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Analysis of Ground Water Quality by Fuzzy Comprehensive Evaluation in Cauvery Delta Region, Tamil Nadu, India

R. Sophia Porchelvi*, P. Selvavathi

Associate professor of Mathematics, A.D.M College for women (Autonomous), Nagapattinam, India

Abstract: The present study consisted of the estimation of some physicochemical parameters and quality of ground water from Cauvery delta region, Tamil Nadu (India).The water sample were collected from various parts of Cauvery delta region. Ten parameters (via TDS, turbidity, pH, Chloride, Calcium, Phosphate, Zinc, Copper, Iron and lead) were selected to assess the ground water quality degree by using fuzzy comprehensive evaluation method. The water samples were grouped into five categories according to Surface Water Quality Standards (as per IS: 2296). Some of the concentrations of PH, turbidity, phosphate were between the class A and class C with the exemption of the parameters (TDS, chloride, calcium, zinc, copper, iron, lead) which exceeds class C. From the analysis, these exceeding parameters were found to be the main pollutant in the sampling areas and hence the study further revealed that the water is not safe for drinking but suitable for aquatic life and also for irrigation purpose in some extend.

Keywords: Ground water, Cauvery delta region, physicochemical parameters, fuzzy comprehensive method, water quality

Citation: R. Sophia Porchelvi, P. Selvavathi, Analysis of Ground Water Quality by Fuzzy Comprehensive Evaluation in Cauvery Delta Region, Tamil Nadu, India. American Journal of Mathematical Analysis, 5 (1). 1-6. January 2017. doi:10.12691/ajma-5-1-1

1. Introduction

Groundwater is an important source of water supply throughout the world. Its use in irrigation, industries and homes continues to increase in the world. Cauvery delta region facing an acute shortage of good drinking water except good potable water supplied by the municipality. Generally, the concentrations of dissolved ions in groundwater are governed by lithology, groundwater flow, nature of geochemical reactions, residence time, solubility of salts, and human activities (Bhatt and Sakalani 1996; Karanth 1987; Nisi et.al. 2008; Schot and Van der Wal 1992) [1,2,3,4]. Moreover, the groundwater quality is mostly affected by either natural geochemical processes such as mineral weathering, dissolution/ precipitation reactions, ion exchange, or various man-made activities such as agriculture, sewage disposal, mining activities and industrial wastages etc. Low quality drinking water can cause gastrointestinal disorder and this water cannot be used for drinking purposes. TDS values are also considered as an important parameter in determining the usage of water, and groundwater with high TDS values is not suitable for both irrigation and drinking purposes (Fetters 1990; Freeze and Cherry 1979) [5,6]. The present study was carried out to evaluated the groundwater quality and its suitability for domestic and agriculture activities in Cauvery Delta Region in Tamil Nadu in India, as the groundwater is the only major source of water for agricultural and domestic purposes due to the lack of surface water and non- perennial nature of Cauvery river which is the major river in this area.

Now, studies on fuzzy comprehensive evaluation, physicochemical characteristics and water quality assessment of water are mainly concentrated on Cauvery Delta Region. This paper focus to study physicochemical characteristics and the quality of surface water of the Cauvery Delta Region. In this research, ten water sampling points were used to analyze the quality of water in Cauvery Delta Region and the characteristics of the ground water distribution throughout the County. For this study, ten important water quality parameters were used which include TDS, turbidity, pH, Chloride, Calcium, Phosphate, Zinc, Copper, Iron and lead. These parameters are widely used in ground water analysis. The fuzzy comprehensive evaluation method, water quality index (WQI), field research, and multivariate statistical methods (such as Pearson’s correlation coefficient), for comprehensive evaluation were applied to analyze the physicochemical characteristic and quality of ground water. These multivariate statistical methods have successfully used in numerous studies [19,20] for the physicochemical characteristic of ground water and to evaluate ground water quality. This study reveals the correlation between surface water samples and determines the ground water quality with health and analyzed its main pollution factor to evaluate the suitability of their waters for drinking and agricultural purpose. In addition, effective measures were put forward to protect the quality of the ground water. The findings of the study could be used as recommendations to prevent ground water pollution and to preserve ecological balance in Cauvery Delta Region.

2. General Situation of the Study Area

Cauvery Delta Zone lies in the eastern part of Tamil Nadu (India) between 10.00-11.30, North latitude and between 78.15 – 79.45 longitude. It is bounded by the Bay of Bengal on the East and the Palk straight on the South, Trichy district on the west, Perambalur, Ariyalur districts on the north west, Cuddalore district on the North and Puddukkottai district on the South West. The present investigation was carried out on Tiruvarur, Thanjavur and Nagapattinam town and their adjacent areas in the Cauvery Delta region in Tamil Nadu.

2.1. Water Sampling

Figure 1.

Table 1. Sampling Points of Cauvery delta region

2.2. Analytical Techniques

The estimation of physicochemical parameters of various methods is given in Table 2.

Table 2. Methods used for estimation of various physicochemical parameters

3. Analysis of Ground water by Fuzzy Comprehensive Evaluation

Fuzzy comprehensive evaluation (FCE) is a quantitative scientific evaluation method, proposed by LA. Zadeh, an U.S. expert in control theory [31], and the contributions of multiple related factors are comprehensively considered according to weight factors, and the fuzziness is decreased by using membership functions [32,33] which is suitable for making decisions in vague and imprecise system. FCE has been widely applied in many fields, such as environmental field [34,35], agriculture field [31,36], engineering field, and other fields [33,39,40,41] as well as to solve the fuzzy and difficult to quantify characteristics of the problem [42]. Water quality management is characterized by imprecision in objectives and water quality standards. The application of fuzzy comprehensive evaluation method involves making of the evaluation theory and method based on comparing the rigorous mathematical model, by fuzzy level of judgment and comprehensive health degree calculation. Therefore, the advantage and disadvantage of water quality can be observed visually. The principle procedure of FCE includes establishing the evaluation factor (via U={U₁,U₂,U₃,…Um}) set and grading level (via V={V₁,V₂,V₃,…Vn})set of evaluated object. This membership function with each category is expressed as [37].

The membership function of level 1 is:

[picture] | (1)

The membership function from level 2 to (n-1) is:

[picture] | (2)

The membership function of last level (level n) is:

[picture] | (3)

Where, rij is the fuzzy membership of indicator i to class j; ci is the monitoring value; sij is the allowable value of water quality indicator.

The fuzzy evaluation matrix R is the comprehensive survey to the index of safety evaluation of ground water and would have i rows and j lines, that's to say R=[rij] [44].

The water quality index, developed by the World Health Organisation (WHO) is an important tool to interpret the overall status of water quality in a simple and understandable manner. Water quality index (WQI) has been extensively applied to monitor water quality in recent years, and is indeed a practical method considering critical environmental variables, which represent the pollution conditions of a water body [47,48,49]. It depicts the composite influence of different water quality parameters [18]. The weight of the water quality indicator is expressed as

[picture] | (4)

Where Pi is the weight of water quality indicator i, Ci is the monitoring value of water quality indicator i, Si is the mean of kinds of water quality standards limit. The normalized weight of each indicator can be calculated by using the formula:

[picture] | (5)

Where Wi means the normalized weight of indicator i, means the sum of weight to all water quality indicators. The fuzzy W consists of weight each water quality indicator.The water quality assessment by fuzzy evaluation matrix R and weight coefficient matrix W,

[picture] | (6)

The fuzzy B is the matrix of membership to each water quality class. Water samples classified to the class with the maximize membership [17].

4. Results and Discussions

4.1. Physicochemical Characteristics of Ground Water

4.1.1. Descriptive Statistics Method

Descriptive statistics method is used for finding the physicochemical characteristics of ground water are given below:

Table 3. Descriptive statistics of ground water samples

The taste of ground water sample number S₁, S₃, S₅, S₈ and S₉ is salty in the Cauvery delta region. The entire collected samples in the Cauvery delta region had lying in an agreeable odour. The entire samples had high Total dissolved substances of ground water and except sample number S₆ had low level to the desirable limit. The Turbidity and pH content of ground water samples was found to be within the BIS describe limit. In sample S₅ had low chloride level and sample S₁ had high level compared with the BIS agreeable limit. The sulphate content of ground water samples was found to be within the BIS described limit. The Calcium content of sample number S₉, Magnesium content of sample number S₃, and Nitrite concentration of sample number S7 are above the desirable limit. The Nitrate concentration of ground water samples was found to be within the BIS describe limit. The Zinc concentration of sample number S7 and S10 had lying the desirable limit other samples had above the desirable limit. The Copper concentration of sample number S₇, the Iron concentration of sample number S₅ and Manganese concentration of sample number S₇ had very high to the desirable limit. The Lead concentration of entire collected samples in the Cauvery delta region had very high level to the desirable limit. The results are also showed in the pie diagram as given below (Figure 2).

Figure 2.

4.1.2. Pearson’s Correlation Coefficient

Pearson correlation coefficient is commonly used to measure and establish the strength of a linear relationship between two variables or two sets of data. It is a simplified statistical tool to show the degree of dependency of one variable to the other [21]. Multivariate statistical method used in this study (Pearson correlation coefficients and multiple linear regressions) helps to find statistically important factors in data variability and thus improve conclusions in environmental impact studies. Pearson correlation matrix was applied to all the collected water samples for identifying the possible statistical relationship between different pairs of ground water quality parameters. A highly strong correlation was observed between Fe and Cu, which gives us an idea about the total hardness of water. A multiple linear regression was used to establish relationship between TDS and other chemical water properties. The positive sign of the regression coefficients indicates that there is a positive relationship between TDS and elements of ground water properties: [EC], [Cl] , [SO₄²⁻], [Ca], [Mg], [NO₃⁻], [Zn], [Mno], [Pb]. It can be concluded that the total dissolved solids is an important physicochemical water quality parameters, because they are correlated with most of the elements in the groundwater.

Table 4. Correlation matrices of physicochemical parameters of ground water

4.2. Water Quality Assessment by Fuzzy Membership

A number of factors were considered for the assessment of water quality but only ten indicators (via, m=10) were selected from the list. This selection was based on their vital importance to the water quality and potential influence on human health. The representative indictors included TDS, turbidity, pH, Chloride, Calcium, Phosphate, Zinc, Copper, Iron and lead. Fuzzy comprehensive evaluation method was used to assess ground water quality and the water quality was described by World Health Organisation (WHO) and Surface Water Quality Standards (as per IS: 2296). Through the formula [(1) – (6)] calculation, the results showed that the fuzzy B is the matrix of membership to each water quality class. The main pollutant in the sampling areas is determined by the maximize membership in the matrix B as compared with the respective standards. From the results, we found that some of the concentrations of PH, turbidity, phosphate were between the class A and class C with the exemption of the parameters (TDS, chloride, calcium, zinc, copper, iron, lead) which exceeds class C. Hence the study further revealed that the water is not safe for drinking but suitable for aquatic life and also for irrigation purpose in some extend.

The adverse health effects caused by ground water contaminated with copper, zinc, lead, calcium, chloride, phosphate and iron are abdominal pain, vomiting, headache, nausea, and diarrhoea, anaemia, liver and kidney damage, concentration problems, high blood pressure, hearing problems, headaches, slowed growth, neurological disorders, reproductive problems in men and women, digestive problems, muscle and joint pain. Studies of these diseases suggest that abnormal incidence in specific areas is related to industrial wastes and agriculture activities that have released hazardous and toxic materials in the groundwater and thereby led to the contamination of drinking water in these areas. These Parameters should be removed from ground water if they are present at high levels for human safety.

5. Conclusions

The quality of ground water sample collected from ten different locations of Cauvery Delta Region, Tamil Nadu (India) is analysed and studied. On the basis of these analytical findings, the following conclusions can be drawn. The fuzzy comprehensive evaluation method showed that some of the concentrations of PH, turbidity, phosphate were between the class A and class C with the exemption of the parameters (TDS, chloride, calcium, zinc, copper, iron, lead) which exceeds class C which means that water qualities of most samples were not good and unsuitable for drinking and suitable for irrigation and in some extend according to World Health Organisation (WHO) and Surface Water Quality Standards (as per IS: 2296). In the correlation regression study, all the parameters are more or less correlated with each other. The mentioned diseases in section 4.2 may occur due to the lack of water quality in the survived region. Hence, rapid and reliable monitoring measures are essential for keeping a close watch on water quality and health environment.

Acknowledgements

The authors are thankful to UGC for supporting this research work through Rajiv Gandhi National Fellowship.

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