot, strong (r = 0.8 to

196
Kanade and Gaikwad

Universal Journal of Environmental Research and Technology



Figure d: Correlation between TDS and TH

in Bore Well sources
Figure b: Correlation between TDS and Na

in Bore Well source
The correlation analysis between EC and TDS, TDS

and Na, TDS and Cl shows strong positive

relationship as r = 1.0, 0.94, 0.92 respectively and

moderate correlation between TDS and chloride r

= 0.7 and very low positive correlation (r = 0.2 to

0.4) with Ca, Mg, Talk, suggesting that aquifer

chemistry is mainly controlled by TDS, EC, Na and

Chloride (Figure b to d, Table 4). Good, medium
and low correlation between chemical parameters
(Table 4) indicates that electrical conductivity and
total dissolved solids is the most appropriate
variable in explaining more than 60% variation in
hardness, sodium, magnesium, sulphate, chloride
and bicarbonate (Pattanaik., 2007).

These strong associations could primarily be put
into two distinct groups: firstly, the elements
contributing to the conductivity of water as of the
strong dependency between EC, TDS, Mg, Cl, Ca,
and Na. Secondly, the strong associations between
NO3, HCO3 and Fe indicated the parameters
contributing to the typical characteristics of the
non-rechargeable and stagnant groundwater
(Khatiwada et al., 2002). The major exchangeable

ions, EC and Na (0.9), Na and Cl (1.0), TDS and Na
Figure c: Correlation between TDS and Cl
(0.9), Ca and Total Hardness (0.8), were found to
in Bore Well source
be correlated positively indicating the origin of

major cations to be dissolution/precipitation
processes(Khatiwada et al., 2002). There are two
outliers, which may be related to different soil
parent materials or geology of the sampling sites.
Cl and Na possess a very good positive correlation
(0.91) between each other. The high Na and Cl
contents detected in certain samples may suggest
the dissolution of chloride salts. The dissolution of
halite in water release equal concentrations of Na
and Cl into the solution (Belkhiri et al., 2010).

197
Kanade and Gaikwad

Universal Journal of Environmental Research and Technology

Table 4: Pearson Correlation Matrix


pH
EC
TDS
Na
K
TH
Ca
Mg
T Alk
Cl
pH
1









EC
-0.2
1.0








TDS
-0.2
1.0
1.0







Na
-0.1
0.9
0.9
1.0






K
-0.1 -0.1 -0.1 -0.2
1.0





TH
-0.5
0.7
0.7
0.5
-0.3 1.0




Ca
-0.3
0.5
0.5
0.4
-0.1 0.8
1.0



Mg
-0.3
0.3
0.3
0.2
-0.2 0.5 -0.2
1.0


Total alkalinity -0.6
0.5
0.5
0.3
-0.2 0.8
0.4
0.6
1.0

Cl
0.0
0.9
0.9
1.0
-0.2 0.6
0.5
0.1
0.3
1.0
NO3
-0.2
0.4
0.4
0.4
-0.1 0.4
0.2
0.3
0.2
0.3
F
0.3
0.6
0.6
0.7
0.0
0.2
0.3
-0.2
0.0
0.7
Fe
0.1
-0.2 -0.2 -0.2 -0.1 0.0
0.0
-0.1
-0.2
-0.1
SO4
-0.3
0.6
0.6
0.6
-0.1 0.5
0.5
0.1
0.3
0.5
Turbidity
-0.2
0.4
0.4
0.3
-0.1 0.1
0.0
0.2
0.2
0.2




5.2 Factor Analysis

PC 1 has high loadings (> 0.50) for EC, TDS, Na, TH,
Kaiser criterion (Kaiser, 1960) applied to determine
Ca, Mg and accounts for 85.6 % of the variance in
the total number of factors for each dataset in this
the hydrochemistry in the area. EC, TDS, Na, TH,
analysis. Under this criterion, only factor with
Ca and Mg showing positive loadings under PC 1
eigenvalues greater than or equal to one will be
and derived from industrial, domestic and
accepted as possible sources of variance in the
agricultural waste in the study area. PC 2, which
data, with the highest priority ascribed to factor
accounts for 11.1 % of the total variance, contains
that has the highest eigenvector sum. The
high loadings for EC, TDS, Na and K, and represents
rationale for choosing 1 is that a factor must have
the contribution of agricultural activities and
a variance at least as large as that of a single
weathering of K - feldspar from underlying
standardized original variable to be acceptable.
geology, a process is accompanied by rise in pH.


Five principle components (PC) were extracted and
PC 3 has high loadings of TH, Na, EC and TDS
rotating using the varimax normalization (Kaiser.
representing 2.2 % of the total variance in the
1960). An initial run using the Kaiser criterion
hydrochemistry. PC 3 represents weathering of
resulted in eight principle components. However it
dolomite from underlying sedimentary material.
was observed that the sixth, seventh and eighth
PC 4, which accounts for 0.8 % of the total
factor would not constitute a unique source of
variance in the hydrochemistry, shows positive
variance in the hydrochemistry since it had only
loadings for EC, TDS, and TH represents the
three loading greater than 0.50. It was therefore
contribution of domestic wastes. PC 5, which
dropped and five factors were chosen for varimax
accounts for 0.3 % of the total variance in the
rotation. The results (Table 5, figure e) shows that
hydrochemistry, shows positive loadings for Na, K,
the five PC account for more than 86% of the total
and TH, representing the weathering of minerals
variance, which is quite good and can be relied
of Na and K in the study area (Liu, 2008). Rotated
upon to identify the main sources of variation in
loading matrix and factor loading plot of
the hydrochemistry.
parameter is shown in Table 5 and figure e

respectively.




198
Kanade and Gaikwad

Universal Journal of Environmental Research and Technology
Table 5: Rotated Loading Matrix (VARIMAX, Gamma = 1.000000)

1
2
3
4
5
PH
-0.008
-0.155
-0.093
0.000
0.054
EC
606.325*
189.813*
85.774*
57.478*
26.434*
TDS
394.111*
123.379*
55.753*
37.361*
17.182*
Na
89.228*
6.724*
2.104*
-13.347
7.352*
K
-0.744
-0.274
-0.927
0.004
-9.841
TH
67.618*
127.795*
46.451*
1.760*
31.386*
Ca
19.403*
46.213*
-21.335
0.490
6.835*
Mg
4.478*
2.593*
24.415
0.120
3.410*

The values with * indicate absolute component loadings higher than 0.5, which are considered significant
contributors to the variance in the hydrochemistry


`Variance' Explained by Rotated Components
1
2
3
4
5
535,883.945
69,770.628
13,679.963
4,881.091
2,188.366


Percent of Total Variance Explained
1
2
3
4
5
85.549
11.138
2.184
0.779
0.349





Figure e: Factor Loading Plot of Correlation between Parameters in Bore Well Sources

5.3 Cluster analysis

observations. The levels of similarity at which
Cluster analysis comprises a series of multivariate
observations are merged are used to construct a
methods that are used to find true groups of data.
dendrogram. Some measure of similarity must be
In clustering, the objects are grouped such that
computed between every pair of objects. In this
similar objects fall into the same class (Danielsson
study, a standardized m-space Euclidian distance
et al., 1999). Hierarchical cluster analysis is the
(Davis,1986). Cluster analysis groups variables into
most widely applied techniques in the earth
clusters
on
the
basis
of
similarities
(or
sciences and is used in this study. Hierarchical
dissimilarities) such that each cluster represents a
clustering joins the most similar observations, and
specific process in a system (Yadina et al., 2008).
then
successively
the
next
most
similar
For cluster analysis single linkage method was
199
Kanade and Gaikwad

Universal Journal of Environmental Research and Technology
used. In this method the distance between the
Table 6: Euclidean Distance using Single Linkage
clusters was determined by the distance of the
Method between chemical parameters
two closest objects (nearest neighbor) in the

different cluster (Systat Software Incorporated), as
Euclidean Members
Clusters Joining
shown in Table 6
Distance
in Group

Fe
F
0.334
2
In this study, the hierarchical cluster analysis (HCA)
Turbidity
Fe
1.869
3
was applied to the raw data for 31 different
pH
Turbidity
6.692
4
locations, using SYSTAT 13 (Systat Software
pH
K
10.245
5
Incorporated). HCA is a powerful tool for analyzing
pH
Temp.
18.241
6
water quality data. A classification scheme using
Mg
pH
31.587
7
the Euclidean distance for similarity measures
Mg
NO3
38.240
8
(Guler et al., 2002).from dendrogram of 16 indexes
Mg
SO4
48.508
9
based on the cluster analysis is depicted in figure f.
Ca
Mg
51.605
10
On the basis of dendrogram of 16 indexes can be
Cl
Na
85.773
2
grouped into three main clusters. First cluster
Cl
Ca
89.219
12
group shows close association between TDS and
Total alkalinity TH
108.288
2
EC. This group associated with group second to a
Total alkalinity
lesser degree having Total alkalinity and Total
Cl
223.760
14
hardness indexes. Group third shows close
TDS
EC
533.519
2
association between chloride and sodium. This
TDS
Total alkalinity
586.861
16
finding corroborates the result of correlation and

cluster analysis. The enrichment of Na and Cl ions
The first component is associated with a
in groundwater is due to the interaction of water
combination of various hydro chemical processes
with rocks and secondly association of TDS with
that contribute to enrich more mineralized water
higher concentration of Na and Cl ions. This
(high value of TDS), as suggested by Hem (Hem,
indicates
anthropogenic
activities
such
as
1989), which supports the contamination in
discharge of sewage and agricultural runoff, which
groundwater from human and animal waste.
support the contamination of groundwater. Yidana
Similar observations have also been reported by
(2008) observed similar results for surface water.
Yidana et al., 2008. The concentration of total

alkalinity in the groundwater is nothing but the

result of reaction of soil CO2 that originates from

H2CO3. The negative loading of pH suggests that
decrease of pCO2 and H2CO3 values during the out
gassing of CO2 decreases there is sharp rise in pH
value which shifts the water towards alkaline side
and concentration of H2CO3 increases. Minerals of
bedrock are subjected to weathering and
subsequently
affected
by
leaching,
which
contribute
dissolved
salts
to
groundwater,
resulting in an increase in TDS and EC.

6. Conclusions
In general the groundwater of the Nashik and
Nipahd taluka is alkaline in nature and hard, but at
some places the groundwater is relatively soft and
falls within the safety limits as prescribed by Indian
Standards. Multivariate statistical techniques are
efficient ways to display complex relationships
among many objects. The concentration of TDS,

EC, TH, Ca, Mg, Na, Cl, HCO3 at most of the

sampling stations in agricultural area having higher
Figure f: The Dendrogram Showing the Clustering
values. Computational analysis of data set of hydro
of Chemical Parameters
chemical constituents in the groundwater suggests

that the aquifer (TDS) is mainly controlled by Cl,

Na, EC and TDS; there is strong positive

relationship between TDS - EC, TDS- Cl, TDS - TH
200
Kanade and Gaikwad

Universal Journal of Environmental Research and Technology
and TDS - Na. The Dendrogram of 16 chemical
groundwater quality parameters in Nambiyar
parameters are plotted and grouped into three
River basin, Tamilnadu, India. Poll Res., 27 (4):
main clusters. It is interesting to note that higher
679 - 683.
values of most of the parameters are found in
10. Gupta, L.P. and Subramanian, V. (1998):
agricultural area, while lower values of parameters
Geochemical factors controlling the chemical
are observed in industrial area. This shows clear
nature of water and sediments in the Gomte
impact of irrigation activities in study area on
River, India. Environmental Geology, 36: 102-
groundwater. Cl and Na possess a very good
108.
positive correlation (0.91) between each other.
11. Guler, C., Thyne, G.D., McCray, J.E. and
The high Na and Cl contents detected in certain
Turner, A.K. (2002): Evaluation of graphical
samples may suggest the dissolution of chloride
and multivariate statistical methods for
salts.
classification of water chemistry data. Hydro.

Hydrogeology Journal, 10:455-474.
7. Acknowledgement
12. Hem,
J.
D.
(1989):
The
Study
and
Authors are thankful to M.V.P. Samaj, Nashik and
Interpretation of the Chemical Characteristics
G.S.D.A, Government of Maharashtra for kind
of Natural Water. 3rd edn. USGS Water Supply
permission to carry out the present investigation.
Paper 2254, US Geological Survey.
Authors also thankful to Dr. Smt. S.S. Ghumare, Dr.
13. Hem, J.D. (1989): Study and interpretation of
Mahesh Sindhikar for inspiration and valuable
the chemical characteristics of natural waters.
suggestions during research work.
US Geological Survey Water Supply Paper,

2254.
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202
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