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
deeksha_dce@rediffmail.com
doi:10.6088/ijes.00202010017
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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Water quality indices used for surface water vulnerability assessment
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.
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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.
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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.
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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)
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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).
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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
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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
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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.
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