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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES Volume 2, No 1, 2011 © Copyright 2010 All rights reserved Integrated Publishing Association Research article

ISSN 0976 – 4402

Water quality indices used for surface water vulnerability assessment

Bharti N, Katyal.D University School of Environment Management, G.G.S.Indrapratha University, Dwarka, New Delhi, India [email protected] ABSTRACT Assessment of water quality can be defined as the analysis of physical, chemical and biological characteristics of water. Water quality indices aim at giving a single value to the water quality of a source reducing great amount of parameters into a simpler expression and enabling easy interpretation of monitoring data. In this study, various water quality indices (WQI) used for assessing surface water quality are discussed. As different National and International Agencies involved in water quality assessment and pollution control defines water quality criteria for different uses of water considering different indicator parameters, so there are numerous WQI specific to any region or area. An attempt to cover all different water quality indices developed worldwide, their background and application area has been made here. In this context, this paper displays a comparative study of many indices and detailed out eight WQI’s perceived as simple, basic and most important indices for water quality assessment. Their mathematical structure, set of parameters, calculation, aggregation formula and flaws have also been detailed out. Keywords: Surface water quality; parameters; water quality index; sub-indices; aggregation formula. 1. Introduction Assessment of surface water quality can be a complex process undertaking multiple parameters capable of causing various stresses on overall water quality. To evaluate water quality from a large number of samples, each containing concentrations for many parameters is difficult (Almeida et al. 2007). To analyze water quality, different approaches like statistical analyses of individual parameter, multi-stressors water quality indices, etc have been considered (Venkatesharaju et al. 2010). Numerous water quality indices have been formulated all over the world which can easily judge out the overall water quality within a particular area promptly and efficiently. For example, US National Sanitation Foundation Water Quality Index (NSFWQI) (Sharifi 1990), Canadian Council of Ministers of the Environment Water Quality Index (CCMEWQI) (Lumb 2006), British Columbia Water Quality Index (BCWQI), and Oregon Water Quality Index (OWQI)(Debels et al. 2005; Kannel et al. 2007; Abbasi 2002). These indices are based on the comparison of the water quality parameters to regulatory standards and give a single value to the water quality of a source (Khan et al. 2003; Abbasi 2002). Through this paper, , an effort has been made to carry out a review of important indices used in water quality assessment and to display updated information about indices composition and structure using a comparative and evaluative analysis among these indices.

Received on July 2011 Published on September 2011

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2. Surface water vulnerability assessment techniques 2.1 Water Quality Index method Water quality indices are tools to determine conditions of water quality and, like any other tool require knowledge about principles and basic concepts of water and related issues (Nikbakht, 2004). It is a well-known method of expressing water quality that offers a stable and reproducible unit of measure which responds to changes in the principal characteristics of water (Brown et al, 1972). WQI is a mechanism for presenting a cumulatively derived numerical expression defining a certain level of water quality (Bordalo et al. 2006). In other words, WQI summarizes large amounts of water quality data into simple terms (e.g., excellent, good, bad, etc.) for reporting to management and the public in a consistent manner. 2.2 History of Water Quality Indices Attempts to categorize water according to its degree of purity date back to the mid-twentieth century (Horton, 1965; Landwehr, 1974). Horton selected 10 most commonly measured water quality variables for his index including dissolved oxygen (DO), pH, coliforms, specific conductance, alkalinity, and chloride. The index weight ranged from 1 to 4 and the index score was obtained with a linear sum aggregation function. The function consisted of the weighted sum of the sub-indices divided by the sum of the weights and was multiplied by two coefficients M1 and M2, reflecting temperature and obvious pollution, respectively. Horton's pioneering effort has been followed up by several workers to formulate various WQI’s and their use has been strongly advocated by agencies responsible for water supply and control of water pollution (Debels et al. 2005; Kannel et al. 2007; Abbasi 2002). Dinius (1972) made an attempt to design a rudimentary social accounting system which would measure the costs and impact of pollution control efforts and applied that index on an illustrative basis to data on several streams in Alabama, USA. Like Horton’s index, it had decreasing scale, with values expressed as a percentage of perfect water quality corresponding to 100%. Another multiplicative water quality index specifically designed for decisison making was developed by Dinius (1987) using index method introduced by Delphi(Helmer & Rescher 1959, Dalkey & Helmer 1963, Abbasi & Arya 2000) have also introduce changes to Delphi method(Dalkey 1968). Lately, Brown and co-workers presented a WQI similar to Horton’s index (Brown et al. 1972). He proposed multiplicative form of the index where weights to individual parameters were assigned based on a subjective opinion based on the judgement and critical analysis of the author. The weight assigned reflected a parameter’s significance for a use and had considerable impact on the index. Later on similar indices have been formulated by Bhargava and dwivedi.(Bhargava et al. 1998; Dwivedi et al. 1997; Bhargava 2006, Devpura, Haridwar). Various researchers have considered similar approaches which brought changes to the methodology depending on the usage and parameters under consideration. Prati et al. (1971) considered 13 different parameters of equal weight in their system (Bolton, 1978). Values of these parameters are rated from 0 to 13 with values more than 8 denoting heavy pollution. Inhaber (1975), however, developed a system based on two distinct sub-indices. The first of these dealt with industrial and domestic wastes and the second with background water quality. Bharti N, Katyal.D International Journal of Environmental Sciences Volume 2 No.1, 2011

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By introducing different parameters to the two sub-indices they are given equal weighting and are averaged to give the WQI which can range from 0 for best water quality to increasingly large numbers for worse quality. Dee et al. (1972, 1973) proposed a system for evaluating the environmental impact of large scale water resources projects. The system included 12 variables (such as DO pH, turbidity, and fecal coliforms), besides pesticides and toxic substances. The index was calculated with and without considering the proposed water resources project. The difference between the two scores provided a measure of the environmental impact (EI) of project. An entirely different system which does not rely on rating curves and weightings has been used by Harkins (1974, 1977). In this system the values are given numerical rankings in relation to selected control values for different parameters. The information is then used to compute the standardized distance from the control values for each parameter to produce an index. Walski and Parker (1974) gave index based on empirical information on the suitability of water for a particular use specifically for the recreational water. The sensitivity functions were determined to assign each parameter a value between one and zero, representing ideal conditions and completely unacceptable conditions respectively. For substances that are inversely related to water quality a negative exponential curve was thought to best represent the sensitivity function. The sub-indices consist of nonlinear and segmented nonlinear explicit functions. To aggregate the sub-indices, a geometric mean was employed. Steinhart et al. (1982) applied a new environmental quality index to summarize technical information on the status of, and trends in Great Lakes Ecosystem. In Canada, the water quality index was introduced in mid 90’s by Water Quality Guidelines Task Group of the Canadian Council of Ministers of the Environment (Rocchini and Swain, 1995; Dunn, 1995; Hebert, 1996). Newly developed CCMEWQI has been employed by various provinces and Ecosystems all across Canada to assess water quality (CCME, 2001a,b; Cash et al., 2001; Husain, 2001; Sharma, 2002; Lumb et al., 2002; Khan et al., 2003; Paterson et al., 2003). These indices have been the product of efforts and research development from governmental agencies in different strata, as well as from masters' and doctorate research. There are various water quality indices (WQI) to compare various physico–chemical and biological parameters (Pandey and Sundaram 2002; Chetana and Somashekar 1997; Ram and Anandh 1996) which have been discussed in the upcoming sections. 2.3 Comparison of WQI Among the first prominent comparisons of water quality indices were Landwehr & Deininger (1976), followed by Ott (1978), who revised water quality indices used in the USA, besides publishing a detailed discussion about the practices and theories of environmental indices. Steinhart et al. (1981) also reviewed more than 20 water quality indices being used till late seventies. In Europe contributions have come from van Helmond and Breukel (1997), who demonstrated that at least 30 water quality indices are of common use around the world. Cooper et al (1994) and Richardson (1997), in South Africa and Australia respectively, have also been occupied in for generating indices for estuaries. In Central America work of Montoya (1997) and León (1998) is evident. Bharti N, Katyal.D International Journal of Environmental Sciences Volume 2 No.1, 2011

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Almost all water quality indices depends upon normalizing, data parameter by parameter according to expected concentrations and interpretation of ‘good’ versus ‘bad’ concentrations. Then parameters are weighted according to their perceived importance to overall water quality and the index is calculated as the weighted average of all observations of interest (e.g., Pesce and Wunderlin, 2000; Stambuk-Giljanovic, 1999; Sargaonkar and Deshpande, 2003; Liou et al., 2004; Tsegaye et al., 2006). Summary of these indices is given in Table 1. Table 1: Summary of water quality indices developed on a national or global level Index The Scatterscore index

Objective Water quality

Method Assesses increases or decreases in parameters over time and space

The Well-being of Nations

Human and Ecosystem

Assesses human indices against ecosystem indices

Environmental Performance Index

Environment al health and ecosystem vitality River health

Uses a proximity-to-target measure for sixteen indices categorized into six policy objectives

Index of River Water Quality Overall Index of Pollution

River health

Chemical Water Quality Index

Lake basin

Water Quality Index for Freshwater Life

Inland waters

Uses multiplicative aggregate function of standardized scores for a number of water quality parameters Assessment and classification of a number of water quality parameters by comparing observations against Indian standards and/or other accepted guidelines e.g. WHO Assesses a number of water quality parameters by standardizing each observation to the maximum concentration for each parameter Assesses quality of water against guidelines for freshwater life

Author Kim and Cardone (2005) PrescottAllen (2001) Levy et al. (2006) Liou et al. (2004) Sargaonka r and Deshpand e (2003) Tsegaye et al. (2006) CCME (2001)

Pesce and Wunderlin (2000) compared the performance of three water quality indices on the Suquía River in Argentina. Then ‘objective’ and ‘subjective’ indices were computed as a function of the normalized values, weights were assigned, and, in the case of the subjective index, a constant that represented the visual impression of the contamination level of a monitoring station. A third ‘minimal’ index was calculated as the average of the normalized values for three parameters only. The study concluded that the third minimal index was well correlated to the objective index, and that both water quality indices were generally correlated to the measured concentrations of different parameters. In a similar study, Stambuk-Giljanovik (2003) compared the performance of several water quality indices on Croatian waters. These indices were similar to the objective index used in Argentina. Findings were that the two modified arithmetic indices were best suited for discriminating sites according to water quality condition. Bharti N, Katyal.D International Journal of Environmental Sciences Volume 2 No.1, 2011

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Liou et al. (2004) developed an index of river water quality in Taiwan selecting some nine parameters and giving them standardized scores based on predetermined rating curves. The index relies on the geometric means of the standardized scores. Kim and Cardone (2005) developed a ‘Scatterscore’ index that evaluates changes in water quality over time and space. It does not rely on water quality standards or guidelines and can include an unlimited number of parameters. It was developed primarily to detect positive or negative changes in water quality around mining sites in the United States, but it could be applied to non-impacted sites as well. Tsegaye et al. (2006) developed a chemical water quality index based on data from 18 streams in one lake-basin in northern Alabama that aggregates the concentration of seven parameters after standardizing each observation to the maximum concentration for each parameter. In general, water quality indices are divided into five main groups (Sobhani, 2003):a. Public indices: these indices ignore the kind of water consumption in the evaluation process, such as NSFWQI, Horton (Ott, 1978) (Horton, 1965). b. Specific consumption indices: Here, the classification of water is on the basis of the kind of consumption and application (drinking, industrial, ecosystem preservation, etc). The most important and applicable of these indices are the Oregon and British Columbia indices (DEQ, 2003). c. Statistical indices: In these indices statistical methods are used and personal opinions are not considered. d. Designing indices: This category is an instrument, aiding decision making and planning in water quality management projects. 3. Review of some important WQI Cude (2001) stated that revisions of these WQI’s is of great interest as various studies have demonstrated new approaches and provided new tools for the development of other indices (Dinius,1987; Kung et al., 1992; Dojlido et al.,1994). After a detailed literature review and going through all of the different types of water quality indices, the ones which are most commonly used and perceived as important are discussed here in detail because covering all WQI’s in this paper is out of our reach. A. Canadian Council of Ministers of Environment (CCMEWQI) CCMEWQI compares observations to a benchmark instead of normalizing observed values to subjective rating curves, where the benchmark may be a water quality standard or site specific background concentration (CCME, 2001; Khan et al., 2003; Lumb et al., 2006). So, this acts as an advantage of the index which can be applied by the water agencies in different countries with little modification. To categorize water quality under this, four categories have been suggested i.e. Excellent, Good, Fair and Poor. Calculating index scores (Khan et al. 2004)  

Find F1: the number of variables whose objectives are not met (scope) F1= [No. of failed variables /Total no of variables]*100 Find F2: the frequency by which the objectives are not met (frequency) Bharti N, Katyal.D International Journal of Environmental Sciences Volume 2 No.1, 2011

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F2= [No of failed tests/Total no of tests]*100 

Find F3: the amount by which the objectives are not met (amplitude) (a) excursioni = [Failed test value/Objectivej ]-1 (b) nse = /No of tests (c) F3= [nse/0.01nse+0.01] CCMEWQI

]

A recent study (Lumb et al. 2006) demonstrated that by using different CCME WQI protocols and sensitivity analyses, the specific problematic parameters that may be contributing towards lowering the index values can be identified. B. National Sanitation Foundation (NSFWQI) Brown et al. (1970) developed a water quality index paying great rigor in selecting parameters, developing a common scale, and assigning weights for which elaborate Delphic exercises were performed. This effort was supported by the National Sanitation Foundation (NSF) and that is why also referred as NSFWQI. This work seems to be the most comprehensive and has been discussed in various papers (Brown et al, 1972; Landwehr & Deininger, 1976). Rating curves were developed by asking the experts to attribute values for variation in the level of water quality caused by different levels of each of the selected parameters (Sharifi, 1990). Having established the rating curves and associated weights, various methods of computing a water quality index are possible, like 1) Additive index1) Where, sub-indices.

, Ii= Sub-index of each parameters, Wi= Weighting factor, n= Number of

C. British Columbia (BCWQI) British Columbia water quality index was developed by the Canadian Ministry of Environment in 1995 as increasing index to evaluate water quality. This index is similar to CCMEWQI where water quality parameters are measured and their violation is determined by comparison with a predefined limit. It provides possibility to make a classification on the basis of all existing measurement parameters. To calculate final index value the following equation is used: BCWQI =

]

The number 1.453 was selected to give assurance to the scale index number from zero to 100. It is important to note that repeated samplings and increasing stations increase the accuracy of British Columbia index. Disadvantages of this method are that this index does not indicate the water quality trend until it deviates from the standard limit and due to usage of maximum percentage of deviation, it cannot determine the number of withdrawals above the maximum limit of standard (Salim et al,2009). Bharti N, Katyal.D International Journal of Environmental Sciences Volume 2 No.1, 2011

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D. Oregon (OWQI) OWQI expresses water quality by integrating measurements of eight water quality variables. It provided the ambient water quality of Oregon's streams for general recreational use and its application to other geographic regions or water body types should be approached with caution (Cude, 2001). The science of water quality has improved markedly since the introduction of the OWQI in the 1970s (Dunnette, 1979). Extensive literature review shows that since 1978, index developers have benefited from increasing understanding of stream functionality (Dinius, 1987; Stoner, 1978; Yu and Fogel, 1978; Joung et al., 1979; Bhargava, 1983; Smith, 1990; Kung et al., 1992; Dojlido et al., 1994). The original OWQI was modeled after the NSFWQI (McClelland, 1974) where the Delphi method was used for variable selection (Dalkey, 1968). Both indices used logarithmic transforms to convert water quality variable results into subindex values. Logarithmic transforms take advantage of the fact that a change in magnitude at lower levels of impairment has a greater impact than an equal change in magnitude at higher levels of impairment. 1. The original OWQI used a weighted arithmetic mean function. 2. The NSF WQI (McClelland, 1974) used a weighted geometric mean function WQI = The unweighted harmonic square mean formula, as a method to aggregate sub-index results, has been suggested as an improvement over both the weighted arithmetic mean geometric mean formula (Dojlido et al., 1994). This formula allows the most impaired variable to impart the greatest influence on the water quality index and acknowledges that different water quality variables will pose differing significance to overall water quality at different times and locations. The formula is given by: WQI=

E. Overall Index of Pollution (OIP) Sargaonkar and Deshpande (2003) developed OIP for Indian rivers based on measurements and subsequent classification of pH, turbidity, dissolved oxygen, BOD, hardness, total dissolved solids, total coliforms, arsenic, and fluoride. Each water quality observation was scored as Excellent, Acceptable, Slightly Polluted, Polluted, and Heavily Polluted, according to Indian standards and/or other accepted guidelines and standards such as World Health Organization and European Community Standards. Once categorized, each observation was assigned a pollution index value and the OIP was calculated as the average of each index value given by the mathematical expression: OIP= th Where Pi = pollution index for i parameter, n = number of parameters. F. Bhargava method To develop this index, Bhargava (1985) identified 4 groups of parameters. Each group contained sets of one type of parameters. The first group included the concentrations of coliform organisms to represent the bacterial quality of drinking water. The second group included toxicants, heavy metals, etc. The third group included parameters that cause Bharti N, Katyal.D International Journal of Environmental Sciences Volume 2 No.1, 2011

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physical effects, such as odor, color, and turbidity. The fourth group included the inorganic and organic nontoxic substances such as chloride, sulfate, etc. The sub-indices were worked out and the simplified model for WQI for a beneficial use is given by: WQI where n is the number of variables considered more relevant to the use and fi(Pi) is the sensitivity function of the ith variable which includes the effect of weighting of the ith variable in the use. The index was applied to the raw water quality data at the upstream and downstream of river Yamuna at Delhi, India. G. Smith’s index Smith (1987) developed an index for four water uses i.e., contact as well as non-contact. It is a hybrid of the two common index types and is based on expert opinion as well as water quality standards. The selection of parameters for each water class, developing sub indices, and assigning weightages were all done using Delphi. The minimum operator technique was used to obtain the final index score: Imin =  min (Isub1, Isub2, ……. Isubn ) Where, Imin equals the lowest sub index value. H. The River Ganga Index of Ved Prakash et al (1990) The index was developed to evaluate the water quality profile of river Ganga in its entire stretch. The index had the weighted multiplication form and was based on the NSFWQI, with slight modifications in terms of weightages to confirm to the water quality criteria for different categories of uses as set by Central Water Pollution Board, India. 4. Conclusion After a thorough study of the above mentioned water quality indices, Table 2 containing indices, sub-indices, their aggregation formula and flaws has been prepared. It has been concluded that NSF, Bhargava, OIP, Oregon and Ved prakash indices which uses the weighted arithmetic average (Stojda & Dojlido, 1983) and the modified weighted sum (Couillard & Lefebvre, 1985) provided the best results for the indexation of the general water quality. Similarly, the weighted geometrical average has been widely used, especially where there is a great variability among samples. A very general flaw has been noticed in NSFWQI is eclipsing which occurs when at least one sub-index reflects poor water quality as explained below: I = w1 I1 + (1 - w1) I2 In situations such as the ones arising when I1 = 50 and I2 = 110 with w1 and both w2 = 0.5, gives I = 80. In other words the overall score indicates acceptable water quality even though one of the constituents as reflected in I2 was above the permissible limit of 100. Here, the index score 'hides' the unacceptable level of one or more constituent parameters. The minimum operator i.e. the Smith’s index appears to be a good candidate for aggregating decreasing scale sub-indices. Moreover, eclipsing does not occur with this aggregation method. When it is important to considerer low values, it is better to use the harmonic mean or its square (Cude, 2001). The latter which is used in CCME and BCWQI is the most sensible Bharti N, Katyal.D International Journal of Environmental Sciences Volume 2 No.1, 2011

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method in a data set with low values, because these take more weight than those with high values. Both of them are interesting case to be considered, because of the integration of three factors, these factors are taken in account on the data and their relation to the objectives. With this concept focusing on the objectives, the agents must worry more in improving the environmental conditions. Table 2: An overview of types of indices, their sub-indices, aggregation functions and flaws Index CCME British Columbia NSF

Subindices Formula Formula

Aggregation function Harmonic Square sum Harmonic Square sum

Implicit nonlinear

Weighted sum

OIP

Weighted Average

Smith Bhargava Oregon

Segmented nonlinear Multiple types Multiple types Nonlinear

Ved prakash

Multiple types

Minimum operator Weighted product Weighted product (arithmetic / geometric ), Unweighted Harmonic Square Mean Weighted product

Flaws Eclipsing region -

Finally, to recognize a unique water quality index for assessing surface water quality of any nation or area with a definitive solution is very difficult. However each institution, agency or researcher should to try to develop a unique method applicable to that particular region and worldwide also. 5. References 1. Abbasi, S.A., (2002), Water quality indices, state of the art report, National Institute of Hydrology, scientific contribution no. INCOH/SAR-25/2002, Roorkee: INCOH, pp 73. 2. Ahmed, S., David, K.S. and Gerald, S., (2004), Environmental assessment: An innovation index for evaluation water quality in streams, Environment Management., 34 pp 406-414. 3. APHA (1989), Standards Methods for the Examination of Water and Wastewater, 17th edition, Washington, D.C.: APHA, AWWA, WPFC. 4. APHA (2005), Standard methods for the examination of water and waste water, 21st edition, American Public Health Association, Washington, DC., USA. 5. Bagde, U.S, Verma, A.K., (1985), Limnological studies of JNU lake, New Delhi. Proc National Sump Pure and Applied Limnology,32, pp 16-23. 6. BCWQI (1996), Ministry of Environment, Lands, and Parks: The Water Quality Section, British Columbia Water Quality Status Report, April, Victoria, BC.

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7. Beldowski, J. and Pempkowiak, J., (2007), Mercury transformations in marine coastal sediments as derived from mercury concentration and speciation changes along source/sink transport pathway (Southern Baltic), Estuarine coastal and shelf science, 72, pp 370-378. 8. Beron P., Valiquette L., Patry G. and Briere F., (1982), Water quality indices, Trib. Cebedeau, 35, pp 385391. 9. Bhargava, D. S., (1983), Use of a Water Quality Index for River Classification and Zoning of the Ganga River, Environmental Pollution (Series B), 6, pp 51-67. 10. Bhargava, D.S., (1987), Nature and the Ganga, Environment Conservation, 14, pp 307318. 11. Bhargava, D. S, Saxena, B. S., and Dewakar, (1998), A study of geo-pollutants in the Godavary river basin in India, Asian Environment, IOS press, pp 36–59. 12. Bhujangaiah, N. S. and Nayak, V.P., (2005), Study of ground water quality in and around Shimoga city, Karnataka, Journal of Indian Council of Chemists, 22(1), pp 42–47. 13. Bolton P.W., (1978), An index to improve water quality classification, Water Pollution Control, pp 271-284. 14. Bordalo, A. A., Teixeira, R., and Wiebe, W. J., (2006), A water quality index applied to an international shared river basin: The case of the Douro River, Environmental Management, 38, pp 910–920. 15. Brabander, K. de., (1992), Comparing biological and chemical parameters as complementary tools for the management of river water quality. In: Newman, P.J., et al. (eds), River Water Quality, ecological assessment and control, EEC-publication EUR 14606 EN-FR. 16. Brown, R. M., McLelland, N.I., Deininger, R. A. and O'Connor, M.F., (1972), A water quality index - crashing the psychological barrier, Indicators of Environmental Quality. 17. Burden, F. R., Mc Kelvie, I., Forstner, U., and Guenther, A., (2002), Environmental Monitoring Handbook, Mc graw- Hill handbooks, New York, pp 3.1–3.21. 18. Buszewski, B. and Kowalkowski, T., (2003), Polands environment – past, present and future state of the environment in the Vistula and Odra river basins, Environmental Science and Pollution Research, 10, pp 343-349. 19. Buyan, K.C., (2005), Multivariate Analysis and its applications, New Central Book Agency pvt. Ltd. Publication, pp 1-2. 20. Cash, K. J., Saffran, K. A. and Wright, C. R., (2001), Application of Canadian Water Quality Index to PPWB Monitoring Program, Technical Report, CCME, March 2001.

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36. Debels, P., Figueroa, R., Urrutia, R., Barra, R., and Niell, X., (2005), Evaluation of water quality in the Chilla’n River (Central Chile) using physicochemical parameters and a modified water quality index, Environmental Monitoring and Assessment, 110, pp 301– 322. 37. Deepa, V. J., (2004), A study of water quality and dissolved trace metal variations in river Godavari at Rajahmundury, Thesis (M. Tech), Dept. of Chemical Engineering, NITK, Surathkal, Deemed University, India, pp 4, 5, 8, 9, 15 18. 38. DEQ (2003), The Oregon Department of Environmental Quality. Available from: http:// www.deq.state.or .us/ lab/ WQM/ WQI/ Wqi main.htm. 39. Dice, L. R., (1945), Measures of the amount of Ecological Association between Species, Ecology, 26, pp 297-302. 40. Dinius, S. H., (1987), Design of an Index of Water Quality, Water Resources Bulletin, 23(5), pp 833-843. 41. Dojlido, J. R, Raniszewski J. and Woyciechowska J., (1994), Water Quality Index Applied to Rivers in the Vistula River Basin in Poland, Environmental Monitoring and Assessment, 33, pp 33-42. 42. Dunn, G. W., (1995), Trends in Water Quality Variables at the Alberta/Saskatchewan Boundary, Prepared for the Committee on Water Quality. 43. Dunnette, D. A., (1979), A Geographically Variable Water Quality Index Used in Oregon, Journal of the Water Pollution Control Federation, 51(1), pp 53-61. 44. Dwivedi, S., Tiwari, I. C., and Bhargava, D. S., (1997), Water quality of the river Ganga at Varanasi, Institute of Engineers, Kolkota, 78, pp 1–4. 45. Esty D. C., Levy M.A., Srebotnjak T., de Sherbinin A., Kim C.H. and Anderson B., (2006), Pilot 2006 Environmental Performance Index, New Haven: Yale Center for Environmental Law & Policy. 46. European Council (1991), Consolidated text produced by the CONSLEG system of the office for official publications of the European Communities. Council Directive of 16 June 1975 concerning the quality required of surface water intended for the abstraction of drinking water in the Member States (75/440/EEC), Office for Official Publications of the European Communities. 47. Faisal, K., Tahir, H. and Ashok, L., (2003), Water quality evaluation and trend analysis in selected watersheds of the Atlantic region of Canada, Environment Monitoring Assessment, 88, pp 221-248. 48. Fintajsl, C. J., (1970), Water Quality (Notes on Lectures), International Standards for Drinking Water in Tehran, Institute of Hydrosciences and Water Resources Technology (WQIHSR), 53, pp 4–5. Bharti N, Katyal.D International Journal of Environmental Sciences Volume 2 No.1, 2011

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