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Evaluation of groundwater quality in and around Peenya
industrial area of Bangalore, South India
using GIS techniques
Anitha Pius &Charmaine Jerome &
Nagaraja Sharma
Received: 22 February 2010 / Accepted: 15 July 2011 / Published online: 11 August 2011
#Springer Science+Business Media B.V. 2011
Abstract Groundwater resource forms a significant
component of the urban water supply. Declining
groundwater levels in Bangalore Urban District is
generally due to continuous overexploitation during
the last two decades or more. There is a tremendous
increase in demand in the city for good quality
groundwater resource. The present study monitors
the groundwater quality using geographic information
system (GIS) techniques for a part of Bangalore
metropolis. Thematic maps for the study area are
prepared by visual interpretation of SOI toposheets on
1:50,000 scale using MapInfo software. Physico-
chemical analysis data of the groundwater samples
collected at predetermined locations form the attribute
database for the study, based on which spatial
distribution maps of major water quality parameters
are prepared using MapInfo GIS software. Water
quality index was then calculated by considering the
following water quality parameters—pH, total dis-
solved solids, total hardness, calcium hardness,
magnesium hardness, alkalinity, chloride, nitrate and
sulphate to find the suitability of water for drinking
purpose. The water quality index for these samples
ranged from 49 to 502. The high value of water
quality index reveals that most of the study area is
highly contaminated due to excessive concentration of
one or more water quality parameters and that the
groundwater needs pretreatment before consumption.
Keywords Groundwater quality .Spatial distribution .
Geographic information system (GIS)
Introduction
Water is a unique natural resource since its total
quantity available on a global basis remains constant
compared to any other renewable resource. In India,
more than 90% of rural and nearly 30% of urban
population depend on groundwater for their drinking
and domestic requirements (Jaiswal et al. 2003).
Groundwater is an invisible and endangered open or
common access resource. Overexploitation of ground-
water beyond the sustainable limit in several parts of
the country has resulted in undesired and progressive
depletion of its level in selected pockets of 370 out of
603 districts in the country (MOWR 2005). Rapid
decline in groundwater levels could reduce India’s
harvest by 25% or more (Singh and Singh 2002). It is
a well-known fact that pure water is absolutely
Environ Monit Assess (2012) 184:4067–4077
DOI 10.1007/s10661-011-2244-y
C. Jerome
Department of Chemistry, Mount Carmel College,
Bangalore, Karnataka, India
A. Pius (*)
Department of Chemistry, Gandhigram Rural Institute,
Gandhigram 624302, Tamil Nadu, India
e-mail: dranithapius@gmail.com
N. Sharma
Hydrology, JSYS,
Sadashivnagar,
Bangalore, India
essential for healthy living. Adequate supply of pure
water is a basic need for all humans, yet it has been
observed that millions of people worldwide are
deprived of this. The consequence of urbanization
and industrialization has caused the deterioration of
water quality. Contamination of drinking water may
occur by percolation of toxics through the soil into
groundwater that is used as a source of drinking water.
Groundwater quality is being threatened by disposal
of urban and industrial wastes and agricultural
chemicals. The rate of depletion of groundwater
levels and deterioration of groundwater quality are
of immediate concern in major cities and towns of the
country. Remote sensing and geographic information
system (GIS) are effective tools for water quality
mapping and land cover mapping essential for
monitoring, modeling and environmental change
detection (Skidmore et al. 1997). GIS can be a
powerful tool for developing solutions for problems
related to water resources, for assessing water quality
and managing water resources on a local or regional
scale(Tjandraetal.2003).Water quality if not
adequately managed can serve as a serious limiting
factor to the future economic development and to the
public health and environment which will result in
enormous long-term costs to society. Thus, the need
for better management of the quality of water is
greatly recognized. The use of maps is common
practice in earth-related sciences in order to evaluate
the evolution of physical phenomena and predict
natural variables as well as assess the risk regarding
surface and groundwater contamination in waste
disposal industrial and other sites (Komnitsas et al.
1998; Komnitsas and Modis 2006; Wingle et al. 1999;
Xenidis et al. 2003). Keeping this in view, observa-
tions of field study are integrated in GIS for the
evaluation of the impact of industrialization on the
groundwater quality in Peenya industrial area of
Bangalore.
Details of the study area
Bangalore is located at a latitude of 12° 58′N and
longitude of 77° 35′E at an altitude of 921 m above
mean sea level (Lokeshwari and Chandrappa 2006).
Bangalore is a cosmopolitan city which is expanding
both in space and in technical development. Greater
Bangalore spread over an area of 800 km
2
lies
between latitudes 12°39′00″to 131°3′00″Nand
longitudes 77°22′00″and 77°52′00″E and is heavily
dependent on groundwater for its water require-
ments. This mega city situated on a N–S trending
highland forms a divide between the rivers
Arkavathi on the west and South Pennar on the
east. This study area is predominantly underlain by
granites and gneisses. These hard rocks have
undergone alteration and decomposition in the
plains and in valleys producing a weathered
mantle ranging in thickness from 10 to 30 m
generally and occasionally extending down to a
maximum depth of 60 m. Laterites of Palaeocene
age are seen at places as cappings on the gneisses.
Granites and gneisses constitute the major aquifer
system in Greater Bangalore. Groundwater occurs
under phreatic conditions in weathered rock and
residuum and under semi-confined conditions in
jointed and fractured granites and gneisses. The
Peenya industrial area (Fig. 1)islocatedonthe
northwestern suburbs of Bangalore city between 13°
1′42″N and 77° 30′45″E. It is the region’slargest
enclave of industrial units, which houses around
3,100 industries dominated by chemical, leather,
pharmaceutical, plating, metal and allied industries
spreadoveranareaof40km
2
of land. The total
population within the zone is 350,000.
Drainage network
The study area is on a water divide. Streams of
different watersheds originate from this area. The area
is sloping towards west. Major part of the study area
is occupied by streams flowing towards west from
this area. A few surface water bodies or tanks are
present in the area but are outside the boundary of
industrial area. Four tanks are close to the boundary in
the western part. Drainage map of the area is shown
(Fig. 2).
Methodology
Data used
Different data products required for the study include
57G/12 toposheets which are obtained from Survey of
India (1:50,000) and GARMIN GPS 60.
4068 Environ Monit Assess (2012) 184:4067–4077
Database creation
Sample locations, drainage network and road network
were all vectorised, and thematic layers were prepared
for analysis. Vector layer for the study area (Fig. 1)is
prepared using MapInfo software. This was georefer-
enced for comparison and analysis with other maps.
Attribute database
Field work was conducted and groundwater samples
from bore wells were collected from 31 predetermined
locations in and around the study area during April 2009
in 2-L high-density polythene containers. A map
showing sampling locations is shown in Fig. 3.
The water samples were then analysed for major
physico-chemical parameters adopting standard proce-
dures (APHA 1992). The physical parameters such as
pH were measured using pH meter of Digisun
Electronics 707. Total dissolved solid s(TDS) were
determined as the residue left after evaporation of the
filtered sample. Total hardness (TH) as calcium
carbonate and calcium (Ca
2+
) were analysed titrimetri-
cally by a complexometric method using EDTA.
Magnesium (Mg
2+
) hardness was calculated from TH
and Ca
2+
. Alkalinity and chloride were determined
titrimetrically. Sulphate and nitrate were analysed by
UV–Visible spectrophotometry. The results were eval-
uated in accordance with the norms prescribed under
‘Indian Standard Drinking Water Specification IS
10500 (1991)’of Bureau of Indian Standards (BIS
1991). The water quality data thus obtained form the
attribute database for the present study (Table 2).
Integration of spatial and attribute database
The spatial and the attribute database generated are
integrated for the generation of spatial distribution
maps of selected water quality parameters like pH,
alkalinity, chlorides, sulphates, nitrates, TDS, total
hardness, Ca, Mg and water quality index (WQI). The
water quality data (attribute) are linked to the
Fig. 1 Location of study area
Environ Monit Assess (2012) 184:4067–4077 4069
77.5 77.51 77.52 77.53 77.54
12.99
13
13.01
13.02
13.03
13.04
13.05
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
0
50
100
150
200
250
300
350
400
450
500
550
600
650
700
750
800
850
SPATIAL DISTRIBUTION OF CHLORIDE
N
(SEASON: Pre monsoon, 2009)
Legend
Sample location
Industrial area
Study area
00.010.020.03
mg/L
km
77.5 77.51 77.52 77.53 77.54
12.99
13
13.01
13.02
13.03
13.04
13.05
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
-10
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
SPATIAL DISTRIBUTION OF NITRATE
N
(SEASON: Pre monsoon, 2009)
Legend
Sample location
Industrial area
Study area
0 0.01 0.02 0.03
mg/L
km
77.5 77.51 77.52 77.53 77.54
12.99
13
13.01
13.02
13.03
13.04
13.05
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
380
400
420
440
460
Water Quality Index map
N
(SEASON: Pre monsoon, 2009)
Legend
Sample location
Industrial area
Study area
1,2,3., Location address
00.010.020.03
mg/L
km
77.5 77.51 77.52 77.53 77.54
12.99
13
13.01
13.02
13.03
13.04
13.05
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
0
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
SPATIAL DISTRIBUTION OF SULPHATE
N
(SEASON: Pre monsoon, 2009)
Legend
Sample location
Industrial area
Study area
0 0.01 0.0 2 0.03
mg/L
km
Fig. 2 Spatial distribution
of water quality parameter,
drainage map and WQI
4070 Environ Monit Assess (2012) 184:4067–4077
sampling locations (spatial) in MapInfo, and maps
showing spatial distribution are prepared to easily
identify the variation in concentrations of the above
parameters in the groundwater at various locations in
the study area.
By integrating the spatial and attribute data in GIS,
we can clearly visualize the spatial distribution of
different parameters. It also helps us to concentrate on
areas marked high for further remedial measures.
Estimation of water quality index
An integral part of any environmental monitoring
programme is the reporting of results. In the case of
water quality monitoring, due to the complexity
associated with analysing a large number of measured
variables, a method which will reduce the multivariate
nature of water quality data is used, by employing an
index that will mathematically combine all water
quality measures and provide a general and readily
understood description of water. Water quality index
(WQI) is a very useful and efficient method for
assessing the quality of water. WQI is also a very
useful tool for communicating the information on
overall quality of water (Pradhan et al. 2001). WQI is
a single unit less number of 100 point scale that
Table 1 Permissible levels and unit weight of water quality
parameters
Parameter Recommended unit standard (Si) Unit weight
pH 7.0–8.5 0.005
TDS 500 0.002
Calcium 75 0.0133
Magnesium 50 0.02
T-hardness 300 0.0033
Chloride 200 0.005
Nitrate 45 0.022
Sulphate 200 0.005
Alkalinity 200 0.005
All units except pH are in milligrams per liter
Fig. 3 Location of source of water samples
Environ Monit Assess (2012) 184:4067–4077 4071
12.99
13
13.01
13.02
13.03
13.04
13.05
1
2
3
4
5
6
7
8
9
10
11
1213
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
0.2
0.6
1
1.4
1.8
2.2
2.6
3
3.4
3.8
4.2
4.6
5
5.4
5.8
6.2
6.6
7
7.4
7.8
8.2
8.6
SPATIAL DISTRIBUTION OF pH
N
(SEASON: Pre monsoon, 2009)
Sample location
Industrial area
Study area
Legend
mg/L
km
100
150
200
250
300
350
400
450
500
550
600
650
SPATIAL DISTRIBUTION OF ALKALINITY
N
(SEASON: Pre monsoon, 2009)
Legend
Sample location
Industrial area
Study area
12.99
13
13.01
13.02
13.03
13.04
13.05
1
2
3
4
5
6
7
8
9
10
11
1213
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
mg/L
km
12.99
13
13.01
13.02
13.03
13.04
13.05
1
2
3
4
5
6
7
8
9
10
11
1213
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
2600
2800
3000
3200
3400
3600
3800
4000
4200
SPATIAL DISTRIBUTION OF TDS
N
(SEASON: Pre monsoon, 2009)
Legend
Sample location
Industrial area
Study area
mg/L
km
-100
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
1300
1400
1500
1600
1700
12.99
13
13.01
13.02
13.03
13.04
13.05
1
2
3
4
5
6
7
8
9
10
11
1213
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
SPATIAL DISTRIBUTION OF TH
(SEASON: Pre monsoon, 2009)
Legend
Sample location
Industrial area
Study area
mg/L
km
77. 5 77.5 1 77.5 2 77.5 3 77.54 77.5 77.5 1 77.5 2 77.5 3 77.54
77 . 5 77.5 1 77 . 5 2 77 .5 3 77.5 4 77.5 77.51 77.52 77 .53 77.54
77 . 5 77.5 1 7 7 .5 2 7 7 .5 3 7 7.54
12.99
13
13.01
13.02
13.03
13.04
13.05
1
2
3
4
5
6
7
8
9
10
11
1213
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
1300
SPATIAL DISTRIBUTION OF CALCIUM
N
(SEASON: Pre monsoon, 2009)
Legend
Sample location
Industrial area
Study area
0
00.010.020.03 00.010.020.03
0 0.0 1 0.02 0.0 3 00.010.020.03
0.01 0.02 0.03
mg/L
km
4072 Environ Monit Assess (2012) 184:4067–4077
provides a pointer to the quality of water source.
Water quality indices have been reported in literature
as early as 1965 (Horten 1965).To determine the
suitability of the groundwater for drinking purposes,
the index developed by Tiwari and Mishra (1985) was
used. In the present study, nine water quality
parameters, namely, pH, alkalinity, TDS, total hard-
ness, Ca hardness, Mg hardness, nitrate, chloride and
sulphate were considered for computing WQI, and the
unit weight Wi of each parameter is obtained depend-
ing upon its weightage, by adopting the following
formula
WQI ¼ΣqiWiðÞ=ΣWiðÞ
Where qi=100(Vi/Si)
qpH ¼100 VpH 7:0ðÞ=8:57:0ðÞ
fg
Wi= K/Si
Where
Qi Quality rating for the ith water quality
parameters (i=1,2,3,….N)
Vi the measured value of the ith parameter at a
given sampling location
Si the standard permissible value for the ith
parameter
The “standard”permissible values for various
pollutants for drinking water, recommended by
WHO, are given in Table. 2for the parameters
considered for WQI. It is well known that the more
harmful a given pollutant is, the smaller is its
permissible value for the standard recommended for
drinking water. So, the “weights”for various water
quality parameters are assumed to be inversely
proportional to the recommended standards for
the corresponding parameters, i.e. Wi=K/Si, where
Wi= unit weight for the ith parameter and K=
constant of proportionality. For the sake of simplic-
ity, assuming that K=1,forpH,assumingthesame
unit weight as that for chlorides, viz., 0.005, the unit
weight Wi, obtained from the above equation with K=1,
is shown in Table 1.
According to this water quality index, the maxi-
mum permissible value is 100. Values greater than
100 indicate pollution and are unfit for human
consumption.
Results and discussion
Spatial distribution of different parameters is pre-
sented in Figs. 2and 4. Of the nine parameters which
are under discussion, the following important infer-
ences could be drawn from the GIS maps for spatial
distribution of chemical parameters—that the high
values of all the parameters fall in the western part of
the area. Total dissolved solids, total hardness and
alkalinity have high values in the central area of the
lower half. The high alkalinity can be associated with
the large number of electroplating units and pharma-
ceutical industries located within this area. High total
hardness values are observed in the central part of
upper half of the area. Chloride and sulphate show
high values in the lower part of western half.
Similarly, high water quality index values are seen
in the western portion of the area.
The spatial distribution of different parameters
(Figs. 2and 4) can be compared with the drainage
map of the area (Fig. 2). It could be seen that stream
network is running towards west which indicates a
westerly slope of the area. The concentration of
different parameters is seen to be high in this area.
Thus, the distribution pattern of the parameters is
controlled by the drainage network in this area.
This inference could be of much use in practical
applications when measures for controlling the trans-
location of the pollution in the area are planned. The
results of the physicochemical analysis are presented
in Table 2.
From the study of the groundwater quality data and
the spatial distribution maps, the following inferences
could be made. The spatial distribution of pH in
groundwater in the area is given in Fig. 4a.The pH of
the water samples in the study area ranges from 6 to
8.5. Samples 9, 12, 13 and17 recorded a pH of less
than 6.5, which can be seen in the central part of the
spatial map for pH. Sample 9 is located near a food
industry. The low pH levels in the effluents could be
due to raw materials such as corn, sorghum, enzymes,
lactic acid and yeast used by the food industries which
in turn affect the groundwater quality. The low pH in
sample 17 is due to effluents from the electroplating
and metal industries located in the vicinity of the bore
well. Samples 12 and 13 are from the bordering
villages. The low pH can be related to the use of
acid-producing fertilizers like ammonium sulphate and
super phosphate of lime as manure for agricultural use.
Fig. 4 Spatial distribution of water quality parameter
Environ Monit Assess (2012) 184:4067–4077 4073
The super phosphate of lime is a basic fertilizer
producing alkaline reaction in the soil. However, excess
phosphates drained out of the vadose zone might have
added to the acidity of the water. Groundwater with low
pH values can cause gastrointestinal disorders such as
hyperacidity, ulcers, stomach pain and burning sensa-
tion. pH values below 6.5 cause corrosion of metal pipes
resulting in the release of toxic metals such as zinc, lead,
cadmium, copper, etc.
Ninety percent of the water samples showed
high alkalinity with concentrations ranging above
200 mg/L which may be due to decay of organic
matter and weathering of rocks and minerals
(Asadi et al. 2007). Three samples with nos.9, 19
and20showedveryhighvaluesofalkalinityandare
located in the western portion. The high value at
these locations is attributed to the effluents from the
pharmaceutical and drug industries which reach
these points due to the westerly slope of the area.
Sample no.14, with the highest value of 670 mg/L,
lies in the upper part of the southern portion. The
high alkalinity value could be due to the action of
carbonates upon the basic material in the soil. The
northeastern portion registered values within the
desirable limit (Fig. 4b).
TDS concentration appears to be a useful indicator
of anthropogenic contamination, with an average
value of 585 mg/L in industrial areas (Choi et al.
Sample pH Alkalinity TDS TH Ca Mg Chloride Sulphate Nitrate
1 7.5 310 1,459 518 482 8.748 494.16 174.49 24.3
2 7.02 350 490 558 228 80.19 288.26 179.9 63.3
3 7.13 200 568 920 760 38.88 426 43.69 78.1
4 6.82 255 1,747 396 182 52.002 462.4 97.571 62.5
5 7.96 220 780 248 78 41.31 31.24 16.8 65.35
6 6.8 200 2,304 558 512 11.178 494 89.405 30.7
7 7.88 255 742.4 1,160 710 109.35 387.1 210.6 32.7
8 7.9 310 899 360 310 12.15 212 38.1 26
9 6.42 565 2,020 1,824 1,457 89.181 910 255.6 84.7
10 6.58 485 2,435 1,060 984 18.468 795 260.1 18.4
11 7.96 315 1,619 296 212 20.412 329.44 58.64 18.5
12 6.34 355 4,224 1,424 1,416 1.944 393.3 216.4 49.6
13 6.01 385 4,256 1,368 98 308.61 724.4 224.39 136.1
14 7.88 670 2,995 784 142 156.01 501.1 67.57 84.9
15 8.18 290 1,785 912 290 151.15 213.3 63.83 1.9
16 7.62 325 920 340 296 10.692 348 71.95 43.05
17 6.48 365 1,012 418 398 4.86 36.9 17.255 46.4
18 6.84 80 1,650 212 74 33.534 74.8 13.476 110.7
19 7.16 520 960 298 160 33.534 375.5 308.12 55.7
20 7.56 540 2,135 494 168 79.218 629.06 316.85 94.05
21 7.21 285 1,900 1,050 542 123.44 411 321.77 99.7
22 7.9 295 1,500 1,242 94 278.96 379 47.962 95.7
23 7.2 220 675 252 86 40.338 79.1 17.552 169.6
24 7.5 275 1,788 202 194 1.944 316.5 13.746 49.6
25 8.01 300 925 518 334 44.712 243.6 47.842 105
26 8.2 255 745 724 176 133.16 278.6 71.97 1.58
27 8.5 225 1,658 96 76 4.86 76.68 36.216 6.5
28 8.2 315 2,130 528 434 22.842 386.2 42.99 2.3
29 8 400 1,082 944 462 117.13 198.8 18.216 159
30 7.6 425 486 486 96 94.77 248.3 48.104 6.95
31 6.8 465 1,384 1,384 1,046 82.134 208 56.148 141
Table 2 Water quality
parameter data
All units except pH are in
milligrams per liter
4074 Environ Monit Assess (2012) 184:4067–4077
2005). In the present investigation, the total dissolved
solids ranged from 486 to 4,256 mg/L and account for
93% non-potability. Water with high total dissolved
solids may induce an unfavourable physiological
reaction in the transient consumer and cause gastro-
intestinal irritation. TDS values as high as 4,100 mg/L
in groundwater in the study area have been reported
by Shankar et al. (2008). Most of the samples with
higher values of TDS are distributed in the upper part
of the southern portion (Fig. 4c). The highest value of
4,256 mg/L recorded by sample 13 is from a dense
residential area outside the industrial estate. The
possible source of pollution could be seepage from
unlined sewage lines, domestic drains and leachates
from waste dumps. A report by Lakshmikantha
(2006) stated that water from bore well samples has
shown increased concentration of almost all physico-
chemical parameters and can be attributed to the
proximity of industrial waste dump sites to these
sample points. Total hardness attributing to 77% of
non-potability showed values higher than the desired
limit (300 mg/L) prescribed by BIS. The total
hardness ranged from 96 to 1,824 mg/L in the
samples. The maximum calcium and magnesium
concentrations are 1,457 and 308.6 mg/L, respective-
ly. The high degree of hardness in the study area can
definitely be attributed to the disposal of untreated or
improperly treated sewage and industrial waste. The
highest value of 1,824 mg/L for total hardness was
recorded in sample 9. Remarkably high values were
also seen in samples12, 13, 21 and 31, which are
located close to dense residential areas, and hence,
industrial effluents may not be the cause, but it may
be due to the geological formation of the area.
Samples with high values are located throughout the
study area as indicated in the spatial distribution map
(Fig. 4c).
The concentration of chloride in 20 samples was
above the desired limit of 250 mg/L, with a maximum
value as high as 910 mg/L. Chloride in excess imparts
a salty taste to water, and people who are not
accustomed to high chloride can be subjected to
laxative effects. Sample 9 located in the western
portion of the study area recorded the highest value of
910 mg/L. The higher concentration of chlorides can
be attributed to the close proximity of these locations
to a number of industries like garments, dyeing and
printing, furnaces glass, refractories and ceramics,
thus indicating definite groundwater contamination
due to chlorides. Exceptionally high values in sample
13 (724.4 mg/L) and sample 20 (629.09 mg/L),
located outside the industrial area, are attributed to
the contamination from a septic system, sewage and
agricultural runoff. Samples with higher concentra-
tions are located in the western and southern
portion of the area (Fig. 2g). High nitrate values
are observed in the northeastern part of the study
area, and a few samples with high values are also
seen in the western and upper part of the central
portion (Fig. 2h). The nitrate values range from 1.6
to 169.6 mg/L. The highest concentration of nitrate
(169.6 mg/l) was observed in sample 23, which is
located close to the agricultural area, and may be due
to contamination from a septic system, sewage and
agricultural runoff that can leach and enter into the
groundwater.
Seventy-five percent of the water samples analysed
contained sulphate within the desired limit. The
values of sulphate ranged from 13.5 to 321.7 mg/L.
Elevated sulphate concentrations were recorded in
samples 19 (308.12 mg/L) and 20 (316.85 mg/L),
while the highest value was observed in sample 21
(321.77 mg/L) located in the village bordering the
industrial area. Use of large amount of fertilizer and
pesticide is the main source of nonpoint pollution
which increases the concentration of sulphate. High
values of sulphate are distributed in the western part
of the study area and lies in the downstream region
(Fig. 2i).
Water quality index is calculated to determine the
suitability of water for drinking purpose. Water
quality index values are shown in Table 3.
The WQI values revealed that out of the 31
groundwater samples studied, samples from 29
locations of the study area were above 100, which is
the permissible limit and therefore cannot be used for
human consumption. The WQI map is shown in
Fig. 2j.The WQI map indicates that the safer zone is
in the northwestern part of the study area, which has a
comparatively lesser number of industries than the
rest of the study area.
Statistical analysis
Using SPSS 15.0, multiple regression analysis was
done to determine the factors contributing to the water
quality index. Multiple regression analysis was done
with water quality index as a dependent variable and
Environ Monit Assess (2012) 184:4067–4077 4075
pH, alkalinity, TDS, Ca, Mg, chloride, sulphate and
nitrate as independent variables (or predictors). B-
coefficient represents the coefficient in the model to
predict the water quality.
SE represents the standard error of beta-coefficient,
which is the standardized beta weight of each
independent variable. This is more important as our
main aim is to assess the influence of predictors on
water quality rather than predict the water quality. The
results of statistical analysis are presented in Table 4.
All the nine physicochemical parameters (total
hardness is a derived parameter using calcium and
Mg) considered for evaluation of WQI are signifi-
cantly contributing to the WQI, of which calcium,
magnesium and nitrate have a larger standardized beta
weight in ranking and have a marked influence on the
water quality index in the industrial area. For the rest
of the water quality parameters, though statistically
significant (p<.001), their contribution to the WQI is
negligible when compared to Ca, Mg and nitrate,
based on the standardized beta coefficient obtained
from the multiple regression analysis performed to
define WQI. Poor quality of water is determined in
order by calcium, magnesium and nitrate The study
thus gives an overview of drinking water quality of
groundwater in the industrial area. Identification of
water quality indicators would help to concentrate on
few attributes during regular water quality check-up
process such as Ca, Mg and nitrate. This saves time,
energy and expenditure to a great extent without
compromising on the quality of output.
Conclusion
The results indicate that most of the water quality
parameters were beyond the permissible limits in the
industrial area and its environs. The overall view of
the water quality index of the present study zone had
a higher WQI value indicating the deteriorated water
quality. These inferences could be made using GIS
techniques. Therefore, a comprehensive sewerage
system for safe disposal of wastes should be devel-
oped to safeguard groundwater quality in the study
area. The analysis of the results drawn at various
stages of the work revealed that GIS is an effective
tool for the preparation of various digital thematic
layers and maps showing spatial distribution of
various water quality parameters. Monitoring of
Table 4 Multiple regression analysis to determine the factors contributing to the water quality index
Physicochemical parameter B coefficient SE Standardized beta Rank Student’stpvalue
pH 4.12 0.000455 0.025 VIII 9051.58 <0.001
a
Alkalinity 0.03 0.000002 0.036 VI 14822.29 <0.001
a
TDS 0.00 0.000000 0.044 V 17458.93 <0.001
a
Ca 0.23 0.000001 0.828 I 324362.82 <0.001
a
Mg 0.55 0.000004 0.391 II 154206.42 <0.001
a
Chloride 0.03 0.000002 0.062 IV 17937.25 <0.001
a
Sulphate 0.03 0.000003 0.030 VII 10303.29 <0.001
a
Nitrate 0.61 0.000006 0.273 III 110456.17 <0.001
a
a
Highly significant
Table 3 Water quality index
Sample no. WQI Sample no. WQI
1 171.710 17 139.742
2 163.843 18 116.026
3 270.582 19 132.434
4 142.378 20 199.063
5 96.958 21 297.131
6 179.219 22 267.497
7 279.629 23 159.778
8 120.407 24 106.248
9 502.102 25 193.724
10 309.057 26 142.767
11 105.726 27 49.210
12 409.939 28 153.794
13 334.172 29 297.993
14 227.644 30 106.039
15 183.359 31 404.097
16 131.405
4076 Environ Monit Assess (2012) 184:4067–4077
pollution patterns and its trends with respect to
urbanization is an important task for achieving
sustainable management of groundwater. A GIS study
proves to be an essential tool to evaluate and quantify
the impact of industrialization on groundwater quality.
Spatial distribution maps of various pollution param-
eters are used to demarcate the locational distribution
of water pollutants in a comprehensive manner and
help in suggesting groundwater pollution control and
remedial measures in a holistic way.
The results show that the overall water quality of
Peenya industrial area and its environs is very poor
and unsuitable for drinking purposes. The results of
the statistical analysis reveal that by controlling the
calcium, magnesium and nitrate levels in the study
area, the water quality can be improved. This in fact is
of vital importance to policy makers. In order to meet
the potability of groundwater, it is recommended that
continuous effective treatment combined with con-
stant monitoring is essential to ensure that it meets the
standards of drinking water.
Acknowledgements The authors are grateful to the Univer-
sity Grants Commission for the fellowship and financial
support granted for the accomplishment of this research work.
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