EVALUATION OF SURFACE DRINKING WATER SOURCES OF DEHRADUN USING MULTIVARIATE ANALYSIS

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1 EVALUATION OF SURFACE DRINKING WATER SOURCES OF DEHRADUN USING MULTIVARIATE ANALYSIS Abhishek Gupta 1*, Rakesh Singh 2, Prashant Singh 3 1,2 Department of Chemistry, DBS (PG) College, Dehradun, Uttarakhand(India) 3 Department of Chemistry, D.A.V. (P.G.) College, Dehradun, Uttarakhand(India) ABSTRACT Evaluation of pollution of surface drinking water sources of district Dehradun, Uttarakhand was done using Multivariate analysis viz. Cluster Analysis (CA), Principal Component Analysis (PCA) and Factor Analysis (FA). Water samples were collected from 5 surface drinking water sources of Dehradun. Water samples were analyzed up to ppm and ppb level for 16 drinking water quality parameters during pre-monsoon and postmonsoon seasons of 2015 using advanced analytical techniques. Cluster analysis grouped 5 sampling sites/sources into three clusters i.e. less, moderate and high polluted based on similarity among water quality parameters. Using Principal Component and Factor Analyses, the useful factors were extracted, which showed that selected sources are mainly contaminated by anthropogenic and hydro-geologic processes. The results of the study inferred that pollution of drinking water sources are caused by municipal waste, untreated or partially treated industrial effluents. The study highlights the treatment of turbidity before domestic supply of water from selected drinking water sources. Keywords: Cluster Analysis, Factor analysis, Multivariate Analysis, Principal Component Analysis, Water Pollution I. INTRODUCTION Dehradun as capital of Uttarakhand is one of the most developed cities of state. Most of the population is dependent on ground as well as surface waters for their daily consumption. The water quality data of Dehradun district has been established by the Central Ground Water Board (CGWB, 2010) by observing different water sources. Water quality characteristics of these sources have been assessed by adopting ph, EC, carbonate, bicarbonate, chloride, total hardness, calcium and magnesium parameters. Most of the analyzed sources express the fresh water quality for domestic and irrigation purposes. Only some sources have high hardness, calcium or magnesium concentration than the desirable limits. According to survey of drinking water quality of India, it was predicted that the drinking water resources of various districts in our country were contaminated due to the predominant concentrations of different ions or bacteria. For this study, water quality of 28 cities was assessed under CLEAN-India programme (IMIS, 2009) and output of the study showed the alarming condition of water. 974 P a g e

2 This study has also predicted the high degree of hardness in water sources of Dehradun district of Uttarakhand state. The evaluation of quality of drinking water sources is necessary to protect the mass population of Dehradun from toxic pollutants. Moreover, identification of causes and sources of contamination of drinking water sources is another important aspect required to be explored. Multivariate technique (Shrestha and Kazama, 2007) is a useful tool for evaluating and characterizing the quality of water and its temporal and spatial variability caused by geogenic and anthropogenic factors. Principal component analysis (PCA), cluster analysis (CA) and factor analysis (FA) are useful in the interpretation and in finding the conclusion of multivariate data sets coming from water quality analysis. The present study includes assessment of water quality of five surface drinking water sources of Dehradun for 16 water quality parameters during pre-monsoon and post-monsoon seasons of 2015 by using multivariate techniques to assess the quality and cause as well as sources of pollution entering into drinking water sources of Dehradun. II. MATERIALS AND METHODS 2.1 Study area Five surface drinking water sources of Dehradun district, which are being used for public supply by Uttarakhand state s drinking water supply and maintenance agency i.e. Uttarakhand Jal Sansthan (UJS), Dehradun were selected and analysed for sixteen water quality parameters namely turbidity, ph, total hardness, alkalinity, chloride, total dissolved solids (TDS), sodium, potassium, calcium, magnesium, sulphate, nitrate, fluoride, lead, zinc and iron for qualitative and quantitative status of drinking water as per BIS: (2012) specification for drinking water. (TABLE 1) Table 1: Water sampling sites of Dehradun district, Uttarakhand, India S.N. Site (Source) Longitude Latitude Height above MSL 1. Kalsi (Jhajreda) N E Bhogpur (Aamgarh Khala) N E Bharatwala (Bhitarligaad) N E Kotala Santure N E Dhauran (Talanighat) N E Sampling procedure Water samples were collected from each site during PRM season (April May) and POM season (October- November) of Water samples were collected in high-density polyethylene Tarson bottles after 2-3 times rinsing with the samples. For metal analysis, water samples were collected in acid-leached polyethylene bottles and preserved by adding ultra pure nitric acid (5 ml/l) to minimize the adsorption and precipitation by reducing ph <2. Water samples were brought to the laboratory at 4 C. Sample preservation and digestion have been performed as per standard methods of BIS and APHA (2012). 975 P a g e

3 2.3 Instrumental Analysis: Analyses of water samples for quantitative and qualitative evaluation of total 16 water quality parameters were carried out in laboratory upto ppm levels. Which included turbidity, ph, total hardness, alkalinity, chloride, total dissolved solids (TDS), sodium, potassium, calcium, magnesium, sulphate, nitrate, fluoride, lead, zinc and iron. Samples were analyzed through AAS, UV-VIS Spectrophotometer and other latest analytical techniques. Suitable protocols and methods as per BIS: (2012) and APHA (2012) have been employed for analysis. III. MULTIVARIATE STATISTICAL TECHNIQUES: CA, PCA and FA are the statistical methods of multivariate techniques (Singh et al. 2005), whose primary purpose is to assess the variation in complete water quality data and illustrate the relationship between variables. Results help to identify the source and causes of these variations up to significant level. The correlation matrix is based on the Pearson correlation coefficient, used for developing relationship between two variables for three significant levels viz. highly significant, moderate and no significant level. CA has been used to subdivide variables into homogenous groups and results may be plotted in the form of Dendrogram. This is used to detect the similarity groups between the sampling sites. The main purpose of PCA is transforming the original variables into small set of variables with minimum loss of original facts. FA is a statistical technique use to minimize the contribution of less significant variables called factors. FA follows PCA. Liu et al., (2003) classified the factor loadings as strong, moderate and weak, corresponding values are >0.75, and , respectively. IV. RESULTS AND DISCUSSION: Common Problem Water Quality Parameters (having values more than Permissible Limits) as per BIS found during PRM and POM seasons of 2015 of the basis on analysed results of 16 physico-chemical drinking water quality parameters are summarized in TABLE 2. Table 2: Common water quality parameters found during PRM & POM seasons of 2015 Name of Problem Water Name of Problem Water Common S.No. Site Name (Source) Quality Parameter during PRM Season Quality Parameter during POM Season Problem Water Quality Parameter during Parameter Value Parameter Value Both PRM and POM Seasons 1. Kalsi (Jhajreda) 9.2 NTU 6.2 NTU 2. Bhogpur (Aamgarh Khala) 10.2 NTU Nil Nil Nil 3. Bharatwala (Bhitarligaad) 9.1 NTU 9.0 NTU 976 P a g e

4 4. Kotala Santure 9.0 NTU 9.1 NTU ph 6.2 Pb mg/l 5. Kalsi (Jhajreda) 9.2 NTU Pb 7.7 NTU mg/l At site no. 2, no common water quality parameter has been found in both seasons of 2015, whereas sites no. 1, 3, 4 & 5 have been found to be associated with turbidity. Though sites 4 & 5 had values more than PL for lead in POM season but were not in common water quality parameters list. 4.1 Factor Analysis (FA)/ Principal Component Analysis (PCA) during Pre-Monsoon (PRM) Season-2015 The Factor analysis (FA)/ Principal component analysis (PCA) were employed on selected 16 water quality parameters namely turbidity, ph, total hardness, alkalinity, chloride, TDS, sodium, potassium, calcium, magnesium, sulphate, nitrate, fluoride, lead, zinc and iron for 5 surface drinking water sources of Dehradun district, Uttarakhand. The statistical treatment of water quality data indicates their association and grouping with three factors. Corresponding variable loadings, eigen values, % of total variance and % of total cumulative variance associated with each other are presented in Table 3. The factors with eigen value of 1.0 or greater are considered to be significant (Shrestha and Kazama, 2007). Further, the factor loadings are classified as strong, moderate and weak correspond to absolute loading values of >0.75, and , respectively (Liu et al., 2003). The PCA extracted three factors and explained % as cumulative total variance. The water quality parameters are grouped into five categories based on factor loadings. Water quality parameters having strong positive and negative loadings with strong correlation co-efficient (>0.75) are highlighted with bold values and presented in TABLE 3. The FA/ PCA have generated 3 significant factors, which explained % of the total variance in data sets. Table 3: Factor Analysis (FA)/ Principal Component Analysis (PCA) of Drinking Water Quality Data of Dehradun during Pre-Monsoon (PRM)-2015 Percentage of Variance after Varimax Rotation D1 D2 D3 % of Total Variance % of Cumulative Total Variance Factor Pattern after Varimax Rotation Water Quality Parameters D1 D2 D ph P a g e

5 Total Hardness (TH) Alkalinity Chloride (Cl - ) Total Dissolved Solids (TDS) Sodium (Na) Potassium (K) Calcium (Ca) Magnesium (Mg) Sulphate (SO 4 ) Nitrate (NO 3 ) Fluoride (F - ) Lead (Pb) Zinc (Zn) Iron (Fe) Bold values indicate Strong Pollution, Underline values indicate Moderate Pollution and Italic values indicate Weak Pollution Factor (D) 1 exhibits % out of total variance of % and has strong positive loadings on total hardness, alkalinity, TDS, calcium and sulphate. However, it was noticed that moderate positive loadings was found on ph, potassium, magnesium, fluoride and zinc, while it was observed negative on iron. Further, weak negative loading was recorded only on one water quality variable i.e. nitrate. High variability in analytical data obtained is indicative of an external source of these elements in respective surface water bodies. The first component/ factor is an indicative of mixed source of pollution comprising natural and anthropogenic sources (domestic sewage as well as industrial discharge) of pollution. Factor (D) 2 accounts for % of the total variance. This factor has negative weak loading only on turbidity. The moderate positive loading was recorded on potassium, fluoride and iron, whereas it was obtained negative on nitrate and zinc. The negative loadings on nitrate and zinc indicate that with increased concentrations of potassium, fluoride and iron, level of nitrate and zinc are decreased and reflects inverse relationship with above-mentioned water quality parameters in surface water bodies. Further, it was observed that strong positive loadings were found on sodium and lead. This component highlights to an organic source of pollution such as agro-based industries. Factor (D) 3 exhibits % of the total variance with moderate positive loadings on magnesium and zinc. Contrary to this positive loading, it was noted negative only on one water quality parameter i.e. ph. The strong positive and negative loadings were noticed only on water quality characteristics viz. chloride and turbidity, respectively. Moreover, weak positive loading was obtained on nitrate. These loadings of the factor might be attributed to the influence of agricultural activities. This factor is also an indicative of natural source of pollution i.e. continuous erosion of soil from nearby locations of water sources Cluster Analysis during Pre-Monsoon (PRM) Season-2015: 978 P a g e

6 The cluster analysis was applied to find out sampling sites having similar characteristics in terms of water quality parameters. The dendrogram has shown grouping of all 5 sampling sites of drinking water sources of Dehradun district, Uttarakhand during pre-monsoon season-2015 into two statistically significant clusters at (D Link /D max )*100<50. A total of 16 water quality variables were used to make the clusters for evaluation of site variation among different sampling sites of Dehradun district. A dendrogram clearly differentiates groups of objects has small distances in the far branches of the tree and large differences in the near branches. Cluster analysis, thus reduces the monitoring efforts and is useful in rapid assessment of water quality as one site from each cluster represents the spatial assessment of water quality for the whole group. Following dendrogram illustrates two clusters and duly shown through Fig. 1. Figure 1: Dendrogram of Cluster Analysis for Different Water Sources of Dehradun during Pre-Monsoon (PRM)-2015 Cluster 1: For water quality data of 2 drinking water sources of Dehradun district, sampling sites no. 3 (Bhitarligaad; Bharatwala) and 5 (Talanighat; Dhauran). These 2 sampling sites primarily receive raw sewage from domestic sources. Cluster 2: The drinking water sources no. 1 (Jhajreda; Kalsi), 2 (Aamgarh Khala; Bhogpur) and 4 (Kotala Santure) together make second cluster of the dendrogram. These sampling sites receive effluents from domestic sources as well as from industrial sources. Cluster 2 is consequence of higher polluted water sources (Boyacioglu and Boyacioglu, 2008). Other researchers have also reported less, moderate and high pollution on the basis of pollution level (Singh et al., 2005 and Kamini et al., 2006). The above two clusters have been summarized in TABLE 4. Table 4: Cluster Analysis Data of Dehradun during Pre-Monsoon (PRM) Season-2015 S.N. District Site/ Source of Cluster 1 Site/ Source of Cluster 2 1. Dehradun Bhitarligaad; Bharatwala Jhajreda; Kalsi Talanighat; Dhauran Aamgarh Khala; Bhogpur 979 P a g e

7 - Kotala Santure 4.2. Factor Analysis (FA)/ Principal Component Analysis (PCA) during Post-Monsoon (POM) Season-2015: Similar to the pre-monsoon season, the Factor Analysis (FA)/ Principal Component Analysis (PCA) were also employed for selected 16 water quality parameters namely turbidity, ph, total hardness, alkalinity, chloride, TDS, sodium, potassium, calcium, magnesium, sulphate, nitrate, fluoride, lead, zinc and iron for five drinking water sources of Dehradun during post-monsoon season. Corresponding variable loadings, eigen values, % of total variance and % of total cumulative variance associated with each other are presented in TABLE 5. The statistical treatment of water quality data indicates their association, which is grouped with three factors as shown by TABLE 5. The PCA extracted three factors and explained % as cumulative total variance. Table 5: Factor Analysis (FA)/ Principal Component Analysis (PCA) of Drinking Water Quality Data of Dehradun during Post-monsoon (POM)-2015 Percentage of Variance after Varimax Rotation D1 D2 D3 % of Total Variance % of Cumulative Total Variance Factor Pattern after Varimax Rotation Water Quality Parameters D1 D2 D ph Total Hardness (TH) Alkalinity Chloride (Cl - ) Total Dissolved Solids (TDS) Sodium (Na) Potassium (K) Calcium (Ca) Magnesium (Mg) Sulphate (SO 4 ) Nitrate (NO 3 ) Fluoride (F - ) Lead (Pb) Zinc (Zn) P a g e

8 Iron (Fe) Bold values indicate Strong Pollution, Underline values indicate Moderate Pollution and Italic values indicate Weak Pollution Factor (D) 1 explains the % of total variance. It has strong positive loadings on ph, total hardness, alkalinity, TDS, calcium, magnesium, sulphate, nitrate and fluoride, while negative loading was recorded on sodium. Negative loading on sodium indicates that as the concentrations of ph, total hardness, alkalinity, TDS, calcium, magnesium, sulphate, nitrate and fluoride are increased, the quantity of sodium is decreased and thus, reflects inverse relationship with above-mentioned water quality parameters in surface water bodies. Moderate loading was not obtained on any of the water quality parameter. However, it also observed weak positive loadings on turbidity and lead. The first component/ factor is indicative of mixed source of pollution and comprises natural and anthropogenic sources (domestic sewage as well as industrial discharge) of pollution. Factor (D) 2 accounts for % of the total variance, which has found moderate positive loadings on two water quality variables viz. turbidity and fluoride. Apart from these loadings, strong positive loadings were obtained on chloride, potassium and iron. While, it was noticed that weak negative and positive loadings were found on sodium and magnesium. This component points to an organic source of pollution. Factor (D) 3 exhibits % of the total variance. This factor has strong positive loadings on turbidity and lead, however it was noted negative only on zinc. In addition, only one moderate positive loading was obtained on magnesium. Weak positive loading was recorded on chloride and sodium, whereas it was found negative only on one water quality parameter i.e. ph. These loadings of the factor might be attributed to the influence of agricultural activities. This factor is also an indicative of natural source of pollution i.e. continuous erosion of soil from banks of drinking water sources analyzed Cluster Analysis during Post-Monsoon (POM) Season-2015: The cluster analysis was applied to find out sampling sites having similar characteristics in terms of water quality parameters related to sources of Dehradun during post-monsoon (POM) season. The dendrogram has shown grouping of all 5 sampling stations of drinking water sources into two statistically significant clusters at (D Link /D max )*100<50. A total of 16 water quality variables were used to make the clusters for evaluation of sites variation among different sampling sites. Following dendrogram (Fig. 2) illustrates two clusters: Figure 2: Dendrogram of Cluster Analysis for Different Water Sources of Dehradun during Post-Monsoon (POM) P a g e

9 Cluster 1: For water quality data of 3 drinking water sources of Dehradun district, sampling sites no. 1 (Jhajreda; Kalsi), 2 (Aamgarh Khala; Bhogpur) and 4 (Kotala Santure) formed first cluster of the dendrogram. These 3 water sources primarily receive raw sewage from nearby domestic sources. Cluster 2: For water quality data of 2 drinking water sources of Dehradun district, sampling sites no. 3 (Bhitarli Gaad; Bharatwala) and 5 (Talanighat; Dhauran) together make second cluster of the dendrogram. These sampling sites are expected to receive effluents from domestic sources as well as from nearby industries. The cluster 2 corresponds to higher polluted water source (Boyacioglu and Boyacioglu, 2008). Thus, technique of cluster analysis reduces monitoring efforts and is useful in rapid assessment of water quality, as one site from each cluster represents the spatial assessment of water quality for the entire group. The above two clusters have been depicted under TABLE 6. Table 6: Cluster Analysis Data of Dehradun District, Uttarakhand during Post-Monsoon (POM) Season-2015 S.N. District Site/ Source of Cluster 1 Site/ Source of Cluster 2 1. Dehradun Jhajreda; Kalsi Bhitarli Gaad; Bharatwala Aamgarh Khala; Bhogpur Talanighat; Dhauran Kotala Santure - V. CONCLUSION: On the basis of physico-chemical parameters, the common problem water quality parameters have been identified as turbidity at four locations i.e. drinking water sources. Different multivariate techniques have been used to evaluate spatial and temporal variations with identification of sources and causes of contamination of drinking water sources of Dehradun district. The results of chemometric analysis performed on 16 water quality parameters, identified three factors controlling their variability in drinking water sources of Dehradun. CA grouped 5 sampling sites into two clusters of similar water quality characteristics in both PRM and POM seasons of This is quite useful to design future sampling strategy, which will reduce the number of sampling sites and associated cost. The FA/PCA helped in identifying the responsible factors/ sources for variations in drinking water quality at five different water sources of Dehradun, Uttarakhand. Acknowledgements Authors are thankful to Uttarakhand State Council for Science and Technology (UCOST), Dehradun and Uttarakhand Jal Sansthan (UJS), Dehradun for support provided for this work. REFERENCES [1.] Central Ground Water Board, CGWB (2010). Ground water scenario of Uttarakhand. Ministry of Water Resources, Government of India P a g e

10 [2.] Integrated Management Information System, IMIS (2009). Department of Drinking Water Supply, Ministry of Rural Development, India. [3.] Shrestha, S. and Kazama, F. (2007). Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan. Environmental Modelling & Software, 22, [4.] Bureau of Indian Standards (BIS) (2012), Second Revision. Specification for Drinking Water. 1S: 10500, Bureau of Indian Standards, New Delhi [5.] American Public Health Association (APHA), American Water Works Association (AWWA) and Water Environment Federation (WEF) (2012). Standards for Examination of Water and Wastewater. 22 nd Ed., American Public Health Association, Washington, DC, USA. [6.] Singh, K.P., Malik, A. and Sinha, S. (2005). Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques a case study. Analytica Chimica Acta, Vol. 538, [7.] Liu, C.W., Lin, K.H. and Kuo, Y.M. (2003). Application of factor analysis in the assessment of groundwater quality in blackfoot disease area in Taiwan. Science of the Total Environment, 313, [8.] Boyacioglu, H. and Boyacioglu, H. (2008). Water Pollution Source Assessment by Multivariate Statistical Methods in the Tahtali Basin, Turkey. Environmental Geology. 54, [9.] Kamini, J., Jayanthi, S. C., and Raghavswamy, V. (2006). Spatio-temporal analysis of land use in urban Mumbai Using multi sensor satellite data and GIS techniques. Journal of Indian Society of Remote Sensing, 34(4), P a g e