Multivariate Analysis for Assessment of Vamsadhara River Water Quality, Andhra Pradesh, India

CH.V. Saikrishna1,2*, L. Vaikunta Rao2 and D. Mallikarjuna Rao3

1Department of Chemistry, GITAM (Deemed to be University), Visakhapatnam, A. P., India.

2Department of Chemistry, Dr.V.S. Krishna GDC(A), Visakhapatnam, A. P., India.

3Akshaya Innotech, Visakhapatnam, A. P., India.

Corresponding Author E-mail: saisrikar.ch@gmail.com

Article Publishing History
Article Received on : 13 Feb 2024
Article Accepted on : 27 Jul 2024
Article Published : 08 Aug 2024

ABSTRACT:

Vamsadhara is a prominent river in north-western Andhra Pradesh. Without enough and reliable knowledge on the quality of the water, people utilize it for home and agricultural purposes quite a lot. This study aims to evaluate the quality of river water for agricultural and domestic consumption. Water samples from 10 sampling stations were collected during the pre and post-monsoon seasons of 2021, and the water quality index and agricultural indices were computed. The Water Quality Index was found to be satisfactory in both the seasons. Correlation and regression analyses are performed to understand the relationship between various hydrochemical parameters. The pre-season and post-season principal components were found to explain 81.3% and 78.9% of the total variance, respectively. The variables in both seasons are solely hydro-chemical and are believed to result from geological processes, suggesting geogenic sources. Water quality parameters for irrigation are within the limits. In irrigated soils, a higher growth of exchangeable Na is typically encouraged by elevated Mg levels. Water with a magnesium hazard level below fifty is considered suitable for irrigation. The Mg hazard was slightly excessive in most of the examined locations of the Vamsadhara River. The river water quality for irrigation is good to excellent, with exemptions from MH. Prioritizing effective sewage water treatment facilities before release into rivers.

KEYWORDS:

Hydrochemical parameters; Magnesium hazard; principal components; Vamsadhara River; WQI

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Saikrishna C. V, Rao L. V, Rao M. Multivariate Analysis for Assessment of Vamsadhara River Water Quality, Andhra Pradesh, India. Orient J Chem 2024;40(4).

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Saikrishna C. V, Rao L. V, Rao M. Multivariate Analysis for Assessment of Vamsadhara River Water Quality, Andhra Pradesh, India. Orient J Chem 2024;40(4). Available from: https://bit.ly/3YD1jKm

Introduction

Water is a vital natural resource for life as we know it. The utmost prerequisite for life is water, and ever since the very start of time, individuals have made efforts to make use of the resources that are accessible. The unregulated use of water for industrial, drinking, and agricultural reasons has resulted in a considerable decline in both water quality and availability. Rivers serve a variety of purposes, such as producing energy, irrigation, drinking water, and fishing1. The physicochemical properties of water determine its quality. Water quality is classified and assessed using physical and chemical factors. The unrestricted use of water for industrial, drinking, and agricultural activities has led to a significant decrease in both water quality and availability. Pesticides, fertilizer usage, pollution movement from highland to lowland, unplanned urbanization, rapidly rising population, and increasing industries reduce water quality and pose a water supply hazard. Water quality drastically declines as pollutants from sewage and agricultural drainage enter the rivers. River streams are contaminated due to both natural and man-made causes. Natural processes, including erosion, the weathering of rocks and minerals, and changes in the geology, are a few of the causes of river pollution2, 3. The main human activities that contribute to pollution are mining, smelting, oil refining, pesticide and fertilizer use, petrochemical manufacture, mining, raw sewage silt, etc.4,5. The level of soluble salts is an essential criterion for irrigation water quality. The categorization of water quality for effective management alternatives is becoming a problem as people become more aware of the importance of fresh water systems for public benefit and aquatic life6. Pollution in aquatic environments occurs due to the introduction of various nutrients from runoff water and sewage discharge7. The impact of water quality on product quality and soil properties is taken into consideration to assess its suitability for irrigation. Irrigation water contains a negligible, but discernible, amount of salt. These are developed due to the degradation of lime stone, gypsum, and fertilizers, conveyed by water, and settled in the land due to the usage of water by-products or the evaporation of surface waters8. Agriculture is a primary sector for most dwellers of the river basin; hence, the socio-economic growth of the people is influenced by the river water quality and water resources9. This study uses multivariate analysis such as correlation, linear regression, and principal component analysis to discover hydro-geochemical elements that are thought to be responsible for declining river water quality10. The Vamsadhara River basin is rich in biodiversity, with diverse flora and fauna inhabiting its riparian zones and surrounding forests. The river is known for its seasonal variability in flow. During the monsoon season, it experiences significant increases in water volume, leading to occasional flooding in the surrounding areas. However, during the dry season, the water levels recede substantially. However, like many rivers in India, the Vamsadhara is facing environmental challenges due to pollution, deforestation, and unsustainable agricultural practices. Efforts are being made to mitigate these issues through conservation measures and sustainable development initiatives; thus, the purpose of this research was to perform multivariate analysis to elucidate the quality of Vamsadhara water for drinking and irrigation.

Materials and methods

Study Area: The Vamsadhara River begins its journey in the Eastern Ghats, a mountain range located along India’s eastern coast. It specifically rises from the hills close to Thuamul Rampur in Odisha’s Kalahandi district. Flowing southwards, the river traverses the districts of Kalahandi, Rayagada, Gajapati, and Ganjam within Odisha. It covers approximately 254 kilometers of distance and eventually merging with the Bay of Bengal at Kalingapatnam in Srikakulam district of Andhra Pradesh state. Rice and cane sugar are the major vegetation in the river basin. The river’s catchment basin covers an area of 10,830 square kilometers. The famous tourist sites of Srikakulam district and the river hold cultural significance for the people living in the regions through which it flows. It has been mentioned in various ancient texts and folklore. Several temples and religious sites are located along its banks, attracting pilgrims and tourists. The catchment area’s primary uses of Vamsadhara Water are agriculture and household use. The ecological significance of the Vamsadhara River makes it remarkable. Like many other rivers in India, the Vamsadhara encounters environmental issues from both natural and man-made sources. The ongoing study’s primary focus is on the Vamsadhara River in North Andhra Pradesh’s suitability for drinking and irrigation water quality assessment at 10 sampling locations.

Sampling and Analysis

Samples were collected from the Vamsadhara River from up to downstream along the river throughout two seasons (pre-monsoon and post-monsoon) and tested for different physicochemical characteristics of water qualities according to the standard methods11. In the study area, samples were analyzed in this investigation from ten different locations. For the qualitative study, water samples were collected before and after the monsoon period of 2021 in Vamsadhara. Clean 2-L plastic bottles cleaned with nitric acid and distilled water were used to collect the samples. During the sample examination, hydro-chemical parameters like pH, electrical conductivity, total dissolved solids, dissolved oxygen, COD, BOD, total organic carbon, salinity, alkalinity, anions, and cations were measured12. Water samples were analyzed according to standard APHA procedures13. Titrimetric methods and UV visible spectrophotometric methods were used to determine the parameters such as total hardness, alkalinity, nitrates, sulphates and phosphates etc. WQI, multivariate statistical analysis like correlation analysis, linear regression analysis, and principal component analysis are performed by using SPSS and water quality parameters for irrigation and agriculture such as sodium absorption ratio, permeability index, sodium percentage, residual sodium carbonate, residual sodium bicarbonate, magnesium hazard, and Kelly’s index14 are determined by using hydrochemical parameters to assess the suitability of Vamsadhara River water  for irrigation in the current study region.

Results and discussion

 Hydrochemical parameters

The mean, minimum, maximum, and standard deviation values of hydrochemical parameters for assessing water quality are shown in Table All the samples’ pH levels were verified to be inside the permissible limit. and in many locations, the water is slightly alkaline. The ability of water to conduct electric current is known as its electrical conductivity (EC), and it is correlated with both the amount of dissolved ionized compounds. As it comes to drinking water, the suggested value of EC is 500μS/cm; in pre-monsoon, EC values are slightly greater than post-monsoon. Alkalinity is a measure of water’s resistance to changes in pH. The suggested alkalinity in drinking water is 20–200 mg/L; the average TA in pre-monsoon is 148.9 mg/L and 148.6 mg/L in post-monsoon. The hardness of water is within the permissible level; according to WHO international standards (2011), the acceptable limit of hardness for potable water is 500.ppm and the desirable limit is 100.ppm. The salinity of river water is influenced by the low and high tide effects; the range of salinity for the Vamsadhara river in the present study region is 0.03-0.0.09 in the preseason and 0.05-0.09 in the postseason. A certain amount of dissolved oxygen is naturally present in water since it is created by aquatic plants and algae as well as absorbed from the surrounding air. A river’s or stream’s dissolved oxygen content can reveal a lot about the quality of the water in it. COD and BOD are crucial for managing the water environment and regulating the overall content of pollution since they are the most extensive indicators of organic pollution15. The mean concentrations of cations in the current examined area are as follows: Ca2+ (31–62 mg/L and 33–51 mg/L) > Mg2+(15-35 mg/L and 18–32 mg/L) > Na+ (14–28 mg/L and 11–25 mg/L) > K+ (5.7–9.2 mg/L and 6.9–9.8 mg/L) and anions are as follows: SO42-(79-101 mg/L & 74-97 mg/L) > Cl-(22-39 mg/L & 21 – 36 mg/L) > PO43- (1.2-2.5 mg/L & 1.6-2.8 mg/L) > NO3– (9-21mg/L & 8 – 19mg/L) > F–(0.3– 0.6mg/L). Surface water naturally contains calcium and magnesium. The concentrations of these elements can be influenced by several factors, primarily the catchment area’s geological structure, land relief, soil type and class, plant cover, and weathering. seasonal variations, and the type and intensity of water supply16. As long as the amount of sodium in food and drink is not above standard limits, it poses no health risks to humans. The companion of sodium is potassium. Potassium remains within the cells; potassium is regarded as an electrolyte that does not allow free circulation. Potassium concentration is necessary to withstand both the pressure created by water movement and the sodium gate pressure17.  SO42- in river water may be attributed to the dissolution of sulfate minerals, atmospheric deposition, and agricultural and industrial discharges18. Establishing timely management plans is crucial despite the difficulty of differentiating between sources of SO42- and NO3– in coastal areas, as both can have detrimental effects on human health19. As fluoride is found in many natural settings and is thought to be essential for human health, it is recommended that people consume a modest dose of fluoride20.  The water’s higher HCO3– concentration signifies that mineral dissolution is prevalent. Phosphates can be tested for their impact on water quality indicators, particularly those that rely on weight, as they are not harmful to humans or animals unless they are present in extremely high concentrations. Recently, the significance of TOC determination has been acknowledged, and measuring it has become standard procedure in environmental investigations21. The TOC values in both seasons are 27 – 61mg/l and 33 – 62mg/l.

Table 1: Descriptive statistics of Hydro chemical parameters

 Hydrochemical Parameters

Dry Season

Wet Season

 

Low

High

Aveg.

S.D

Low

High

Aveg.

S.D

pH

6.70

8.10

7.33

0.47

7.10

8.10

7.51

0.36

E.C

144.00

288.00

217.80

47.00

131.00

268.00

198.70

46.44

TDS

244.00

388.00

309.10

46.26

225.00

355.00

281.50

47.07

TA

98.00

198.00

148.90

33.62

105.00

195.00

148.60

29.87

SALINITY

0.03

0.09

0.07

0.02

0.05

0.09

0.07

0.01

TH

198.0

310.0

242.9

39.63

181.0

288.0

224.2

37.51

DO

5.90

6.50

6.24

0.20

6.10

7.10

6.54

0.39

BOD

1.90

2.40

2.11

0.17

1.70

2.20

1.98

0.18

COD

28.00

38.00

33.10

3.54

21.00

31.00

26.90

3.07

CO32-

6.00

19.00

13.80

4.18

6.00

18.00

13.00

3.80

HCO3–

16.00

56.00

30.10

11.88

15.00

55.00

28.50

11.88

Cl–

22.00

39.00

31.00

5.48

21.00

36.00

27.10

4.56

F–

0.30

0.60

0.43

0.12

0.30

0.70

0.45

0.12

NO3–

9.00

21.00

12.80

3.74

8.00

19.00

12.50

3.66

SO42-

79.00

101.00

90.90

6.72

74.00

97.00

86.50

7.65

PO43-

1.20

2.50

1.89

0.47

1.60

2.80

2.20

0.41

Na+

14.00

28.00

21.30

4.76

11.00

25.00

18.20

4.73

K +

5.70

9.20

7.34

1.41

6.90

9.80

8.46

0.99

Mg2+

15.00

35.00

28.20

6.27

18.00

33.00

26.90

5.38

Ca2+

31.00

62.00

43.80

10.62

33.00

51.00

41.50

6.15

TOC

27.00

61.00

44.10

11.33

33.00

62.00

45.90

9.01

 

Water quality Index

A unitless single number obtained by uniting various water quality parameters is termed WQI.A distinctive type of quantitative averaging function that combines various values and units into one value evaluates the various hydrochemical parameters22. The water quality index is determined by the Pesce and Wunderlin (2000) approach shown below23. 

Ci is the normalized value of a parameter; Pi is the relative weight; and k is a constant. The quality of water is categorized based on WQI values24. If the WQI value is above 90, it is considered very good, and if it is below 25, it is very poor. Water quality is good if it is between 71 and 90. The wqi values in the study region ranged from 69.5 to 73 in the pre-monsoon and 72 to 78 in the post-monsoon season. The wqi values at all locations are given in Table 2.

Table 2: Water quality index in both the seasons

Sampling Location

Longitude

Latitude

Pre-Monsoon (%)

Post Monsoon (%)

S-1

18.79866oN

83.92986oE

69.5

77.5

S-2

18.69277oN

83.96816oE

72.0

76

S-3

18.63050oN

83.95956oE

73.0

73.7

S-4

18.59000oN

83.96406oE

69.0

73.1

S-5

18.53964oN

83.97176ooE

72.2

74.2

S-6

18.45068oN

83.94959oE

72.6

77.9

S-7

18.37153oN

83.99312oE

71.0

73.7

S-8

18.34895oN

84.00594oE

73.7

75.7

S-9

18.3330oN

84.05364oE

71.0

75.2

S-10

18.35109oN

84.09499oE

72.1

72.1

 

Principal component analysis

A multivariate statistical technique that is intended to reduce a complex set of variable inputs with a vast amount of data into new, uncorrelated variables that are linear combinations of the original variables25. The suitability of the current dataset for PCA was assessed using the Kaiser-Meyer-Olkin (KMO) and Bartlett’s tests of sphericity. The factor loadings are classified as’strong,”moderate,’ ‘weak’ based on absolute loading values greater than 0.75, 0.75-0.50, and 0.50-0.30, respectively26. The PCA of study locations in two seasons is given in Table 3. In premonsoon, the three PCs explained 72.53% of the total variance, in post monsoon, 68.5% of the total variance was explained. In preseason, PC1 explains 39.7% of variance in preseason with strong factor loadings of EC, Cl–, Na+, SO42- (m), PC2 explains 20.9% with strong loadings of Mg2+, TH, Ca2+, TOC, HCO3–(m), PC3 explains 12.01% with strong loadings of Na+, SO42-. On the other hand, in postseason, PC1 explains 30.54% of variance in preseason with strong factor loadings of EC, Na+, Mg2+, Cl–, PC2 explains 24.1% with strong loadings of TH, Mg2+, Ca2+, TOC, and PC3 explains 13.8% with strong loadings of NO3–, PO43-, which indicates geogenic process and dissolution sediment nutrient effluents are responsible. The scree plots and biplots to understand the data structure are given in Figs. 1 and 2.

Table 3: Factor loadings of Principal Components

 

Pre monsoon

Post monsoon

 Principal components

1

2

3

4

1

2

3

4

pH

-0.96

-0.03

-0.07

0.02

-0.87

0.03

-0.37

0.13

EC

0.86

0.04

0.43

0.02

0.99

0.03

0.05

0.07

TDS

0.44

0.32

0.79

0.07

0.04

0.5

0.08

0.32

TA

-0.95

-0.17

-0.07

-0.01

-0.88

-0.08

-0.21

0.07

SALINITY

0.38

0.58

0.51

0.03

-0.10

0.52

-0.09

0.25

TH

-0.06

0.89

0.41

0.06

0.09

0.96

0.10

-0.02

DO

0.16

0.04

-0.13

0.31

0.04

-0.33

0.21

-0.86

BOD

0.14

-0.40

0.05

0.79

0.02

-0.22

0.54

0.18

COD

0.22

0.11

0.22

-0.07

0.49

0.13

-0.35

-0.50

CO3

-0.74

0.27

-0.22

-0.05

-0.61

0.30

-0.11

0.12

HCO3

-0.54

0.59

-0.08

0.28

-0.56

0.70

0.19

0.34

Cl

0.84

0.11

0.47

0.08

0.92

0.06

-0.08

0.26

F

0.29

0.00

0.88

0.09

0.22

-0.06

0.08

0.78

NO3

-0.07

-0.05

0.02

0.98

0.04

0.01

0.94

-0.08

SO4

0.55

0.18

0.48

0.23

0.65

0.12

0.07

-0.08

PO4

0.20

0.03

0.49

0.71

0.48

0.13

0.79

0.07

Na

0.86

0.02

0.42

0.01

0.99

-0.02

0.01

0.05

K

-0.67

-0.40

0.29

-0.31

-0.26

-0.41

0.12

0.01

Mg

0.06

0.91

-0.07

-0.07

0.04

0.89

0.26

-0.26

Ca

0.03

0.80

0.32

-0.43

0.36

0.82

-0.26

0.16

TOC

0.20

0.86

-0.11

-0.31

-0.16

0.82

-0.27

0.20

Eigen Values

8.33

4.38

2.52

1.85

6.406

5.070

2.907

2.188

cumulative% of Variance

39.67

60.52

72.53

81.33

30.505

54.648

68.490

78.908

 

Correlation analysis

Itis a statistical technique used to evaluate the strength and direction of the linear relationship between two quantitative variables. The goal is to determine whether and to what extent changes in one variable are associated with changes in another variable. The most common correlation coefficient is Pearson’s correlation coefficient (denoted by “r”). It ranges from -1 to 1. In general, r values > 0.7 are regarded as strong correlations, and 0.5 to 0.7 considered as moderate. Strong correlations were observed between all the measures in both seasons, suggesting that physico-chemical factors had an impact on the quality of the water in the Vamsadhara River, negative correlation was observed between dissolved oxygen and BODdue to the rise in organic and inorganic pollutants27, strong positive correlation between magnesium and other parameters was observed, which indicates the cause of domestic and agricultural pollutants present in surface water. The correlation matrix for both seasons is shown in Tables 4 & 5.

Table 4: Correlation matrix of pre-monsoon

 

pH

E.C

TDS

TA

SALINITY

TH

DO

BOD

COD

CO32-

HCO3-

pH

1

                   

E.C

-0.92

1

                 

TDS

-0.54

0.78

1

               

TA

0.98

-0.89

-0.59

1

             

SALINITY

-0.39

0.55

0.67

-0.42

1

           

TH

-0.04

0.2

0.6

-0.15

0.69

1

         

DO

-0.29

0.31

0.14

-0.23

0.10

0.13

1

       

BOD

-0.17

0.22

0.04

-0.09

-0.09

-0.26

-0.38

1

     

COD

-0.34

0.49

0.41

-0.30

0.16

0.3

0.72

0

1

   

CO32-

0.67

-0.65

-0.3

0.62

-0.3

0.18

0.04

-0.18

-0.13

1

 

HCO3-

0.56

-0.53

-0.17

0.47

0.19

0.48

-0.28

0

-0.49

0.58

1

Cl-

-0.89

0.99

0.81

-0.88

0.62

0.27

0.27

0.25

0.41

-0.62

-0.43

F-

-0.3

0.62

0.82

-0.31

0.47

0.32

-0.16

0.15

0.29

-0.38

-0.19

NO3-

0.1

-0.05

0.03

0.06

-0.06

0.03

0.26

0.73

-0.06

-0.07

0.23

SO4 2-

-0.57

0.69

0.75

-0.6

0.7

0.33

0.32

0.11

0.24

-0.38

-0.27

PO43-

-0.25

0.42

0.58

-0.3

0.23

0.31

0.29

0.5

0.34

-0.39

-0.13

Na+

-0.92

1.00

0.76

-0.89

0.53

0.18

0.31

0.24

0.47

-0.65

-0.53

K +

0.54

-0.42

-0.17

0.62

-0.32

-0.15

-0.06

-0.1

-0.07

0.33

-0.02

Mg2+

-0.16

0.11

0.34

-0.29

0.4

0.83

0.24

-0.44

0.34

0.22

0.32

Ca2+

-0.12

0.22

0.46

-0.19

0.62

0.85

-0.05

-0.57

0.33

0.08

0.3

TOC

-0.15

0.07

0.2

-0.29

0.48

0.63

-0.32

-0.62

-0.09

0.09

0.42

 

Cl-

F-

NO3-

SO4 2-

PO43-

Na+

K +

Mg2+

Ca2+

TOC

 

Cl-

1

                   

F-

0.65

1

                 

NO3-

-0.01

0.09

1

               

SO4 2-

0.73

0.47

0.18

1

             

PO43-

0.44

0.55

0.75

0.5

1

           

Na+

0.99

0.61

-0.07

0.66

0.39

1

         

K +

-0.44

-0.1

-0.26

-0.31

-0.19

-0.4

1

       

Mg2+

0.14

-0.09

-0.09

0.19

0.12

0.09

-0.31

1

     

Ca2+

0.25

0.26

-0.46

0.1

-0.09

0.22

-0.09

0.76

1

   

TOC

0.12

0

-0.35

0.07

-0.25

0.06

-0.52

0.73

0.76

1

 

Table 5: Correlation matrix of post-monsoon

 

pH

E.C

TDS

TA

SALINITY

TH

DO

BOD

COD

CO32-

HCO3-

pH

1

                 

 

E.C

-0.89

1

               

 

TDS

-0.58

0.78

1

             

 

TA

0.96

-0.91

-0.61

1

           

 

SALINITY

0.38

-0.10

0.40

0.29

1

         

 

TH

-0.07

0.13

0.29

-0.11

0.46

1

       

 

DO

-0.35

-0.01

-0.33

-0.22

-0.5

-0.21

1

     

 

BOD

-O.13

0.08

0.14

0.02

0.04

-0.03

-0.05

1

   

 

COD

-0.29

0.41

0.38

-0.3

0.13

0.14

0.17

-0.3

1

 

 

CO32-

0.66

-0.61

-0.19

0.66

0.46

0.28

-0.45

0.32

-0.14

1

 

HCO3-

0.49

-0.5

-0.16

0.46

0.44

0.68

-0.4

0.08

-0.45

0.63

1

Cl-

-0.7

0.91

0.86

-0.74

0.09

0.12

-0.23

-0.01

0.28

-0.52

-0.4

F-

-0.07

0.23

0.54

-0.05

0.22

-0.04

-0.6

0.16

0.02

0.05

0.12

NO3-

-0.42

0.07

0.12

-0.24

-0.19

0.12

0.45

0.55

-0.16

-0.01

0.15

SO4 2-

-0.48

0.58

0.71

-0.44

0.21

0.22

-0.04

0.24

0.22

-0.09

-0.22

PO43-

-0.6

0.5

0.64

-0.5

0.24

0.22

0.25

0.42

0.04

-0.28

-0.03

Na+

-0.88

0.99

0.76

-0.89

-0.16

0.08

0

0.06

0.45

-0.62

-0.54

K +

0.42

-0.31

-0.04

0.54

0.39

-0.34

0.07

0.23

-0.01

0.09

-0.12

Mg2+

-0.16

0.08

0.18

-0.17

0.33

0.93

0.05

0.08

0.15

0.32

0.6

Ca2+

-0.14

0.39

0.47

-0.25

0.46

0.85

-0.43

-0.25

0.32

0.01

0.41

TOC

0.24

-0.13

0.08

0.03

0.48

0.64

-0.41

-0.56

-0.02

0.23

0.62

 

Cl-

F-

NO3-

SO4 2-

PO43-

Na+

K +

Mg2+

Ca2+

TOC

 

Cl-

1

                 

 

F-

0.32

1

               

 

NO3-

-0.12

0.14

1

             

 

SO4 2-

0.73

-0.02

0.07

1

           

 

PO43-

0.45

0.28

0.72

0.48

1

         

 

Na+

0.9

0.26

0.06

0.56

0.45

1

       

 

K +

-0.17

0.15

-0.01

0.01

0.19

-0.31

1

     

 

Mg2+

-0.01

-0.25

0.32

0.25

0.29

 0.03

-0.41

1

   

 

Ca2+

0.43

0.16

-0.25

0.24

0.08

0.37

-O.33

0.63

1

 

 

TOC

-0.02

-0.01

-0.31

-0.14

-0.17

-0.18

-0.45

0.53

0.64

1

 

 

Linear regression analysis

Linear regression is a powerful tool for understanding and predicting relationships between variables. It is a statistical technique used to find the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data. The aim is to find the best-fitting line that minimizes the difference between the observed values and the values predicted by the model. The equation for a simple linear regression with one independent variable is typically written as: Y = aX + b, where Y is the dependent variable and X is the independent variable. The regression plots with squared R values for hydrochemical parameters with high correlation (r > 0.7) are given in Figs. 3 and 4.

 Water Quality parameters for Irrigation

The physical and chemical characteristics such as pH, conductivity, Na+, Mg2+, K+, Ca2+, SO42-, Cl–, carbonate and bicarbonate, etc. are used to establish the appropriateness of water quality for agriculture and irrigation purposes28,29. The assessment of water quality for irrigation is ascertained by calculating some indices such as sodium percentage, residual sodium carbonate, residual sodium bicarbonate, sodium adsorption ratio, magnesium hazard, Kelly’s index, and permeability index. The formulas to calculate these parameters are in table 6