 Open Access
 Total Downloads : 3
 Authors : Bhaswati Dutta , Bibhash Sarma
 Paper ID : IJERTV7IS060179
 Volume & Issue : Volume 07, Issue 06 (June 2018)
 Published (First Online): 25062018
 ISSN (Online) : 22780181
 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, Guwahati781013, Assam, India
Bibhash Sarma2
2Associate Professor, Civil Engineering Department, Assam Engineering College,
Guwahati781013, 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 interrelated. 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


Premonsoon 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 physicochemical 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 wellknown 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 physicochemical 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 (noncarbonated 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
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 RÂ² = 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
RÂ² = 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 RÂ² = 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 RÂ² = 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
RÂ² = 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 RÂ² = 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 RÂ² = 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 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 
MgCO3TH 
MgCO3 = 12.98 + 0.17TH 
61.03% 
TurbidityFe 
Turbidity = 3.55 + 3.7Fe 
60.45% 
ClTH 
Cl = 19.02 + 0.34TH 
47.25% 
MnFe 
Mn = 0.44 + 0.16Fe 
33.14% 
TurbidityMn 
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% 
CaCO3TH 
CaCO3 = 53.18 + 0.3TH 
22.22% 
AlkalinityTH 
Alkalinity = 5.75 + 0.67TH 
22.08% 
MgCO3Fe 
MgCO3= 22.61 +5.01Fe 
17.82% 
ClCaCO3 
Cl = 21.68 + 0.32CaCO3 
16.85% 
pHMn 
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 
Hydrogenion concentration (pH) 
6.58.5 
6.58.5 
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 
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