assessment of water quality of a river using an indexing approach during the low-flow season

ASSESSMENT OF WATER QUALITY OF A RIVER USING AN INDEXING APPROACHDURING THE LOW-FLOW SEASONy

MUHAMMAD TOUSIF BHATTI AND MUHAMMAD LATIF*

Centre of Excellence in Water Resources Engineering, University of Engineering and Technology, Lahore, Pakistan

ABSTRACT

The River Chenab is one of the largest rivers in Pakistan with an average annual flow of 5.29 billion cubic metres (BCM). The

river traverses a total length of 576 km through a number of densely populated and industrial cities in the Punjab province of

Pakistan. In the present study, a segment of 292 km was monitored for a variety of cardinal water quality constituents during the

low-flow months of 2006–07 and 2007–08. Water quality indices (WQIs) were calculated for three uses of the river water, i.e.

irrigation, drinking and aquatic life, using the CWQI 1.0 model developed by the task group of the Canadian Council of

Ministers of the Environment (CCME). The results revealed that the lower river reach (185–233 km) was more polluted than the

upper 185 km segment. In this river reach, overall WQI ranking was poor for drinking and marginal for both irrigation and

aquatic life. The WQIs for all three uses were ranked poor at the sampling station located at 233 km along the river. Copyright

# 2009 John Wiley & Sons, Ltd.

key words: monitoring; water quality; index; contamination; water uses

Received 25 August 2008; Revised 11 August 2009; Accepted 13 August 2009

RESUME

Le fleuve Chenab est l’un des plus grands fleuves du Pakistan avec un ecoulement annuel moyen de 5.29 milliards de metres

cubes. Le fleuve, d’une longueur totale de 576 kilometres, traverse un certain nombre de villes densement peuplees et

industrielles dans la province du Pendjab du Pakistan. Dans la presente etude, un segment de 292 kilometres a ete l’objet d’un

suivi au niveau d’une serie de constituants cardinaux de qualite de l’eau pendant les mois d’etiage de 2006–07 et de 2007–08.

Des index de qualite de l’eau (WQIs) ont ete calcules pour trois usages de l’eau de riviere – irrigation, eau potable, vie aquatique

– utilisant le modele CWQI 1.0 developpe par le groupe de travail du Conseil canadien des ministres de l’environnement

(CCME). Les resultats ont indique que la partie la plus en aval du fleuve (185 a 233 kilometres) etait plus polluee que les 185

kilometres en amont. Dans cette partie du fleuve, le classement global de WQI indiquait une qualite de l’eau mauvaise pour la

consommation et mediocre pour l’irrigation et la vie aquatique. Les WQIs pour chacun des trois usages indiquaient une eau de

mauvaise qualite a la station de prelevement situee a 233 kilometres le long du fleuve. Copyright# 2009 John Wiley & Sons,

Ltd.

mots cles: surveillance; qualite de l’eau; index; contamination; usages de l’eau

INTRODUCTION

The disposal of untreated wastewater into natural water

bodies is a serious water quality issue in many developing

countries and Pakistan is no exception. Due to unmanaged

and large-scale addition of effluents, the water quality of

rivers is degraded near densely populated cities and towns

especially during low-flow months of the year. Another

problem of developing countries is the non-availability of

water quality data. Unfortunately, there is no established

water quality monitoring programme in Pakistan. Current

monitoring, largely done by some government agencies,

is temporally and spatially fragmented. There is also an

inadequate use of information gathered by monitoring

activities to formulate useful indigenous criteria for waste

disposal in natural water bodies.

IRRIGATION AND DRAINAGE

Irrig. and Drain. 60: 103–114 (2011)

Published online 8 December 2009 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/ird.549

*Correspondence to: Dr. Muhammad Latif, Centre of Excellence in WaterResources Engineering, University of Engineering and Technology, Lahore,Pakistan. E-mail: [email protected] de la qualite de l’eau de riviere pendant l’etiage par uneapproche d’indexation.

Copyright # 2009 John Wiley & Sons, Ltd.

A recent report of theWorldWildlife Fund (WWF) (2007)

stated that rapid population growth, urbanization and

unsustainable water consumption practices have placed

immense stress on the quality as well as the quantity of water

resources in Pakistan. There is very little separation of

municipal and industrial effluents in the country. Both

effluents flow directly into nearby natural water bodies

(rivers or canals) through open drains that are rarely lined.

Unfortunately, no surface water quality standards have yet

been established in Pakistan. National standards are

available only for waste water (industrial and municipal

effluents) but these are rarely enforced.

The water quality issue in Pakistan, considering its

importance, has not yet received sufficient attention. A

comprehensive water quality monitoring programme is

indispensable to assess the water quality status of the

national rivers. Once the water quality monitoring data are

collected, there is a further need to translate the data into a

form that is easily understood and effectively interpreted. A

water quality index (WQI) plays an important role in such a

translation process. It is a communication tool for transfer of

water quality data (Ball and Church, 1980). The communi-

cation of water quality data is especially challenging when

the intended audience is the general public who not directly

interested in water quality data. Members of the public are

more interested in the information that the water quality data

conveys and are even more interested in the knowledge that

follows from the information (Khan et al., 2005). Political

decision-makers, non-technical water managers, and the

general public usually have neither the time nor the training

to study and understand a traditional, technical review of

water quality data. A number of indices have been developed

to summarize water quality data in an easily expressible and

easily understood format (Couillard and Lefebvre, 1985).

As a synthetic indicator, WQI provides overall summaries

of water quality and potential trends on a simple and

scientific basis (Kaurish and Younos, 2007). The concept of

indices to represent gradations in water quality was first

proposed by Horton (1965). The need for such readily

understood evaluation tool was ultimately realized, and

several researchers (e.g. Brown et al., 1970; Prati et al.,

1971; Walski and Parker, 1974; Landwehr, 1979; Bhargava,

1985; Dinius, 1987; Smith, 1990; Swamee and Tyagi, 2000;

Kaurish and Younos, 2007) have developed their own rating

schemes. A remarkable contribution in WQI development is

a model proposed by the Canadian Council of Ministers of

the Environment (CCME). Khan et al. (2003) reviewed and

analysed the water quality of three watersheds using the

CWQI 1.0 model.

The purpose of the present study was to characterize the

water quality status of the River Chenab, an urban river in

Pakistan. The study presents spatial variations of a variety of

water quality parameters. Moreover, WQIs with respect

to different water uses are also determined. The CWQI

1.0 model was used for calculation of WQIs at selected

sampling stations along the river.

THE STUDY AREA

The River Chenab has its source in the Himachal Pradesh

province of India. Up to its confluence with the Indus River

in Pakistan, it travels a distance of 1240 km with average

annual flow of 5.29 billion cubic metres (BCM). The total

drainage area of the river is 67 500 km2 (38 500 km2 lies in

Pakistan). Pakistan has the exclusive rights on water of this

river according to the Indus Waters Treaty (IWT) of 1961

between Pakistan and India. Four barrages have been

constructed on the River Chenab in Pakistani territory,

namely at Marala, Khanki, Qadirabad and Trimmu. Four

link canals (a link canal transfers water from one river to an

adjacent one) and three irrigation canals offtake from these

barrages and irrigate a total of 2.39 million ha. Table I shows

the discharge capacity of the irrigation and link canals.

After it enters Pakistan, the River Chenab traverses a

number of densely populated industrial cities (e.g. Sialkot,

Gujranwala, Gujrat, Hafizabad, Faisalabad, Sargodha and

Jhang) in the Punjab province of Pakistan. According to a

recent census, the population of these cities was 19.94

million in 1998 (Government of Pakistan (GoP), 1998).

The industrial and municipal effluents are disposed of

directly into the river without any prior treatment through a

contiguous system of surface drains. Designated beneficial

uses of the River Chenab include irrigation, watering of

livestock, bathing, municipal supply, industrial supply and

propagation of aquatic life.

A total river length of 576 km lies in Pakistani territory out

of which a segment of 292 km (from the Marala headworks

to the Trimmu headworks) was selected in the present study

for detailed analysis. River water samples were collected

from seven locations along the course of this segment as

shown in Figure 1. Table II outlines a summary of selected

Table I. Irrigation and link canals originating from the RiverChenab

Name of barrage Offtaking canals Dischargecapacity(m3 s�1)

Marala Barrage Upper Chenab Canal 477Marala–Ravi Link Canal 622

Khanki Barrage Lower Chenab Canal 477Qadirabad Barrage Qadirabad–Balloki Link Canal 623Trimmu Barrage Trimmu–Sidhnai Link Canal 354

Rangpur Canal 76Haveli Link Canal 146

Copyright # 2009 John Wiley & Sons, Ltd. Irrig. and Drain. 60: 103–114 (2011)

DOI: 10.1002/ird

104 M. T. BHATTI AND M. LATIF

monitoring points along the river starting from the Marala

headworks, the point where it enters Pakistani territory from

India. A river schematic showing the locations of major

hydraulic structures, canals and drains is presented in

Figure 2.

MATERIALS AND METHODS

Identification of low-flow season

The low-flow regime of a river can be analysed in a variety

of ways depending on the type of data initially available and

the type of output information required. Consequently, there

exist a variety of low-flow measures and indices (Smakhtin,

2001). In the present study, low-flow months of the year

were identified based on statistical analysis of 60 years’

(1947–48 to 2006–07) flow records of the River Chenab at

Marala. A flow duration curve (FDC) was developed

(Figure 3) for the River Chenab at Marala using mean

monthly flow data over 60 years. The figure shows a

distribution of flows ranging from floods to low flows. The

river flow at Marala ranged between 4381 and 122m3 s�1.

The ‘‘low-flow section’’ of an FDC is of most interest for

low-flow studies. It is that part of the FDC which may be

arbitrarily determined as that part of the curve with flows

below the median flow which corresponds to the discharge

equalled or exceeded 50% of the time, Q50 (Smakhtin,

2001). The median flow (Q50) was computed as 685m3 s�1

from Figure 3. The river discharge below Q50 may be taken

Figure 1. Map of the study area

Table II. Water quality monitoring stations selected for the water quality study (2006–08) along the River Chenab

Station code Description Distance (km) Latitude Longitude

SS1 Marala Headworks 0 328 190 1300 748 280 5100SS2 Khanki Headworks 57.5 328 280 6700 738 580 2200SS3 Qadirabad Headworks 84.8 328 240 2000 738 580 2200SS4 Chiniot Bridge 185 318 450 170 728 570 3800SS5 5 km upstream of Faqirian Sillanwali Drain outfall 218.6 318 370 3400 728 340 5000SS6 10 km downstream of Faqirian Sillanwali Drain outfall 233.2 318 370 3500 728 340 5000SS7 Trimmu Headworks 292 318 380 4100 728 310 5800


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ASSESSMENT OF RIVER WATER QUALITY USING AN INDEXING APPROACH 105

as the low flow part of the FDC and was regarded as the

upper bound of low flow in the river during the low-flow

season.

Monthly average flow data are plotted in Figure 4 for the

long-term (60 years) flow records. The median flow (Q50)

from the FDC is also plotted in the figure, showing the upper

bound of the low flows. It is clear from this figure that the

average river flow remains below Q50 during the months

from October to March. This period may be considered as

the low-flow season of the year. Due to less water availability

in the river, its capacity to assimilate the added pollution

decreases during the low-flow season resulting in severe

water quality problems. Since more discharge flows into the

river during high-flowmonths, therefore more dilution of the

added effluents takes place. In this context water quality

monitoring activities were restricted to low-flow months

(October to March) in the present study.

Water quality monitoring

Surface water quality was monitored using grab sampling

with a short holding time (< 1 day). Sampling was

performed once a month during the low-flow season of the

Figure 2. Schematic representation of the selected reach of the River Chenab

Figure 3. Flow duration curve for the River Chenab at Maralausing mean monthly data of sixty year period (1947–48 to 2006–07)


DOI: 10.1002/ird


year (October to March) due to constraints of resources and

time. Water samples were collected from various depths and

at different points across the river cross-sections and finally

mixed to form a representative composite sample. A variety

of cardinal water quality parameters were used to examine

the water quality condition of the River Chenab with

reference to its intended uses including pH, total dissolved

solids (TDS, mg l�1), electrical conductivity (EC,mS cm�1),

total Kjeldhal nitrogen (TKN, mg l�1), sodium adsorption

ratio (SAR), residual sodium carbonate (RSC, meq l�1),

dissolved oxygen (DO, mg l�1), biochemical oxygen

demand (BOD, mg l�1), total and faecal coliform bacteria

(No. in 100ml) and chemical oxygen demand (COD,

mg l�1). Effluents of all the contributing drains, before

mixing with the river stream, were also monitored regularly

in the low-flow seasons (2006–07 and 2007–08). The study

considered both in situ and laboratory analysis of the

collected samples with minimum instrumental and analyti-

cal errors (1–5%). The selection of water quality parameters

was made keeping in view the presence of high concen-

trations of various selected parameters in the rivers of

Pakistan during some sparse monitoring studies. Another

basis for their selection was the availability of analysis

facilities and guidelines/standards.

An important consideration during the present study was

to adopt the best-suited water quality criteria for WQI

calculation. Numerous sets of standards, or guidelines for

water quality, have been issued from time to time by various

agencies and authorities (e.g. United States Environmental

Protection Agency (EPA), World Health Organization

(WHO), European Union (EU), and other countries)

intending to define the maximum acceptable limit of water

pollution by various pollutants. Standards for ambient water

quality are commonly designated according to the intended

use of the water resource (e.g. drinking water, fishing water,

irrigation).

In Pakistan, very little attention has been given to

formulate surface water quality standards according to

different water uses. National Environmental Quality

Standards (NEQS) established in 1993 are available only

for municipal and liquid industrial effluents and do not

provide any guideline for the receiving water bodies.

Similarly, some other national institutions (e.g. Pakistan

Standards Institution (PSI), 1987; Pakistan Council of

Research in Water Resources (PCRWR), 2002) have drafted

water quality standards for drinking and irrigation waters

but their enforcement is still pending. The most recent

advancement in the establishment of water quality guide-

lines was a stakeholder workshop organized by WWF

Pakistan in November 2006 to disseminate the devised

guidelines. In this workshop, stakeholders from different

government departments (irrigation, agriculture and

environment), industry water management institutes, and

non-governmental organizations (NGOs) working on water

issues were invited to review the criteria and guidelines in

detail and voice their comments. Their comments and

suggestions were incorporated in the proposed guidelines by

WWF (2007). These guidelines were used as a preferred

source in the present study for defining water quality criteria

in the CWQI 1.0 model.

The model

The CWQI 1.0 model consists of three measures of

variance from selected water quality objectives: scope (F1),

Figure 4. Long term average (1947–48 to 2006–07) of mean monthly discharge in the River Chenab at Marala


DOI: 10.1002/ird


the number of variables not meeting water quality

objectives; frequency (F2), the number of times these

objectives are not met; and amplitude (F3), the amount by

which the objectives are not met. The index produces a

number between 0 (worst water quality) and 100 (best water

quality). These numbers are divided into five descriptive

categories to simplify presentation. The model calculates the

WQI as follows:

� Scope (F1) represents the extent of water quality

guideline non-compliance over the time period of

interest:

F1 ¼ Number of failed variables

Total number of variables� 100 (1Þ

In Equation (1), variables indicate the water quality

parameters tested during the time period for index

calculation.

� Frequency (F2) represents the percentage of individual

tests that do not meet the objectives, i.e. failed tests.

F2 ¼ Number of failed tests

Total number of tests� 100 (2Þ

� Amplitude (F3) represents the amount by which failed

test values do not meet their objectives. F3 is calculated

in three steps as follows.

Step 1. The number of times by which an individual

concentration is greater than (or less than, when the objec-

tive is a minimum) the objective is termed an ‘‘excursion’’

and is expressed as follows. When the test value must not

exceed the objective:

excursioni ¼ Failed test valuei

Objectivej

" #� 1 (3)

For the cases in which the test value must not fall below

the objective:

excursioni ¼Objectivej

Failed test valuei

� �� 1 (4)

where i and j¼ 1, 2, 3, .. . ., n.

Step 2. The collective amount by which individual tests

are out of compliance is calculated by summing the

excursions of individual tests from their objectives and is

divided by the total number of tests (both those meeting

objectives and those not meeting objectives). This variable,

referred to as the normalized sum of excursions, or NSE, is

calculated as

NSE ¼Pni¼1

excursioni

Number of tests(5)

Step 3. Finally F3 is calculated by an asymptotic

function that scales the normalized sum of the excursions

from objectives (NSE) to yield a range between zero and

100:

F3 ¼ NSE

0:01NSEþ 0:01

� �(6)

The WQI is then calculated as

WQI ¼ 100�ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiF1 þ F2 þ F3

p1:732

� �(7)

TheWQI values are then converted into rankings by using

the index categorization schema as presented in Table III.

The rankings range from poor to excellent based on theWQI

scores. This ranking schema was more suitable for aquatic

and irrigation uses but in the case of drinking use, it was

better to convey a clearer message (e.g. fit or unfit) due to the

involvement of potential risks of human health hazards.

Therefore, a different ranking criterion was set for drinking

use as presented in Figure 5.

The CWQI model provides a mathematical framework for

assessing ambient water quality conditions relative to water

quality objectives. It is flexible with respect to the type and

number of water quality variables to be tested, the period of

Table III. CCME WQI categorization schema for aquatic and irrigation uses

Rank WQI value Description

Excellent 95–100 Water quality is protected with a virtual absence of threat or impairment; conditions very close to natural orpristine levels. These index values can only be obtained if all measurements are within objectives virtuallyall of the time

Good 80–94 Water quality is protected with only a minor degree of threat or impairment; conditions rarely depart fromnatural or desirable levels

Fair 65–79 Water quality is usually protected but occasionally threatened or impaired; conditions sometimes departfrom natural or desirable levels

Marginal 45–64 Water quality is frequently threatened or impaired; conditions often depart from natural or desirable levelsPoor 0–44 Water quality is almost always threatened or impaired; conditions usually depart from natural or

desirable levels


DOI: 10.1002/ird


application, and the type of water body (stream, river reach,

lake, etc.) tested. These decisions are left to the user.

Therefore, before the index is calculated, the water body,

time period, variables, and appropriate objectives need to be

defined (Canadian Council of Ministers of the Environment

(CCME), 2001).

In the present study, WQIs were calculated at seven

locations along the River Chenab. The calculations were

made for three different water uses: drinking, irrigation

and aquatic life. Many of the selected water quality

parameters affect the suitability of water for two or all

three uses, but some of them were exclusively associated

with one water use (e.g. SAR and RSC for irrigation). For

each water use, different sets of parameters were used.

Their selection was made depending on the relevance of

parameters to a particular water use in a regional context,

availability of data and water quality guidelines. The water

quality parameters and guidelines used in the calculation

of WQIs are presented in Table IV. The table shows the set

of parameters used for each water use and the guidelines

for each parameter. The input data used in the CWQI 1.0

model were selected from the monitoring programme

conducted during the low-flow season for two years, i.e.

2006–07 and 2007–08.

RESULTS AND DISCUSSION

Water quality assessment

Figure 6 presents the results of water quality analysis of

the River Chenab at selected locations during the low-flow

months of 2006–07 and 2007–08. The figure shows

percentiles and medians plotted for individual water quality

parameters along with water quality guidelines for different

uses. The results indicate that EC (Figure 6(a)) remained

relatively constant (�225mS cm�1) for the upper 85 km of

the selected river reach followed by a slight increase of

approximately 100mS cm�1 at SS3 and SS4. Median EC

increased drastically in the lower river reach (218–233 km)

due to the intervention of two major drains (i.e. Faqirian

Figure 5. Criterion for the fitness of river water for drinking

Table IV. Water quality standards for different water uses

Water quality parameters Unit Water uses

Drinking Aquatic Irrigation

Total dissolved solids (TDS) mg l�1 800 1000 1000Electric conductivity (EC) mS cm�1 1250 1500 1500pH Minimum 6.5 6.5 6.4

Maximum 8.5 8.5 8.4Total Kjeldhal nitrogen (TKN) mg l�1 0.5� 1.2� N/ASodium adsorption ratio (SAR) N/A N/A 8Residual sodium carbonate (RSC) meq l�1 N/A N/A 2.3Dissolved oxygen (DO) mg l�1 >6 >5 >4Biochemical oxygen demand (BOD) mg l�1 2 8 8Carbonaceous oxygen demand (COD) mg l�1 25� 50� 70�

Coliform bacteria (total) No. per 100ml 0 N/A 1000Coliform bacteria (faecal) No. per 100ml 0 N/A 500Total hardness mg l�1 300 N/A N/AChloride (Cl) mg l�1 250 N/A 100Sodium (Na) mg l�1 200 N/A N/A

N/A¼ not applicable or not available.�Official Gazette of the Turkish Government (1991).Unless otherwise specified data have been adopted from the WWF Water Quality Guidelines 2007.


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Figure 6. Percentiles and medians for different water quality parameters during low flow months of 2006–7 and 2007–8


DOI: 10.1002/ird


Sillanwali (FS) and Chakbndi Drain) in this river segment. It

showed higher values than permissible for drinking and

irrigation at SS6. Addition of fresh water from the River

Jehlum at 284 km (see Figure 2) caused dilution, resulting in

decrease in EC at SS7. An identical trend is observed in the

case of TDS in Figure 6(e). The range between the 10th and

90th percentiles denotes the variation in observed values at

different times. This range is smaller for EC, TDS, SAR,

BOD and total coliforms upstream of SS6 than at or

downstream of this sampling station. For all water quality

parameters except pH and TKN, the median line formed an

approximately similar wedge shape in the lower river reach.

The median values of pH remained in the range 8.1–8.3 at

all sampling stations. Thus, pH of the river water is not

affected much by the drain effluents. Median TKN

(Figure 6(i)) peaked at SS2 (2.8mg l�1) followed by a fall

at SS3 (0.8mg l�1). Furthermore, a very high range between

the 10th and 90th percentiles at SS7 shows high variability

of TKN concentrations among the sampled months. For the

downstream reach (84–292 km) TKN remained relatively

constant (�1mg l�1), except for a slight increase at SS6

(1.5mg l�1) which shows that the drain effluents caused a

decreasing effect on TKN concentrations due to unknown

chemical processes. The median value of TKN exceeded the

permissible limit for irrigation use at SS6; on the other hand

it remained higher than the guideline for drinking at all the

sampling stations.

SAR and RSC are very important water quality

parameters for the determination of irrigation water quality,

particularly in Pakistan. The application of irrigation water

with high EC, SAR and RSC may cause salinity and sodicity

problems in the receiving soils that may result in a decrease

in crop yields, ultimately leading to degradation in soil

fertility. Median SAR (Figure 6(c)) increased longitudinally

from 2.2 to 6 between SS1 and SS5, peaked at SS6 (48,

which is 500% higher than the allowable limit) and then

decreased to 17.6 at SS7. Median SAR was found higher

than the allowable concentration for irrigation at SS6, SS7

and SS8. Median RSC remained higher than the permissible

limits for irrigation at four sampling sites, i.e. SS1, SS2, SS6

and SS7. The highest value was noted at SS6 where the

median RSC exceeded the limits by 113%.

The median line of total coliforms (Figure 6(h)) shows a

steep upward slope between SS2 and SS6. This trend is the

effect of untreated sewage being added to this river segment

from the surrounding cities.

Organic waste includes both dissolved and particulate

matter composed principally of proteins, carbohydrates, and

fats. Biodegradable organics were measured in terms of

biochemical oxygen demand (BOD) and chemical oxygen

demand (COD). For median COD and BOD (Figures 6(f)

and (g)), small variations were noted in the upstream reach

of the river (zero to 218 km). The median of both

constituents peaked at SS6 (28.5 and 99mg l�1 respectively)

followed by a drop at SS7.

Water quality indices

Table V presents a summary of three measures of

variance, i.e. F1 (scope), F2 (frequency) and F3 (amplitude)

for selected water uses. The table shows that among all water

uses except drinking, F1 has higher values than F2 and F3 at

all the selected river stations. It denotes that there is a higher

percentage of failed variables than the percentage of

individual failed tests and the amount by which they failed.

Table V further denotes that F1 values show an increasing

trend from SS1 to SS6 followed by a drop at SS7. This trend

infers that more water quality variables failed (did not meet

their objectives) in the downstream reach polluted by the

surface drains. Thus, from these results, it can be concluded

with confidence that the quality of river water deteriorates

from the upper to lower reaches except for the last one.

The highest values of F2 are observed for drinking and

lowest for irrigation use. It shows that the percentage of

individual failed tests is highest in the case of drinking and

lowest for irrigation. Similarly, F3 values are also higher in

the case of drinking as compared to the aquatic life and

irrigation uses of the river water. The reason is that for the

uses of aquatic life and irrigation, the values of failed

variables do not exceed as much from their objectives as for

drinking.

Table VI provides a detailed insight of the water quality

situation with respect to different water uses at the selected

sampling stations and summarizes the calculation of WQIs.

The table lists those water quality parameters that exceeded

the permissible limits for different uses most of the time

during the sampling period. The water quality parameters

with the highest value of normalized sum of excursion

(NSE) are also given in the table. It is clear from the table

that faecal coliform, BOD and SAR constituted the largest

Table V. Scope, frequency and amplitude for different water usesalong River Chenab

Samplingstations

Water uses

Drinking Aquatic life Irrigation

F1 F2 F3 F1 F2 F3 F1 F2 F3

SS1 38 32 48 50 25 13 22 11 3SS2 50 38 60 38 30 23 22 16 9SS3 50 41 63 50 24 32 44 17 21SS4 62 49 72 50 28 41 56 26 27SS5 62 49 69 50 24 34 56 22 22SS6 88 62 80 75 38 52 78 43 54SS7 62 47 72 50 22 40 56 30 37


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values of use for drinking, aquatic life and irrigation uses

respectively.

Irrigation. Figure 7 presents WQIs along the river for

aquatic life and irrigation uses. The river water was ranked

‘‘good’’ for irrigation at SS1 and SS2 where the WQI score

was greater than 80. The WQIs ranged in ranking from

‘‘marginal’’ to ‘‘fair’’ at sampling sites SS3, SS4, SS5 and

SS7. But the results of individual parameters (Figure 6) show

the presence of high median SAR and RSC values at these

stations (except SAR at SS3), higher than the maximum

allowable limits for irrigation. This is further confirmed

from the results presented in Table VI where the variables

with the most failed tests are SAR and RSC. The only

Figure 7. Water quality indices for irrigation and aquatic life at different locations along the River Chenab

Table VI. Summary of water quality index calculations for different water uses at selected sites of the River Chenab

StationNo.

Wateruses

Number ofvariables tested

Number offailed variables

Variables withmost failed tests

Variables withhighest NSE

Drinking 10 3 FC FCStation 1 Aquatic 8 3 BOD BOD

Irrigation 9 2 RSC RSCDrinking 10 3 TKN, FC FC

Station 2 Aquatic 10 3 COD, BOD BODIrrigation 8 3 RSC RSCDrinking 9 2 FC FC

Station 3 Aquatic 10 3 BOD BODIrrigation 8 2 SAR SARDrinking 9 3 FC FC

Station 4 Aquatic 10 4 BOD BODIrrigation 9 3 SAR CODDrinking 10 2 FC FC

Station 5 Aquatic 8 2 BOD BODIrrigation 9 2 SAR SARDrinking 10 5 TKN, FC FC

Station 6 Aquatic 8 5 COD, BOD CODIrrigation 9 5 RSC, SAR SARDrinking 10 5 TKN, FC FC

Station 7 Aquatic 8 4 BOD, COD BODIrrigation 9 4 RSC, SAR RSC

Variables: FC¼ fecal coliforms, BOD¼ biochemical oxygen demand, COD¼ carbonaceous oxygen demand, TKN¼ total Kjeldahl nitrogen, SAR¼ sodiumadsorption ratio, RSC¼ residual sodium carbonate.


DOI: 10.1002/ird


sampling station with a ‘‘poor’’ ranking for irrigation was

SS6 where the WQI score was 40. Fortunately, there are no

canal offtakes in the lower river reach (84–292 km). On the

bases of the results of the present study it is inadvisable to

plan any irrigation canal from the river in that area,

especially between SS5 and SS6.

Aquatic life. For aquatic life, the river water was of

‘‘fair’’ quality from SS1 to SS2. Downstream of SS2, the

WQI ranked ‘‘marginal’’ except at SS6 (‘‘poor’’) where the

river is intensely polluted by surface drains. WQI for aquatic

life remained at 40 at SS6 followed by some improvements

(58 at SS7) due to the contribution of fresh water from the

River Jehlum.

Drinking. Table VII indicates that the river water was

‘‘not fit’’ for drinking at all the sampling stations. The results

in Table VII were based on the criterion adopted to

decide the suitability of river water for drinking (Figure 5).

The worst water quality situation was again at SS6 where the

WQI score was only 40. At sampling stations SS1 and SS2

the WQIs were greater than 80, but the river water still

contained pathogens and therefore could not pass the fitness

criteria.

CONCLUSIONS

In this study, a 292 km length of the River Chenab was

monitored for water quality at seven locations along the river.

The WQIs were calculated using the CWQI 1.0 model. To

simplify presentation, these indices were divided into five

descriptive categories (‘‘poor’’ to ‘‘excellent’’) for aquatic life

and irrigation uses. A different categorization method was

developed to assess the water quality for drinking. In this case,

instead of ranking into categories, the river water was declared

‘‘fit’’ or ‘‘unfit’’ based on WQI scores as well as presence or

absence of pathogens.

The results of the study revealed that the water quality of

the river is made worse by effluents added through surface

drains along its course. Water quality degradation is

prominent in the river reach from 84 to 233 km. For the

majority of the selected water quality parameters, an abrupt

increase in median values is observed in this reach. The

availability of fresh water from the River Jehlum at 284 km

compensates the situation to some extent in the last river

segment (233–292 km). The WQIs are mostly ranked as

‘‘marginal’’ or ‘‘fair’’ for aquatic and irrigation uses.

However, the river water is not fit for drinking at any

sampling stations. Sampling station SS6 is the most polluted

point with the lowest scores of WQIs for all three water uses.

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assessment of water quality of a river using an indexing approach during the low-flow season

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