A NEW HYBRID METHOD FOR ASSESMENT OF SUBSURFACE WATER FOR POTABILITY- A CASE STUDY OF TIRUCHIRAPPALLI CITY,
S .INDIA
ABSTRACT
A new hybrid fuzzy Simulink model has been developed to assess the groundwater quality levels in Tiruchirappalli city, S. India. Water quality management is an important issue in the modern times. The data collected for Tiruchirappalli city have been utilized to develop the approach. This is illustrated with seventy nine groundwater samples collected from Tiruchirappalli Corporation, S.India. The characteristics of the groundwater ground water for this plain were monitored during the years 2006 and 2008. The quality of groundwater at several established stations within the plain were assessed using Fuzzy Logic simulation. The results of the calculated Fuzzy logic Simulink and the monitoring study have yielded good agreement. Groundwater quality for potability indicated high to moderate water pollution levels at Srirangam, Ariyamangalam, Golden Rock and K.Abisekapurm zones of the study area, depending on factors such as depth to groundwater, constituents of groundwater and vulnerability of groundwater to pollution. Fuzzy Logic simulation approach has shown to be a practical, simple and useful tool to assess groundwater quality assessment for potability. This approach is capable of showing the water quality assessment for drinking.
KEY WORDS
Groundwater, fuzzy simulation, potability, and Tiruchirappalli
1. Introduction
Ground water is one among the Nation's most important natural resources. Very large volumes of ground water are pumped each day for industrial, agricultural, and commercial use. Ground water is the drinking-water source for about one-half of the Nation's population, including almost all residents in rural areas. Information on the quality and quantity of ground water is important because of the Nation's increasing population and dependency on this resource. The population dependent on public water systems that used ground water for drinking-water supplies increased during last 50 years. The estimated withdrawal increased about five-fold during last half century.The quality and availability of ground water will continue to be an important environmental issue. Long-term conservation, prudent development, and management of this natural resource are critical for preserving and protecting this priceless national asset.
As per the International norms, if per capita water availability is less than 1700 m3 per year then the country is categorized as water stressed and if it is less than 1000 m3 per capita per year then the country is classified as water scarce. India is water stressed and is likely to be water scarce by 2050 [8].
Continued research, guidance and regulations by government agencies and pollution abatement programmes are necessary to preserve the Nation's groundwater quality and quantity for future generations. The impact of Industrial effluents is also responsible for the deterioration of the physico-chemical and bio-chemical parameters of groundwater [16].The environmental impacts on the groundwater contaminations may seriously affect the socio-economic conditions of the country. Knowledge on water chemistry is important to assess the quality of aquatic resources for understanding its suitability for various needs [17].Water quality monitoring and enforcement by regulatory bodies mandate information on the status and changing trends in water quality [10].Deterministic approach in Decision making by comparing threshold values of water quality indices with prescribed limits. [9,14].
There are two areas in which the literature is far from complete and has the gaps which are to be bridged and these are: 1. The decision on the water quality assessment (desirable, acceptable or not acceptable) using fuzzy logic and 2. The sets of calculated data and limits was not concise but but as fuzzy sets (with vagueness). To avoid the complexity in handling uncertainty in water quality assessment the margin of safety or degree of perception is introduced .
Prior to apply to single value to potability standards, fuzzy technique was used for computing in the field of water science by various researches [12,13and 18]Keeping the importance of uncertainty handling in the Potable water quality assessment and versatility of the fuzzy set theory in decision-making in the imprecise environment, an attempt is made to classify the groundwater from Tiruchirappalli City Corporation of Tamilnadu, South India for the potable use [20].
2. Study area and Sampling
Tiruchirappalli City of Tamil Nadu, India is selected for the study. The general topology of Tiruchirappalli is flat and lies at an altitude of 78 m above sea level. Tiruchirappalli is fed by the rivers Cauvery and Kollidam. There are reserve forests along the river Cauvery. Golden Rock and the Rock Fort are the prominent hills. The southern/south-western part of the district is dotted by several hills which are thought to be an offset of the Western Ghats Mountain range and the soil is considered to be very fertile. Figure 1 shows the study area.
Figure 1 Study area of Tiruchirappalli city
The water samples are collected during March 2006 and December 2008. The water from these bore wells are used for drinking, house hold utilities and bathing by the residents. As per the standard laboratory procedures sixteen physico chemical water quality parameters were evaluated [1].The groundwater hydrochemistry records of the study area are used for the preparation of maps[21] and[22]. The resulted maps are observed by geospatial methodology (Kriging) and are represented in the form of equal ion contour concentration limits [19,20 and 24 ] . The MATLAB V.2008 (a) software was also used to analyse the data. The process of groundwater quality data are used as the hidden layer for the preparation of base maps. These features are the boundary lines between mapping units, other linear features (streets, rivers, roads, etc.) and point features (bore well points, etc.). The contours are developed for pH, EC, Cl-, Na+, Ca++, Mg++, Total Hardness, Alkalinity, F, SO4-, Coliform and NO- 3 values for the pre monsoon and post monsoon values. The monitoring and sampling program was initiated in 2006 and finalized the year 2008. A total of seventy nine monitoring stations were established of them represented groundwater conditions .
3. A hybrid of Fuzzy and Simulation
The theory of fuzzy sets was first introduced by[22]to model uncertainty in subjective information. Fuzzy sets are defined as sets whose members are vague objects. Data can generally be received in terms of linguistic judgments and beliefs (natural language), which can then be converted to the form of fuzzy sets in order to provide a base for logical and mathematical reasoning[23]. Simulink models and sub models representing the complex interaction between various parameters are framed and used for twelve selected parameters. A typical block diagram showing Simulink (Fuzzy Information Process) of first group water quality parameters viz. pH, EC, Cl-, Na+ is presented in Fig. 2. Data collected from the study area for various seasons are used as the input for simulation model. The simulation was used for the collected data for seasonal variations. Based on expert knowledge 66 rules are designed for main potability parameters in Group I, where as 73 rules are designed for Group II. Results from group one and two are combined with Group III to assess the final classification of water.A total of 27 rules are implemented for the final evaluation of groundwater quality. The results from all the three groups are aggregated to assess the final classification of water as shown in Fig 3. The processes are applied to all the seasonal water samples and the results obtained are as shown in Fig. 4.
The rule based decision on expert's perception was fired using Mamdani implification of maximum and minimum operator [16]. To assess the potability of water quality, 181 rules are employed of groundwater samples.
Figure. 2 Block Diagram for Simulink process of FIP -First Group water quality parameters
Figure.3 Block Diagram for Simulink process of FIP- Second Group water quality parameters.
Figure. 4 Block Diagram showing Simulink process of FIP -Third Group water quality parameters
Figure. 5 Block Diagram for the fuzzy Simulink process of FIP for Water quality assessment
Figure 6. Subsurface water potable frequency during premonsoon periods ( a) 2006, ( b) 2007, ( c) 2008
3.1 Approach towards groundwater classification
A fuzzy rule based system is generated in which users classify the water according to given data in Desirable,Acceptable,Not acceptable, Rejected quality with respect to different parameters, all connected using AND operator. The user's feedback is also taken with respect to overall quality for different parameters connected by AND operator. For example, one of the feedbacks taken may be like this, If TDS = good AND pH = medium and Sulphate = good then, overall water quality = ?.After this, Delphi's technique is applied to converge the feedback of various users to a single value. A degree of match is computed between the user's perception and field data for different parameters and for every type of water quality viz. good,(Desirable) medium (Acceptable) or bad (Not Desirable). The water quality for which degree of match is the highest is considered to represent the quality of the water sample.
4. RESULTS AND DISCUSSIONS
Comparison of existing eight water quality parameters with the point value of the prescribed limits target to the precise computation of physico chemical groundwater quality. In case groundwater quality model approach, these 8 parameters were divided in the four categories on the basis of expert opinion according to their significance to drinking water quality criteria.
The hydro chemical analyses revealed that water samples in the study area is characterized by hard to very hard, fresh to brackish and alkaline in nature. The highly turbid water may cause health risk as excessive turbidity can protect pathogenic microorganisms from the effects of disinfectants and also stimulate the growth of bacteria during storage. Characteristic by pH values, most of the water samples were alkaline in nature which are well within permissible limit (6.5 – 8.5) and some of the samples have been found acceptable for usage and the ranges are between 6.5 and 9.2 meeting BIS standards of IS:10500:1991 and WHO (2006) guidelines.Based on Electrical Conductivity (Ec) values measured all water samples Zone-I (Srirangam) are desirable (
Figure 7 (a) Potability map of Premonsoon 2006
Figure 7 (b) Potability map of Premonsoon 2007
Figure 7 (c) Potability map of Premonsoon 2008
5. Conclusion
The quality of the groundwater of the Tiruchirappalli city was monitored in 79 sampling wells for 3 years and recorded data revealed that the concentrations of cations and anions were above the maximum, desirable for human consumption. The Electrical Conductivity was found to be the most significant parameter within input parameters used in the modeling. The developed model enabled well to test the data obtained from 79 samples of bore wells of Tiruchirappalli city. Therefore, with the proposed model applications, it is possible to manage groundwater resources in a more cost-effective and easy way.
References
[1]American Public Health Association(2005).Standard method for examination of water and waste water,21st edition American Public Health Association,Washington, DC.
[2]Bureau of Indian Standard(1991). Indian Standard specification for drinking water, BIS Publication No. IS: 10501,New Delhi,.
[3] Chen,Z.; Huan, G.H.; Chakma,A.,(2003). Hybrid fuzzy-stochastic modeling approach for assessing environmental risks at contaminated groundwater systems.,Journal of Environmental Engineeing.,129(1),79-88.
[4] Chitu Okoliand Suzanne D.Pawlowski (2004).The Delphi method as a research tool an example ,design considerations and applications.,Information and Management., 42(1),15-29.
[5] Cude,C.O., (2001).Water quality Index: a tool for evaluation water quality management effectiveness, J.Am.Water Ressour.Assoc.,37, 125-137.
[6] Chang,N.,Chen,H.W.,King,S.K., (2001).Identification of river water quality using the fuzzy synthetic evaluation approach,J.Environ.Manage.,63,293-305.
[7] Dahab, M. F., Lee, Y. W. and Bogardi, I.: 1994, 'A rule based fuzzy-set approach to risk analysis of nitrate contaminated groundwater',Water Sci. Technol.30(7), 45-52.
[8] Deshpande, R.D., and Gupta. S.K., (2004) Water for India in 2050: First order assessment of available options,Current Science, Vol. 86, No. 9, 1216-1224.
[9] Dahyia, S., Datta,D., and Kushwaha, H.S., (2005) A fuzzy synthetic evaluation approach for assessment of physio-chemical quality of groundwater for drinking purposes,Environmental Geology,8, 158-165.
[10] Dahiya. S., B. Singh, S. Gaur, V.K. Garg and H.S. Kushwaha,(2007)Analysis of groundwater quality using fuzzy synthetic evaluation,Journal of Hazard Materials. Vol. 147, 3, 938-946.
[11] Jamshidi, M.: 2003, 'Tools for Intelligent Control: Fuzzy Controllers, Neural Networks and Genetic Algorithms',Phil. Trans. R. Soc.361, 1781-1808.
[12] Liou,S.,Lo.S.A.Wang., (2004). Generalized water quality index for Taiwan,Environ.Monit.Assess.9635-52.
[13] Liou.S.,Lo,S.L., (2004)A fuzzy index model for tropic status evolution of reservoir waters,Water Res.96 35-52.
[14] Mamdani, E.M ., (1976) Advances in the linguistic synthesis of fuzzy controllers,International Journal of Man-Machine Studies,8, 669-678.
[15] Khaiwal, R, and Garg, V. K., (2006) Distribution of fluoride in groundwater and its suitability assessment for drinking purposes,Intern. J. Environ. Health Res,16, 163-166.
[16] Sivasankar, K., and Gomathi, R., (2009) Fluoride and other Quality parameters in the Groundwater samples of Pettaivaithalai and Kulithalai Areas of Tamil Nadu, Southern India,Water Quality Exposure Health, Vol. 1, 123-134.
[17] Subramani, T, Elango, L, and Damodarasamy, S.R, (2005) Groundwater quality and its suitability for drinking and agricultural use in Chithar River Basin, Tamil Nadu, India,Environmental Geology,47, 1099-1110.
[18] Schulz,K.,Howe,B., (1999) .Uncertainity and sensistivity analysis of water transport modeling in a layered siol profile using fuzzy set theory,J.Hydroinform. 1,127-138.
[19] Venkat Kumar, N., Samson Mathew and G. Swaminathan.,(2008) A Preliminary Investigation for Groundwater Quality and Health Effects–A Case Study –,Asian Journal of Water, Environment and Pollution.Vol.5, No.4, pp.99-107.
[20] Venkat Kumar, N., Samson Mathew , G. Swaminathan., (2009)FuzzyInformation Processing for Assessment of Groundwater Quality.International Journal of Soft Computing, vol. 4, No.1, pp 1-9.
[21]World Health Organisation (1984.)Guidelines for Drinking Water Quality Recommendation, Vol II, World Health Organisation, Geneva,
[22] Zedah, L.A. (1965) ‘Fuzzy set',Information control, Vol. 8, No.3, pp.338-353.
[23] Zedah, L.A. (1975) ‘The concept of a linguistic variable and its application to approximate reasoning',Information Science,Vol. 8, pp. 199-249
[24] Zekai Sen (2009) Fuzzy Groundwater classification Rule Derivation from Quality Maps,Water Quality Exposure Health, Vol.1, 115-112.
