321

KEYWORDS

ISSN: 0974 - 0376

NSave Nature to Survive

: Special issue, Vol. III:

www.theecoscan.inAN INTERNATIONAL QUARTERLY JOURNAL OF ENVIRONMENTAL SCIENCES

Prof. P. C. Mishra Felicitation Volume

Paper presented in

National Seminar on Ecology, Environment &Development

25 - 27 January, 2013

organised by

Deptt. of Environmental Sciences,

Sambalpur University, Sambalpur

Guest Editors: S. K. Sahu, S. K. Pattanayak and M. R. Mahananda

Kumar Manoj et al.

Cluster analysis

Permeability index

Principal component analysis

Sodium absorption ratio

Water quality index

321 - 330; 2013

SPATIAL ASSESSMENT AND CHARACTERISATION OF THE

SUBARNAREKHA RIVER WATER THROUGH INDEX ANALYSES

APPROACHES AND CHEMOMETRICS

322

KUMAR MANOJ, BALWANT KUMAR AND PRATAP KUMAR PADHY*

Department of Environmental Studies, Institute of Science, Visva – Bharati

Santiniketan - 731235, Birbhum, West Bengal, INDIA

E-mail: pkpadhy@visva-bharati.ac.in

INTRODUCTION

Water is one of the most vital natural resources central to the survival of the living

beings. Fresh water accounts for just 2.6% of the total water available on the

earth. Out of this only a small fraction is readily available in rivers, lakes and as

groundwater. Rivers have always been the cradles of civilisation because of their

fertile basin and abundant freshwater resources. River water is used for drinking,

washing, animal husbandry, industrial, agricultural and recreational purposes.

However, unchecked anthropogenic activities, especially in the developing

countries, are increasingly making river water a stressed and unsustainable naturalresource. Industrial effluents, agricultural runoff, mining discharges, domesticwastes and municipal sewage all contribute to the pollution of river waters. Forbetter management and sustainable development of river water resourcescontinuous monitoring and assessment is necessary.

Subarnarekha River is one of the most important east flowing interstate rivers ofIndia with a total catchment area of about 19, 296km2. It flows through threeIndian states viz. Jharkhand, West Bengal and Odisha covering a distance ofaround 470km. According to the Subarnarekha Barrage Project a barrage is to beconstructed near Bhasraghat (West Medinipore district, West Bengal) to irrigate1,14,198h of land annually through a left bank canal and its distribution systemcovering a cultural command area of 96,860h (http:/ /pib. nic. in/ newsite /erelease.Aspx ?relid =33636). Moreover, Government of Odisha also plans to developdeep water; all weather port at Kirtania near the mouth of the Subarnarekha Riverfor which a memorandum of understanding with a Chennai based company hasbeen signed (http:// www. orissadiary. com/ Show Bussiness News. asp?id=3556).Based on these important developmental activities pre-projects level study of theriver water becomes necessary to compare with any future studies of the region.

Environmental monitoring and assessment of freshwater resources provide

feedback on the status of their quality degradation based on performance of

developmental programmes and projects. The major objectives of the present

study were to collect the most recent physicochemical data of the Subarnarekha

river in West Bengal and Odisha to develop water quality index (WQI) indicating

the ecological status of the surface water and apply chemometrics, which uses

multivariate statistical modeling, like principal component analysis (PCA) /factor

analysis (FA) and agglomerative hierarchical cluster analysis (AHCA) to further

interpret the surface water quality data. Suitability of water for irrigation was also

tested using indices like sodium adsorption ratio (SAR), percentage sodium (%Na)

and permeability index (PI) including salinity graphics.

MATERIALS AND METHODS

Study area

The study area consisted of two districts West Medinipore (21º36´ to 22º57´ N

and 86º33´ to 88º11´E) and Balasore (21º03' to 21º59' N and 86º20' to 87º29'

NSave Nature to Survive QUARTERLY

Subarnarekha River is one of the most impor-

tant east flowing rivers of India. The river in

West Bengal and Odisha is site for the

Subarnarekha Barrage Project and Kirtania port

respectively. The major objectives of the

present study were to investigate the pre-

projects level water quality of the river basin

through development of water quality index

(WQI) and application of chemometrics. Suit-

ability of water for irrigation was also tested

using sodium adsorption ratio (SAR), percent-

age sodium (%Na) and permeability index (PI).

Most of the parameters were found to be

within the permissible limits of prescribed stan-

dards. WQI classified river water into good

and medium reflecting the water to be eco-

logically suitable and sustainable.

Chemometrics further helped in clear inter-

pretation of the water quality. SAR, %Na and

PI indices displayed water to be suitable for

irrigation. The river water was found to be

ecologically suitable and sustainable because

of absence of any major anthropogenic influ-

ence in the region as compared to upper in-

dustrial and populated belts like Jamshedpur

and Ghatsila in Jharkhand state. This study

can be used as reference to observe the status

of quality of water during ongoing projects

and post projects scenario.

ABSTRACT

*Corresponding author

323

E) lying in the Indian states of West Bengal and Odisha

respectively (Fig. 1). The western portion of the West

Medinipore is a part of the Chottanagpur plateau formed of

hard lateritic stone. The eastern portion is a part of the coastal

plain and is formed of alluvial soil. Subarnarekha River flows

through the upland region of the western portion of the district.

Balasore district is a coastal district of the Odisha situated on

the northern most part of the state. It is bounded by West

Medinipore on its north and by the Bay of Bengal on the east.

Subarnarekha River flows through the north-western hills, inner

alluvial plain and the coastal belt before draining into the Bay

of Bengal near Talsari. The climate of the study area is mostly

hot and humid. Summer season starts from middle of March

and continues till May. Rainy season lies from June to

September with South-western monsoon causing maximum

rain. However, maximum rain is experienced during the July-

August period. The region experiences winter season from

December to February (http:/ /www. indianetzone. com/5/

balasore. htm).

Sampling procedure and preservation

Water samples were collected from thirteen locations along

the route of the Subarnarekha River basin in West Medinipore

and Balasore districts (Fig. 1) during post-monsoon, October

2011, period and analysed for their physicochemical

properties. Standard procedures as described by APHA (2005)

were followed for the Sample collections, stabilisation and

transportation to the laboratory as well as storage. All the

samples were processed within 6 hours of their collection.

Sample analyses

Water samples were analysed for nineteen parameters to

determine the overall quality with respect to pH, temperature

(Temp), dissolved oxygen (DO), electrical conductivity (EC),

total suspended solids (TSS), total dissolved solids (TDS),

bicarbonate (HCO3

-), hardness (Hard), calcium (Ca2+),

magnesium (Mg2+), biological oxygen demand (BOD),

chemical oxygen demand (COD), nitrate (NO3

-), phosphate

(PO4

-3), fluoride (F-), chloride (Cl-), sulphate (SO4

-2), sodium

(Na+) and potassium (K+) following the methods described by

APHA (2005). Temp, pH and DO were measured on the field.

All the reagents were of analytical grade purchased from

Merck, India. To prepare all the reagents and calibration

CHARACTERISATION OF THE SUBARNAREKHA RIVER WATER

standards double glass distilled and deionised waters were

used.

Basic statistical and correlation analyses

Descriptive statistics of the studied physicochemical variables

for all studied sampling sites was determined. Correlation

analysis using Pearson correlation matrix was done to

determine the interrelationship between the variables.

Water quality index (WQI) determination

WQI was determined following Pesce and Wunderlin (2000).

WQI =

Where,

k = subjective constant and represents the visual impression

of the river. Its value ranges from 0.25 to 1.00. k = 0.25 for

highly contaminated water, 0.5 for moderately contaminated

water, 0.75 for light contaminated water and 1.00 for apparently

good quality water. Ci and Pi are normalised and relative weight

values assigned to each parameter respectively (values of Ci

and Pi can be obtained from the paper authored by Pesce and

Wunderlin (2000). Higher relative weight has been assigned

to the parameters (like DO = 4) considered most important for

the aquatic. Whereas, parameters considered least important

have been assigned comparatively lower relative weights (like

temperature and pH = 1). To do away with the subjective

evaluation of the water quality, constant k was not considered

in the present study. Similar provisions have also been used

by authors like Abrahão et al. (2007) and Sánchez et al. (2007).

The value of WQI ranges from 0 – 100 which can be classified

into five categories. WQI values between 0 – 25 classified as

“very bad”; WQI values between 26 – 50 classified as “bad”;

WQI values between 51 – 70 classified as “medium”; WQI

values between 71 – 90 classified as “good” and WQI values

between 91 – 100 classified as “excellent” (Jonnalagadda and

Mhere, 2001; Sánchez et al., 2007).

Agglomerative hierarchical cluster analysis (AHCA)

Cluster analysis (CA) is one of the most important multivariate

statistical methods for sorting objects into groups or clusters. It

is an exploratory data analysis tool for solving classification

problems. Members of the same cluster have strong degree of

association as compared to the members belonging to the

other clusters (Kašparová et al., 2009). CA can be done using

Figure 2: Piper trilinear diagram of major ions in river waterCations Anions

Mg

2+

SO4

2- +

Cl-

CO

32- +

HC

O3

-

Ca2+

+ M

g2-

SO4 2-

Na+

+K+

Cl -Ca2+

S.1

S.2

S.3

S.4

S.5

S.6

S.7

S.8

S.9

S.10

S.11

S.12

Explanation

Figure 1: Study area and the sampling sites

S.1 Gopiballabhpur

S.2 Bhasraghat

S.3 Sonakanla

Rajghat S.4S.5 Baliapal Branch

S.6 Asti

S.7 PantaiJamkunda

S97Batagramcanal

S.10 Kirtanla

S.11 Kirtanla muhana

S.13DighaNalah

Bay ofBengal

Talsari S.12S.8 Bhusandeswar

WestBengal

Sampling sites along the subarnarekha river basin

Sampling sites in west medinipore

district

Sampling sites in balasore district

West Bengal

India

Odisha

Odisha

∑

∑

i

i

Pi

CiPi

k

324

both hierarchical and non-hierarchical methods. In this study

AHCA was performed for sorting variables into easily

explainable clusters using Ward’s method and squared

Euclidean distance as a distance measure. Hierarchical

agglomerative techniques start from the finest possible structure

(each data point forms a cluster), compute the distance matrix

for the clusters and join the clusters that have the smallest

distance. This step is repeated until all points are united in

one cluster (Härdle and Simer, 2003).

Principal component analysis (PCA)

The main objective of the PCA is to reduce the dimension of

the observations while retaining the variability in the

multivariate dataset. Low dimensional linear combinations are

often easier to interpret which makes the complex data analysis

easier (Härdle and Simer, 2003). PCA can be performed on

the correlation or covariance matrix of the dataset. Principal

components are the smaller set of independent or uncorrelated

variables obtained by transforming a larger set of inter-

correlated variables. These components provide useful

information on the most significant parameters that explain

the variability in the full dataset while excluding the less

significant ones (Juahir et al., 2011). The principal component

is expressed as:

KUMAR MANOJ et al.,

Figure 6: USSL diagram for classification of river waters for irrigation

So

diu

m h

azard

Low

Hig

hM

ed

ium

So

diu

m A

bso

rpti

on

Rati

o (SA

R)

Very

High

Conductivity (μS/cm)

Very HighHighMediumLow

Salinity hazard

Figure 5: Gibbs plot for TDS versus Na+: (Na+ + Ca2+)

Rock WeatheringDominance

Evaporation CrystallizationDominance

Precipitation/RainDominance

Na+: (Na+ + Ca2+)

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

TD

S (m

g/L

)

TD

S (m

g/L

)

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Cl-: (Cl- + HCO3

-)

Figure 4: Gibbs plot for TDS versus Cl-: (Cl- + HCO3

-)

Rock WeatheringDominance

Evaporation CrystallizationDominance

Precipitation/RainDominance

Figure 3: Agglomerative hierarchical cluster analysis depicted as

dendrogram

EC TDS HCO3

-T SO4

-2 F - TSS K+ DO Hard Mg2+ pH Ca 2+NO3

- PO4

3- BOD C O D Cl -Na+

Resc

ale

d d

ista

nce c

lust

er

co

mb

ine

Figure 7: Wilcox diagram for classification of river waters for

irrigation

Conductivity (μS/cm)

Excell

en

t to

Go

od

Go

od

to

Perm

issi

ble

Un

suit

ab

le

Do

ub

tfu

l to

Perm

issi

ble

Perc

en

tage o

f so

diu

m (%

Na)

mjim

...........

3ji32ji21ji1ij xaxaxaxaZ ++++=Where z is the component score, a is the component loading,

325

Table 1: Physicochemical properties of the water samples

Parameters Sampling sites

S.1 S.2 S.3 S.4 S.5 S.6 S.7 S.8 S.9 S.10 S.11 S.12 S.13

pH 7.20 7.40 7.80 7.50 7.80 7.30 7.70 7.30 7.60 7.90 7.90 7.70 8.20

Temp (ºC) 27.0 26.80 26.90 27.10 27.50 27.30 26.90 27.10 27.30 27.30 27.10 26.80 27.80

DO (mg/L) 5.60 5.90 5.80 5.20 5.60 5.30 5.10 4.60 4.70 4.90 5.20 5.50 6.50

EC (μS/cm) 456.00 453.00 447.00 493.00 475.00 439.00 394.00 528.00 504.00 436.00 462.00 427.00 792.00

TSS (mg/L) 100.00 150.00 152.00 116.00 103.00 123.00 149.00 135.00 147.00 112.00 152.00 264.00 510.00

TDS (mg/L) 296.00 292.00 286.00 325.00 304.00 283.00 251.00 342.00 323.00 281.00 295.00 284.00 519.00

HCO3

- (mg/L) 40.10 36.40 33.40 45.10 33.00 25.20 32.50 34.00 51.3 24.00 36.00 20.00 99.10

Hard (mg/L) 62.79 77.45 78.92 101.09 89.70 80.01 61.43 69.84 75.33 97.00 72.00 96.00 96.50

Ca2+ (mg/L) 33.56 35.56 32.56 45.00 40.20 34.67 25.56 22.50 36.00 37.00 16.00 36.00 36.50

Mg2+ (mg/L) 7.10 10.18 11.27 13.63 12.03 11.02 8.72 11.50 9.56 14.58 13.61 14.58 14.58

BOD (mg/L) 0.56. 3.76 2.96 1.10 0.93 1.87 1.59 2.80 2.24 1.51 3.39 0.54 1.88

COD (mg/L) 9.00 160.00 86.00 45.00 35.00 77.00 67.00 141.00 120.00 112.00 168.00 16.00 98.00

NO3

- (mg/L) 1.78 1.10 1.23 1.10 0.44 0.44 0.86 0.59 0.44 0.43 0.40 0.46 0.54

PO4

3- (mg/L) 3.76 1.76 1.80 1.46 1.50 1.50 1.00 0.86 1.50 2.10 2.40 0.80 2.90

F- (mg/L) 0.10 0.15 0.11 0.19 0.22 0.26 0.29 0.23 0.20 0.15 0.12 0.21 0.30

Cl- (mg/L) 36.11 31.65 35.06 30.12 30.34 29.23 33.00 39.10 36.20 35.10 35.12 35.00 35.20

SO4

3- (mg/L) 109.00 115.00 110.00 144.23 141.30 138.54 105.00 163.00 123.23 128.00 112.00 128.00 176.00

Na+ (mg/L) 43.68 40.26 43.06 36.56 34.45 38.34 45.00 68.70 74.64 33.00 35.00 11.80 66.30

K+ (mg/L) 33.33 35.56 32.56 23.12 29.45 21.03 12.56 9.10 7.12 25.00 49.50 46.12 95.20

Table 2: Use of water as proposed by *CPCB and #BIS.

Classification of water for different uses designated by CPCB

Designated best use Class of water Criteria

Drinking water source without A pH between 6.5 – 8; DO 6 mg/L or more;

conventional treatment but after disinfection BOD at 20ºC 2 mg/L or less

Outdoor bathing (organized sector) B pH between 6.5 – 8.5; DO 5 mg/L or

more; BOD at 20°C 3 mg/L or less

Drinking water source after conventional C pH between 6 – 9; DO 4 mg/L or more;

treatment and disinfection BOD at 20°C 3 mg/L or less

Propagation of wild life and fisheries D pH between 6.5 – 8.5; DO 4 mg/L or

more; Free ammonia (as N) 1.2 mg/L or less

Irrigation, industrial cooling, E pH between 6.0 – 8.5 mg/L or less; Electrical

controlled waste disposal conductivity at 25ºC μmhos/cm maximum 2250

Indian standard specifications for the tolerance levels for palatability; BIS; IS:10500; 2004

Parameters Desirable limit Undesirable effect outside the desirable Permissible limit

limit

Cl- 250 Beyond this limit taste, corrosion and 1000

palatability are affected

Ca2+ 75 Encrustation in water supply structure 200

and adverse effects on domestic use

Mg2+ 30 Encrustation in water supply structure and 100

adverse effects on domestic use

NO3

- 45 Beyond this methaemoglobinemia takes No relaxation

place/ may be indicative of pollution

SO4

2- 200 Beyond this causes gastro-intestinal irritation 400

TDS 500 Beyond this palatability decreases 2000

and may cause gastro-intestinal irritation

Hardness 300 Encrustation in water supply structure and 600

adverse effects on domestic use*Central Pollution Control Board; # Bureau of Indian Standard

x is the measured value of the variable, i is the component

number, j is the sample number, and m is the total number of

variables. In this study PCA was performed using correlation

matrix among the variables. VARIMAX normalised rotation

was applied to make the results more interpretable as rotated

component matrix helps to clarify the doubts which may be in

the original component matrix (Hani, 2010).

Suitability of river water for irrigation

Sodium adsorption ratio (SAR), percentage sodium (%Na) and

permeability indices were used to assess the suitability of the

Subarnarekha River water for irrigation. The ions used for

calculating these indices were first converted into

miliequivalent (meq)/L from mg/L.

meq/L =

Sodium hazard is typically expressed as SAR. This index

describes the concentration of sodium in relation to the

combined concentrations of calcium and magnesium. SAR

was calculated as given below (USSL, 1954):

weightequivalent

mg/L

CHARACTERISATION OF THE SUBARNAREKHA RIVER WATER

326

SAR =

Table 3: Descriptive statistics for the studied physicochemical parameters

Parameters Min Max Mean SD Median Variance Skewness Kurtosis

pH 7.20 8.20 7.64 0.29 7.70 0.084 0.176 -0.488

Temp 26.80 27.80 27.14 0.29 27.10 0.084 0.878 0.658

DO 4.60 6.50 5.38 0.52 5.30 0.272 0.513 0.430

EC 394.00 792.00 485.08 98.58 456.00 9718.58 2.848 9.170

TSS 100.00 510.00 170.23 110.13 147.00 12129.69 2.864 8.702

TDS 251.00 519.00 313.92 65.82 295.00 4331.91 2.854 9.218

HCO3

- 20.00 99.10 39.24 19.85 34.00 393.98 2.545 7.708

Hard 61.43 101.09 81.39 13.44 78.92 180.73 0.062 -1.318

Ca2+ 16.00 45.00 33.16 7.67 35.56 58.85 -0.991 1.074

Mg2+ 7.10 14.58 11.72 2.41 11.50 5.83 -0.391 -0.720

BOD 0.54 3.76 1.93 1.05 1.87 1.09 0.338 -0.938

COD 9.00 168.00 87.23 52.23 86.00 2727.86 0.036 -1.065

NO3

- 0.40 1.78 0.75 0.43 0.54 0.185 1.313 1.138

PO4

3- 0.80 3.76 1.79 0.84 1.50 0.701 1.134 1.323

F- 0.10 0.30 0.19 0.07 0.20 0.004 0.115 -1.050

Cl- 29.23 39.10 33.94 2.87 35.06 8.25 -0.220 -0.549

SO4

2- 105.00 176.00 130.25 21.75 128.00 473.01 0.876 0.099

Na+ 11.80 74.64 43.91 17.03 40.26 289.96 0.378 0.364

K+ 7.12 95.20 32.28 22.89 29.45 524.14 1.791 4.436

river. BOD slightly exceeded the permissible limit (class ‘A’

range d” 2.0 mg/L) at sites S.2, S.3, S.8, S.9 and S.11 prescribed

by Central Pollution Control Board (CPCB, 2008). Readings of

DO and BOD point to moderate organic pollution load of the

river. This is expected as the river water is used for washing,

fishing, bathing, domestic waste disposal etc. Very low

concentrations of fluoride were obtained in the river water.

The standards of BIS and CPCB are given in Table 2.

Interpretation of hydrochemical regime

To better understand the hydrochemical regime of the studied

region some major ions determined in the water samples were

plotted on the Piper diagram (Piper, 1994). The Piper trilinear

diagram for important sampling sites of the Subarnarekha River

is displayed in Fig. 2. The diagram consists of two lower

triangles and a middle quadrilateral. The left and right triangles

represent the distribution of major cations and major anions

respectively. The quadrilateral summarises the dominant

cations and anions to indicate the final water type. The water

types are designated according to the area in which they occur

on the diagram segments (Tatawat and Chandel, 2007). The

distribution of cations indicates samples from five locations to

be of no dominant type and samples from the remaining sites

to be of Na+-K+ type. In anion distribution triangle samples

from all locations fall under zone representing SO4

2- type with

a small number of samples plotted very close to the no

dominant type water. Two distinct water types can be identified

from the Piper diagram. Samples from S.4, S.5, S.6, S.10 and

S.12 sites is classified into Ca2+-Mg2+-SO4

2--Cl- water type

whereas, samples from S.1, S.2, S.3, S.7. S.8, S.9 and S.11

belong to Na+-K+-Cl--SO4

2- water type.

Basic statistical analyses

Basic statistics for the water quality of the Subarnarekha River

is given in Table 3. Skewness values indicate parameters like

EC, TDS, Ca2+, Mg2+and Cl- to be negatively and moderately

skewed. Parameters like DO, TSS, BOD, NO3

-, PO4

3- and SO4

2-

are found to be positively and moderately skewed. Thus, we

can say that the concentrations of the parameters are not

uniformly distributed which can also be confirmed by their

high variance values.

KUMAR MANOJ et al.,

PI =

%Na is also used to evaluate sodium hazard as water with

higher %Na may result in sodium accumulations which can

cause undesirable changes in the physical properties of the

soil and reduce soil permeability. %Na was obtained from the

equation given below (Wilcox, 1955):

[ ][ ]

2

MgCa

Na

22 ++

+

+

Permeability index (PI) is used to assess probable influence ofwater quality on the physical properties of soil. Long timeeffects of irrigation water quality on physical properties of soildepend mainly on total salts, Na+, HCO

3

- and CO3

-

concentrations of irrigation water and on initial soil properties(Kirda, 1997). The PI was calculated from the equationprovided below (Doneen, 1964):

x100KNaMgCa

KNa22 ++++

++

++++

%Na =

RESULTS AND DISCUSSION

Physicochemical characterisation

Physicochemical characterisation of surface water bodies

widely reflects the pollution load and anthropogenic influence

on the water systems (Suthar et al., 2009). The results obtained

for nineteen physicochemical parameters in this study are

presented in Table 1. The pH range indicates moderately

alkaline water of river Subarnarekha. Temperature did not

vary much among the sampling sites. Electrical conductivity

of the Subarnarekha river water was significantly different along

the basin route. Similar results were obtained for TDS and

TSS. Hardness values indicate relatively soft nature of river

water. Concentration of Cl; SO4

2-, NO3

-, Ca2+ and Mg2+ were

found to be within the permissible limits of Bureau of Indian

Standards (BIS, 2004). DO values were less than the regulatory

standard (e” 6.0 mg/L) at most of the sampling sites. DO levels

are important in the natural self-purification capacity of the

x100NaMgCa

HCONa

22

3

+++

−+

++

⎟⎠

⎞⎜⎝

⎛ +

327

Tab

le 4

: C

orr

ela

tio

n a

naly

sis

of

the s

tud

ied

ph

ysi

co

ch

em

ical

para

mete

rs

pH

Tem

pD

OEC

TSS

TD

SH

CO

3

-H

ard

Ca2

+M

g2+

BO

DC

OD

NO

3

-PO

4

3-

F-

Cl-

SO4

2-

Na

+K

+

pH

10

.48

10

.37

00

.45

50

.60

8*

0.4

45

0.4

45

0.4

64

-0.0

10

0.6

35

*0

.03

70

.14

4-0

.46

10

.11

10

.19

00

.08

10

.19

2-0

.03

20

.62

5*

Tem

p1

0.2

11

0.7

32

**

0.4

64

0.7

16

**

0.6

51

*0

.39

90

.25

50

.34

2-0

.16

50

.04

3-0

.46

80

.29

80

.45

7-0

.07

00

.69

6*

*0

.42

90

.41

1

DO

10

.49

40

.63

5*

0.5

00

0.5

26

0.2

71

0.2

96

0.1

38

-0.0

09

-0.1

97

0.2

81

0.4

68

0.0

44

-0.2

50

0.1

45

-0.1

42

0.8

18

**

EC1

0.8

28

**

0.9

98

**

0.9

36

**

0.3

36

0.1

37

0.3

49

0.0

60

0.1

58

0.1

69

0.3

56

0.4

15

0.2

30

0.7

68

**

0.5

70

*0

.70

6*

*

TSS

1-0

.83

9*

*0

.77

8*

*0

.37

20

.07

00

.44

9-0

.01

80

.03

1-0

.23

70

.20

30

.48

50

.20

30

.54

30

.23

00

.84

9*

*

TD

S1

0.9

30

**

0.3

64

0.1

61

0.3

69

0.0

30

0.1

28

0.1

60

0.3

47

0.4

18

0.2

25

0.7

79

**

0.5

41

*0

.71

6*

*

HC

O3

-1

-0

.19

70

.15

60

.14

60

.06

70

.12

70

.00

80

.45

70

.35

60

.15

80

.54

6*

0.6

15

*0

.66

3*

Hard

10

.68

3*

0.8

26

**

-0.3

27

-0.2

07

-0.3

54

-0.1

14

0.1

55

-0.3

15

0.5

20

-0.3

30

0.3

98

Ca2

+1

0.1

52

-0.5

21

-0.5

25

0.1

44

0.0

13

0.0

90

-0.5

03

0.2

48

-0.1

67

0.0

54

Mg

2+

1-0

.04

00

.12

5-0

.59

1*

-0.1

65

0.1

40

-0.0

38

0.5

11

-0.3

18

0.4

96

BO

D1

0.9

08

**

-0.0

88

-0.0

68

-0.2

18

0.1

73

-0.1

20

0.3

48

0.0

02

CO

D1

-0.3

34

-0.0

54

-0.1

08

0.2

74

0.0

36

0.4

19

0.0

03

NO

3

-1

0.4

59

-0.4

89

0.0

15

-0.4

08

-0.0

23

-0.0

68

PO

4

3-

1-0

.41

90

.16

4-0

.08

80

.12

50

.51

3

F-

1-0

.20

40

.58

7*

0.2

85

0.1

11

Cl-

10

.05

60

.44

70

.05

2

SO4

2-

10

.37

70

.35

7

Na

+1

-0.0

72

K+

1*. C

orr

elat

ion is

sig

nific

ant a

t the

0.0

5 le

vel (

2-tai

led); *

*. C

orr

elat

ion is

sig

nific

ant a

t the

0.0

1 le

vel (

2-tai

led).

Correlation analysis

Correlation coefficients obtained between different

physicochemical parameters is presented in Table 4. Most of

the components show good correlation among themselves

indicating interrelationships and interactions between the

parameters. DO show positive correlation with TSS and K,

whereas negative correlation is obtained between DO and

TDS. TDS and conductivity reveal to be most significantly

correlated. Highly significant relationships are shown by ions

like SO4

2-, Na+ and K+ on the overall conductivity of water.

TSS reveals positive correlation with HCO3

- and K+ and negative

correlation with TDS. Total hardness displays highly significant

relationship with Ca2+ and Mg2+. As expected, BOD and COD

showed significantly high positive correlation.

Water quality index determination

WQI was determined involving fifteen parameters which were

normalised and weighted prior to index calculation. WQI

calculated at different locations along the river basin is shown

in Table 5. WQI value ranged from 61.72 to 78.28 with

minimum and maximum values recorded at S.13 and S.1

respectively. Overall the WQI values can be categorised into

two categories viz. medium and good. Since, River

Subarnarekha is a part of the national river action plan (India)

WQI can be successfully applied for its overall classification

and characterization. WQI can be an effective tool of water

quality characterisation and trend analysis as it is easily

understandable to the general public, policy makers and

regulatory bodies.

Agglomerative Hierarchical cluster analysis

Chemometrics uses multivariate statistical modeling techniques

like AHCA and PCA for exploratory data analysis. This can be

regarded as an analytical branch of environmental chemistry

for better interpretation of data matrix. Cluster analysis was

applied to the standardised water quality dataset for multivariatestatistical modeling using Ward’s method and squaredEuclidean distances as criteria for forming clusters of thevariables. The AHCA dendrogram displaying clustering of thestudied physicochemical parameters is plotted in Fig. 3. Fourclusters are observed from the dendrogram each of themfurther subdivided into sub-clusters. EC, TDS, HCO

3

-, Temp,SO

4

2- and F- showing very high correlations among themselvesform cluster 1. Similarly TSS, K+ and DO being significantlycorrelated form cluster 2. The third group is dominated mainly

Table 5: WQI and classification of the river waters

Sampling sites WQI Values Water Classification

S.1 78.97 Good

S.2 66.21 Medium

S.3 68.28 Medium

S.4 74.48 Good

S.5 75.52 Good

S.6 71.03 Good

S.7 72.07 Good

S.8 65.17 Medium

S.9 66.89 Medium

S.10 68.62 Medium

S.11 65.52 Medium

S.12 74.83 Good

S.13 62.41 Medium

CHARACTERISATION OF THE SUBARNAREKHA RIVER WATER

328

Table 6: Rotated component matrix of the water quality data

Parameters Principal Components

1 2 3 4 5 6 Communalities

pH 0.158 0.531 0.615 0.088 -0.083 0.161 0.725

Temp 0.789 0.117 0.311 -0.067 -0.066 -0.097 0.751

DO 0.125 0.883 -0.034 -0.060 0.189 -0.332 0.946

EC 0.862 0.463 0.156 0.056 0.014 0.065 0.989

TSS 0.502 0.750 0.240 -0.046 -0.195 0.149 0.935

TDS 0.854 0.468 0.170 0.023 0.014 0.057 0.981

HCO3

- 0.807 0.523 -0.041 0.065 0.082 0.019 0.938

Hard 0.222 0.114 0.795 -0.257 0.033 -0.443 0.958

Ca2+ 0.245 -0.051 0.172 -0.484 0.173 -0.724 0.880

Mg2+ 0.112 0.194 0.943 0.026 -0.090 -0.040 0.950

BOD -0.022 0.048 -0.086 0.979 0.036 0.069 0.974

COD 0.138 -0.087 0.118 0.945 -0.002 0.190 0.970

NO3

- -0.167 0.133 -0.645 -0.205 0.582 -0.124 0.858

PO4

3- 0.244 0.405 -0.178 -0.078 0.744 0.109 0.827

F 0.448 0.144 -0.015 -0.170 -0.847 -0.087 0.975

Cl 0.213 -0.069 -0.004 0.071 0.216 0.889 0.893

SO4

2- 0.790 0.033 0.348 -0.093 -0.261 -0.088 0.832

Na+ 0.782 -0.155 -0.378 0.322 -0.044 0.264 0.953

K+ 0.279 0.876 0.319 -0.011 0.156 0.036 0.973

Eigenvalues 4.826 3.407 2.977 2.373 1.883 1.843

% variance 25.399 17.932 15.666 12.489 9.911 9.701

explained by

component

Cumulative 25.399 43.331 58.997 71.487 81.398 91.099

% varianceExtraction method: Principal component analysis

Table 7: Major ions and salinity indices

Sampling sites Na+ (meq/L) K+ (meq/L) Ca2+ (meq/L) Mg2+ (meq/L) HCO3

-(meq/mL) SAR %Na PI

S.1 1.90 0.85 1.68 0.59 0.66 1.78 54.78 64.99

S.2 1.75 0.91 1.78 0.85 0.60 1.52 50.28 57.53

S.3 1.87 0.83 1.63 0.94 0.55 1.65 51.23 58.78

S.4 1.59 0.59 2.25 1.14 0.74 1.22 39.14 49.20

S.5 1.50 1.28 2.01 1.00 0.54 1.22 48.01 49.45

S.6 1.67 0.54 1.73 0.92 0.41 1.45 45.47 53.47

S.7 1.96 0.32 1.28 0.73 0.53 1.96 53.15 67.76

S.8 2.99 0.23 1.13 0.96 0.56 2.93 60.64 73.62

S.9 3.25 0.18 1.80 0.80 0.84 2.85 56.88 71.28

S.10 1.43 0.64 1.85 1.22 0.39 1.46 40.27 45.56

S.11 1.52 1.27 0.80 1.13 0.59 1.24 59.11 66.38

S.12 0.51 1.18 1.80 1.22 0.33 1.41 35.88 30.59

S.13 2.88 2.44 1.83 1.22 1.62 0.76 63.56 69.98

by Hard, Ca2+ and Mg2+ which is expected as these ions have

highly significant impact on the hardness property. In cluster

4 three sub-clusters are observed: NO3

- - PO4

3-, BOD - COD

and Cl- - Na+. Clustering of NO3

- - PO4

3- and Cl- - Na+ reflects

potential interaction among the ions.

Principal component analysis

PCA was applied to the water quality dataset obtained for the

Subarnarekha River water to more accurately evaluate the

clustering behaviour. VARIMAX rotation with Kaiser

Normalisation was used for this intelligent data analysis

technique for better interpretation of the results. The result of

PCA is displayed in Table 6. Six principal components (PC)

were obtained with a cumulative variance of 91%. PC 1

accounts for 25% of the total variance and is dominated by

EC, TDS, HCO3

-, Temp, SO4

2- and Na+ with very high factor

loadings. PC 2 accounts for 18% of the total variance and is

dominated by TSS, K+ and DO with significantly higher factor

loadings. PC 3 is dominated by Hard and Mg2+ accounting for

16% of the total variance. PC4 is loaded by BOD and COD

accounting for 12% of the total variance. PC 5 accounts for

around 10% of the total variance and is highly contributed by

NO3

- and PO4

3-. PC 6 also accounting for around 10% of the

total variance is loaded with Cl- ion. Though Na+ and Cl- ions

were clustered in AHCA, because of relatively good

interrelationship between them, in PCA they belong to different

principal components. The concentrations of Cl ion did not

vary much along the river route but wide variations in Na+

values were noted. This could be the possible reason for their

separation in PCA. Analytical chemical data of the water

samples can be used to assess the functional sources of

dissolved ions in waters. High positive loadings of HCO3

-,

SO4

2- and Na+ on PC 1 indicate these ions to be the principal

ions controlling the TDS and conductivity of the river water.

Similarly Mg2+ ions seem to be the most important ions

influencing hardness. These phenomena suggest various

hydrogeochemical processes affect the salinity behaviour of

river water. High positive loading of Na+ ion reflects ion

KUMAR MANOJ et al.,

329

exchange on the clay materials and the process of dissolution

of Na+ and Cl- suggest a higher rate of weathering (Bhardwaj

et al., 2010). High positive loadings for Na+ and Cl- ions were

obtained in PCA indicating weathering of rock minerals as

principal sources of these ions in river water. Similarly the

presence of HCO3

- ions can principally be attributed to the

dissolution of silicate minerals and its reaction with soil CO2.

However, the concentration of NO3

- and PO4

3- ions in water is

mainly attributed to the agricultural runoff which is also

indicated by their high factor loadings in PC 5. The areas

along the project sites are underdeveloped and lack major

industrial activities. These sites lie downstream of some

important industrial and populated belts of Jharkhand state

like Ranchi, Jamshedpur and Ghatsila.

The dominance of geogenic activities responsible for

dissolved ions is also confirmed by the Gibbs plot. According

to Gibbs (1970) evaporation-crystallisation, rock weathering

and precipitation are the three dominant processes that control

the water chemistry. The weighted ratios of Cl-: (Cl- + HCO3

-)

and Na+: (Na+ + Ca2+) plotted as a function of TDS in Gibbs

plot (Fig. 4 and Fig. 5) reveal the ionic composition of waters

to be mainly controlled by the rock weathering processes.

Suitability of river water for irrigation

The calculated values of SAR, %Na and PI are displayed inTable 7. SAR index is typically used to express sodium hazardin more reliable manner. According to Todd (1980) waterswith SAR values < 10 are excellent for irrigation related works.Presence of excess Na+ in the irrigation water is hazardous asit can replace adsorbed Ca2+ and Mg2+ from the soils makingthem more compact and impervious. This tendency of irrigationwaters to enter into cation exchange reactions with soil can bepredicted using SAR. In this study SAR values vary from 0.76to 2.93 indicating suitability of the river water for irrigation.Salinity hazard for crop productivity is measured in terms ofelectrical conductivity. Higher the conductivity less is the wateravailable for use by the plants. Waters with conductivity from250 – 750 μS/cm are considered well for irrigation whereas,waters with conductivity > 750 μS/cm are considereddoubtful. All major sampling sites from S.1 to S.12 in thisstudy display conductivity to be < 750 μS/cm showing thesuitable nature of the river water for irrigation. The United

States Salinity Laboratory (USSL, 1954) diagram describes

relation between SAR and conductivity of water samples and

places them into irrigation water categories based on the

combination of the two parameters (Anim et al., 2011). Salinity

diagram for the Subarnarekha River is displayed in Fig. 6. It is

observed that all major sites from S.1 to S.12 sites fall into

C2S1 category and only S.13 site come under C3S1 category.

This indicates suitability of the river water for irrigation and

other agricultural activities. In case of TDS, waters with TDS <

1000mg/L are considered best for irrigation related activities.

TDS at much higher concentrations may prove injurious to

the plants by altering osmotic activities and preventing

adequate aeration. All sampling sites from S.1 to S.12 showed

TDS to be < 1000mg/L. Plotting %Na against conductivity,

designated as Wilcox diagram (Fig. 7), can also be used for

water classification varying from excellent to unsuitable

(Wilcox, 1955). From Fig. 7 it becomes clear that the river

water comes under excellent to good category. Waters can be

classified as Class I, Class II and Class III based on PI values.

Class I and II waters are categorised as good for irrigation

having PI values of 75% or more. Class III waters are

categorised unsuitable with 25% maximum permeability

(Nagaraju et al., 2006). PI values in the present study were

obtained from 30.59 – 73.62. Accordingly, the river water

samples can be categorised as belonging to Class II reflecting

the suitability of the Subarnarekha River water for irrigation

purposes.

CONCLUSIONS

WQI classified Subarnarekha River water along the project

sites into good and medium categories. Chemometrics

identified geogenic activities to be principally responsible for

the dissolved ions in the river water. Three important indices

SAR, %Na and PI along with USSL diagram and Wilcox diagram

revealed river water to be suitable for agriculture. These

arguments clearly show overall quality of the river water along

the project sites to be unpolluted and ecologically suitable

and sustainable. Continuous monitoring and assessment will

keep checking the pollution status of the river water during

and after completion of the projects. This study can be used

as reference to observe the status of quality of water during

ongoing projects and post projects scenario.

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