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Environmental Monitoring and Assessment (2006) 119: 201–231 DOI: 10.1007/s10661-005-9023-6

c Springer 2006 

QUALIDEX – A NEW SOFTWARE FOR GENERATING WATER QUALITY INDICE CHINMOY SARKAR and S. A. ABBASI∗ Centre for Pollution Control and Energy Technology, Pondicherry University, Pondicherry-605014, India (∗ author for correspondence, e-mail: prof [email protected])

(Received 06 May 2005; accepted 22 August 2005)

Abstract. Water quality indice are necessary for resolving lengthy, multi-parameter, water analysis reports into single digit scores. This, in turn, is essential for comparing the water quality of different sources and in monitoring the changes in the water quality of a given source as a function of time and other influencing factors. In this paper we present the computer-automated tool QUALIDEX (water QUALIty inDEX), which has been developed by us to generate and operate water quality indice. Several popular indice – such as the Oregon Water Quality Index developed in the 1970s by Oregon Department of Environmental Quality and later updated in 1995, the Aquatic Toxicity Index developed by Wepener and coworkers for protection of aquatic life at the Olifants river, Kruger National Park, South Africa, the water quality index developed by Dinius in 1987, the Overall Index of Pollution (of surface waters) developed at the National Environmental Engineering Research Institute (NEERI), and the water quality index of the Central Pollution Control Board (CPCB) – have also been added to QUALIDEX in modular form. There is also a New water quality index sub-module which enables the user to generate his or her own index and compare its performance with these well-known indice. The package is also capable of analyzing the variations in the water quality of different sites at different times. The software has been coded in Visual C++ and has been integrated with MS Access database. Keywords: water quality indice, drinking water, software, monitoring, virtual instrument

1. Introduction Of all the natural resources, water is unarguably the most essential and precious. The crucial role of water as the trigger and sustainer of civilizations has been witnessed throughout the human history. But, up till as late as 1960s, the overriding interest in the water has been vis a vis its quantity. Except in manifestly undesirable situations, the available water was automatically deemed utilizable water. Only during the last three decades of the twentieth century the concern for water quality has been exceedingly felt so that, by now, water quality has acquired as much importance as water quantity. But expressing water quality, especially to a lay person, is enormously more difficult than expressing water quantity. The later can be expressed in precise terms as volume contained in a lotic water body, for example in m3 or Km3 ; or flow in a lentic

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one, for example cusec (m3 sec−1 ). But water quality is a multi-parameter attribute; a large number of physical, chemical and biological factors together determine the water quality. Further, unlike the water quantity, water quality is a function of the nature of water utilization. To wit, assessing and communicating water quality is fraught with the following complications: 1. The water quality varies according to the type of use. A certain water quality may be good enough for the purpose of irrigation but it mayn’t be good enough for drinking. Furthermore, the criterion of an ‘acceptable water quality’ varies from region to region and from time to time depending upon the prevailing conditions. 2. Different agencies have developed different standards for various uses of water which differ in terminologies for classification scheme, selection of indicator parameters, classification scheme etc. 3. A given water sample may contain hundreds of constituents – element in neutral or ionic form, anions, organics, suspended solids pathogens, radioactivity, color, odor etc. The quality of water is defined in terms of these physical, chemical, biological and bacteriological parameters each of which are expressed in different units of measurements. The extent of importance level of each of these parameters in influencing the overall water quality for a given type of water use is unknown and needs to be determined. 1.1. THE

CONCEPT OF WATER QUALITY INDEX

On account of the multi-dimensional nature of the various environmental subsystems, the concept of environmental indice has gained currency to characterize the individual sub-systems (Otto, 1978). The philosophy of employing indice to characterize the status of the environment is not a novel one; the concept has its roots in subjects like economics and commerce (Fisher, 1922; Diewert, 1993). A considerable bulk of literature on the index theory has evolved over time by the work of various economists. The most extensively employed is the consumer price index (CPI) which is a single value indicator of the status of the market at any given instant, on the basis of prices of certain communities. Then there is also the share price index which gives an indication of the overall status of a country’s economy. Subjective environmental indice that are presently employed are thus, the offshoots of the axiomatic indice devised by economists to quantify the spatial and temporal changes in economy. Water quality Index have gained currency only during the last three decades but the concept in its rudimentary form was first introduced more than 150 years ago – in 1848 – in Germany where the presence or absence of certain organisms in water was used as indicator of fitness or otherwise of a water source. Since then various European countries have developed and applied different systems to classify the quality of water within their regions. These water classification

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systems are usually of two types: (i) Those concerned with the amount of pollution present, and (ii) Those concerned with living communities of macroscopic or microscopic organisms. Rather than assigning numerical values to represent the water quality, these classification systems categorized water bodies into one of several pollution classes or levels. By contrast, indice that use a numerical scale to represent gradation in water quality levels are a recent phenomenon, beginning with Horton’s index in 1965. Horton (1965) set for himself the following criteria when developing the first ever modern WQI: (1) The number of variables to be handled by the index should be limited to avoid making the index unwieldy. (2) The variables should be of significance in most areas. (3) Only such variables, of which reliable data is available, or obtainable, should be included. Horton selected 10 most commonly measured water quality variables for his index including dissolved oxygen (DO), pH, specific conductance, alkalinity, and chloride. Specific conductance was intended to serve as an approximate measure of total dissolved solids (TDS), and carbon chloroform extract (CCE) was included to reflect the influence of organic matter. One of the variables, sewage treatment (percentage of population served), was designed to reflect the effectiveness of abatement activities on the premise that chemical and biological measures of quality are of little significance until progress has been made in eliminating discharges of raw sewage. The index weight ranged from 1 to 4. Notably Horton’s index didn’t include any toxic chemicals. The index score is obtained with a linear sum aggregation function. The function consists of weighted sum of sub-indice divided by the sum of weights and multiplied by two coefficients M1 and M2 , which reflect the temperature and obvious pollution, respectively: n wi Ii M1 M2 (1) WQI = i=1 i=1 wi Horton’s index is easy to compute, even though the coefficients M1 and M2 require some tailoring to fit the individual situations. The index structure, its weights, and rating scale are highly subjective as they are based on the judgment of the author and a few of his associates. Horton’s pioneering effort has been followed up by those of several workers who have striven to develop less and less subjective but more and more sensitive and useful water quality indice.

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A water quality index basically acts as a mathematical tool employed to transform the bulk of water quality data into a single digit, cumulatively derived, numerical expression indicating the level of water quality. This, in turn, is essential for comparing the water quality of different sources and in monitoring the changes in the water quality of a given source as a function of time and other influencing factors. The concept also aims at eliminating the subjective assessment of water quality and the individual biases of water resource managers. Numerous variations of water quality indice have been addressed in literature over the past four decades (Horton, 1965; Brown et al., 1970; Prati et al., 1971; Dinius, 1972, 1987; Walaski and Parker, 1974; Dunette, 1979; Bhargava, 1983, 1985; Smith, 1987, 1989; Schultz, 2001, Said et al., 2004, etc).

1.2. THE

MAIN STEPS INVOLVED IN THE DEVELOPMENT OF A

WQI

There are four main steps involved in the development of a water quality index (Otto, 1978; Abbasi, 2002). However, depending upon the sophistication being aimed at, additional steps may be taken. 1. Parameter selection – A given water sample may contain hundreds of constituents; however one needs to select the optimum set of parameters which, together, reflect the overall water quality with respect to a given end use. In order to overcome the uncertainty and subjectivity involved in the process, enormous care, attention, experience and consensus gathering skills are necessary. Approaches such as Delphi, have commonly been employed in order to reduce the subjectivity involved in the process. 2. Transformations of the parameters of different units and dimensions into a common scale – Different parameters occur in different ranges, are expressed in different units and have different behavior in-terms of concentration-impact relationships. Sub-indice are developed for the respective parameters in order to transform them into a common scale. The nature of variations in the sub-index curves indicates the impact of change in the value of the parameter upon the water quality. The sub-index functions are generated on the basis of an in depth understanding of the impact of a particular concentration level of a parameter upon the health of human beings and other biota. The sub-index curves may be linear, non-linear, segmented-linear, segmented-nonlinear etc. 3. Assignment of suitable weightages to the respective parameters – In most of the water quality indice, the selected water quality parameters are assigned weights depending upon its importance in impacting the water quality. Several methods have been employed thus far for the assignment of weightages to the individual parameters. The earlier indice such as the one developed by Horton (1965) depended upon the developer’s criteria for associating weightage to the respective component parameters on the basis of its closeness to a desired level of

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water quality standard. However, on account of the high level of subjectivity associated with this method, it has been subsequently discarded. Opinion gathering techniques like Delphi (Abbasi, 1995; Abbasi and Arya, 2000) has also been employed to assign weightages to individual parameters (Brown et al., 1970; Dinius, 1987; Smith, 1987). They minimize the subjectivity and thereby enhance the credibility of the index. Some indice have been developed thus far which don’t employ any weightage system. The ones developed by Prati et al. (1971) and Sargaonker et al. (2003) are based on the pre-existing water quality standards. The Aquatic Toxicity Index (Wepener et al., 1992) doesn’t employ any weighing system since the developers felt that too little was known about the importance of one determinant parameter compared to another under varying local conditions. Said et al. (2004) have also developed a water quality index, in which an attempt has been made to simplify the calculations through the elimination of the sub-indice. Five water quality parameters namely DO, total phosphates, fecal coliform, turbidity, and specific conductivity were identified and weighted. A final form was selected which, according to the authors keeps the index in a simple equation and a reasonable numerical range. A logarithmic aggregation function was employed to obtain small index scores. A sensitivity analysis was performed to test the performance and in the final index, the powers of the variables in the WQI were adjusted to indicate the optimal reflection of the impact of each variable upon the water quality. The index was designed to range from 0 to 3. In case of very good water quality, the index had a value of 3, it ranged from 2–3 for acceptable quality while it was less than 2 in case of marginal quality. The index gives a value less than 2 if one or two of the variables are at undesirable levels; it gives a value less than 1 if most of the variables deviate substantially from the ideal levels. Apparently, the index is suited to provide a reasonable assessment of water quality for general uses under natural conditions. 4. Evaluation of the final index score through the aggregation of the respective sub-indice– Different indice have employed different variants of aggregation techniques depending upon the type of end use. Some of the commonly employed aggregation functions are weighted sum (Horton, 1965; Brown et al., 1970; Prati et al., 1971; Deininger and Landwehr, 1971; Dinius, 1972; McDuffie and Haney, 1973; Stoner, 1978; Giljanovic, 1999; Sargaonker et al., 2003), weighted geometric mean (Deininger and Landwehr, 1971; Walski and Parker, 1974; Bhargava, 1985), and weighted product (Dinius, 1987). The additive aggregation function modified by Solway (House and Ellis, 1980) was used in the Aquatic Toxicity Index (Wepener et al., 1992) while the unweighted harmonic mean square formula was employed in index developed by Dojlido et al. (1994) as also in the Oregon Water Quality Index (Cude, 2001). However, one of the drawbacks associated with most of these indice is that they involve aggregation of scores of individual water quality parameters resulting in hiding of valuable information related to the raw water quality (Smith, 1987, 1989, 1990). A low

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sub-index score for one variable may severely limit the water use, but it may be eclipsed or hidden by high sub-index scores for other variables. In order to avoid this tendency of eclipsing valuable information, Smith advocated the use of minimum operator approach that uses the lowest sub-index value as the final index score. But then this approach has its own shortcomings: it fails to provide a composite picture of water quality. Swamee and Tyagi (2000) have conceived a mathematical formulation for developing an aggregate index which has been demonstrated to be free from the problems of ambiguity and eclipsing. On account of the different behavior of the water quality parameters, they have divided the sub-indice in to three categories – uniformly decreasing, wherein the sub-indice monotonically decrease with the increase in the level of the water quality variable, non-uniformly decreasing, in which the sub-indice change at a rate different from the rate of change in the levels of the water quality variables, and unimodal, where the sub-indice show maxima at an optimum value of the water quality variable, but decrease as the water quality variable departs from optimum. They have proposed a full range of sub-index function equations for a number of parameters which has been calibrated with the help of the sub-index curves of NSF-WQI. In some of the water quality indice, the technique of devising sub-indice and their aggregation have been replaced by employing suitable statistical techniques to determine the parameters of importance and the extent of their importance. These approaches reduce the subjective assumptions associated with the traditional indice. The statistical approaches mainly focus on deriving the correlations expressing the associations among water quality parameters to determine the importance of each as a determinant of water quality. Factor analysis (Joung, et al., 1978), principal component analysis (Shin and Lam, 2001; Parinet et al., 2004), Kendall’s nonparametric classification (Harkin, 1974), uniformity indexing method (Ball and Church, 1980), non-linear regression (Cude, 2001) are some of the commonly employed statistical tools in the formulation of water quality index. Another approach that has evolved in the 1990s involves the classification of water quality based on the fuzzy theory. Kung et al. (1992) employed fuzzy clustering analysis for the classification of water quality while the fuzzy synthetic evaluation technique has been used by Lu et al. (1999) and Bin Chang (2003) to analyze and compare the levels of reservoir eutrophication in Taiwan. 1.3. SOME

FREQUENTLY USED INDICE

Some of the water quality indice that have been frequently employed in public domain for the purpose of water quality assessment are the NSF Water Quality Index (NSFWQI), British Columbia Water Quality Index (BCWQI), Canadian Water Quality Index (CWQI), Oregon Water Quality index (OWQI), and the Florida Stream Water Quality Index (FWQI) (Said et al., 2004).

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Of these the NSFWQI has been the forerunner of many indice and its methodology continues to be adapted to this day. To generate important scores for the NSFWQI, a survey was conducted among 142 water quality scientists and 35 parameters were considered for possible inclusion. In the final form, NSFWQI relied on nine parameters (Brown et al., 1970; Mitchell and Stapp, 1996). The British Columbia Ministry of Environment, Lands and Parks in Canada have developed the British Columbia Water Quality Index (BCWQI). The index has been developed at great effort, over a long time span (Zandbergen and Hall, 1998). However, BCWQI has been found to be extremely sensitive to sampling design and has been applied to variety of uses – drinking, recreation, irrigation, livestock watering, wild life and aquatic life. In 1997, the Water Quality Guidelines Task Group of the Canadian Council of Ministers of Environment (CCME) decided to study the different existing approaches to index formulation and their efforts led to the development of a standardized system – an unified water quality index that has been employed throughout Canada for the purpose of water quality assessment (CCME, 2001). The Canadian Water Quality Index (CWQI) was based on the concept of British Columbia Water Quality Index (BCWQI). Instead of the conventional Delphi approach, the CWQI employed three factors, each of which has been scaled between 0 and 100 namely scope, frequency and amplitude. They are the measures of variance from selected objectives of water quality which are combined together to create a vector in an imaginary ‘objective exceedance’ space. ‘Objectives’ may refer to Canada-wide water quality guidelines or site specific water quality objectives. The length of the vector is then scaled to range between 0 and 100, and subtracted from 100 to produce an index which is 0 or close to 0 for very poor water quality, and close to 100 for excellent water quality (CCME, 2001; Khan et al., 2003). The index was subsequently revised to overcome the problems that arose due to the formulations for estimating the frequency and amplitude. The revised index have been employed by Khan et al. (2003) to analyze the water quality trends in three selected watersheds of Atlantic region: Mersey River, the Point Wolfe River, and the Dunk River sites. The Oregon Water Quality Index, developed by the Oregon Department of Environmental Quality (ODEQ) in the late 1970s and updated several times since then is another frequently used WQI in public domain (Cude, 2001). The Florida Stream Water Quality Index (FWQI), developed in 1995 under the Strategic Assessment of Florida’s Environment program is an arithmetic average of twelve water quality parameters namely, water clarity (turbidity and total suspended solids), dissolved oxygen, oxygen demanding substances (biochemical oxygen demand, chemical oxygen demand, total organic carbon), nutrients (phosphorus and nitrogen), bacteria (total and fecal coliform), and biological diversity (natural or artificial substrate micro-vertebrate diversity and Beck’s biotic index). Index values ranging from 0 to less than 45 represents good quality, 45 to less than 60 represents fair quality, and 60 to 90 represents poor quality (SAFE, 1995).

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2. The Proposed Water Quality Software In this paper we have presented the computer-automated tool – QUALIDEX (water QUALIty inDEX), which has been developed by us to generate and operate water quality indice. This PC-based water quality software has been developed within the object oriented architecture in Visual C++. The primary objective is to fulfill the urgent need of a diagnostic tool for the overall assessment of the status of water quality of individual water sources such as wells, ponds and lakes in any region, with respect to the different water uses at regular intervals. 3. Basic Architecture of QUALIDEX QUALIDEX comprises of four modules, namely – the database module, the index generation module, the water quality comparison module and the report generation module. The basic architecture of QUALIDEX is depicted in Figure 1. 3.1. D ATABASE

MODULE

The water analysis data covering the parameters needed for each of the five indice available in the QUALIDEX are stored in the database module. The module comprises of five MS Access files, one for each of the component indice. A typical dataset as keyed into this module is depicted in Figure 2. For each of the index, the corresponding parameter values of a specific site at a particular date and time are stored in the respective MS Access spreadsheet. The MS Access files have been connected to the software through the Open Database Connectivity (ODBC) data source administrator of windows. A user friendly interface has been developed within the software itself to enable the users to enter, save and edit raw water quality data as shown in Figure 4. 3.2. WATER

QUALITY INDEX GENERATION MODULE

The software enables the user to generate the following well-known water quality indice for any water source of the water quality use: • • • •

The Oregon Water Quality Index (OWQI) Aquatic Toxicity Index (ATI) Dinius Water Quality Index (DWQI) Overall Index of Pollution (OIP) of National Environmental Engineering Research Institute (NEERI), India • Water quality index of Central Pollution Control Board (CPCB), India In addition the software contains the New Water Quality Index Sub-module with which new index may be generated on the basis of the parameters chosen by the

Figure 1. Conceptual framework of QUALIDEX.

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Figure 2. Typical data set keyed into the MS Access database module of the Overall Index of Pollution (OIP).

210 C. SARKAR AND S. A. ABBASI

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user, and the concerned weightage, applicability range, sub-index, and aggregation function defined by the user. 3.2.1. The Indice Included in QUALIDEX 3.2.1.1. Oregon Water Quality Index (OWQI). The OWQI was developed in the 1970s by the Oregon Department of Environmental Quality for the purpose of summarizing and evaluating water quality trends and status (Dunette, 1979). It was employed to express the water quality status and trends for legislatively mandated Water Quality Status Assessment Reports. It was modeled after the National Sanitation Foundation’s WQI and it employed the Delphi Technique for the selection of water quality variables. The water quality variables were classified according to the impairment categories i.e. oxygen depletion, eutrophication or potential for excess biological growth, dissolved substances and health hazards. However, the original OWQI was discontinued in 1983 on account of the enormous resources required for calculating and reporting the results. With the advancements in the computer technology, enhanced tools of data display and visualization and a better understanding of water quality, the OWQI was updated in 1995 by refining the original sub-indice, adding temperature and total phosphorus sub-indice, and improving the aggregation calculation (Cude, 2001). The resulting index indicates the water quality of Oregon’s streams with respect to general recreational use including fishing and swimming. The overall water quality is expressed as a single digit by integrating measurements of eight different water quality variables namely temperature, dissolved oxygen, biochemical oxygen demand, pH, ammonia+nitrate nitrogen, total phosphorus, total solids and fecal coliform. The sub-index transformation formulae were derived using non-linear regression, from the transform table developed from the originally hand drawn OWQI sub-index transformation curves (Dunette, 1980). It was felt that the minimum operator aggregator proved to be too sensitive to the most impacted variable and didn’t integrate the other variables (Cude, 2002). Consequently, the unweighted harmonic square mean formulae was employed for the purpose of aggregation of the sub-index scores as an improvement over the weighted arithmetic mean formula used in the original version:  n WQI = n 1 (2) i=1 Si2

where WQI is the water quality index result, n is the number of sub-indice and Si is the sub-index i. The OWQI helps to evaluate the effectiveness of water quality management activities. It may also be employed to develop environmental indicators, such as percentage of river monitoring sites with significantly improving water quality, or the percentage of sites with excellent water quality. 3.2.1.2. Aquatic Toxicity Index (ATI). It was developed by Wepener et al. (1992) to assess the health of aquatic ecosystems. Since extensive toxicity database are

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available for fishes, the toxic effects of different water quality to fishes have been employed as health indicators of the aquatic ecosystem. The physical water quality parameters employed were pH, dissolved oxygen and turbidity while the chemical determinant included ammonium, total dissolved salts, fluoride, potassium and orthophosphates and the potentially hazardous metals chosen were total zinc, manganese, chromium, copper, lead and nickel concentrations. An ATI scale, similar to the WQI scale proposed by Smith (1990) for salmonid spawning was used. The Solway modified unweighted additive aggregation function (House and Ellis, 1980) was initially employed to aggregate the values obtained from the rating curves 2  n 1 1 I = · qi (3) 100 n i=1 where I is the final index score, qi is the quality of the ith parameter (a value between 0–100) and n is the number of determinants in the indexing system. Wepener et al. didn’t employ the weighted sum system, as too little information is available about the importance of one determinant compared to another under different local conditions and the inherent chemistry of the system as a whole. Moreover, it is impossible to compare the factors which have a direct and interactive effect upon one another. In order to avoid concealing the identity of the determinant which limits the water’s suitability for use, the minimum operator function was also employed. A computer software package (WATER), written in Pascal with “Turbo Pascal Version 6” was developed to compute both the additive and minimum operator final index values. Wepener et al. (1999) assessed the spatial and temporal trends of water quality of Olifants river and Selati river in Kruger national park during the course of metal mining project over a two year period (February 1990–April 1992) based on their index. 3.2.1.3. Dinius Water Quality Index (DWQI). It is a multiplicative water quality index developed by Dinius (1987) for six categories of water uses: public water supply, recreation, fish, shellfish, agriculture and industry. He employed the liberal use of Delphi for decision making. The index included 12 parameters: dissolved oxygen, 5-day BOD, coliform count, E-coli count, pH, alkalinity, hardness, chloride, specific conductivity, temperature, color and nitrate. The weightage of each parameter was assigned based on the evaluation of importance by the Delphi panel members. The individual sub-index functions were combined with the help of a multiplicative aggregation function as follows IWQ =

n 

Iiwi

(4)

i=1

where IWQ is the Dinius water quality index whose value ranges from 0–100, Ii is the sub-index function of the pollutant parameter, Wi is the unit weight of the

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pollutant parameter whose value ranges from, 0–1 and n is the number of pollutant parameters. 3.2.1.4. Overall Index of Pollution (OIP). It was developed by Sargaonker et al. (2003) at National Environmental Engineering Research Institute (NEERI), Nagpur, India in order to assess the status of surface waters, specifically under Indian conditions. A general classification scheme has been formulated based on a concept similar to the one proposed by Prati et al. (1971) and giving due consideration to the classification scheme developed by CPCB. The scheme reflects the status of water quality in terms of pollution effects of parameters under consideration. Five classes namely C1 : Excellent/pristine, C2 : Acceptable/requires disinfection, C3 : Slightly Polluted/requires filtration and disinfection, C4 : Polluted/requires special treatment and disinfection, C5 : Heavily Polluted/cant be used have been considered. The concentration levels of the parameters were classified into these classes or categories on the basis of the well-established standards/criteria employed by CPCB, ISI 10500 or other agencies. In order to bring the different water quality parameters into a commensurate unit, an integer value 1, 2, 4, 8, 16 is assigned to each of the five classes C1 , C2 , C3 , C4 , C5 respectively in geometric progression.

Figure 3. Main menu of graphic user interface of QUALIDEX.

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These numbers are termed as class index and they indicate the level of pollution in numeric terms. The parameter concentration is then assigned to the respective mathematical expression to obtain a numerical value called an index (Pi ) which indicates the level of pollution for that parameter. The Overall Index of Pollution (OIP) is then evaluated as a mean of all the individual pollution indice (Pi ) as follows  Pi OIP = i (5) n where Pi is the pollution index for the ith parameter, i = 1, 2, . . . n and n = number of parameters. The index value ranges between 1–16. The Overall Index of Pollution (OIP) was found to be very useful for the assessment of surface water status as well as the formulation of pollution control strategies in terms of treatment required at different levels. The index was employed to ascertain the suitability of water at a few sampling stations along the Yamuna river. 3.2.1.5. Water quality index of Central Pollution Control Board (CPCB), India. It was developed by Ved Prakash et al. (1990) and is primarily based on the WQI

Figure 4. User-friendly interface for entering, editing and saving raw water quality data into the database module.

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of National Sanitation Foundation (Abbasi, 2002). However, slight modifications were made in terms of assignment of weightages so as to conform to the water quality criteria for different categories of water uses set by the Central Pollution Control Board, India. Four important water quality parameters- dissolved oxygen (DO), biochemical oxygen demand (BOD), pH and fecal coliform were selected through Delphi. A weighted sum aggregation function was used to evaluate the overall water quality index. WQI =

P 

wi Ii

(6)

i=1

where Ii = subindex for the ith water quality parameter; wi = weight associated with the ith water quality parameter; P = number of water quality parameters The developed index was employed to evaluate the water quality profile of river Ganga in its entire stretch and to identify areas requiring urgent pollution control measures. The indice described above have been coded within this module. Table I lists the complete details of each of these indice. To evaluate any particular water quality

Figure 5. Common interface to analyze the individual parameters of the Overall Index of Pollution (OIP).

Weightage

–

–

–

–

Parameter

Temperature (◦ C)

DOC (mg/l) DOS (% saturation)

BOD, 5day (mg/l)

pH

DOS > 275% BOD ≤ 8 BOD > 8 pH < 4 4 ≤ pH < 7 7 ≤ pH ≤ 8 8 < pH ≤ 11 pH > 11

DOC ≥ 10.5 100% < DOS ≤ 275%

T > 29 DOC < 3.3 3.3 < DOC < 10.5

T ≤ 11 11 < T < 29

Range applicable(x) SIT = 100 SIT = 76.54 + 4.172T − 0.1623T2 − 2.0557E − 3T3 SIT = 10 SIDO = 10 SIDO = −80.29 + 31.88DOC − 1.401DO2C SIDO = 100 SIDO = 100 exp((DOS − 100)∗ − 1.197E − 2) SIDO = 10 SIBOD = 100 exp(BOD∗ − 0.1993) SIBOD = 10 SIpH = 10 SIpH = 2.628 exp(pH∗ 0.5200) SIpH = 100 SIpH = 100 exp((pH − 8)∗ − 0.5188) SIpH = 10

Sub-index function(SIi )

Oregon Water Quality Index (OWQI)

WQI =



1 i=1 S 2 i

n

n

Aggregation function

TABLE I Sub-index functions and the weightages of the component indice of QUALIDEX

(Continued on next page)

10–59 – Very poor 60–79 – Poor 80–84 – Fair 85–89– Good 90–100 – Excellent

Water quality classification

216 C. SARKAR AND S. A. ABBASI

Weightage

–

–

–

–

Weightage

–

–

Parameter

Total solids (mg/l)

Ammonia+NO3 -N

Total Phosphorus

Fecal Coliform (nos./100 ml)

Parameter

DO (mg/l)

pH

0 ≤ DO ≤ 5 5 < DO ≤ 6 6 < DO ≤ 9 DO > 9 –

Range applicable (x) y y y y y

= 10x = 20x − 50 = 10x + 10 = 100 = 98 exp[−(x − 8.16)2 · (0.4)] +17 exp[−(x − 5.2)2 · (0.5)] +15 exp[−(x − 11)2 · (0.72)]+2

Sub-index function (y)

Aquatic Toxicity index (ATI)

SITS = 100 SITS = 142.6 exp(TS − 8.862E − 3) SITS = 10 SIN = 100 exp(N∗ − 0.4605) SIN = 10 SIP = 100–299.5P − 0.1384P2 SIP = 10 SIFC = 98 SIFC = 98 exp((FC − 50)∗ − 9.9178E − 4) SIFC = 10

TS < 40 40 < TS ≤ 220 TS > 220 N≤3 N>3 P ≤ 0.25 P > 0.25 FC ≤ 50 50 < FC ≤ 1600 FC > 1600

Sub-index Function (SIi )

Range applicable (x)

Oregon Water Quality Index (OWQI)

TABLE I (Continued)

Aggregation function

Aggregation function

(Continued on next page)

Water quality classification

Water quality classification

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217

– – – –

–

–

– – – – – –

Manganese Nickel Fluoride Chromium

Lead

Ammonium

Copper Zinc Orthophosphates Potassium Turbidity Total dissolved salts

0.02 < NH+ 4 ≤ 0.062 0.062 < NH+ 4 ≤ 0.05 NH+ > 0.5 4 – – – – – – y y y y y y y y y

= −500x + 110 = 40/(x + 0.65)2 = −5.8x + 32.5 = −26 ln(x − 18) + 180 = −22 ln(0.001(x − 20)) + 16 = 100 exp−2.4x = 150 exp−0.02x +8 = −220 ln(0.001 ln(x) + 30) − 689 = 117 exp−0.00068x −7

y = 100

NH+ 4 ≥ 0.02

= 0.115 exp−0.05 · exp0.0013x +5 = −28 ln(x − 10) + 211 = −71 ln(0.001(x + 2.5)) − 235 = −40 ln(0.1(x + 150)) + 210

y = −27 ln(0.1(x − 30))

y y y y

Sub-index function (y)

–

– – – –

Weightage Range applicable (x)

Parameter

Aquatic Toxicity index (ATI)

TABLE I (Continued)

I =

1 1 . 100 n

 i=1

n 

qi

2

Aggregation function

(Continued on next page)

60–100 – Suitable for all fish life 51–59 – Suitable only for hardy fish species 0–50 – Totally unsuitable for normal fish life

Water quality classification

218 C. SARKAR AND S. A. ABBASI

Weightage 0.019 0.097 0.090 0.116 0.063 0.065 0.074 0.079 0.077

0.090 0.077 0.063

Parameter

DO (% saturation) BOD, 5 day (mg/l) Coli (MPN Coli/100 ml) E. Coli (Coli/100 ml) Alkalinity (ppm CaCO3 ) Hardness (ppm CaCO3 ) Chloride (mg/l) Sp. Conductance (micro-mhos/cm)

pH

Nitrate (mg/l) Temperature (◦ C) Color (Color units – Pt std)

0.82DO + 10.56 108 (BOD)−0.3494 136 (Coli)−0.1311 106 (E-Coli)−0.1286 110 (Alk)−0.1342 552 (Ha)−0.4488 391 (Cl)−0.3480 306 (Sp.C)−0.3315 100.6803+0.1856( p H ) 1 103.57−0.2216 (pH) 125(N)−0.2718 102.004−−0.0−−382(T a−T s) 127(C)−0.2394

7.1 – – –

Sub-index function (y)

– – – – – – – –

Range applicable (x)

Dinius water quality index

TABLE I (Continued)

IWQ = i=1

n 

Ii wi

Aggregation function –

(Continued on next page)

Water quality classification

QUALIDEX

219

Weightage

–

–

–

–

– –

–

Parameter

Turbidity

pH

Color

% DO

BOD TDS

Hardness

Pi = 1 Pi = exp(y + 42.5)/205.58 Pi = (y + 500)/125

≤75 75–500 >500

=1 = (y/5) = (y + 43.9)/34.5 =1 =1 = exp((y − 7)/1.082 = exp((7 − y)/1.082 = (y + 130)/140 = y/75 = exp(−(y − 98.33)/36.067) = (707.58 − y)/14.667 = (y − 79.543)/19.054 =1 = y/1.5 =1 = exp((y − 500)/721.5) = (y − 1000)/125 = y/375

Pi Pi Pi Pi Pi Pi Pi Pi Pi Pi Pi Pi Pi Pi Pi Pi Pi Pi

Sub-index Function (Pi )

≤5 5–10 10–500 7 7 >7 105

Range applicable (x) IDO = 0.18 + 0.66x IDO = −13.5 + 1.17x IDO = 163.34 − .62x IBOD = 96.67 − 7.0x IBOD = 38.9 − 1.23x IPH = 16.1 + 7.35x IPH = −47.61 + 20.09x IPH = 316.96 − 29.85x IPH = 96.17 − 8.0x Icoli = 42.33 − 7.75 log10 x Icoli = 2

Sub-index Function (Ii )

Ved Prakash Index (CPCB WQI)

TABLE I (Continued)

WQI =

i=1

P 

wi Ii

Aggregation function

63–100 – Good to excellent 50–63 – Medium to good 38–50 – Bad