**Open Access**-
**Total Downloads**: 3 -
**Authors :**Bhaswati Dutta , Bibhash Sarma -
**Paper ID :**IJERTV7IS060179 -
**Volume & Issue :**Volume 07, Issue 06 (June 2018) -
**Published (First Online):**25-06-2018 -
**ISSN (Online) :**2278-0181 -
**Publisher Name :**IJERT -
**License:**This work is licensed under a Creative Commons Attribution 4.0 International License

#### Correlation Study and Regression Analysis of Ground Water Quality Assessment of Nagaon Town of Assam, India

Bhaswati Dutta1,

1M.E. Student,

Civil Engineering Department, Assam Engineering College, Guwahati-781013, Assam, India

Bibhash Sarma2

2Associate Professor, Civil Engineering Department, Assam Engineering College,

Guwahati-781013, Assam, India

#### Abstract – In this study, the Nagaon district of the state Assam, India is selected as the study area to assess the groundwater quality for drinking purpose. Therefore efforts have been made to evaluate the status of potability of 77 groundwater samples from boring or tube wells representing groundwater resource that have been collected from September 2017 to January 2018 from different locations in the Nagaon town. 12 physical, chemical and biological water quality parameters, viz. fluoride, iron, manganese, nitrate, pH, turbidity, alkalinity, chloride, total hardness, calcium hardness, magnesium hardness and bacteria test are selected for analysis and to study whether the groundwater of the study area is potable for use or not. There is a relationship between variables which shows that one variable actually causes changes in another variable. In this paper, a statistical regression analysis method of the drinking water samples is carried out. This technique is based on the study and calculating the correlation coefficients between various physicochemical parameters of drinking water. The results were further compared with drinking water quality standards as per BIS (I.S. 10500:2012) and it was deduced that most of the water samples are potable. The results proved to be a useful mean for rapid monitoring of water quality with the help of systematic calculations of correlation coefficient.

Index Terms- Statistical regression analysis method, Water quality parameters, Correlation coefficient.

INTRODUCTION

Water is a public good and every person has the right to demand drinking water. Human life, as with all animal and plant life on the planet, is dependent upon water. Not only do we need water to grow our food, generate our power and run our industries, but we need it as a basic part of our daily lives. Water, sanitation and health are closely inter-related. In wealthier communities this connection is taken for

granted but in poor developing communities the connection is a stark daily reality.

Water and health are related in a number of ways. Firstly, there is the direct impact of consuming contaminated water – this is known as 'waterborne and there is chemically contaminated water such as water containing excessive amounts of arsenic or fluoride. Some contaminants are added to drinking water as a result of natural processes and some due to human activities such as industry and mining. Poor communities, especially in urban fringe areas, are particularly susceptible to dangers from polluted water from a variety of sources due to lack of or poorly enforced regulation of water pollution. The most prominent factors that elevates the level of water pollution are exploding population, increasing industrialization and urbanization. Various treatment methods are adopted to raise the quality of drinking water. Water should be free from the various detoxifications such as Organic and Inorganic pollutants, Pesticides, Heavy metals etc. As well as all its parameter like fluoride, iron, manganese, nitrate, pH, turbidity, alkalinity, chloride, total hardness, calcium hardness, magnesium hardness should be within acceptable limit. A novel approach of regression method is adopted to assess quality of water.

STUDY AREA

Nagaon town is selected as the study area since it is a developing town in Assam and much work has not been done in assessing the potability of the groundwater source. Parts of the Nagaon town are affected with contamination of groundwater by water quality entities or parameters with very high concentrations due to human interference.

TABLE 1: NAGAON DISTRICT AT A GLANCE

SL NO

ITEMS

STATISTICS

1.

GENERAL INFORMATION

i) Geographical Area (in sq.km AS PER

411030

2011 CENSUS)

ii) Population

2826006

iii) Average Annual Rainfall (mm)

1541

iv) No of sub division

03

2.

GEOMORPHOLOGY

Piedment plain, flat alluvial plain (older and younger alluvial) and Inselberges (Granites & Gneisses) Brahmaputra and its tributaries mainly Kolong, Kopili, Sonai and Diyang.

3.

LAND USE (sq. km.) as on 2011

i) Forest Area

88024

ii) Net Area Sown

235626

iii) Total cropped area

291339

iv) Area sown more than once

55713

4.

Major soil types

Alluvial soil

5.

PREDOMINANT GEOLOGICAL FORMATIONS

Vast river borne sediment, Older and Younger alluvium.

6.

HYDROGEOLOGY

i) Major water bearing formation

ii) 2.23 4.48 mbgl

7.

MAJOR GROUND WATER PROBLEMS AND ISSUES

Higher conc. of iron in ground water and Arsenic & Fluoride in some pockets.

Major Physiographic units

Major drainage

Sand and pebble aquifer zone down to 300 m depth and weathered and fracture zones up to 100 m depth in consolidated rocks

1.861 – 4.07 mbgl

No significant change observed

Pre-monsoon water level

Post monsoon water level

Long term water level trend

MATERIALS AND METHODS

Drinking ground water samples were collected from different sampling locations covering the entire Nagaon town as in Table 2. The collected samples were analyzed in Kaliabor and Nagaon Public Health Engineering Department as per convenience.

Collection, Preparation of Water Samples and Analysis For sampling in the study area, groundwater samples were collected by grab sampling from different pinpoint locations representing the actual groundwater resource of the study area. The samples were collected in plastic PET bottles to get representative samples. 500 ml of each of the samples

were collected for groundwater quality analysis. All the sampling bottles were filled to the top with the groundwater samples and tightly capped. After that the filled sample bottles were transported to the laboratory of PHED. Samples were protected from direct sunlight during transportation. The samples were stored in the laboratory at room temperature until analyzed.

Water Quality Analysis

The water samples were analyzed for physicochemical parameters with the help of equipment that have been used in the limits of precise accuracy and chemicals used were of analytical grade as mentioned in the table 2 below.

TABLE 2: The physico-chemical parameters, their units and the methods/equipment of analysis

Parameter

Unit

Method/Instrument

Fluoride, F

mg/l

Spectroquant Pharo 100 Spectrophotometer

Iron, Fe

mg/l

Spectroquant Pharo 100 Spectrophotometer

Manganese, Mn

mg/l

Spectroquant Pharo 100 Spectrophotometer

Nitrate, NO3

mg/l

Spectroquant Pharo 100 Spectrophotometer

Total Hardness, TH

mg/l

Titration

Calcium Hardness, CaCO3

mg/l

Titration

Magnesium Hardness, MgCO3

mg/l

Titration

Alkalinity

mg/l

Titration

Chloride

mg/l

Titration

pH

Field Water Testing Kit

Turbidity

NTU

Field Water Testing Kit

Bacteria test

Blue Bacta Vial

TABLE 3: Analysis results of 77 groundwater samples collected from the Nagaon town of Nagaon district, Assam

Sample No.

Fluoride (F) in mg/l

Iron (Fe) in mg/l

Manganese (Mn) in mg/l

Nitrate (NO3) in mg/l

Total Hardness (TH) in mg/l

Calcium Hardness (CaCO3) in mg/l

Magnesium Hardness (MgCO3) in mg/l

Alkalinity in mg/l

Chloride (Cl) in mg/l

pH

Turbidity in NTU

Bacterial test

Well Depth in feet

1

2

3

4

5

6

7

8

9

10

11

12

13

1

0.16

0.21

0.45

2.7

272

200

17.57

50

96

6.5

4

negative

23

2

0.24

1.25

0.28

2.1

280

220

14.64

40

74

7

6

negative

30

3

0.17

0.87

0.44

2.7

236

180

13.66

40

46

6.5

4

negative

23

4

0

0.26

0.37

1.3

192

120

17.57

40

26

7

5

negative

30

5

0

0.36

0.85

4.4

244

200

10.74

36

88

6

5

negative

26

6

0.30

1.12

0.45

1.6

256

200

13.66

58

78

6.5

4

negative

26

7

0.17

0.25

0.46

6.1

252

200

12.69

42

50

6.5

5

negative

26

8

1.03

4.73

0.63

1.9

236

150

20.98

52

66

6.5

10

negative

26

9

0.14

1.26

0.54

1.2

240

190

12.20

38

60

7

8

negative

30

10

0.21

0.80

0.57

1.6

300

190

26.84

68

96

6

5

negative

23

11

0.43

0.11

0.46

3.1

184

150

8.30

130

40

6.5

3

negative

25

12

0.10

0.11

0.34

4.6

252

185

16.35

210

58

6.5

6

negative

40

13

0.24

0.12

0.29

2.7

268

200

16.59

204

78

6.5

3

negative

30

14

0.30

0.19

0.30

1.5

124

95

7.08

110

18

6

5

positive

30

15

0.22

0.29

0.45

2.2

160

140

4.88

90

32

6

5

positive

20

16

0.31

0.34

0.48

2.5

232

125

26.11

180

46

6

4

negative

30

17

0.44

0.12

0.79

8.4

260

175

20.74

140

112

6

5

negative

20

18

0.20

0.06

0.35

7.3

256

190

16.10

150

106

6

5

negative

25

19

0.27

0.09

0.29

6.6

164

105

14.40

100

76

6

4

negative

25

20

0.43

0.08

0.45

3.1

224

110

27.82

250

28

7.5

5

positive

20

21

1.24

1.26

0.68

2.5

176

150

6.34

148

14

6.5

25

negative

25

22

0.54

0.19

0.54

2.4

204

175

7.08

160

14

6

5

negative

33

23

0.10

0.22

0.63

2.2

128

95

8.05

154

50

6

5

negative

30

24

0.36

0.24

0.35

1.9

160

95

15.86

138

28

7

6

negative

25

25

0.28

0.32

0.36

1.7

220

75

35.38

198

30

6.5

5

negative

44

26

0.10

0.26

0.32

3

316

115

49.04

204

74

6

5

negative

25

27

0.19

0.21

0.33

2.6

312

115

48.07

236

76

6.5

5

negative

25

28

0.21

0.52

0.37

3.1

312

115

48.07

168

60

6

6

negative

25

29

0.22

0.21

0.31

2.6

236

110

30.74

222

70

6.5

5

negative

33

30

0.21

0.81

0.46

3.1

204

70

32.70

162

66

6.5

10

negative

25

31

0.35

1.14

0.57

1.9

236

200

8.78

230

34

6

10

negative

80

32

0

0.27

0.36

1.8

308

170

33.67

306

80

7

5

negative

40

33

0.70

0.38

0.32

1.8

176

80

23.42

116

16

6

5

negative

30

34

0.09

0.37

0.35

2.1

244

120

30.26

186

86

6.5

5

negative

25

35

0

0.34

0.32

2.3

296

120

42.94

220

78

7

5

negative

25

36

0.25

0.25

0.34

2.4

172

80

22.45

190

8

7.5

6

negative

30

37

0.10

0.21

0.53

2.9

252

100

37.09

240

72

6.5

6

negative

25

38

0

0.20

0.29

2

208

75

32.45

250

6

7.5

5

negative

30

39

0.14

0.28

0.37

2.4

188

80

26.35

126

54

6

6

negative

30

40

0.12

0.74

0.44

2.4

164

100

15.62

190

68

6.5

10

negative

25

41

0.21

0.22

1.14

2.7

240

175

15.86

36

92

6

5

negative

30

42

0.16

0.45

0.87

2.2

208

140

16.59

76

32

6

5

negative

26

43

0.05

0.20

0.50

2.4

132

40

22.45

46

18

6

5

negative

30

44

0.06

5

1.88

2.7

352

120

56.61

74

96

6

25

negative

26

45

0.11

0.54

1.40

2.7

212

100

27.33

50

76

6

5

negative

25

46

0.11

0.73

0.75

2.6

208

125

20.25

56

46

6

6

negative

25

47

0.94

4.77

1.57

2.1

248

105

34.89

60

38

6

30

negative

26

48

0.42

4.34

1.24

2.3

196

75

29.52

58

44

6

10

negative

26

49

0.22

1.14

0.50

3.9

180

90

21.96

36

78

6

5

negative

26

50

0.15

0.22

0.36

2.5

208

110

23.91

90

10

6.5

5

negative

30

51

0.10

4.54

1.15

2.8

288

105

44.65

106

120

6

25

negative

26

52

0.41

3.98

1.37

3.5

304

90

52.22

90

110

6

25

negative

26

53

0.25

0.22

0.79

5.3

308

150

38.55

272

104

6

5

negative

30

54

0.12

3.81

1.31

3.9

400

175

54.90

342

128

6

30

negative

150

55

0.11

0.58

0.44

3.2

280

75

50.02

286

70

7

5

negative

30

56

0

2.03

0.73

2.1

360

120

58.56

280

78

6

10

negative

55

57

0.09

0.51

0.39

8.6

196

85

27.08

176

52

6

5

negative

26

58

0

0.95

0.45

5.1

164

70

22.94

146

58

6

5

negative

30

59

0.14

3.77

0.69

2.8

240

60

43.92

244

46

6.5

6

negative

26

60

0.16

4.55

0.80

2.4

420

125

71.98

414

124

6

25

negative

26

61

0.07

1.72

0.54

1.8

180

90

21.96

198

10

6.5

5

negative

190

62

0.05

0.36

0.33

5.1

200

75

30.50

196

58

6

5

negative

30

63

0.08

1.07

1.71

2.3

68

45

5.61

342

88

6

5

negative

30

64

0.07

0.39

1.74

2.5

212

155

13.91

308

86

6

5

negative

26

65

0

0.62

0.76

8.5

280

75

50.02

120

44

6

5

negative

30

66

0.22

1.23

0.74

4.5

420

225

47.58

348

164

6

5

negative

30

67

0.10

1.28

0.48

2.9

216

130

20.98

226

4

6.5

6

negative

30

68

0.18

0.70

0.31

6.1

276

120

38.06

198

52

6

5

negative

30

69

0.19

0.84

0.41

2.8

192

85

26.12

202

56

6

5

negative

26

70

0.07

0.88

0.43

6.4

284

90

47.34

208

56

6

5

negative

26

71

0.76

0.72

0.38

12.1

212

110

24.89

152

70

6.5

5

negative

30

72

0.49

0.94

0.33

4.9

208

95

27.57

160

58

6

5

negative

26

73

0.09

0.71

0.35

8.4

272

125

35.87

202

32

6

5

negative

26

74

0.52

0.92

0.34

2.8

168

80

21.47

162

20

6

5

negative

30

75

0.45

2.52

0.82

2.2

260

115

35.38

204

34

6

5

negative

26

76

0.53

0.67

0.62

3.4

484

185

72.96

602

166

6

5

negative

30

77

0.29

0.68

1.13

2.5

144

90

13.18

102

20

6

5

negative

30

TABLE 4: Statistics of the analytical results

Sl. No.

Water quality parameter

Minimum value

Maximum value

Mean

Standard deviation

1

Fluoride (F) in mg/l

0

1.24

0.24

0.24

2

Iron (Fe) in mg/l

0.06

5

1.04

1.32

3

Manganese (Mn) in mg/l

0.28

1.88

0.61

0.38

4

Nitrate (NO3) in mg/l

1.2

12.1

3.37

2.04

5

Total Hardness (TH) in mg/l

68

484

238.49

71.36

6

Calcium Hardness (CaCO3) in mg/l

40

225

124.54

45.31

7

Magnesium Hardness (MgCO3) in mg/l

4.88

72.96

27.80

15.62

8

Alkalinity in mg/l

36

602

165.45

101.69

9

Chloride (Cl) in mg/l

4

166

60.99

34.83

10

pH

6

7.5

6.28

0.41

11

Turbidity in NTU

3

30

7.39

6.27

Linear Regression Model

The relationship of water quality parameters on each other in the samples of water analyzed was determined by determining correlation coefficients (r) by using the mathematical formula as given below. Let x and y be any two variables (water quality parameters in the present investigation) and n = number of observations. Then the correlation coefficient (r), between the variables x and y is given by the relation.

R = n(xy)

y = Ax +B

To correlate x and y, the constant A and B are to be determined by fitting the experimental data on the variables x and y. According to the well-known method of least squares, the value of constants A and B are given by the relations

And B = ymean – Axmean

Where, xmean = x ; ymean = y

Where,

f(x)f(y)

A = n(xy)

()2

f(x) = n(x2) (x)2; f(y) = n(y2) (y)2 and all the summations are to be taken from 1 to n. If the numerical value of the correlation coefficient between two variables x and y is fairly large, it implies that these two variables are highly correlated. In such cases, it is feasible to try a linear relation of the form

By using these relations, with the help of Microsoft Excel the values of correlation coefficients (R) are found which has been given below in Table 5.

TABLE 5: Correlation coefficients (R) among various water quality parameters

Depth

F

Fe

Mn

NO3

TH

CaCO3

MgCO3

Alkalinity

Cl

pH

Turbidity

Depth

1

F

-0.118

1

Fe

0.174

0.247

1

Mn

0.082

0.047

0.576

1

NO3

-0.095

0.004

-0.154

-0.117

1

TH

0.099

-0.088

0.323

0.119

0.106

1

CaCO3

0.043

0.091

-0.088

-0.026

0.003

0.471

1

MgCO3

0.08

-0.162

0.422

0.151

0.116

0.781

-0.182

1

Alkalinity

0.215

-0.098

-0.009

-0.007

0.046

0.47

-0.021

0.539

1

Cl

-0.038

-0.138

0.232

0.316

0.193

0.687

0.411

0.476

0.361

1

pH

-0.011

-0.04

-0.199

-0.383

-0.244

-0.079

0.036

-0.113

0.032

-0.273

1

Turbidity

0.203

0.284

0.778

0.552

-0.124

0.324

-0.014

0.371

0.036

0.243

-0.154

1

The correlation coefficient (R) measures the degree of association that exists between two variables, one taken as dependent variable. The greater the value of regression coefficient, the better is the fit and more useful the regression variables (Daraigan Sami G.,2011). Correlation is the mutual relationship between two variables. Direct correlation exists when increase or decrease in the value of one parameter is associated with a corresponding increase or decrease in the value of other parameter. In this study, the numerical values of correlation coefficient (R) for the eleven water quality parameters and depth are tabulated in Table 5.

RESULT AND DISCUSSIONS

MgCO3

In the studied area, water used for drinking purposes should be colourless, odourless and free from slight turbidity and excess salts. The important physico-chemical characteristics of analyzed water samples viz., Mean and Standard

Deviation (SD) have been presented in Table 4. It shows that variation among the measured values of these parameters at different locations is not too high and variation range is very narrow.

The regression equation was used as a mathematical tool to calculate different dependent characteristics of water quality by substituting the values for the independent parameters in the equations. The regression analysis carried out for which the water quality parameters found to have better and higher level of significance in their correlation coefficient are studied below.

Correlation between magnesium hardness and total hardness A graph of magnesium hardness and total hardness in mg/l of the groundwater samples is plotted to establish the relationship between the two variables.

80

70

60

50

40

30

20

10

Series1

Linear (Series1)

y = 0.171x – 12.975 RÂ² = 0.6103

0

-10

0

200

400

600

TH

FIG.1: A graph of magnesium hardness and total hardness

The plotted graph revealed a direct linear and positive relationship between the two variables. Linear regression was carried out to find the regression coefficient (R) value for the relationship.

From the graph, it can be seen that the magnesium hardness are found dependent on the total hardness, such that an increase in total hardness related to an increase in magnesium hardness. This relation indicates the presence of

stratum of high mineral content of limestone and chalk which are largely made up of calcium and magnesium carbonates and bicarbonates in the Nagaon town area.

Correlation between calcium hardness and total hardness

CaCO3

A graph of calcium and total hardness in mg/l of the groundwater samples is plotted to establish the relationship between the two variables.

250

200

150

100

50

Series1

Linear (Series1)

y = 0.2992x + 53.179

RÂ² = 0.2222

0

0

200

400

600

TH

FIG.2: A graph of calcium hardness and total hardness

The plotted graph revealed a direct linear and positive relationship between the two variables. Linear regression was carried out to find the regression coefficient (R) value for the relationship.

From the graph, it can be seen that the calcium hardness are found dependent on the total hardness, such that an increase in total hardness related to an increase in calcium hardness. This relation indicates the presence of stratum of high

mineral content of limestone and chalk which are largely made up of calcium and magnesium carbonates and bicarbonates; and also presence of calcium sulphate and calcium chloride in the geology of the Nagaon town area.

Correlation between chloride and total hardness

Cl

A graph of chloride and total hardness in mg/l of the groundwater samples is plotted to establish the relationship between the two variables.

180

160

140

120

100

80

60

40

20

0

Series1

Linear (Series1)

y = 0.3355x – 19.025 RÂ² = 0.4725

0 200 400 600

TH

FIG.3: A graph of chloride and total hardness

The plotted graph revealed a direct linear and positive relationship between the two variables. Linear regression was carried out to find the regression coefficient (R) value for the relationship.

From the graph, it can be seen that chloride is found dependent on the total hardness, such that an increase in total hardness is related to an increase in chloride. This relation indicates the presence of stratum of high mineral content of limestone and chalk (carbonate or temporary

Alkalinity

hardness) and also presence of calcium sulphate, calcium chloride, magnesium sulphate, and magnesium chloride (non-carbonated or permanent hardness) in the Nagaon town area that adds to the total hardness and as a result the chloride also increases from chlorides of calcium and magnesium.

Correlation between alkalinity and total hardness

A graph of alkalinity and total hardness in mg/l of the groundwater samples is plotted to establish the relationship between the two variables.

700

600

500

400

300

200

100

Series1

Linear (Series1)

y = 0.6696x + 5.7491

RÂ² = 0.2208

0

0

200

400

600

TH

FIG.4: A graph of alkalinity and total hardness

From the graph, it can be seen that alkalinity is found dependent on the total hardness, such that an increase in total hardness is related to an increase in alkalinity. This

relation basically indicates that alkalinity and hardness changes depending on the pH or mineral content of the stratum.

Correlation between alkalinity and magnesium hardness

A graph of alkalinity and magnesium hardness in mg/l of the groundwater samples is plotted to establish the relationship between the two variables.

700

600

500

Alkalinity

400

300

200

100

0

Series1

Linear (Series1)

y = 3.5095x + 67.878 RÂ² = 0.2906

0 20 40 60 80

MgCO3

FIG.5: A graph of alkalinity and magnesium hardness

From the graph, it can be seen that alkalinity is found dependent on the magnesium hardness, such that an increase in magnesium hardness is related to an increase in alkalinity.

This relation basically indicates that alkalinity and hardness changes depending on the pH or mineral content of the stratum.

Correlation between chloride and magnesium hardness

A graph of chloride and magnesium hardness in mg/l of the groundwater samples is plotted to establish the relationship between the two variables.

180

160

140

120

Cl

100

80

60

40

20

0

Series1

Linear (Series1)

y = 1.061x + 31.487

RÂ² = 0.2264

0 20 40 60 80

MgCO3

FIG.6: A graph of chloride and magnesium hardness

From the graph, it can be seen that chloride is found dependent on the magnesium hardness, such that an increase in magnesium hardness is related to an increase in chloride.

This relation indicates the presence of magnesium carbonates and bicarbonates; and also magnesium chloride in the stratum of the study area.

Correlation between chloride and calcium hardness

A graph of chloride and calcium hardness in mg/l of the groundwater samples is plotted to establish the relationship between the two variables.

180

160

140

120

Cl

100

80

60

40

20

0

Series1

Linear (Series1)

y = 0.3156x + 21.682 RÂ² = 0.1685

0 50 100 150 200 250

CaCO3

FIG.7: A graph of chloride and calcium hardness

From the graph, it can be seen that chloride is found dependent on the calcium hardness, such that an increase in calcium hardness is related to an increase in chloride. This

relation indicates the presence of calcium carbonates and bicarbonates; and also calcium chloride in the stratum of the study area.

Correlation between turbidity and iron contents

A graph of turbidity (NTU) and iron contents in mg/l of the groundwater samples is plotted to establish the relationship between the two variables.

35

30

25

Turbidity

20

15 Series1

Linear (Series1)

10 y = 3.7024x + 3.5506

5 RÂ² = 0.6045

0

0 1 2 3 4 5 6

Fe

FIG.8: A graph of turbidity and iron contents

From the graph, it can be seen that the turbidity is found dependent on the iron contents, such that an increase in iron contents related to an increase in turbidity. This relation indicates that iron in groundwater occurs in two forms Fe2+,

is very soluble and Fe3+, will not dissolve appreciably may cause turbidity in the groundwater samples in the Nagaon town area.

Correlation between manganese and iron contents

A graph of manganese and iron contents in mg/l of the groundwater samples is plotted to establish the relationship between the two variables.

2

1.8

1.6

1.4

Mn

1.2

1

0.8

0.6

0.4

0.2

0

Series1

Linear (Series1)

y = 0.1653x + 0.442

RÂ² = 0.3314

0 2 4 6

Fe

FIG.9: A graph of manganese and iron contents

From the graph, it can be seen that Mn and Fe are correlated in the study area aquifer system. Since Mn is not found as a free element in nature; it is often found in minerals in combination with iron. From the analysis of the groundwater

samples of the Nagaon town it is observed that the study area stratum have Fe and Mn minerals mostly so these two parameters in some sampling location have exceeded the permissible limits.

Correlation between magnesium hardness and iron content

A graph of magnesium hardness and iron content in mg/l of the groundwater samples is plotted to establish the relationship between the two variables.

80

70

60

MgCO3

50

40

Series1

30

Linear (Series1)

20 y = 5.0051x + 22.614

10 RÂ² = 0.1783

0

0 2 4 6

Fe

FIG.10: A Graph Of Magnesium Hardness And Iron Content

From the graph, it can be seen that magnesium hardness and iron are correlated in the study area aquifer system. It is

observed that an increase in iron is related to an increase in magnesium hardness.

Correlation between turbidity and manganese content

Turbidity

A graph of turbidity in NTU and manganese content in mg/l of the groundwater samples is plotted to establish the relationship between the two variables.

35

30

25

20

15

10

5

Series1

Linear (Series1)

y = 9.1584x + 1.7721 RÂ² = 0.3048

0

0

0.5

1

Mn

1.5

2

FIG.11: A graph of turbidity and manganese content

From the graph, it can be seen that turbidity and Mn are correlated in the study area aquifer system. It is observed that an increase in Mn is related to an increase in turbidity.

8

7

6

5

pH

4

3

2

1

0

Correlation between pH and manganese content

A graph of pH and manganese content in mg/l of the groundwater samples is plotted to establish the relationship between the two variables.

Series1

Linear (Series1)

y = -0.4137x + 6.5395 RÂ² = 0.1464

0 0.5 1 1.5 2

Mn

FIG.12: A graph of pH and manganese content

The plotted graph revealed a direct linear and negative relationship between the two variables. Linear regression was carried out to find the regression coefficient (R) value for the relationship.

As a negative correlation is found to exist between pH and Mn values, it can be said empirically that when Mn content in the groundwater increases, the value of pH decreases i.e. the water is acidic mainly in the study area.

In the current study it is evident from Table 5 that distribution of magnesium hardness MgCO3, calcium hardness CaCO3, chloride and alkalinity were significantly correlated (R > .46) with total hardness (TH). Alkalinity and chloride were significantly correlated (R > 0.47) with magnesium hardness MgCO3. Turbidity, manganese Mn and magnesium hardness MgCO3 were also significantly correlated (R > 0.42) with iron Fe. A high correlation value was observed between MgCO3 and TH (R=0.78). A low negative correlation was observed between pH and Mn (R=0.38). A considerably low correlation was observed between turbidity and MgCO3 (R=0.37) and Chloride and alkalinity (R=0.36). Fluoride F is negatively correlated with most of the water parameters and some parameters like nitrate NO3 and F; CaCO3 and NO3 are insignificantly correlated. This is perhaps due to highly variable nature of chemical concentrations and minerals in the stratum of the study area. Finally, it can be concluded that the correlation studies of the water quality parameters have great significance in the study of water resources.

CONCLUSION

The statistical regression analysis has been found to be a highly useful technique. Finding linear correlation between various physicochemical water parameters can be treated as a unique step ahead towards the drinking water quality management. The mathematical models used to access water quality involve two parameters to describe realistic groundwater situations. This technique has been proven as a very useful tool for monitoring drinking water and has a good accuracy. A significant relationship obtained from a systematic correlation and regression in this study has been established among different pairs of physicochemical parameters. The method of linear correlation has been found to a significant approach to get an idea of quality of the ground water by determining a few parameters experimentally. It can be concluded that the iron, manganese, alkalinity, chloride, turbidity, total hardness and magnesium hardness are important physicochemical parameters of drinking water, because they are correlated with most of the water quality parameters in the study area. This study has revealed the facts that all the physicochemical parameters of drinking water in Nagaon town of Assam are correlated in some or the other ways. But iron Fe, manganese Mn, turbidity and total hardness TH are the parameters exceeding the permissible limits of the drinking water quality parameters in the study area and since groundwater is available in the study area through boring or tube well in shallow depth of 20 feet onwards so significant correlation of Fe, Mn, turbidity and TH with depth could not be established. Thus the study could be more enhanced by studying groundwater quality in more depth in the near future.

TABLE 6: Linear correlation coefficient and regression equation for some pairs of parameters which have significant value of correlation

Pairs of parameters | Regression equation | R square |

MgCO3-TH | MgCO3 = -12.98 + 0.17TH | 61.03% |

Turbidity-Fe | Turbidity = 3.55 + 3.7Fe | 60.45% |

Cl-TH | Cl = -19.02 + 0.34TH | 47.25% |

Mn-Fe | Mn = 0.44 + 0.16Fe | 33.14% |

Turbidity-Mn | Turbidity = 1.77 + 9.16Mn | 30.48% |

Alkalinity- MgCO3 | Alkalinity = 67.88 + 3.51MgCO3 | 29.06% |

Cl- MgCO3 | Cl = 31.49 + 1.06 MgCO3 | 22.64% |

CaCO3-TH | CaCO3 = 53.18 + 0.3TH | 22.22% |

Alkalinity-TH | Alkalinity = 5.75 + 0.67TH | 22.08% |

MgCO3-Fe | MgCO3= 22.61 +5.01Fe | 17.82% |

Cl-CaCO3 | Cl = 21.68 + 0.32CaCO3 | 16.85% |

pH-Mn | pH = 6.54 9.16Mn | 14.64% |

TABLE 7: Comparison of the analytical results of 77 groundwater samples with I.S. 10500:2012

0

Sl. No. | Water quality parameter | Desirable limit | Maximum permissible limit | Samples below the desirable limit | Samples exceeding the desirable limit but within the maximum permissible limit | Samples exceeding the maximum permissible limit |

1 | Fluoride (F) in mg/l | 1 | 1.5 | 75, 97.4% | 2, 2.6% | 0 |

2 | Iron (Fe) in mg/l | 0.3 | 1.0 | 27, 35.1% | 29, 37.7% | 21, 27.2% |

3 | Manganese (Mn) in mg/l | 0.1 | 0.3 | 0 | 5, 6.5% | 72, 93.5% |

4 | Nitrate (NO3) in mg/l | <45 | 45 | 77, 100% | 0 | |

5 | Alkalinity in mg/l | 200 | 600 | 51, 66.2% | 25, 32.5% | 1,1.3% |

6 | Chloride (Cl) in mg/l | 250 | 1000 | 77, 100% | 0 | 0 |

7 | Total hardness (TH) in mg/l | 200 | 600 | 23, 29.9% | 54, 70.1% | 0 |

8 | Calcium hardness (as CaCO3) in mg/l | 75 | 200 | 11, 14.3% | 64, 83.1% | 2, 2.6% |

9 | Magnesium hardness (as MgCO3) in mg/l | 30 | 150 | 48, 62.3% | 29, 37.7% | 0 |

10 | Hydrogen-ion concentration (pH) | 6.5-8.5 | 6.5-8.5 | 46, 59.7% | 31,40.3% | 0 |

11 | Turbidity in NTU | 1 | 5 | 0 | 53, 68.8% | 24, 31.2% |

12 | Bacteriological parameter | Absent | Absent | 74, 96.1% | 3, 3.9% | 0 |

REFERENCES | water quality data for Al -MukallaCity, Hadhramout, Yemen | ||

[1] | Course Manual on Training of Chemists for the Water Testing Laboratories in Assam, Department of Sanitary Engineering, All | International Journal of Environmental Sciences, Science and Technology (HUST),Mukalla , Hadhramout, Yemen. | |

India Institute of Hygiene and Public Health, Kolkata and PHED, | [7] | Soni Chaubey and Mohan Kumar Patil Correlation Study and | |

[2] | Government of Assam. Ground Water Information Booklet Nagaon District, Assam, | Regression Analysis of Water Quality Assessment of Nagpur City, India. | |

Central Ground Water Board North Eastern Region Ministry of Water | [8] | Training Course Manual on Removal of Fluoride from Drinking | |

[3] | Resources Guwahati November 2013. Kar D., Sur P., Mandal S.K., Saha T. and Kola R.K., Assessment of | Water, under HRD Programme, RGNDWM Ministry of Rural Areas and Employment, Government of India. | |

Heavy Metal Pollution in Surface Wter, International Journal of | [9] | Training Course on Water Analysis, Department of Public Health | |

[4] | Environmental Science and Technology, 2008. Koshy, M. and T. V. Nayar (1999). Water quality aspects of River | [10] | Engineering, Government of Assam. Volume II: City Appraisal, Central Public Health and |

Pamba. Pollut. Res., 18 : 501510. | Environmental Engineering Organization (CPHEEO). | ||

[5] [6] | M. Chandra Sekhar & K. Surender Reddy Regression Models For Prediction Of Water Quality In Krishna River. Sami G. Daraigan , Ahmed S. Wahdain , Ahmed S. BaMosa and | [11] | Ward A.D. and Elliot W.J., Environmental Hydrology, eds. CRC Press, Boca Ratan, 1995. |

Manal H. Obid 2011Linear correlation analysis study of drinking |