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

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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.

  1. 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.

  2. 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.

    1. Major Physiographic units

    2. Major drainage

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

      1. 1.861 – 4.07 mbgl

      2. No significant change observed

    1. Pre-monsoon water level

    2. Post monsoon water level

    3. Long term water level trend

  3. 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.

      1. 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.

      2. 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

      3. 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.

  4. 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 R² = 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

    R² = 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 R² = 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

    R² = 0.2208

    0

    0

    200

    400

    600

    TH

    FIG.4: A graph of alkalinity 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 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 R² = 0.2906

    0 20 40 60 80

    MgCO3

    FIG.5: A graph of alkalinity and magnesium 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 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

    R² = 0.2264

    0 20 40 60 80

    MgCO3

    FIG.6: A graph of chloride and magnesium 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 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 R² = 0.1685

    0 50 100 150 200 250

    CaCO3

    FIG.7: A graph of chloride and calcium 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 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 R² = 0.6045

    0

    0 1 2 3 4 5 6

    Fe

    FIG.8: A graph of turbidity and iron contents

    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 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

    R² = 0.3314

    0 2 4 6

    Fe

    FIG.9: A graph of manganese and iron contents

    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 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 R² = 0.1783

    0

    0 2 4 6

    Fe

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

    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 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 R² = 0.3048

    0

    0

    0.5

    1

    Mn

    1.5

    2

    FIG.11: A graph of turbidity and manganese content

    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 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 R² = 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.

  5. 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

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