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“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)
Cluster No. N Range Minimum Maximum Mean Variance
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 %
Cluster No. N Range Minimum Maximum Mean Variance
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)
Cluster No. N Range Minimum Maximum Mean Variance
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)
Cluster No. N Range Minimum Maximum Mean Variance
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++)
Cluster No. N Range Minimum Maximum Mean Variance
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++)
Cluster No. N Range Minimum Maximum Mean Variance
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++)
Cluster No. N Range Minimum Maximum Mean Variance
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)
Cluster No. N Range Minimum Maximum Mean Variance
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)
Cluster No. N Range Minimum Maximum Mean Variance
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
Cluster No. N Range Minimum Maximum Mean Variance
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
Scree Plot of PH-Mg/ca ra
Eigen analysis of the Correlation Matrix
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
Scree Plot of PH-Mg/ca ra
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
Scree Plot of PH-Mg/ca ra
Eigen analysis of the Correlation Matrix
Eigenvalue 3.1469 1.7675 1.6554 1.2358 1.0835 Proportion 0.286 0.161 0.150 0.112 0.099 Cumulative 0.286 0.447 0.597 0.710 0.808
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
Eigenvalue 3.2183 2.0543 1.9508 1.2026 1.0058 Proportion 0.293 0.187 0.177 0.109 0.091 Cumulative 0.293 0.479 0.657 0.766 0.857
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.
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