Statistical Analysis of Quality of Water in Various Water Shed for Kozhikode City, Kerala, India

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ScienceDirect Aquatic Procedia 4 (2015) 1078 – 1085

INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE 2015)

Statistical Analysis of Quality of Water in Various Water Shed for Kozhikode City, Kerala, India Narayanan C Viswanatha*, PG Dileep Kumarb, KK Ammadb a Department of Mathematics, Govt. Engg. College, Thrissur, Thrissur, 680009, India Department of Civil Engineering, Govt. Engg. College, Kozhikode, Kozhikode, 673005,

b

Abstract Ground water is the principal source for drinking water and other activities in Kozhikode town. Monitoring the ground water quality therefore is of utmost importance. In this paper a multivariate statistical analysis of quality of ground water has been carried out, based on thirty data points of water shed in Kozhikode city, Kerala, India. The parameters examined were PH, Electric conductivity, Total Dissolved Solids, Total Hardness, Chlorides, Calcium and Magnesium, Sodium, Potassium, Bicarbonates and Sulphates. A methodology for characterizing ground water quality of watersheds based on the above data that mingle multiple linear regression, principal component analysis and structural equation modeling is presented. The aim of this work is to analyze hydro chemical data in order to explore the groundwater samples and the origin of water mineralization, using mathematical method and modeling. Thus, with the help of MATLAB, a regression equation is explored for the sampled ground water. The structural equation modeling which has been carried out using IBM-SPSS Amos, allows a simultaneous analysis of the entire system of parameters. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2015 The Authors. Published by Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of ICWRCOE 2015. Peer-review under responsibility of organizing committee of ICWRCOE 2015 Keywords:Ground Water Quality, Multiple Linear Regression, Principal Component Analysis, Structural Equation Modeling

1. Introduction This study is a follow up of the one that we conducted in Viswanath et al. (2014), which analyzed the ground water quality of about 2344 km2 area of the Kozhikode district, Kerala, Inida, during the month of July 2014.

* Corresponding author. Tel.: +91-9495851753. E-mail address:[email protected]

2214-241X © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of ICWRCOE 2015 doi:10.1016/j.aqpro.2015.02.136

Narayanan C. Viswanath et al. / Aquatic Procedia 4 (2015) 1078 – 1085

Combining multiple linear regression (MLR) modeling, principal component analysis (PCA) and structural equation modeling (SEM), the above paper succeeded in developing a regression model for predicting total dissolved solids (TDS) in terms of bicarbonates (HCO3-), chloride (Cl-), sulfate (SO42-), sodium (Na+), calcium(Ca2+) magnesium (Mg2+) and nitrates (No3-). Here we reexamine the regression model developed in the above study, to test its relevance during the post south west monsoon season, which starts in the beginning of the month of June and lasts till the end of September; and also in a restricted study area of 118.52 square kilometer, which comprises of the Kozhikode city, Kozhikode District, Kerala, India which is more exposed to pollution (Kerala Vision 2030) than the rural places. Multivariate statistical methods like PCA, MLR and SEM have been used extensively in the literature for studying water quality data. We refer to the studies (Chenini and Khemiri 2009, Viswanath et al. 2014) and the references therein for details on the above methods. 2. Materials and Methods

Fig. 1. The study area and sampling locations.

The study area is the Kozhikode city, which comprises of about 118.52 square kilometer in the Kozhikode District, Kerala, India. It lies between North latitude 11º.25' and East longitude 75º.45'. The region comprising Kozhikode Corporation and peri-urban blocks belong to the low- and midlands in the typical classification of land in Kerala as low-, mid- and highlands. Lagoons and backwaters characterise the lowland, which receives runoff from the rivers. Kozhikode features a tropical monsoon climate . Like many other parts of the Kerala state, Kozhikode receives ample rain from the South-west monsoon from June to September and from the North-East Monsoon from second half of October through November (Kozhikode 2014). A total of 30 water samples were collected in the end of the month of September 2014 (Fig. 1). All the samples, collected in tight capped high quality polyethylene bottles, were immediately transported to the laboratory under low temperature conditions in ice-box and stored in the laboratory. All analyses were completed within a week time in laboratory. The measured variables included the characteristic water quality parameters. All other parameters were determined in laboratory. The ground water samples were analyzed for parameters which

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include pH, electrical conductivity (EC), total dissolved solids(TDS), bicarbonates (HCO3-), chloride (Cl-), sulfate (SO4+), sodium (Na+), calcium (Ca2+) magnesium (Mg2+) nitrates (No3-)and Total Hardness(TH). Among the major cations sodium, calcium and magnesium were analyzed by flame photometer. Total hardness was analyzed by gravimetric analysis. Major anions were analyzed by Research Grade Analyzer. The analytical data quality was ensured through careful standardization in accordance with standard method of American Public Health Association (APHA 1988). The ground water ionic strength is dominated by major cations and anions. Results of the analysis are given in Table 1. Table 1. Physicochemical parameters of drinking water at studied watersheds for Kozhikode city (Fig. 1), Kerala State, India (all parameters are in mg/l (ppm) except pH; EC in μ mhos/cm ) NO.

TDS

Ph

EC

Cl-

SO4+

Na+

HCO3-

TH

Ca2+

Mg2+

NO3-

K

1

153

7.81

340.7

24.5

31.8

0.95

20.2

98.635

17.6

4.37

0.95

0.12

2

170.9

7.74

366.9

15.3

12.8

1.01

31.45

101.55

23.5

4.9

1.01

0.15

3

185.2

7.36

409.2

16.7

8.9

2.17

19.65

105.33

25.3

5.1

2.17

0.23

4

142

7.17

271.8

18.7

16.8

4.73

2.65

76.85

12.8

3.4

4.73

0.95

5

276.6

7.04

605.9

20.6

32.5

4.77

39.75

180.2

32.8

4.37

4.77

1.1

6

189

7.36

419.2

18.7

10.9

3.17

2.65

107.33

24.5

5.02

3.17

0.36

7

102.9

7.34

222.9

9.9

11.5

0.68

3.975

64.925

11.8

2.9

0.68

0.12

8

140.5

7.28

305.8

24.1

16.6

4.49

2.98

51.65

12.1

3.01

4.49

1.12

9

58.29

7.15

126.9

10.2

1.8

5.08

0

18.2

3.2

2.43

5.08

1.23

10

62.62

7.17

138.9

6.83

3.6

4.66

0

38.425

5.6

2.44

4.66

1.14

11

66.1

5.58

132

28

5.1

21

0

18.55

8.8

1.23

0

5.23

12

122

5.25

234

59

25.1

35.6

0

28.55

12.8

1.48

0.2

11.23

13

251

6.77

475

124

55.1

48.3

3.975

80.825

19.23

8.23

31.2

14.45

14

133

6.12

254

42

5.2

51

0

21.2

8.01

1.02

0

15.2

15

128

6.67

244

36

24.1

32.2

3.971

37.1

9.62

2.23

0

10.25

16

56.5

5.4

115

20

0

22.7

0

9.275

7.23

1.12

12.7

5.41

17

60.8

5.48

117

20

0

27.2

0

27.95

7.23

1.08

14

6.15

18

98.8

6.32

196

30

23.5

14.4

0

35.775

9.62

2.03

0

1.89

19

155

6.13

293

58

16.1

34.3

1.325

46.375

8.92

1.12

0.8

8.93

20

252

7.25

474

80

61.2

40

1.325

39.75

16.03

3.81

4.1

8.99

21

195

4.98

370

60

11.2

29.8

0

58.2

15.23

3.63

46.1

7.23

22

131

5.76

247

36

29.1

37.5

0

23.85

9.63

1.86

29.3

9.23

23

82.9

5.47

163

30

5.2

22.2

0

19.875

6.4

1.12

16

6.56

24

275

6.03

530

108

80.1

74.4

29.15

56.975

24.01

5.23

46.1

19.23

25

151

5.84

286

48

39.2

39.9

0

37.1

9.54

1.86

24.7

9.63

26

76.6

5.64

148

22

4.1

38.2

0

25.3

4.81

0.23

21.2

9.25

27

211

7.34

405

80

38.2

70.7

1.325

38.425

8.82

1.02

1.8

18.56

28

166

7.43

311

52

21.5

40.8

1.325

50.35

16.09

3.63

0

10.23

29

179

8.57

340

62

18.2

46.9

0

46.375

8.02

1.23

2.1

14.69

30

158

6.64

352

58

69.23

54.7

1.325

41.075

15.23

3.9

2.01

15.69

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Following the method adopted in Chenini and Khemiri 2009 and Viswanath et al. (2014), we first conducted a principal component analysis (PCA) for reducing the dimension of the analysis. After PCA, a multiple linear regression (MLR) analysis was carried for developing a regression model for predicting TDS in terms of main components. PCA and MLR were performed using MATLAB 2009a.This regression model is then validated using structural equation modelling (SEM) with IBM SPSS AMOS 22.0. 3. Results and Discussion The level of TDS is one of the characteristics, which decides the quality of drinking water. According to Indian standard Drinking Water- Specification IS 10500: 2012, acceptable limit for total dissolved solids in drinking water is 500 mg/L. Table 1 shows that in the present study, TDS is ranging from 55-280 mg/L. The acceptable limit of total hardness of drinking water is 200 mg/L according to IS 10500: 2012. In the present study, TH is ranging from 9-110 mg/L. So we conclude that the quality of water is good drinking and agricultural purposes. A linear relation of the form TDS=k*EC between TDS and EC (Wood 1976; Hem 1985) has been explored for the data in Table 1, which revealed k=0.4965 with an R2 value 0.9684 and root mean square error (RMSE) equal to 11.37. The TDS/EC ratio, k for the data from the Kozhikode city (Table 1) can be found to be higher than 0.4638, the corresponding value obtained for the data (Table 1, Viswanath et al. (2014)) from a broader area of the Kozhikode district. Mostly, k lies between 0.55 and 0.75 (Hem 1985) and lesser values suggest lower presence of ions (Ali (2010)). This is again confirmed from the higher k obtained for the city area which is more vulnerable to different kind of pollution, when compared to rural areas. However, notice that the k value 0.4965 for the Kozhikode city is below its usual range suggesting a lower presence of ions even in the city of Kozhikode. For developing a MLR model for predicting TDS, we performed a PCA of the data in Table 1. The percentages of variance explained by the first four principal components (PC) were 77.3, 6.6, 4.5 and 2.8 respectively, which accounts for around 91 percentage of variance. Table 2 gives the component loadings of the first four PCs. It shows the variable pH has low loadings, especially for the first two components, which explains almost 81 percentage of variance. The significance of TDS and the insignificance of pH can be easily viewed in Figure 2, which is a Biplot of the PCA. This made us to form a regression model for predicting TDS excluding pH. As can be viewed in Fig. 2, the variable Mg2+ is not much significant than pH; however, we decided to include Mg2+ in the initial regression model, since it is a chemical element and may contribute in the regression model especially in this case where the data has been collected from the city not far from the Arabian sea. We started an initial regression model for predicting TDS in terms of Cl-, SO42-, Na+, HCO3-, Ca2+, Mg2+, NO3- and K, results of which are given in Table 3. It shows that the p-values of all the variables except Cl- and Ca2+ are greater than 0.05, suggesting that Cl- and Ca2+ are the only variables which are statistically significant for the regression model. This made us to consider a second regression model for predicting TDS, which involved only Cl- and Ca2+. Results from this study are given in Table 4, which shows a slight decrease in the R-square value indicating a probably inferior model with respect to the data. However, a decrease in Mse and an increase in the F-statistic together with a decrease in the corresponding p-value indicate a better fit of the data by the second model. Further in the second model, only the p-value associated with the constant term is just above 0.05 and the other p-values are less than 0.0000. All these facts point out that the second regression model better fits the data when compared to the first model. High t-value for the regression coefficient of Ca2+ in Table 4 suggest that Ca2+ is the most significant parameter for the regression model. Finally, validity of the MLR models developed was tested using SEM. The first structural model, SEM1, corresponds to the first MLR model, in the sense that the relation between TDS and other parameters are the same. It is to be noted that SEM is more general than the corresponding MLR model, since it involves other subregression models between the parameters Cl-, SO42-, Na+, HCO3-, Ca2+, Mg2+, NO3- and K. Figure 3 shows SEM1. Many fit indices have been proposed in the literature for testing whether a proposed SEM fits the data (Schermelleh-Engel 2003, Barrett 2007, Hooper et al. 2008). Among these indices, we considered the following indices: i) The F2 statistic, the degrees of freedom and the corresponding p-value ii) The root mean square error of approximation (RMSEA) iii) The root mean square residual (RMR) iv) The goodness of fit statistic (GFI) and the adjusted goodness of fit statistic (AGFI) (v) The parsimony goodness of fit index (PGFI) (vi) The normed fit index (NFI) and the comparative fit index (CFI). As a reference for the range of these indices which suggest good, acceptable and bad fit of the data by the SEM, we consider Table 1 in Schermelleh-Engel 2003. Table 5 presents the fit indices for SEM1. It shows that the fit index GFI shows an acceptable fit; AGFI

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doesn’t even indicate an acceptable fit and all other indices points to a good fit in accordance with Table 1, Schermelleh-Engel 2003. Comparing Fig. 3 and Table 3 one finds that the regression coefficients of different parameters are almost the same in the first MLR and SEM1. As in the case of MLR, p- values of different regression coefficients except that of Cl- and Ca2+ are found greater than 0.05. This substantiates the rejection of the first MLR model for predicting TDS. Now, a second structural model SEM2 corresponding to the second MLR model is analyzed. Fig. 4 shows SEM2. Table 5 presents the fit indices for SEM2. It shows the indices GFI and AGFI are not in the acceptable ranges. However, the chi-square statistic and other indices point to a good fit of the data by the SEM. A comparison of Table 4 and Fig. 4 shows almost same regression coefficients for predicting TDS. Also, the pvalues of different regression coefficients are now less than 0.05 as in the case of second MLR model. Thus the SEM study further validates the selection of second MLR model. We thus select the MLR model for predicting TDS as TDS = 6.0728*Ca2+ + 1.1843*Cl-+ 19.7015 Here we note that the regression model obtained here is entirely different from what we obtained in Viswanath et al. 2014. Notice that in contrast to the previous study, the present study has been concentrated in those areas nearer to the sea, which could be one reason behind the dominance of Ca2+ and Cl- over the other parameters. Another reason for the difference could be that the present study has been carried out in the post south west monsoon season. 4. Conclusion Applying multivariate statistical methods, we studied the physicochemical parameters of the water quality of the Kozhikode city, Kerala, India. The TDS/EC ratio was analyzed using linear regression. A PCA helped us to isolate the less significant parameter pH and lead us to a MLR model for predicting TDS in terms of HCO3-, SO42-, No3-, Cl-, Ca2+, Mg2+, K and Na+. It was found that Ca2+ and Cl- are the only statistically significant parameters in the model. Hence a second regression model was developed involving these two variables only. A SEM study further substantiated the rejection of the first MLR model and validated the final regression model. It was found that Ca2+ is the most significant parameter in the regression model for predicting TDS. Acknowledgements The authors gratefully acknowledge anonymous reviewers for their constructive comments towards improving the scientific quality of the manuscript. TDS PH Cl-

Table 2. Loadings of the first four principal components -0.8996 0.3518 -0.0826 0.1666 -0.0034 0.0149 0.0309 0.0190 -0.3142

-0.6004

-0.0944

0.1162

SO4

-0.2345

-0.2186

0.6147

-0.6898

+

Na HCO3Ca2+

-0.1454 -0.0724 -0.0702

-0.5900 0.2150 0.1410

0.0362 0.0219 0.0046

0.2414 -0.2772 -0.1012

Mg2+ NO3K

-0.0161 -0.0622 -0.0400

0.0214 -0.1621 -0.1639

-0.0044 -0.7767 0.0217

-0.0475 -0.5713 0.0928

2-

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0.4

TDS HCO3-

0.2

Component 2

Ca2+ Mg2+

PH

0

K

-0.2

NO3-

SO4-0.4

Cl-

-0.6 -0.8

-0.6

-0.4

Na+ -0.2 Component 1

0

0.2

0.4

Figure 2: Coefficients of first two PCs’ (blue vectors) and representation of Table 1 data in the PC space (red dots)

Table 3. Results of the first regression analysis Regression t-value p-value Coefficients 0.1327

Table 4. Results of the second regression analysis Regression Coefficients

t-value

p-value

Constant

19.7015

1.8600

0.0738

Cl-

1.1843

7.5396

0.0000

9.5187

0.0000

Constant

25.6584

1.5643

Cl-

1.3731

2.9194

0.0082

SO42-

0.2336

0.5129

0.6134

Na+

0.2689

0.1512

0.8813

Ca2+

6.0728

HCO3-

0.1927

0.2292

0.8210

R-square

0.8677

Ca2+

6.5019

3.8400

0.0010

Mse

582.06

Mg2+

-5.9994

-0.8911

0.3830

NO3-

-0.1829

-0.3850

0.7041

F-value

88.57

K

-1.9837

-0.3482

0.7312

R-square

0.8789

Mse

685.26

F-value

19.05

1.4x 10-12

5.4 x 10-8

Table 5. Fit indices for SEM

Model

2

F

df

p-value

RMSEA

RMR

GFI

AGFI

SEM1

15.06 17 0.59

0.00000 136.9

0.91

0.76

SEM2

17.62 23 0.78

0.00000 117.6

0.89

0.78

PGFI 0.34 0.45

NFI 0.955 0.95

CFI 1.0 1.0

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Figure 3. SEM corresponding to first regression model

Figure 4. SEM corresponding to the second regression model

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References Ali, M. H., 2010. Fundamentals of Irrigation and On-farm Water Management: Volume 1. Springer. APHA, 1988. Standard methods for the determination of water and wastewater 20th Edn. American Public Health Association, American Water Works Association and Water Environment Federation. Washington DC American Public Health Association Barrett, P., 2007. Structural Equation Modeling: Adjudging model fit; Personality and Individual Differences; Vol. 42, No.5, pp.815-824 Chenini, I., Khemiri, S., 2009. Evaluation of ground water quality using multiple linear regression and structural equation modeling. Int. J. Environ. Sci. Tech. 6(3):509-519 Hem, J. D., 1985. Study and Interpretation of the Chemical Characteristics of Natural Water. U.S. Geological Survey, Water Supply Paper 2254 Hooper, D., Coughlan, J., Mullen, M. R., 2008. Structural equation modeling: Guidelines for determining model fit. The Electronic Journal of Business Research Methods 6(1):53-60 Kerala Vision 2030, 2014. http://www.kerala.gov.in/docs/reports/vision2030 Kozhikode, 2014. In Wikipedia, The Free Encyclopedia. Retrieved 07:31, September 2, 2014, from http://en.wikipedia.org/w/index.php?title= Kozhikode&oldid=623449938 Schermelleh-Engel, K., Moosbrugger, H., Muller, H., 2003. Evaluating the fit of structural equation models: Tests of significance and descriptive goodness - of - fit measures. Methods of Psychological Research 8(2):23-74 Viswanath, N. C., Dileep Kumar, P. G., Ammad, K. K., Usha Kumari, E. R., 2014. A Study of ground water quality of the Kozhikode district, Kerala, India applying multivariate statistical methods. (Communicated) Wood, W. W., 1976. Guidelines for collection and field analysis of ground-water samples for selected unstable constituents. USGS Techniques of Water-Resource Investigation : 01-D2