“BIO-STATISTICAL MODEL OF IMPACT OF SUGAR ... I hereby declare that the project report entitled...

“BIO-STATISTICAL MODEL OF IMPACT OF SUGAR

INDUSTRY ON GROUNDWATER QUALITY IN MALEGAON

(TAL. BARAMATI)”

A PROJECT REPORT ENTITLED

“BIO-STATISTICAL MODEL OF IMPACT OF SUGAR

INDUSTRY ON GROUNDWATER QUALITY IN MALEGAON

(TAL. BARAMATI)”

SUBMITTED TO

BOARD OF COLLEGE AND UNIVERSITY DEVELOPMENT (BCUD)

PUNE UNIVERSITY, PUNE

SUBMITTED BY

Dr. VAISHALI VILAS PATIL ASSISTANT PROFESSOR,

PRINCIPAL INVESTIGATOR

AND

Prof. AVINASH S. JAGTAP Prof. NEETA K. DHANE ASSOCIATE PROFESSOR ASSISTANT PROFESSOR

CO-INVESTIGATOR CO-INVESTIGATOR

DEPARTMENT OF STATISTICS

TULJARAM CHATURCHAND COLLEGE, BARAMATI, DIST. - PUNE (MS) INDIA

MARCH, 2014

ACKNOLEDGEMENT

This is right time to express my feeling to the Hon’ble Vice Chancellor, Pune University Pune, Director, BCUD, Pune University, Pune, Dr. Ravindra Jaybhaye, OSD, BCUD, Pune university, Pune and Dr. S. J. Sathe, ARC, T. C. College, Baramati for providing me the opportunity and encouragement for the research.

I am very much thankful to my inspiration Dr. Chandrashekhar V. Murumkar, Principal, Tuljaram Chaturchand College, Baramati, who is always behind me for constant inspiration and support. I extend my thanks to him for providing all necessary research facilities and spare time throughout the period of this project work.

I am thankful to Director, Krushi Vigyan Kendra, Baramati for providing guidance and facilities for the collection and chemical analysis of the sample.

My sincere thanks to my colleagues of the department for their help throughout the research work. I extend my thanks to non teaching staff of the department.

Last but not the least I am very much thankful to my family for their understanding, patience and endless support.

Place: Baramati Dr. Vaishali V. Patil

Date: Principal Investigator

DECLARATION

I hereby declare that the project report entitled “Bio-Statistical Model of Impact of Sugar

Industry on Groundwater Quality in Malegaon (Tal. Baramati)” completed and written by me under

the financial support of BCUD, Pune University, Pune at Tuljaram Chaturchand College,

Baramati has not been previously published or formed the basis of any Degree, Diploma,

Research project or any other similar title.

Place: Baramati Dr. Vaishali V. Patil

Date: Principal Investigator

CERTIFICATE

This is to certify that the research project entitled “Bio-Statistical Model of

Impact of Sugar Industry on Groundwater Quality in Malegaon (Tal. Baramati)”

being submitted herewith for the research project sanctioned under the financial

support by BCUD, Pune University, Pune is the result of original work completed

by Dr. V. V. Patil and not the part of any earlier submission for any award of

degree, project or similar title.

Dr. Chandrasekhar V. Murumkar

Principal

Tuljaram Chaturchand College, Baramati

Place: Baramati

Date:

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1. Literature Review

The exhaustive literature survey indicates the extensive studies on Water Quality have been

carried out by the various research workers.

Shenbagavalli.S, Mahimairaja.S, Kalaiselvi.P (2011). In this paper they studied the

boimethanated distillery spentwash from the Salem Cooperative Sugar Mills Ltd, Mohanur,

Namakkal district in farmers’ field, particularly in dry land, as a source of plant nutrients and

irrigation water. The impact of spent wash application on soil and groundwater quality was

assessed in areas previously applied with the distillery spent wash.

The ground waters samples were collected from open wells near the spentwash applied

fields in Namakkal district contained large amount of salts, particularly K + and Cl suggesting

that the water contamination is mainly due to the application of distillery spent wash. More than

50% of the samples were found unsuitable for irrigation purpose as they have shown greater

potential for salinity hazards. Though no marked evidence was observed on the soil

characteristics, the nutrients (N, P and K) and salt (Na, Ca, Mg, Cl and SO4) contents were

relatively higher in these soils previously amended with the spentwash.

The main objective of the paper Sheth R. C. and Kalshetty B. M. (2011) is to identify

the quality of ground water especially in the industrial area and to calculate water quality index

for different ground water sources at industrialized area. The investigation of quality assessment

of water resources around Jamakhandi sugars in different three unions.

Pawar N. J., Pondhe G.M., Patil S.F.(1998) In this article groundwater pollution probably

caused by the disposal of effluent from the Mula Sugar factory is discussed. This article revealed

spatial as well as temporal changes in the chemical properties of groundwater. While the

temporal changes have been attributed to dilution and concentration phenomenon governed by

climate factors, the spatial variation in the geochemical characteristics of groundwater appeared

to be related to pollution due to effluents from the Mula Sugar Factory. A Statistical Model for

Evaluating Water Pollution in Jiulong River Watershed Heshan Guan∗1, Weiping Wang2,

Qingshan Jiang3, Huasheng Hong2, Luoping Zhang2. A Multifactor Statistical Model For

Analysing The Physico-Chemical Variables In The Coastal Area At ST-Louis And Tamarin,

Mauritius.

Chandel et al (2008) have studied quality of ground water of Jaipur city and its suitability for

domestic and irrigation purpose. They reported groundwater quality of Jaipur city experienced

degradation due to rapid urbanization and industrialization. Gadhave et al (2008) have studied

water quality in industrial area near Shrirampur Maharashtra. They reported that the natural

quality of ground water tends to be degraded by human activities. Ilangeswaran et al (2009)

studied assessment of Quality of Groundwater in Kandarvakottai and Karambakudi Areas of

Pudukkottai District, Tamilnadu. They found that almost all the parameters for most of the

samples in permissible limits. Mukherjee et al (2005) studied assessment of groundwater quality

in the south parganas (province), west Bengal coast. They reported that the concentrations of

various ions are above the permissible limits for drinking and irrigation purposes. Mohan et al

(1988) studied Fluoride Concentration in Ground Water of Prakasham District in India and they

reported, groundwater samples contained high concentrations of fluorides compared to open well

and pond water samples. Shyamala et al (2008) studied Physicochemical analysis of bore-well

water samples of Telungupalayam area in Coimbatore (Shyamala, 2008), they reported the 28

WaterR & D Vol. 1 | No.1 | 27-35 | January-April | 2011Quality of Ground Water and its

Suitability for drinking purpose R. P. Dhok , A.S. Patil and V.S. Ghole ground water is fit for

domestic and drinking purpose and need treatments to minimize the contamination especially the

alkalinity. The objective of the scientific investigations is to determine the hydrochemistry of the

ground water and to classify the water in order to evaluate the water suitability for drinking and

domestic uses and its suitability for drinking purpose( Todd, 1980).

2. Introduction In recent years there has been a tremendous change in attitude among individual growers

towards environmental issues plus an increasing adoption of more sustainable farming practices.

Freshwater is a finite resource, essential for agriculture, industry and even human

existence. Freshwater is the most essential but scarce resource in our country. Presently the

quality & the availability of the fresh water resources is the basic need of the many

environmental challenges on the national horizon. However, indiscriminate exploitation and

unplanned use of ground water for agricultural purpose diminishes in qualitative and quantitative

terms. The health of community is affected by water that they consume. Rapid increase in

population and industrialization together with the lack of wisdom to live in harmony with nature

has led to the deterioration of quality of water thus resulting in water pollution.

In recent years, an increasing threat to ground water quality due to human activities has

become of great importance. The adverse effects on ground water quality are the results of man's

activity at ground surface, unintentionally by agriculture, domestic and industrial effluents. The

quality of ground water is of great importance in determining the suitability of particular ground

water for a certain use.

The principal environmental and public health dimensions of the global freshwater

quality problem are highlighted below:

Five million people die annually from water-borne diseases.

Ecosystem dysfunction and loss of biodiversity.

Contamination of groundwater resources.

Global contamination by persistent organic pollutants.

Experts predict that, because pollution can no longer be remedied by dilution (i.e. the flow

regime is fully utilized) in many countries, freshwater quality will become the principal

limitation for sustainable development in these countries early in the next century. This "crisis"

is predicted to have the following global dimensions:

Decline in sustainable food resources (e.g. freshwater and coastal fisheries) due to

pollution.

Cumulative effect of poor water resource management decisions because of inadequate

water quality data in many countries.

Many countries can no longer manage pollution by dilution, leading to higher levels of

aquatic pollution.

Millions of Indians currently lack access to clean drinking water, and the situation is only getting

worse. India’s demand for water is growing at an alarming rate. The problem of groundwater

system in several parts of the country has become so acute and that unless urgent steps for

abatement are taken, groundwater resources may be damaged.

Fresh water becomes polluted due to three major reasons, excess nutrients from sewage,

Industries, mining and agriculture.

A vast majority of ground water quality problems are caused by contamination, over-

exploitation, or combination of the two. One of the reasons of pollution in groundwater is the

industrial effluents.

Water is polluted due to different phenomenon. The dissolved solutes determine the

usefulness of water for various purposes. Industrial water entering in to ground water is the

major source of organic and inorganic pollutants. Sewage on the water quality, the waste

products can change the water Chemistry. Water gives life to Industries, but, Industries kill the

water Chemistry. The waste water which emerges out after uses from industries have no definite

composition, the pollutants associated with industrial effluents such as organic matter, inorganic

dissolved solids, fertilizer materials, suspended solids, heavy metals from toxic pollutants and

micro organisms and also pathogens. The industrial wastes are responsible for water color,

turbidity, odour, hardness, toxic elements, bacteria and micro organisms. Industrial wastes

contain poisonous chemicals which are difficult to remove from its homogeneous solution state.

Due to rapid growth of industrialization, much sewage is disposed off that generates fair

changes of ground water pollution. Safe drinking water is the primary need of every human

being. All ground water sources are not always safe due to rapid Industrialization, Urbanization.

Therefore, pollution of water resources needs a serious and immediate attention through

periodical check up of water quality.

India is the largest sugar producing country in the world. The sugar industry plays an

important role in Indian economy. It is the second largest industry in the country, next to textiles.

Indian Sugar mills generate 0.16-0.76 m3 of waste water for every tone of cane crushed by them.

The sugar industry in Maharashtra is a key factor in the rural economy of the state. Growing

demand for the sugar industries in all parts of the state is an indication of the importance of the

industry to the rural development. In spite of the fact that the sugar Industry is the backbone of

the rural economy of the Maharashtra, the need has arisen to review and recognize

environmental problems associated with sugar industry. The problems of groundwater quality are

particularly severe in many sugar industry areas and are threatening the rural population. Sugar

may be responsible for more biodiversity loss than any other crop, due to its destruction of

habitat to make way for plantations, its intensive use of water for irrigation, its heavy use of

agricultural chemicals, and the polluted wastewater that is routinely discharged in the sugar

production process.

Sugar industry is basically seasonal in nature and operates only for 120 to 200 days in a year

(early November to April). Significantly large volume of waste is generated during the

manufacture of sugar and contains a high amount of pollutional load particularly in terms of

suspended solids, organic matter, and press mud, bagasse and air pollutants. Therefore an

attempt will made to take an overview of waste management in sugar industry.

For over 40 years, about 60% of the existing land area in Baramati Taluka was covered with

sugarcane plantation. Over these years, a large amount of pesticides and fertilizers have been

used in order to achieve a high yield but as side effects, these agricultural practices have

represented particular risks to water sources. The fertilizers and pesticides have been penetrating

the ground water sources and runoff during rainfall, thus adding to the level of contaminants in

the surface waters.

In addition, over the past two decades, several surface water bodies have been receiving

industrial effluent discharges containing chemicals and trace of metals. Metal residues and textile

slurries with high trace metal concentration have caused great deal of pollution. Hence there are

strong reasons to believe that groundwater near the Malegaon Sugar Industry of Malegaon may

possibly be undergoing degradation of the water quality arising from the presence of biological

and chemical pollutants.

Realizing the importance of groundwater quality and its deterioration, we decided to study

the impact of Malegaon Sugar Industry on groundwater level of the concern area.

Therefore, pollution of water resources needs a serious and immediate attention to

understand the importance and control of water quality.

Keeping in view the main objective of the present investigation is the evaluation of physico-

chemical aspects of ground water quality from the selected locations around Malegaon Sugar

Industry, to specify accurately and timely information regarding the quality of river water at

industrial effluent disposal point and both at upstream and downstream flow of water. The

present findings may be helpful to shape sound public policy and to implement water quality

improvement program effectively as well as efficiently.

In order to study the physico-chemical activities in the groundwater near the Malegaon Sugar

Factory of Malegaon, several studies have been performed and the levels of various metals,

nutrients, physical and chemical parameters were recorded. These studies have shown the

presence of the toxic metals. Since the water pollution is hazardous for the human beings it is

important to find out whether the presence of toxic substances is significantly increasing due to

the impact of industrial sewage. The aim of this project is to develop and analyze a multifactor

bio-statistical model to assess the extent of water pollution in the Malegaon Sugar Industry of

Malegaon.

3. Objectives This study found that the continuous disposal of industrial effluents on land, which has

limited capacity to assimilate the pollution load, has led to groundwater pollution. The quality of

groundwater surrounding the factory locations has deteriorated, and the application of polluted

groundwater for irrigation has resulted in increased salt content of soils. In some locations dug

wells, bore wells and some of the hand pumps also have a high concentration of salts. However,

if the pollution continues unabated it could pose serious problems in the future.

Most of the samples, due to contamination of spentwash, were found unsuitable for

irrigation purpose. Hence we set the following objectives :

Study the pollutants in the groundwater.

To identify factors important for establishing the bio-statistical model.

Develop a Statistical index for impact of Sugar Industry on groundwater quality.

Testing of the developed index.

4. Methodology The quality of ground water is the resultant of all the processes and reactions that have

acted on the water from the moment it condensed in the atmosphere to the time it is discharged

by a well. Therefore, the quality of ground water varies from place to place, with the depth of

water table, and from season to season and is primarily governed by the extent and composition

of dissolved solids present in it.

This project will definitely help the Industry to decide where to use funds properly for the

prevention of environmental pollution regarding water.

4.1 Sample Collection Baramati is located in the eastern part of Pune district of Maharashtra state, having

geographical coordinates such as 18° 9' 0" North, 74° 35' 0" East. It lies between 74.82

Longitude and 18.31 Latitude. The Baramati city is located on the bank of river ‘Karha’ and

Malegaon sugar factory is 7 Km away from Baramati. Baramati is a Taluka place.

The groundwater samples were collected from hand pumps, dug wells and bore wells in

and around different areas of Malegaon around the Sugar Industry, Malegaon, Baramati,

District Pune, Maharashtra state (India) are selected randomly and analyzed for their physico-

chemical characteristics. The various physic-chemical parameters such as PH, Electrical

conductivity, Ca++ , Mg++, Na++, HCO3-, Cl-, SO4 2-, etc. were determined using standard

procedures of APHA.

Ground water samples from different hand pumps and Bore wells of various sampling

sites. The distance between two sampling sites was kept more than 200 meters. The depths of

collected water from bore wells and hand pumps were in the range of 20 to 50 feet.

Water samples were collected in a good quality polyethylene bottle of one-liter capacity

during period (October 2013 to January 2014) and analyzed on the same day or one day after the

collection. The samples after collection were immediately kept in dark boxes and analyzed in

laboratory for various parameters at earliest.

Map of Baramati District

4.2 Physico-Chemical Analysis Samples were analyzed in the laboratory by using standard methods of analysis

(APHA,1998). High purity (A.R. grade) chemicals and double distilled water was used for

preparing solution for analysis. Various physical parameters like pH, and EC were determined

within two hours with the help of digital portable pH meter and Conductivity meter in the

laboratory. Calcium (Ca2+), Magnesium (Mg2+), Chloride (Cl–), and Bicarbonate (HCO3 –)

were determined by volumetric titration methods; while Sodium (Na+) and Potassium (K+) by

Flame photometry as recommended by APHA. All parameters are studied in the laboratory

within a one day after collection of samples.

The irrigation quality parameters like Sodium Absorption Ratio (SAR), Residual sodium

Corbonate (RSC) were calculated with the help of Calcium (Ca2+), Magnesium (Mg2+),

Chloride (Cl–), Sodium (Na+) and Potassium (K+) in milliequivalent per liter (Me/l). These

(Me/l) values of respected cation and or anions were used in following calculations of respective

parameter of irrigation quality for getting its index or ratios.

Sodium Absorption Ratio (SAR) : The index is used for predicting the sodium hazard of water in agriculture use. It is the

concentration of sodium and the proportion of sodium to calcium and magnesium. SAR is

calculated as,

SAR (Me/l) = ேమశ

ඥమశାெమశ/ଶ

Residual sodium Corbonate (RSC) : If the water contains carbonate and bicarbonate in excess of calcium and magnesium then

this excess is denoted as RSC and was calculated by following formula.

RSC (Me/l) = (HCO3 – CO3) – (Ca2+ + Mg2+)

4.3 Statistical Approach

The objective of this research is to evaluate the mutual correlations among the various

water quality parameters to reveal the primary factors that affect reservoir water quality, and the

differences among the various water quality parameters in the watershed.

In this research, the water quality data has been collected over one and a half years so that

sufficient sets of water quality data are available to increase the stability, effectiveness, and

reliability of the final statistical analysis results. These data sets can be valuable references for

managing, regulating, and remediating water pollution in the mentioned are.

Multivariate statistical approaches show that the polluted surface water is strongly

influencing the quality of ground water in the study area of the data collected at 400 sites around

the Malegaon Sugar Industry, Baramati.

A statistical approach is used to express the magnitude of pollution. Initially, correlation

matrices of the major parameters and trace elements followed by principal componant analysis

on them are presented to quantify the aspect of pollution.

Mathematical models, especially water quality models, will be play an important role for

environmental study. We develop a Bio-Statistical Model of Impact of Sugar Industry on

groundwater quality in Malegaon on the basis of major contaminants as nitrates, metals,

inorganic constituents, organic components etc. in the groundwater in the concern area.

In this project we develop a water quality model with the help of statistical tools like

correlation analysis, cluster analysis, principal component analysis, etc. The model evaluate the

status of water pollution correctly, the value of the water quality index given by model is all-

around reflection of water pollution.

5. Statistical Analysis Correlation

Table-1: Correlation Coefficient Between Various Physico-Chemical Parameters of Ground Water

Samples at Different Locations. PH EC SODIUM RSC SAR CA MA NA HCO3 CL ratio PH 1 -.015 .220(**) .124(*) -.099 -.256(**) -.179(**) .062 .089 -.122(*) .012 EC -.015 1 .138(*) .234(**) .174(**) .388(**) .447(**) .508(**) .321(**) .633(**) .030 SODIUM .220(**) .138(*) 1 .358(**) .383(**) -.324(**) -.375(**) .562(**) .272(**) -.17(**) -.016 RSC .124(*) .234(**) .358(**) 1 .153(**) -.117(*) -.071 .230(**) .634(**) .021 .058 SAR -.099 .174(**) .383(**) .153(**) 1 -.077 -.128(*) .359(**) .135(*) .001 -.005 Ca++ -.256(**) .388(**) -.324(**) -.117(*) -.077 1 .399(**) .124(*) -.094 .622(**) -.29(**) Mg++ -.179(**) .447(**) -.375(**) -.071 -.128(*) .399(**) 1 .192(**) .110(*) .584(**) .297(**) Na++ .062 .508(**) .562(**) .230(**) .359(**) .124(*) .192(**) 1 .336(**) .330(**) .096 HCO3 .089 .321(**) .272(**) .634(**) .135(*) -.094 .110(*) .336(**) 1 .094 .105 Cl -.122(*) .633(**) -.167(**) .021 .001 .622(**) .584(**) .330(**) .094 1 .035 Mg/Ca ratio

.012 .030 -.016 .058 -.005 -.297(**) .297(**) .096 .105 .035 1

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Result: Above correlation matrix the correlation followed by * or ** shows that, the correlation is significant between some of the parameters.

Descriptive Statistics for all valid samples (after ignoring missing parameter values and

extreme parameter values) Table-2: Analytical Results of Ground Water samples at different locations.

Variables N Range Minimum Maximum Mean Variance

PH (6.5-8.4) 334 2.38 6.56 8.94 7.833443 0.110229

EC (0-0.75) 334 7.23 0.18 7.41 1.505808 1.158062

SODIUM% (0-60%) 334 76.71 3.71 80.42 38.72249 203.4103

RSC (0-1.5) 334 18.4 0 18.4 5.02482 16.60852

SAR (0-1.8) 334 21.52 0 21.52 2.158413 2.707845

Ca++ (0-1.5) 334 9.8 0.2 10 2.010479 2.153466

Mg++ (1-5.0) 334 18.7 0.2 18.9 3.343293 5.341687

Na++ (0-4.0) 334 9.3 0.4 9.7 3.266617 2.718786

HCO3 (1-8.5) 334 21.8 1.2 23 9.313174 13.15063

Cl (0-6.0) 334 58.2 1 59.2 6.06497 41.71958

Mg/Ca Ratio (0-3.0) 334 21.48 0.02 21.5 2.288144 7.486144

Result: From above Table we observe that the maximum values of all parameters are too much greater than the permissible level of corresponding maximum values. Also we observe that, there is large variability in some parameters. Hence we classify the data into clusters using “Cluster Analysis” technique.

Cluster Analysis:

Number of cases included in different clusters

Number of observations

Cluster1 5 Cluster2 166 Cluster3 69 Cluster4 70 Cluster5 24

Dendrograms of Different Clusters

Cluster 1

Cluster 1

26 141 1 271 291

100.00

66.67

33.33

0.00

Observations

Similarity

Cluster 2

Cluster 2

0.00

33.33

66.67

100.00

Similarity

Observ ations

Cluster 3

Cluster 3

277

266

42265

120

287

262

31259

172

269

151

106

211

278

263

46228249

240

66243

9452300

246

33301

49267

19279

114

227

184

260

173

258

171

264

83767172694839159

32745877517927248

234

178

165

125

41235

44282

333

111

179

25

0.00

33.33

66.67

100.00

Similarity

Observ ations

Cluster 4

Cluster 4

247

316

213

21273137

225

199

1039050195

20270309

303

129

3138131720275

20699310

231

196

29664242

147

146

143

164

192

11922132

127

237

118

250388429299

31988208

207

13345307

244

198

200

32592189

186

140

18818187

167

174

3055587

0.00

33.33

66.67

100.00

Similarity

Observ ations

Cluster 5

Cluster 5

59 163 54 304 160 161 177 169 256 185 194 57 190 191 40 251 145 253 36 89 97 46 170 257

100.00

66.67

33.33

0.00

Observations

Similarity

Clusterwise Descriptive Statistics for Different Variables

1) Ph

Cluster No. N Range Minimum Maximum Mean Variance

1 5 0.69 7.19 7.88 7.492 0.094

2 166 2.38 6.56 8.94 7.8443 0.117

3 69 1.24 7.01 8.25 7.7394 0.08

4 70 1.78 6.73 8.51 7.8624 0.102

5 24 1.4 7.26 8.66 8.015 0.112

Result : From above table and diagram we observe that, cluster 5 has maximum Ph.

01020304050607080

1 2 3 4 5

Ph

Cluster No.

Bar diagram of mean Ph for different clusters

2) Electric Conductivity (EC)


1 5 4.03 3.38 7.41 4.818 2.699

2 166 4.85 0.19 5.04 1.2724 0.691

3 69 4.49 0.22 4.71 1.4423 1.061

4 70 5.33 0.18 5.51 1.7069 1.38

5 24 3.62 0.25 3.87 2.0263 0.864

Result : From above table and diagram we observe that, cluster 1 has maximum electric conductivity.

01020304050607080

1 2 3 4 5

EC

Cluster No.

Bar diagram of mean EC for different clusters

3) Sodium %


1 5 33.94 8.87 42.81 25.156 151.232

2 166 25.38 24.83 50.21 36.6614 38.005

3 69 33.63 3.71 37.34 21.0399 42.431

4 70 20.8 41.94 62.74 52.23 25.573

5 24 20.74 59.68 80.42 67.245 31.028

Result : From above table and diagram we observe that, cluster 5 has maximum sodium percentage.

0

10

20

30

40

50

60

70

80

1 2 3 4 5

sodi

um %

Cluster No.

Bar diagram of mean sodium % for different clusters

4) Residual Sodium Carbonate (RSC)


1 5 16.5 0 16.5 8.24 51.863

2 166 18.05 0 18.05 4.3336 11.932

3 69 10.8 0 10.8 2.681 7.079

4 70 16.6 1.1 17.7 7.7399 15.841

5 24 18.17 0.23 18.4 7.9554 23.821

Result : From above table and diagram we observe that, cluster 4 has maximum Residual Sodium Carbonate (RSC).

0

0.5

1

1.5

2

2.5

3

3.5

1 2 3 4 5

R.S.

C.

Cluster No.

Bar diagram of mean R.S.C. for different clusters

5) Sodium Absorption Ratio (SAR)


1 5 4.67 0 4.67 2.364 2.901

2 166 21.39 0.13 21.52 1.9998 3.017

3 69 2.9 0.28 3.18 1.3007 0.484

4 70 5.78 0.12 5.9 2.696 1.322

5 24 9.3 0.2 9.5 4.1108 4.402

Result : From above table and diagram we observe that, cluster 4 has maximum Sodium Absorption Ratio (SAR).

00.5

11.5

22.5

33.5

1 2 3 4 5

S.A.

R

Cluster No.

Bar diagram of mean SAR for different clusters

6) Calcium (Ca++)


1 5 6.5 3.5 10 7.84 6.778

2 166 5.4 0.2 5.6 1.914 0.987

3 69 6.8 0.4 7.2 2.6226 3.239

4 70 6.4 0.2 6.6 1.5214 0.989

5 24 1.7 0.4 2.1 1.1296 0.259

Result : From above table and diagram we observe that, cluster 4 has maximum Calcium.

0

0.5

1

1.5

2

2.5

3

3.5

1 2 3 4 5

Ca++

Cluster No.

Bar diagram of mean Ca++ for different clusters

7) Magnesium (Mg++)


1 5 9.8 3.7 13.5 9.66 13.553

2 166 8.7 0.2 8.9 3.1883 2.148

3 69 18.7 0.2 18.9 4.5184 12.071

4 70 7.59 0.31 7.9 2.6861 2.022

5 24 2.6 0.3 2.9 1.6379 0.48

Result : From above table and diagram we observe that, cluster 4 has maximum Magnesium.

0

0.5

1

1.5

2

2.5

3

3.5

1 2 3 4 5

Mg+

+

Cluster No.

Bar diagram of mean Mg++ for different clusters

8) Sodium (Na++)


1 5 6.29 1.9 8.19 5.432 5.474

2 166 4.56 1.1 5.66 2.9513 0.93

3 69 6.36 0.4 6.76 2.0022 1.94

4 70 8.1 1.6 9.7 4.3747 2.981

5 24 5.13 3.1 8.23 5.4 2.039

Result : From above table and diagram we observe that, cluster 4 has maximum Sodium.

0

0.5

1

1.5

2

2.5

3

3.5

1 2 3 4 5

Na+

+

Cluster No.

Bar diagram of mean Na++ for different clusters

9) Bicarbonate (HCO3)


1 5 5.6 6.2 11.8 8.16 5.368

2 166 20.6 2.2 22.8 9.1187 12.137

3 69 13.6 1.2 14.8 7.9986 7.809

4 70 20 3 23 10.5557 15.574

5 24 19.2 2 21.2 11.0542 18.98

Result : From above table and diagram we observe that, cluster 4 has maximum Bicarbonate.

00.5

11.5

22.5

33.5

1 2 3 4 5

HCO

3

Cluster No.

Bar diagram of mean HCO3 for different clusters

10) Chloride (Cl)


1 5 28.4 30.8 59.2 43.6 120.8

2 166 16.4 1 17.4 4.8922 11.51

3 69 24.8 1.4 26.2 7.7768 47.156

4 70 17 1 18 4.8743 8.619

5 24 8.2 2.4 10.6 4.9083 4.462

Result : From above table and diagram we observe that, cluster 4 has maximum Chloride.

00.5

11.5

22.5

33.5

1 2 3 4 5

Cl

Cluster No.

Bar diagram of mean Cl for different clusters

11) Mg/Ca ratio


1 5 7.83 0.9 8.73 2.65 11.581

2 166 21.4 0.1 21.5 2.0921 6.057

3 69 10.23 0.02 10.25 2.293 3.885

4 70 20.3 0.2 20.5 2.9231 15.828

5 24 6.78 0.23 7.01 1.7025 2.057

Result : From above table and diagram we observe that, cluster 4 has maximum Magnesium to Calcium ratio.

0

0.5

1

1.5

2

2.5

3

3.5

1 2 3 4 5

Mg/

Ca ra

tio

Cluster No.

Bar diagram of mean Mg/Ca ratio for different clusters

Clusterwise Principal Component Analysis 1) Principal Component Analysis for first Cluster

2 4 6 8 10

0

1

2

3

4

5

Component Number

Eig

enva

lue

Scree Plot of PH-Mg/ca ra

Eigen analysis of the Correlation Matrix

Eigenvalue 4.5866 3.3883 2.1592 Proportion 0.417 0.308 0.196 Cumulative 0.417 0.725 0.921

Variable PC1 PC2 PC3 PH -0.430 0.167 0.007 E.C 0.293 0.415 0.101 SODIUM % -0.382 0.242 0.244 R.S.C 0.438 -0.087 0.207 S.A.R -0.151 -0.480 -0.215 CA ++ 0.396 0.044 -0.339 MA ++ 0.378 0.172 -0.318 NA++ -0.124 0.503 -0.149 HCO3 0.158 0.281 0.508 CL -0.072 0.295 -0.204 Mg/ca ra -0.154 0.228 -0.551

Since there are 11 predictors, there are 11 eigen values and corresponding 11 principle

components. Eigen values are expressed in descending order. We consider principal component

is significant if Eigen value > 1. For the cluster 1 first three Eigen values > 1 and explain 92.1%

information . Therefore we select first three PCs.

To group 11 predictors in to disjoint groups we consider absolute value of load > 0.30

Significant. Using this criterion we have the following grouping of variables and corresponding

to each PC, scores are computed as a linear combination of loads and respective selected

predictors.

Z1 = -0.43x1-.382x3+.438x4+.396x6+0.378x7

Z2 = 0.415x2-.48x5+.503x8+0.295x10

Z3 = 0.508 x9-0.551 x11

Using these variables we computed the equation for water quality index

Water Quality Index 1=0.452769 Z1+0.334419Z2+ 0.212812 Z3 Water quality index in terms of original variables

WQI_1=-0.19469 x1 + 0.138784 x2 - 0.17296 x3 + 0.198313 x4 - 0.160521 x5 + 0.179297 x6 + 0.171147 x7 + 0.168213 x8 + 0.108108 x9 + 0.295 x10 -0.117259 X11

2) Principal Component Analysis for Second Cluster

2 4 6 8 10

0

1

2

3

Component Number

Eig

enva

lue



Eigenvalue 2.9131 1.7593 1.5615 1.3584 1.0510 Proportion 0.265 0.160 0.142 0.123 0.096 Cumulative 0.265 0.425 0.567 0.690 0.786

Variable PC1 PC2 PC3 PC4 PC5 PH 0.011 0.138 -0.358 0.279 0.606 E.C -0.400 -0.091 -0.051 0.159 0.005 SODIUM % -0.011 0.041 0.646 0.032 0.528 R.S.C -0.306 0.364 0.149 0.477 -0.192 S.A.R -0.031 -0.095 0.519 -0.154 -0.376 CA ++ -0.084 -0.650 -0.165 0.126 -0.096 MA ++ -0.397 0.090 -0.253 -0.464 -0.016 NA++ -0.382 -0.270 0.219 -0.301 0.389 HCO3 -0.450 0.164 0.075 0.343 -0.120 CL -0.448 -0.243 -0.087 0.025 -0.017 Mg/ca ra -0.175 0.490 -0.092 -0.453 -0.022

Z1= -0.4x2-.45x9+.448x10

Z2= -0.65x6+0.49x11

Z3= 0.646x3+0.519x5

Z4= 0.477x4-0.464x7

Z5= 0.606x1+0.389x8

Using these variables we computed the equation for water quality index Water Quality Index 2 = 0.33715 Z1 + 0.20356 Z2 + 0.18066 Z3 + 0.15649 Z4 + 0.12213 Z5

Water quality index in terms of original variables

WQI_2= 0.074015 x1-0.13486 x2 + 0.116708 x3 + 0.074645 x4 + 0.093764 x5 -0.13232 x6 + 0.072611 x7 + 0.047511 x8 -0.15172 x9 + 0.151043 x10 +0.099745 x11

3) Principal Component Analysis for Third Cluster

2 4 6 8 10

0

1

2

3

4

Component Number

Eig

enva

lue


Eigenvalue 4.1052 1.8799 1.4192 1.0215 Proportion 0.373 0.171 0.129 0.093 Cumulative 0.373 0.544 0.673 0.766

Variable PC1 PC2 PC3 PC4 PH 0.143 -0.241 -0.170 0.708 E.C -0.443 -0.203 0.158 0.021 SODIUM % -0.220 0.391 -0.207 0.368 R.S.C -0.017 -0.123 -0.658 -0.411 S.A.R -0.267 0.233 -0.406 0.221 CA ++ -0.366 0.216 0.140 -0.255 MA ++ -0.327 -0.436 0.190 -0.074 NA++ -0.448 0.059 -0.044 0.169 HCO3 -0.201 -0.386 -0.462 -0.098 CL -0.408 -0.098 0.182 0.099 Mg/ca ra 0.121 -0.529 0.036 0.164

Z1 = -0.443x2-.366x6-.448x8-.408x10

Z2= 0.391x3-0.436x7-0.529x11

Z3= -0.658 x4 -0.406x5-0.462x9

Z4= 0.708 x1

Using these variables we computed the equation for water quality index Water Quality Index3= 0.486945 Z1 + 0.223238 Z2+ 0.168407 Z3+ 0.12141 Z4

Water quality index in terms of original variables

WQI_3=0.085958 x1-0.21572 x2 + 0.087286 x3 - 0.11081 x4 -0.06837 x5 -0.17822 x6 -0.09733 x7 -0.21815 x8 -0.0778 x9 -0.19867 x10 -0.11809 x11

4) Principal Component Analysis for Fourth Cluster

2 4 6 8 10

0

1

2

3

Component Number

Eig

enva

lue




Variable PC1 PC2 PC3 PC4 PC5 PH 0.005 0.157 0.070 0.028 0.870 E.C 0.448 0.045 -0.233 -0.064 -0.028 SODIUM % 0.235 -0.008 -0.236 0.615 0.090 R.S.C 0.063 0.687 0.005 -0.132 -0.164 S.A.R 0.280 0.056 -0.056 0.566 -0.259 CA ++ 0.105 0.005 -0.580 -0.357 -0.170 MA ++ 0.392 -0.128 0.308 -0.260 -0.139 NA++ 0.480 -0.121 0.013 0.005 0.188 HCO3 0.171 0.661 0.131 -0.016 0.015 CL 0.461 -0.154 -0.041 -0.288 0.166 Mg/ca ra 0.159 -0.079 0.657 0.036 -0.172

Z1 = 0.448 x2 +0.392 x7+0.48 x8 + 0.461 x10

Z2= 0.687x4+0.661x9 Z3= -0.58x6+0.657x11

Z4= 0.615x3+0.566x5

Z5= 0.87x1

Using these variables we computed the equation for water quality index Water Quality Index4=0.35396 Z1 + 0.19926 Z2+ 0.1856 Z3+ 0.1386 Z4 + 0.12252 Z5

Water quality index in terms of original variables WQI_4= 0.106592 x1 + 0.158574 x2 + 0.085245 x3 + 0.136892 x4 + 0.078453 x5 -0.10767 x6 +0.138752 x7 + 0.169901 x8 +0.131711 x9 + 0.163176 x10 + 0.121965 x11

5) Principal Component Analysis for Fifth Cluster

Eigenanalysis of the Correlation Matrix


Variable PC1 PC2 PC3 PC4 PC5 PH 0.238 0.108 0.544 0.043 -0.007 E.C 0.425 0.179 0.258 -0.173 0.103 SODIUM % 0.428 -0.189 -0.105 -0.050 -0.284 R.S.C 0.432 -0.188 -0.343 0.152 0.100 S.A.R 0.044 0.307 0.057 -0.424 0.725 CA ++ -0.162 0.310 -0.005 0.683 0.261 MA ++ -0.347 -0.435 0.153 0.112 0.231 NA++ 0.147 -0.429 0.247 0.351 0.326 HCO3 0.384 -0.152 -0.362 0.130 0.334 CL 0.246 0.005 0.513 0.183 -0.142

Mg/ca ratio -0.121 -0.546 0.164 -0.333 0.118

Z1= 0.425 X2 + .428 X3 + 0.432 X4 + 0.384 X9

Z2= -0.435 x7 - 0.429 x8 - 0.546 x11

Z3= 0.544 x1 + 0.513 x10

Z4= 0.683 x6

Z5= 0.725 x5

Using these variables we computed the equation for water quality index Water Quality Index 5 = 0.34189 Z1 + 0.21823 Z2 + 0.20653 Z3 + 0.127189Z4 + 0.10618 Z5

Water quality index in terms of original variables WQI_5= 0.112354 x1 + 0.145303 x2 +0.146329 x3 + 0.147696 x4 + 0.076983 x5 + 0.086869

x6 - 0.09492 x7 - 0.09361 x8 +0.131286 x9 + 0.105952 x10 - 0.11914 x11

6. Result and Discussion Suitability of Ground Water for Agricultural Purpose

Physico-chemical properties of ground water samples from different locations are shown in Table 2. pH

The pH of natural water is important index of hydrogen ion activity and it is resulting value of the acid - base interaction of a number of mineral and organic components in water. pH is an important ecological factor and is a term used and universally to express the intensity of the acid and alkaline condition of the water samples. Most of the water samples were slightly alkaline due to the presence of carbonates (CO3’’) and bicarbonates (HCO3’). pH-value determines the equilibrium between free CO2, HCO3’ and CO3’’.It is clear from the table 2 that the pH value of water samples were varying from 6.56 to 8.94 and these values are within the prescribed limits. Electrical Conductivity (EC)

Electric conductivity is caused due to presence of electrolytes which dissociate in to cations and anions. It is a measure of water capacity to convey electric current. It is an indicator of the degree of mineralization of water. The EC is correlated with total dissolved solids. In the present investigation the EC values of water sample during monitoring periods ranged in between0.18 to 7.4 mmhos/cm and indicate the presence of some ionic matter such as Ca, Mg, Cl, SO4, CO3, HCO3 and some trace elements. Sodium % In the present investigation the sodium % values of water sample ranges between 3.71 to 80.42%, which shows in some cases sodium % is above permissible level. Also it has more variability. Residual Sodium Carbonate (RSC) In our study, we observe that RSC is ranges between 0 to 18.4 meq/l and its maximum permissible level is 1.5meq/l. It shows detoriation of water quality. Sodium Absorption Ratio (SAR)

Increase of SAR of the irrigation water had an adverse impact on water infiltration for all soil types. As per standards it’s range is in between 0-1.8, above 1.8 they consider water quality is savior. We observe that, the SAR for our sample observations varied from 0 to 21.52 and it shows that water quality is very low as per SAR concern.

Calcium (Ca++)

In the present investigation the calcium values of water sample ranges between 0.2 to 10 meq/l, which shows that calcium is present in large amount. Magnesium (Mg++)

In our study, we observe that Magnesium is ranges between 0.2 to 18.9 meq/l and its maximum permissible level is 5 meq/l. It shows detoriation of water quality. Sodium (Na+)

Sodium levels in ground waters vary widely; depends upon geological formation. In surface water generally the sodium concentration ranges in between 1 and 300 ppm depending upon the geographical area. Excessive intake of sodium chloride causes vomiting. In the present investigation the sodium concentration of the water samples were found in between the ranges of 0.4 to 9.4 meq/l, this indicates the more concentration of sodium in the industrial effluent point due to the chemical combination of compounds leads to change in the quality of water. Bicarbonate (HCO3)

Alkalinity of water is acid neutralizing capacity of the water to predestinated pH. Alkalinity in water is mainly due to CO3’’ HCO3’ and OH- content. Borates, phosphates, silicates or other bases if present also contribute for alkalinity. In the present study, Bicarbonate concentration are varying from 1.2 to 23 meq/l which is above permissible level. Chloride (Cl-)

The chloride concentration serves as an indicator of pollution by sewage, industrial effluents. Chloride occurs in all ground waters widely in varying concentration. Excessive chloride in potable water is not particularly harmful. Chloride in excess imparts a salty taste to water. In the present investigation the chloride values ranged from 1 to 59.2 meq/l.

All Physico-chemical parameters have maximum average in cluster 4 except pH, EC

and sodium percentage. It shows that water quality of cluster 4 is sever.

7. Concluding Remarks The overall concluding remarks about water quality index are as follows:

• In general, in this study we observe that water quality index for study area ranges from 0

to 25.

• If quality index is ranges below 10 then water quality is good for irrigation.

• If quality index is ranges between 10-15 then water quality is moderate for irrigation.

• If quality index is ranges above 15 then water quality is severe.

We observe that water quality indices near to the malegaon sugar factory

water samples are high and it is ranges between 18 to 22. These samples are included in

cluster 4. The overall groundwater quality of the study area is not good.

To improve or to keep the quality of water at the effluent discharge point,

the industrial waste should be treated properly before disposal into the river stream.

Hence, there should be continuous monitoring of the pollution level.

The polluted water due to the disposal of industrial effluent, used for

irrigation which would not improve the soil fertility but also reduces the enrichment of

nutrients present in the soils.

8. Biblography Bartram J. and Balance R. (1996), Water Quality Monitoring - A Practical

Guide to the Design and Implementation of Freshwater Quality Studies and

Monitoring Programmes UNEP/WHO

Basavaraj M. Kalshetty, Shobha.N, M.B.Kalashetti and Ramesh Gani, “Water

Quality of River Tungabhadra due to the Discharge of Industrial Effluent at

Harihar, District Davanagere, Karnataka State”, India American Journal of

Advanced Drug Delivery, Vol. 1-1 (2014), 120-132.

Dhok R. P., A.S. Patil and V.S. Ghole, “Quality of Ground Water and its

Suitability for drinking purpose” Water Research & Development, Vol. 1-1

(2011), 27-35. Heshan Guan, Weiping Wang, Qingshan Jiang, Huasheng Hong, Luoping Zhang,

“A Statistical Model for Evaluating Water Pollution in Jiulong River

Watershed” Environnemental Informatics Archives, Vol. 3 (2005), 185 – 192.

Mushtaq Hussai TVD and Prasad Rao “Multivariate statistical analysis of

heavy metals in ground water - A case study of Bolaram and Patancheru

Industrial Area, Andra Pradesh, INDIA” International Journal of Advanced

Research, Vol. 2- 2 (2014), 876-883.

Pawar N. J., Pondhe G.M., and Patil S. F., “Groundwater Pollution due to

Sugar-Mill Effluent, at Sonai,Maharashtra, India.” Envioronmental

Geology, Vol. 34 (2/3) (1998), 151-156.

Ratnakant C.Sheth and Basavaraj M.Kalshetty, “Water Quality Assessment of

Ground Water Resources around Sugar Factory of Jamkhandi Town,

Bagalkot District, Karnataka State” International Journal of Applied Biology

and Pharmaceutical Technology, Vol. 2-1 (2011), 188-207.

Vandna Jowaheer, “A Multifactor Statistical Model for Analyzing the Phisico-

Chemical Variables in the Costal Area at St-Louis and Tamarin, Mauritius”

Journal of Applied Qualitative Methods, Vol. 5-1 (2010), 89-97.

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