assessment of water quality of a river using an indexing approach during the low-flow season
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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: drmlatif@yahoo.comyEvaluation 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
Copyright # 2009 John Wiley & Sons, Ltd. Irrig. and Drain. 60: 103–114 (2011)
DOI: 10.1002/ird
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)
Copyright # 2009 John Wiley & Sons, Ltd. Irrig. and Drain. 60: 103–114 (2011)
DOI: 10.1002/ird
106 M. T. BHATTI AND M. LATIF
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
Copyright # 2009 John Wiley & Sons, Ltd. Irrig. and Drain. 60: 103–114 (2011)
DOI: 10.1002/ird
ASSESSMENT OF RIVER WATER QUALITY USING AN INDEXING APPROACH 107
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
Copyright # 2009 John Wiley & Sons, Ltd. Irrig. and Drain. 60: 103–114 (2011)
DOI: 10.1002/ird
108 M. T. BHATTI AND M. LATIF
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.
Copyright # 2009 John Wiley & Sons, Ltd. Irrig. and Drain. 60: 103–114 (2011)
DOI: 10.1002/ird
ASSESSMENT OF RIVER WATER QUALITY USING AN INDEXING APPROACH 109
Figure 6. Percentiles and medians for different water quality parameters during low flow months of 2006–7 and 2007–8
Copyright # 2009 John Wiley & Sons, Ltd. Irrig. and Drain. 60: 103–114 (2011)
DOI: 10.1002/ird
110 M. T. BHATTI AND M. LATIF
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
Copyright # 2009 John Wiley & Sons, Ltd. Irrig. and Drain. 60: 103–114 (2011)
DOI: 10.1002/ird
ASSESSMENT OF RIVER WATER QUALITY USING AN INDEXING APPROACH 111
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.
Copyright # 2009 John Wiley & Sons, Ltd. Irrig. and Drain. 60: 103–114 (2011)
DOI: 10.1002/ird
112 M. T. BHATTI AND M. LATIF
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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