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American Journal of Civil and Environmental Engineering

2018; 3(4): 68-82

http://www.aascit.org/journal/ajcee

A Comprehensive Assessment of Spatial Interpolation Method Using IDW Technique for the Groundwater Quality Evaluation of an Industrial Area in Bangalore, India

Bangalore Shankar*, Shobha

Department of Civil Engineering, East Point College of Engineering and Technology, Bangalore, India

Email address

*Corresponding author

Citation Bangalore Shankar, Shobha. A Comprehensive Assessment of Spatial Interpolation Method Using IDW Technique for the Groundwater

Quality Evaluation of an Industrial Area in Bangalore, India. American Journal of Civil and Environmental Engineering.

Vol. 3, No. 4, 2018, pp. 68-82.

Received: June 11, 2018; Accepted: June 27, 2018; Published: July 19, 2018

Abstract: Assessment and mapping of quality of groundwater is extremely important because the physical and chemical

characteristics of groundwater determine its suitability for agricultural, industrial and domestic usages. Geographic information

system (GIS) is an efficient and effective tool in solving problems where spatial data are important. Geographical Information

System can be an effective and powerful tool for mapping, monitoring, modelling and assessing water quality, and detecting

environmental changes, determining water availability, preventing flooding and managing water re-sources on a local or

regional scale. In the present study the spatial variations in ground water quality is carried out in the Peenya industrial area of

Bangalore district in India. This interpolation has been done by using the Inverse Distance Weighting (IDW) technique. In the

present study ground water samples were collected from 30 locations in the study area. The ground water quality information

maps of the entire study area have been prepared using GIS spatial interpolation techniques for all the parameters during both

the pre and post-monsoon seasons of 2017. The results obtained in the study and the spatial database established in GIS will be

helpful for monitoring and managing ground water pollution in study area. The water quality index for the groundwaters have

also been calculated and it is found that WQI exceeds 100 (the limit for safe drinking water) at 12 out of the 30 sampling

stations during pre-monsoon and 13 stations during post-monsoon, that is, 40% and 43.33% of the samples during these

seasons are deemed unfit for potable purpose without suitable treatment.

Keywords: Geographic Information System, Groundwater, Inverse Distance Weighting, Quality, Spatial Distribution,

Water Quality Index

1. Introduction

1.1. Water Quality and Evaluation Index

Water is one of the most essential natural resources for

eco-sustainability and is likely to become critically scarce in

the coming decades [1]. About one-third of the world’s

population relies on groundwater for drinking purposes. Due

to population explosion, industrial improvement and

agricultural development, extraction of groundwater has

increased [2]. The ground water quality is normally

characterized by physical characteristics, chemical

composition, and biological parameters. These quality

parameters reflect inputs from natural sources including the

atmosphere, soil and water rock weathering, as well as

anthropogenic influences of various activities such as mining,

land clearance, agriculture, acid precipitation, and domestic

and industrial wastes [3]. Variations in availability of water

in time, quantity and quality can cause significant

fluctuations in the economy of a country [4]. Hence, the

conservation, optimum utilization and management of this

resource for the betterment of the economic status of the

country become paramount [5]. Water quality assessment, as

a basic issue related to human survival, has a strong

theoretical and practical significance [6]. The continuous

pollution of both surface and underground water sources has

69 Bangalore Shankar and Shobha: A Comprehensive Assessment of Spatial Interpolation Method Using IDW

Technique for the Groundwater Quality Evaluation of an Industrial Area in Bangalore, India

reduced the quality and quantity of water needed for general

agricultural requirements such as meeting crop water

requirement [7]. The safe and sustainable use of groundwater

requires a regular evaluation of its quality [8]. Understanding

of contamination and its control, is actually a necessity due to

the fact its far-reaching impact on human health [9]. This

calls for proper practical mechanisms to safeguard the natural

quality of groundwater.

It is in this connection that a quality evaluator is assessed

to give a composite picture of the groundwater status in the

form of water quality index

The Water Quality Index (WQI) is considered as an

effective tool to convey the information about overall water

quality in a comprehensible and useful manner [10]. A water

quality index (WQI) may be defined as a rating reflecting the

composite influence of a number of water quality parameters

on the overall quality of water. The main objective of WQI is

to turn complex water quality data into information that is

understandable and useable by the public. WQI based on

some important parameters can provide a simple indicator of

water quality. It gives the public, a general idea of the

possible problems with water in a particular region. Thus, a

water quality index synthesizes complex scientific data into

an easily understood format [11].

Therefore, the present study focuses on the groundwater

quality analysis of Peenya industrial area using GIS including

spatial interpolation for groundwater quality evaluation In

addition, water quality indices of the study area has also been

evaluated to identify the suitability of water samples for

human consumption and domestic utility.

1.2. Spatial Interpolation Method for

Groundwater Quality Evaluation

As many professionals point out, groundwater quality

mapping over extensive areas is the first step in water

resources planning [12] and groundwater can be optimally

used and sustained only when the quantity and quality is

properly assessed [13]. The spatial distribution of quality

groundwater shows some heterogeneity and the measurement

of quality parameters at every location is not always feasible

on account of time as well as cost of the data collection.

Therefore, prediction of values based on selectively

measured values is one alternative while minimizing errors

and enhanced rate of calculation accuracy. Geographical

Information System (GIS) is a leading tool and has great

potential for use in environmental problem solving in several

areas, including engineering and environmental fields [14].

The usage of geospatial technologies has smartly reduced the

complexities involved in the evaluation of natural resources

and their related environmental concerns.

Due to the emergence of geostatistical analyst as an

innovative tool to fill up the gap between geostatistics and

GIS, many researchers widely used it for the analysis of

spatial variation of groundwater characteristics [8].

Several researches have been undertaken to compare

different interpolation methods in a variety of situations,

using GIS in areas such as groundwater depth, groundwater

contamination, groundwater quality, etc. [15, 16]. Kriging,

Inverse Distance Weighting (IDW), and Radial Basis

Functions (RBF) are three well-known spatial interpolation

techniques commonly used for characterizing the spatial

variability and interpolation between sampled points and

generating prediction maps [17].

Local polynomial method and IDW were the best methods

to estimate EC and pH, respectively in a study carried out in

Hamedan-Bahar plain, west of Iran [18]. But according to

[19], kriging and co-kriging methods are superior to IDW.

In an other study, total suspended solids (TSS), dissolved

oxygen, ammonia nitrate (NH3–N), biochemical and

chemical oxygen demands (BOD and COD) and pH were

measured from seven sampling points to examine the water

quality of Bertram River, a main stream in the rapidly

growing tourist destination of Cameron Highlands, Malaysia

[20]. They preferred IDW method for the generation of water

quality surface data as it is more intuitive and efficient. IDW

method has also been used in water quality index zonation

and in the production of spatial distribution maps of water

quality parameters [21].

The IDW makes predictions using a linear weighted

combination based on the inverse of the distance between the

points [22]. It is computationally fast and has the ability to

accommodate barriers that reflect the linear discontinuity in

the surface.

The present study investigated the best interpolation

method by using IDW to illustrate the spatial distribution of

the water quality parameters in the groundwaters of Peenya.

In addition, the water quality indices of the study area have

been evaluated to assess the comprehensive water quality

status.

2. Details of the Study Area

Bangalore city lies between North Latitude 12052

121

11 to

1306

10

11 and East Longitude 77

00

145

11 to 77

032

125

11 covering

an area of approximately 400 square km. The study area,

Peenya Industrial area, is covered in part of the Survey of

India Toposheet No 57 H/9. The area covering about 40

square kilometres lies to the Northern part of Bangalore city

and houses more than 2100 industries dominated by chemical,

leather, pharmaceutical, plating and allied industries.

3. Materials and Methods

3.1. Sampling, Geodatabase and Analysis

A total of 30 sampling stations were selected in the study

area as illustrated in Figure 1. The samples were collected by

composite sampling method during pre-monsoon and post-

monsoon seasons of the year 2017 and a GPS Survey was

done. These samples were drawn from the open wells, bore

wells, hand pumps and municipal water supply schemes,

which are being extensively used for drinking and other

domestic, industrial and agriculture purposes. The samples

were collected in two litre PVC containers, sealed and were

American Journal of Civil and Environmental Engineering 2018; 3(4): 68-82 70

analyzed for 20 major physico-chemical parameters in the lab.

However, for the calculation of WQI, only 10 parameters (for

which the B.I.S limits have been stipulated) have been

considered. The location map of the study area with the

sampling stations have been presented in figure 1, while the

details of the sampling locations/sources and latitude

/longitude details have been presented in table 1.

The physical parameters such as pH and Electrical

Conductivity were determined in the field at the time of

sample collection. The chemical characteristics including

metals were determined as per the Standard methods [23] for

the examination of water and wastewater (APHA, 2002). The

results obtained were evaluated in accordance with the

standards prescribed under ‘Indian Standard Drinking Water

Specification IS 10500: 2003’ of Bureau of Indian Standards

[24]. The results of the physico- chemical analyses have been

presented in table 2.

3.2. Concept of IDW Interpolation Methods

Spatial interpolation is the process of using points with

known values to estimate values at other unknown points. In

GIS, spatial interpolation can be applied to create a raster

surface with estimates made for all raster cells. The results of

interpolation analysis can then be used for analysis that cover

the whole area.

There are many interpolations methods. In the present

study area interpolation method called Inverse Distance

Weighting (IDW) has been used.

The sampling locations were captured as latitude /

longitude data in degrees, minutes, and seconds (DMS)

Format. The data was converted to decimal degrees (Long

DD and Lat DD) for all sampling locations. Sorting this in

Excel format file, it was exported as text file structure. This

converted text file structure was used for the analysis. The

spatial analyst tool in the GIS Software was employed for

interpretation of data. The results were stored as Raster files

upon analysis [5].

Steps of IDW Method employed in Arc Map 10.1

Software.

1 Click the point layer in the ARC MAP table of contents

that contains the attributes you are interested in.

1. Start Geostatistical wizard

2. Under the methods section, choose Inverse Distance

Weighting, which is located under Deterministic

methods

3. The lower portion of geostatistical wizard shows

information about inverse distance weighted

interpolation. A dialogue box is seen

4. Under the input section, choose the data field that you

want to interpolate. In addition we can specify a weight

field, this will weigh the data values and alter the

interpolated values

5. Click next

6. Modify the power value, which can vary between 1 and

100.

7. Specify the output path

8. Set the environmental settings so that latitude and

longitude is distributed overall

9. Once you are satisfied with the model, click finish. A

Method Report window appears.

10. Click ok to produce the surface

11. The method report contains window a summary

showing the dataset, attribute, interpolation method and

parameter values used to create the surface.

A typical Screenshot of IDW method in ARC GIS 10.1 is

shown in Figure 2.

Table 1. Details of Sampling Stations along with the Latitude and Longitude.

Sample no. and code Sampling Locations Source Latitude Longitude

P1 Aditya apparels, Peenya Istg, Peenya industrial area BW 77.5153 13.0175

P2 Vinayaka mosquito coil mfg co, Peenya industrial area MWS 77.5167 13.0177

P3 Opp Hitco tools ltd, III Ph, Peenya industrial area BW 77.5187 13.0249

P4 Rexroth Bosch India ltd, III Ph, Peenya industrial area BW 77.519 13.025

P5 Zuman exports, III Ph, Peenya industrial area BW 77.5188 13.0249

P6 Opp Shakthi mosaics, sanjay gandhi nagar slum, PIA HP 77.4617 13.0367

P7 Peenya industrial estate, bangalore north HP 77.523 13.0288

P8 Near super tax labels, II stage PIA BW 77.5061 13.0164

P9 Auma industried limited, II stage, Peenya dasarahalli BW 77.5078 13.0169

P10 Opp industrial electrocontrols, III stage, II Ph, peenya MWS 77.4572 13.0256

P11 Malnad furnitures, T-Dasarahalli, Peenya BW 77.4872 13.0236

P12 Near unique instruments, III main, IV Ph, PIA HP 77.513 13.0279

P13 Power plastics, III main, IV Ph, PIA HP 77.5161 13.028

P14 M/S Paragon footwear pvt ltd, Iiph, PIA BW 77.5283 13.0268

P15 Honeyhills energy system, PIA BW 77.5244 13.0397

P16 Fine tools India ltd, IV ph, PIA BW 77.5144 13.03

P17 Byraveshwara stores, nandini layout, I stage, II block BW 77.538 13.0136

P18 Petrol bunk, nandini layout BW 77.5367 13.0103

P19 Hi-power equipments pvt ltd, II ph, PIA OW 77.5161 13.0244

P20 M/S Biopharma drugs and pharmaceuticists, PIA BW 77.525 13.025

P21 Simco insulator manufacturing company, II ph, PIA BW 77.527 13.0261

P22 Hitachi koki India ltd, I ph, PIA BW 77.5189 13.0265

P23 Venus engineering industries, III ph, PIA BW 77.5172 13.0249

P24 Near shruthi innovations, Peenya BW 77.5356 13.033

P25 CMC-water Peenya BW 77.5383 13.032



Sample no. and code Sampling Locations Source Latitude Longitude

P26 Hanuman weaving factory, I ph, PIA MWS 77.4928 13.0391

P27 Hind Hivac pvt ltd, I ph, PIA BW 77.5289 13.0405

P28 John crane sealing systems, I ph, PIA BW 77.5258 13.0389

P29 Trident fabricants, KIADB, I ph, PIA BW 77.4967 13.0367

P30 CMTI, Peenya BW 77.535 13.0325

BW: Borewell, OW: Open well, MWS: Mini water supply scheme, HP: Hand pump

Figure 1. Location map of Peenya Industrial Area with Sampling Stations.


Figure 2. Screenshot of IDW method in ARC GIS 10.1.

3.3. Development of Water Quality Index

In the formulation of a WQI, the importance of various

water quality parameters depends on the intended use of

water. This paper attempts to evaluate the water quality

indices from the viewpoint of suitability of water for human

consumption. The ten parameters chosen for the present

study are shown in the first column of table 2. The second

column of this table gives the drinking water standards for

these parameters as recommended by the BIS. The method of

evaluating the WQI has been briefly discussed here.

Weightages are assigned based on the importance of each

parameter. Weighing means the relative importance of each

water quality parameter that play some significant role in

overall water quality and it depends on the permissible limit

in drinking water set by National and International agencies

[25].

In the first place, the more harmful a given pollutant of

water, the smaller in magnitude is its standard for drinking

water. So the unit weight Wi for the ith

parameter Pi is

assumed to be inversely proportional to its recommended

standard Si (i=1, 2….., n) and n= no. of parameters

considered= 10 in the present case). Thus,

Wi= K / Si (1)

where the constant of proportionality K has been assumed to

be equal to unity for the sake of simplicity. These unit

weights Wi, for the 10 water quality parameters used here are

shown in the last column of Table 4, where pH has been

assigned the same weight as chloride.

The quality rating qi for the ith

parameter P is given, for all

other parameters except pH, by the relation

qi =100 (Vi / Si) (2)

Where Vi is the observed value of the ith

parameter and S

is its recommended standard for drinking water. For pH, the

quality rating qpH can be calculated from the relation

qpH =100[(VpH~7.0)/1.5] (3)

Where VpH is the observed value of pH and the symbol “~”

means simply the algebraic difference between VpH and 7.0.

Finally, the water quality index (WQI) can be calculated

by taking the weighted arithmetic mean of the quality rating

qi, thus,

WQI= [Σ (qi Wi) / ΣWi] (4)

where both the summations are taken from i=1 to i=10 (the

total no. of parameters considered).

Table 2. Water Quality parameters their standards and unit weights.

Parameter (Pi) Standard (Si) Unit weight (Wi)

pH 6.5-8.5 0.004

Total Hardness 300 0.003

Calcium 75 0.013

Magnesium 30 0.033

Chloride 250 0.004

Nitrate 45 0.022



Parameter (Pi) Standard (Si) Unit weight (Wi)

Sulphate 200 0.006

TDS 500 0.002

Fluoride 1 1

Iron 0.3 3.33

ΣWi = 4.4183

4. Results and Discussion

4.1. Results of Physico-chemical Analysis

The results of the physico- chemical analysis of the

groundwater samples of Peenya industrial area during the pre

and post-monsoon seasons is presented in tables 3 and 4

respectively. Out of the thirty samples analysed, 22 samples

(73.33%) were found to be non-potable as per Bureau of

Indian Standards. The critical constituents for the non

potability of the samples are total hardness and nitrates, each

of which accounted for 43.33% non-potability whilst sulphate

and total dissolved solids accounted for 40% and 16.67% of

non-potability, followed by other parameters such as

magnesium and calcium, as a result of which 30% and 26.67%

of the samples were found to be non-potable. Fluorides

accounted for 23.3%, of non-potability, while pH was outside

the permissible limits in 16.67% of the samples examined. Iron

contributed to the non- potability of 10% of the samples.

Table 3. Results of Pre-Monsoon Physico-Chemical Analysis of Groundwater Samples.

Sample

no PH

Total Hardness,

mg/l as CaCO3 Ca, mg/l Mg, mg/l Fe, mg/l Cl, mg/l NO3, mg/l SO4, mg/l TDS, mg/l F, mg/l

1 7.72 1030 208 124 1.14 330 17 603 1530 2.9

2 6.05 607 128 70 1.02 220 64 308 976 1.4

3 7.41 456 102 49 0 208 34 170 840 0.8

4 8.1 139 36 12 0 40 10 152 342 2.1

5 7.9 695 145 81 0.24 350 68 406 1442 1.3

6 7.94 772 130 109 0.14 504 82 210 1468 0.6

7 7.96 1155 214 151 0.18 598 319 192 2110 0.76

8 7.78 716 142 88 0.14 274 83 62 902 0.38

9 5.12 596 131 66 0 440 58 32 919 0.61

10 6.14 378 92 36 0 210 42 113 670 2

11 6.41 319 80 29 0 150 11 60 484 1.4

12 6.11 2960 514 408 0 2038 136 504 3848 5.88

13 7.11 3070 591 388 0.04 1680 101 358 3420 6.12

14 7.06 502 86 70 0 422 42 40 880 0.6

15 7.01 1212 222 160 0.38 582 36 588 1978 0.42

16 7.18 1050 228 117 0.22 452 20 524 1642 0.4

17 7.8 432 124 30 0.04 220 18 93 719 1.4

18 7.24 294 75 26 0.1 148 42 65 580 0.14

19 7.7 398 130 18 1.44 80 58 220 624 1.42

20 7.48 404 78 51 0.04 140 18 138 680 0.44

21 8.2 546 120 60 0.8 410 20 106 937 1.3

22 7.9 626 162 54 0 320 24 68 902 1.9

23 6.89 310 91 20 0.36 244 81 52 698 0.74

24 7.01 1446 336 148 0.52 360 34 880 2148 0.38

25 7.59 1348 270 164 0.22 808 52 806 2575 0.93

26 8.3 372 100 30 0 150 54 63 688 1.3

27 7.01 124 30 12 0.36 100 10 30 300 2.3

28 6.52 583 148 52 0.1 160 17 104 740 1.4

29 6.9 514 102 63 0 240 28 73 812 1.39

30 7.1 98 28 7 0.08 60 10 20 248 1.2

Table 4. Results of Post-Monsoon Physico-Chemical Analysis of Groundwater Samples.

Sample

no PH

Total Hardness,

mg/l as CaCO3 Ca, mg/l Mg, mg/l Fe, mg/l Cl, mg/l NO3, mg/l SO4, mg/l

TDS,

mg/l F, mg/l

1 7.72 1074 216 130 1.26 356 22 640 1600 2.96

2 6.07 620 140 66 1.15 242 80 322 1030 1.42

3 7.42 458 98 52 0 230 40 190 870 0.8

4 8.1 172 44 15 0 50 12 170 370 2.2

5 7.92 716 152 82 0.27 410 86 442 1560 1.27


Sample

no PH

Total Hardness,

mg/l as CaCO3 Ca, mg/l Mg, mg/l Fe, mg/l Cl, mg/l NO3, mg/l SO4, mg/l

TDS,

mg/l F, mg/l

6 7.97 840 142 118 0.22 600 114 228 1610 0.66

7 7.96 1242 234 160 0.2 710 344 216 2300 0.8

8 7.79 738 128 102 0.2 304 104 80 970 0.4

9 5.12 590 128 66 0 440 58 32 920 0.61

10 6.16 406 100 38 0 240 54 120 740 2.1

11 6.4 322 88 25 0 172 16 66 510 1.38

12 6.11 2996 522 412 0 2120 164 542 4010 5.92

13 7.14 3040 596 378 0.08 1860 122 380 3630 6.12

14 7.07 538 74 86 0 470 40 50 940 0.66

15 7.02 1262 236 164 0.38 640 40 604 2080 0.48

16 7.2 1084 230 124 0.42 508 22 540 1730 0.44

17 7.82 372 110 24 0.08 244 20 98 700 1.5

18 7.24 322 80 30 0.18 124 38 68 560 0.22

19 7.72 432 140 20 1.51 98 56 242 670 1.44

20 7.5 388 80 46 0.06 168 22 138 700 0.45

21 8.21 542 112 64 0.84 396 18 114 920 1.4

22 7.9 670 160 66 0 360 24 68 940 1.9

23 6.88 318 88 24 0.45 270 98 62 770 0.8

24 7.02 1502 352 152 0.55 412 40 934 2290 0.48

25 7.57 1418 288 170 0.27 904 58 880 2780 0.98

26 8.22 356 100 26 0 164 68 80 730 1.32

27 7.02 128 25 16 0.44 110 16 27 320 2.4

28 6.54 536 136 48 0.17 182 26 110 740 1.42

29 6.92 542 110 65 0 264 36 90 860 1.44

30 7.12 136 38 10 0.05 74 14 24 300 1.32

The maximum, minimum and mean concentrations of

nitrates in the study area during pre-monsoon season are

found to be 319 mg/L, 10 mg/L and 52.97mg/L respectively

and 344 mg/L, 12 mg/L and 61.74 mg/L respectively during

post-monsoon season. Beyond 45 mg/L, nitrates may cause

methemoglobinemia or blue baby disease in infants. It may

also be carcinogenic in adults [26].

The maximum, minimum and mean concentrations of total

hardness in the study area during pre-monsoon season are

found to be 3070 mg/L, 98 mg/L and 771.7 mg/L respectively

and 3040 mg/L, 128 mg/L and 792 mg/L respectively during

post-monsoon season. The maximum, minimum and mean

concentrations of calcium in the study area during pre-

monsoon season are found to be 591 mg/L, 28 mg/L and

161.43 mg/L respectively and 596 mg/L, 25 mg/L and 160.64

mg/L respectively during post-monsoon season. The

maximum, minimum and mean concentrations of magnesium

in the study area during pre-monsoon season are found to be

408 mg/L, 7 mg/L and 89.76 mg/L respectively and 412 mg/L,

92.64 mg/L and 10 mg/L respectively during post-monsoon

season. The calcium and magnesium salts which impart

hardness are also obviously higher in these areas.

The maximum, minimum and mean concentrations of TDS


3848 mg/L, 248 mg/L and 1203.4 mg/L respectively and

4010 mg/L, 300 mg/L and 1271.67 mg/L respectively during

post-monsoon season. Waters with high TDS (>2000mg/L)

are of inferior palatability and may induce an unfavourable

physiological reaction in the transient consumer and gastro

intestinal irritation [27].


sulphate in the study area during pre-monsoon season are

found to be 880 mg/L, 20 mg/L and 234.67 mg/L

respectively and 934mg/L, 24 mg/L and 251.9 mg/L

respectively during post-monsoon season. Higher

concentration of sulphate (>250 mg/L) may cause cathartic

action and malfunctioning of alimentary canal and

gastrointestinal irritation in human beings. High

concentration may also induce diarrhoea [28].


fluorides in the study area during pre-monsoon season are

found to be 6.12 mg/L, 0.38 mg/L and 1.46 mg/L

respectively and 6.12 mg/L, 0.4 mg/L and 1.507 mg/L

respectively during post-monsoon season. High concentration

of fluoride causes dental fluorosis, which is nothing but

disfigurement of the teeth and dental mottling or spotting of

teeth [29]. Hence, it is essential to maintain fluoride

concentration between 0.6 to 1.2 mg/L in drinking water and

the upper limit is 1.5mg/L (BIS 10500, 2003). Intake of

excess fluoride causes dental, skeletal and non-skeletal

fluorosis. Gastrointestinal complaints, constipation and

intermittent diarrhoea and flatulence in expectant and

lactating mothers, hardworking young adults, foetus and

children may be some of the other disorders associated with

excess fluorides.

The maximum, minimum and mean concentrations of iron


1.44 mg/L, 0 and 0.252 mg/L respectively and 1.51 mg/L, 0

and 1.51 mg/L respectively during post-monsoon season.

The desirable limit of iron as per BIS 2003 is 0.30 mg/L

and maximum permissible limit 1.0mg/L. Beyond this limit,

taste and appearance are affected and has adverse effects on



domestic uses such as staining of clothes and utensils. If the

concentration of iron exceeds 0.3 mg/L, it affects water

supply structures as well as promotes iron bacteria [30]. The

higher values may be due to rusting of casing pipes, non-

usage of borewells for long periods and disposal of scrap iron

in open areas due to industrial activity [31].

The average values of the various physico-chemical

parameters of groundwater sampling locations collected

during pre and post –monsoon seasons of the year 2017 for

the study area are presented in the figures 3 and 4

respectively in the form of bar charts. The major physico-

chemical parameters mapped in these figures are TH, Ca, Cl,

NO3, SO4 and TDS. From the figure 5, it is observed that by

comparing the pre-monsoon and post monsoon season

concentrations, the values are more or less similar, with a

slight increasing trend in the post monsoon season.

The spatial distribution maps for the key parameters during

pre and post monsoon seasons have been presented from

figures 6 to 10.

Figure 3. Average values of major physico-chemical parameters during pre-monsoon season.

Figure 4. Average values of the major physico-chemical parameters of water samples collected during post-monsoon season.

Figure 5. Comparison of Pre and Post Monsoon Analysis with Respect to Average Values.


Figure 6. Interpolated Distance Weighted map of (a) Total hardness (b) Calcium (c) Magnesium (d) pH during pre-monsoon season.



Figure 7. Interpolated Distance Weighted map of (a) Iron (b) Chloride (c) Nitrate (d) Sulphate during pre-monsoon season.


Figure 8. Interpolated Distance Weighted map of (a) Total Dissolved Solids (b) Fluoride during pre-monsoon (c) Total Hardness (d) Calcium during post-

monsoon season.



Figure 9. Interpolated Distance Weighted map of (a) Iron (b) Nitrate during post-monsoon season.


Figure 10. Interpolated Distance Weighted map of (a) Sulphate (b) Chloride (c) Total Dissolved solids (d) Fluoride during post-monsoon season.

4.2. Results of Ground Water Quality Indices

A sample calculation of WQI for the first sampling station

(pre-monsoon) is shown in detail in Table 5. In this table, 10

water quality parameters are listed in the first column, while

their actual values are given in the second column. The third

column in table shows the quality ratings q for these

parameters, while the last column gives sub-indices (qiwi).

The water quality index for the first sampling station is

calculated and shown in the last row of table and found to be

equal to 357.07. In the same way, the Water quality indices

for all the 30 sampling stations of Peenya industrial area have

been calculated using the ground water quality data using the

equations 1-4 during both the pre-as well as post-monsoon

seasons and the complete results have been presented in

Table 5.

The numerical value of the water quality index, as

formulated in the previous section [Equations 2 and 3],

implies that the water under consideration is fit for human

consumption if it’s WQI<100, and is unfit for drinking

without treatment if it’s WQI>=100. Moreover, the larger the

value of WQI, the more polluted the water concerned.

From Table 6, the overall quality of the ground water of

this area is reflected in the average value of WQI, which is

found to be 100.52 and 112.0 during the pre and post-

monsoon reasons respectively. It is found that WQI exceeds

100 (the limit for safe drinking water) at 12 out of the 30

sampling stations during pre-monsoon and 13 stations during

post-monsoon, that is, 40% and 43.33% of the samples

during these seasons are deemed unfit for potable purpose

without suitable treatment.

Table 5. Sample calculation of the water quality index for sampling station-1.

Parameter (Pi) Observed

Value (Vi)

Quality rating

(qi)

Sub index

(qiwi)

pH 7.72 48 0.192

Total Hardness 1030 343.33 1.03

Calcium 208 277.33 3.60

Magnesium 124 413.33 13.64

Chloride 330 132 0.528

Nitrate 17 37.78 0.831

Sulphate 603 301.5 1.809

TDS 1530 306 0.612

Fluoride 2.9 290 290

Iron 1.14 380 1265.4

WQI = [Σ (qi.wi) / Σ wi]= 357.07

Table 6. Water quality indices for the groundwaters of Peenya Industrial

area during pre-monsoon and post-monsoon seasons.

Sampling station Water Quality Index (WQI)

Pre-monsoon Post-monsoon

1 357.07 388.86

2 291.46 324.72

3 20.50 20.65

4 48.33 50.74

5 93.80 100.97

6 53.57 75.73

7 71.41 78.01

8 47.87 63.96



Sampling station Water Quality Index (WQI)

Pre-monsoon Post-monsoon

9 17.12 17.10

10 47.34 49.84

11 33.09 32.64

12 148.91 150.34

13 163.52 173.67

14 16.50 18.23

15 111.29 112.90

16 69.27 120.67

17 43.52 55.65

18 29.93 51.91

19 395.87 414.01

20 22.11 27.30

21 233.09 245.45

22 45.69 46.01

23 109.21 133.47

24 145.87 155.98

25 83.45 97.55

26 31.48 32.00

27 143.14 165.65

28 59.23 77.22

29 34.07 35.40

30 47.74 43.10

Average 100.52 112.00

5. Conclusion

The water quality parameters of Peenya industrial area

were analyzed for better understanding using spatial analysis

tools of ArcGIS software. The spatial distribution of

interpolated maps for the parameters TH, Ca, Cl, NO3, SO4

and TDS during the year 2017 have been presented in this

paper. The IDW maps showing the spatial distribution of the

above mentioned physico-chemical parameters were

developed using GIS, which facilitated in identifying the

potential zones of drinking water quality. The water quality

index for the groundwaters have also been calculated and it is

found that WQI exceeds 100 (the limit for safe drinking

water) at 12 out of the 30 sampling stations during pre-

monsoon and 13 stations during post-monsoon, that is, 40%

and 43.33% of the samples during these seasons are deemed

unfit for potable purpose without suitable treatment.

Acknowledgements

The authors are extremely grateful to the Principal and

management of EPCET for their perpetual support,

encouragement and inspiration along with the excellent

library facilities provided to the authors during the course of

this work.

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