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20120140502018

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  • 1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 156-162, © IAEME 156 WATER QUALITY OF BUDHABALANGA RIVER IN THE VICINITY OF BALASORE TOWN BY CORRELATION AND REGRESSION METHOD Nibedita Pattnayak Associate Professor, Department of Chemistry, OEC, Bhubaneswar, Orissa, India ABSTRACT In industrial zone different types of industries emits highly concentrated polluted water. The concentrated of pollution depends upon the situation of point with respect to the industries that emits the polluted water. As there are large number of variables, the only statistical methods are useful for prediction of concentration of pollutants. This study aims to calculate the various parameters by correction and regression techniques. Key words: Pollutants, Water Quality Index, Correlation and Regression Method. INTRODUCTION Water is one of the abundantly available substances in nature. Pollution of land, water and air through water generated as a result of increasing is a challenge of serious dimensions. The main purpose of water analysis is to evaluate methods of treatments of ground water with to reuse or dispose, ascertain quality of water. Correlation among water quality parameters in specific environmental condition have been shown to be useful [1]. Utilization of such methodology will thus greatly facilitate the task of rapid monitoring of the status of pollution of water body or waste waters or effluents and achieve economy in matters of collection and analysis of samples [2]. The water quality of ground water can be predicted with sufficient accuracy just by the measurement of EC alone. This provides a means for easier and faster monitoring of water quality in a location [3]. The correlation study and correlation coefficient values can help in selecting treatments to minimize contaminates in ground water [4]. Water quality index has been regarded as one of the most effective way to communicate water quality [5]. INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 5, Issue 2, February (2014), pp. 156-162 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2014): 4.1710 (Calculated by GISI) www.jifactor.com IJARET © I A E M E
  • 2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 156-162, © IAEME 157 The WQI value [8] indicate the overall pollution of drinking water: 0 – 25 Excellent 25 – 50 Good 50 – 75 Poor 75 – 100 Very poor > 100 Unfit for drinking. MATERIAL AND METHOD Water samples were collected from Budhabalanga river of Balasore. The water samples were collected in every months. The correlation coefficient (r) among 23 water quality parameters, such as Chloride, Nitrate, sulfate, sulfide, phosphate, Nickel, Lead, Chromium, Cobalt, Zinc, Manganese, Iron, Turbidity, pH, electrical conductivity, Total alkalinity, Total hardness, calcium, magnesium, TDS, Sodium, Potassium, DO. of different categories of waters samples were calculated. The sampling of water was carried out during the period of one year (December-2012 to November- 2013). The water samples were collected by holding glass stoppered sterile bottle near its base in the hand and plugging it and transported in the laboratory in an ice-box to avoid unpredictable changes in physico-chemical characteristic after measuring temperature. Physico-chemical analysis for water were done following the standard methods by APHA[6]. Correlation coefficient and Linear regression Correlation coefficient ‘r’ has been calculated between each pair of parameters by using experimental data [7]. Let X and Y be the two variables, then the correlation ‘r’ between the variables X and Y is given by 2 2 Σ Σ Σ XY r X Y = × where, X X X= − and ΣX X n = Y Y Y= − and ΣY Y n = n = no. of samples. If the values of correlation coefficient ‘r’ between two variables X and Y are fairly large it implies that these two variables are highly correlated. In such cases it is feasible to try linear relation of the form :Y = AX + B to correlate X and Y, where A and B are the constants. For high ‘r’ values linear equation is found for two variables X and Y i.e. the concentration of certain parameters can be predicted when one is determined. RESULT AND DISCUSSION 1. The value of ‘r’ in positive correlation was between 0 to 0.99 and for negative correlation 0 to 0.99. 2. For Budhabalanga river, there was a negative correlation between sulfide and zinc (r= –0.864). There was some positive correlation between sulfide and pH (r=0.807), E.C. and TDS (r = 0.831), total alkalinity and TDS (r=0.831), total hardness and calcium (r=0.891).
  • 3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 156-162, © IAEME 158 3. Least square fitting for linear relation Y = AX+B (X and Y = Parameters, A and B = regression coefficient, r = correlation coefficient OPTIMUM PROCEDURE OF PREDICTING CONCENTRATION OF EACH CONTAMINANT This method aims to predict the concentration of all parameters when one is used to calculate all parameters[fig 1 to 4]. 1. Calculate Zinc from equation: Zinc = –0.1297 (Sulfide) + 0.060 2. Calculate pH from equation: pH=1.4872 (Sulfide)+ 7.150 3. Calculate TDS from equation: TDS =0.4976 (EC) + 8.8538 4. Calculate TDS from equation: TDS = 0.6072 (Total alkalinity) + 8.8432 5. Calculate Calcium from equation: Calcium = 0.627 (Total hardness) + 1.3729 Category of water sample X Y A B r Regression equation Y=AX+B Budhabalanga river water Sulfide Zinc -0.1297 0.060 -0.864 Zinc = –0.1297 (Sulfide) + 0.060 Sulfide pH 1.4872 7.150 0.807 pH=1.4872 (Sulfide)+ 7.150 EC TDS 0.4976 8.853 0.831 TDS =0.4976 (EC) + 8.8538 Total alkalinity TDS 0.6072 8.8432 0.831 TDS = 0.6072 (Total alkalinity + 8.8432 Total hardness Calcium 0.627 –1.3729 0.891 Calcium = 0.627 (Total hardness) + 1.3729
  • 4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 156-162, © IAEME 159 Table -1 Correlation coefficient ‘r’ among various parameters of Bhudhabalanga river water of Balasore Chlorid e Nitrat e Sulfat e Sulfid e Phosphat e Nicke l Lead Chromiu m Cobal t Zinc Mangane se Iron Turbidit y pH Ele. Cond Total Alkal. Total Hard Calciu m Magnesiu m TDS Sodiu m Potassiu m DO Chloride 1.000 Nitrate 0.248 1.000 Sulfate 0.585 0.173 1.000 Sulfide -0.367 - 0.060 - 0.293 1.000 Phosphate -0.098 - 0.192 - 0.301 0.076 1.000 Nickel -0.401 0.195 0.101 - 0.076 0.001 1.000 Lead 0.149 0.070 0.197 - 0.620 -0.316 0.288 1.000 Chromium -0.100 - 0.117 - 0.020 - 0.368 -0.071 - 0.149 0.192 1.000 Cobalt -0.312 - 0.480 - 0.273 - 0.185 0.311 0.199 - 0.073 0.158 1.000 Zinc 0.374 0.310 0.496 - 0.864 -0.194 0.273 0.690 0.144 - 0.195 1.000 Manganes e -0.099 - 0.025 - 0.086 0.299 0.288 - 0.352 - 0.012 0.137 0.005 - 0.315 1.000 Iron -0.005 0.140 0.424 - 0.179 0.121 0.097 - 0.122 0.231 0.387 0.143 0.323 1.000 Turbidity 0.040 0.736 0.133 0.422 -0.243 0.020 - 0.287 -0.296 - 0.765 - 0.054 0.055 - 0.118 1.000 pH -0.313 - 0.002 - 0.175 0.807 0.303 - 0.161 - 0.569 -0.325 - 0.421 - 0.607 0.392 - 0.185 0.550 1.000 Ele. Cond -0.515 0.394 - 0.416 - 0.199 -0.072 0.430 0.393 -0.094 0.079 0.273 0.050 - 0.016 0.160 - 0.114 1.000 Total Alkal. -0.515 0.394 - 0.416 - 0.199 -0.072 0.430 0.393 -0.094 0.078 0.273 0.050 - 0.016 0.161 - 0.114 1.000 1.000 Total Hard 0.331 0.491 0.068 - 0.542 -0.466 - 0.008 0.267 -0.052 - 0.177 0.506 -0.561 - 0.218 0.196 - 0.477 0.368 0.368 1.000 Calcium 0.008 0.229 - 0.238 - 0.377 -0.522 - 0.032 0.282 -0.006 - 0.014 0.251 -0.490 - 0.367 0.028 - 0.403 0.487 0.487 0.891 1.000 Magnesiu m 0.663 0.671 0.480 - 0.564 -0.200 0.030 0.140 -0.096 - 0.340 0.670 -0.439 0.083 0.359 - 0.394 0.051 0.051 0.760 0.381 1.000 TDS -0.604 0.036 - 0.313 - 0.316 0.096 0.570 0.511 0.070 0.144 0.371 -0.066 - 0.094 -0.118 - 0.095 0.831 0.831 0.169 0.325 -0.121 1.000 Sodium -0.120 - 0.220 0.391 - 0.210 -0.334 0.174 0.127 0.581 0.239 0.127 -0.216 0.455 -0.340 - 0.315 -0.243 -0.243 -0.066 -0.014 -0.115 0.002 1.000 Potassium -0.318 0.135 0.061 0.082 -0.276 0.261 0.230 -0.104 0.320 - 0.022 0.354 0.593 -0.077 - 0.169 0.430 0.430 -0.141 -0.025 -0.251 0.206 0.256 1.000 DO -0.161 - 0.560 - 0.242 0.572 -0.004 - 0.112 - 0.414 -0.043 0.441 - 0.790 0.035 - 0.137 -0.347 0.133 -0.507 -0.507 -0.461 -0.225 -0.616 - 0.477 0.146 0.022 1.00 0 Fig. 1: Linear plot between Sulfide and Zinc values y = -0.1297x +0.0606 R =-0.864 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0 0.1 0.2 0.3 0.4 0.5 [Sulfide], mg/l [Zinc],mg/l
  • 5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 156-162, © IAEME 160 Fig.2: Linear plot between E.C. and TDS values Fig. 3: Linear plot between Total Alkalinity and TDS values Fig. 4: Linear plot between Total Hardness and Calcium values y = 0.6072x + 8.8432 R = 0.831 0 10 20 30 40 50 60 70 80 90 95 100 105 110 115 120 [Total Alkalinity], mg/l [TDS],mg/l y = 0.627x - 1.3729 R = 0.891 0 2 4 6 8 10 12 14 16 18 0 5 10 15 20 25 30 [Total Hardness], mg/l [Calcium],mg/l
  • 6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 156-162, © IAEME 161 METHOD OF CALCULATION OF WATER QUALITY INDEX (WQI) Water quality indices were calculated using the method proposed by Tiwari and Mishra [5]. In this method following equations have been used. (i) Quality rating, [ ]100 ( )/( )n n i s iq V V V V= − − . where, Vn = Actual amount present in polluted water on nth parameter. Vi = The ideal value of this parameter. Vi = 0 for the suitable water except pH and DO Vi = 7.0 mg/l for pH and Vi = 14.6 mg/l for DO. Vs = Its standard. (ii) Unit weight for various parameters is inversely proportional to the recommended standard (Sn) for the corresponding parameters. n n Kw S = ( nw = Unit weight) 11 1 1n Si K V= = ∑ ( iSV = Standard value of its parameters) K = constant of proportionality which is determined from the condition and K=1 for sake of simplicity (iii) Sub-indices, ( ) ( ) nw n nSI q= . (iv) The overall WQI was calculated by taking geometric mean of these sub-indices (SI)n. WQI 21 21 =1 =1 Π ( ) Π ( )n n n n n SI q w= = or, WQI = Antilog10 21 10 1 logn n n w q =       ∑ Categories Water Quality Index S1 Budhabalang river : 25.187 > 25
  • 7. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 156-162, © IAEME 162 CONCLUSION In this case study the relation between various parameters has been derived by using correlation coefficient. As this is a statistical calculation there are more or less errors present, in spite of errors this method is very important, because analysis of all parameters is very time consuming and costly. To determine pollution load, it is a tough task every day. This method gives better alternative. It reduces cost analysis as well as time. The important application of WQI could be (i) identification and ranking of different activities for environmental degradation (ii) prioritization of pollution prevention resources (iii) determination of seasonal changes in environmental indices[9]. REFERENCE 1. Patnaik, N., P.K. Mohapatra and G. Mishra, Correlations among water quality parameters of a textile mill effluent, Indian Jour. Env. Protection, 10(6): 418-423 (1990). 2. Tewari, T.N. and Manzoor Ali, Correlations among water quality parameters of industrial waters, Indian J. Env. Prot. 8(1): 44 (1988). 3. Kalyanaraman, S.B. and G. Geetha, Correlation analysis and prediction of characteristics parameters and water quality index of ground water, Pollution Resource, 24(1): 197-200 (2005). 4. Achuthan Nair, G.I. Mohamad Abdullah and Moholy Fadiel Mahamoud, Physico-chemical parameters and correlation coefficients of ground water on North-East Libya, Pull. Res. 24(1): 1-6 (2005). 5. T.N. Tiwari and M. Mishra, A preliminary assessment of WQI to major India rivers. Ind. J. Env. Prot. 5(4): 276-279 (1985). 6. American Public Health Association, Standard Methods for the Examination of water and waste water, 14th ed. Washington, DC. American Water Works Association, 1976, p. 131. 7. A.D. Gavos, P.B. Lokhande and H.A. Mujawar, Charcterisation of waste water by correlation and regression method, Ind. J. Env. Prot. 27(12), 1117-1128 (2007). 8. R.K. Patel and P.C. Mishra, Some aspects of the quality of water in and around Rourkela (2005). 9. Alaka Panda and Nibedita Pattnayak, Costal Water Pollution Index-a tool assessing coastal water quality along the North East Coast of India, IJSER, Vol.4, ISSUE 6, June-2013. 10. R. S. Sapkal and Dr. S. S. Valunjkar, “Development and Sensitivity Analysis of Water Quality Index for Evaluation of Surface Water for Drinking Purpose”, International Journal of Civil Engineering & Technology (IJCIET), Volume 4, Issue 4, 2013, pp. 119 - 134, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316.

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