Assessment of Groundwater Contamination Due to Co Disposal of … · Borewell clustering using hierarchical cluster analysis identified three clusters illustrating varying degree

International Journal of Environmental Engineering and Management

ISSN 2231-1319 Volume 7, Number 1 (2016), pp. 35-67

© Research India Publications

http://www.ripublication.com

Assessment of Groundwater Contamination Due to

Co –Disposal of Municipal Solid Waste and

Secondary Wastewater on Land at Lawspet in

Puducherry, India

N. Suresh Nathan1, R. Saravanane2 and T. Sundararajan2

1 Research Scholar, Department of Civil Engineering,

Pondicherry Engineering College, Puducherry, India.

e-mail:[email protected]. 2 Professor, Department of Civil Engineering,

Pondicherry Engineering College, Puducherry, India.

Abstract

Water quality assessment is one of the most important aspects in groundwater

studies. The hydro-geochemical study reveals the quality of groundwater for

drinking, agricultural and industrial purposes. Further, it is feasible to study

the change in water quality due to rock water interaction or any type of

anthropogenic activities like municipal solid waste dumping or secondary

wastewater land application. At Lawspet area in Puducherry, India, a unique

situation of co-disposal of solid waste dumping and secondary wastewater

disposal on land prevails simultaneously within the same campus. So an

attempt was made to assess the combined effect of this co-disposal on the

environmental quality and pollution effects on groundwater quality with a

view to correctly monitor the situation. The study area falls in Zone V

(Lawspet) area, wherein the STP and solid waste landfill are located in the

same campus. 68 borewells were considered for investigation and water

samples were collected every month in order to study the seasonal and spatial

variations. In toto 929 water samples were collected and tested for 17 physio-

chemical and bacteriological parameters and the study is restricted to the

pollution aspects of ten physio-chemical parameters only. In the present study,

Multivariate Statistical Analysis such as Correlation Analysis, Regression

Analysis, Descriptive Analysis, Cluster Analysis etc., were employed.

Correlation analysis revealed that the water quality parameters were

significantly correlated with one another indicating perfect, very good

correlation, good and moderate correlation. Using Regression Analysis

36 N. Suresh Nathan1, R. Saravanane and T. Sundararajan

multiple linear models were developed for various parameters. Borewell

clustering using hierarchical cluster analysis identified three clusters

illustrating varying degree of pollution. Cluster-wise Descriptive analysis was

made for Minimum, Maximum, Mean, Range, Skewness, Kurtosis and

Normality. Also three major components viz. Anthropogenic, Hardness and

Geogenic responsible for groundwater quality in the study area were identified

using hydro-geo chemical analysis.

Keywords: Solid waste, Secondary Wastewater, Leachate, Groundwater

contamination

1. INTRODUCTION

The importance of groundwater can be seen from its ability to act as a large reservoir

of freshwater. Aquifers are able to offer natural protection from contamination so the

degree of potability of untreated groundwater is safer than its untreated surface water

equivalent. Such storage enables timely use of water, which can be pumped out if

need be when corresponding surface resources such as rivers or reservoirs are unable

to provide enough water. The extraction of groundwater from aquifers is relatively

and organizationally easy to develop and cheap to use, as the borewells can often be

drilled close to where the water supply is needed. Many aquifer systems possess a

natural capability to attenuate the pollution levels. As it is impossible to completely

avoid aquifer pollution, this capacity should not be underestimated, instead taken as

an advantage to minimize the consequences to water supplies. Although groundwater

is not easily contaminated, if this occurs it is difficult to remediate, and in the

developing countries, such remediation may prove practically impossible. This can be

especially important for urban water supply, where most of groundwater contains a

wide variety of dissolved inorganic chemicals due to the movement of groundwater

through the different geological formations. These dissolved constituents are

responsible for physical and chemical characteristics of groundwater. With respect to

groundwater degradation from human activities at Puducherry we then have two

significant aspects:

Contamination based on non-engineered Solid Waste (SW) dumping.

Partially treated or Secondary Wastewater (SWW) application on land.

1.1 Solid Waste Leachate

The source of the contaminated liquids can be associated with many factors, including

the water generated during the decaying of waste. In addition, when the rain touches

the surface of the landfill it may percolate through the waste and give rise to

groundwater table. This relies on the type of landfill like soil and surface

characteristics, type and quantity of vegetation, gradient of layers, etc. When leachate

mingles with groundwater it forms a plume that spreads in the direction of flow,

thereby contaminating the groundwater of the area. The degree of contamination in

the aquifers relies on the transport rate of contaminants, collection and disposal

conditions at the site, as the contaminants infiltrate through the soil media. It was

Assessment of Groundwater Contamination Due to Co –Disposal of Municipal … 37

established that the total contaminants concentrations decrease drastically from the

solidwaste to the soil in the unsaturated zone. The groundwater contamination has

become a long-term problem where contamination persists in aquifers for decades

without treatment because groundwater travel slowly with time.

1.2 Land Application of Secondary Wastewater

The large amounts of treated wastewater generated by modern society, are potentially

a valuable resource, for irrigation of crops, industrial cooling water, recharge of

groundwater, etc. By contrast, wastewater is generally untreated or partially treated in

many large cities where uncontrolled and unorganised infiltration often occurs due to

non availability of wastewater treatment facilities. Irrigation with wastewater effluent

has been practiced successfully over a long period. Reuse attenuates the stress on

other water sources and also helps to evade direct discharge into natural water bodies

where surplus nutrients can cause eutrophication, algal blooms and contamination

which may occur due to pathogens, heavy metals, salts, and organic micro pollutants.

One of the possible risks to soil is salt accumulation which has negative effects on

plant growth. Another one is soil sodicity which refers to the amount of sodium

present in soil and its increase is caused by the application of SWW. High soil

sodicity causes increase in bulk density, soil surface crusting, reduction in soil

permeability, infiltration, porosity and soil hydraulic conductivity. Organic

compounds behave differently when compared to heavy metals when applied to soil.

This was attributed to the complex combinations of these compounds and their

possible breakdown to their elemental states. Land applications were effective in

removing low concentrations of organic pollutants.

In order to minimize health risks, it is important to know both the total amount of

hazardous pollutants and the chemical forms in which they are present in the

groundwater. The presence of wastewater contaminants in water that is exploited for

drinking, is undesirable and microorganisms in particular can have dire consequences,

chiefly for vulnerable members of society such as children or the elderly.

The present research was initiated with the aim to provide data on the groundwater

quality due to solid waste and SWW leachate. Considering the variations and

transformations over a period of time, the specific objective of this study was to

evaluate the effectiveness of inorganic compound transport using multivariate

statistical and hydro-geochemical analyses including the interaction between

biodegradation of contaminants in leachate during the landfilling operations and

SWW land application.

2. STUDY AREA AND PRESENT SCENARIO

Puducherry is a Union Territory in India with an extent of 293 sq. km. The entire

urban and sub urban areas of Puducherry are divided into nine zones for the purpose

of water supply and comprehensive underground drainage system.

The study area falls in Zone V (Lawspet) area, wherein the STP and solid waste

landfill are located in the same campus at Karuvadikuppam, Lawspet at 11o 58’16” N


Latitude and 79 o48’11” E Longitude on the northern part of Puducherry, India

(Figure 1). The terrain declines from North to South and the ground elevation ranges

from 53 m to 6 m as shown in Figure 2. The region is characterized by tropical

climate with a mean annual precipitation of 1200mm. 35% of the rainfall occurs

during the South-West monsoon from June to September and the remaining 65%

occurs during the North-East monsoon i.e. from October to December. The STP and

solid waste landfill dump site are located at sewage farm of area 125 acres, out of

which 44 acres are being used for cultivation purposes. Presently 15 MLD of

wastewater is treated using four serially connected facultative oxidation ponds of

capacities 2.9 MLD(1980), 2.9 MLD(1991), 2.2 MLD(1997) and 4.8 MLD(2001) and

1 UASB of capacity 2.5 MLD(2006). Domestic sewage of BOD 250mg/L is treated

with a removal efficiency of 65%. Nearly, 12.5 MLD of partially treated SWW is

discharged into recharge pond area of 18 acres, since 1980.

Figure 1: Location Map of Study Area


Figure 2: Elevation of Study Area

A portion of STP site at Karuvadikuppam is used as solid waste landfill. Solid waste

tipping started in 2004 and discontinued in 2013 only and it spreads over an area of 21

acres approximately. It is a non-engineered low lying open dump. The land fill is

unlined and the solid waste has been dumped indiscriminately in an unscientific way

and irregular fashion. Nearly, 125 lorry loads (200 MT) of solid waste has been

dumped daily. The solid waste landfill height varies from 2m to 6m. The solid waste

dumped includes household wastes which are bio-degradable in nature like kitchen

waste, paper, cardboard, clothes, waste from poultry/fish market etc., and non-

biodegradable wastes like plastic materials, glassware, non-infectious hospital waste,

etc. The borewells in this area will be the main source of water supply for coastal

zones viz.,ZONE II (Muthialpet) and ZONE V (Lawspet) as the current water supply

in these zones are contaminated due to sea water intrusion.

The study area is divided into four components:

1) Solid Waste Dump area

2) Recharge Pond area

3) Sewage Farm area

4) Peripheral area around Sewage Farm


3. GEOMORPHOLOGY

12 borewells were sunk for study purposes for various depths ranging from 30m to

53m especially for soil stratification as detailed below:

1) Solid Waste Dump area - 6 borewells

2) Recharge Pond area - 6 borewells

Soil samples were collected at every 1 m depth in all 12 borewells. Nearly 480 soil

samples were collected and lithological particulars of all borewells in Solid Waste

Dump area and Recharge Pond area are documented in Tables 1 and 2.

4. METHODOLOGY

4.1 Sample Collection and Field Measurements

4.1.1 Monitoring Borewells

Nearly 125 water supply and agricultural borewells are located in and around STP

within a radial distance of 2.5 Km from STP and solid waste landfill. Out of which,

20 Public Works Dept. (PWD) borewells supply drinking water to ZONE II

(Muthialpet) and ZONE V (Lawspet) and the remaining are private domestic

borewells.

To accurately represent the groundwater quality, a sampling strategy was designed to

cover a wide range of borewells at the key locations. In toto, 68 borewells were

identified in and around the study area as detailed below and depicted in Figure 3. All

the borewells were considered for investigation and water samples were collected

every month from Jan 2014 - Oct 2015 from solid waste dump area, recharge pond

area, sewage farm area (existing) and peripheral area (private & Govt.) in order to

study the seasonal and spatial variations.

1) Solid Waste Dump area - 2 borewells (newly sunk)

2) Recharge Pond area - 5 borewells (newly sunk)

3) Sewage Farm area - 3 borewells (existing)

4) Peripheral area - 58 borewells (private & Govt.)

Table 1: Classification of Soil at Recharge Pond Area

Lithological Description Range (Below ground

level) in metres

Reddish colour top soil 0-5

Reddish colour sandstone 3-9

Reddish colour sandstone mixed with clay 19-55

Coarse to medium red sand stone with clay 7-27

Coarse to medium red sand stone 17-40

Fine to medium reddish sand stone with pebbles, felspar and

quartz 26-44

Medium to fine red to yellow sand with clay and laterite 30-41

Coarse to very coarse red sand mixed with red clay 32-48

Medium to fine red to yellow clay sand 36-49

Yellowish colour clay 47-53


Table 2: Classification of Soil at Solid Waste Dump Site

Lithological Description Range (Below ground level)

in metres

Medium to fine reddish clay top soil 0-3

Medium to fine red sand mixed with clay and laterite 3-14

Coarse to medium red sand with clay 3-27

Medium to fine red sand 7-26

Coarse to medium red sand with laterite 15-34

Medium to fine mottled clay with red sand 22-41

Coarse to medium red sand 28-46

Coarse to very coarse red sandstone 26-47

Coarse to very coarse sand with pebbles 36-50

Figure 3: Distribution of borewells in Study Area

4.2 Physio-Chemical Analysis of Groundwater

Water samples were collected from the borewells after pumping for 15 minutes. The

samples were analyzed in the Public Health Laboratory, PWD, Puducherry, India. In

Toto 929 water samples were collected and tested for 17 physio-chemical and

bacteriological parameters viz. EC, pH, TDS, Alkalinity, Bicarbonate, Total

Hardness, Calcium, Magnesium, Iron, Chloride, Sulphate, Nitrate, Sodium, Fluoride,

Potassium, Phosphate, Silica, B.O.D, C.O.D, total coliforms and faecal coliforms. The

water samples were tested according to the standard methods [1], [2] for both physio-


chemical and bacteriological parameters as depicted in Table 3. Eventhough the water

samples were tested for all physio-chemical and bacteriological parameters , the study

is restricted to the pollution aspects of 10 key physio-chemical parameters viz. EC,

TDS, Bicarbonate, Total Hardness, Calcium, Magnesium, Chloride, Sulphate, Sodium

and Potassium.

Table 3: Standard methods used for the determination of parameters

Table 4: Mean Physio-Chemical Test Results of 68 Borewells (Jan. 2014-Oct. 2015)

BW No. EC TDS TH HCO3 Cl SO4 Na Ca Mg K

Cluster1

BW1 1328 847 352 207 303 83 164 79 33 5

BW5 1333 826 327 207 335 69 176 71 36 6

BW14 1176 742 369 372 191 65 134 80 45 4

BW15 1106 697 491 364 191 50 71 109 46 4

BW40 1454 946 309 324 309 74 184 74 37 4

Parameters Method/Procedure Parameters Method/Procedure

EC Instrumental Method Iron Phenanthroline Method

Colorimetric-NH2OH-HCl

pH Electrometric Method Chloride Argentometric Titration

Method

TDS Instrumental Method Sulphate Turbidimetric Method

Alkalinity Titration Method Nitrate Phenol Disulphonic Acid

Digestion Method,

colorimetric

Total Hardness Titration-EDTA Phosphate Vanadomolybdo - Phosphoric

Acid Colorimetric Method

Calcium Titration-EDTA Fluoride Colorimetric-SPADNS

Magnesium Calculation-

Titrimetric

Silica Molybdo Silicate

Sodium Flame Emission

Photometric

C.O.D Open Reflux, Titration Method

Potassium Flame Emission

Photometric

Total

Coliforms

MPN Method

Faecal Coliforms MPN Method


BW41 1558 983 338 288 352 67 204 69 43 3

BW42 1474 919 328 307 321 68 191 77 33 3

BW43 1287 807 268 271 276 66 188 62 24 2

BW47 1057 666 241 174 234 63 118 57 24 3

BW48 1284 808 279 276 265 68 162 61 35 3

BW49 1222 770 268 262 262 63 163 63 31 3

BW51 1424 921 315 322 297 65 188 70 38 4

BW52 1325 839 284 285 270 57 182 57 38 4

BW53 1230 775 271 240 252 63 167 59 30 3

BW54 1232 773 294 246 253 65 154 64 35 4

BW57 1179 743 232 149 273 69 148 55 29 4

BW58 1197 756 256 235 252 67 157 54 33 4

BW59 1068 669 229 187 226 71 152 61 24 3

BW60 1022 640 239 200 198 59 123 56 25 3

BW62 1128 711 243 204 232 66 146 63 22 4

BW63 955 601 234 160 198 70 117 55 27 3

BW67 1621 1022 317 273 380 79 210 70 39 5

BW68 1413 888 320 214 311 73 182 64 41 4

BW69 1076 679 269 227 227 62 136 65 24 3

BW70 1140 715 265 205 244 65 145 60 32 5

BW71 1053 640 244 234 207 58 152 58 24 4

BW72 1002 631 222 156 221 76 128 59 21 3

BW78 1151 722 251 205 263 61 156 56 30 3

Cluster2

BW7 1957 1232 353 427 457 84 275 74 39 7

BW8 2024 1278 414 320 481 86 287 89 47 8

BW9 1984 1250 409 371 447 94 264 88 51 8

BW10 2149 1353 440 424 494 57 262 83 53 10

BW No. EC TDS TH HCO3 Cl SO4 Na Ca Mg K

BW11 2102 1324 371 461 427 126 273 64 57 10

BW13 1786 1126 363 330 423 75 269 89 38 8

BW44 1811 1148 377 281 452 71 226 85 52 4

BW45 2145 1328 452 417 497 107 301 66 68 4

Cluster3

BW17 437 275 168 137 62 18 45 43 19 3

BW18 191 120 79 62 30 24 18 19 7 2


BW19 766 481 362 438 57 24 42 79 42 4

BW20 579 363 259 311 36 18 38 60 30 2

BW21 347 217 129 181 21 22 22 33 11 2

BW22 282 178 122 124 24 14 13 36 10 2

BW23 302 191 119 115 24 18 16 34 8 1

BW24 397 251 169 190 36 17 22 47 15 2

BW25 311 197 122 102 34 9 24 33 9 2

BW26 388 244 132 100 45 17 30 41 8 1

BW27 401 252 180 205 32 22 24 45 16 2

BW28 478 301 213 240 31 19 24 48 17 2

BW30 315 198 89 92 32 21 24 31 7 2

BW32 703 444 263 368 44 54 70 57 27 3

BW35 330 206 156 174 21 13 16 46 16 2

BW36 441 279 178 270 19 13 33 42 18 2

BW37 348 218 140 172 27 11 26 40 9 2

BW38 723 454 220 326 55 35 65 66 23 2

BW39 154 97 59 70 17 7 27 15 6 1

BW46 654 412 231 341 44 36 50 55 24 3

BW50 565 367 124 50 117 49 70 32 13 3

BW55 765 504 179 93 158 54 93 43 18 3

BW64 267 174 73 43 43 35 29 24 6 2

BW75 697 438 301 59 41 87 36 64 27 2

BW76 620 387 173 132 79 58 65 46 16 3

BW77 843 521 189 111 183 63 109 50 15 3

BW79 202 126 78 68 27 16 15 19 9 2

BW80 364 230 105 77 49 34 39 29 9 2

BW81 406 252 144 77 52 78 33 36 13 1

BW82 286 179 98 75 33 44 26 26 10 2

BW84 484 304 120 101 90 31 61 25 16 3

BW85 422 273 119 75 62 30 42 31 12 3

Note: EC - µS/cm, all other parameters – mg/L

4.3 Multivariate Statistical Analysis

As there is wide spatial variation (2 to 3 Km radius) with ground elevation (more than

45m) among the borewells it is appropriate to investigate the hydro-geological

chemistry of groundwater in the entire study area of all the 68 borewells. The mean

values of all the parameters under study are given in Table 4.


In this research work, different multivariate statistical techniques were applied [3-5].

These techniques were used to establish relationship among various parameters and

the sampling sites, to identify the factors and sources influencing groundwater quality

and to suggest useful tools for both management of water resources and monitoring of

groundwater quality [6-8]. In the present study, Correlation Co-efficient, Regression

Analysis, Normality Tests, Descriptive Analysis such as Mean, SD, Range, Skewness,

Kurtosis, etc. were used to evaluate Physio-chemical parameters of groundwater

samples [9], [10]. The statistical software package SPSS Version 21 was used for the

multivariate statistical analysis of the data.

5. RESULTS AND DISCUSSION

5.1 Hierarchical Cluster Analysis (HCA)

Using HCA it is feasible to plan optimal sampling strategy which reduces the number

of sampling stations thereby reducing recurring costs. HCA visualizes intra-

relationship among the parameters for a better understanding of the studied system

[11],[12]. Different sampling locations in the study area can be grouped into clusters

to spatially explain the similarity in chemical composition of groundwater quality

among the borewells. The selected ten hydro-chemical parameters viz, EC, TDS,

Bicarbonate, Total Hardness, Calcium, Magnesium, Chloride, Sulphate, Sodium and

Potassium in the study area were subjected to HCA. The clustering procedure

generated three well defined clusters.

Cluster 1 involves 28 borewells, forming 41% of the sampling stations. Cluster 2

comprises of 8 borewells, representing 12% of the sampling stations. Cluster 3

accounts for 32 borewells comprising of 47% of the sampling stations. Clusters 1, 2

and 3 correspond to polluted, highly polluted and non-polluted regions of the study

area. The final results of cluster formations are presented in Table 5 and the regional

distribution of borewells in different clusters are shown in Figure 4.


Figure 4: Borewell Locations in Different Clusters

Table 5: Cluster Classification

Cluster

No.

Borewell Designation Borewell

Nos.

Location Remarks

C1 1,5,14,15,40,41,42,43,47,48,

49,51,52,53,54,57,58,59,60,62,

63,67,68,69,70,71,72 and 78

28 Solid waste dump

area, Recharge pond

area and South-East

and South-Western

parts of study area

Polluted

C2 7,8,9,10,11,13,44 and 45 8 Solid waste dump

area, Recharge pond

area and South-

Western parts of

study area.

Highly

Polluted

C3 17,18,19,20,21,22,23,24,25,26,

27,28,30,32,35,36,37,38,39,46,50,

55,64,75,76,77,79,80,81,82,84 and

85

32 North-East, North-

West and South-East

and South-Western

parts of study area

Not

polluted


5.2 Correlation Analysis

Correlation coefficient is commonly used to measure and establish the relationship

between two variables. It is a simplified statistical tool to show the degree of

dependency of one variable with the other. Linear correlation between various

parameters is determined between +1 and -1, when the correlation co-efficient of a

parameter is seen to +1 or -1, the relationship between the variables is significant.

Positive correlation indicates common evaluation pattern and features between

different elements in solid waste dump site and SWW recharge pond area. If the

correlation co-efficient is negative it represents an alternation of one parameter

against another in the study area. From 10 parameters that have been considered for

analysis, a curve fit procedure for linear model has been adopted to find out the

possible correlation between selected parameters, using Karl Pearson Correlation. The

correlation matrix of the parameters in Clusters 1, 2 and 3 are presented in Tables 6,7

& 8. Perfect Correlation (r = 0.9 to1.0), Very Good Correlation (r = 0.8 to 0.9), Good

Correlation (r = 0.7 to 0.8) and Moderate Correlation (r=0.6 to 0.7) between the

variables were established. The relationship among the parameters depending on the

degree of correlation in all the three clusters are consolidated in Table 9.

Table 6: Correlation Matrix (Cluster 1)

Parameter EC TDS TH HCO3 Cl SO

4 Na Ca Mg K

EC 1.000

TDS .997 1.000

TH .430 .433 1.000

HCO3 .504 .516 .737 1.000

Cl .919 .908 .211 .162 1.000

SO4 .382 .394 -.131 -.283 .528 1.000

Na .847 .835 -.042 .207 .859 .445 1.000

Ca .284 .293 .939 .678 .088 -.092 -.177 1.000

Mg .624 .630 .818 .720 .398 -.038 .238 .623 1.000

K .244 .242 .311 .016 .295 .242 .126 .232 .366 1.000



4 Na Ca Mg K

EC 1.000

TDS .996 1.000

TH .690 .665 1.000

HCO3 .744 .735 .142 1.000


Cl .621 .601 .834 .123 1.000

SO4 .377 .354 -.050 .482 -.255 1.000

Na .581 .536 .391 .488 .365 .491 1.000

Ca -.567 -.533 -.044 -.769 -.077 -.713 -.419 1.000

Mg .670 .636 .700 .332 .478 .474 .250 -.577 1.000

K .304 .362 -.100 .432 -.242 .021 .099 .088 -.291 1.000



4 Na Ca Mg K

EC 1.000

TDS .999 1.000

TH .800 .792 1.000

HCO3 .507 .496 .760 1.000

Cl .656 .665 .145 -.160 1.000

SO4 .582 .582 .319 -.161 .538 1.000

Na .817 .820 .339 .126 .901 .582 1.000

Ca .794 .786 .957 .768 .147 .261 .336 1.000

Mg .765 .758 .953 .780 .152 .262 .356 .885 1.000

K .637 .646 .475 .362 .549 .235 .610 .406 .578 1.000

Table 9: Degree of Correlation among Parameters

Correlation Cluster 1 Cluster 2 Cluster 3

Perfect Cor.

(0.9-1)

a) EC with TDS & Cl

b) TDS with Cl

c) TH with Ca

a) EC with TDS a) EC with TDS

b) TH with Ca & Mg

c) Cl with Na

V.Good Cor.

(0.8-0.9)

a) EC with Na

b) TDS with Na

c) Cl with Na

a) TH with Cl a) EC with TH and Na

b) TDS with Na

c) Ca with Mg

Good Cor.

(0.7-0.8)

a) TH with HCO3

b) HCO3 with Mg

a) EC with HCO3

b) TDS with HCO3

c) TH with Mg

a) EC with Ca and Mg

b) TDS with TH, Ca & Mg

c) TH with HCO3

d) HCO3 with Ca & Mg

Moderate Cor.

(0.6-0.7)

a) EC & TDS with Mg.

b) Ca with HCO3 and

Mg

a) EC with TH & Cl &

Mg

b) TDS with TH, Cl &

Mg

a) EC with Cl & K

b) TDS with Cl & K

c) Na with K


5.3 Regression Analysis

Statistical methods like regression models are best suited for establishing relationship

between dependent and one or more independent variables. Multiple Linear

Regression (MLR) technique was used to develop models relating hydro-chemical

parameters to a set of statistically significant independent hydro-chemical parameters

[13]. MLR is based on method of least squares, where sum of squared error between

actual and predicted values should be minimum.

The general MLR model is given by the following equation.

Y=a+b(TDS)+c(TH)+d(HCO3)+e(Cl)+f(SO4)+g(Na)+h(Ca)+i(Mg)+j(K)

Where ‘Y’ – EC, TH,HCO3, Cl, SO4,Na, Ca, Mg and K (Input parameters)

a - constant

b,c,d,e,f,g,h,i & j - co-efficients of MLR model.

The most significant MLR models for predicting EC, TH, HCO3, Cl, SO4, Na, Ca, Mg

and K from various combinations of known concentrations of other chemical

constituents in groundwater are presented in Tables 10, 11 and 12 for Clusters 1, 2

and 3.

Table 10: MLR Model Co-efficient for Selected Water Quality Parameters

(Cluster 1)

Dependent

Variable

‘Y’

Constant

‘a’

Independent Variable R

R2

TDS

‘b’

TH

‘c’

HCO3

‘d’

Cl

é'

SO4

'f''

Na

‘g’

Ca

‘h’

Mg

‘i’

K

‘j’

EC 81.69 1.13 0.298 0.037 0.469 -0.35 0.683 -0.42 0.286 -2.76 0.999 0.997

TH -1.24 - - -0.15 -0.04 -0.99 0.216 3.992 3.28 0.099 0.989 0.978

HCO3 -10.5 - -0.68 - -0.86 -2.36 1.941 5.942 5.26 -8.6 0.952 0.906

Cl -63.05 - -0.11 -0.53 - -0.41 1.719 2.703 2.415 -2.48 0.957 0.915

SO4 28.55 - -0.24 -0.12 -0.03 - 0.216 1.272 0.857 0.054 0.793 0.629

Na 28.51 - 0.174 0.335 0.478 0.725 - -2.09 -1.46 1.762 0.968 0.936

Ca 3.667 - 0.203 0.065 0.047 0.269 -0.13 - -0.72 0.279 0.986 0.972

Mg 1.247 - 0.201 0.069 0.051 0.218 -0.11 -0.87 - 0.706 0.956 0.914

K 0.595 - 0.001 -0.01 -0.01 0.002 0.015 0.038 0.08 - 0.573 0.329


Table 11: MLR Model Co-efficient for Selected Water Quality Parameters (Cluster 2)

Table 12: MLR Model Co-efficient for Selected Water Quality Parameters (Cluster 3)

In Cluster 1, the Co-efficient of Determination (R2) varied from 0.329 to 0.997, the

least being MLR model for K and best being MLR model for EC. R2 for EC is 0.997,

in otherwords 99.7% of the variance has been explained by the said model. In Cluster

2, R2 varied from 0.733 to 1, the least being MLR model for K. It may be seen from

Table 11, there is perfect correlation “1” between the predicted values and observed

values for all models viz. EC, TH, HCO3, Cl, SO4, Na, Ca and Mg except K. In

Cluster 3, R2 ranged from 0.645 to 0.999, the least being MLR model for K. The best

model was identified as for EC (R2 = 0.999) in relation to other parameters where

99.9% of variance has been explained by the model.

Dependent

Variable

‘Y’

Constant

‘a’

Independent Variable R R2

TDS

‘b’

TH

‘c’

HCO3

‘d’

Cl

‘e’

SO4

‘f’

Na

‘g’

Ca

‘h’

Mg

‘i’

K

‘j’

EC -20.476 - 0.887 0.509 3.222 2.731 -1.06 -1.82 - 24.47 1 1

TH -298.87 - - 0.18 0.235 -0.47 0.631 2.583 3.665 -0.07 1 1

HCO3 1659.94 - 5.554 - -1.31 2.61 -3.5 -14.3 -20.4 0.372 1 1

Cl 1271.03 - 4.253 -0.77 - 1.998 -2.68 -11 -15.6 0.285 1 1

SO4 -636.02 - -2.13 0.383 0.5 - 1.342 5.497 7.798 -0.14 1 1

Na 473.963 - 1.586 -0.29 -0.37 0.745 - -4.1 -5.81 0.106 1 1

Ca 115.71 - 0.387 -0.07 -0.09 0.182 -0.24 - -1.42 0.026 1 1

Mg 81.56 - 0.273 -0.05 -0.06 0.128 -0.17 -0.71 - 0.018 1 1

K -25.064 - - 0.049 -0.02 0.027 -0.01 0.275 -0.01 - 0.856 0.733

Dependent

Variable

‘Y’

Constant

‘a’

Independent Variable R R2

TDS

‘b’

TH

‘c’

HCO3

‘d’

Cl

‘e’

SO4

‘f’

Na

‘g’

Ca

‘h’

Mg

‘i’

K

‘j’

EC 1.449 1.26 0.247 0.01 -0.02 0.113 0.975 0.584 -0.38 -3.05 0.999 0.999

TH -4.792 - - 0.015 0.078 0.338 -0.3 2.384 4.233 -1.02 0.987 0.974

HCO3 -9.221 - 0.162 - -2.16 -2.1 3.527 2.541 3.594 8.488 0.938 0.881

Cl -8.537 - 0.074 -0.2 - -0.34 1.501 0.473 -0.35 7.288 0.958 0.919

SO4 11.351 - 0.331 -0.2 -0.35 - 0.949 -0.31 -0.13 -3.36 0.838 0.702

Na -0.804 - -0.1 0.112 0.526 0.323 - -0.02 0.153 0.095 0.961 0.923

Ca 8.547 - 0.248 0.025 0.052 -0.03 -0.01 - -0.59 -1.73 0.97 0.94

Mg -3.233 - 0.125 0.01 -0.01 -0 0.014 -0.17 - 1.749 0.971 0.944

K 1.544 - -0 0.001 0.011 -0.01 0 -0.02 0.083 - 0.803 0.645


5.4 Descriptive Statistics

During this study some important physio-chemical properties from shallow

groundwater in the study area were obtained and evaluated. The main objective of

this study is to assess and define the groundwater quality, distribution and magnitude

of potential contamination in the area. In addition to statistically documenting the

present condition of shallow groundwater for future comparisons, the study should

also be valuable to the authorities responsible for groundwater exploitation and

management. Cluster wise important physio-chemical attributes (such as EC, TH,

HCO3, Cl, SO4, Na, Ca, Mg and K) of water quality affected by SW & SWW

operations, taking cognizance of the overall geology and environmental situation, are

statistically assessed and presented in Tables 13, 14 and 15.

Table 13: Descriptive Statistics (Cluster 1)

Parameter Guideline

Value

Range Minimum Maximum Mean Std.

Deviation

Variance Skewness Kurtosis

EC 750 666 955 1621 1231.96 172.721 29832.70 .526 -.392

TDS 500 421 601 1022 776.29 112.410 12635.91 .517 -.498

TH 300 269 222 491 287.68 56.922 3240.152 1.780 4.805

HCO3 200 223 149 372 242.64 59.753 3570.386 .491 -.341

Cl 250 189 191 380 262.25 49.639 2464.046 .532 -.206

SO4 200 33 50 83 66.50 6.818 46.481 .174 1.020

Na 50 139 71 210 157.07 30.145 908.735 -.645 1.099

Ca 75 55 54 109 65.29 11.369 129.249 2.291 7.247

Mg 30 25 21 46 32.11 7.156 51.210 .237 -.869

K 10 4 2 6 3.68 0.863 0.745 .702 .706


Parameter Guideline

Value


Deviation


EC 750 363 1786 2149 1994.75 140.208 19658.21 -.486 -1.130

TDS 500 227 1126 1353 1254.88 83.552 6980.982 -.553 -1.023

TH 300 99 353 452 397.38 36.789 1353.411 .351 -1.488

HCO3 200 180 281 461 378.88 63.298 4006.696 -.335 -1.342

Cl 250 74 423 497 459.75 28.449 809.357 .117 -1.493

SO4 200 69 57 126 87.50 21.640 468.286 .562 .271

Na 50 75 226 301 269.63 21.778 474.268 -.873 2.245

Ca 75 25 64 89 79.75 10.334 106.786 -.755 -1.337

Mg 30 30 38 68 50.63 9.694 93.982 .395 .405

K 10 6 4 10 7.38 2.326 5.411 -.606 -.743



5.4.1 Electrical Conductivity (EC)

EC is the ability of a solution to conduct electricity. It is governed by the migration of

ions in the solution and it depend on the nature and number of ionic species in that

solution. EC signifies the amount of total dissolved salts. In Cluster 1, EC values

ranged from 955 to 1621 µS/cm. In Cluster 2, EC values ranged from 1786 to 2149

µS/cm. All the borewells in Clusters 1 and 2 indicated the presence of high amount of

dissolved inorganic substances. In Cluster 3, it ranged from 180 to 843 µS/cm. In

Clusters 1 & 2, 100% of the borewells exceeded the acceptable limit of 750 µS/cm for

good water. In Cluster 3, only 9% of the borewells exceeded this limit.

5.4.2 Total Dissolved Solids (TDS)

TDS is the common measure which correlates with the concentration of dissolved

minerals in water samples and it indicates the salinity behaviour of groundwater. In

Cluster 1, TDS values ranged from 601 to 1022 mg/L. In Cluster 2, TDS values varied

from 1126 to 1353 mg/L. All the borewells in Clusters 1 and 2 exhibited higher TDS

values than the prescribed limit of 500 mg/L. 100% of the borewells in Clusters 1 & 2

are affected. In Cluster 3, it ranged from 97 to 521 mg/L. Only 6% of the borewells

are affected in Cluster 3.

5.4.3 Total Hardness (TH)

In groundwater, Bicarbonates, Carbonates, Sulphates and Chlorides of Calcium and

Magnesium contribute significantly to TH. So the principal hardness causing ions are

calcium and magnesium. TH is a measure of the occurrence and abundance divalent

cations that is Ca2+ and Mg 2+. High hardness indicate the non potability of

groundwater. In Cluster 1, the values ranged from 222 to 491 mg/L. In Cluster 2, the

values ranged from 353 to 452 mg/L. In Cluster 3, the values varied from 59 to 362

mg/L. In Cluster 1, 36% of the borewells exceeded the threshold limit of 300mg/L. In

Cluster 2, 100% of the borewells exceeded the limit. In Cluster 3 only 16% of the

Parameter Guideline

Value


Deviation


EC 750 663 180 843 452.94 184.871 34177.41 0.554 -0.741

TDS 500 424 97 521 285.41 117.769 13869.53 0.519 -0.745

TH 300 303 59 362 159.16 69.396 4815.814 1.061 1.186

HCO3 200 395 43 438 155.59 105.070 11039.66 1.205 0.549

Cl 250 166 17 183 50.78 38.246 1462.757 2.265 5.222

SO4 200 80 7 87 30.97 20.273 410.999 1.224 0.930

Na 50 96 13 109 38.97 23.226 539.451 1.407 1.745

Ca 75 64 15 79 40.47 14.712 216.451 0.555 0.266

Mg 30 36 6 42 15.19 8.118 65.899 1.437 2.550

K 10 3 1 4 2.22 0.706 0.499 0.245 0.191


borewells are affected. The classification guidelines for hardness of water is given in

Table 16.

Table 16: Classification of Groundwater based on TH

TH (mg/L) as CaCO3 Water Classification

0 – 75

75 – 150

150 – 300

>300

Soft

Moderately Hard

Hard

Very Hard

In Cluster 1, 18 borewells are classified as hard and 10 borewells as very hard. In

Cluster 2, all the borewells are classified under very hard category. In Cluster 3, 2

borewells are under soft category, 15 borewells are classified as moderately hard, 13

borewells are hard type and 2 borewells are under very hard category.

5.4.4 Bicarbonate (HCO3)

HCO3 in water is mainly associated with Ca and Mg. In Cluster 1, the values ranged

from 149 to 372 mg/L. In Cluster 2, the values ranged from 281 to 461 mg/L. In

Cluster 3, the values ranged from 43 to 438 mg/L. In Cluster 1, 82 % of the borewells

exceeded the threshold limit of 200 mg/L. In Cluster 2, 100% of the borewells

exceeded the limit whereas in Cluster 3, only 25% of the borewells exhibited higher

values.

5.4.5 Chloride (Cl)

Chloride is an ion found in varying amount in groundwater. It may be present

naturally in groundwater or may originate from diverse sources such as weathering,

leaching of sedimentary rocks, sea water intrusion etc., In the study area, excessive

amount of Cl is added to groundwater due to solid waste leachate and SWW leachate

which impart salinity to groundwater. In Cluster 1, it ranged from 191 to 380 mg/L. In

Cluster 2, the values ranged from 423 to 497 mg/L. In Cluster 3, the values ranged

from 17 to 183 mg/L. In Cluster 1, 17 borewells showed higher values which

attribute to 61% of the total borewells. All the borewells in Cluster 2 showed higher

values than the prescribed limit of 250 mg/L. In Cluster 3, no borewell is affected.

5.4.6 Sulphate (SO4)

Sulphur in groundwater is generally present in SO4 form. SO4 may enter into

groundwater through weathering of sulphide bearing deposits. In Cluster 1, it ranged

from 50 to 83 mg/L. In Cluster 2, it ranged from 57 to 126 mg/L. In Cluster 3, it

ranged from 7 to 87 mg/L. All the borewells in Clusters 1, 2 & 3 exhibited values less

than the prescribed limit of 200 mg/L.

5.4.7 Sodium (Na)

Na is a mineral which naturally occurs and is prevalent in groundwater. All Na

compounds are soluble in water and remain in aqueous solution. The groundwater in

contact with igneous rocks will dissolve Na from its natural source. In drinking water


higher concentration of Na may cause heart problem. Higher concentration of sodium

ion when used for irrigation purposes may cause salinity problems. Excessive amount

of Na ion in groundwater affects the palatability in general. In Cluster 1, it varied

from 71 to 210 mg/L. In Cluster 2, it ranged from 226 to 301 mg/L. In Cluster 3, it

ranged from 13 to 109 mg/L. In Clusters 1 & 2, 100% of borewells exceeded

threshold limit. In cluster 3, 25% of borewells are affected. Anthropogenic activities

like SW leachate and SWW land application contribute to increased sodium levels.

The classification guidelines for irrigation quality of water based on Na% is furnished

in Table 17 [14].

Table 17: Classification of Groundwater based on Na%

Na% Water Classification

<20

20 - 40

40 - 60

60 – 80

>80

Excellent

Good

Medium

Bad

Very Bad

In Cluster 1, Na% varied from 24.88 to 61.38%. The mean Na% is 52.33, and can

be classified as medium type water fit for irrigation purposes. In Cluster 2, Na%

ranged from 53.27 to 62.82%. The mean Na% is 58.43, and may be concluded as

medium from irrigation point of view. In Cluster 3, Na% varied from 15.97 to

55.47%, The mean Na% is 33.06, and can be concluded that the groundwater in

Cluster 3 is highly suitable for irrigation also.

5.4.8 Calcium (Ca)

Calcium is an important element in groundwater. It may dissolve readily from

carbonate rocks and lime stones or may be through leaching of soils. The other

sources include primarily industrial and municipal discharges. Calcium is of

nutritional value to the human beings and it helps in maintaining the structure of plant

cells and soils. Ca is directly related to hardness. In Cluster 1, it ranged from 54 to

109 mg/L. In Cluster 2, it ranged from 64 to 89 mg/L. In Cluster 3, it varied from 15

to 79 mg/L. 4 borewells in Cluster 1, exceeded the threshold limit of 75mg/L which

formed 14% of the total borewells. In Cluster 2, 63% of borewells exceeded the limit.

In cluster 3, only 1 borewell is affected.

5.4.9 Magnesium (Mg)

Magnesium is also found abundant in groundwater. It’s deficiency may lead to

protein malnutrition. Mg is another mineral which is directly responsible to hardness.

In Cluster 1, it varied from 21 to 46 mg/L. In Cluster 2, it varied from 38 to 68 mg/L.


In Cluster 3 it ranged from 6 to 42 mg/L. In Cluster 1, 64% of the borewells exceeded

the threshold limit of 30 mg/L. In Cluster 2, 100% of the borewells are affected. In

Cluster 3, only 6% of the borewells are affected.

5.4.10 Potassium (K)

Potassium is an important cation which plays a vital role in intermediate metabolism.

It is an important nutrient for both plant and animal life. Ingestion of excessive

amount may prove detrimental to human beings. All the 68 borewells in Clusters 1,2

& 3 have showed lesser values than the prescribed limit of 10 mg/L.

A consolidated report on the contamination level in the borewells is demonstrated in

Table 18.

Table 18: Level of Contamination in borewells

Parameters Cluster 1 Cluster 2 Cluster 3

No. of

Borewells

%

affected

No. of

Borewells

%

affected

No. of

Borewells

%

affected

EC

TDS

TH

HCO3

Cl

SO4

Na

Ca

Mg

K

28

28

10

23

17

Nil

28

4

18

Nil

100

100

36

82

61

Nil

100

14

64

Nil

8

8

8

8

8

Nil

8

5

8

Nil

100

100

100

100

100

Nil

100

63

100

Nil

3

2

5

8

Nil

Nil

8

1

2

Nil

9

6

16

25

Nil

Nil

25

3

6

Nil

5.5 Skewness and Kurtosis

In Cluster 1, the skewness value is more than zero for all parameters except Na. So

the parameters viz., EC, TH, HCO3, Cl, SO4, Ca, Mg and K fall in right-tailed

distribution. Na falls in left tailed distribution. Further all the parameters are

moderately skewed (-1 to +1) except TH and Ca (>1) which are highly skewed. The

Kurtosis value is less than three for all the parameters except TH and Ca and so they

are platykurtic. TH and Ca are leptokurtic as the Kurtosis value is more than three.

In Cluster 2, the skewness value for parameters, EC, TH, HCO3, Cl, SO4, Na, Ca, Mg

and K, is less than zero. So they fall in left tailed skewed distribution and they are

moderately skewed. For parameters TH, Cl, SO4 and Mg, the skewness value is more

than zero, so they are under right tailed skewed distribution and they are also

moderately skewed. The Kurtosis value is less than three for all the parameters, so

they are platykurtic.


In Cluster 3, the skewness value for all parameters are positive, so they are under right

tailed skewed distribution. The parameters such as TH, HCO3, Cl, SO4, Na and Mg

are highly skewed. Parameters viz. EC, TDS, Ca and K are moderately skewed. The

Kurtosis value is less than three for all parameters except Cl, so they are platykurtic ,

whereas Cl is leptokurtic (>3).

5.6 Normality

For the study of statistical distribution of variables in the Clusters normality tests

using two methods viz., Kolmogorov-Smirnov and Shapiro-Wilk were conducted for

all the parameters of interest and the test results are demonstrated in Tables 19, 20

and 21. The cluster wise box whisker plot for all parameters are depicted in Figures 5,

6 and 7. As the sample size is small i.e. less than 50, Shapiro-Wilk test is more

appropriate. For testing of normal distribution two hypotheses viz., Ho (Null

hypothesis) and H1 (Alternate hypothesis) were resorted to.

The assumptions are as follows:-

1. Ho – Null hypothesis : The data exhibit a normal distribution.

2. H1 – Alternate hypothesis : The data do not follow a normal distribution.

In Cluster 1, for p>0.05, the parameters EC, TDS, HCO3, Cl, SO4, Na and Mg follow

normal distribution whereas the parameters TH, Ca and K are not normally distributed

and hence H1 (Alternate hypothesis) is accepted.In Cluster 2 all the parameters are

normally distributed. In Cluster 3, the parameters like EC, TDS and Ca follow

normal distribution whereas the parameters TH, HCO3, Cl, SO4, Na, Mg and K are

not normally distributed and H1 (Alternate hypothesis) is rejected.

Table 19: Tests of Normality (Cluster 1)

Parameter Kolmogorov-Smirnov Shapiro-Wilk

Statistic Sig. Statistic Sig.

EC .107 .200 .966 .471

TDS .112 .200 .961 .378

TH .151 .102 .850 .001

HCO3 .117 .200 .961 .378

Cl .105 .200 .962 .382

SO4 .107 .200 .978 .807

Na .094 .200 .966 .474

Ca .189 .012 .785 .000

Mg .125 .200 .956 .274

K .248 .000 .859 .001


Figure.5: Box Plot for various parameters (Cluster 1)




EC .155 .200 .905 .321

TDS .171 .200 .920 .426

TH .210 .200 .925 .468

HCO3 .227 .200 .934 .549

Cl .164 .200 .920 .431

SO4 .153 .200 .976 .943

Na .238 .200 .926 .479

Ca .248 .157 .834 .065

Mg .153 .200 .946 .675

K .231 .200 .859 .117






EC .151 .062 .935 .055

TDS .147 .078 .939 .072

TH .132 .169 .928 .035

HCO3 .195 .003 .852 .000

Cl .228 .000 .726 .000

SO4 .197 .003 .874 .001

Na .167 .024 .858 .001

Ca .088 .200 .975 .636

Mg .148 .074 .876 .002

K .309 .000 .830 .000



5.7 Hydro-geochemical Analysis

Cluster wise Hydro-geochemical Analysis has been investigated for most prominent

cations and anions in groundwater. The observed order of affinity in groundwater

contacting the aquifer matrix is Cl->HCO3->SO4

2- = Na+>Ca2+>Mg2+>K+ for Clusters

1 and 2. For Cluster 3, the order is HCO3->Cl->SO4

2- = Ca2+>Na+>Mg2+>K+. Cation

exchange is the most feasible explanation of the observed changes. Strong increase in

chloride in groundwater due to solid waste dumping and SWW land application in

Clusters 1 and 2 can be explained as mixing of waters.

5.8 Sources of Nacl in Groundwater

5.8.1 Evapotranspiration

In general evaporation causes an increase in the concentration of all constituents in

the groundwater. Climatic conditions affect the rate of evapotranspiration, and it is an

effective indicator of concentration of dissolved ions in groundwater [15].

Groundwater samples are plotted with Na+/Cl- against EC. If points are found to

spread horizontally, it means that the Na+/Cl- ratio does not change significantly with

the increase of EC. Consequently the process of evapotranspiration might not be the

main cause for the increase in Na+ and Cl- concentration in groundwater. As such for

Clusters 1,2 and 3, Na+/Cl- has been plotted against EC and given in Figures 8, 9 and

10. In all the clusters the borewell points spread horizontally without any significant


change with increase of EC, indicating that evapotranspiration does not play a part in

the increase of Na+ and Cl- concentration in groundwater.

5.8.2 Ion exchange

Wet and dry climatic conditions may promote weathering and this mineral weathering

can contribute to groundwater chemistry. One of the important processes responsible

for the concentration of ions in groundwater is ion exchange [16],[17]. Chloro-

Alkaline Index (CAI) strongly suggest the occurrence of ion exchange process and is

given by

CAI = Cl-– (Na+ + K+) /Cl-

When there is an exchange between Ca or Mg and Na or K in groundwater in the

aquifer, CAI is negative and if there is reverse ion exchange then the index is

positive. Accordingly CAI has been calculated for borewells in Clusters 1,2 and 3.

In Clusters 1 and 2 all the borewells (100%) showed positive CAI values indicating

reverse ion exchange process played a dominant role. In Cluster 3, 19 borewells

(60%) showed negative results demonstrating ion exchange process, whereasreverse

ion exchange was noticed in 13 borewells (40%).

5.8.3 Compositional Relation

The compositional relation among various dissolved ions can be used to investigate

the origin of solutes and other hydro geochemical processes. The stoichiometric

relation of the dissolved ions account for their sources or origin [18]. Weathering of

silicate rocks is one of the major processes, responsible for high concentration of Na+.

The molar ratio Na+/Cl- plays a very prominent role in identifying excess of Na+ or

Cl- due to dissolution/weathering of rocks or anthropogenic activities like solid waste

dumping and SWW land application.

Further if the molar ratio Na+/Cl- ≈1, then halite dissolution is responsible for Na+. If

Na+/Cl- >1, it is interpreted as Na+ released from silicate weathering, indicating that

ion exchange process is dominant. Hence, Na+ has been plotted against Cl- for

Clusters 1, 2 and 3 in Figures 11,12 and 13.

In Cluster 1, the molar ratio Na+/Cl- varied from 0.57 to 1.13. Na+/Cl- is <1 for 22

borewells (79%) indicating reverse ion exchange process (Figure 11) where Cl- ions

dominate over Na+ ions. The anthropogenic activities like solid waste dumping and

SWW land application are responsible for excess Cl- ions in groundwater. For 6

borewells (21%) Na+/Cl- ≥1, indicating that excess Na+ ions are due to halite or

silicate weathering.

In Cluster 2, the molar ratio varied from 0.77 to 0.99, all the borewell samples

(100%) lie below the trend line 1:1 (Figure 12). Usually wastewater is enriched with

Na+ and Cl-. Here the reverse ion exchange process played a dominant role, indicating

the anthropogenic activities play a vital role for Cl- ions in groundwater.


In the Reverse Ion Exchange Process which is dominant in Clusters 1 and 2, sodium is

removed from solution and calcium is released and the replacement order due to solid

waste and SWW leaching is Na+>Mg2+>Ca2+.

In Cluster 3, the molar ratio ranged from 0.84 to 2.68 the groundwater samples of 29

borewells (91%) lie above the trend line 1:1 i.e. Na+/Cl- >1 (Figure 13) indicating that

excess Na+ ions in groundwater is mainly due to weathering and dissolution of rocks.

Here reverse ion exchange process is less prominent.

Figure 8 : Na/Cl Versus EC (Cluster 1)


Figure 11 : Na Versus Cl (Cluster 1)

Figure 9 : Na/Cl Versus EC (Cluster 2)



Figure 10 : Na/Cl Versus EC (Cluster3)



CONCLUSION

The study has examined the groundwater quality in a part of Lawspet area, on the

northern part of Puducherry, India, which has a wide spatial variation (2 to 3 Km

radius) and with ground elevation (more than 45m) among 68 borewells. Here a

unique situation of solid waste dumping and SWW land disposal prevails

simultaneously within the same campus.

12 new borewells were sunk for depths from 30m to 53m and soil litho-

particulars are documented. Monthly water sample surveys for 17 hydro-

chemical parameters were made. 929 samples from 68 borewells were analyzed

in order to evaluate the temporal and spatial variations of the groundwater.

Hierarchical Cluster analysis was carried out with highly correlated parameters

viz., EC, TH, HCO3, Cl, SO4, Ca, Mg and K for all 68 borewells. 3 well defined

clusters namely C1, C2 and C3 were formed. The borewells in Clusters 1 and 2

are polluted and located in and around solid waste dump site and recharge pond

area. The borewells in Cluster 3 are located in the peripherals of the study area

and are not polluted.

Correlation analysis revealed perfect to very good correlation. Also good and

moderate correlation among parameters were noticed. The MLR analysis showed

that all the models are highly correlated in other words, the observed values and

the predicted values are highly significant except for model for K in Cluster 1.


Generally the Co-efficient of Determination i.e. R2 varied from 0.9 to 1, showing

that the models are perfectly fit for simulations.

Cluster-wise Descriptive analysis was made for Minimum, Maximum, Mean,

Range, Skewness , Kurtosis and Normality. In Clusters 1 and 2, for parameters

EC and TDS 100% of borewells exceeded the acceptable limit. For TH, 36% of

borewells exceeded the limit in Cluster 1, whereasin Cluster 2, 100% of

borewells have exceeded the limit. Further the borewells are categorized as very

hard and hard in Clusters 1 and 2.

For HCO3, 82% of borewells in Cluster 1 and 100% of borewells in Cluster 2

have exceeded the threshold limit. As far as Cl is concerned, 68% of borewells in

Cluster 1 and 100% of borewells in Cluster 2 showed excess values than the

prescribed limit. In Cluster 3 no borewell is affected. For SO4, all borewells in

Clusters 1, 2 and 3 showed lesser values than the allowable limit.

For Na, 100% of borewells exceeded the threshold limit in Clusters 1 and 2.

From irrigation point of view, as the Na% is between 20% and 60%, the water is

fit for agricultural purposes. For Ca, 14% of borewells are affected with excess

Ca in Cluster 1 and 63% of borewells are affected in Cluster 2. For Mg, 64% and

100% of borewells have exceeded the allowable limit in Clusters 1 and 2. As far

as K is concerned, no borewell is affected in any cluster.

In Cluster 1, all the parameters are normally distributed except TH, CA and K.

In Cluster 2, all the variables are normally distributed. In Cluster 3, EC, TDS and

Ca are normally distributed and others are not.

The hydro-geochemical analysis revealed that the order of cations and anions in

Clusters 1 and 2 is Cl>HCO3>SO4 = Na>Ca>Mg>K. In Cluster 3 the order is

HCO3>Cl>SO4 = Ca>Na>Mg>K. Further analysis showed that Reverse Ion-

Exchange process played a dominant role in Clusters 1 and 2, indicating that

anthropogenic activities are mainly responsible for groundwater contamination.

In Cluster 3, Ion-Exchange process dominated the hydro-geology of the area

indicating that weathering and dissolution are responsible for excess Na ions in

groundwater.

The result of this study illustrates that multivariate statistical and hydro-

geochemical methods are excellent exploratory tools for interpreting complex

water quality data sets and for understanding spatial variations, also they are

useful and effective for water quality management. Additionally the results may

be used to reduce the number of samples analyzed both in space and time without

much loss of information. This will assist the decision makers to identify

priorities to improve water quality that has deteriorated due to pollution from

various anthropogenic activities like indiscriminate and unscientific solid waste

dumping and SWW land application.


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