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“SEDIMENT CHARACTERIZATION OF SOME

SECTIONS OF RIVER TAWI AND ITS IMPACT ON

MACRO-BENTHIC INVERTEBRATE FAUNAL

DIVERSITY”

SUBMITTED TO THE UNIVERSITY OF JAMMU FOR

THE AWARD OF DEGREE OF

DOCTOR OF PHILOSOPHY

IN ZOOLOGY

BY

VIPULAB SHARMA

UNDER SUPERVISION OF

Prof. K.K. SHARMA Dr. ARTI SHARMA

Supervisor Co-Supervisor

P.G. DEPARTMENT OF ZOOLOGY

UNIVERSITY OF JAMMU

JAMMU-180006

P.G. DEPARTMENT OF ZOOLOGY

UNIVERSITY OF JAMMU, JAMMU-180006

No. JU/ZOOL/………

DATED ……………..

CERTIFICATE

This is to certify that:

1. The thesis entitled, “Sediment characterization of some sections of river

Tawi and its impact on macro-benthic invertebrate faunal diversity”

embodies the work done by the candidate- Ms. Vipulab Sharma.

2. The candidate has worked under my supervision for the period required

under the rules.

3. The candidate has put the required attendance in the department during the

period required under the statutes.

4. The thesis being submitted is worthy of consideration for the award of

Ph. D degree of the University of Jammu, Jammu.

5. The conduct of the research scholar remained satisfactory during the period

of research.

Prof. Roopma Gandotra Prof. K.K. Sharma

Head of the Department Supervisor

Dr. Arti Sharma

Co- Supervisor

ACKNOWLEDGEMENTS

First and foremost thanks to the Creator and Guardian, “GOD”

for being my strength and guide in writing this thesis with patience.

„Thank you‟ for providing me an opportunity to proceed successfully

to bring this task to realization.

It is exclusively a unique opportunity to express my deepest

sense of gratitude to my esteemed supervisor Prof. K.K. Sharma, Dean

Life Sciences and former Head of Department. It is because of his

able guidance, optimistic approach and research aptitude that

inspired me to reach my goal. I will always remain indebted to him

for his scholastic insight, untiring effort and worthy suggestions,

along with critical approach that polished my work in better form.

It was a real privilege and an honour for me to share his

exceptional scientific knowledge and extraordinary human

qualities that helped me in developing patient and calm attitude

that a researcher should possess.

A word of respects and honour for my Co- supervisor, Dr. Arti

Sharma for her timely suggestions, sincere advice, constant

encouragement, valuable instructions and motherly treatment that

made my journey more smooth and comfortable. Her incessant

guidance really helped a lot in bringing this thesis in its present

form.

I owe my special and sincere thanks to Prof. Prof. Roopma

Gandotra, (Present Head of the Department) and Prof. Kadambri

Gupta, (Former Head of the Department) for their commendable

opinions, benediction and for extending all facilities whenever

required.

I would fail in my duty if I don‟t extend my word of

appreciation to the entire teaching faculty of the department viz;

Prof. D.N. Sahi, Prof. N.K. Tripathi, Prof. J.S. Tara, Prof. Seema

Langer, Dr. Sanjay Bhatia, Dr. Parwinder Kumar and Dr. Sarabjeet

Kour for their support and cooperation.

My thanks are due to the non-teaching staff of the Department

of Zoology, especially, Mr. Pardeep, Mr. Suresh Gupta, Mrs. Sureshta,

Mr. Makhan Lal Koul, Mr. Roop Singh, Mr. Purshotam, Mrs. Kavita,

Mr. Manu Malhotra, Mrs. Sunita, Mr. Puran, Mr. Harbans, Mr.

Lucky and all others for their help.

Deep sense of gratitude to Dr. Shvetambri Jasrotia, Dr.

Surinder Pal Sharma and Dr. Rajinder for their kind words and

help rendered during the compilation.

I am sincerely thankful to Directorate of Agriculture, Jammu

for allowing me to use their infrastructure and equipments.

Highly indebted to the University of Jammu for providing me

financial assistance in the form of scholarship.

I express my appreciation to Dr. Chandrakiran, Dr. Ruchi

Sharma, Meenakshi Saini, Komal Bangotra, Deep Novel Kour, Dr.

Samita Chowdhary, Sadhana Sharma, Neha, Aarti, Bipu, Vikas,

Kunal, Hiteshi, Aman, Neha, Devinder, Meenu, Neha, Harjeet,

Shiwali, Chhavi, Rashmi, Irfan, Surbhi, Ritika for valuable

cooperation and friendly working atmosphere.

Extremely thankful to Sakshi Koul & Rohit Bhardwaj for

encouraging cooperation and invaluable advice.

Grateful to my friends Ravi, Rupinder, Priya, Pawandeep,

Uzma, Ashima, Shweta and Ruksana for their prompt help

whenever needed.

This thesis is fruit of the efforts of my mother Mrs. Suman

Sharma and my father Mr. Vidya Sagar Sharma who dreamt it to be

and made it happen with their tireless efforts. Thanks to all my

elders who blessed me and stood by me in every thick and thin till

now and younger brothers and sisters for their live presence.

It would be selfish not to mention my indebtness towards my

father-in-law- Sh. Sham Lal Sharma and mother-in-law (late)

Smt. Kamlesh Sharma who absolved me from all domestic liabilities

and helped me in carrying my dream forward.

Not possible to pen down my regards to my life partner, Mr.

Vishal Sharma who stood by and hold me strongly during tough

times throughout the whole venture.

VIPULAB SHARMA

LIST OF TABLES

TABLE NO.

1. - IV of river Tawi

from March, 2011 to February, 2013.

2. Monthly variations in sediment colour at station I- IV of river Tawi from

March, 2011 to February, 2013.

3. Monthly variations in moisture content (%) at station I- IV of river Tawi


4. Monthly variations in sediment texture (%) at station I of river Tawi from


5. Monthly variations in sediment texture (%) at station II of river Tawi from


6. Monthly variations in sediment texture (%) at station III of river Tawi from


7. Monthly variations in sediment texture (%) at station IV of river Tawi from


8. Mean particle size distribution and texture at different stations of river Tawi

(March, 2011 to February, 2013).

9. Monthly variations in Total Organic Carbon (TOC) (%) at stations I- IV of

river Tawi from March, 2011 to February, 2013.

10. Monthly variations in Total Organic Matter (TOM) (%) at stations I- IV of


11. Monthly variations in sediment pH at stations I- IV of river Tawi from


12. Monthly variations in sediment electrical conductivity (µS/cm) at stations I-

IV of river Tawi from March, 2011 to February, 2013.

13. Monthly variations in Bicarbonate (mg/g) at stations I- IV of river Tawi from


14. Monthly variations in Chloride (mg/g) at stations I- IV of river Tawi from


15. Monthly variations in Calcium (mg/g) at stations I- IV of river Tawi from


16. Monthly variations in Magnesium (mg/g) at stations I- IV of river Tawi from


17. Monthly variations in Nitrate (mg/g) at stations I- IV of river Tawi from


18. Monthly variations in Phosphate (mg/g) at stations I- IV of river Tawi from


19. Monthly variations in Sulphate (mg/g) at stations I- IV of river Tawi from


20. Monthly variations in sediment load (SL) (m2S

-1mg/l) of river Tawi at

stations I- IV from March, 2011 to February, 2013.

21. Variations in sediment parameters along different stations during the study

period March, 2011 to February, 2013.

22. Pearson correlation matrix showing correlation among the various

parameters

of sediments of river Tawi from March, 2011 to February, 2013.

23. 2-way Analysis of Variance (ANOVA) showing variations in different

parameters of sediments of river Tawi from March, 2011- February, 2013

24. Monthly variations in air temperature (o

c) at stations I- IV of river Tawi


25. Monthly variations in water temperature (o



26. Monthly variations in depth of water (cm) at stations I- IV of river Tawi from


27. Monthly variations in velocity of water (m/s) at stations I- IV of river Tawi


28. Monthly variations in transparency of water (cm) at stations I- IV of river

Tawi from March, 2011 to February, 2013.

29. Monthly variations in pH of water at stations I- IV of river Tawi from


30. Monthly variations in Dissolved Oxygen (DO) (mg/l) of water at stations I- IV

of river Tawi from March, 2011 to February, 2013.

31. Monthly variations in Free carbon dioxide (FCO2) (mg/l) of water at stations

I- IV of river Tawi from March, 2011 to February, 2013.

32. Monthly variations in Carbonate (mg/l) of water at stations I- IV of river


33. Monthly variations in Bicarbonate (mg/l) of water at stations I- IV of river

Tawi from March, 2011 to February Variations in sediment parameters

along different stations during the study period March, 2011 to February,

2013.

34. Monthly variations in Chloride (mg/l) of water at stations I- IV of river Tawi


35. Monthly variations in Calcium (mg/l) of water at stations I- IV of river Tawi


36. Monthly variations in Magnesium (mg/l) of water at stations I- IV of river


37. Monthly variations in Biological Oxygen Demand (mg/l) of water at stations


38. Monthly variations in Nitrate (mg/l) of water at stations I- IV of river Tawi


39. Monthly variations in Phosphate (mg/l) of water at stations I- IV of river


40. Monthly variations in Sulphate (mg/l) of water at stations I- IV of river Tawi


41. Monthly variations in total suspended solids (TSS) (mg/l) in water of river

Tawi at stations I- IV from March, 2011 to February, 2013.

42. Monthly variations in Total dissolved solids (TDS) (mg/l) of water at St I- IV


43. Monthly variations in Discharge value (Q) (m3S

-1) in water of river Tawi at


44. Variations in water parameters along different stations during the study

period March, 2011 to February, 2013.

45. Pearson correlation matrix showing correlation among the various

parameters of water of river Tawi from March, 2011 to February, 2013.

46. 2-way Analysis of Variance (ANOVA) showing variations in different

parameters of water of river Tawi from March, 2011- February, 2013.

47. Monthly variations in macro- benthic invertebrates at station I of river Tawi

during the study period (2011-2012).

48. Monthly variations in macro- benthic invertebrates at station I of river Tawi


49. Monthly variations in macro- benthic invertebrates at station II of river Tawi


50. Monthly variations in macro- benthic invertebrates at station II of river Tawi


51. Monthly variations in macro- benthic invertebrates at station III of river

Tawi during the study period (2011-2012).

52. Monthly variations in macro- benthic invertebrates at station III of river


53. Monthly variations in macro- benthic invertebrates at station IV of river


54. Monthly variations in macro- benthic invertebrates at station IV of river


55. Monthly variations in the total macro-benthic count with the contributions

made by different groups from March, 2011- February, 2013.

56. Different diversity indices to compare the benthic community structure at

various stations of river Tawi from March, 2011- February, 2013

57. Different similarity indices to compare the benthic community structure at

various stations of river Tawi from March, 2011- February, 2013.

58. Correlation coefficient (r) between the macro-benthic invertebrate fauna and

various physico- chemical parameters of sediments of river Tawi from


59. Correlation coefficient (r) between the macro-benthic invertebrate fauna and

various physico- chemical parameters of water of river Tawi from March,

2011 to February, 2013.

60. Correlation coefficient (r) between physico- chemical parameters of

sediments and water of river Tawi from March, 2011 to February, 2013.

LIST OF FIGURES

FIGURE NO.

1. - IV of river Tawi


2. Monthly variations in moisture content (%) at station I- IV of river Tawi


3. Sediment Composition of river Tawi during first and second year of study

period (2011- 2013).

4. Overall sediment composition of river Tawi during study period (2011- 2013)

5. Monthly variations in Total Organic Carbon (TOC) (%) at stations I- IV of


6. Monthly variations in Total Organic Matter (TOM) (%) at stations I- IV of


7. Monthly variations in sediment pH at stations I- IV of river Tawi from

March, 2011 to February, 2013

8. Monthly variations in sediment electrical conductivity (µS/cm) at stations I-

IV of river Tawi from March, 2011 to February, 2013.

9. Monthly variations in Bicarbonate (mg/g) at stations I- IV of river Tawi from


10. Monthly variations in Chloride (mg/g) at stations I- IV of river Tawi from


11. Monthly variations in Calcium (mg/g) at stations I- IV of river Tawi from


12. Monthly variations in Magnesium (mg/g) at stations I- IV of river Tawi from


13. Monthly variations in Nitrate (mg/g) at stations I- IV of river Tawi from


14. Monthly variations in Phosphate (mg/g) at stations I- IV of river Tawi from


15. Monthly variations in Sulphate (mg/g) at stations I- IV of river Tawi from


16. Monthly variations in sediment load (SL) (m2S

-1mg/l) of river Tawi at


17. Monthly variations in air temperature (o



18. Monthly variations in water temperature (o



19. Monthly variations in depth of water (cm) at stations I- IV of river Tawi from


20. Monthly variations in velocity of water (m/s) at stations I- IV of river Tawi


21. Monthly variations in transparency of water (cm) at stations I- IV of river


22. Monthly variations in pH of water at stations I- IV of river Tawi from


23. Monthly variations in Dissolved Oxygen (DO) (mg/l) of water at stations I- IV


24. Monthly variations in Free carbon dioxide (FCO2) (mg/l) of water at stations


25. Monthly variations in Carbonate (mg/l) of water at stations I- IV of river


26. Monthly variations in Bicarbonate (mg/l) of water at stations I- IV of river

Tawi from March, 2011 to February Variations in sediment parameters

along different stations during the study period March, 2011 to February,

2013.

27. Monthly variations in Chloride (mg/l) of water at stations I- IV of river Tawi


28. Monthly variations in Calcium (mg/l) of water at stations I- IV of river Tawi


29. Monthly variations in Magnesium (mg/l) of water at stations I- IV of river


30. Monthly variations in Biological Oxygen Demand (mg/l) of water at stations


31. Monthly variations in Nitrate (mg/l) of water at stations I- IV of river Tawi


32. Monthly variations in Phosphate (mg/l) of water at stations I- IV of river


33. Monthly variations in Sulphate (mg/l) of water at stations I- IV of river Tawi


34. Monthly variations in total suspended solids (TSS) (mg/l) in water of river

Tawi at stations I- IV from March, 2011 to February, 2013.

35. Monthly variations in Total dissolved solids (TDS) (mg/l) of water at St I- IV


36. Monthly variations in Discharge value (Q) (m3S

-1) in water of river Tawi at


37. Percent contribution of different benthic groups to the total macrobenthic

population during the study period (March, 2011- February, 2013) at river

Tawi.


population during the study of two years (2011- 2012) at river Tawi.


population at Station I.


population at Station II.


population at Station III.


population at Station IV.

43. Monthly variations in total macrobenthic count with contributions made by

different groups from March, 2011- February, 2013.

LIST OF PLATES

PLATE NO.

PLATE 1: STUDY AREA

Figure 1: Satellite image of river Tawi

Figure 2: Map of river Tawi

PLATE 2: ANTHROPOGENIC STRESS AT STUDY STATIONS ON RIVER TAWI

Figure 1: Sand mining

Figure 2&3: Stone quarrying

Figure 4: Fishing

Figure 5&6: Cattle and human bathing

Figure 7&8: Garbage and sewage disposal

Figure 9&10: Disposal of non- biodegradable and religious wastes.

PLATE 3: DIFFERENT STUDY STATIONS AT RIVER TAWI

Figure1: Station I (Nagrota)

Figure 2: Station II (Circular Road)

PLATE 4: DIFFERENT STUDY STATIONS AT RIVER TAWI

Figure 1: Station III (Gujjar Nagar)

Figure 2: Station IV (Satwari)

PLATE 5: MACRO-BENTHIC INVERTEBRATE FAUNAL DIVERSITY

Figure1: Tubifex tubifex

Figure2: Branchiura sp.

Figure3: Nais sp.

Figure 4: Dero digitata

Figure 5: Earthworm

Figure 6: Hirudinaria sp.


Figure 1: Caenis sp.

Figure 2: Baetis sp.

Figure 3: Ephemerella sp.

Figure 4: Odonata larva.

Figure 5: Hydropsyche sp.

Figure 6: Ceratopsyche sp.


Figure 1: Micronecta sp.

Figure 2: Canthydrus sp.

Figure 3: Regimbertia sp.

Figure 4: Hydroglyphus sp.

Figure 5: Berosus sp.

Figure 6: Pentaneura sp.


Figure 1: Limnophilla sp.

Figure 2: Simulium sp.

Figure 3: Erastalis larva.

Figure 4: Culicoides sp.

Figure 5: Focripomyia sp.

Figure 6: Tabanus larva.


Figure 1: Lymnea sp.

Figure 2: Physa sp.

Figure 3: Gyraulus sp.

MATERIAL AND METHODS

Page 40

Study area

Jammu and Kashmir, paradise on earth is located in the northern part of the Indian

sub continent in the vicinity of the Karakoram and Western Himalayan mountain ranges.

Formerly, one of the largest princely states of India has an area of 39, 146 sq. miles and a

population of 12,548296 (Census, 2011). The state is bordered by China in the North, Tibet

in the East, Pakistan in the West and by Himachal and Punjab in the South. Vast majority of

the state is mountainous and its physiography is divided into seven zones that are closely

associated with structural components of Western Himalayas containing plains, foothills and

valleys. State is divided into three regions: Jammu, Kashmir, and Ladakh. Jammu is the

winter capital and second largest city in the state. Also, remarked as the „city of temples‟,

Jammu is situated along the river Tawi in the foot hills of Himalayas.

Physiography of Jammu

Though the climatic conditions of Jammu vary from dry, sub-humid to arid, yet it

experiences four well defined seasons:

i. Winter (November- February)


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ii. Spring (March- April)

iii. Summer (May- August)

iv. Autumn (September- October)

Average rainfall ranges between 100- 120 cm (with 80% contributed by monsoon downpour

and the remaining 20% by localized rains). Humidity is the highest during monsoon. Day length

varies from 11 hours in December to 14 hours in mid- June.

Geography of Jammu

Jammu, the southernmost part of J&K is located in the foothills of lower Shivaliks. It

is situated in between74 19ꞌ E to 75

20ꞌ E longitude and 32

27ꞌ to 33

50ꞌ N latitude and has

an elevation of 325 meters.

Our state is blessed with several lentic and lotic systems prime of which are Jhelum,

Ravi, Indus, Chenab and Tawi. Our study is primarily confined to river Tawi, which flows

through Jammu and support life line of the inhabitants of the city.

Origin of river Tawi

The river originates from the Central Himalayan axis (Dhaulandhar range) below

Seoj Dhar peak, from the lapse of Kali Kundi glacier and the adjoining area of Southwest of

Bhaderwah in Doda district, has latitudinal position 32◦ 35ꞌ- 33

◦ 5ꞌ N and longitudinal position

74◦ 35ꞌ- 75

◦ 45ꞌ E, is the left bank tributary of river Chenab. It flows between Jug dhar and

Trisul dhar in a westerly direction till Udhampur where it takes a southerly bend across the

Shivalik range and again resumes a westerly course passing along the Jammu city and it joins

Chenab River in Sialkot in Pakistan (Plate 1). The total catchment area of the river Tawi is

more than two thousand and one hundred square kilometers. Total stretch of the river adds up

to 141kilometers and traverses through Doda, Udhampur and Jammu (Plate 1).

Historical Status

Tawi river has great religious and historical importance attached to it. The river is

also known as Surya putri, i.e Daughter of Sun God. It is a belief of the inhabitants of the city


Page 42

that the river was brought to Jammu by “Raja Pehar Devta” to cure his father. The water of

the river was crystal clear and was the sole source of drinking water to the inhabitants.

Present Status

River fulfills all the domestic, commercial and other needs of the folk. As much 26

mgd (million gallons per day) water is pumped from the Tawi to cater the needs of folk of

Jammu region at three water treatment plants: Sitlee, Dhountli and Boria. River water is also

used for irrigation, recreation, sewage disposal, fishing etc. The river flowing through the

steep hills on either side (excepting the lower reach of 35 km) is fed by number of streams of

1st to 5

th order through middle and sub Himalayas till it emerges into plains in Jammu city.

The flow of the river is perpetual and considerable, though of much varying volume. The

river is also liable to floods which occur at the time of periodical rains. It is these floods that

deposit the stones, mud, silt etc. in the form of sediments on the shores and riverbed carried

by the river across for several kilometers. The river is an ecological heritage of the area with

its characteristic flora and fauna.

Passing through the urban settlements, river Tawi is inevitably used as depository for

untreated domestic sewage, garbage, animal excreta, dead animals, agricultural runoff

(fertilizers and pesticides) and detergents. Frequent dredging of the bottom and shores for

extraction of sand and stones has altered shoreline morphology and disturbed the ecological

imbalance in biotic community (Plate 2). The river was famous for its crystal clear water, but

with the passage of time, the quality of water has so much degraded that it is even not

suitable for bathing.

So, the present research problem has been undertaken to assess the impact of

sediment characteristics on the benthic fauna of river Tawi. For carrying out the study, four

sampling stations were selected along the longitudinal profile of River Tawi (Plate 3 and 4)

depending upon the anthropogenic stress viz;


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Station I (St. I)

The area was located near Sainik School, Nagrota, where water was comparatively

clean and pollution free. This station witnessed least anthropogenic pressure (Plate 3, Figure

1).

Station II (St. II)

It was situated at Circular Road, near Peer kho temple and was about 6 kilometers

from station I. this station received organic load in the form of religious wastes, crematorium,

garbage and sewage etc. (Plate 3, Figure 2).

Station III (St. III)

This station was situated under Tawi bridge at Gujjar Nagar, at a distance of about 4

kilometers from station II and 10 kilometers from station I. The station was a victim of

pollution arising out of crematorium, sewage, garbage, religious wash offs, bathing, washing,

sand mining and vehicle washing etc. (Plate 4, Figure 1).

Station IV (St. IV)

Located under Tawi Bridge, near Satwari at a distance of about 6 kilometers from

station III, was the revival zone of the river. The site was a clean water zone without any

pollution related activity (Plate 4, Figure 2).

Methodology

Bottom sediments and water samples were procured from the four stations of river

Tawi to analyze their physico – chemical characteristics with simultaneous sampling of biotic

components every month for a period of two consecutive years (March, 2011 to February,

2013.).


Page 44

A. Abiotic parameters

a) Sediments

b) Water

a) Sediments: Sampling and Analysis

Sampling

Sediments were collected from shore of the river using a sampler once a month

from March, 2011 to February, 2012, at each sampling station and stored in well labeled

zip lock polyethylene bags and kept in an ice-chest box before transferring to the

laboratory. Samples were analyzed for moisture content prior to drying and then were air

dried at room temperature in the laboratory. Some of the parameters were assessed using

air dried sediments but for remaining parameters, samples were dried in the oven at 60 ◦ C

and finally stored in air tight containers for further analysis.

Analysis

Bottom sediments were analyzed for the physico-chemical parameters according

to the standard methods:

(a) Physical Parameters:

1. Temperature: It was recorded by immersing the mercury bulb thermometer in the

sediment samples immediately after its dredging in the field.

2. Colour: The colour of the sediment samples was done according to the Munshell Soil

Colour Charts (1954).

3. Texture: Particle size was estimated using Hydrometeric method (Bouyoucos, 1961). The

results so obtained were plotted in the USDA Textural Triangle which was confirmed using

Gerakis and Baer (1999) Textural triangle software.


Page 45

4. Moisture content: Moisture content of the sediment samples was determined by oven

drying method (Adoni, 1985; Srivastava and Banerjee, 2004).

5. Suspended sediment load value (SL): The calculation of suspended Sediment Load value

was based on the discharge value of the river, Total suspended solids (TSS) value and area of

sampling basin (A) using the following formula:

SL= Q x TSS / A.

where, SL=Sediment Load value.

Q= Discharge value.

TSS= Total suspended solids.

A= Area of sampling basin.

The data analyzed would be used to detect changes in the concentration of suspended

matter and its relationship with hydrological parameters and other variables (Kamarudin et

al., 2009).

(b) Chemical parameters:

1. pH: 20 gm of dried sediment samples were taken in a beaker and a suspension was made

in 50 ml distilled water. After shaking it well, pH was recorded using a standard digital

pH meter (Hanna).

2. Electrical Conductivity (EC): Sediment samples for measuring conductivity were

processed as in case of pH and the aliquot was kept overnight. Then the (EC) was

measured by using conductivity meter (Systronic make, type 304).

For the remaining parameters sediment suspension was made of fresh dilution (1: 10

w/v) with distilled water. Suspension was filtered over Whatman filter paper number 44 to

analyze the parameters as follows:

3. Carbonate and Bicarbonate: Carbonates and Bicarbonates in the sediment samples

were estimated following APHA (1985) and Adoni (1985).


Page 46

4. Chloride: Chloride content in the sediments was analyzed following Argentotitrimetric

method (Kanwar and Chopra, 1967).

5. Calcium and Magnesium: Calcium and Magnesium content were estimated following

EDTA- Titrimetric method (Kanwar and Chopra, 1967).

6. Total Organic Matter (TOM): Organic Matter was determined by ignition method

(Wilde et al., 1972).

7. Total Organic Carbon (TOC): Organic Carbon was calculated by multiplying the value

of organic matter with a factor of 0.48 (Westlake, 1981).

8. Nitrate: Nitrate content of the sediment samples was analyzed following Adoni (1985).

9. Phosphate: Phosphate was extracted using sulphuric acid and determined

spectrophotometrically (Adoni, 1985).

10. Sulphate: It was estimated in the samples by extracting the sediments with calcium

choride (Williams and Steinberg, 1959) and measured using turbiditimetric method.

b) Water: Sampling and Analysis

Sampling

Water samples were collected monthly in dark glass containers from each station.

Estimation of some physico- chemical parameters viz; Temperature, Depth, Transparency,

pH, DO, FCO2, CO32-

, HCO3-, Cl

-, Ca

2+ and Mg

2+ was done on the spot while analysis of

NO3-, PO4

2-, SO4

2- and BOD of water samples were carried out in the laboratory.

Analysis

(a) Physical Parameters:

1. Atmospheric Temperature: Air temperature was recorded with the help of a mercury

bulb thermometer while avoiding its direct exposure to the sunlight (Welch, 1952).


Page 47

2. Water Temperature: Water temperature was recorded with the help of a mercury

centigrade thermometer graduated up to 110 ◦

C. This was done by dipping the

thermometer vertically into the water (Welch, 1952).

3. Transparency: The transparency of the water was determined by Secchi disc of 20 cm in

diameter (painted black and white on the upper surface) and computed by the formula:

T = X + Y / 2 (Welch, 1952)

where, T = transparency in cm.

X = depth at which disc becomes invisible.

Y = depth at which disc reappeared while pulling the rope upward.

4. Depth: A graduated meter rod was used for recording the depth (Adoni, 1985)

5. Speed: Speed of the water was calculated using Flow meter using the formula:

V = d / 1.2t

where, V = velocity (m /s)

d = distance travelled by flow meter

t = total time taken by flow meter to cover the distance

(b) Chemical Parameters:

1. pH: pH of water sample was determined with the help of a portable field pH meter

(Hanna) by lowering its bulb directly in to the water.

2. Free carbon Dioxide (FCO2): Titrimetric method was adopted for the estimation of the

free carbon dioxide (APHA, 1985)

3. Dissolved Oxygen (DO): It was determined by Sodium Azide Modification of Winkler‟s

Method (APHA, 1985).

4. Carbonates and Bicarbonates: These were estimated following APHA (1985).

5. Chlorides: Argentometric method was employed to determine chloride content of water

samples (APHA, 1985).


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6. Calcium and Magnesium: The estimation of calcium and magnesium was done by the

EDTA – Titrimetric method as suggested in APHA (1985).

7. Nitrate: Nitrate was determined by Phenol Disulphonic Acid method using

spectrophotometer (APHA, 1985; Adoni, 1985 and Chopra and Kanwar, 1991).

8. Phosphate: It was determined by Stannous Chloride method using spectrophotometer

(APHA., 1985; Adoni, 1985)

9. Sulphate: Turbiditimetric method using spectrophotometer was employed to estimate

sulphate content of the water samples (APHA, 1985; Adoni, 1985).

10. Total Dissolved Solids (TDS): Amount of TDS in the water was determined by

Gravimetric method (Prasad and Patil, 2008 and a Protocol of Hydrology Project (HP)

Training module file, 1999).

11. Total Suspended Solids (TSS): Amount of TSS in the water was determined by

Gravimetric method (Adoni, 1985; Prasad and Patil, 2008 and a Protocol of Hydrology

Project (HP) Training module file, 1999).

12. Discharge Value of river (Q): Discharge value is the product of velocity and area. It

was computed using the formula:

Q = v x A.

where, Q = Discharge value of the river.

v = Velocity of the river in m/s.

A = Area of river which is the product of depth and width.

B. Biotic Parameters

Collection of Macrobenthic Invertebrates

For the study of macrobenthic invertebrate fauna, bottom sediments were collected

using a sampler and collections were washed through standard sieve number 40 (256meshes/


Page 49

cm2) to collect the benthic organisms. The washed sediment with the benthic macro-

invertebrates were poured into a white enamel tray and sorted in the laboratory. For effective

sorting, moderate volume of water was added into the container to improve visibility.

Forceps were used to pick large organisms while smaller ones were sorted out using soft

brush. The macro-invertebrates were poured into a wide mouth labeled plastic container and

preserved with 5% formalin solution to which Rose Bengal (dye) had been added. The Rose

Bengal dye (strength was 0.1%) selectivity colored all the living organisms in the sample

(Zabbey, 2002; Idowu and Ugwumba, 2005). The preserved benthos were later identified to

their lowest taxonomic group under light and stereo dissecting microscope and counted.

Qualitative Analysis of Macrobenthic Invertebrates:

The identification was done using the keys by Ward and Whipple (1959), Pennak

(1978), Tonapi (1980), Adoni (1985),) and Hart (1994).

Quantitative Analysis of Macrobenthic Invertebrates:

Total number of macrobenthic invertebrates / m2 was computed using the following

formula:

N = O/ A. S X 10, 000

where, N = number of macroscopic organisms/ m2.

O = number of organisms counted.

A = area of metallic sampler in square meter.

S = number of samples taken at each station.

C. Statistical Analysis of Data

1. Standard deviation (SD)

Standard deviation was calculated by using the formula:

SD = √ (∑ d2/ n)

where, d = deviation from the mean (x- x-).

n = total number of observations.

2. Shanon – Weaver diversity index (Hꞌ)


Page 50

Hꞌ = -∑ si = 1 (pi) ln. pi or Hꞌ = -∑

si = 1 (ri / N) (log2 ri / N)

where, Hꞌ = information content of the sample (bits / individual).

S = number of species.

pi = proportion of total species belonging to ith

class.

ri = Total number of individuals in i species.

N = total number of individuals in sample.

As the number and distribution of species (biotic diversity) within the community

increases, so does the value of Hꞌ.

3. Simpson’s Index (D)

D = ∑ si = 1 (pi)

2

where, pi = proportion of total number of each species.

S = number of species in the community.

This index places relatively little weight on rare species and more weight on common

species (Krebs, 1994). Its values range from 0, indicating a low level of diversity, to a

maximum of 1.

4. Marglef’s Index

dꞌ = S – 1/ Ln (N).

where, dꞌ = species diversity.

S = total number of individuals of all the species.

N = total number of individuals in a sample.

5. Equitability Index

J = Hꞌ/ LnS

where, Hꞌ = species diversity.

S= total number of species.

Ln = Natural log.

6. Sorenson’s index

Ss = 2a / 2a+ b+ c

where, Ss = Sorenson‟s quotient

a = number of species in sample A and B

b = number of species in sample B not in sample A


Page 51

c = number of species in sample A not in sample B

7. Morisita- Horn index

CMH = 2∑ Xij X Xik / [(∑ Xij2/Nj

2) + [(∑ Xik

2/Nj

2)] NjNk

where, CMH = Morisita- Horn index

Xij, Xik = number of individuals of species i in sample j and k.

Nj = ∑ Xij = total number of individuals in sample j

Nk = ∑ Xik = total number of individuals in sample k

8. Pearson’s Correlation

Pearson‟s Correlation analysis was done for the data by using Microsoft Excel (MS

Office, 2007).

9. Analysis of Variance (ANOVA)

2-way ANOVA was calculated with the help of SPSS Software (Ver. 16.0).

RESULTS AND DISCUSSION

Page 52

4.1 Sediment Analysis

A review of literature revealed that sediments play an outstanding role in limnological

studies as they can both reflect and affect what is occurring in the overlying waters.

Sediments are highly dynamic and active in character primarily due to various

biogeochemical reactions and transformations occurring within the water body. While

investigating the sediments in riverine system, Stronkhorst et al. (2004) suggested that

sediments always provide a natural buffer system and an important habitat for aquatic

organisms. Because of their variable physical and chemical properties, sediments not only act

as a source and sink of nutrients in an aquatic ecosystem, but also provide a record of

river’s pollution history (Matisoff et al., 1985; Mucha et al., 2003 and Tsai et al., 2003).

Unfortunately, overpopulation, local soil erosion and extensive urbanization adversely affects

physico- chemical and biological properties of the sediments (Davies and Tawari, 2010),

eventually deteriorating the productivity of the overlying waters (Bragadeeswaran et al.,

2007 and Rauf et al., 2009).

River Tawi has been analyzed by many workers on the different aspects but there is

no information on its sediment quality despite of various human activities going on and

within the river. In this context it becomes extremely crucial and important to get an insight


Page 53

in the various physico- chemical properties of bottom sediments viz; temperature, colour,

moisture content, pH, electrical conductivity (EC), total organic carbon (TOC), total organic

matter (TOM), particle size, sediment texture, calcium, magnesium, bicarbonates, chloride,

nitrate, phosphate and sulphate. The study of sediments will act as a useful tool for future

researchers for planning the actual assessment of environmental pollution of this aquatic

system.

4.1.1 Seasonal variations in physical parameters of sediments of river Tawi

Sediment Temperature

Most of the biogeochemical processes occurring in sediments affect both water

column and pore water chemistry are temperature dependent (Nimick et al., 2003). The

exchange of heat between sediments and overlying water column is significant and has a

moderating effect on water temperature variations. Near the bottom as the water temperature

increases or decreases, the effect of surface heating or cooling is passed downward into the

sediments. Also, the storage and release of heat from sediments produce temperature changes

which can impact both chemical and biological processes (Smith, 2002).

The seasonal variations in sediment temperature during the study period fluctuated

from 14.2 c + 0.21 (January) to 33 c + 2 (June) during the first year (2011-2012), While in

the consecutive year (2012- 2013), it fluctuated from 15.8 c + 0.22 (January) to 35.6 c +

0.41 (June) (Table 1 and Figure 1), thereby indicating maximum sediment temperature

during the summer months (June) after which it followed declining trend till it attained the

minimum values during the winter months (January). This trend in the fluctuation of

sediment temperature was observed to repeat during both the years of study. The temperature

variations in the sediments closely followed the water temperature overlying it as both these

parameters were correlated with each other (r= 0.99) (Table 60). Sarvankumar et al. (2008)

also advocated that water temperature and sediment temperatures are correlated to each

other. William and Lewis (1976) reported that warming of sediments proceeds almost as

rapidly as the warming of overlying waters.


Page 54

A general survey of literature also revealed that the temperature of sediment may be

affected due to:

Morphometery ( William and Lewis, 1976)

Air and water temperature (Sarvankumar et al., 2008)

Depth of water body (Smith, 2008)

During the present study the statistical analysis revealed that the F- value of 2-way ANOVA

(Table 23) showed significant variations both among stations and months.

Sediment Colour

Sediment colour signifies the composition and nature of sediments, type of minerals

present in it and is affected by the environment of the water body. Sediment colour is also

suggestive to be indicative of productive capabilities of lotic as well as lentic ecosystems.

Colour of bottom sediments of river Tawi at different stations during the study

period (extending from March, 2011 to February, 2013) varied from Gray (5Y 5/1), Dark

Brown (5YR 5/2), Gray Brown (10YR5/2), Brown (10YR 5/3) and Yellowish Brown

(10YR5/4) at all stations (Table 2). Overall, hue of the riverine sediments varied from 2.5Y

to 10YR (Munshell soil colour charts, 1954).

A look at the table 2, further revealed varied sediment colour at different stations of

the river. The grayish colour of the sediments recorded at stations I and IV may be due to

predominance of sand and low organic matter. The observations made by Sinha et al. (1992)

too indicated that gray colour reflects low organic matter in the bottom sediments. On the

other hand, the variation in the sediment colour (i.e. various shades of brown) observed at

stations II and III may be attributable to the higher content of organic matter (Chandrakiran,

2011). Saraladevi et al. (1992) while studying the organic carbon in the Periyar river,

associated the grayish- black colour of the sediments to the sandy- clayey bottom and

brownish colour to the clayey- silty bottom of the river. Remani et al. (1981) attributed the

brown colour of sediments to the oxidized conditions while the gray colour of the sediments

to the terrigenous matter. However, Peverill et al. (1999) referred that the existence of

organic matter and iron oxide impart brown colour to the sediments whereas dominance of

silt and fine sand impart olive green colour to the sediments according to Osleger et al.

(2008).


Page 55

Moisture Content

Sediment moisture is an important factor affecting the physico- chemistry of the

sediments. Sediment hydration exerts influence on the mechanical and chemical composition

of riverine sediments.

Perusal of the table 3 and figure 2 indicated that the percentage moisture content of

bottom sediments ranged from a minimum of 1.9 % + 0.58 (June) to 12.92 % + 2.05

(August) during the first year of present study (2011-2012) while during the second year of

study (2012- 2013), the recorded percentage of moisture content varied from 2.1 % + 0.48

(June) to 12.82 % + 1.97 (August) thereby indicating average lowest value in summer season

followed by the highest value in monsoon season.

Low moisture content in the summer may be attributable to the high water

temperature as the increased air temperature and water temperature during summer months

cause high rate of evaporation of water from the interstitial spaces of sediment particles.

High moisture recorded during monsoon months could be ascribed to the frequent rains due

to which water percolated in the sediments.

Perusal of table 21 revealed that on an average, the variation in the moisture content

of sediments varied as: StationIII> StationII> Station IV > Sation I. These variations may be

attributable to the:

Particle size as, the large particles like that of sand hold less water in the

interstitial spaces but the finer particles hold more water (Saraladevi et al.,

1992; Hoque et al., 2008 and Khalik et al., 2013).

Sediments rich in organic matter and clay can retain more moisture than sandy

sediments (Lipsius, 2002 and Kumar et al., 2012)

Bulk density (Gupta and Larson, 1979).

Moisture content showed positive correlation with silt (r = 0.53), clay (r = 0.49)

while negative correlations with sand (r = -0.45) (Table 22).

Sediment Texture

Mineral soils are often composed of inorganic particles of varying sizes called soil

separates and the relative proportion of various separate or size groups of individual soil


Page 56

grains in a mass of soil is referred to as ‘sediment texture’ (Ezekiel et al., 2011). Texture

gives general physical appearance or character of sediment which strongly influence

properties like porosity, permeability, bulk density and organic matter (Last, 2001).

The particle size distribution (the percentages of sand, silt and clay) in the bottom

sediments of river Tawi during the study period (March, 2011 to February, 2013) has been

tabulated (Table 4- 7 and Figure 3&4). A look at the table 4- 7 and figure 3- 4 further

revealed the sediment texture at different stations and it was observed that at station I

texture varied from pure sand to sandy loam while station II was characterized by four

different textural classes viz., sand, loamy sand, sandy loam and loam. On the other hand,

sediments at station III of river Tawi were dominated by loamy sand and sandy loam type of

textural class while station IV was observed to be predominated by sand followed by loamy

sand and sandy loam type of sediments.

During the present study period (March, 2011 to February, 2013), it was observed

that among all the sediment components, sand dominated the composition of sediments

followed by silt and clay at all the stations (Figure 4). Dominance of sand in the sediment

quality of bottom sediments in the river Tawi may be due to the topographical features of the

area (Sesamal et al.,1986), sediments brought from headwaters by weathering of rocks

(Lewis et al., 2001 and Gurumayum and Goswami, 2011) and frequent dredging of

sediments (Davies and Abowei, 2009). Moreover, the anthropogenic activities like

constructional works and disposal of concrete materials (Plate 4) also tend to increase the

sand component of sediments. Dominance of sand in bottom soil of river Tawi in the present

findings corroborate with that of Pathak et al. (2000 & 2001) in tributaries of river

Brahmaputra and Mahanadi and Singh and Mahaver (1997 and 1998) and Singh et al. (1999)

from the rivers Ganga and Ghaghra while low value of clay and silt was also observed by the

op. cit. authors in the lower stretches of their respective water bodies.

Comparatively, while analyzing the sediments at all the stations (Table 8), it was

found that the particle size of the sediments of river Tawi ranged from sand, silt to clay. At

stations I and IV sand was found to be dominating component of the sediments. At both of

these stations, sediments were sandy in texture which may be due to various factors as

already discussed. Also, both of these stations had the least anthropogenic stress in the form


Page 57

of garbage and sewage. The predominance of sand in the sediments is in agreement with the

observations by Ekwere et al. (1992) and Khalik et al. (2013). At stations II and III

sediments were of sandy loam in nature with higher percentage of silt and clay as compared

to other two stations. Although, sand dominated, yet silt and clay also contributed

significantly to the composition of sediments. High concentration of silt and clay was due to

the deposition, decomposition and degradation of organic matter at these sites received

through sewage and garbage of the city. Similar observations have already been reported by

George et al. (2010) who observed that the station with the highest percentage of clay also

had the highest percentage of silt. Increased silt and clay values at downstream were reported

by Singh et al. (1999) in Gharga river. Kumar and Khan (2009) advocated that these

variations in the sediment content at different stations are directly related to the variations of

sediment texture brought about by variations in a circulation pattern during different

environmental conditions of location at study.

Seasonal variations in the bottom sediments of river Tawi revealed higher percentage

of sand during monsoon season and lowest percentage was recorded during the summer

season. The higher percentile value of sand may be attributed to the winnow activity of the

monsoonal flood, which is in agreement with the findings of Sesamal et al. (1986) from

Diamond harbour and Sagar Island and Rajasegar et al. (2002) from Vellar estuary. This may

also be due to the influence of the increased current velocity of the river during the monsoon

season, which could cause coarser sediment to be brought to the downstream area (Jamil et

al., 2004). According to Kamaruzzaman (1994) and Kamaruzzaman et al. (2002) areas which

have strong currents would comprise mainly coarser sediments. Jamil et al. (2004) observed

that during the monsoon season, the sediment was slightly coarser than it was during the non-

monsoon season. The coarser sediment occurring during the wet season may be due to the

heavy rainfall and higher energy of water movement from the upstream, where the finer

sediments were transported out (Ong et al., 2012).

Contrarily, the average silt and clay content exhibited their maxima during summer

followed by a decline in monsoon season. The higher value during summer may be due to the

fluctuations and settling of finer fractions (Rajasegar et al., 2002). Also, the biodegradation


Page 58

of organic matter results in the high percentage of silt apart from the death and decaying of

vegetation (Prasanthan, 1999).

Furthermore, a decline in sand content and increase in silt and clay during winters

may be attributed to the low water levels and algal blooms on the shore line of the river

which adds organic matter, consequently, decreasing the sand content and increasing the silt

and clay composition of the sediments. Moreover, Nair and Ramachandran (2002) too

advocated that the physical processes of transportation, flow of river and deposition in the

water body primarily influence the grain size variations of sediments. These spatial and

temporal variations are directly related to the variation of sediment texture brought by

variations in a circulation pattern during different environmental conditions of location at

study (Kumar and Khan, 2009).

Correlation matrix (Table 22) indicated positive correlation between silt and clay

(r = 0.83), silt and TOC (r = 0.48), while a negative correlation was recorded between sand

and silt (r = -0.94), sand and clay (r = -0.85) and sand and TOC (r = -0.50).

Analysis of 2- way ANOVA (Table 23) indicated sand and silt showed significant

variations between both stations and months while clay showed significant variations among

the stations only.

4.1.2 Seasonal variations in chemical parameters of sediments of river Tawi

Total Organic Carbon (TOC)

Total Organic Carbon is an organic pollutant that provides information on all organic

substances in sediments and is of considerable interest in an aquatic ecosystem as a potential

source of food for the benthic fauna. The decomposition of organic matter releases organic

carbon into the water which finally accumulates in the sediments.

The percentage of TOC in sediments of river Tawi varied from 0.12 % + 0.08

(August) to 0.82 % + 0.55 (June) and from 0.12 % + 0.08 (August) to 0.92 % + 0.49 (June)

during both the years of study (2011-2013) (Table 9 and Figure 5).


Page 59

The highest percentage of TOC was recorded during the summer season with lowest

percentage during the monsoon season. Significant increase in percentage of TOC was also

recorded during the post monsoon followed by a decline during winter months. An increase

in TOC concentration in the summer months may be influenced by:

An increase in temperature which in-turn increases the rate of decomposition

thereby releasing more of TOC in the sediments. Such an increase in TOC

with increase in temperature was also noted for some British rivers by Tipping

et al. (1997).

Active biological life in rivers that are enriched with many nutritious substances

(Siepak, 1999).

Niemirycz et al. (2006) during their study of Total Organic Carbon (TOC) in the

sediments of Odra river, found an increase in the TOC content in the spring/ summer period

in comparison to the autumn/ winter months. Similar changes in the TOC concentration in

waters were observed for the Verdu river by Parks and Baker (1997).

Decrease in TOC concentration levels during monsoon months may be linked with:

Removal of top layer of sediments where the decomposition of organic matter

takes place.

Decrement of pollutants in river sediments after enormous flooded conditions

during the season. (Niemirycz et al., 2006)

Incessant stirring up of the sediments releasing the organic carbon from the

sediment to the water column (Bragadeeswaran et al., 2007).

It is well documented that the process of decomposition of organic matter is

temperature dependent phenomenon which releases organic carbon that finally accumulates

in the sediments. The low temperature and slow decomposition pace during the winter

months could be accounted for the low percentage of TOC during the winters (Davies and

Tawari, 2010).

Furthermore, a closer look at the table 21 revealed that the highest percentage of TOC

observed at station II and station III, followed by station I and station IV during both the


Page 60

years of study. Such variations in the distribution of organic carbon at different stations of

river Tawi may be associated with:

the distribution of organic matter (Elewa et al., 1997)

particle composition of the sediments (Abdo, 2004)

and,

The allochthonous organic load entering into the river Tawi along with the

sewage effluents as already been advocated by Abdel- Satar (2005).

The present observations also gets the support from the observations made by Abdel

– Satar (2005) who also observed high percentage of OC and OM in sediments rich in silt

and clay as compared to sandy sediments.

Correlation matrix showed (Table 22) that TOC depicted positive correlation with silt

(r = 0.48) and negative correlation with sand (r = -0.50). Such findings are in conformity with

findings of Abdel- Satar (2005) who emphasized that sites containing high percentage of silt

and clay also contained a higher percentage of OC and OM, while the sites containing low

percentage of silt and clay also recorded a low percentage of OC and OM. Kumary et al.

(2001) and Khalik et al. (2013) advocated that the distribution of total organic carbon is

linked with a higher percentage of clay.

2-way ANOVA (Table 23) when applied showed significant variations both among the

stations and months.

Total Organic Matter (TOM)

Total Organic matter is one of the basic components of sediments that play a major

role in the aquatic eco-system as it affects various biogeochemical processes, nutrient

cycling, biological availability, chemical transport and interactions.

The findings of Murty and Veerayya (1972), suggested that oxidation of organic

matter in the sediments may be due to:

high oxygen content

high temperature

and,

shallowness of the system


Page 61

The percentage of Total Organic Matter (TOM) showed a variation in the range of

0.21 % + 0.13 (August) to 1.42 % + 0.96 (June) in the first year of study (2011- 2012).

During the consecutive year, the percentage of TOM followed the range of 0.21 % + 0.13

(August) to 1.58 % + 0.85 (June) (Table 10 and Figure 6). On an average, the seasonal

variations in TOM of sediments of river Tawi recorded a peak in summer months (March –

June) followed by a gradual decline during the monsoon months.

Peak observed during the summer months could be attributed to high rate of

decomposition which is directly related to temperature. The decomposition of organic matter

releases the organic carbon into the water which finally accumulates in the sediments. Davies

and Tawari (2010) also recommended that Total Organic Matter (TOM) is directly

proportional to the Total Organic Carbon (TOC). As the TOC is directly related to the TOM

the present observations also revealed an increment in TOC and TOM both in the summer

months which may be due to the more active biological life in rivers that are enriched with

many nutritious substances (Niemirycz et al., 2006).

Lowest percentage of TOC observed during the monsoon months may be due to the

dilution effects caused by rains and runoff (Davies and Tawari, 2010) while winter minima in

the percentage of organic matter could be due to low temperature which slows down the pace

of decomposition.

Close look at the table 21 also indicated variations in distribution of TOM along the

four studied stations. Stations II and III recorded the highest percentage of TOM followed by

station I and IV. Such variations in TOM followed the same pattern as that of TOC. Highest

percentage of TOM on station II and III may be dependent upon the particle size composition

of sediments (where silt and clay were in considerable amount) and allochthonous organic

load entering into the river in the form of garbage (solid wastes) and sewage (liquid wastes)

(Abdel- Satar and Elewa, 2001; Abdo, 2004 and Abdel- Satar, 2005). Higher retention of

organic matter on fine grained material were also well on records by Russel (1960). Whereas,

on the other hand, the low percentage of TOM at station I and IV could be due to the fact that

the sand was the major contributor to the sediment composition and low anthropogenic stress


Page 62

at these sites. Boey (1997) regarded the sandy sediments to be poor in the nutrients and thus

authenticates the presence of low percentage of OC and OM at station I and IV.

Thus, on the basis of above findings it was observed that the sites containing high

percentage of mud, exhibited the highest values of OM, while the sites containing low

percentage of mud has the minimum value of OM. These findings are in line with the

findings of Abdel- Satar (2005).

The present findings gets further strengthen by a significantly positive correlation

between OM and percentage of silt (r = 0.50) and clay (r = 0.62) (Table 22). A negative

correlation was found between the OM and sand content (r = -0.52) also supports present

findings. The analysis of data also revealed that TOC and TOM shared significant positive

correlation with each other (r = 0.99). The observations made by Griggs (1975) indicated that

if the concentration of OM exceeds 1% in the sediments it is said to have higher organic

content.

Significant F- values of 2-way ANOVA analysis showed that variations in Total

Organic Matter (TOM) are significant among both stations and months (Table 23).

pH

pH is a measure of the concentration of hydrogen ions present in the soil or sediments

or water and is known to be related to the availability of macro and micro nutrients in op.

cited medium. It is an extremely important parameter, since most of the chemical reactions in

aquatic environment are controlled by change in its value and also plays an important role in

deciding the quality of the sediments.

The values of pH recorded during the study period varied from a minimum of 6.92+

0.08 to 9.15+ 0.88 during the months of June and January respectively (2011-2012) and from

7.92+ 0.08 to 8.82+ 0.28 during the months of March - April and November respectively

(2012-2013) (Table 11 and Figure 7).

During the present study it has been observed that pH of the sediments of river Tawi

fluctuated from slightly acidic to alkaline conditions. A significant low pH value was


Page 63

recorded during the summer months which implies to the acidity of the sediments. The low

pH value during the summer may be associated with increased temperature that tends to

increase the process of decomposition thereby releasing acidic by- products. Elewa and

Ghallab (2000) also observed the release of hydrogen sulphide and the formation of organic

acids and other breakdown products by the decomposition of organic matter

Slight increase in pH towards alkalinity was recorded during monsoon, post monsoon

and winter months. Alkaline pH recorded during monsoon season may be attributed to the

dilution by rain water. Similar observations have already been reported by Nath (2001) from

Narmada River. The alkalinity in the value of pH during winter months could also be related

to low temperature, lower decomposition rate and more release of bicarbonate ions in the

sediments. A much alkaline value (7.01- 8.75) was reported in winters from river Ghaghara

by Singh and Mahaveer (1997). The increment in pH in post monsoon and winter season may

also be accredited to the decrease in OC and OM which favors less release of acids and hence

shifting of pH towards alkaline side (Elewa and Ghallab, 2000). Changes in the pH values

could also be attributed to the ongoing redox reactions in sediments (Chapman, 1996).

Braide et al. (2004) reported alkaline range of 6.9 to 7.8 of the sediments of collected

from fresh water stream of Minichida whereas Abdel- Satar (2005) studied the sediment

quality of River Nile and observed variations in pH ranging from 7.1 - 9.0.

A look at the table 21 further revealed the lowest pH values at stations II and III,

while higher values were recorded at stations I and IV. It was thus inferred from the present

observations that the station II and III were having slightly less pH which may be due to the

comparatively more release of organic acids as a result of decomposition of total organic

matter. Moreover, the sediment texture at these stations i.e., sandy loam that could retain

more of OM than sandy sediments of station I and IV (George et al., 2010).

Correlation matrix showed significant negative correlation between pH and TOM (r =

-0.70) (Table 22).


Page 64

Electrical Conductivity (EC)

It is well documented that Electrical Conductivity (EC) is the ability of a material to

conduct (transmit) an electrical current. It is a good measure of dissolved solids and is used

to determine mineralization in sediments. Soil electrical conductivity is an indirect

measurement that correlates very well with several soil physical and chemical properties.

During the period of present investigations (2011- 2013), the mean value of electrical

conductivity (EC) of sediments varied from 0.03 µS/ cm + 0.01 (August) to 0.96 µS/ cm +

0.08 (June) and from 0.03 µS/ cm + 0.01 (August) to 0.76 µS/ cm + 0.71 (June) (Table 12

and Figure 8).

Visualization of table 12 and figure 8 indicated that the peak in EC content of

sediments was observed during the summer season (March - June), succeeded by a decline

during the monsoon and post monsoon period and again a gradual rise was seen during the

winter season.

The presently recorded elevated values of EC of sediments during summer months

may be ascribed to:

The elevated temperature that may cause the faster evaporation of the riverine

water leaving behind the dissolved salts (Michaud, 1991).

The retention of organic matter on the bottom sediments (Chandrakiran,

2011).

Bhatt et al., (1999) and Gupta and Paul (2013) also reported maximum conductivity

during summers months.

Decrement in EC during monsoons may be linked with the increased volume of

water as a result of frequent rains which caused a decline in salt/ ion concentration in water

thereby reducing the values for EC in sediments (Chandrakiran, 2011). The observations

made by Hoque et al. (2008), also indicated the lowest EC content during the monsoon

season.

Relative increase in the amount of EC content of sediments during winters may be

due to the building up of ions in the bottom sediments brought about by monsoonal rains


Page 65

and low level of overlying waters. Hoque et al. (2008) also recorded the similar trend in the

variations of EC content of soils.

Inquisition of the table 21 revealed that on an average, the maximum EC at station II

and III whereas, a decrement in EC content was recorded at station I and IV.

According to Grisso et al. (2007) EC of the sediments is strongly affected by:

particle size

soil texture

on the amount of moisture held by soil particles

All the above discussed factors could be accounted for low EC at stations I and IV

and high EC at stations II and III. Sands have a low conductivity, silts have a medium

conductivity, and clays have a high conductivity (Braide et al., 2004; Reddy et al., 2006 and

Grisso et al., 2007). Braide et al. (2004) and Reddy et al. (2006) referred it to the ionization

difference in sandy and clayey soils. Solanki and Chavda (2012) have already shown strong

relationship of EC to particle size and texture of sediments.

Gupta and Paul (2013) regarded that the conductivity in rivers and streams is affected

primarily by geology of the area through which the water flows. Streams that run through

areas with granite bed rock tend to have lower conductivity but on the other hand streams

running through clay and soils have higher conductivity.

Correlation matrix showed significantly negative correlation of EC to sand (r = -

0.79) but positive correlation to silt (r = 0.82) and clay (r = 0.77) (Table 22).

Bicarbonate (HCO3-)

The Bicarbonate is the major anion present in the soil solution of calcareous soils or

sediments. The concentration of bicarbonate is intimately associated with the inter-related

other variables viz; pH, calcium-ion concentration and the partial pressure of carbon dioxide

in the soil atmosphere (Lee and Woolhouse, 1969). As suggested by many workers in

freshwater ecosystems, the composition of alkalinity is variable, depending upon the

chemical composition of water. Moreover, Preskley and Kaplan (1968) also suggested that

microbiological reduction of sulphate has been postulated to lead to the production of


Page 66

bicarbonate ions and the acid thus generated dissolves calcite or dolomite and generates more

bicarbonate.

2(CH2O) + SO42-

–‒› 2HCO3- +HS

- +H

+

CaCO3 + H+ –‒› Ca

2+ + HCO3

-

Throughout the first year of present study (2011- 2012), the values for bicarbonates

varied from 0.34 mg/g + 0.08 (August) to 2.46 mg/g + 0.15 (January). However in the

following year (2012-2013), the bicarbonate content ranged from 0.87 mg/g + 0.10 (August)

to 2.55 mg/g + 0.25 (January) (Table 13 and Figure 9).

Perusal of the table 13 and figure 9 indicated that the bicarbonate content of the

sediments recorded average maxima during the winter months (November-February) while

the minima was observed during the monsoon (July- August). A decrease in the bicarbonate

content during the monsoons may be attributed to the dilution effects caused by the frequent

rains which ultimately diluted the OC and OM thereby flushing out the deposited ions.

The peak in the bicarbonate ions in sediments during the winter months could be

referred to the process of bacterial degradation of organic matter (aerobic and anaerobic)

which releases the bicarbonate ions thereby increasing the alkalinity and stimulate the

decaying rates by increasing the buffer capacity and pH of sediments (Heide et al., 2010).

Similar variations have also been studied by Boxma (1972) who noticed that during the

summer season the bicarbonate level decreased to a minimum after which it rose again in the

autumn. It was documented by Hutchison (1957) that at pH of 8-9, 97.2% or 96.6% inorganic

carbon may occur as bicarbonates. Also, Patra et al. (2011) noticed that carbonic acid

dissociates into bicarbonates in the alkaline conditions.

The process of decay in turn produces carbonic acid which immediately dissociate

into H+ and HCO3

- ions thereby increasing content of bicarbonate ions also result in the

production of OH- ions and CO2 ions as shown in the reaction as proposed by Dhar (1976):

2HCO3- ‒› CO2 + CO3

- +H2O


Page 67

HCO3- ‒› CO2 + OH

-

CaCO3 +H2O ‒› Ca (HCO3) 2

Ca (HCO3) 2 ‒› Ca2+

‒› 2HCO3-

In the present study, it was further observed that in the spring- summer season

bicarbonates in the sediments exhibited a decline which may be due to the bicarbonate

produced as calcium- bicarbonate shown in above reaction which is more soluble and rapidly

diffuses into overlying water column followed by a continuous uptake of carbon dioxide as a

result of breakdown of bicarbonate ion by phytoplanktonic community (Chandrakiran, 2011).

Decline could also be accounted to the behavior of carbonic acid that remains un-dissociated

in the acidic conditions (Patra et al., 2011).

A close perusal of table 21 indicated maximum content of bicarbonate ions at the

stations II and III and minimum at stations I and IV, which could be due to the reason that the

decomposition of organic matter released carbonates and bicarbonates which ultimately gets

deposited at bottom sediments.

The finding of Matissof et al. (1981) also indicated that the concentration of

bicarbonate is a direct process of:

Organic decomposition

Dissolution of calcium carbonate mineral phase (20%)

Dissolution of magnesium, iron and manganese carbonate mineral

phase (20%).

Moisture content in the soil or sediments (Boxma, 1972)

Bicarbonates were positively correlated with calcium (r = 0.92) and pH (r = 0.64) but

negatively correlated to total organic carbon (r = -0.56) (Table 22). Significant variations

were recorded for both stations and months for 2-way ANOVA analysis (Table 23).


Page 68

Chlorides (Cl-)

Chloride is one of the essential micronutrient that occurs in all natural waters in

widely varying concentrations. In the sediment/ soil chlorine occurs predominantly as

chloride anion (Cl-). The chloride anion does not form complexes readily and shows little

affinity in its adsorption to soil components. Moreover, presence of these ions affect structure

of sediments/ soil making it sodic and limiting water infiltration and drainage (Anu et al.,

2010). It is one of the first elements removed from the minerals by weathering as soils are

formed. Measurements of these ions in the sediments provide information on salinity

problems.

The chloride content in the sediments of river Tawi varied from 0.18 mg/g +0.03

(July) to 0.61 mg/g + 0.08 (June) and 0.15 mg/g + 0.02 (July) to 0.66 mg/g + 0.22 (June)

(Table 14 and Figure 10) during the present study (2011- 2013) thereby indicating the

maximum values during the summer months and a decline during winter season.

Maximum salinity or chloride content of the sediments during the summers could be

attributed to faster evaporation of water leaving behind the salts making sediments rich in

chloride content (Kumar and Khan, 2009). Higher concentration of these ions all the stations

during summer months (Mar- June) could be accounted to the higher rate of mineralization of

accumulated organic matter at the bottom releasing more of chloride ions (Chandrakiran,

2011).

Least amount of the chlorides in the monsoons could be rendered to the run off owing

to the frequent rains that could reduce the chloride content in the sediments. These results are

in conformity with the findings of Kumar and Khan (2009). Decrement in chloride content in

monsoons could also be due to the release of chloride from sediment into overlying waters as

also suggested by De et al. (2009).

The variations in chloride content in different eco-systems may be linked with:

topography,

tides (high and low)

fresh water inflow


Page 69

as suggested by Kumar and Khan (2009)

Perusal of Table 21 further indicated the variation in the chloride content along the

longitudinal profile of river Tawi. During study period, the maximum accumulation of

chloride ions was recorded at stations II and III and it could be accounted to the prevalent

anthropogenic stress in the form of domestic sewage and garbage, religious wastes, animal

and human excreta, and wastes from cremation ground etc. Moreover, chloride content of

sediments also varied with texture of sediments. Also, being an anion chloride is not easily

adsorbed on the soil exchange complex because of which chloride moves rapidly with soil

water and is more prone to loss in sandy soils (Freeman et al., 2006).

The conclusions drawn by Franzen et al. (1994) thus suggest that chloride content is

related to the percentage of soil moisture and bulk density. On the basis of the fact that

clayey soils have more organic matter and thus retain more water than the sandy soils which

also advocates and the higher chloride content at station II and III as compared to at station I

and IV.

Chloride and total organic matter (TOM) showed significantly positive correlation to

each other (r = 0.72) (Table 22). While analysis of 2-way ANOVA showed significant

variations both among stations and months (Table 23)

Calcium (Ca2+

) and Magnesium (Mg2+

)

Calcium and Magnesium are present in adequate amount in soils. Both are a

component of several primary and secondary minerals in soil which are essentially insoluble

for agricultural considerations. They exist in the form of complexes such as calcite (CaCO3)

and Dolomite (Ca Mg (CO3)2). Calcium is also present in relatively soluble forms as a cation

adsorbed to the soil colloidal complex. The ionic form of both of these minerals is considered

to be available to crops (Shivakumar and Srikantaswamy, 2012). Calcium and Magnesium

along with the other cations and anions significantly contributes to the salinity of the soil.

Well marked seasonal variations in calcium content have been recorded from the

sediments of river Tawi (Table 15 and Figure 11) and during the first year of study (2011 -

2012), the calcium content varied from 0.07 mg/g + 0.02 (August) to 0.45 mg/g + 0.39 mg/g


Page 70

(January) while it varied from 0.14 mg/g + 0.02 mg/g (June) to 0.60 mg/g + 0.34 mg/g

(January), during the second consecutive year (2012- 2013).

Magnesium content of sediments also recorded seasonal variations during the study

period (2011- 2013) which varied from 0.06 + 0.22 mg/g (August) to 0.23 + 0.06 mg/g

(January) and from 0.12 + 0.02 mg/g (June and July) to 0.43 + 0.10 mg/g (January) (Table 16

and Figure 12).

Perusal of the table 15 and 16, figures 11and 12 further revealed a high concentration

of both of these ions during post- monsoon and winter months and a fall in the monsoon

months (July- August). Low values of calcium and magnesium recorded during pre-

monsoon may be due to uptake of these ions by the flora and fauna, whereas, the least ionic

concentration recorded during the monsoonal months may be attributable to the dilution

effects caused by heavy rains and frequent flooding, thus, bringing the variations in their

contents. Shivakumar and Srikantaswamy (2012) also observed the lower ranges of these

cations in monsoon as compared to the post and pre monsoon season. While Mishra and Puri

(1954) advocated that increase or decrease in calcium and magnesium content may be

associated with the uptake of these ions by living organisms and their ultimate release as an

outcome of decomposition in the water bodies.

Increased values in post- monsoon and winter months may be linked with the entry of

new rain water in soil or sediments during the monsoon from the surrounding catchment

area. Similar observations have also been cited by Solanki and Chavda (2012). These

findings also get strengthened by a direct relationship between calcium and bicarbonate

(r = 0.92) (Table 22) as suggested by Prasad and Saxena (1980). Contrarily, earlier workers

like Abdel- Satar (2005) reported the decrease in the calcium and magnesium during cold

seasons and increase during the hot seasons.

The observations made during the present study also indicated that along the

longitudinal profile of the river Tawi (Table 21), higher levels of calcium and magnesium

were recorded at stations II and III as compared to stations I and IV which could be related

to:


Page 71

The impact of more anthropogenic stresses at these sites. The present findings

also gets support from the observations made by Verma and Saksena (2010)

who observed high calcium and magnesium in contaminated soils or

sediments than the uncontaminated ones and also reported the highest value of

magnesium at the point where sewage load was quite high.

The amount of calcium and magnesium is higher in organic matter rich soils

than in other soils (Verma and Saksena, 2010).

Soil texture may also be one of the factors responsible for enrichment of these

ions at stations II and III. As also advocated by Jones and Jacobsen (2001)

who reported that soils dominated by clay have small pores that prevent water

from draining freely.

Very high surface areas in fine textured soils give numerous binding places

and high abilities to retain nutrients to such soils (Jones and Jacobsen, 2001).

Structural chemistry of these ions which are positively charged and bind

strongly to the negatively charged clay particles (Jones and Jacobsen, 2001).

High Molluscan density reported at stations II and III also resulted in release

of these ions during their death and decay. According to Sugirtha and Sheela

(2013) the large fragments of molluscan shell, cause elevations in the calcium

and magnesium concentrations.

Kumar and Ramachandra (2003) during their investigations on Sharavati river

in Kerala assessed the dependence of these ions on the parent materials or

rock.

Calcium and Magnesium showed significantly negative correlation to temperature (r

= -0.71; r = -0.75) (Table 22). Analysis of 2-way ANOVA on the recorded data showed

significant variations of calcium and magnesium both among stations and months (Table 23).

Nitrate (NO3-)

Nitrates are a form of nitrogen which is found in several different forms in terrestrial

and aquatic ecosystems. Formation of nitrates is initiated by breakdown of organic matter by

bacteria and fungi to produce ammonium ions (NH4+). When the sediments are aerobic, the


Page 72

ammonium is oxidized to nitrite (NO2-) and then to nitrates (NO3

-) through the nitrification

process (Morse et al., 2004). Together with phosphorus, nitrates in excess amount can

accelerate eutrophication thus causing dramatic increases in aquatic plant growth and

changes in the biotic life inhabiting the stream.

A complete absence of nitrates was recorded in monsoon months during the study

period while maximum value was recorded in the summer months 2.98 mg/g + 0.35 (June)

(2011-2012) and 2.02 mg/g + 0.68 (June) (2011-2012) (Table 17 and Figure 13).

High nitrate content in the sediments during summers may be due to the oxidation of

organic matter which has settled on the top layer of sediment (Bragadeeswaran et al., 2007;

Sarvankumar et al., 2008; Rita et al., 2012). The high levels of nitrates observed in the

summer season is also in agreement with Wolfhard and Reinhard (1998) and Adeyemo et al.

(2008) who concluded that nitrates usually built up during dry seasons. Kelso et al. (1997)

also confirmed the records of high content of nitrites in summer.

The complete absence of nitrates observed throughout the monsoon season may be

ascribed to the low levels of organic matter and removal of top layer of sediments during

heavy floods (Bragadeeswaran et al., 2007 and Sarvankumar et al., 2008). The available

form of nitrogen are very water soluble which move rapidly with rainfall (Mussa et al., 2009)

and thus, can be accounted for less nitrates in monsoon. According to Wolfhard and Reinhard

(1998) the rains flush out deposited nitrate from near surface soils reducing the levels

drastically as rainy season progresses. Reduced levels of nitrate during monsoons have been

observed by Adeyemo et al. (2008).

Overall, the nitrate content of sediments ranged from 0 mg/g to 3.4 mg/g which

implies to the low nutrient level of the river Tawi. Ezekiel et al. (2011) also observed the

nitrate range 2.6 to 4.1mg/kg mean nitrate content of 3.65mg/kg and considered the value to

be low and attributed it to the low nutrient level of Sombreiro river, Niger delta.

Comparing, the values of nitrate station wise, as it is evident from the table 21, the

highest value of nitrates was recorded at stations II and III. Conversely, lower values were

observed at other two stations.


Page 73

As the sandy sediments are nutritionally poor while the clayey and silty sediments are

rich in nitrogenous compounds, and can be accounted for the richness of nitrate at stations II

and III. Dye (1978) observed high nitrogen content in finer substrate and suggested that it

was probably due to trapping of detritus by finer particles resulting in an increase in bacterial

population which may also be a reason for high level of nitrogen at specific sites as

encountered in the present study at stations II and III.

Non- uniform distribution of nitrate at all the four stations could also be attributed to

the differences in sediment nutrient input from the drainage systems of the various stations

as referred by Ezekiel et al. (2011) as stations II and III receive high nutrients in the form of

sewage, garbage and animal and human excreta. Land disturbing activities may also cause

introduction of large amounts of sediment into nearby streams and rivers (David et al., 1981).

Drusilla et al. (2009) referred the unequal distribution to the low adhesion of nitrates to the

inorganic contents of sediment as observed at stations I and IV which is mainly dominated by

sand.

The factors which affected phosphate concentration also affect nitrate distribution

(Ezekiel et al., 2011).

Correlation matrix showed significantly positive correlation to TOC (r = 0.69),

phosphate (r = 0.88) but negative correlation to sand (r = -0.71) (Table 22). Application of 2-

way ANOVA showed significant variations between stations and months (Table 23).

Phosphate (PO42-

)

Phosphates generally considered as ‘pollutants’ enter the riverine system through

super phosphate fertilizer, washed from catchment area and from chemicals used to improve

the performance of detergents (Abowei and Sikkoi, 2005) which ultimately get deposited in

the riverine sediments that may be released back to the overlying waters under certain

conditions (Singare et al., 2011). The ion is a highly particle- reactive molecule and thus the

sorption properties of sediments are crucial for phosphorus retention capacity.

During the first year of study, the mean phosphate ion concentration in the sediments

of river Tawi varied from 0 (July- August) to 2.79 mg/g + 0.38 (June) and form 0 (August) to


Page 74

1.54 mg/g + 0.73 (June) (Table 18 and Figure 14), during the study period thereby revealing

a peak in the sedimentary phosphate levels during the summer months (May- June). The peak

started up building throughout the spring season (March- April) which ultimately elevated

during the summer months. High levels of phosphate during summer can be related to

increased decomposition rate of organic matter at elevated temperature. Such an increase has

also been observed by Owens and Walling (2002) and Vincente et al. (2003).

Monsoon season revealed the lowest value of phosphate which could be attributed to

the removal of top layer of sediments by floods (Sarvankumar et al., 2008). It could also be

attributed to the low level of organic matter during the monsoons. In context to the present

observations Boynton and Kemp (2000) and Cerco (2000) perceived that although the rainy

period is related to an increase in the dissolved phosphate in water, the increase in freshwater

flow re- suspends the recently deposited sediment and hampers the deposition of particles

brought about by the river.

High levels of phosphate during post- monsoon and winter season could be attributed

to the slow sorption of the ions brought about by the monsoonal rains. As opinioned by

Pardos et al. (2003) and Jarvie et al. (2005) the stability of the chemical forms of phosphorus

in association with the environmental conditions, regulate the retention and release of

phosphates in the sediment-water interface. Also, as the temperature is the main factor

controlling the phosphate content in the sediments accounted for the least concentration of

phosphates during autumn and winter months (Bostrom et al., 1988). As the decomposition

during these months is slow which ultimately lowers the organic matter content and hence

the phosphates content in the sediments.

Spatial distribution of phosphorus along the profile of the river Tawi revealed its

lowest values at stations I and IV, while the highest values at stations II and III (Table 21).

The spatial distribution of phosphates was mainly influenced by granulometry and sediment

texture. This is in confirmation with the findings of Gasper et al. (2013) who linked the

highest phosphorus concentrations to the fine sediment distribution at Botafogo river. These

results are also in agreement with those found by Smil (2000) and Katsaounos et al. (2007)


Page 75

according to whom the concentration of organic phosphorus in sediments is related to the

fine sediment distribution.

As a matter of fact, the sorption reactions of phosphorus are enhanced as a function of

decreasing sediment grain size. Large –sized particles have poor affinity to adsorb anions

such as phosphate (Abdel- Satar, 2005). Khalik et al. (2013) during their study on sediment

quality of Bertam river reported the retention of phosphorus by high clay content in

sediments at a particular area which could be accounted for high values of phosphates at

stations II and III as these sites were rich in clay and silt content. However, the significant

correlation observed in the present studies between the concentrations of OP and OM could

also account for high value of phosphates at stations II and III as compared to stations I and

IV as identified by Gasper et al. (2013) during their research on spatial and seasonal

sediment phosphorus species.

Pollutants received at stations II and III in the form of sewage and garbage and other

religious wastes of the city may increase the amount of phosphates in the sediments of these

sites. Correll (1999) and Ezekiel et al. (2011) backend the increased phosphate concentration

at sites subjected to the drainage system and sewage effluent discharge. Abowei and Sikkoi

(2005) reasoned the phosphate fertilizers and chemicals used to improve the performance of

detergents as the stock increasing the phosphate concentrations at particular site.

Phosphates showed significantly positive correlation to nitrates (r = 0.88) and

sulphate (r = 0.87) but negative correlation to sediment load (r = -0.41) (Table 22).

Application of 2- way ANOVA showed significant variations between stations and months

(Table 23).

Sulphate (SO42-

)

Sulphate (SO42-

) is the main inorganic form of sulphur along with some reduced

forms such as thiosulphate (SO2-) present in most well drained and well aerated sediments.

Whereas, under anaerobic conditions the main form of inorganic sulphur in soils and

sediments is sulphide and often elemental sulphur. Inorganic sulphur tends to dominate in

polluted sediments while organic sulphur compounds, normally make up the largest fraction


Page 76

in unpolluted sediments (Mitchell et al., 1984; Nriagu and Soon, 1985). Influx of industrial

and agricultural runoff of sulphate containing chemicals, atmospheric deposition, sewage

effluents which ultimately get deposited in the riverine sediments could be the probable

sources of sulphate in the sediments. Sulphur contents in soils generally range between 0.1

and 0.5g/kg (Mitchell et al., 1984) but polluted sediments may contain more than 5g/kg of

sulphate.

During the first year of present investigations, the sulphate varied from 0 (July-

August) to 3.38 mg/g + 1.44 (June). During the second year of study, sulphate also showed

marked variations from 0 (July- August) to 3.49 mg/g + 0.27 (June) (Table 19 and Figure

15).

In presently studied river a peak in the sulphate concentration was recorded during

the summer season. Variations in the sulphate content of the sediments followed the same

pattern as the variations in phosphate and nitrate as also confirmed by a positive correlation

between these parameters (r = 0.88). Mussa et al. (2009) also determined same behavior of

sulphate to that of phosphate in the soils or sediments.

Maxima of sulphate values during summer months is in conformity with the

observations of Otene and Iorchor (2013) who also recorded sulphate values to be the highest

in the dry months (summer months) than the wet seasons (monsoons months). Such summer

maxima could be due to the elevated temperature that accelerates the process of

decomposition of organic matter thereby releasing sulphate ions along with other nutrients in

the sediments. House and Denizen (2002) and Gudasz et al. (2010) referred temperature as

an important variable influencing carbon, nitrogen, phosphorus and sulphur bio-geochemical

cycles in the aquatic environments. As, the decomposition of sedimentary organic matter is a

process that can be stimulated by warming (White et al., 1991; Arnosti et al., 1998 and

Gudasz et al., 2010), biogeochemical fluxes from sediment may be enhanced at higher water

temperatures and result in altered fluxes sulphate and nitrate in response to warming. This

could be accounted for the peak of increasing suphate concentrations during the spring to

summers. Similar temperature dependence of Sulphur Reduction Rate (SRR) has also been

reported by Moeslumd et al. (1994) and Thamdurp et al. (1994).


Page 77

The lowest concentration of sulphate ions during the monsoon may be due to the

dilution effects caused by rains and thus the removal of top layer of sediments where organic

matter settles down. Jorgensen (1977) concluded in his work that seasonal variations in

sulphate reducing activity is not a simple temperature effect but also reflects variations in

amount of labile organic substrate available for anaerobic decomposition.

High sulphate reduction rates are found in sediments containing high amount of

organic matter (Panutrakul et al., 2001). Also, Skyring (1987) reviewed the sulphate

reduction rates and noted that they vary seasonally and between sediment types.

A look at table 21, along the profile at river Tawi, revealed varied sulphate

concentrations at different stations. Thus, the high value of sulphates at stations II and III

may be due to availability of high amount of organic matter as compared to station I and IV.

This is further confirmed by the correlation values between these two parameters (r = 0.59).

Works have shown that biologically mediated transformation of C, N, P, and S are highly

dependent on organic matter quality because heterotrophic microbes require a carbon source

(Kaushal and Lewis, 2005 and Zhang et al., 2012). Skyring (1987) also advocated that

sulphate reduction rate vary seasonally and between sediment types. Zak et al. (2006)

analyzed the sediments of river Spree in Germany and found the sediments to be poor in

organic matter (5-13%) and also poor in decomposition process and regarded sulphate

reduction and phosphate mobilization to be limited by the availability of decomposable

organic matter.

High sulphate content was observed at station III, followed by station II, station IV

and station I which could be attributed to the particle size of the sediments. This indicated

significantly higher amounts of sulphur were present in the particle size class <0.002 mm,

followed by particle size class 0.002- 0.02 mm and 0.02- 2.0 mm. Scherer et al. (2012) found

that the values of sulphur in smallest soil separates were up to five times higher and ranged

between 455 and 630 mg S/ kg than the values of the largest separates, ranging between 115-

275 mg S/ kg. Solomon et al. (2001) also reported the total S increased with decreasing size

of soil particles. Acquaye and Kang (1987) also obtained a positive correlation between the

relative sulphate (SO42-

) adsorption capacity and the clay content of soils


Page 78

High percentage of sulphate concentration at station II and III could also be related to

the rate of sewage discharge along with other effluents at these sites. Rita et al. (2012)

attributed the high sulphate content to the enormous discharge of sewage water and other

effluents along with surface run off. Similar findings have also been reported by Babale et al.

(2011). They found the discharge of untreated tannery effluents responsible for the elevated

sulphate levels in the sediments during their study on sediment of river Challawa, in Nigeria.

Correlation matrix showed significantly positive correlation to TOC (r = 0.59),

chloride (r = 0.89) but negative correlation to sand (r = - 0.61) (Table 22). Application of 2-

way ANOVA showed significant variations between stations and months (Table 23).

Sediment load

Sediment yield is the total sediment outflow from a catchment per unit of time and

areas, measured at a given cross section of the river. It should include both bed loads as well

as suspended load sediment yield is mainly affected by factors such as climate, relief, soil,

vegetation and human. It provides integrated measure of soil erosion, influence sediment

transport and deposition (Nicholos, 2006).

During the first and second year of study sediment load of the river varied from

0.0000026 (m2S

-1mg/l) + 0.0000018 (December) to 0.039 (m

2S

-1mg/l) + 0.063 (August) and

0.0000082 (m2S

-1mg/l) + 0.0000086 (January) to 0.102 (m

2S

-1mg/l) + 0.168 (July). A close

perusal of the table 20 and figure 16 revealed that maximum sediment load was accessed

during the monsoon months and minimum load during the winter months.

Increased flow rate during monsoons results in increased sediment load (Kusimi,

2008 and Kamarudin et al., 2009). Moreover, Bloom (1998) recommended sediment load as

the power function of velocity and Subramanian (1996) regarded sediment yield as an

integrated measure of erosion, transport and deposition processes. Minimum sediment load

observed during winter months may be due to low discharge values of the river.

A close perusal of the table 21 further revealed the maximum sediment load at

stations III and IV of river Tawi which could be due to:


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Downstream location of these sites (Laronne and Reid, 1993; Tooth, 2000

Kusimi, 2008).

Reduction in vegetative cover, much erosion by runoff into river channel

(Kusimi, 2008).

Increase in catchment area (Kusimi, 2008).

Dumping of Garbage and other activities contributing to increasing sediment

load (Kamarudin et al. 2009).

4.2 Water Analysis

The physico- chemical characteristics of water reflect not only the status of water

quality and nutrients but also have a direct influence on the organisms. Seasonal fluctuations

of these parameters play an important role in the distribution, periodicity, qualitative and

quantitative composition of biota in aquatic ecosystem (Chhetry and Pal, 2012). Since

limnochemical and limnobiological components of any water body interact with each other

(Kulshreshtha et al., 1992), so it becomes inevitable to investigate these parameters in any

limnological study.

During the present investigations, along the longitudinal profile of river Tawi various

abiotic parameters viz; temperature (air and water), depth, transparency, velocity, pH, free

carbon dioxide, dissolved oxygen, carbonates, bicarbonates, chlorides, calcium, magnesium,

biological oxygen demand, nitrate, phosphate and sulphate, total suspended solids, total

dissolved solids and discharge value showed monthly variations as depicted in tables 24 to

43.

4.2.1 Seasonal variations in physical parameters of water of river Tawi

Temperature

A look on the table 24 and figure 17 revealed the variation in air temperature from

15 2 c + 0.21 (January) to 39. 25 c + 1. 29 (June) and 15 2 c + 2 ( anuary to 2 c

+ 1. 29 (June) during the study period (2011- 2013).


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Water temperature plays an important role in influencing the periodicity, occurrence

and abundance of organisms. Review of table 25 and figure 18 shows that surface water

temperature closely followed air temperature and varied from 15. c + 0.21(January) to 3

c + 2 (June) and c + 0 (January) to 34.87

c + 1.67 (June) during both the years of study

(2011-2013).

Obtained maximum temperature during summers may due to:

Clear atmosphere and greater insolation from the sun (Ahmed, 2004).

The longer day length as a consequence of increased photoperiod as has been

reported earlier by Kaushik and Saksena (1999), Sawhney (2008) Manjhare et

al. (2010).

An increase in suspended solids brought by rains also account elevated levels

of heat absorbed and therefore high temperature levels as also has been

suggested earlier by Walia (1983), Singh (1988), Sharma (2002), Singh

(2004) and Sharma et al. (2006)

Rapid heat exchange due to shallow water as suggested by Kant and Raina

(1990), Sawhney (2008), Shinde et al. (2011).

Maximum air and water temperature during the summers has also been reported by

Sharma (2002), Singh (2004), Kour (2006) and Sharma (2013).

Decline in air and water temperature in winters may be due to:

Reduced illumination and shorter day length (Fasihuddin and Kumari, 1990;

Sawhney, 2004; Sawhney, 2008 and Shinde et al. 2011).

Less turbidity as recorded by Butler (1962).

The oblique incident rays, shorter photoperiod

Increased condensation due to higher percentage of water vapors in air

(Aguado and Burt, 2004 and Pidwimy, 2006).

Iqbal et al. (2004) and Lashari et al. (2009) also recorded maximum temperature

during summer season and minimum temperature during winter months.


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Variations of temperature both air and water temperature has been recorded along the

stations (Table 44). The probable reason for the variations may be:

The distance between stations

The difference in timings to reach a station (Jayaraman et al., 2003)

Heat produced due to decomposition of organic wastes discharged into the

station II and III (Adeogun and Fafioye, 2011).

A close relationship between air and water temperature has been recorded in the

presently studied water body and water temperature seems to closely follow air temperature

(Parray et al. 2010) as also strengthened by positive correlation (r = 0.98) (Table 45). A close

relationship between air and water temperature has been opinioned by large number of

workers (Baba, 2002; Sawhney, 2004; Abdel- Satar, 2005; and Essien – Ibok et al., 2010 and

Ogbuagu et al., 2011).

The 2- way ANOVA calculated for variations in the parameter should significant

variations both among months and stations (Table 46).

Depth

Water depth plays an important role in governing the water quality of any riverine

system (Garg et al., 2009) and its fluctuations are mainly due to various climatic factors that

operate in an aquatic ecosystem, for e.g., evaporation of water due to increased atmospheric

temperature during the day time, wind velocity, rainfall and humidity (Welch, 1952). Water

depth exhibits an indirect correlation with the differential activities and the life processes of

aquatic biota (Kaushik and Saksena, 1991)

Perusal of Table 26 and Figure 19 revealed an annual variation in the depth of river

Tawi. During the first year (2011- 2012), it fluctuated between 12.87 cm + 2.70 (December)

to 42.75 cm+17.07 (August). But during the second year of study (2012- 2013), the water

level varied from 9.5 cm + 3.64 (December) to 28.87 cm + 7.33 (August).

Maximum depth was recorded during monsoon season which may be attributed to the

rains that cause frequent floods and massive fresh water influx (Sawhney, 2004; Singh, 2004;


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Sawhney, 2008; Essien- Ibok et al., 2010 and Chowdhary, 2011). A decline in depth was

reported in peak summer and winter season. Winter minima in depth may be attributed to the

reduction in water flow from the source. Sunder (1988), Sawhney (2008) and Chowdhary

(2011) also recorded a decline in depth in their respective lotic systems due to reduction in

water from hills. A fall in depth recorded during the summers (May- June) may be attributed

to an increased evaporation of water at a higher temperature. Similar observations of summer

decrease in water depth are in accordance with the findings of Puri (1989), Zutshi (1992) and

Sharma (1999).

A careful insight of table 44 revealed maximum average depth at station III followed

by station IV, station I and station II during the first year of investigations which could be

ascribed to the fact that this station receives a continuous discharge of sewage and domestic

effluents of almost whole Jammu City (Sharma, 2013). But contrarily, during the second year

of study, maximum depth was at station I and minimum at stations II and III. Maximum

depth at station I could be attributed to the activities such as sand mining and dredging that

alters the topography of the system (Ayoola and Kuton, 2009). Critical fall in depth at station

III could be attributed to the diversion of river water due to construction of barrage.

Correlation matrix revealed that depth is significantly positively correlated with air

temperature (r = 0.77) and water temperature (r = 0.46) (Table 45). Moreover, recorded depth

in different months and at different stations during the present study period showed

significant variation as deputed by 2-way ANOVA results (Table 46).

Velocity

The water velocity and the associated physical forces collectively represent the most

primary environmental factor in regulating the growth and distribution of aquatic biota as

it erodes the channel and determines the type of particle deposition and thus nature of

stream bed (Wetzel, 2001). The velocity was found to be directly proportional to the

flood level and also with the gradient of river stretch. The water current is known to the

dissipate materials and heat within the system thereby affecting loading and self –

purification capacity of lotic ecosystem. Moreover, dissolved oxygen content of water is

also correlated with the rate of flow water current (Wetzel, 2001).


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The present investigative studies extending from 2011 to 2013 registered variations in

velocity form 0.25 m/s + 0.13 (December, 2011) to 0.98 m/s + 0.48 (August, 2011) and 0.20

m/s + 0.21 (November, 2012) to 0.92 m/s + 0.24 (August, 2012) (Table 27 and Figure 20). It

is a well established fact that velocity is directly proportional to water level which is quite

evident from present investigations (r = 0.90) as maximum speed in river Tawi was recorded

in the monsoonal months (July-August) which coincided with the peak in water level caused

by heavy floods due to precipitation and increased run off.

Such observations gets strengthened from those recorded by Chopra et al. (1990),

Joshi (1994), Sharma (1999), Sawhney (2004), Barzani et al. (2007), Sawhney (2008) and

Baig et al. (2010) and Essien-Ibok et al. (2010).

Decrement in the velocity during winters may be attributed to the reduction of water

level recorded during this period (Sawhney, 2008; Naik, 2009). Contrarily, Singh et al.

(2010) recorded the minimum velocity from Manipur river system during the summer

months and maximum velocity during winter months.

Among stations (Table 44), maximum velocity was recorded at stations I and IV

compared to stations II and III which could be due to:

Pooled nature of river at stations II and III (Sawhney, 2008).

Anthropogenic influence.

Lower silt and clay deposition.

Application of Pearson’s correlation revealed significantly positive correlation of

velocity to depth (r = 0.90) (Table 45). River velocity showed significant monthly as well as

station- wise variations on application of 2-way ANOVA (Table 46).

Transparency

Transparency or light penetration is essentially a function of the reflection of light

from water surface that depends on the intensity of sunlight, suspended soil particulate,

turbid water received from catchment area and density of plankton etc. (Mishra and Saksena,

1991; Singh, 1999; Kulshrestha and Sharma, 2006). Also, transparency exhibits an inverse

relationship with turbidity of water (Iqbal et al., 2004).


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A perusal of table 28 and figure 21 revealed that transparency of river Tawi showed

variations during both the years of study. During the first year of study (2011-2012),

transparency varied from 9.57 cm + 3.49 (August) to 21.75cm + 3.11 (February). During the

second year of study (2012- 2013), transparency varied from 7.5cm + 1.5 (August) to 21.5

cm + 3.57 (April).

Seasonal variations in water transparency indicated higher values during winter

(2011-2012) and summer seasons (2012-201), whereas lower values were evident in the

monsoon season during both the years of study. These findings are in conformity with the

findings of Saksena et al. (2008) while working on river Chambal.

Least transparency observed in monsoons may be due to:

High current which erodes the bank of the river and more run off from the

catchment areas (Singh et al., 2010)

Turbid flood water

Suspended matter and dissolved particles (Garg et al., 2009)

Similar observations of least transparency during monsoons have also been cited by

Zutshi (1992), Essien- Ibok et al. (2010), Singh et al. (2010), Verma and Saksena (2010) and

Sharma et al. (2013).

The maximum transparency observed during the winter months during first year of

study could be due to:

Low rains (Singh, 2004).

Increase in infiltration of phytoplankton, bacteria and particulate organic

matter by bivalves which further enhance water clarity in the column (Vagun

and Hakenkamp, 2001).

Reduction in turbulence and low record of suspended matter (Sharma, 1999

and Kaul, 2000).

Similar findings have been reported by Singh et al. (1999), Nath and Srivastava

(2001), Shaikh and Yeragi (2004), Singh et al. (2010).


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However, during the second year of study (2012- 2013) a peak in the transparency

was recorded in the summer months which might be attributed to settlement of suspended

particles on substratum (Ahwange et al., 2012), clear water and shallowness due to

construction of barrage. Garg et al. (2009) related transparency to biological productivity,

suspended particles and water colour. They during their study observed that water was turbid

in monsoon season with yellow brown colour, while green colour in winter and transparent

green in summer season.

A careful look at the table 44 revealed that the water at stations I and IV was the most

transparent while at stations II and III, the water was least transparent which could be related

to the presence of high suspended load at stations II and III. Panigrahi et al. (2007) also

regarded suspended load as the key factor governing the light penetration in an ecosystem.

Sewage and other polluted effluents entering the river at stations II and III could also be

accounted for the low transparency at these stations. Annalakshmi and Amsath (2012), when

compared the transparency values of Arasalar and Cauvery rivers, found the low

transparency in river Cauvery and accredited it to the turbid condition of the river due to the

mixing of effluents and sewage. The finding of Kamal et al. (2007), Moustafa et al. (2010)

on their respective aquatic systems is in support of present findings.

The negative correlations between transparency and TSS (r = -0.53) (Table 45)

further confirm the findings. Similar correlations have also been studied by Abdel- Satar

(2005). 2-way ANOVA application showed significant variations both along the stations and

among the months (Table 46).

4.2.2 Seasonal variations in chemical parameters of water of river Tawi

pH

The pH of water is very important because it governs solubility of nutrients. It

regulates most of the biological processes and biochemical reactions (Verma et al., 2006).

Most ecosystems are sensitive to changes in pH due to due to climatology and pollution

factors and these changes can be indicative of an industrial pollutant, photosynthesis or the

respiration of the organisms (Ugwu and Wakawa, 2012).


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A look at the table 29 and figure 22 revealed that the pH of water varied from 7.6 +

0.18 (June) to 8.6 + 0.12 (December- January) and 7.7+ 0.07 (June) to 8.6+ 0.12 (December)

during both the years of study (2011- 2013).

Interestingly, it was noted that the pH showed a distinct pattern during both the years

of study and remained alkaline throughout the study period. It showed a decline throughout

the summer season followed by an increasing trend during monsoons and winters. A peak in

its value was recorded during winter season and consequent fall was seen during the

summers.

During summer months with an increase in water temperature, as the organic

substances decay, carbon dioxide is liberated, more the carbon dioxide produced, lowered the

pH of water body. Such a relationship between free carbon dioxide and pH has already been

administered by various workers viz; Cole (1975), Reid and Wood (1976) and Goldman and

Horne (1983). Also, another factor, i.e. rise in temperature could also be a causative factor

for decrement in dissolved oxygen and increment in free carbon dioxide as a result of

metabolic rate of aquatic biota; a reason which has already been suggested by Joshi (1996),

Hassan et al. (1998), Sharma (2002), Shawney (2008) and Sharma (2013). Ashfaque and

Alfasane (2004) observed that in river Thames pH attained a value of 8.5 in spring at the time

of phytoplankton maximum and was relatively low in summer.

During monsoons and post- monsoon, the high value of pH was recorded which may

be due to river’s enormous size and constant water movements which are expected to bring

changes in the levels of carbon dioxide and hence increases pH value towards alkaline side

(Ahmed, 2004). The high value of pH during the rainy season in the present work possibly

resulted from increased rate of pollutants from the surrounding areas along with rain water.

These findings corroborate with the findings of Singh et al. (2013).

Overall pH of the river fluctuated from 7.5 to 8.7 indicating mildly neutral to alkaline

nature of the river water (Gupta and Banerjee, 2012). Ugwu and Wakawa (2013) noted the

variation in pH from 6.89 to 7.53 during summers, 7.19 to 7.89 in rainy season and 7.12 to

7.73 during winters. Singh et al. (2010) during their investigations on physico- chemical

properties of water samples from Manipur river system also witnessed a shift of pH of 6.5

during summers to an alkaline pH of 7.9 during the winters.

The maxima in the pH value during the winter months could be due to:


Page 87

High oxygen level as the oxygen and pH share positive correlation with each

other (r = 0.72) (Table 45).

Abundance of phytoplankton growth (George, 1961 and Ashfaque and

Alfsane, 2004)

Less organic matter that produced less carbon dioxide thus shifting pH

towards alkaline side.

Kant and Kachroo (1971) reported pH of water changes with change in

climatologically and vegetation factors. Generally, fluctuation in pH values in different

seasons of the year is attributed to factors like removal of carbon dioxide by photosynthesis,

through bicarbonate degradation, dilution by freshwater influx, low primary productivity,

reduction of salinity and temperature decomposition of organic material by Karuppasamy

and Perumal (2000) and Rajasegar (2003)

Careful observation of table 44 showed variations along the profile of river Tawi

which clearly revealed minimum pH at stations III and II while maximum at stations I and

IV. The relative decrease of pH values at the stations III and II (sewage discharge points)

may be attributed to the bacterial and fungal action in the sediments that liberated methane

and hydrogen sulphide as well as the formation of organic acids and other breakdown

products (Lenz, 1977; Ravindra et al., 2003). Decline in pH at sewage receiving sites has

already put in records by Laxminarayana (1965), Pehwa and Mehrotra (1966), Goel et al.

(1980), Prasad and Saxena (1980) and Kamal et al. (2007). Sahu et al. (1995) related

fluctuation in pH with input loads of pollutants in the river system.

It is well documented that pH is directly related to carbonate (r = 0.78) and dissolved

oxygen (DO) (r = 0.72) and inversely related to free carbon dioxide (r = -0.91) (Zafar, 1964)

(Table 45). 2- Way ANOVA when applied showed significant variations both along the

stations and among the months (Table 46).

Dissolved Oxygen(DO)

Dissolved oxygen, an indicative of health of an aquatic ecosystem, is considered as an

important parameter in assessment of degree of pollution in natural water, directly affecting

survival and distribution of flora and fauna inhabiting in it. The quantity of dissolved oxygen

in water is directly or indirectly proportional to water temperature, partial pressure of oxygen


Page 88

in air, amount of chlorophyll content etc. (Welch, 1952; Wetzel, 1975). Optimum

concentration of DO is essential for maintaining aesthetic qualities of water as well as for

supporting life.

During the present investigations, in the year 2011- 2012, (Table 30 and Figure 23),

the dissolved oxygen content (DO) in river Tawi varied from 3.9 mg/l + 0.72 (June) to 12.5

mg/l + 1.76 (January) and in the succeeding year (2012- 2013), it followed the same pattern

and varied from 3.9 mg/l + 0.60 (June) to 12.5 mg/l + 1.68 (January).

Well marked monthly and seasonal variations were recorded in DO content of water

during the present investigative studies. DO levels revealed the least value during the

summer months with the maximum rise during the winters.

Minima in the dissolved oxygen content during summers may be attributed to:

Increased water temperature (Abdel – Satar and Elewa, 2001; Ahmed, 2004)

as the increment in temperature decreases the solubility of atmospheric

oxygen (Ueda et al., 2000)

Increase in temperature also increased the oxidative processes of organic

matter in water body (Ueda et al., 2000; Abdel – Satar and Elewa, 2001 and

Mahmoud, 2002) which inturn consumes the dissolved oxygen.

Increase in free carbon dioxide which may result from breakdown of organic

matter, respiration of biota etc. (Sahu et al., 2000; Koroosh et al., 2009).

Prakash et al. (2009) reported that the concentration of DO is inversely

proportional to the concentration of FCO2.

Increased day length and light intensity after acquiring optimal limit started

acting as a limiting factor for photosynthesis and hence result in decline in DO

production (Pandey et al., 1992 and Singh, 2004).

Similar observations of low DO during summer is well in accordance to several

workers viz; Singh et al. (1998), Abdel- Satar and Elewa, (2001), Ahmed (2004), Iqbal et al.

(2004), Abdel- Satar (2005), Naz and Turkman (2005), Garg et al. (2009) and Moustafa et al.

(2010).


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The present observation reveals that Dissolved Oxygen (DO) after acquiring

minimum levels during summers started increasing to acquire maximum level during winters

through the intermediate values during the monsoon season. Higher dissolved oxygen

concentration observed during the monsoons (5.73 mg/l) might be due to the higher wind

activity (Das et al., 1997) leading to increase in aeration level with increased flow. Similar

observations have been cited by Gupta and Mehrotra, (1991), Gupta et al. (1996) and Singh

et al. (2013). According to Hutchinson (1975) increase in DO was due to physical aeration

rather than biological event. Post monsoonal rise in DO might due to increased

photosynthetic activity facilitated by increased pH and decreased turbidity. The observations

by Iqbal et al. (2004) and Sachidanandamurthy and Yajurvedi (2006) are in conformity with

the present findings.

A look at the figure 23 indicated a peak in DO level during winter season which may

be due to:

Low temperature that enhanced the oxygen retaining capacity of water i.e.

high solubility of oxygen at low temperature (Suthar et al., 2005). Ahmed

(2004) also attained maximum level of DO in river Padma during winter

(February).

Decreased rate of decomposition and respiration activities of biota might be

among the several other reasons affecting the DO in aquatic system.

Short photoperiod. Iqbal et al., (2004) established a negative relationship

between with photoperiod and DO concentration and obtained maximum DO

(9.3mg/l) when the photoperiod was minimum (10. 25 h) during their study on

river Soan.

The present findings also get strengthened by a significantly positive

correlation between with pH and DO (r = 0.72). Such relationship between

with pH and DO has also been observed by Abdel-Satar (2005).

Present findings of maximum DO levels during the winter season also gets

strengthened by observations made by Chakraborty et al. (1959), Pehwa and Mehrotra


Page 90

(1966), Tripathi et al. (1991), Bisht (1993), Ahmed (2004), Iqbal et al. (2004), Hassan et al.

(2008), Saksena et al. (2008), Sawhney (2008), Garg et al. (2009) and Sharma (2013).

During the present investigative period (2011-2013), all the stations on river Tawi

witnessed great variations in the DO content (Table 44). Maximum DO content was recorded

at station I followed by station IV, while minima in DO was recorded at stations II and III

(the sewage receiving site).

Low value of dissolved oxygen at downstream stations (Stations II and III) may be

due to:

High load of organic matter in suspension and at the bottom and its microbial

decomposition (Sahu et al., 2000; Das and Acharya, 2003; Mishra et al.,

2009; Verma and Saxena, 2010 and Rita et al., 2012).

Absence of macrophytes.

Discharge of oxygen consuming effluents (Bharti and Murthy, 1990 and

Tripathi et al., 2008).

Consumption of oxygen in decomposition of organic matter present in the

water due to various religious activities as well as mixing of domestic wastes

(Telang et al., 2009).

Heavy sewage load (Shyamla et al, 2008) and degradation of organic matter

by bacteria present in sewage load lead to depletion of O2 (Hynes, 1970;

Welch, 1980 and Kamal et al., 2007). Reduction in DO as a result of sewage

out fall into the river has been reported by Saxena et al. (1966), Bulusu et al.

(1967), Prakasam and Johnson (1992), Zutshi (1992), Khanna et al. (1997)

and Begum and Harikrishna (2008).

High level of dissolved oxygen was observed at stations I and IV. Torrential nature of

the river and its gradient may be held responsible for average high value DO at station I

(Bhadra et al., 2003) while high DO at station IV could be due to the distance between this

station and others. Because of the large distance, the allochthonous load gets deposited far

away from this site. Also, the self purification capacity of the river is highly responsible for


Page 91

high levels of DO at station IV. High DO at stations I and IV could also be due to the least

anthropogenic stress as revealed during the present investigations.

DO showed negative correlation with air temperature (r = -0.91) and water

temperature (r = -0.90) (Table 45). Similar observations have also been cited by Abdel- Satar

(2005). Application of 2- Way ANOVA when applied showed significant variations in the

value of DO both along the stations and among the months (Table 46).

Free Carbon Dioxide (FCO2)

Free carbon dioxide is the normal component of all natural waters and also acts as a

chief parameter required for photosynthesis as the seasonal carbon flow forms the base of

photo pyramid. The amount of free carbon dioxide depends on the decomposition of top soil

and chemical nature of underlying rocks.

A perusal of table 31 and figure 24 revealed that FCO2 varied from 0 mg/l to 8 mg/l +

4.30 (June) and 0 mg/l to 4.62 mg/l + 3.13 (June) during both the years of study.

Maximum levels of free carbon dioxide observed in summer season at all the stations

of River Tawi during both the years of study may be ascribed to:

Increased temperature (Talling, 1957, Sakhre and Joshi, 2002 and Garg et al.,

2009).

De-oxygenation (Talling, 1957, Singh et al., 2010).

Increased decomposition of dead organic matter at high temperature, utilizing

dissolved oxygen and liberating more carbon dioxide (Hutchinson, 1957;

Goldman and Horne, 1983; Patil et al., 1985, Zutshi, 1992; Kaul, 2000,

Sakhre and Joshi, 2002; Koroosh, 2009; Singh and Gupta, 2010 and Ishaq and

Khan, 2014).

Increased respiratory activities of aquatic organisms (Singh, 1999 and Saksena

et al., 2008).

Inverse relationship of dissolved oxygen and free carbon dioxide is well on

records (Reid and Wood, 1976; Jhingran, 1982; Goldman and Horne, 1983;

Annalakshmii and Amsath, 2012).


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Similar increase in free carbon dioxide in the water during summers is well

documented by several workers viz; Singh et al. (1998) Garg et al. (2009), Essien –I bok et

al. (2010) and Singh et al. (2010).

Complete absence of free carbon dioxide was during the monsoon season which

primarily may be due to:

Its utilization in photosynthetic activity or it was being inhabited by the

presence of appreciable amount of carbonates in water (Sahai and Sinha,

1969; Annalakshmii and Amsath, 2012).

Shorter photoperiod (Baig et al., 2010, Chowdhary and Sharma, 2013 and

Sharma, 2013)

Slow decomposition due to low temperature (Goldman and Horne, 1983;

Kumar et al., 1987)

High amount of dissolved oxygen as DO and FCO2 are inversely proportional

to each other (Prakash et al., 2009).

A look at the table 44 revealed comparatively higher record of free carbon dioxide at

downstream sites which primarily be due to:

Continuous discharge of sewage (Malviya et al., 1990; Trivedy et al.,

1990 and Kaul, 2000).

Microbial decomposition of allogenic and autogenic organic matter

(Kumar et al., 1987; Singh and Gupta, 2010)

An inverse relationship of DO with FCO2 is on records (Odum, 1971;

Khatri, 1984; Joshi et al., 1996).

Less turbulence due to low gradient and pooled nature of the sites (Welch,

1952 and Sharma, 2013).

Correlation matrix revealed that free carbon dioxide shared significant positive

correlation with air temperature (r = 0.60) while significantly negative correlation with pH (r

= -0.91) (Table 45). Cole (1975), Ahmed (2004) and Kaul (2000) also recorded the inverse

relationship between free carbon dioxide and pH because increase in carbon dioxide

concentration in water results in decrease of its pH due to the formation of carbonic acid.

Both FCO2 and DO were observed to show an inverse relationship in the present study (r = -


Page 93

0.61) (Table 45). Similar inverse relationship between DO and FCO2 was also recorded by

Welch (1952), Wetzel (2001), Ahmed (2004), Annalakshmii and Amsath (2012). 2-way

ANOVA when applied showed significant variations in the values of FCO2 both among the

stations and months (Table 46).

Carbonate (CO32-

)

Carbonates constitute principal anion in fresh waters which is resulted from the

removal of CO2 by photosynthesis of plants (microbes, algae, floating phytoplankton) and

also by changes in temperature, evaporation or mixing of masses, resulting in the

precipitation of low- Mg calcite (Flugel, 2010). It exists in a number of polymorphic and

hydrated forms, calcium carbonate being the most important that occurs principally as calcite

and rarely as metastable aragonite. Moreover, it is considered to be the common form of

inorganic carbon when FCO2 is absent (House, 1984 and Stumn and Morgan, 1995).

In river Tawi the level of carbonates showed seasonal variations which varied from 0

(February) to 46.5 mg/l + 6.53 (January) and from 0 (May and August) to 46.5 mg/l + 6.53

(January) during both the years of study (2011- 2013) (Table 32 and Figure 25).

Higher values of carbonates recorded in winter season may be due to the complete

uptake of free carbon dioxide by growing phytoplankton which finally led to the total

absence or low free carbon dioxide levels, therefore, explains shift of pH towards alkalinity

and thereby enhanced precipitation of carbonates (Sharma, 1992).

The plausible reason for the presence of carbonate may be:

Complete absence of FCO2 due to uptake of phytoplankton community

(Sharma, 1999; Sharma, 2002 and Singh, 2004).

Enhanced precipitation of carbonates due to alkaline condition when complete

absence or low concentration of free carbon dioxide was observed (Sharma,

1992).

When pH of water was >8.4 (Reddy, 1981; Wurts and Durborow, 1992 and

Kumar et al., 2008).

Inverse relationship with free carbon dioxide (Ahmed, 2004).


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Absence of carbonates during some months of both the years of study (2011-2013)

may be attributed to the presence of free carbon dioxide during those months leading to

dissolution of carbonates into bicarbonates (Sharma 2002 and Sharma, 2013).

Station wise analysis of carbonates from the waters of river Tawi (Table 44) gives an

insight view to the uneven distribution of these anions maximum values were recorded from

the stations I and IV. On the other hand, stations II and III showed complete absence or low

record of carbonates. Such findings could be attributed to the continuous discharge of carbon

dioxide enriched sewage and other organic wastes from the vicinity which tends to increase

the free carbon dioxide levels thereby decreasing the carbonates.

On the basis of above findings an inverse relationship between FCO2 and CO32-

was

deduced presently which also gets strengthened by significant negative correlation (r = -0.63)

(Table 45). Similar inverse relationship between these parameters is also emphasized by

several workers viz; Hutchinson (1957), Cole (1975), Jhingran (1982), Goldman and Horne

(1983), Patil et al.(1985), Puri (1989), Khajuria (1992), Sharma (1999), Sharma (2002),

Sawhney (2004), Kour (2006) and Sawhney (2008).

Carbonates varied significantly along the stations and among months when analyzed

for 2-way ANOVA (Table 46).

Bicarbonate (HCO3-)

The capacity of water to neutralize a strong acid is known as alkalinity and it is

primarily a function of carbonate, bicarbonate and hydroxide content and formed due to

dissolution of carbon dioxide in water (Murthuzasab et al., 2010). The bicarbonate ions so

formed has two important functions.

The first one providing main buffer system for the resulting pH of water.

Second one was to provide CO2 for photosynthesis (Golterman, 1975).

A perusal of the table 33 and figure 26 indicated that the bicarbonate content in the

water of river Tawi varied from 129.5 mg/l + 65.02 (May) to 434.9 mg/l + 27.79 (January)


Page 95

during the first year of study (2011- 2012). During the second year of study (2012- 2013),

bicarbonates varied from 197.8 mg/l + 42.14 (August) to 459.2 mg/l + 22.91 (January).

Higher value of bicarbonate witnessed during winter at all the stations of river Tawi

may be due to:

Low free carbon dioxide concentration during these months (Table 31 and

Figure 24) (Ahmed, 2004).

Reduced photosynthetic activity in winters resulted in decreased uptake of

bicarbonates as a source of carbon in photosynthesis (Sharma, 2013).

Conversion of insoluble marls into soluble bicarbonate by free carbon dioxide

which entered the stream waters (Singh, 1980).

Release of compounds previously locked up in bottom (Sawhney, 2008 and

Chowdhary, 2011).

Increase in bicarbonates with decrease in water level (Singh et al., 1980.,

Lashari, 2009)

Direct relationship with pH (r = 0.79) and DO (r = 0.76) (Table 45).

A similar winter maxima in bicarbonate values is also witnessed by Rutne (1963),

Ahmed (2004) and Moustafa et al. (2010).

Low alkalinity recorded during monsoonal months (July- August) may be attributed

to low rate of nutrient cycling by reduced microbial activity and frequent flooding (Lashari,

2009). Decline in bicarbonates due to dilution of water and influx of rain water has also been

put on records by Laxminarayana (1965), Kaul (2000) and Sawhney (2004).

Fall in bicarbonate content in summer during both the years of study could be due to

presence of high levels of free carbon dioxide as both share negative correlation with each

other (r = -0.75) (Table 45). Moreover, bicarbonates were perhaps also utilized by growing

phytoplanktonic population as a source of inorganic carbon for photosynthesis

(Chandrakiran, 2011). Lowest alkalinity recorded during summers has also been put forward

by Ahmed (2004) during his study on river Padma. Decline could also be attributed to the

decomposition of organic matter (Prashar et al., 2006).


Page 96

Reviewing the table 44 carefully, it was observed that bicarbonates remained high at

stations II and III which may perhaps be due to sewage and other effluents of the city

entering the river at these downstream sites. Such an effect of pollutants on bicarbonate

content is well on records by Shastree et al. (1991), Moustafa et al. (2010) and Vaishali and

Parikh (2013). Compared to stations I and IV, increased alkalinity at stations II and III could

also be accounted to frequent religious activities occurring at these sites (Prasad and Patil,

2008).

Less decomposition of dead organic matter at stations I and IV releases less quantity

of salts, thereby lowering the levels of bicarbonates at these sites (Hayes and Anthony, 1958;

Kadlec, 1962; Kumar et al, 1987; Zutshi, 1992 and Kaul, 2000). Also, the conversion of

insoluble marls (present in effluents) into soluble form by free carbon dioxide may account

for higher records of bicarbonates at stations II and III.

Pearson’s correlation application on the data showed bicarbonates shared

significantly positive correlation to pH (r = 0.79) and DO (r = 0.76) but negative correlation

to free carbon dioxide (r = -0.75) and chloride (r = - 0.76) (Table 45). 2 way ANOVA

analyses indicated that the values of bicarbonate showed significant variations both among

the stations and months (Table 46).

Chloride (Cl-)

Chloride is one of the essential micronutrients and naturally occurring anion that

occurs in all the natural waters in widely varying concentrations. It behaves as a conservative

ion in most aqueous environments that is considered to equalize the cation and anion balance

of aquatic systems. It originates from the dissociation of salts such as sodium chloride or

calcium chloride in water.

NaCl (s ‒› Na+

(aq) + Cl- (aq)

CaCl2 (s ‒› Ca2+

(aq) + 2Cl- (aq)

The ecological significance of chloride lies in its potential to regulate salinity of water

and exert consequent osmotic stress on biotic communities (Shinde et al., 2011). Chloride is


Page 97

non toxic to humans but elevated levels make water unpotable due to salty taste. It is

apparent that chloride can be taken as one of the indices of water pollution and high

concentration can act as ‘advance warning’ of the presence of more toxic contaminants

Inspecting the table 34 and figure 27, it was noticed that during the first year of

present study (2011- 2012), chloride varied from 15.41 mg/l + 3.06 (January) to 31.99 mg/l +

1.23 (June) and 17.35 mg/l +1.69 (Jan) to 40.91 mg/l +14.42 (June). The figure 27 further

revealed that the summers recorded maxima in the values of chloride which could be

attributed to:

High temperature and increased decomposition of organic matter

Increased evapo- transpiration leaving the salts behind (Raghavendra and

Hosmani, 2002 and Shiddamallayya and Pratima, 2008)

Low water level (Harrison, 1999; Ahmed, 2004., Singh et al., 2010 and

Prabhakar et al., 2011)

Present findings of summer maxima also gets strengthened by the works of Harshey

et al. (1982), Saksena et al. (2008), Garg et al. (2009), Singh et al. (2010), Gadhia et al.

(2012) and Sunkad (2013).

The winter months on the other hand, witnessed the minimum values of chloride ions

at all the stations of river Tawi which could probably be due to low temperature that lowered

the rate of decomposition of organic matter thereby lowering the release of chloride ions.

Least value of chloride ions could also be due to dilution effect as advocated by Chourasia

and Adoni (1985). Sedimentation rate on relatively stable environmental condition and low

water temperature could also be accounted for the winter minima in the chloride values

(Gonzalves and Joshi, 1946; Zafar, 1964; Kaushik and Saxena, 1991; Kaushik and Saxena,

1999; Ahmed, 2004; Sawhney, 2008 and Shinde et al., 2011). Lower chloride levels could

also be due to the reason that when water level rises due to winter rains, the consequent

dilution decreases the chloride concentration (Harrison, 1999).

A look at the table 44 gives an insight view of spatial distribution of the chloride

content of river Tawi. The stations II and III receiving pollutants in the form of garbage and


Page 98

sewage showed higher chloride content compared to the stations I and IV. Moreover,

Bhuvaneshwaran et al. (1999), Kumari et al. (2013) and Sunkad (2013) advocated that the

contamination of water from domestic sewage can be monitored by chloride essays of the

water bodies. The higher values of chloride ion at stations II and III downstream location of

these sites (Sunkad, 2013), animal wastes and human excreta (Sunkad, 2013) and organic

matter decomposition (Chattopadhyay et al., 2005).

Increased concentration of chloride is always regarded as an indicator of

eutrophication (Hynes, 1960), pollution due to sewage (Chaurasia and Adoni, 1985) and

deteriorated effect of the effluents on water quality (Moustafa et al., 2010).

Chloride showed significantly positive correlation to free carbon dioxide (r = 0.92)

and sulphate (r = 0.71) but negative correlation to dissolved oxygen when analyzed for

Pearson’s correlations (Table 45). Application of 2-way ANOVA showed variations along

the stations and among the months (Table 46).

Calcium (Ca2+

)

Calcium is an important micro nutrient in an aquatic environment, being present in

high quantities in rocks and plays an important role in growth and metabolism of aquatic

organisms. The calcium ion contributes to the hardness of water. In addition, it also plays a

significant role in the buffering of pH and also affects the carbonate- bicarbonate system in

water bodies (Goldman and Horne, 1983).

During the present studies on river Tawi (2011-2013) (Table 35 and Figure 28),

calcium ion fluctuated between 19.24 mg/l + 1.27 (June) to 43.32 mg/l + 2.61 (January) and

19.03 mg/l + 1.06 (May) to 39.79 mg/l + 0.77 (January).

The maxima recorded in calcium in river Tawi during winter season may be

attributed to:

Low temperature resulting in reduced evaporation. This observation is in line

with (Nath and Srivastava, 2001and Jan, 2005).


Page 99

Increased solubility resulting due to fall in temperature which finds support

from Sunder (1988), Sawhney (2004), Abdel- Satar (2005), Mahdi et al.

(2006), Sawhney (2008), Garg et al. (2009) and Singh and Gupta (2010).

Decline in depth (Chowdhary, 2011).

Whereas, the monsoon months revealed minima in the value of calcium ions which

may be due to:

Frequent rains and dilution by more mixing of fresh water (Moustafa et al.,

2010 and Gadhia et al., 2012).

Similar trend of maxima in calcium ions during winters and minima in monsoon has

also been witnessed by Sah et al. (2000), Singh et al. (2009) and Singh et al. (2010).

A meager rise was seen in the values of calcium during pre-monsoons which might be

due to high salinity, tidal flow and low fresh water mixing (Gadhia et al., 2012).

Higher values of calcium were recorded at polluted stations II and III compared to

stations I and IV (Table 44) which might be due to:

Sewage discharge enriched with Ca2+

ions (Abdel – Satar, 2005).

Release of salts during microbial decomposition of dead organic matter at St

II and III (Hayes and Anthony, 1958).

Structural chemistry as these are positively charged and bind strongly to

negatively charged clay particles (Jones and Jacobsen, 2001).

Large fragments of molluscan shells may also cause elevations in the calcium

and Magnesium concentration (Sugirtha and Sheela, 2013).

The present findings are in accordance with the findings of Rai (1978), Singh and

Bhowmick (1985), Malviya et al. (1990), Kulshreshtha et al. (1991). Nandan and

Patel (1992) and Kaul (2000).

According to Spence (1967) natural waters may be categorized on the basis of

calcium concentration as nutrient poor (up to 15 mg/l), moderately nutrient rich (15 mg/l to


Page 100

60mg/l) and nutrient rich (above 60mg/l). In the presently studied water body the calcium

level fluctuated from17.37 mg/l to 42. 17 mg/l (Table 34) and thus on the basis of the

observations of op. cited author, the present water body can classified as moderately nutrient

rich.

Calcium shared significantly positive correlation to magnesium (r = 0.94) and pH (r =

0.89) (Table 45). Calcium varied significantly both along the stations and among the months

on application of 2-way ANOVA (Table 46).

Magnesium (Mg2+

)

Magnesium is often associated with calcium in all kinds of waters and is required by

chlorophyllus plants as the porphyrin component of chlorophyll molecules and as a

micronutrient in enzymatic transformation of organisms (Dagaonkar and Saksena, 1992).

Magnesium concentration is relatively conservative and therefore, exhibits less fluctuation

both in hard and soft waters. Having higher solubility than calcium, adds to the hardness of

the water body together with the calcium and both of them play an important role in

antagonizing the toxic effects of various ions by neutralizing excess acid produced

(Munawar, 1970).

A look at the table 36 and figure 29 indicated, that magnesium level fluctuated from

17.37 mg/l + 0.94 (May) to 42.17 mg/l + 2.48 (January) and 17.86 mg/l + 0.99 (June) to

38.91 mg/l + 0.61 (January) during both the years of study (2011-2013).

The maxima of magnesium concentration recorded during winter season may be

attributed to:

Increased solubility at low temperature (Otsuki and Wetzel, 1974; Sawhney,

2008 and Chowdhary, 2011).

Decreased evaporation and sharp decline in depth (Sunder, 1988; Baba, 2002

and Sawhney, 2008).

Perusal of the table 35 and figure 28 depicted that magnesium showed minimum

values during the monsoon months which may be due to frequent rains that causes dilution of


Page 101

the river water by adding more of fresh water. This observance is in accordance with

Moustafa et al. (2010). In context to present observations Sah et al. (2000), Garg et al. (2009)

and Singh et al. (2010) also found minimum magnesium hardness during rainy season and

maximum in winter season in Thoubal River.

Variations in magnesium concentration have been attributed to different bio

geochemical activities in water ecosystems (Murthuzasab et al., 2010). Along the profile of

river Tawi higher values of magnesium were recorded at polluted stations II and III,

compared to I and IV (Table 45) which might be due to:

Sewage discharge enriched with Mg ions (Abdel – Satar, 2005).

Release of salts during microbial decomposition of dead organic matter at

stations II and III (Hayes and Anthony, 1958).

Structural chemistry as these ions are positively charged and bind strongly to

negatively charged clay particles (Jones and Jacobsen, 2001).

Large fragments of molluscan shells may also cause elevations in the calcium

and magnesium concentration (Sugirtha and Sheela, 2013).

The present findings are in accordance with the findings of Malviya et al. (1990),

Kulshreshtha et al. (1991) and Kaul (2000).

During the present study levels of magnesium showed lower values than that of

calcium in river Tawi which may be due to:

Preponderance of Ca over Mg in sedimentary rocks (Abdel – Halim, 1993).

Also, the behavior of dissolved carbon dioxide in water which may affect the

concentration of magnesium in solution, when carbon dioxide present in

appreciable concentration, it reacts with calcium salts more than with

magnesium, thus converting large quantities of calcium into soluble

bicarbonates (Abdel – Halim, 1993; Abdel – Satar, 2005), Malik and Pandit,

2006; Garg et al., 2009 and Murthuzasab et al., 2010).


Page 102

Magnesium shared significantly positive correlation to calcium (r = 0.94) and pH (r =

0.89) (Table 45). Magnesium varied significantly both along the stations and among the

months on application of 2-way ANOVA (Table 46).

Biological Oxygen Demand (BOD)

BOD a pollution indicator, is the amount of oxygen utilized by micro organisms for

the oxidation of decomposable organic matter. The biodegradation of organic materials

exerts oxygen tension in the water and increases the BOD (Abida and Hari Krishna, 2008). It

determines the strength of sewage, effluents and other polluted waters and provides data on

the pollution load in all natural waters. According to Bureau of Indian Standards (BIS)

specifications, BOD value of potable water should be zero. But BOD value of 3-6 mg/l is

permissible. BOD therefore, is an important parameter indicating the scenario of freshwater

bodies (Bhatti and Latif, 2011).

During the study period, BOD was observed to oscillate between 0.52 mg/l + 0.19

(October- November) and 2.27 mg/l + 0.70 (May) during the first year of study and between

0.42 mg/l + 0.12 (Jan) to 2.9 mg/l + 0.71 (June) during the second year of study (Table 37

and figure 30). BOD content was found to be maximum during summer and minimum in

winter season. Similar trend in the variations in BOD has also been reported by Ahipathy and

Puttaiah (2006).

High values of BOD attained during summer might be attributed to:

High rate of organic matter decomposition (Gadhia et al., 2012 and Sunkad,

2013)

Rising water levels (Ahmed, 2004)

Highest biological activity (Ashu and Kumar, 2010)

High temperature (Dubey and Ujjania, 2013)

Less water current (Sanap et al., 2006 )

Similar observations have also been made in different water bodies by

Bhuvaneshweari and Devika (2005), Garg et al. (2009) and Heety et al. (2011).


Page 103

The minima obtained in the BOD values during winter months could be due to lower

water level (Ahmed, 2004), lower temperature which ultimately lowered the process of

decomposition (Dubey and Ujjainia, 2013 and Kumari et al., (2013) and decrease in

microbial activity (Shiddamallayya and Pratima, 2008). Moreover, high levels of DO

obtained during winter season could be responsible for low levels of BOD. Such findings are

further strengthened by a negative correlation between DO and BOD (Table 45). Such

correlation has also been reported by Sunkad (2013).

A winter minimum in BOD values has also been reported by Raghavendra and

Hosmani (2002), Sachidanandamurthy and Yajurvedi (2004), Pathak and Mudgal (2004),

Heety et al. (2011).

A further look at the table 44, revealed that minimum level of BOD was present at

stations I and IV while higher levels were recorded from stations II and III. Such variations

along the longitudinal profile of the river could be attributed to:

Organic enrichment (Kumari et al., 2006 and Saksena et al., 2008).

Decay of plants and animal matter in the river (Saksena et al., 2008).

Agricultural wastes (Murthuzasab et al., 2010).

Domestic sewage (Ahmed, 2004; Moustafa et al., 2010 and

Murthuzasab et al., 2010).

Similar observations have already been put in records by Kamal et al., (2007) and

Kumari et al., (2013).

BOD shared significantly positive correlation to free carbon dioxide (r = 0.84)

but negative correlation to dissolved oxygen (r = -0.61) and pH (r = - 0.87) (Table 45).

BOD varied significantly both along the stations as well as among the months (Table

46).

Nitrate (NO3-)

Nitrate, which is the highly oxidized form of nitrogen, is the end product of aerobic

decomposition, found in aquatic environment and a vital nutrient for growth, reproduction


Page 104

and survival of organisms. Presence of nitrates in water indicates the final stage of

mineralization (Nema et al., 1984). The presence of nitrates in a lotic system depends mostly

upon the activity of nitrifying bacteria, stream currents and catchment characteristics (Singh

et al., 2010). Nitrate in surface water is an important factor for water quality assessment

(Johnes and Burt, 1993).

The nitrates were found to be completely absent at all the stations in monsoon months

during the study period (2011-2013) and showed the peak in summer months and varied from

0 (July- August) to 0.74 mg/l + 0.08 (June, 2011) and from 0 (August) to 0.78 mg/l + 0.22

(June, 2012) (Table 38 and Figure 31).

The summer maxima in the value of nitrates during the study period may be due to:

Nitrates usually built up during dry seasons (Adeyemo et al., 2008 and

Ahwange et al., 2012)

Oxidation of ammonia by nitrifying bacteria and biological nitrification

(Swami et al., 1996)

Increased phytoplankton excretion (Swami et al., 1996; Govindasamy et al.,

2000).

Bacterial decomposition of planktonic detritus present in the environment

(Swami et al., 1996; Govindasamy et al., 2000).

Organic matter decomposition (Paulose and Maheshwari, 2007)

Increased evaporation (Garg et al., 2009)

A similar summer maximum was also recorded by Gurumayum et al. (2001),

Banerjee and Gupta (2010), Murthuzasab et al. (2010), Thakre et al. (2010), Khondker and

Abed (2013) and Singh et al. (2013).

The absence of nitrates during monsoons may be due to:

Influence of river discharge washing out of nitrates during the period of high

rain without assimilation, short residence time , and slow uptake rate of nitrate

by phytoplankton (Xavier et al., 2005) ,

Dilution caused by fresh water inflow (Ishaq and Khan, 2014).


Page 105

Moreover nitrate content of sediments also depicted their complete absence in

monsoons (Table 17 & 60).

Well marked variations in nitrate content were observed in all the sampling stations

(Table 44). Among the stations, maximum content was recorded from stations II and III

compared to stations I and IV. Higher values along these stations II and III could be due to

High load of organic matter ( Singh et al., 1998) ,

Waste discharge from the city area (Prakasam and Johnson, 1992; Royer et

al., 2004) ,

Decomposition and biodegradation of organic matter (Sunkad, 2013 and

Sharma et al., 2011).

Sewage disposal in the river (Chattopadhyay et al., 2005 and Singh et al.,

2013).

Run- off water from agricultural lands (Royer et al., 2004 and Singh et al.,

2010).

Nitrate levels over 10mg/l in natural water normally indicate man made

pollution but the presently measured values in river Tawi were found to be within the

limit range as shown in table 38.

Nitrates showed significant correlation to phosphates (r = 0.88) and sulphates (r =

0.84) (Table 45). Application of 2-way ANOVA showed significant variations only among

the months (Table 46).

Phosphate (PO42-

)

Phosphorus being an important factor in ecological studies is available as phosphate

(PO42-

) in natural waters. In fresh water, it is the first limiting nutrient for plants which

regulates the phytoplankton production in presence of nitrogen. Phosphate plays a dynamic

role in growth of organisms and has a great significance in water quality analysis. It occurs in

low to moderate concentration, but as non toxic to people and other organisms. Excess

accumulation of phosphates in rivers, is responsible for eutrophication.


Page 106

Seasonal variations in phosphate levels in waters of river Tawi revealed its complete

absent during monsoons at all the stations and fluctuated from 0 mg/l (July- August) to 0.07

mg/l + 0.009 (June) and from 0 mg/l (July- August) to 0.32 mg/l + 0.11mg/l (June) during

the study period (2011- 2013) (Table 39 and Figure 32) .

The summer maxima in the phosphate could be related to:

Increased temperature.

Greater evaporation (Swaranlatha and Rao, 1998 and Garg et al. 2009,).

Concentration of water during summer (Swaranlatha and Rao, 1998).

Autochthonous origin (Swaranlatha and Rao, 1998).

Accelated decomposition of organic matter (Ahwange et al. 2012).

Also, low water circulation could be implicated in the high PO42-

content

(Ahwange et al. 2012).

Similar observations have been put forth by Blum (1957), Khanna et al. (2006), Garg

et al. (2009), Jemi and Balasingh (2011), Dubey and Ujjania (2013). Contrarily,

Murthuzasab et al. (2010) and Singh et al. (2010) found high phosphate content in monsoons

and lower during summer.

The variations in the phosphate followed the same pattern as the variations in nitrates

and showed complete absence during the monsoons which could be attributed to the slow

pace of decomposition, dilution effects caused by the rains (Ishaq and Khan, 2014) and

higher water level. Also, monsoonal rains flush out the nitrate and phosphate along with

other nutrients accumulated during the pre- monsoonal months as also supported by the

observations laid by Blum (1957), Jemi and Balasingh, (2011) Khondker and Abed (2013).

They further regarded that monsoons play a significant role in self purification capacity of

river. Similar observations have already been reported by Patra et al. (2011).

Spatial variations in values of phosphate revealed an increase from upstream to

downstream sites which could due to:

Release of untreated sewage (Welch, 1952, Shaikh and Yeragi, 2004, Saksena

et al., 2008).


Page 107

Frequent washing and bathing activities at stations II and III (Raut et al.,

2011).

Agricultural drainage (Saksena et al., 2008, Thakre et al., 2010).

Faecal matter of human and animal origin (Sharma et al., 2010)

Relation with organic matter as evidenced (Abdel- Satar, 2005).

Lowering in input of the pollutant at stations I and IV (Singh et al., 2013)

Due to joining of many inlets downstream (Thakre et al., 2010)

Relation to sediment phosphate values (Table 60).

In the presently studied water body the phosphate level was found to be ranging from

0.07 mg/l to 0.32mg/l which was found to be in permissible limit as the values above 0.5mg/l

indicate polluted conditions (Jain et al., 1996).

Phosphate ions when analyzed for Pearson’s correlation showed significantly positive

correlation to free carbon dioxide (r = 0.82), chloride (r = 0.75) and negative correlation to

velocity (r = -0.10), dissolved oxygen (r = -0.37) (Table 45). 2-way ANOVA (Analysis of

Variance) showed significant variations along the stations and among the months (Table 46).

Sulphate (SO42-

)

The sulphate (SO42-

) is the most abundant form of sulphur, usually second to

carbonate, occurring in the fresh waters. Ecologically, the ion is important for the growth of

plants and shortage may inhibit the development of planktons. The ion being important in

protein metabolism is equally important in determining the suitability of natural waters for

public and industrial supplies.

The monthly variation in the sulphate content varied from 0mg/l (July-August) to

0.08 mg/l + 0.036 (June) and 0 mg/l (August) to 0.87 mg/l + 0.006 (June) (Table 40 and

Figure 33) during both the years of study (2011- 2013).

Sulphate ions recorded maxima during the summer months which may be due to:

Greater evaporation (Garg et al., 2009)


Page 108

Biological oxidation of sulphur species may add sulphate to water (Raut et al.,

2011; Ganga et al., 2014).

Low water level (Raut et al., 2011).

While monsoon witnessed minima/ complete absence in the sulphate values which

could be due to dilution by addition of new rain water (Raut et al., 2011) add due to lesser

rate of decomposition of organic matter. Moreover, absence of any industrial pollution in the

vicinity of sampling stations could also account for lower sulphate contents in monsoon

season as also reported by Umamaheshwari and Saravanan, (2009). Monsoon minima/

complete absence in the sulphate values could also be correlated to the minima in the

sediment sulphate values in river Tawi (Table 19 & 60).

Similar summer maxima and winter minima in sulphate values is well documented on

different water bodies by different workers viz., Ashu and Kumar (2010), Sharma et al.

(2010), Venkateshraju et al. (2010), Sujitha et al. (2012), Sunkad (2013) and Ganga et al.

(2014). Maya et al. (2007) advocated that sulphate being many fold higher in non- monsoons

season than that of monsoons in Periyar river in Kerala.

Perusal of the table 44 indicated that station- wise variation of sulphate followed the

pattern as: station III > station II, station IV > station I with maximum concentration at

stations II and III while minimum at stations I and IV which could be due to:

Use of detergent and soap by city dwellers (Kamal et al., 2007 and Ganga et

al ., 2014)

Sewage discharge (Kumari et al., 2006 and Ganga et al., 2014).

High organic matter decomposition at these sites (Vaishali and Parikh, 2013

and Ganga et al., 2014).

Dumping site of garbage.

Anthropogenic stress (Grasby et al., 1997).

Pearson’s correlation coefficient showed significant positive correlation to total

dissolved solids (r = 0.58) and sulphate (r = 0.93), chloride (r = 0.75) (Table 45). Application

of 2-way ANOVA revealed significant variations between stations and months (Table 46).


Page 109

Total Suspended Solids (TSS)

TSS is a physical property of water that affects the light scattering of the water. TSS

is typically composed of fine clay or silt particles, plankton, organic or inorganic compounds

or other micro-organisms ranging from 10 nm to 0.1 nm. The ability of the water body to

support diversity of aquatic life begins to lose as the levels of TSS increases in a particular

aquatic system.

In the present study TSS ranged from (Table 41 and Figure 34) 47.5 mg/l +19.52

(December) to 722.5 mg/l + 652.39 (July) during the first year (2011- 2012), while during the

second year (2012- 2013), it ranged from 45.5 mg/l + 21.69 (December) to 560 mg/l +

200.49 (August).

As is evident from the table 41 and figure 34 that maximum amount of TSS were

observed during the monsoons (July- August) which could be accredited to siltation,

deterioration and heavy precipitation (Raut et al., 2011). Such high value of TSS could also

be due to high discharge in the rainy season bringing in soil and other sediments and the

turbulent flow which stirred up the non- living matter like silt and sand at the bottom of the

river (Sachidanandamurthy and Yajurvedi, 2004; Shiddamallayya and Pratima, 2008; Ishaq

and Khan, 2014). High TSS during monsoons could also be related to lower transparency

(Sujitha et al., 2012).

On the other hand, the minima recorded during winters could be referred to the slow

velocity of the river with smooth flow that causes the least erosion of the banks of the river

resulting in low TSS. Such relationship of velocity and TSS has also been shown by Ishaq

and Khan (2014). TSS can be influenced by changes in pH that will cause some of the solutes

to precipitate or settle down (Sujitha et al., 2012).

Khabode et al., (2002), Khanna and Bhutiani (2003) and Sujitha et al., (2012)

reported maximum TSS in monsoon, moderate in summer and minimum in winters, which

supports the present observations.

Perusal of the table 44 indicated maximum TSS at station III followed by station II,

IV and I which could be due to:


Page 110

Anthropogenic stress in the form of discharge of municipal wastes (Akan et

al., 2012)

Domestic sewage (Kuamri et al., 2006; Heety et al., 2011)

Organic matter concentration

The nature of bottom also contributed to the varied levels of TSS.

In context to present observations Patra et al., (2011) observed high value in

downstream stations.

TSS showed significantly negative correlation to transparency (r = -0.53) and DO (r = -0.55)

(Table 45).

Total Dissolved Solids (TDS)

TDS is a measure of inorganic salts that mainly consists of carbonates, bicarbonates,

chlorides, sulphate along with organic materials and other dissolved materials in the water.

Water with TDS indicates more ionic concentration, which affects the quality of running

waters and is unsuitable for any other purposes.

During the study period the values of TDS observed to fluctuate between 72.5 mg/l

+12.99 (October) to 322.5 mg/l + 40.23 (January) and 60 mg/l +18.73 to 13.75 mg/l + 52.36

during the study period (Table 42 and Figure 35).

The observed values showed maxima during winter months coinciding with the peak

shown by the inorganic ions viz; Ca, Mg, CO32-

, and HCO3- (Table… Similar observations

have also been documented by Lashari et al., (2009). It could also be attributed to the fact

that river water carries different types of solid wastes during winter season (Prabhakar et al.,

2011). Also, the lowered water level in river leads to the concentration of dissolved minerals

in water (Mohamed, 2008; Elsayed, 2009; Moniruzzaman et al., 2009).

Consequently, minimum values of TDS obtained during monsoons could be

suggestive of the fact that runoff water during the rainy season only contributed to the

dilution of the river (Izonfuo and Bariweni, 2001; Moniruzzaman et al., 2009). Gadhia et al.

(2012) also observed higher TDS in dry season than in rainy season.


Page 111

The minima in TDS and maxima in TSS give an expression of inverse relationship

between the two parameters which in turn is supported by the findings of Charkhabi and

Sakizadeh, 2006 and Patra et al. (2011).

Along the longitudinal profile of river Tawi (Table 44), maximum TDS was recorded

from station III and minimum from station I which could be due to:

Discharge of untreated sewage water (Heety et al., 2011; Annalakshmi and

Amsath, 2012)

Washing and Bathing

Anthropogenic stress

Organic matter

Kamal et al., (2007) also observed higher values in downstream stations. TDS content

in fresh water ranges from10 to 500 mg/l (Kumari et al., 2013) whereas in the present studies

it varied from 60mg/l to 322mg/l.

Discharge value of river (Q)

Discharge value (Q) is a factor that could influences the mobility and deposition of

sediments at a particular basin. Increased discharge values along with the total suspended

solids increased the turbidity of the system.

During the present study (March, 2011- February, 2013), the discharge values varied

from 1.15 m3s

-1 + 0.85 (December) to 25.41 m

3s

-1 + 25.04 (August) and 0.42 m

3s

-1 + 0.16

(February) to11.90 m3s

-1 + 8.26 (August) as shown in table 43 and figure 36.

Maxima in the discharge during monsoons could be due to high velocity that

gradually eroded the banks of the river and increased its width and area while minima

recorded in winters could be related to the lowest depth and width of river during the season.

As indicated by the table 44, station III showed maximum discharge value while

station II showed the minimum discharge value during first year of study, whereas during the

second year of study, the stations II and III showed the minimum discharge values and


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station I showed the maximum discharge values which could again be related to the location

and area of these sites (Kusimi, 2008).

Kusimi (2008), opinioned that discharge is affected

By reduction in vegetative cover.

Erosion by runoff into the river channel.

Increase in catchment area.

Discharge value shared significantly positive correlation to velocity (r = 0.92) and

TSS (0.57) and negative to transparency (r = -0.11) (Table 45).

4.3 Biotic parameters

Seasonal and spatial variations in the macro- benthic invertebrate diversity play a

central role in exploring the ecology of aquatic ecosystems. During the present investigative

studies extending from March, 2011 to February, 2013, a total of 27 species of macro-benthic

invertebrates belonging to three phyla: Annelida, Arthopoda and Mollusca were recovered

from river Tawi (Table 47- 54, Figure 37 and Plate 5- 9). Phylum Annelida was

taxonomically represented by classes Oligochaeta [Family Tubificidae (4 sp.) and

Lumbricidae (1 sp.)] and Hirudinea (1sp.). This phylum contributed 19.02% to the overall

population during study period of two years while the diversity recorded was 41.42% and

11.19% during first and second years respectively (Figure 37, 38 A&B).

In river Tawi Phylum Arthropoda was categorized as the prime contributor thus

contributing 70.27% (Figure 37) to the total macro- benthic population. During the first and

second year of study, their percentage contribution was 43.94% and 79.47% respectively.

This phylum was taxonomically represented by 6 orders viz; Ephemeroptera (3 sp.),

Trichoptera (2sp.), Odonata (1 sp.), Hemiptera (1 sp.), Coleoptera (4 sp.) and Diptera (7 sp.)

belonging to class Insecta (Figure 38 A&B).

Phylum Mollusca represented by single class Gastropoda (3 sp.), though contributed

10.71% (Figure 37), to the overall diversity but shared 14.64% and 9.34% during the first

and second years of study (Figure 37, 38 A&B).


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4.3.1 Seasonal variations in macro-benthic invertebrate fauna

Perusal of table 55 and figure 43 further revealed the seasonal variations in the

diversity and density of macro-benthic invertebrates and observed a well marked peak in the

quantitative distribution of macro-benthic invertebrate fauna during the summer and winter

season with a fall during monsoon season and winter rains.

Maximum abundance of benthic macro-invertebrate faunal assemblage in river Tawi

during the summer season may be due to:

Reduced water inflow (Sawhney, 2008), that gives an opportunity to benthic

organisms to colonize.

Increased temperature that caused increased organic production (Sharma,

2002; Sawhney, 2008).

Increased rate of decomposition producing large amount of detritus which

serve as dietary item for benthic invertebrates (Saini, 2009 and Elipek et al.,

2010).

Reduced water depth (Sawhney, 2008 and Scharold et al., 2010).

The present findings draw support from the works of Barna (2007), Mushtaq (2007)

and Sawhney (2008).

In river Tawi the macro-benthic population abundance observed during summer

months could be attributed to the numerical density of Oligochaetes in general and Tubifex

tubifex in particular (Table 55), such a peak during summer months could be attributed to:

Organic enrichment as Oligochaetes favors organically rich conditions and

remains dominant in severally polluted conditions (Hawkes, 1979; Takeda,

1999; Nocentini et al., 2001; Callisto et al., 2005; Bouchard, 2004;

Chakraborty and Das, 2006; Gasim et al., 2006 and Manoharan et al., 2006).

Provision of favorable environment for inhabitation of Oligochaetes and

Hirudinea (Sheyla et al., 2006).

Considerable oxygen depletions as oligochaetes can live in extremely polluted

waters (Brinkhurst and Cook, 1974).


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Availability of suitable food materials (Slepukhina, 1984; Lauristen et al.,

1985; Monakov, 1998).

Higher water temperature as Oligochaetes exhibited greater reproduction rate

at higher temperature within optimum limits (Aston, 1973; Sunder and Subla,

1986; Nijboer et al., 2004).

Population of Hirudinea was also observed that could be accounted to the accidental

emergence of these individuals along with the bathing of cattle.

Phylum Mollusca represented by class Gastropoda showed seasonal variations with

maximum abundance in summer months during both the years of study. The maxima during

summer season could be related to organically rich bottom that provides suitable food

materials and reproduction site for the individual as reported by Jose and Salas (2007). A

higher count of Gastropods recorded during summers may be due to the effect of

reproduction of these macro- benthic invertebrates as gelatinous egg mass and small sized

Molluscs were observed in collection during this period (Dutta and Malhotra, 1986 and

Sharma et al., 2010). Polluted conditions caused by increased rate of decomposition of

organic matter during summers could also account for the abundance of Lymnaeidae and

Gastropods as they are known to respond to polluted environment by increase in abundance.

The present findings also draw support from the works of Bouchard (2004). Adeogun and

Fafioye (2011) related the presence of Gastropods to their tolerance of some levels of

pollution.

The second peak acquired by benthic fauna during winter season may be due to:

Low predation pressure (Habeeba et al., 2012 )

Low turbidity (Sharma et al., 2010).

High dissolved oxygen favoring growth of the organisms.

Low water temperature (ChandraKiran, 2011).

Reduced water depth (Bechara, 1996).

In the contribution to the total macro- benthic invertebrate population a second peak

acquired by fauna was due to the dominance of Pentaneura sp. belonging to order Diptera


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(Table 55 and Figure 43) of family Chironomidae. Similar abundance of chironomids in the

macro- benthic invertebrate fauna was also recorded by Arab et al. (2004), Sawhney (2008),

Aura et al. (2011), Chandrakiran (2011), Sharma et al. (2011), and Sharma et al. (2013).

Pentaneura sp. larvae showed its peak during winter months which could be

attributed to low water level in the river (12.87 to 9.5 cm), along with sluggish movement of

water during these months as suggested by Paolette et al. (1980), Dutta and Malhotra (1986)

and Sunder and Subla (1986). During winter season low predation pressure, high dissolved

oxygen and low turbidity makes favorable condition for benthic communities (Habeeba et

al., 2012). Dipterans were noticed throughout the river as they have the capacity to adapt

varied aquatic habitats due to their extraordinary organization (Verma and Saksena, 2010).

Specific abundance of Pentaneura sp., indicates the pollution status of stream as

Chironomids are known to prefer polluted water with high nutrients and low oxygen as

suggested by Callisto et al. (2005), Clemente et al. (2005), Olomukoro and Ezemonye

(2006), Manoharan et al. (2006) and Sharma et al. (2011). Chatzinikolaou et al. (2006) also

observed maximum abundance of Diptera (Chironomidae) at sites under human pressure.

Perusal of the table 55 also revealed that the numerical density of other orders

belonging to phylum Arthropoda i.e., Ephemeroptera, Trichoptera, Odonata, Hemiptera and

Coleoptera was much lower as compared to Diptera. Lower abundance of these groups in

comparison to Diptera could be due to the sensitivity of these groups to anthropogenic stress

and wide adaptive capacity of dipterans (Verma and Saksena, 2010). Such lower abundance

has already been reported by Fernandez and Ruff (2006) and Risservato et al. (2009).

The characteristics fall in the peak of benthic invertebrates during monsoonal (July-

August) and winter rains (January-February) could be attributed to:

Flushing of benthic bed causing instability in the existing substratum

(Ysebaert et al. 2003; Sawhney, 2008).).

Increased siltation along with raised water level captured benthic beds (Singh,

2004).


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Influx of allochthonous and autochthonous materials along with rain water

increased turbidity and lowered the transparency due to increased sediment

load (Sharma et al., 2010; Collin et al., 2011; Habeeba et al., 2012,).

High predation pressure (Ishaq and Khan, 2013).

A fall in the population of benthos during the flooding conditions has also been

observed by Zutshi (1992), Thakial (1997), Sharma (1999), Sawhney, (2004) and Beche et

al. (2006).

4.3.2 Population dynamics of macro-benthic invertebrate fauna

Along the longitudinal profile of river Tawi, the percentage contribution of different

taxonomic groups, exhibited dominance of Arthropods followed by Annelids and Molluscs

(Table 47- 54 and Figure 39- 42) at all the stations. Malhotra et al. (1990), Duran (2006) and

Mohan et al. (2013) also recorded similar order of dominance in their respective water

bodies. The orders of dominance of different taxonomic groups to the total macro-benthic

assemblages revealed at different stations have been depicted as:

Station I

First year: Ephemeroptera (81.23%)> Trichoptera (12.20%)> Diptera (5.74%)> Odonata

(0.82%).

Second year: Ephemeroptera (61.16%)> Diptera (21.09%)> Trichoptera (17.75%).

Station II

First year: Diptera (76%)> Gastropoda (11%)> Oligochaeta (8.96%)> Coleoptera (6.90%)>

Hemiptera (3.45%)> Hirudinea (0.58%).

Second year: Oligochaeta (50.50%)> Diptera (46.86%)> Hemiptera (1.55%)> Gastropoda

(1.09%).


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Station III

First year: Oligochaeta (50.34%)> Diptera (25.89%)> Gastropoda (21.16%)> Coleoptera

(0.45%)> Hirudinea (0.16%).

Second year: Diptera (79.83%)> Gastropoda (12.4%)> Oligochaeta (7.77%).

Station IV

First year (2011-2012): Oligochaeta (56.5%)> Trichoptera (17.5%) = Diptera (17.5%)>

Gastropoda (5 %)> Ephemeroptera (2.5%)> Hirudinea (1%).

Second year: Diptera (91.42%)> Coleoptera (3.10%)> Gastropoda (2.07%)>

Ephemeroptera (1.45%)> Trichoptera (1.33%)> Oligochaeta (0.63%).

Review of table 47- 54, longitudinally revealed numerical variations in the benthic

invertebrates along the profile at different stations of river Tawi. Maximum species diversity

was recorded at station II (during the first year of study, 2011-2012) and at station IV

(during the second year of study, 2012-2013) which could be related to pooled nature of the

river created due to embankment which resulted in accumulation of allochthonous materials

thereby enriching the site in organic matter (Dutta and Malhotra, 1986; Thakial, 1997;

Sharma, 1999; Sharma, 2002 and Singh, 2004). Also, species diversity is higher in

heterogeneous sediments than in the homogenous sediments (Boyden and Little, 1973).

While station I recorded the lowest species diversity (during both the years of study) which

could be due to high velocity of water and high water currents which does not allow the

benthic organisms to flourish. Presence of Ephemeroptera, Trichoptera at station I clearly

reveal clean and clear status of the station with the least anthropogenic influence as these

are known to be the indicators of clean water conditions (Ocun and Capitulo, 2004).

Diversity indices give better information about the environmental conditions under

which the organisms live (Gaufin 1973; Hawkes 1979; Teles 1994) than a consideration of

individual taxa alone. Diversity indices are important to understand a particular biotic

community and reflect the changes in the community structure with pollution stress in the

environment (Stoyanova et al., 2010). Indices viz; Shannon- Weaver index (Hꞌ) (1949),


Page 118

Simpson’s dominance index (c ( 4 , Margalef’s richness index (dꞌ) (1958) Pielou’s

equitability index (E) (1966), Sorenson’s index (Sorenson, 4 and Morisita- Horn index

(Morisita, 1959) when applied on macro-benthic fauna of river Tawi, exhibited well marked

variability at all the stations of river during both the years of study (Table 56).

Perusal of the table 56 further revealed higher value for Shannon-Weaver diversity

and Margalef’s richness at stations II and IV. Higher species diversity at stations II and IV

could be due to pooled nature of the river and heterogeneous texture of sediments that

supports the higher species diversity (Boyden and Little, 1973). Conclusively, higher index

values were recorded when the number of species was high. Hence Margalef index (d)

appears to be dependent upon the number of species and not on the number of individuals as

also supported by Nkwoji et al. (2010).

Although both Shannon-Weaver and Simpson’s index takes into account the

proportional abundance of species, but Shannon- index is more sensitive to rare species and

Simpson’s index puts more emphasis on commonly occurring species Thus, the high values

of Simpson’s index at stations I, IV (during first year) and at station III (during second year)

indicated that these stations are inhabited or colonized by few common or dominant species.

Moreover, Shannon- Weaver and Simpson’s indices were found to be inversely related to

each other as depicted by the table 56. Similar observations have also been put on record by

Simpson (1949) and Green (1993).

Shannon- Weiner diversity index can be utilized to access the status of a water body

and its value less than 2 reflect heavily polluted water (Shekhar et al., 2008). During the

present investigation period, the values of Shannon- Wiener index oscillated within 1.42 to

1.94 (2011-2012) and 0.35 to 1.83 (2012-2013) at all the stations which clearly reflected

heavy pollution in river Tawi Similarly, Margalef’s index varied from 1.14 to 1.63 (2011-

2012) and 0.50 to 0.95 (2012-2013) which again confirmed the polluted nature of river Tawi

as the values of Margalef’s index above are indicative of non- influenced conditions.

Comparison made between stations by using qualitative presence- absence type,

Sorenson’s quotient of similarity (Q/S , stations II and IV were found more similar with

highest value of 52.17% while stations I and III were found least similar with the value of


Page 119

9.5% during the first year of study (2011-2012) (Table 57). On the other hand, during the

second year of study (2012-2013), highest Sorenson’s Quotient value was found between

stations II, III and III, IV i.e., 66.66% while lowest value of 30.76% was found between

stations I, II and I, III (Table 57) . Based on the meristic data i.e. counts of individuals

referring quantitative indices, Morisita-Horn index showed maximum similarity between

stations I and II and lowest similarity between station I and III during the first year of study

(2011-2012). But during the second year of study (2012-2013), maximum similarity was

shown between station III and IV while minimum similarity was shown between station I and

II (Table 57). Mathews (1986) concluded that Morisita –Horn index below 0.50 indicated

low similarities in the relative abundance of species whereas index above 0.75 indicated high

similarities.

COORELATIONS

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5.1 Between sediments and macro-benthic invertebrates

Sediments are the source of organic and inorganic nutrients, and are considered to be

an important abiotic factor responsible for the qualitative and quantitative distribution of

benthic fauna. The benthos, as a result of their direct association with sediments may act as

accumulators and conductors of contaminants by physical, chemical and biological processes

within the sediment they inhabit (Reynoldson, 1987) and are widely used as indicators of

ecological condition because of their variety of responses to human disturbances such as

sedimentation (Rosenberg and Resh, 1993).

Various workers viz; Percival and Whitehead (1929), Cummins (1964), Cummins and

Lauf (1969) also considered substrate as a deciding factor in distribution of macro-benthic

invertebrates. Sediment temperature, sediment texture and organic matter are the most

plausible cause for the variations in macro-benthic invertebrate fauna of any aquatic

biocoenoses. Damodaran (1973), Herman et al. (2001) and Ingole et al. (2002) also

emphasized on the importance of these above given factors for the distribution of macro-

benthic fauna.

During the period of present investigations (March 2011 to February, 2013), sand was

observed to be the main component of sediments besides silt and clay in varying proportions

at all the stations of river Tawi. Substratum at stations I and IV was mainly composed of

COORELATIONS

Page 121

pebbles, stones, boulders, coarse sand with negligible percentage of silt, clay, TOC and TOM

and could be the plausible reason for low diversity and density at these stations as the

pollution free habitat provides habitation to pollution- sensitive taxa viz; Caenis sp., Baetis

sp., Hydropsyche sp. etc.

Presence of these taxa at stations I and IV could be accounted on the fact that such

substratum provide suitable place for the clinging of these organisms. Langford and Bray

(1968) collected the nymphs of Ephemeroptera from clean sand substratum and the

Trichopterans were recorded from the stony substratum by Pliuraite and Kesminas (2004).

Such homogenous type of substratum inhabits lower species diversity (Boyden and Little,

1973). Complete absence of annelids and molluscs at station I could be because of lack of

any anthropogenic activity at or near the site and lack of availability of nutrients at the

bottom. Hassan et al. (2014) found minimum density of molluscs due to absence of clayey or

muddy bottom. While at station IV all the pollutants on their way gets sedimented on the

margins of the river. Moreover, due to self purification capacity of lotic systems, conditions

improve and cause reappearance of Ephemeropterans and Trichopterans at station IV. A

different invertebrate composition in sandy habitats has also been reported by Barton (1988).

In the presently studied water body Ephemeroptera and Trichoptera were found to be

negatively correlated to silt (r = -0.44; r = - 0.57.)

Substratum at stations II and III was sandy loam due to rich contributions from silt,

clay, total organic carbon, and total organic matter. The variations in TOC and TOM are

found to be well in range and could be related to high density and diversity at these stations.

As evidenced by Hyland et al. (2000) TOC levels below 0.05% and above 3% are related to

the decreased benthic abundance and may be an indicative of stressful environment High

diversity and density of macro-benthic invertebrates at stations II and III could also be

correlated to:

Heterogeneous sediments (soft bottoms rich in silt and clay) as soft-bottomed

sediments provide suitable burying substratum to soft- bodied worms

(Slepukhina, 1984; Waters, 1995; Schenkova et al., 2001; Harrison et al.,

2007; Jose and Salas, 2007; Baturina, 2012; Nijboer et al., 2004).

COORELATIONS

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Domestic sewage along with other anthropogenic effluents provide efficient

food materials in the form of organic matter for the growth and reproduction

of benthic invertebrates (Callisto et al., 2005; Clemente et al., 2005;

Olomukoro and Ezemonye, 2006; Manoharan et al., 2006 and Sharma et al.,

2011).

Correlation analysis also revealed a significant positive correlation between Annelids

and sediment temperature (r = 0.57), silt (r = 0.51), clay (r = 0.74), TOC (r = 0.74) and TOM

(r = 0.74) but a negative correlation of the Annelids was observed with sand (r = -0.61)

(Table 58).

The arthropods (due to predominance of Diptera) showed positive correlation to silt (r

= 0.42) and clay (r = 0.64), (Table 58). Also, Cummins and Lauf (1969) reviewed the

relationships between benthic organisms and the substrate type and reported the associations

between chironomid larva and organic matter. Negative correlation was shown to sediment

temperature (r = -0.25) which may be due to the fact that at increased temperature larval

forms of order Diptera have tendency to moult in the successive instars and thereby into adult

terrestrial forms resulting in decline in their number in bottom sediments (Yildiz et al.,

2005). Negative correlation to sand (r = -0.41) could be explained on the fact that coarse sand

value exceeding 69% and above are limiting to Chironomids (Ezekiel et al., 2011).

The macro- benthic invertebrate fauna recorded from river Tawi may be functionally

categorized as:

Sub-surface deposit feeders (Oligochaetes)

Deposit feeders (Pentaneura sp., Culicoides sp., Forcipomyia sp.)

Predators (Odonates, Coleopterans)

Collectors and Scrapers (Ephemeropterans)

Piercers (Hemipterans).

As discussed above the categorization and classification of benthic organisms is

based on the feeding pattern as proposed by Cummins (1973), Edmunds et al. (1976),

Osborne et al. (2000) and Risservato et al., (2009) and Chandrakiran (2011).

COORELATIONS

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5.2 Between physico- chemical features of water and macro-benthic invertebrates

Various water quality parameters viz; temperature velocity, pH,

FCO2, DO, BOD,

hardness (Ca2+

and Mg2+

), alkalinity (CO32-

and HCO32-

) play a direct as well as indirect role

in the distribution of macro-benthic diversity (Ishaq and Khan, 2014) thus supporting the

resistant and sensitive species accordingly (Table 59). On the basis of anthropogenic stress

and changes in water quality (Table 44), stations I and IV, could be inferred as least polluted

sites, while stations II and III may be regarded as the polluted- sites.

At station I, Ephemeropterans, Trichopterans and Odonates dominated the overall

diversity with the meager representation of Diptera while, station IV showed the presence of

Diptera and Gastropods along with Ephemeroptera, Trichoptera and Odonates.

Ephemeropterans, Trichopterans and Odonates are known to be pollution- sensitive and their

number decreased with increased pollution load and vice- versa (Kalyoneu and Zeybek,

2011). The results of the present study also recorded the similar pattern and distribution of

benthic organisms. Such variations in the density and abundance in the distribution of benthic

organisms may be associated with:

High pH (Ocun and Capitulo, 2004).

Increased oxygen content (Ocun and Capitulo, 2004).

Clean water and high transparency with low TSS as these are clean water

indicators (Ocun and Capitulo, 2004).

Less nutrient load and anthropogenic stress (Ocun and Capitulo, 2004).

Analysis of macro-benthic invertebrate fauna from river Tawi revealed high density

and diversity at stations II and III which could be correlated to water quality parameters viz;

low DO, increased FCO2 along with other organic and inorganic nutrients. Moreover, pre-

dominance of pollution- tolerant taxa (Pentaneura sp., Simulium sp., Erastalis sp., etc.) at

stations II and III could be due to:

High temperature as Annelids, Diptera and Molluscs exhibited greater

reproduction at higher temperature (Dutta and Malhotra, 1986; Gasim et al.,

2006; Manoharan et al., 2006; Sharma et al., 2010).

COORELATIONS

Page 124

Reduced water flow causes these dwellers to colonize easily (Sawhney, 2008),

whereas Molluscs are mostly related to lentic ecosystems (Olomukoro and

Dirisu, 2014).

High nutrient load and availability of dissolved nutrients like NO3-, PO4

- etc.

(Elipek et al., 2010)

Enhanced organic decomposition leading to acidic conditions (Sawhney,

2008)

Low DO level and high FCO2 and BOD as the above discussed organism can

tolerate stress conditions (Brinkhurst and Cook, 1974).

Molluscans density at these sites could be related to calcium and magnesium

content, as they incorporate these ions in their shells (Sugirtha and Sheela,

2013).

It is well on records that alteration of aquatic conditions by the domestic and

industrial waste- waters and local environmental conditions leads to variations in the

composition, abundance and distribution of macro-benthic invertebrates. Hence, on the basis

of benthic macro-invertebrate diversity and their different degrees of tolerance to their

environment, station I could be regarded as oligosaprobic whereas, stations II and III as

mesosaprobic. While, conditions at station IV were found to be of mixed type, hence it was

the revival zone of the river.

5.3 Between physico-chemical features of water and sediments

Sediments are highly dynamic in nature and play an outstanding role in limnological

studies of overlying waters (Stronkhorst et al., 2004). Exchange of nutrients between

sediments and overlying water depends upon chemical characteristics of water and that of

sediments (Mortimer, 1971; Wildung et al., 1974). Thus, it becomes inevitable to elucidate

any relationship between chemistry of sediments and that of overlying waters.

Temperature is of immense importance in the study of any aquatic ecosystem. Both

sediment temperature and water temperature in the presently studied water body were found

to be significantly positively correlated (r = 0.99) (Table 60) to each other due to the fact that

the warming of sediments proceeds almost as rapidly as the warming of overlying waters.

COORELATIONS

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This is also confirmed in the findings of William and Lewis (1976) and Sarvankumar et al.,

(2008).

Since most of the chemical reactions in aquatic environment are controlled by change

in pH value, it is an extremely important parameter. pH of overall aquatic ecosystem

(sediment & water) ranged from slightly acidic to alkaline. A significantly positive

correlation (r = 0.73) was recorded between sediment pH and water pH (Table 60).

In any aquatic system DO, FCO2, pH and Temperature of water are directly or

indirectly correlated to total organic matter and total organic carbon of sediments influencing

the distribution of ions in water and sediments. The correlation between chemical properties

of water and sediments revealed that at high temperature decomposition of organic matter

deposited in the bottom sediments increases utilizing the dissolved oxygen, thereby raising

the organic carbon (in sediments), free carbon dioxide and BOD (in water) in the aquatic

system and vice-versa. The present findings also get strengthened from the negative

correlation between DO and OC (r = -0.69), while OC and BOD (r= 0.92), (Table 60)

revealed significant positive correlations.

Inorganic nutrients of both sediment and water (calcium, magnesium, bicarbonates,

chlorides, nitrate, sulphate and phosphate) showed significantly positive correlations (Table

60) to each other which could be due to the ‘sink and source’ of sediments to the overlying

waters (Mucha et al., 2003). Such correlations can also be explained on the fact these

nutrients in both sediments and water showed same pattern of variations between different

stations and seasons.

Another interesting finding observed in river Tawi during the study period was

increased level of limnological parameters in water than that of sediments. Such observations

may be attributable to the low clay content in sediments. This implies that the absence of

sediment sink (low clay) enhances the capacity of nutrients to remain mostly in suspension or

in solution in the river, thus increasing the lifetime of the pollutants in the water column and

their accessibility to the biota (Eteswin et al., 2013).

SUMMARY AND CONCLUSION

Page 126

During the study period, extending from March, 2011 to February, 2013

investigations were carried to study the physico- chemical characteristics of bottom

sediments and water along with qualitative and quantitative assessment of macro-benthic

invertebrates. Realizing the impact of bottom sediments in the distribution of macro-benthic

invertebrates, a comprehensive study involving both these components was practiced. Apart

from this, physico-chemical parameters of water were also analyzed on monthly basis so as

to get a cumulative effect of sediments on water quality.

Bottom sediments of river Tawi were analyzed for several physical and chemical

parameters viz; sediment temperature, pH, moisture content, electrical conductivity, total

organic carbon, total organic matter, sand, silt, clay, bicarbonate, calcium, magnesium,

chloride, nitrate, phosphate, sulphate and sediment load on monthly basis for a period of two

years (March, 2011 to February, 2013). Well marked Spatio-temporal variations were

revealed by all the above discussed parameters.

Physical characterization of sediments revealed that sediment temperature was high

during summers and low during winters showing its close association with water

temperature. pH mostly remained alkaline throughout the study period. Hue of the sediments

varied from grey to brown due to the varied concentration of organic carbon and organic


Page 127

matter. Electrical conductivity (EC) and moisture content was poor for the sandy soils and

high for silt and clay. Sediment texture revealed sand as the major component following silt

and clay. Texture was found to affect the distribution of nutrients, organic carbon and organic

matter. Organic matter (OM) and organic carbon (OC) content in sediments recorded

comparatively low values at all the stations and in sandy sediments their concentration was

found to be low as compared to clayey sediments. Maximum sediment load was recorded

during the monsoons at all the stations due to high amount of total suspended solids carried

by incessant rains to the riverine system. Chemical parameters viz; calcium, magnesium,

chloride, bicarbonate, nitrate, phosphate and sulphate also showed variations in their

distribution along the profile due to variations in texture and anthropogenic load (sewage,

garbage, religious wastes etc).

The results of 2-way ANOVA indicated that all the Limnological variables showed

highly significant variations both along the stations and among the months. When computed

for Pearson’s correlation, several sediment characters were found to be significantly

correlated with each other, thus suggesting a close proximity between them.

Monthly analysis of physico-chemical parameters of water of river Tawi viz; air and

water temperature, pH, depth, transparency, velocity, dissolved oxygen (DO), biochemical

oxygen demand (BOD), free carbon dioxide (FCO2), carbonate, bicarbonate, calcium,

magnesium, chloride, nitrate, phosphate, sulphate, total suspended solids (TSS) and total

dissolved solids (TDS), resulted in well marked seasonal as well as station- wise variations.

Temperature was maximum in summers and minimum in winters while pH, DO, FCO2, BOD

etc. were found to be related to temperature and anthropogenic stress. The limnological

variables showed significant and non- significant variations to each other when correlation

matrix was applied. Application of 2- way ANOVA depicted significant monthly as well as

station- wise variations.

A total of 27 taxa were collected during the assessment of macro- benthic invertebrate

diversity of river Tawi, belonging to three phyla viz; Annelida, Arthropoda and Mollusca.

Phylum Arthropoda dominated at all the stations with Insecta as the single class succeeded

by 6 orders namely Ephemeroptera (3 sp.), Trichoptera (2sp.), Odonata (1 sp.), Hemiptera (1


Page 128

sp), Coleoptera (4 sp.) and Diptera (7 sp.). Order Diptera was numerically abundant of all the

other insect orders being characterized by the dominance of chironomids in general and

Pentaneura sp., in particular. Annelids were represented by classes Oligochaeta (with

numerical abundance of Tubifex sp.) and Hirudinea. Phylum Mollusca was represented by

single class Gastropoda with dominance of Physa sp.

Various diversity indices viz; Shannon-Weaver, Simpson, Margalef and Pielou’s

when applied showed wide fluctuations at all the stations. Sorenson’s index as well as

Morisita- Horn index was applied to record the similarity between the stations. Both these

indices showed the least similarity between station I and III.

Studies on the impact of sediment characteristics on macro-benthic invertebrates

revealed a well established relationship between physico-chemical parameters of sediments

and macro-benthic community. Among the several physico-chemical parameters, sediments

texture, organic carbon and organic matter were observed to be of prime importance. Sandy

texture with stones, pebbles, boulders, low OC and OM was observed to support population

of Ephemeroptera and Trichoptera (which are potentially known to be as pollution-sensitive

groups) at stations I and IV. While the bottom rich in silt, clay and organic matter at stations

II and III provide suitable habitat for the tube- dwelling forms (Tubificidae and

Chironomidae) as these are known to prefer a substratum with moderate sand fraction due to

unsuitability of sand as tube building material.

Physico-chemical variables of water and sediments of river Tawi were though within

permissible limits (as prescribed by Indian Standard specifications), but in some cases were

found to affect the occurrence and abundance of macro-benthic invertebrates. A relationship

between water quality parameters and macro-benthic diversity also revealed that the

distribution of macro-benthic invertebrates to be affected by variations in dissolved oxygen

(DO) and pollution load at the studied sites. Prevalence of Ephemeroptera and Trichoptera at

the least stressed sites and Oligochaetes, Diptera and Gastropods at highly stressed sites

clearly indicates their association with the aquatic environment.

Hydrological parameters of sediments and water showed significant correlations to

each other. But it is pertinent to mention here that sediments which are known to be


Page 129

prominent sink to an aquatic ecosystem, which is not a case as observed in the present

studies. During the study it was observed that the value of the chemical parameters was more

in water than in sediments which could be attributed to the low clay content in the overall

composition of sediments. This implies that the absence of a sediment sink (low clay)

enhances the pollutants capability to remain in suspension or in solution in the river.

Thus, it could be inferred from the characterization of bottom sediments and water,

their impact on macro-benthic invertebrates implies to the alterations in trophic status of

river. The river in its native form was clean with sandy bottom with boulders and pebbles but

external loading of the anthropogenic stress (in form of domestic and sewage waste, washing,

bathing, cattle bathing, garbage disposal etc.) is eventually deteriorating the basic nature of

this water body. These activities are not only polluting the water of this pristine ecosystem

but also poisoning the sediments.

Unfortunately, a sharp decline in the diversity of pollution- sensitive groups and

prevalence of pollution- tolerant groups over them is a serious matter of concern as it is

simply implying to the eutrophic state of water body. Such an alteration in the physico-

chemical as well as biological characterization of river Tawi demands a quick and

comprehensive attention of general masses, limnologists and conservation biologists.

Recommendations

Environmental problems are intricately interwoven between terrestrial and aquatic

habitats. Outcome of the human activities in the form of pollution ultimately enters the water

bodies thereby affecting its flora and fauna. Therefore, there is a need to set common

objectives and implement compatible policies and programs.

Direct disposal and dumping of waste at the riverside should be checked.

Sewage run into the river through various local drains should be treated so

that the eco- biology of the system may not be disturbed.

Various religious practices should be within the limits of eco-tolerance of the

river. The purpose of the present investigations is not to draw a picture of


Page 130

horror and discourage religious activities but to develop baseline data on

physical and biological aspects of this sacred ecological unit.

Frequent activities like bathing, washing, vehicular washing that adds to the

ionic concentration of the river should be avoided.

Frequent dredging of the sediments by sand and stone collectors that

eventually disturbs the natural habitat of the macro-benthic invertebrates

should be under the strict surveillance of the concerned authorities.

Various methods like sediments capping (either by mechanical or active

barrier) and sediment oxidation should be applied to inhibit the release of

nutrients from bottom sediments at highly polluted sites.

Regular monitoring of sediment quality along with bio-monitoring should be

practiced so as to keep a check on the status of this fluvial system.

Citizens need to play a central role in communicating a sense of urgency and

responsibility to their elected officials and other decision-makers and in public

education campaigns to make the connection between contaminated sediment

and ecosystem and economic health.

Therefore there is a need to implement certain policies, programs and laws to

conserve this aquatic ecosystem and preserve its sanctity.

Data and information generated should be disseminated to the citizens and

school children to heighten the awareness among them.

The existing situation if mishandled can cause irreparable ecological harm in the

long-term well masked by short term prosperity.

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International Research Journal of Environment Sciences________________________________ ISSN 2319–1414

Vol. 2(3), 51-55, March (2013) Int. Res. J. Environment Sci.

International Science Congress Association 51

Sediment Characterization of Lower sections of a Central Himalayan river,

Tawi, Jammu (J&K), India

Sharma V., Sharma K.K. and Sharma A. Department of Zoology, University of Jammu, Jammu (180006), J&K, INDIA

Available online at: www.isca.in Received 1st March 2013, revised 4th March 2013, accepted 17th March 2013

Abstract

Information on sediment quality and characterization is an important requirement for water resources development and

management. This paper presents the results of a study that was conducted to assess the sediment physico- chemistry of

river Tawi during a period of twelve months. Sediments were analyzed according to the standard methodology for sediment

particle size (sand, silt and clay), sediment texture, pH, EC, TOC and TOM. Analysis of Variance (ANOVA) and Pearson

correlation coefficient were used to analyze the data. Sand was found to be the major contributor to the sediment texture.

The values of TOC and TOM do not exceeded the acceptable limits but tend to be increasing gradually. Therefore,

environmental surveillance of these parts of the river is strongly recommended.

Keywords: Sediment characterization, sediment texture, TOC, TOM, river Tawi.

Introduction

Sediments play an outstanding role in Limnological studies as

they can both reflect and affect what is occurring in the

overlying waters. They are highly dynamic and active in

character primarily due to various biogeochemical reactions and

transformations occurring within the water body. Sediments in

our rivers provide a natural buffer system and an important

habitat for aquatic organisms1. Because of their variable

physical and chemical properties, they not only act as source

and sink of nutrients in an aquatic system, but also provide a

record of river’s pollution history2- 4

. Sediments act as site for

decomposition of organic matter carried by bacteria which

promotes biological changes and affect the water quality by re

suspension and nutrient release5. Sediments can be either

organic or inorganic, carried by water, wind and ice or other

naturally occurring agents to lakes, streams and rivers. Sediment

texture specifically refers to the proportions of sand, silt and

clay below 2000 micrometers (2mm) in diameter in a mass of

sediment6 (table 1). Sediments comprise many shapes and sizes

ranging from silt, sand, small pebbles to boulders. Sand is

coarse and gritty, silt is smooth and clay is sticky and plastic

when wet7. Unfortunately, overpopulation, local soil erosion and

extensive urbanization adds organic matter to the river bed

which on decomposition releases TOC in the sediments that

adversely effects physico- chemical and biological properties of

the sediments8, eventually deteriorating the productivity of the

overlying waters9- 10

.

The above discussed factors have been deteriorating River Tawi

in Jammu region. Many works have been carried out on the

different aspects of this water body such as physico-chemistry,

plankton and bacterial aspects, benthos, fish and fisheries. But

there has been no information on the sediment quality of river

Tawi, despite of various human activities going on and within it.

The study was necessary to assess the sediment fractions and

some physico-chemical parameters of this aquatic system. So,

the present study was carried out on the sediments of river Tawi

in order to assess the moisture content, particle size, sediment

texture, pH, EC, TOC and TOM and TN. The study of

sediments will be a useful tool for future researchers for actual

assessment of environmental pollution of this aquatic system.

Material and Methods

Study area: River Tawi, (figure 1) a major River in Jammu

region is the left bank tributary of river Chenab originating from

the lapse of Kali Kundi glacier in Bhaderwah, flows through

some parts of Doda district, Udhampur reaches Jammu from

where it finally merges into Chenab in Pakistan. It is an open

and bare river lacking any proper macrophytic growth and

vegetation. This aquatic body receives effluents discharges from

the water front communities, dredging company, manual,

dredging, sewages and garbage disposal etc. Thus, it is essential

in this context to study the sediments of the water body as these

act as ultimate sink for wastes.

Sampling stations: Four sampling stations were selected along

the longitudinal profile of River Tawi, viz; S1, S2, S3 and S4.

Station1 (S1) (near Sainik School, Nagrota), water was

comparatively clean with the bottom composed of stones

andboulders. Station 2(S2) (Circular Road) was about 6

kilometers from st.1, and receives organic load in the form of

religious wastes, crematorium etc. Station 3(S3) (Gujjar Nagar)

at a distance of about 4 kilometers from station 2 and 10

kilometers from station 1. It receives heavy pollution load and

organic matter in the form of sewage and garbage. Station 4(S4)

(near Satwari) is the revival zone of the river which is located at

a distance of about 6 kilometers from station 3 which is again a

clean water zone with the bottom of stones and gravels.

http://www.isca.in/

International Research Journal of Environment Sciences______________________________________________ ISSN 2319–1414



Sediment sample collection and laboratory analysis: River

bed sediments were collected using Ekman’s dredge once a

month from March, 2011 to February, 2012. Sediments were

collected at each sampling station and stored in well labeled zip

lock polyethylene bags and kept in an ice-chest box before

transferring to the laboratory.

Samples were analyzed for moisture content prior to drying.

Sediment samples were then air dried at room temperature in the

laboratory. The dried samples were further crushed to fine

texture using 2.0 mm mesh sized sieve for the estimation of

physico-chemical parameters. Physico-chemical parameters

were determined according to standard methods: Moisture

content: by oven drying method11

, pH: by digital pH meter12

,

electrical conductivity (EC): by using conductivity meter13

,

particle size: by Bouyoucous hydrometer14

, Texture: by textural

triangle software15

, total organic carbon (TOC) and total organic

matter (TOM): by Walkley and Black rapid titration method16

,

total nitrogen (TN): by Kheldahl’s method17

.

Data analysis: Analysis of variance (2- way ANOVA) and

Pearson correlation coefficient were used to analyze the data

using SAS (2003) and Microsoft excel (2007) packages.

Results and Discussion

Sediment particle size: The calculated range, mean and

standard deviation of all the parameters are presented (table2).

Across all the stations, the sand component was found to in

highest proportion over silt and clay. Percentage sand content

ranged from 84.60 % (St.1) to 61.89 % (St.3). Maximum

percentage of silt content ranged from 23.32% (St.3) to 10.42%

(St.1). Highest value for clay was recorded as 11.79 % (St.3)

and lowest of it was recorded as 4.94% (St. 4). Texture was

observed to be Loamy sand at station 1and 4; while it was

observed sandy loam at station 2 and 3. Sediments depend on

the parent material available and deposits of materials18

. At

station 1and 4, sediments were mainly of loamy sand nature

with sand as the major component which may be due

topographical features of the concerned area, due to the

weathering of rocks and frequent dredging of sediments19- 21

. At

station 2 and 3 sediments were of sandy loam nature with silt

and clay in high proportion compared to the sand. High

concentration of silt and clay was due to the deposition and

decomposition of organic matter as these sites received through

sewage and garbage of the city22

. Station with the highest

percentage of clay also had the highest percentage of silt22

.

Variations in the sand, silt and clay content in the bottom

sediments at different stations are also strengthened by 2- way

ANOVA (table 4) which recorded highly significant values for

all components of bottom sediments among stations of the river

Tawi. Sand exhibited significant negative correlation with clay

(r= -0.997) and silt (r= -0.999). But silt and clay shared

significant positive correlation with each other (r= 0.994)22,8

(table 5).

Figure-1

Whole map of study area (a) Station 1(a) (Sainik School, Nagrota) 1(b) Station 2(Circular road) 1(c) Station 3(Gujjar

Nagar) 1(d) Station 4(Satwari)




Table-1

Size limits of sediment particle size in the United State department of Agriculture (USDA) and International Soil Science

Society (ISSS) Schemes

USDA Scheme ISSS Scheme

Name of the Particle Size Diameter Range(µm) Name of the Particle Size Diameter Range(µm)

Very Coarse sand

Coarse sand

Medium sand

Fine sand

Very fine sand

Silt

Clay

2000- 1000

1000-500

500- 250

250- 100

100- 50

50- 2

<2

Coarse sand

Fine sand

Silt

Clay

2000- 200

200- 20

20- 2

<2

Coarse Fragments

Gravels

Cobbles

Stones

2000- 75000µm (2-75mm)

75000- 25400µm (75-254mm)

>254000µm (>254mm)

Table-2

Sediment particle size in river Tawi (from March2011 to February, 2012)

Parameters St.1 St.2 St. 3 St. 4 Range Mean+ S.D

Sand % 84.60 68.21 61.89 84.59 61.89-84.60 74.82+10.02

Silt % 10.42 21.34 23.32 10.47 21.34-10.47 16.38+5.98

Clay % 4.98 10.45 11.79 4.94 4.94- 11.79 8.04+3.11

Textural class Loamy sand Sandy loam Sandy loam Loamy sand

Table-3

Physical and chemical parameters of sediments in river Tawi (from March2011 to February, 2012)

Parameters St.1 St.2 St. 3 St. 4 Range Mean+ S.D

Ph 7.7 7.8 7.7 7.7 7.7-7.8 0.037+3.43

E.C (µs) 0.23 0.24 0.24 0.14 0.14-0.24 0.212+0.04

Moisture % 4.26 5.29 5.56 4.26 4.26-5.56 4.842+0.59

TOC % 0.22 0.33 0.35 0.24 0.22-0.35 0.285+0.05

TOM % 0.38 0.57 0.61 0.42 0.38-0.61 0.495+0.09

TN % 0.019 0.0285 0.0305 0.00105 0.001-0.03 0.019+0.01

Table-4

Values of Analysis of Variance (ANOVA) for stations

Parameters Value

Sand *10.99

Silt *20.05

Clay *6.61

TOC *91.77

*Values are significant at 5%

Table-5

Pearson’s correlation coefficient of Sediment Texture and TOC

Parameters Sand Silt Clay TOC

Sand -

Silt - 0.999* -

Clay -0.997* 0.994* -

TOC -0.987* 0.983* -

*Values are significant at 5%




Sediment physico-chemical parameters: The results of the

physical and chemical parameters of sediments of river Tawi

have been tabulated (table 3). The pH value of the sediments

represented alkaline conditions and fluctuate between 7.7 to 7.8

which may be attributed to the land drainage pollution arising

from commercial and anthropogenic activities like disposal of

industrial wastes andwashing of vehicles etc23,24

. EC of

sediments is strongly affected with particle size and soil texture.

Sands have low EC and clays and silts have high EC23,25

. EC of

the sediments, on an average, was observed to be low. High EC

of 0.24µs/cm was recorded (St. 2 and 3) and low EC was

recorded as 0.14µs/cm (St.4). EC content of sediments of station

2 and 3 was more as they had sandy loam type of sediments

(more clay and silt as compared to sand). Contrarily, station 1

and 4 had low EC having more percentage of sand. High

percentage of moisture content recorded was 5.56% (St.3) and it

was recorded as low 4.26% (St.4). Moisture content is the

quantity of water contained in soils or sediments. Sandy loam

sediments (St.2 and 3) have high moisture content while Loamy

sand (St.1 and 4) have low moisture content which may be

attributed to the fact that moisture content depends on the

particle size, organic matter and bulk density26

. Also, the clayey

soils have more organic matter and thus retain more water than

sandy soils27, 28

.

The TOC percentage ranged from 0.35% (St.3) to 0.22%

(St.1).TOM and TN followed TOC and found to be ranged from

0.61 % (St.3) to 0.38 % (St.1); whereas TN ranged from 0.03 %

(St.3) to 0.01% (St.1 and 4) (table 3). TOC also showed

significant value for 2-way ANOVA which inferred that stations

showed greater variation in TOC (table 4). Total organic carbon

and total organic matter were high (St. 3); which could be

attributed to the fact that this station received heavy organic

matter in form of municipal wastes, agricultural wastes, sewage,

human and cattle excreta. As TOC is directly proportional to

TOM thus, the deposition and decomposition of organic matter

released organic carbon in water which ultimately gets

accumulated in the sediments8,10,21

. Total organic carbon shared

significant positive correlation with silt (r=0.983) and clay (r =

0.991)8,21

but significant negative correlation with sand (r = -

0.987)10

. TOC also shared significant positive correlation with

moisture content (table 5). Particle size distributions and TOC

percentage of all the stations has also been graphically

represented (figure 2).

Figure-2

Graphical representation of particle size distribution and TOC on all the four stations

0

10

20

30

40

50

60

70

80

90

st.1st.2

st.3st.4

sand %

silt %

clay %

TOC




Conclusion

The results of the study indicated that the sediments of the river

Tawi were having sand as the major contributor followed by silt

and clay. Percentages of TOC, TOM and TN indicated the effect

of incorporation of the effluents on the natural sediments of the

river Tawi. However, the concentration and dispersal pattern of

these parameters were moderate and comparatively lower than

the average value. It is therefore strongly recommended that

strict measures should be taken against the disposal of wastes on

the river sites so that the natural nature of the sediments should

be conserved andpreserved.

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tillage and conventional tillage, Agric Eng Int: CIGR Journal, 14(1), 26-37 (2012)

AARJMD VOLUME 1 ISSUE 17 (JANUARY 2014) ISSN : 2319 - 2801

Asian Academic Research Journal of Multidisciplinary

www.asianacademicresearch.org

212

A Peer Reviewed International Journal of Asian

Academic Research Associates

AARJMD

ASIAN ACADEMIC RESEARCH

JOURNAL OF MULTIDISCIPLINARY

OCCURRENCE AND ABUNDANCE OF MACRO-BENTHIC INVERTEBRATES

INHABITING RIVER TAWI, JAMMU (J&K), INDIA

K.K. SHARMA*; ARTI SHARMA**; VIPULAB SHARMA***

*Department of Zoology,

University of Jammu, Jammu- 180006

**Department of Zoology,


***Department of Zoology,


Abstract

The present study was undertaken to know the distribution of macro-benthic invertebrates

inhabiting river Tawi. A total of 24 taxa were identified during the study. Phylum Annelida was

the dominant followed by Arthropoda and Mollusca. Class oligochaeta was dominant

contributing 53.21 % of total macrobenthic diversity followed by class Insecta which showed

peak due to numerical abundance of Pentaneura sp. Class Gastropoda was the only representative

of phyla Mollusca. The information which is included here can be used to measure the impact of

pollution, to conserve biodiversity of the area and can be used for further study.

Keywords: macro-benthic invertebrates, taxa, annelida, arthropoda, mollusca, pollution.




213

Introduction

Benthos is the organism that inhabits the bottom of the water body, play an important role in

aquatic community, involved in mineralization, promoted mixing of sediments, flux oxygen into

sediments, cycling of organic matter and in assessment of the quality of inland waters (Idowu

and Ugwumba, 2005). Macro benthic invertebrates form an integral part of aquatic environment

and are of ecological and environmental importance as they maintain various levels of

interaction between the community and environment (Anderson and Sedel, 1979).

Benthic macro invertebrates are key components of aquatic food webs that link organic matter

and nutrient resources present in the sediments with higher trophic levels (Wallace and Webster,

1996). Macro benthic invertebrates can be used as a barometer of overall density in an aquatic

ecosystem (Chatzinikotaou et al., 2006) and any negative effect caused by pollution in the

community structure can in turn affect trophic relationships, those of fish and bird population

directly or indirectly (Sharma and Chowdhary, 2011). With the sensitive life stage (Hutchinson,

et al., 1998) sedentary habits and relatively long life span (Pratt, and Coler, 1976) they have the

ability to integrate the environmental effects. Because of their extended residency period in

specific habitats and presence or absence of a particular benthic species in a particular

environment these can be used as bioindicators of specific environment and habitat (Sarang and

Sharma, 2009). Also, aquatic invertebrates have the ability to clean rivers as they utilize the

organic acid and detritus matter (Sharma and Chowdhary, 2011).

When a water body is subjected to the influence of sewage and industrial pollution, a

considerable stress on their faunal communities results in population elasticity of macro benthic

invertebrates (Ram kumar et al., 2010). Furthermore, benthic invertebrates are a ubiquitous and

diverse group of long lived species that react strongly and often predictably to human influences

mainly caused by disposal of wastes, sewage etc.

Histologically, invertebrates have received considerable attention in the study of running water

ecosystems as they act as bio indicators of a particular aquatic ecosystem.




214

Materials and methods

Study area

The study was carried for a period of one year on river Tawi in Jammu district (J&K), having

latitudinal position 32◦ 35’- 33◦ 5' N and longitudinal position 74◦ 35'- 75◦ 45' E. It is one of the

rivers that drains the major portion of Jammu city and is also the source of drinking water to the

inhabitants.

Laboratory analysis

Benthic analysis

The soil samples were washed through a sieve of 1 mm × 1 mm mesh size to collect the benthic

organisms. The washed sediment with the benthic macro-invertebrates were poured into a white

enamel tray and sorted in the laboratory. For effective sorting, moderate volume of water was

added into the container to improve visibility. Forceps were used to pick large organisms while

smaller ones were sorted out using soft brush. The macro-invertebrates were poured into a wide

mouth labeled plastic container and preserved with 5% formalin solution to which Rose Bengal

(dye) had been added. The Rose Bengal dye strength was 0.1% selectivity colored all the living

organisms in the sample (Zabbey, 2002; Idowu and Ugwumba, 2005). The preserved benthos

were later identified to their lowest taxonomic group under light and stereo dissecting

microscope and counted. The identification was done using the keys by Ward and Whipple

(1959), Tonapi (1980), Adoni (1985) and Hart (1994). The percentages of each class, order and

phyla of macro benthic invertebrates were estimated.

Statistical analysis

Collected data was statistically analyzed using Simpson’s index (1949), Dominance, Shannon-

Wiener index (1949), Equatibility and Margalef’s richness index (1958).

Results and Discussions

Qualitative analysis

Qualitatively analysis of macrobenthic invertebrates showed the presence of three phylum

Annelida, Arthropoda and Mollusca. Phylum Annelida was survived by class Oligochaeta and

Hirudinea supporting 5 taxa. 17 taxa of phylum Arthropoda belonging to class Insecta and 4

orders viz; Ephemeroptera, Trichoptera, Hemiptera and Diptera were recorded. Phylum Mollusca

contributed only 3 taxa belonging to class Gastropoda.




215

Quantitative analysis

This is clear from the table 1 that altogether 24 taxa belonging to three phyla: Annelida,

Arthropoda and Mollusca were collected from the study site during the study period. Of these,

phylum Annelida contributed the largest share constituting 53. 35 % of the total macro-benthic

invertebrate fauna followed by the phylum Arthropoda sharing 30. 47 % and phylum Mollusca

contributing 16.18% (Table1 and Figure 1). Among phylum Annelida, Tubifex tubifex was the

most dominant species and it contributed 61.41 % and Hirudinea was the least dominant which

contributed 0.25% to the total Annelid population. Phylum Arthropoda was mainly represented

by numerical abundance of Pentaneura sp., which contributed 27.96% whereas Ceratopsyche sp.,

Hydroglyphus sp., contributed least i.e., 0.65% to the overall population of Arthropods. Group

Mollusca was the third dominant group of the macro benthic invertebrates inhabiting river Tawi

represented by singly by class Gastropoda which in turn was represented majorly by Physa sp.,

constituting 50.20 % of the total molluscan fauna.

Diversity and density of the macro benthos is largely dependent on chance settlement of pelagic

larval forms of different species, affinity to suitable substratum and also the degree of stress

caused by strong waves and tide currents (Olive et al. 2002).

It is clear from the findings that the river Tawi was inhabited by 3 major groups belonging to

phyla Annelida, Arthropoda and Mollusca(Figure 1). Out of which phyla Annelida contributed

the most followed by Arthropoda and Mollusca. As the benthic fauna is bio- indicator of

ecological conditions of any aquatic system (Sarang and Sharma, 2009), their presence or

absence depicts the impact of anthropogenic stress at a particular site (Sharma and Chowdhary,

2011). Abundance of group Annelida could be due to availability of soft bottom for borrowing

and availability of food materials (Schenkova et al., 2001 and Nijboer et al., 2004). Numerical

abundance of Tubifex sp. and Nais sp. could be due to the organic matter enrichment and

deposition of algae and mosses on large stones and other hard substrates as these provide feeding

materials to the members of this group (Battish and Sharma, 1997; Baturina, 2012).

Among arthropods, Pentaneura belonging to family Chironomidae was the most dominant

organism as these groups have hemoglobin pigment in their blood, these have broad oxygen

tolerance(Laguaze ‘re et al., 2009). Least abundance of Ephemeroptera, Trichoptera and

Hemiptera could be due to anthropogenic stress at the river site and their sensitivity to




216

environmental stress (Hall et al., 2006). Yap et al., 2003 also regarded EPT as the bio indicators

of clean ecosystems.

Besides, other faunal groups, Molluscs also form an important faunal component (Sarvankumar

et al. 2009). Although phylum Mollusca had the least contribution to the overall benthic fauna of

river Tawi, yet was mainly represented numerically abundant Physa sp of class Gastropoda .

Gastropods play a vital role at the debris interface as they consume living and decaying plant and

animal materials (Brendan et al., 2007). Abundance of Physa sp. could be attributed to soft rich

bottom and to the tolerance of Gastropods to some levels of pollution (Garg et al., 2009 and

Sharma et al., 2013).

The macro-benthic invertebrate fauna was analyzed statistically for Species diversity, Species

richness, Dominance, Simpson’s index and Equitability which showed great variations (Figure

2). The value of Simpson’s index ranged from dsimp=0.28 to dsimp= 0.96. Values of dominance

ranged from D= 0.03 to D= 0.10. The value of Shannon- Wiener index ranged between H’=0.87

to H’= 2.29 and the recorded values in the following order: Annelida> Arthropoda > Mollusca.

The minimum equitability value was ranged between E = 0.55to E = 0.80. Margalef species

richness varied between d= 0.44 to d= 1.91. All indices values other than Simpson’s index were

high when the observed number of species was high. Nkwoji et al. (2010) also cited similar

observations.

Conclusion

From the above studies it could be concluded that the abundance and dominance of some of the

pollution indicator species like Tubifex tubifex, Branchiura sp., Pentaneura sp., and Physa sp.,

but inadequacy of Ephemeroptera and Trichoptera clearly indicates the shifting status of the river

Tawi towards eutrophication. Anthropogenic stress showed alarming shift or total elimination of

sensitive biotic community form the habitat but consequent increase in pollution tolerant species

as these species have the potency to tolerate the organic pollution. This bio-survey of the macro

benthic invertebrate fauna gives an important insight into the health of the river and appends the

knowledge and understanding of the management strategies involving bio-monitoring as a

significant tool in the river restoration studies.




217

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Table 1: Check list of various benthic groups inhabiting river Tawi

Phyla class Order Name of genus %age of class&

order

%age of phyla

Arthropoda Insecta

Ephemeroptera Caenis sp. 5.61% 30.47%

Baetis sp.

Ephemeraella

Trichoptera Hydropsyche sp. 1.4%

Ceratopsyche sp.

Hemiptera Berosus sp. 1.52%

Micronecta sp.

Hydroglyphus sp

Regimbartia sp.

Diptera Culicoides sp.&

Forcipomyia sp.

21.95%

Tabanus sp

Erstalis larvae

Simulium sp.

Pentaneura sp.

Limnophilla sp.

Annelida Oligochaeta

Tubifex sp. 53.21% 53.35%

Pheretima sp.

Branchiura sp

Nais sp.

Hirudinea Hirudinea sp. 0.13%

Mollusca Gastropoda Lymnea sp. 16.18% 16.18%

Physa sp.

Gyraulus sp.




221

Figure1. Pie-chart showing distribution of different phylum of macro-benthic invertebrates

inhabiting river Tawi.

Figure2. Graphical representation of various indices applied to macro benthic invertebrates

inhabiting river Tawi

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