1

Chapter – 1

Introduction

Earth, often referred to as `the water planet', is unique amongst planets of our solar

system largely because of its abundant water - in oceans, in the atmosphere, in glaciers and

as fresh water on land. While the world's oceans may seem unbounded, the amount of fresh

water actually available to people is finite. Freshwater ecosystems are critical elements of

earth‟s dynamic processes and essential to human economies and health. They, invariably,

provide water for drinking, sanitation, agriculture, transport, electricity generation and

recreation apart from serving as abode for a diverse range of animals and plants. Fresh

water systems serve as hot spots of biodiversity; they contain 2.4% of all known species

despite only occupying 0.8% of the terrestrial surface and only representing 0.3% of the

water of the planet (Margalef 1983; McAllister et al. 1997).

2

Even though water is abundant, the amount of potable fresh water available is a tiny

fraction of the total amount of water in the world. As per WHO (2006) estimates, only

0.007% of all water on earth is readily available for human consumption. The fresh water

resources are vulnerable to human abuse and not evenly distributed in both time and space.

Fresh water resources around the world have been overused, polluted, fought over and

squandered with little regard for human health and ecological consequences (Lavado et al.,

2004). The finite nature of renewable fresh water makes it a critical natural resource to

examine in the context of population growth. However, despite the importance of

freshwater resources in our lives and well-being, we are increasingly beginning to take this

resource as being infinite, and for granted.

Due to growing industrialization on one hand and exploding population on the other,

the demands of water supply have been increasing tremendously. Moreover considerable

part of this limited quality of water is polluted by sewage, industrial waste and a wide range

of synthetic chemicals (Malmqvist and Rundle, 2002; Nilsson et al., 2005; Sabater and

Stevenson, 2010; Belenguer et al., 2014). The World Health Organization estimates that

more than 20% of the world population (around 1.3 billion people) has no safe drinking

water and that more than 40% of all populations lack adequate sanitation (Oastridge and

Trents, 1999). The lack of safe drinking water and adequate sanitation measures lead to a

number of water borne diseases such as cholera, dysentery, salmonellosis and typhoid

(Nwidu et al., 2008). Throughout the world, about 2.3 billion people suffer from diseases

that are linked to water related problems (WHO, 1997) which, continue to kill millions of

people yearly, debilitate billions, thereby undermining developmental efforts (Nash, 1993;

Olshansky et al., 1997; Warner, 1998; Herschy, 1999).

India is a blessed with fresh water resources in the form of numerous rivers and

lakes. It is often referred to as the “Land of Rivers”. It has 14 major, 55 minor and

numerous small rivers. In fact riverbanks first hosted human civilizations in India as

elsewhere in the world. The spiritual reverence for rivers in India still remains intact. But

the physical well-being of the rivers is increasingly been challenged by the rapid growth in

industrialization to support the country‟s growing population and economy. According to

the scientists of National Environmental Engineering Research Institute, Nagpur, India,

about 70 % of the available water in India is polluted (Pani, 1986). Studies showed that

domestic and industrial sewage, agricultural wastes etc. have polluted almost all of Indian

rivers. Most of these rivers have turned into sewage carrying drains. It is estimated that

3

community waste from human activities accounts for four times as much wastewater as

industrial effluents, most of which is discharged untreated/partially treated into the water

courses in India (Sahu, 1993).

India is heading towards a freshwater crisis due to improper management of water

resources and environmental degradation, which has led to lack of access to safe water

supply to millions of people. This poses a serious health problem as millions of people

continue to depend on this polluted water from the rivers. The harmful effects of river

pollution are not limited to human population only. Pollution of river has affected all

species of animals, sometimes threatening their very existence. It seriously impairs the

reproductive ability of animals in general and fish species in particular thus forcing them

towards extinction.

The beginning of the new millennium seems to be characterized by a steadily

increasing attention being paid to the environment. The dramatic increase in public

awareness and concern about the state of global and local environments has been

accompanied and partly prompted by an ever-growing body of evidence on the extent to

which pollution has caused severe environmental degradation. In addition, the costs of these

effects in the depreciation of resources, lost productivity and in cleaning up or improving

polluted environments are high and are increasingly occupying the attention of governments

and politicians around the world.

Aquatic ecosystem ordinarily supports a variety of physical, chemical and biological

mechanisms by which waste may be assimilated in bio-systems without causing serious

changes in abiotic and biotic characteristics of water. Now-a-days, contaminants reach

levels in excess of the assimilative capacity of receiving waters. Owing to the large quantity

of effluent discharged to the receiving waters, the natural processes of pathogen reduction

are inadequate for protection of public health. The extent of discharge of domestic and

industrial effluents is such that rivers receiving untreated effluent cannot provide the

dilution necessary for their survival as good quality water sources, thereby affecting the

organisms directly or indirectly by altering physico-chemical environment.

Moreover, over the past several decades, increase in use of metals in industry has led

to serious environmental pollution (Phillips, 1980; Sericano et al., 1995). The gradual rise in

the levels of such metals in aquatic environment has become a problem of primary concern.

This is due to their persistence since they are not usually eliminated either by

biodegradation or by chemical means, in contrast to most organic pollutants. The deadlier

4

diseases like edema of eyelids, tumor, congestion of nasal mucous membranes and pharynx,

stuffiness of the head and gastrointestinal, muscular, reproductive, neurological and genetic

malfunctions caused by heavy metals have been documented (Madsen et al., 1990; Bent and

Bohm, 1995; Abbasi, et al., 1998; Johnson, 1998; Bruins et al., 2000; Tsuji and

Karagatzides, 2001; Fatoki and Mathabatha, 2001). Therefore, monitoring these metals is

important for safety assessment of the environment in general and human health in

particular.

Before water can be described as potable, it has to comply with certain physical,

chemical and microbiological standards. Although the standards vary from place to place,

the objective anywhere is to reduce the possibility of spreading water borne diseases to the

barest minimum (Edema et al., 2001).

Microorganisms are widely distributed in nature, and their abundance and diversity

may be used as an indicator for the suitability of water (Okpokwasili and Akujobi, 1996).

The bacteriological examination of water has a special significance in pollution studies, as it

is a direct measurement of deleterious effect of pollution on human health. The higher the

level of indicator bacteria, the higher the level of faecal contamination and the greater the

risk of water-borne diseases such as cholera, dysentery, salmonellosis, typhoid etc.

(Hodegkiss, 1988; Vaidya et al., 2001). Coliforms are the major microbial indicator of

monitoring water quality. The detection of Escherichia coli provides definite evidence of

faecal pollution (Pipes, 1981; Kataria et al., 1997; Byamukama et al., 2000; Harwood et al.

2001; Pathak and Gopal, 2001; Kistemann et al., 2002).

Ecological health of an aquatic ecosystem can be analyzed through biological

indicators also. Fish has been particularly identified as one of the best biological indicators

for evaluating aquatic health, owing to its wide distribution and easy identification.

Fish assemblages have been used as an indicator of environmental degradation

(Scott and Hall, 1997; Arunachalam, 2000). Fish diversity in aquatic ecosystems is

considered as a diagnostic tool to highlight the impact of environmental changes (Das and

Chakrabarty, 2007). Loss of fish diversity or decline in their number determines the severity

of habitat degradation of an aquatic ecosystem (Ganasan and Hughes, 1998).

Fish growth is considered as biomarker for riverine pollution because it integrates all

effects within fish. Understanding of growth in fish is very important for more specific

fishery management. Basheer et al. (1993) opined that length- weight relationship of fish

varies depending upon the condition of life in aquatic environment. The study of length-

5

weight relationship is of paramount importance in fishery science, as it assists in

understanding the general well-being and growth patterns in a fish population.

Condition of the fish is strongly influenced by environmental conditions and can be

used as an index to assess the status of the aquatic ecosystem in which fish live. The

condition factor is used in the pollution studies in order to compare the "fatness" or

wellbeing of the fish. And it is based on the hypothesis that heavier fish of a particular

length are in a better physiological condition.

Water pollution has also been reported to exert deleterious effects on fish

reproduction viz. gonad‟s maturation, spawning behaviour, duration and number of eggs

per spawn, the egg and embryo viability, survival of fry etc. It is understood that the

reproduction is basic to the survival of the maximum number of young and, hence the

success of the fish species. Long-term exposure to environmental stressors causes

detrimental effects on important features such as metabolism, growth, reproduction and,

ultimately, the condition and survival of fish (Barton et al., 2002; Benejam et al., 2008).

Therefore, the river water quality management is essential to ensure the protection

of the drinking water resources, provision of healthy environment for aquatic flora and

fauna and encourage recreational activities. The proper monitoring is a prerequisite of water

quality management.

Globally, people need to appreciate the fact that our environment is a delicately

balanced substance and, indeed that it is a system. Consequently, the task of protecting it is

not a regional issue, neither is it a continental affair. It goes beyond local initiates. But then,

we have to start from somewhere. While thinking globally we must act locally. Thus, the

research becomes necessary at the present location, where there is little awareness about the

dangers inherent in contaminated water. It is all in an attempt to direct attention to issues of

effective management of the environment and the protection of the water qualities therein.

River Tawi is the only lifeline for Jammu city and its outskirts, whose water is used

for various purposes including drinking water supply. The catchment area of 141 km long

river Tawi is delineated by latitude 32°35'-33°5'N and longitude 74°35'-75°45'E. The

catchment area up to Indian border (Jammu) is 2168 km² and falls in the districts of Jammu,

Udhampur and a small part of Doda. Due to urbanization and industrialization, the pollution

level in river Tawi is increasing day by day and its water is fast losing its nectar clear

shimmering look. There is drastic decline in quantity as well as quality of the fish

inhabiting it.

6

Keeping this in mind, the present research work has been carried out to evaluate the

degree of pollution level at upstream and downstream regions of river Tawi so as to assess

its potability and the impact of pollution on fishes. The present study has been undertaken

with following aims and objectives:

Assessment of physico-chemical parameters of different selected sections of the

river Tawi.

Quantitative and Qualitative estimation of heavy metals present in aquatic

ecosystem.

Quantitative estimation of bacterial load to assess its suitability for human

consumption.

Identification and characterization of pathogenic bacteria affecting drinking water

quality and edible fish fauna.

Assessment of impact of pollution on various biological parameters of fishes viz.

fish assemblage and abundance, growth and reproduction potential.

The present research work is a small initiative towards generating important

information which would be communicated to the general public, government and

environmentalists to make them aware about the present status of pollution level in river

Tawi and its impact on fish fauna, thereby helping in the conservation of this precious fresh

water resource of our area.

7

Chapter – 2

Review of Literature

WATER QUALITY ANALYSIS:

Physico-chemical analysis of water:

During the last several decades, the water quality of the Indian rivers has been

deteriorating due to continuous discharge of industrial wastes and domestic sewage.

The surveys carried out by several researchers on important sources of water, revealed

that most of them were polluted. Works of Kothandaraman et al., 1963; Saxena et

al., 1966; Munawar, 1970; Safi et al., 1978; Zutshi and Vass, 1978; Dyniel and

Wood, 1980; Philips, 1980; Zutshi and Vass, 1982; Agrawal and Srivastava, 1984;

Unnai, (1984); Bagde and Verma, 1985; Pathak et al., 1991; Shaw et al., 1991;

Sharma, 1991; Dublin-Green, 1992; Philips and Rainbow, 1993; Martin, 1994;

Srivastava and Sinha, 1996; Carpenter et al., 1998; Middelkoop, 2000; Sivakumar

et al., 2000; Hanif et al., 2005; Sachidanandamurthy and Yajurvedi, 2006;

Soliman, 2006; Krishnan et al., 2007; Duran and Suicmez, 2007; Smitha et al.,

2007; Kumar and Bahadur, 2009; Ololade et al., 2009; Hassan et al., 2010; Kurup

8

et al., 2010; Verma and Saxena, 2010; Sharma et al., 2011 etc. testified the rising

pollution levels of various water sources of importance.

Jebanesan et al. (1989) studied the Cooum river in the city limits of Madras

and analyzed chemical characteristics like DO, BOD & COD. BOD and COD were

higher in downstream stations indicating accumulation of heavy load of sewage and

industrial effluents.

Kant and Raina (1989) analyzed the physico-chemical characteristics and

pollution load in the river Tawi at Jammu. They suggested BOD, COD, high turbidity

and low content of dissolved oxygen (DO) as indicators of pollution.

Prakash et al. (1989) examined the seasonal physico-chemical characteristics

viz. temperature, pH, dissolved oxygen, free carbon-dioxide, carbonate, bicarbonate

etc. of raw sewage water discharged into the river Tawi.

Tripathi et al. (1991) analyzed Varanasi city sewage discharged into the river

Ganga for its physico-chemical properties such as temp., DO, BOD, acidity, alkalinity

etc. An analysis of variance revealed significant variation in most of the parameters

with respect to months as well as sites.

The physico-chemical characterization of effluents of local textile industries of

Faisalabad, Pakistan was carried out by Nosheen et al. in 2000. The results indicated

that all samples had high values of pH, EC, chloride, phenols, BOD and COD. Only

sulphate contents were found to be present within the permissible limits thus

concluding that the textile effluents were highly polluted.

In 2001, Saha et al. analyzed physico-chemical characteristics in relation to

pollution and phytoplankton production potential of a brackishwater ecosystem of

Sundarbans in West Bengal. The deteriorating condition of the system was indicated by

the high under saturation of dissolved oxygen, high chemical oxygen demand and high

level of total ammonia preferably in the premonsoon season.

A comprehensive data regarding seasonal variations in physico-chemical

characteristics of river Yamuna, Haryana was published by Ravindra et al. in 2003.

The river in Delhi upstream was of better quality whereas the Delhi downstream stretch

was polluted as indicated by very low DO and high total dissolved solids (TDS),

electric conductivity (EC), total hardness, Na, K, Cl, F and SO4. The differences in

various parameters were found to be statistically significant (p < 0.01)

9

In 2004, Iqbal et al. published the results of a study designed to demonstrate

the seasonal variations in physico-chemical parameters (water temperature, light

penetration, surface tension, density, specific gravity, boiling point, turbidity, pH,

dissolved oxygen, free CO2, alkalinity, acidity, carbonates, bicarbonates, total solids,

total dissolved solids and total dissolved volatile solids.) of river Soan, Pakistan. These

parameters were compared with water quality standards to indicate probable pollution

in the river.

The impact of refinery effluent on the physico-chemical properties of a water

body in the Niger delta was documented by Otokunefor and Obiukwu in 2005. The

study encompassed the investigation of physico-chemical qualities of a refinery

effluent as well as water body receiving it. The treated refinery effluent contained very

high concentrations of phenol (11.06 mg/l), oil and grease (7.52 mg/l), ammonia (8.52

mg/l), COD (91.76 mg/l), TDS (390.6 mg/l) and phosphate (6.2 mg/l), but low in

sulphide, nickel, lead, copper and chromium, which were undetectable.

Spasojevic et al. in 2005 evaluated the water quality of Zapadna Morava river.

The physicochemical analyses results showed that the river water was of lower quality.

Throughout the greater part of the year, the water was polluted with ammonia. Nitrite

and phenol concentrations were also occasionally higher. The presence of toxic

substances in the water was due to a discharge of non-purified municipal and industrial

waste waters.

Sachidanandamurthy and Yajurvedi (2006) investigated changes in physico-

chemical parameters of Bilikere lake, Mysore. They declared it to be mesotrophic lake

and suggested immediate corrective measures before it became eutrophic.

A study involving qualitative assessment of river Gomti in Lucknow, India, was

published by Tripathi et al. in 2006. Results showed that there was increase in the

river's BOD, COD, alkalinity, chlorides, total hardness, conductivity, total solids and

heavy metals (Fe, Pb, Cu, Zn, Cr) but the dissolved oxygen (DO) and pH decreased.

Except Iron, all other heavy metal such as lead, copper, zinc and chromium were found

to be well within permissible limits.

A physico-chemical assessment of the Bebar river (Malaysia) was performed

by Gasim et al. (2007) by analyzing pH, temperature, Dissolved Oxygen (DO), Total

Dissolved Solids (TDS), Total Suspended Solids (TSS), conductivity and turbidity.

10

Results showed that the pH and DO value were very low, indicating the water was not

potable.

A comprehensive study was made public by Murugesan et al. in 2007 wherein

the authors assessed the physico-chemical and biological quality of river Chittar at

Courtallam, Tamil Nadu (India). Analysis of various water samples revealed that

although the river was not highly polluted, biological quality was significantly poor.

Excluding sulphate, all the other physico-chemical parameters analyzed were found

within the permissible limits. However, the total and faecal coliforms exceeded the

permissible limits, indicating a poor status of the river.

Bhandari and Nayal (2008) studied the physico-chemical parameters of river

Kosi, Uttarakhand and observed that all parameters of its water were within the

maximum permissible limit set by WHO except turbidity and BOD which recorded a

high value.

The physico-chemical parameters (pH, EC, TDS, TS, BOD and DO etc.) of

Krishna river water were studied by Prasad and Patil (2008) to conclude that most of

the parameters were within the permissible limit of ICMR and WHO.

While studying the physico-chemical parameters to assess the water quality of

river Ganga for drinking purpose in Haridwar district, Joshi et al. (2009) observed that

the WQI value of water from some sampling stations were quite unfit for drinking

purpose because of high value of dissolved solids and sodium.

Ramakrishnaiah et al. (2009) assessed the water quality index (WQI) for the

groundwater of Tumkur. The high value of WQI was found to be mainly from higher

values of iron, nitrate, total dissolved solids, hardness, fluorides, bicarbonate and

manganese in the groundwater. The analysis revealed that the groundwater of the area

needed some degree of treatment before consumption.

Kumar et al. (2010) studied the physico-chemical characteristics (Temp., pH,

total hardness, total alkalinity, BOD, COD, turbidity) of some Uttarakhand rivers

(Devprayag, Gangotri, Haridwar, Rudraprayag, Dakpathar and Yamunotri) and found

that Haridwar is most polluted.

Water samples were analyzed for physico-chemical parameters viz. pH,

electrical conductivity, turbidity, total dissolved solids, alkalinity, total hardness,

dissolved oxygen, BOD, COD and anions (Ca, Mg, Fe, Mn, NO3-, NO2

-,SO4

2-,PO4

2-,F

-

11

and Cl-) by Helen et al. (2011). Concentrations of most of the investigated parameters

in the water samples exceeded the permissible limit of WHO.

An investigation was carried out by Patra et al. (2011) on the Karala River, a

tributary of the river Teesta to study the seasonal change of physico-chemical factors

and ichthyofaunal diversity. The results indicated fluctuations in physic-chemical

factors owing to anthropogenic influences.

Khan et al. (2012) analyzed water samples of river Jhelum for physico-

chemical parameters and observed the concentration of nitrates and nitrites above the

permissible limits of WHO, which could pose threat to all kinds of life directly or

indirectly.

Khound et al. (2012) presented a comprehensive assessment of surface water

quality of Jia-Bharali river basin. The major ion contents show the following trend

Ca>Na>Mg>K while anion composition follow the trend HCO3>Cl>SO4>PO4>NO3.

Spatio - temporal variability of the physico-chemical parameters (within permissible

limits of WHO) from this study may be used as future baseline data to monitor and

manage any changes with changing land use.

The ground water quality of some villages of Gujarat (India) was determined by

physico-chemical methods by Chaudhari et al. (2013). The water samples showed

deviations from water quality standards of WHO, indicating groundwater

contamination.

Jena et al. (2013) assessed the physico-chemical parameters (turbidity, pH, EC,

total dissolved solid, total suspended solid, conductivity, dissolved oxygen, biological

oxygen demand, chloride, total alkalinity, hardness, sodium, potassium, calcium,

magnesium and sulphate) of Kharoon river. Anthropogenic activities had led to

increased values of various parameters but within permissible limits of WHO.

Yadav et al. (2013) analysed the water quality of Satak reservoir, M.P. (India)

using physico-chemical characteristics (chloride, total hardness, alkalinity,

temperature, pH) and suggested that the reservoir was under the category of

mesotrophic water body slightly inclined towards eutrophication.

While studying seasonal variation of the water quality of Senegal river

(Mauritania), N’diaye and Kankou (2014) found that all physico-chemical parameters

measured were within tolerable values except turbidity and TSS that exceeded the

maximum values of 980 NTU and 674 mg/L in the rainy season.

12

Heavy metal analysis of water:

Freshwater ecosystems exhibit a high natural variability in their physical and

chemical properties due to local differences in geology and climate. They are therefore

more susceptible to anthropogenic influences than the more consistent and stable

marine environments (Rainbow and Dallinger, 1993). Consequently, both quality and

quantity of water are affected by an increase in anthropogenic activity (Sanders, 1997).

Any pollution, either physical or chemical, cause changes in the quality of the

receiving waters (Noble et al., 1971; Wittmann and Forstner, 1977; Sanders, 1997).

These changes may include increased dissolved nutrients which may result in

eutrophication, changes in stream temperatures and bottom characteristics which lead

to habitat destruction and alteration of species diversity, and the addition of toxic

substances which can have either acute or chronic effects on aquatic organisms

(Gaufin, 1973; Sanders, 1997; Farkas et al., 2000).

According to Mason (1991), heavy metal pollution is one of the five major

types of toxic pollutants commonly present in surface waters. The important

environmental pollutants are those that tend to accumulate in organisms, those which

are persistent because of their chemical stability or poor biodegradability, and those

which are readily soluble and therefore environmentally mobile (Hellawell, 1986;

Freedman, 1989; Sanders, 1997).

All metals are natural constituents of the environment and are found in varying

levels in all ground and surface waters (Martin and Coughtrey, 1982). Some are

essential elements that are required for the normal metabolism of organisms, while

others are non-essential and play no significant biological role (Prosi, 1979; Cross and

Sunda, 1985; Rainbow, 1985; Rainbow and White, 1989 & Sanders, 1997). Living

organisms do however require trace amounts of some heavy metals. Excessive levels of

essential metals can however be detrimental to the organism.

Although essential heavy metals are generally considered to be less toxic than

non-essential metals (Batley, 1983), they are toxic when present in elevated

concentrations in the environment (Bryan, 1971; Du Preez and Van Vuren, 1994;

Sanders, 1997). Various environmental factors like temperature, pH, water hardness,

dissolved oxygen, light, salinity and organic matter can influence the toxicity of metals

in solution (Bryan, 1976).

13

Sanders (1997) stated that it is important to monitor pollution levels of heavy

metals in aquatic systems carefully, so that approximate measures of the potential

hazard can be attained. These measures should give an estimation of the type of effects

that could be expected after exposure to heavy metals.

Water characteristics of the Ceyhan River studied by Yilmazer and Yaman

(1999) exhibited seasonal variations. Water was contaminated with Fe, Al and Ni, and

excessively contaminated with Pb, and Cd elements. The amount of suspended matter,

Pb, Fe and Cd elements do not permit it to be used for drinking purposes. However, the

Cd element concentration exceeds the allowable limit of irrigation waters, salinity of

the water is suitable for irrigation purpose.

Fatoki et al. (2002) studied trace metal pollution in Umtata river. High levels

of Al, Cd, Pb, Zn and Cu were observed, which may affect the “health” of the aquatic

ecosystem and also the health of the rural community that uses the river water directly

for domestic use without treatment.

While studying heavy metal pollution in a sewage-fed lake of Bhopal,

Shrivastava et al. (2003) found that the concentrations of iron and manganese were

within acceptable limits, whereas others including chromium, nickel, zinc and lead

were not within acceptable water quality limits.

Olaifa et al. (2004) studied heavy metal contamination of Clarias gariepinus

from a lake and fish farm in Ibadan, (Nigeria). Their findings suggested that although

the heavy metals (Mn, Cu, Zn, Cr & Fe) were present in measurable quantities in fishes

yet these were still within safe limits for consumption.

Khaniki et al. (2005) provided a review article on mercury contamination in

fish and public health aspects, explaining various ill effects of mercury in humans

entering via aquatic food chain.

While comparing heavy metal levels (Fe, Cu, Ni, Cr, Pb and Zn) of Grey

Mullet (Mugil cephalus L.) and Sea Bream (Sparus aurata L.), Yilmaz (2005)

observed higher levels of metal concentrations in M. cephalus than S. aurata which

might have resulted from the difference in foraging habits of the two species.

Staniskiene et al. (2006) analyzed the distribution of heavy metals in tissues of

freshwater fish in Lithuania and concluded that the concentration of heavy metals in

inner organs of fish was from several to twelve times higher than that in flesh. The

largest amount of heavy metals was found in liver.

14

Lalwani et al. (2006) studied arsenic level in public water supply of Delhi and

suggested mixing of ground water and contamination through broken or leaking

channel could be the possible reason of higher arsenic level in tap water.

The determination of physico-chemical parameters and trace metal contents of

drinking water samples in Akure, Nigeria was carried out by Abulude et al. (2007).

The mean levels (mg / L) of the metals showed range i.e. 4.8(Fe), 0.3 (Cr), 0.1(Cd),

0.2(Pb), 0.2(As), 0.1(Ni). However, Co and Zn were not detected. Comparison of the

metal contents in the water sample with WHO limits showed that the mean levels of all

the metals were below the maximum permissible levels for drinking water

Asonye et al. (2007) analyzed heavy metal profiles of Nigerian rivers, streams

and waterways. Their studies revealed the fact that Pb, Cd, Cr, Zn and Mn levels in

some of the samples were above the guidelines of WHO, indicating serious risks to the

health of communities residing around and using these surface waters for domestic,

commercial and socio-cultural purposes.

Kar et al. (2008) assessed heavy metal pollution in surface water and observed

a significant positive correlation of conductivity with Cd and Cr of water wheras Mn

showed negative correlation with conductivity.

Vinodhini and Narayanan (2008) studied the bioaccumulation of heavy

metals in organs of fresh water fish, Cyprinus carpio exposed to heavy metal

contaminated water system. The order of heavy metal accumulation in the gills and

liver was Cd > Pb > Ni >

Cr and Pb > Cd > Ni > Cr. Similarly, in case of kidney and flesh tissues, the order was

Pb > Cd > Cr > Ni and Pb > Cr > Cd > Ni. In all heavy metals, the bioaccumulation of

lead and cadmium proportion was significantly increased in the tissues of the fish.

While analyzing heavy metals in water and fish samples of Madivala lakes,

Begum et al. (2009) found heavy metal concentration in water was in the order Pb > Cr

> Cd > Ni. The maximum concentration of heavy metals was found in kidney and liver

and the order of heavy metal level in various argons was muscle >gills >liver >kidney.

Al-Kahtani (2009) studied accumulation of heavy metals in Oreochromis

niloticus. According to him, the concentrations of metals in water were found in the

following order - Fe2+

>Zn2+

>Cu2+

>Pb2+

>Mn2+

>Cd2+

. The levels of heavy metals

recorded were generally low in water as compared to WHO and USEPA recommended

levels, except iron which was found to be higher than the recommended levels. In fish

15

samples collected from the polluted spring, metal levels were significantly higher than

the levels in water, indicating bioaccumulation.

A study was conducted by Rauf et al. (2009) to determine heavy metal

(cadmium and chromium) concentrations in gills, kidneys, liver, skin, muscles and

scales of three fish species (Catla catla, Labeo rohita and Cirrhina mrigala) from river

Ravi. Catla catla showed higher levels of metal concentrations than Labeo rohita and

Cirrhina mrigala. Metal contamination was highest at Baloki Headworks, probably

due to inclusion of more effluents from industrial and sewage water.

Olowu et al. (2010) determined heavy metals in fish tissues, water and

sediment from Lagoons (Nigeria) and according to them, the concentration of Zn is

within the limits permitted by the Lagos State Environmental Protection Agency

(LASEPA) of 1.0 mg/l set for water. None of the trace metals investigated was above

the maximum permissible level set by WHO.

Surface water and groundwater samples of certain locations around Tumkur

were assessed by Vijaya bhaskar et al. (2010) for pH, EC and heavy metals Cd, Cu,

Fe, Hg, Mn, Zn and Ni. All the surface water samples except samples of Honnudike

and Hebbur were found to be having normal concentrations of heavy metals and fit

for irrigation purpose. However, Hebbur and Honnudike samples have iron

concentrations exceeding the limit set for irrigation purpose.

The study of Lawson (2011) revealed that the concentrations of detected heavy

metals (Fe, Zn, Mn, Cd, Cr and Pb) were above the maximum contaminant level

(MCL) recommendations of USEPA. The water parameters favoured the production of

brackish water fish. However, it is highly contaminated and therefore not suitable for

drinking purpose in man.

Surface water samples from Warri river of Nigeria were analyzed quantitatively

for the concentration of heavy metals by Wogu and Okaka (2011). The results

showed that Cd, Cr, Mn and Nil had higher concentrations than values in standard

guidelines for potable water, pointing to the existence of risks to public health.

Trace metal levels in water and sediments of River Benue were determined by

Maitera et al. (2011). The results suggest that trace metal concentrations in River

Benue were generally greater in the sediment samples than water samples.

Choudhary (2012) performed quantitative analysis of seven heavy metals viz:

Cu, Fe, Hg, Zn, As, Cd and Pb in the surface water of Kaliasote water reservoir of

16

Bhopal (Madhya Pradesh). The results obtained were compared with safe limits in ppm

for heavy metals laid down by BIS, WHO, ICMR.

Farooq et al. (2012) conducted study to analyze heavy metal concentration in

surface water of river Indus and concluded that in pre-monsoon, generally the

concentration of cadmium, lead, mercury and copper exceeded the WHO recorded

permissible limits while in the post-monsoon samples cadmium, lead and mercury

were above the prescribed limits.

Heavy metals (Iron, Zinc, Nickel and Manganese) concentrations were

determined in surface water and tissues of fish (Clarias gariepinus) from River Owan

by Alex et al. (2013). Heavy metal concentration in surface water was in the order:

Fe>Ni>Mn>Zn and in fish the order was Fe>Zn>Mn>Ni. Fe and Ni had higher

concentrations than recommended benchmarks of WHO and FEPA, an indication of

risk to human health.

Butu and Bichi (2013) examined the spatial variation in concentration of some

heavy metals in Galma Dam, Nigeria and concluded that Fe whose sources are related

to weathering activities seem to be high in the upper parts of the dam while

anthropogenic sources related elements such as Cu, Cr, Cd, Co and Zn showed some

dominance in the lower part of Galma dam.

Spatio-temporal variation of heavy metals in Cauvery River basin was studied

by Raju et al. (2013). The seasonal heavy metal concentration in river water was

maximum for Fe, Zn, Mn, Cu, Co and Cd during pre-monsoon and Pb, Ni and Cr

during post-monsoon season whereas, in the sediment samples higher concentration of

Ni, Cr, Mn, Cu, Co and Cd was found during pre-monsoon and Fe, Pb and Zn in post-

monsoon.

The work of Adewumi et al. (2014) revealed that most of the water parameters

determined for Ala River were within the W.H.O (1984), permissible limit for portable

water, except for Mn, Ni, and Pb which are beyond the limits.

The mean concentrations of physico-chemical quality parameters of River

Benue at Yola in Adamawa State monitored for seven months in dry season by Haliru

et al. (2014) indicated elevated levels of turbidity, total solids, and total suspended

solids due to flooding and anthropogenic activities. The presence of high levels of

concentration of Cd, Cu, Cr and Pb at Gindin Goruba and Shinko sampling stations are

a source of serious concern to public health using the river water. In general, heavy

17

metals concentrations in the river water are in the following trend, Mn > Zn > Cu > Fe

> Cd > Cr > Pb.

Bacteria as water quality indicators:

The sanitary significance of finding various coliforms along with

Streptococci sp. and Clostridium perfringens was recognized by bacteriologists by

the start of the twentieth century (Hutchinson & Ridgway, 1977).

The use of bacteria as indicators of the sanitary quality of water probably

dates back to 1880 when Von Fritsch described Klebsiella pneumoniae and K.

rhinoscleromatis as microorganisms characteristically found in human faeces

(Geldreich, 1978).

The total coliform group and faecal streptococci are considered excellent

indicators of water bacterial contamination, because these bacteria are always

present in the normal intestinal tract of humans and other warm-blooded animals

and are eliminated in large numbers in faecal wastes (Isquith and Winters, 1987;

Gonul and Karapinar, 1991 & Rompre et al., 2002). Coliforms were considered as

prime indicator to assess the biological or faecal contamination of water

(Geldreich, 1966 & Gleeson and Gray, 1997). These coliform groups of bacteria

have been used for decades as an indicator of water quality; the absence of fecal

coliforms was considered evidence of safe water free of waterborne pathogens (US

Environmental Protection Agency, 1989 and WHO, 1997).

Lechevallier et al. (1980) while analyzing raw and chlorinated water supplies

found that Actinomycetes and Aeromonas species were the two most common groups

of SPC bacteria in chlorinated distribution water whereas Aeromonas sp. and

Enterobacter agglomerans were the two most common groups of SPC bacteria in raw

water.

Municipal water samples were analyzed for characterization of indicator

bacteria by Clark et al. (1982). They concluded that some bacterial species like

Escherichia coli, Enterobacter aerogenes, Aeromonas hydrophila, Klebsiella

pneumoniae and Citrobacter freundii were present mostly in all types of water

samples.

18

After comprehensive field investigation in various parts of India Manja et al.

(1982) revealed that the presence of coliforms in drinking water is associated with

hydrogen sulphide producing organisms.

Feacal indicator bacteria at fish farms were studied by Niemi and Taipalinen

in the year 1982. They concluded that Total Coliform (TC) bacteria identified mainly

belonging to the genera Enterobacter, Citrobacter and Aeromonas. However, the

majority of Faecal Coliform (FC) strains were Escherichia coli.

Bej et al. (1990) suggested that PCR amplification of lac Z and lam B provides

a basis for a method to detect indicators of faecal contamination of water, and

amplification of lam B in particular permits detection of E. coli and enteric pathogens

(Salmonella and Shigella sp.).

Kataria et al. (1997) carried out bacteriological study with physico-chemical

parameters to assess the water quality status of Halali River (Bhopal). The study

revealed that coliform bacteria increased up to 2400/100ml in monsoon months and

minimum 240/100 ml in summer months.

While studying the correlation of bacterial indicator organisms with Salmonella

sp., Staphylococcus aureus and Candida albicans in sea water, Efstratiou et al. (1998)

observed strong positive association of all indicators with Salmonella and moderate

positive correlations with Staphylococcus aureus and Candida albicans.

Kravitz et al. (1999) performed quantitative bacterial examination of domestic

water supplies in the Lesotho Highlands. E. coli and total coliforms were used by them

as indicators for water quality.

The degree of pollution and the sanitary-bacteriological state of pelagic waters

of Lake Wigry of Poland was studied by Niewolak (1999). He used Total Viable Count

20ºC (TVC) and Total Viable Count 37ºC as indicators of pollution and Total coliform

(TC), Faecal coliform (FC) and Faecal streptococci (FS) as indicators of sanitary state.

Potentially pathogenic microorganisms in water and bottom sediments in the

Czarna Hancza river were studied by Niewolak and Opieka (2000). Their findings

suggests that the number of Pseudomonas aeruginosa, Aeromonas hydrophila and

Staphylococcus sp. as well as the number of the indicators bacteria of a sanitary state

(total coliforms, faecal coliforms and faecal streptococci) should be taken into account

while estimating the usefulness of river water for recreation.

19

Payment et al. (2000) analyzed Saint Lawrence River water of Canada, which

was used by drinking water treatment plants and determined the level of occurrence of

bacterial indicators (total coliforms, fecal coliforms and Clostridium perfringens) and

pathogens (Giardia lamblia, Cryptosporidium, human enteric viruses).

Maipa et al. (2001) observed high microbiological pollution during summer

period and studied seasonal fluctuation of E. coli and other coliforms as pollution

indicators in the marine environment.

Microbiological evaluation of the Mhlathuze River (S. Africa) was performed

by Bezuidenhout et al. (2002). Their studies revealed elevated levels of indicator

micro-organisms (both faecal and total coliforms) and heterotrophic plate count

bacteria and suggest that surface water temperature and rainfall during the study period

appeared to be some of the factors affecting the increased bacterial counts.

De Donno et al. (2002) estimated the level of hygiene and sanitation on the

brackish water basin of Acquatina used for extensive and semi-intensive fish culture.

After analyzing microbiological parameters viz., total and faecal coliforms, faecal

streptococci and Salmonella sp., in channels and basins, they suggested that fresh water

introduced by channels could be a potential source of faecal contamination.

Karaboz et al. (2003) determined TVC, TC, FC from Izmir Bay and isolated

water borne bacterial pathogens viz. Salmonella, Shigella, Legionella, Campylobacter,

Yersinia and Vibrio from coastal area having high domestic and industrial wastewater

discharge.

Tortorello (2003) summarized the history, use and analytical methods for the

most commonly used indicator organisms, including the aerobic plate count, yeasts and

molds, the coliform groups, Escherichia coli, Enterobacteriaceae, and Listeria.

Anazoo and Ibe (2005) studied the sanitary quality of Ulasi river (Nigeria).

After microbiological assessment, it was concluded that the river water is not safe for

drinking without pretreatment.

Studies on microbial ecology in the runoff of the glacier in relation to pollution

have been studied by Baghel et al. (2005). Study clearly revealed that there was

significant presence of bacterial indicators of faecal pollution in middle and lower

stretch. Thus concluded that the situation of Gangotri glacier is not very serious but

alarming.

20

Ramaiah et al. (2005) performed quantitative analysis of pollution indicator

and pathogenic bacteria in Mumbai from Ballast waters. They also viewed seasonal

variations of bacterial groups (E. coli, Shigella sp., Salmonella sp., Vibrio cholerae, V.

parahaemolyticus, Campylobacters & Aeromonads) and suggested avoidance of

ballasting and deballasting in Mumbai harbor region.

Tallon et al. (2005) summarized the history of indicator organisms, the

evolution of the analytical methodologies and suggested the advantages and limitations

of current faecal indicator microorganisms.

Bacteriological water quality status in terms of total coliform and faecal

coliform count was studied on both - east and west banks of river Yamuna by Anand et

al. (2006). The results indicated increase in bacterial total count from upstream to

downstream stretch of river Yamuna. The bacterial count at all the locations was

reduced tremendously in the monsoon months due to flushing effect, while count was

high in the Pre-monsoon month due to receiving of surface runoffs.

Drucker and Panasyuk (2006) while analyzing potentially pathogenic bacteria

in Lake Baikal reported thirty-one species of bacteria which were representatives of 14

genera. Most doinant genera were Acinetobacter, Pseudomonas, Citrobacter, and

Enterobacter often found in littoral zones. Growth of the species diversity and

abundance of these bacterial groups was correlated with anthropogenic load.

Prevalence of coliform bacteria in Kodaikanal and Yercaud lakes (Tamilnadu)

was detected by Rajakumar et al. (2006) suggesting that water was polluted by faecal

contaminants to such extent that it was not potable and also unsuitable for recreational

activities.

Bacteriological analysis of water samples from Tsunami hit coastal areas of

Kanyakumari was done by Rajendran et al. (2006). Bacterial species isolated from

samples were Aeromonas hydrophila, Pseudomonas aeruginosa, Escherichia coli,

Citrobacter freundii, Enterococci etc. There was no report of acute diarrhoeal diseases

or typhoid illness during the post tsunami period.

Microbiological analysis of drinking water of Kathmandu valley was performed

by Prasai et al. (2007). Total plate and coliform count of water samples crossed the

WHO standards and thus rendering water unsafe for public use.

21

During the study of microbiological characteristics of waters in the major rivers

in Kainji Lake National Park, Ajibade et al. (2008) revealed the fact that water during

wet season is not potable due to high feacal pollution during respective season.

Nagvenkar and Ramaiah (2009) studied the abundance and types of various

pollution indicator bacterial populations from tropical estuaries. Comparative

assessment of pollution indicator and human pathogenic bacteria (Vibrio sp.,

Aeromonas sp., Acrobacter sp., E. coli etc.) was helpful to infer that the tropical

estuaries of Mandovi and Zuari experienced impacts of sewage outfalls.

A study was conducted to investigate the water quality of seven important lakes

in North India by Sharma et al. (2010). The high MPN values, presence of faecal

coliforms and streptococci in the water samples suggests the potential presence of

pathogenic microorganisms

Ajeagah et al. (2012) evaluated the bacteriological water quality of Danube

river basin in Galati area of Romania. During the study, they found persistence of E.

coli confirming the fact that there is continuous faecal pollution leading to

contamination of water quality.

In 2012, Oku et al. studied the occurrence of faecal coliforms and other

heterotrophic bacteria in Great Kwa river, Nigeria. Results showed the presence of

Escherichia coli (33.3%), Staphylococcus aureus (12.5%), Bacillus spp. (4.17%),

Clostridium spp. (8.33%), Enterobacter spp. (12.5%), Corynebacterium spp. (4.17%),

Pseudomonas spp. (8.33%), Serratia marcesan (8.33%), and Streptococcus spp.

(8.33%).

Nikseresht and Salmanov (2013) surveyed the coliform pollution in Iranian

river (Maragheh city) and observed highest coliform count (28.5/100 cm3) and faecal

coliforms (E.coli = 9/100cm3) to be highest in summer due to high temperature and

anthropogenic disturbances.

The study made by Sivaraja and Nagarajan (2014) revealed that, the study

area of the river Cauvery and Bhavani were grossly polluted in respect of coliform

assessment which is mainly attributed to the open defecation, high amount of raw

sewage and barrage sedimentation, which require continuous monitoring and proper

disinfection if the water is to be used for drinking purposes. The severely contaminated

stations of rivers Cauvery and Bhavani with respect to total coliforms are

Vairapalayam (1800 MPN/100ml) and Bhavani Sagar (920 MPN/100ml).

22

Physico-chemical factors and Bacteria:

Growth of bacteria is affected by variety of physical and chemical factors

which in multitude of ways act with or against one another. They influence not only the

size and composition of microbial populations, but also the morphology and

physiology of individual bacteria. Thus, in some species temperature above or below

the optimum as well as salt concentration or pH below or above the optimum may lead

to considerable changes in metabolism, cell morphology and reproduction. Physical

conditions include depth, turbidity, temperature and light penetration. Chemical

condition includes pH, free carbon dioxide, dissolved oxygen, carbonate bicarbonate,

chloride, calcium and magnesium.

Several workers have studied physico-chemical characteristics and their

seasonal variations in riverine system of India, suggesting increasing level of pollution

(Joshi and Sharma, 2003; Mishra and Joshi, 2003; Pandey and Pandey, 2003 & Kumar

et al., 2004).

The bacterial populations have been determined as between 101

and 106

bacterial cells/ml in river system (Carney et al., 1975; Spancer and Ramsay, 1978;

Geesey and Costerton, 1979 & Bell et al., 1980). Yoshimizu and Kimura (1983)

reported 101

cells/ml of heterotrophic bacteria in water during spring and 103

cells/ml in

autumn. Austin and Austin (1985) also recorded similar results from two fish farms in

England.

While studying the impact of mass bathing on the water quality of river

Godavari on the eve of „Holi Mela‟, Gyananath et al. (2000) revealed the fact that

water was getting polluted due to mass bathing and was unfit for both drinking as well

as bathing. The finding was based on water quality parameters viz. pH, temp., DO,

chloride, hardness, alkalinity, COD, BOD, MPN etc.

During the study, Kagalou et al. (2002) observed the relationships between

indicator bacteria and organic load in Lake Pamvotis (Greece). They noticed increase

in TC and FC levels near heavily polluted areas and during rainfall periods. Low or

negative correlations were also observed between bacteriological indices and BOD &

COD levels.

Das and Acharya (2003) studied the impact of domestic sewage on the lotic

water in and around Cuttack (India) and observed high values of NH4+, NO3

-, BOD,

23

Total Viable Count (TVC) & Escherichia coli. The nutrient characteristics showed

highest concentration during summers. Various physico-chemical and microbial

parameters confirmed domestic sewage mixing points as the most contaminated zones.

Parashar et al. (2003) observed the impact of collective bathing and increased

organic load on quality of Ganga canal water. Analysis of water was done before and

during mela period and they observed minor changes in physico-chemical

characteristics and considerable changes in microbiological characteristics of water.

Giannoulis et al. (2004) assessed the microbiological and physico-chemical

quality of potable water in North Western Greece. The drinking water quality standards

were analyzed with respect to the presence of total coliforms, faecal coliforms and

faecal streptococci along with some physico-chemical parameters like temperature, pH,

and total dissolve solids.

While analyzing the physico-chemical status of the river Jamuna in District

Auraiya (U.P.) Kumar et al. (2004) observed increase in pollution as we go down the

river due to regular discharge of sewage and industrial waste throughout its course.

Roslev et al. (2004) studied the effect of oxygen on survival of faecal pollution

indicators in drinking water. They concluded that faecal pollution indicators other than

E. coli may persist longer in drinking water under anaerobic conditions.

Thilaga et al. (2004) investigated fresh water lake of Ooty (The Nilgiris) for

some physico-chemical parameters like temperature, total solids, pH, total alkalinity,

dissolved CO2, BOD, COD etc. and suggested that lentic water resources situated near

human settlements receive huge wastes and are getting polluted day by day, thereby

increasing per capita water need.

Agbogu et al. (2006) studied bacteriological and physico-chemical indicators

of pollution of surface waters in Zaria (Nigeria) and suggested a positive correlation

between faecal coliform count with most of the physico-chemical parameters

(temperature, pH, turbidity, chloride, BOD etc.).

The drinking, borewell and sewage water in areas of Sivakasi has been studied

by Krishnan et al. (2007) Most of the physico-chemical characters (pH, total solids,

total dissolved solids, total suspended solids; chemical parameters like total alkalinity,

acidity, free CO2, dissolved oxygen, total hardness, calcium, magnesium, chloride,

salinity) of drinking and borewell water were within the ISI permissible level. However

24

in water samples from all the sites, bacterial count (standard plate count, total coliform

count, faecal coliform count) exceeded recommended permissible level of WHO.

Physico-chemical (pH, TDS, hardness, conductivity, dissolved oxygen and

chemical oxygen demand) and microbial (MPN count) analysis of the water samples of

Anand district (Gujarat) was conducted by Mishra and Bhatt (2008).They concluded

that the quality of water samples subjected to study was acceptable from majority of

physico-chemical parameters while as per the bacteriological standards, the water needs

to be treated before using it in domestic applications.

Raja et al. (2008) evaluated physico-chemical parameters of river Kaveri

(Tamil Nadu) and found low DO levels and higher levels of solids, total hardness,

COD, the total viable count (TVC) or standard plat count (SPC) at Station 2 as a result

of domestic sewage contamination.

A study was conducted to investigate the water quality (physico-chemical as

well as bacteriological) of seven important lakes in North India by Sharma et al. in

2010. Total viable count exceeded the maximum permissible limits in all the lakes

irrespective to different seasons. The high most probable number (MPN) values and

presence of faecal coliforms and streptococci in the water samples suggests the

potential presence of pathogenic microorganisms which might cause water borne

diseases.

While studying the physico-chemical and bacteriologiocal parameters of water

samples from different sources, Ahmedabad, Gujarat, Saxena et al. (2011) concluded

that the quality of water samples subjected to study was acceptable from majority of

physico-chemical parameters while as per the bacteriological standards, the water

needs to be treated before using it for domestic purposes including drinking.

The present study of Srivastava and Srivastava (2011) revealed that water

quality of river Gomti from upstream to downstream was found to be more polluted

with reference to bacteriological parameters rather than all physico-chemical

parameters. The high values of sewage pollution indicator bacteria detected, suggested

that the microbiological quality of water of river Gomti was very poor, unsafe and not

acceptable for any purpose especially in Lucknow and Barabanki districts.

Adejuwon and Adelakun (2012) examined the physiochemical and

bacteriological analysis of surface water in Lala, Yobo and Agodo rivers (Nigeria). The

samples were analyzed for pH, TDS, TH, electrical conductivity, chloride, calcium,

25

nitrate and faecal coliform. The implication of these results is that the rivers pose a

health risk to the rural communities who rely primarily on them as the only source of

domestic water supply.

The physico-chemical (pH, DO, BOD, COD, total hardness Ca2+

, Mg2+

) and

microbial parameters (bacterial and fungal colonies) of pond water samples collected

from various sites in and around Coimbatore city (India) were analyzed to assess the

quality of water by Rajiv et al. (2012). The results suggested that the pond water

samples collected from various sites were above the limit of WHO standards. Pre

treatment is needed for these waters for drinking purpose and human consumption.

Water quality assessment of river Gomti in Lucknow was done by Kumar et

al. (2013). The increase in value of chloride, nitrate, total hardness and heavy metals

were also due to domestic discharges. The DO, TSS, TDS, nitrate, nitrite and other

parameters at some of the sites were beyond permissible limit, thus water was declared

polluted and not suitable for beneficial uses without conventional treatments.

Physico-chemical and microbiological profile of water sources (river Kabul,

tube well, open well and filter water) evaluated by Javed et al. (2014) showed that

most of the parameters TDS, TCC, TBC and faecal colifom in River Kabul samples

were observed more during the monsoon season (July), followed in post monsoon

season (December) and then in pre monsoon season (April). TDS, TBC and TCC

values for river Kabul and Open well sources were found greater than the WHO limit,

whereas no faecal coliform was found in filter water.

The study was conducted to examine the quality of surface water of river

Umkhrah in Shillong by Khongwir et al. (2014). The physico-chemical and

bacteriological parameters investigated were found to be above the permissible limits

of WHO. Presence of E.coli indicated that the water is polluted with faecal matter.

Bacterial micro-flora of fish:

Fishes are continually bathed in aqueous systems, so they remain in contact

with the microorganisms of the surface and bottom waters. Bacteria could accumulate

at the site of damage or wound. They may enter into the mouth with water and pass

down to alimentary canal or may be trapped on gills. Studies reveal that the surface of

gills is populated by bacterial species reflecting the bacteria normally present in water

(Liston, 1957; Evelyn & Mc Dermott, 1961; Colwell, 1962; Pacha & Porter, 1968;

26

Austin, 1982; 1983; Allen et al., 1983 and Nieto et al., 1984). The mucous secretion

from fish provides good growth conditions (Liston, 1957).

However, the surface of fresh water fishes include Acinetobacter, Aeromonas

hydrophila, Alcaligenes piechaudii, Enterobacter aerogenes, Escherichia coli,

Flexibacter sp., Micrococcus luteus, Morarxella sp., Pseudomonas fluorescens and

Vibrio fluvialis (Christensen, 1977 and Allen et al., 1983). Nieto et al. (1984) have

observed Aeromonads, Cornyeforms, Enterobacteria, Gram positive cocci,

Pseudomonads and Vibrios on the gills of healthy fingerlings of rainbow trout.

Gilmour et al. (1976), Kaper et al. (1981) and Ninagawa et al. (1983) have

reported that Pseudomonas fluorescens, Aeromonas hydrophila, Edwardsiella tarda,

Vibrios sp. and Myxobacteria are ubiquitous to aquatic environment and are usually

found on body surface and intestine of fishes.

The intestinal micro flora of marine fish dominated by genus Vibrio has been

reported by Shewan (1961). Trust & Sparrow (1974) and Yoshimizu et al. (1980)

isolated Pseudomonas, Bacillus, Vibrio, Achromobacter, Alcaligenes, Micrococcus,

Clostridium, E. coli, Streptococcus and Xanthomonas from intestinal flora of healthy

fishes. Certain fish pathogenic bacteria like Aeromonas salmonicida, Pasteurella

piscida, and Yersinia ruckeri are generally not found in fresh water free from diseased

or health carrier fishes and are considered as obligate pathogens (Nieto et al., 1990).

Allen et al. (1983) reported that species of Aeromonas, Escherichia, Klebsiella,

Pseudomonas, Salmonella and Vibrio collected from polluted water fish multiply in the

gut, mucus & tissues, thus render fish a potential vector of human disease.

While examining intestinal contents of Striped bass, Mc Farlane et al. (1986)

reported predominance of opportunistic fish pathogens especially Aeromonas

hydrophila. Other isolates included Vibrio, Pseudomonas, Flavobacterium,

Alcaligenes, Enterics, Micrococcus, Bacillus, Corynebacterium and Acinetobacter.

Cahill (1990) described bacterial floras isolated from eggs, skin, gills and

intestines for a limited number of fish species. He suggested that generally, the range

of bacterial genera isolated is related to the aquatic habitat and varies with factors such

as salinity of habitat & bacterial load in water.

Apun et al. (1999) bacteriologically examined various organs of cyprinid fish

species and isolated various Gram positive and Gram negative bacterial species mainly

from intestine. Among Gram negative, common species observed were Aeromonas

27

hydrophila, Citrobacter freundii, Escherichia coli, Enterobacter aerogenes, Klebsiella

sp., Pseudomonas sp., Vibrio anguillarium and among Gram positive, Bacillus sp.,

Listeria and Staphylococcus sp. were isolated.

A DNA-based assay was developed by Gustafson et al. (1999) to detect

Aeromonas salmonicida from infected fish by analyzing tissues, feaces, and the tank

water in which the infected fish were held.

Wiklund et al. (2000) detected Flavobacterium psychrophilum from fish tissue

and water samples by using nested PCR with DNA probes against a sequence of the

16S rRNA genes.

DeSousa and Silva-Souza (2001) analyzed bacterial community associated

with fish and water from Congonhas river (Brazil). They isolated Pseudomonas,

Acinetobacter, Aeromonas, Bacillus, Enterobacteriaceae, Micrococcus and

Lactobacillus from fish and in addition, observed occurrence of Flavobacterium in

water coupled with absence of Micrococcus and Lactobacillus.

Ampofo and Clerk (2002) isolated twenty different bacterial species from

fishermen and members of communities associated with seven fish ponds with

different fertilizer treatments, and an open system. E. coli was the predominant species

in all the communities, while Escherichia coli, Klebsiella pneumoniae, Proteus

mirabilis, Proteus vulgaris, Pseudomonas sp., Shigella sp. and Streptococcus faecalis

were common in individuals of communities of sewage-fed pond.

Tiirola et al. (2002) carried out identification of the putative pathogen in

Flavobacterial outbreaks by using broad range bacterial PCR method with universal

16S rDNA targeting primers and bacterial culture.

Al-Harbi (2003) investigated different samples (pond water, sediment, and

intestine of hybrid tilapia and pigeon faeces) monthly to assess total bacterial load,

total coliforms & faecal coliforms. A sample analysis for coliforms was done using the

multiple-tube fermentation technique and the abundance of normal bacteria coliforms

was found to be greater in warmer months than in the cold months. Escherichia coli

were the only coliform organism found in different analyzed samples.

While examining bacterial micro flora of gastrointestinal tract of Nile Tilapia

(Oreochromis niloticus) cultured in semi-intensive system, Molinari et al. (2003)

isolated various Gram negative bacilli viz. Aeromonas hydrophila, Aeromonas veronii,

28

Citrobacter freundii, Chromobacterium violaceum, Burkholderia sp., Escherichia coli,

Flavimonas oryzihabitans and Plesiomonas shigelloides.

Guzman et al. (2004) recovered E. coli in fresh water fish, Jenynsia

multidentata and Bryconamericus iheringi. Quantification of E. coli was done by MPN

method. They observed positive linear relationship between E. coli loads in water and

those found in digestive tract and muscle of fish.

Novotony et al. (2004) studied human infections via fish and observed bacteria

as causative agents. Following bacteria were recorded as infecting agents

Mycobacterium sp., Streptococcus iniae, Photobacterium damselae, Vibrio

alginolyticus, V. vulnificus, V. parahaemolyticus, V. cholerae, Erysipelothrix

rhusiopathiae, Escherichia coli, Aeromonas sp., Salmonella sp., Staphylococcus

aureus, Listeria monocytogenes, Clostridium botulinum, C. perfringens,

Campylobacter jejuni, Delftia acidovorans, Edwardsiella tarda, Legionella

pneumophila, and Plesiomonas shigelloides.

Khan et al. (2005) performed qualitative and quantitative analysis of fish and

revealed the fact that the Standard Plate Count (SPC) and Most Probable Number

(MPN) were far exceeding than the prescribed standards for fish and fish products. The

bacteria isolated were Pseudomonas, Arthrobacter, Micrococcus and Staphylococcus.

Basti and Misaghi (2005) studied the microbial status of cultivated fish in

Gilan (Iran) and found Listeria monocytogenes, Escherichia coli, Vibrio

parahaemolyticus and Salmonella sp. as potential pathogens.

Total heterotrophic bacteria, total coliforms, faecal coliforms and E. coli,

Pseudomonas and total vibrio count were analyzed from Parangipettai landing centres

(Tamil Nadu) by Das et al. (2005). By revealing the presence of human pathogenic

bacteria in the major food fishes (sardines, mackerels, silverbellies etc.); they assumed

significance in the backdrop of consequences to human health.

Lalitha and Surendran (2005) analyzed farmed fresh water prawn, before and

after icing, for bacteriological quality. Total Aerobic mesophilic bacteria (TPC), faecal

streptococci, faecal coliforms were enumerated. The levels of faecal streptococci and

faecal coliforms were above the acceptable limits. On icing, a reduction was noticed.

They also isolated some pathogenic bacteria viz. Enterococcus sp., Staphylococcus

aureus, Aeromonas hydrophila, Aeromonas veronii biovar sobria and Clostridium

perfringens.

29

Kapetanovic et al. (2008) described the bacterial community associated with

salmonids from the Krka river. Diversity analysis demonstrated that majority of the

recovered bacteria were related to Aeromonadaceae group.

In 2010, Kumaran et al. isolated bacteria from fin rot of Asian Sea bass, Lates

calcarifer. Based on different biochemical tests and sequence of 16S rDNA, the

causative bacteria were identified as Pseudomonas sp. KUMS3.

Musefiu et al. (2011) examined and compared bacteria flora of wild and

cultured Clarias gariepinus (African catfish) and Orepchromis niloticus (Nile Tilapia)

randomly collected from different aquatic environments in Nigeria. The bacterial

species isolated belonged to genera Bacillus, Proteus, Pseudomonas, Klebsiella,

Streptococcus, Salmonella, Staphylococcus, Micrococcus, Serratia and Escherichia.

Latha and Ramachandra (2013) analyzed the bacterial microflora of fish in

both polluted and non-polluted water. They observed that the bacterial load in polluted

water fish tissues were comparatively higher. Maximum bacterial counts were seen in

intestine followed by gills and skin. The bacterial genera isolated during study were

Pseudomonas, Acientobacter, Aeromonas, Enterobacteriaceae, Micrococcus, Bacillus,

and Lactobacillus.

A survey of the bacteriological and physico-chemical analysis of three

freshwater fish ponds stocked with Clarias gariepinus was conducted by Torimiro et

al. (2014). The total aerobic bacterial count ranged from 1.50 x 104

CFU/mL – 1.13 x

106 Cfu/mL. Twenty-five bacterial isolates belonging to thirteen genera were

identified whereas the values of the physico-chemical parameters were found to be

within the acceptable limit.

Pathogenic Bacteria and Fish diseases:

There are numerous reports of fish pathogens of marine and fresh water.

Various workers suggested Vibrio sp. as the causal agent (Smith, 1961; Mc Carthy et

al., 1974; Egi-dius et al., 1981).

Genus Aeromonas was reported as one of the most important fish pathogens

(Gopal-Krishnan, 1961; Haley et al., 1967; Amlacher, 1970; Bootsma et al., 1977;

Hazen et al., 1978; Lallier et al., 1981; Newman, 1983; Cornick et al., 1984;

Karunasagar et al., 1986; Trust, 1986).

30

Mycobacterium causing Mycobacteriosis in both fresh and marine water fishes

in temperate as well as tropical waters has been described by various authors (Ross,

1970; Giavenni et al., 1980; Santacana et al., 1982; Arakawa and Fryer, 1984;

Chinabut et al., 1990).

Genus Pseudomonas was described as pathogenic to all species of fishes

(Meyer and Collar, 1964). Some Pseudomonas species have been reported to be

causative agents of fish diseases. P. anguilliseptica was described as a pathogen in eel,

Anguilla japonica (Wakabayashi & Egusa, 1972) and ayu, Plecoglossus altivelis

(Nakai et al., 1985), Pseudomonas fluorescens in carp (Shiose et al., 1974), sea bream,

Evynnis japonica (Kusuda et al., 1974) and tilapia, Sarotherodon niloticus (Miyashita,

1984), Pseudomonas chlororaphis in amago trout, Onchorhynchus rhodurus (Hatai et

al., 1975) and Pseudomonas putida in yellow tail, Seriola quinqueradiata (Kusuda and

Toyoshima, 1976; Muroga,1990; Wakabayashi et al.,1996). Streptococcus sp. also

causes disease in fishes (Bell, 1961; Hendricks and Leek, 1975; Foo et al., 1985).

Cann and Taylor (1984) and Eklund et al. (1984) described Clostridium

botulinum as an agent of disease. Edwardsiella sp. (Hawke, 1979; Waltman et al.,

1985) and Yersinia sp. (Lee et al., 1981; Green and Austin, 1983; Dwilow et al., 1987;

Wei- Liang Chao et al., 1988) have been reported as a causative agent of various fish

diseases.

Flexibacter sp. (Ordal and Rucker, 1944; Pacha and Ordal, 1970),

Flavobacterium sp. (Meyer et al., 1959; Kluge, 1965), Pasteurella (Bullock and

Hastein, 1976) has been described as an aetiological agent. Among Enterobacteria

Edwardsiella ictaluri, E. tarda, Proteus roettgeri and Yersinia ruckeri have been

established as fish pathogen (Austin and Austin, 1987). In addition to these, there have

been references to pathogenic role of Erwinia sp. (Starr and Chatterjee, 1972), Serratia

(Liewellyn, 1980; Nieto et al., 1990) and Citrobacter freundii (Sato et al., 1982;

Karunasagar et al., 1992).

Most of the bacteria that cause disease of fish can only attack if the fish have

been injured or weakened, but some species have greater pathogenic capacity and may

even attack completely healthy undamaged fishes. The bacterial diseases (cotton wool,

tail or fin rot, scale protrusion, dropsy, tuberculosis, furunculosis etc.) in fishes and

their treatment have been described by various workers (Snieszko, 1954; Conroy,

1962; Van Duijn, 1973; Austin and Austin, 1987).

31

Bacteria and Human health:

Escherichia coli is not usually considered to be pathogenic. Only a small group

of E. coli strains cause diseases. The majority of strains are the natural inhabitants,

commensal bacteria of the gastrointestinal tract of warm-blooded animals. It can be a

common cause of Extra intestinal disease such as neonatal meningitis (Sarff et al.,

1975), urinary tract infections and septicemia (Orskov and Orskov, 1975). Certain

strains produce enterotoxins that commonly cause traveler‟s diarrhea and occasionally

cause very serious foodborne disease.

Almost all members of genus Samonella are potentially pathogenic. Typhoid

fever, caused by Salmonella typhi, is the most severe illness caused by any member of

the genus Salmonella. A less severe gastrointestinal disease caused by other

Salmonellae is called Salmonellosis. Salmonellosis is one of the most common forms

of foodborne illness. Salmonella sp. is found to be pathogenic causing enteric fevers,

gastro-enteritis and septicemia (Krieg and Holt, 1984; Baired-Parker, 1990; Kramer et

al., 1996).

Pseudomonas sp. is an opportunistic human pathogen that causes eye

infection, diarrhea, ear infection, burns, wound and skin infections and nosocomial

infections in compromised hosts. (Lombardi et al., 2002; Bouallegue et al., 2004;

Iwalokun et al., 2006). Pseudomonas sp. is also responsible for bacteremia and sepsis

in neonatal, neutropenic and cancer patients, as well as urinary tract infections

(Martino et al., 1996; Ladhani and Bhutta, 1998; Lombardi et al., 2002 and Pertz et al.,

2005, Foysal et al., 2011; Suryawanshi and Khindria, 2013).

Proteus vulgaris is commonly involved in urinary tract infections, wound

infections, pneumonia, septicemia and occasionally infant diarrhea (Krieg and Holt,

1984; Talaro and Talaro, 1996).

Aeromanas sp. have been associated with several human infections such as

gastroenteritis, peritonitis, endocaditis, urinary tract and wound infections (Daily et al.,

1981; Krovacek et al., 1994; Golas et al., 2002; Bari et al., 2007; Igbinosa et al., 2012).

Illness through Staphylococcus aureus range from minor skin infection such as

pimples, boils, cellulites, toxic shock syndrome, impetigo, and abscesses to life

threatening disease such as pneumonia, meningitis, endocarditis, and septicemia.

32

(Masud et al., 1988; Soomro et al., 2003; Singh and Prakash, 2008; Suryawanshi and

Khindria, 2013).

V. cholerae of Genus Vibrio cause the severe diarrheal disease “cholera”.

Some other sp. of Vibrio cause gastrointestinal infections, blood infections as well as

wound and ear infections (Cheasty et al., 1999; Cottingham et al., 2003, Sack et al.,

2004).

Apart from above mentioned bacteria, Shigella sp. (dysentery), Streptococcus

sp. (meningitis and skin infections), and Campylobacter sp. (campylobacteriosis) are

also potent human pathogens (Ampofo and Clerk, 2002; Karaboz et al., 2003; Ramaiah

et al., 2005).

IMPACT OF POLUTION ON BIOLOGICAL CHARACTERS OF FISH:

Pollution and Fish diversity:

Fish has been identified as suitable for biological assessment due to its easy

identification and economic value (Ogbeibu and Victor, 1989; Simon and Lyons, 1995;

Yamazaki et al., 1996; Smith et al., 1999; Siligato and Bohmer, 2001; Idodo-Umeh,

2002 & Oguzie, 2003).

Fish assemblages have been regarded as an effective biological indicator of

environmental quality and anthropogenic stress in aquatic ecosystems (Plafkin et al.,

1989; Fausch et al., 1990; Simon, 1991; Scott and Hall, 1997; Arunachalam, 2000;

Zampella et al., 2006 & Das and Chakrabarty, 2007) not only because of its iconic

value, but also because of sensitivity to subtle environmental changes (Karr, 1981).

Loss of fish species diversity determines the severity of habitat degradation of an

aquatic ecosystem (Ganasan and Hughes, 1998).

Influence of environmental factors on structure and function of fish

assemblages in streams have been studied in several countries (Gafny et al., 2000;

Bhat, 2004; Magalhaes et al., 2002; Li and Gelwick, 2005; Snodgrass et al., 1996;

Davis et al., 2003 & Belliard et al., 1999).

A considerable quantity of research has been carried out on the physico-

chemical parameters of riverine water and their impact on aquatic biota in India

(Adebisi, 1980; Pande et al., 1988; Ray et al., 1996; Samanta and Chakrabarti, 1997;

33

Chakraborty, 1998; Dhanapakiam et al., 1999; Shastri, 2000; Barat and Jha, 2002;

Shahnawaz et al., 2009; Sarkar et al., 2010).

A loss in diversity attributable to anthropogenic impact has been shown by

many studies (Tsai, 1968; Harrel and Hall, 1991; Grall and Glemarec, 1997; Chow-

Fraser et al., 1998; Boet et al., 1999; Gafny et al., 2000; Martin et al., 2000 &

Guyonnet et al., 2003).

The effects of urban wastewaters on the diversity and abundance of the fish

population of Ogba river in Benin City, Nigeria were assessed by Obasohan and

Oronsaye (2009). The influx of drainage effluents into Ogba river has invariably

affected the diversity and abundance of the fish community in the river. The

comparatively low species diversity, abundance and the poor fish condition, which

have emerged from this study, suggested the presence of stress-inducing factors in

Ogba river.

A study was conducted to determine fish species diversity in Igbesa, Itele and

Iba tributaries of river Ore in Southwest, Nigeria by Emmanuel and Modupe (2010)

and concluded that the discharges from industries that surrounded the adjacent Ologe

lagoon and the domestic wastes from boundaries settlements and the farming activities

along the tributaries might have contributed to low species diversity.

The evaluation of fish community in the chronically polluted middle Elbe river

was done by Jurajda et al. (2010). Significant differences in fish species richness and

density were registered among individual sites within study sections. Sites downstream

the weirs had significantly higher species richness and density than the other two sites

in the middle and upstream weirs.

The fish community of the Bhadra reservoir in relation to physico-chemical

parameters was studied by Thirumala et al. (2011). 33 fish species identified during

the study belongs to Cyprinidae 18 species, Channidae 2 species, Bagridae and

Siluridae with 3 species and a species each of Mastacembelidae, Ambassidae,

Cichlidae, Claridae, Notopteridae, Cobitidae and Heteropneustidae. The species

diversity is peak in post monsoon and the diversity was low in premonsoon. A rapid

decline in fish diversity at discharged zone (polluted) of the Bhadra river was observed.

The physical and chemical characteristics as well as the fish species diversity of

the coastal fishing grounds of the Lagos lagoon was assessed by Amaeze et al. (2012).

Unregulated burning of sawdust at Okobaba and oil pollution at the ports has led to

34

depleted fish catch. Fish diversity significantly varied with sampling zones (p<0.05)

and generally areas receiving organic waste had higher fish diversity compared to those

receiving chemical waste.

The fish diversity and associated environment of 12 strategically selected

intertidal stations along the extremely polluted Thane creek on the west coast of India

were studied by Goldin and Athalye (2012). 12 species of fish were recorded along

the entire length of the creek with dominance of only 5 species that occurred

throughout the year, namely Mugil cephalus, Mystus gulio, Mystus shingala, Tilapia

mossambica and Scylla serrata where as the other fishes were rare in their occurrence.

A comparison with the past literature for the study area revealed decline in the fish

diversity.

The fish community of the Kamala basin reservoir in relation to physico-

chemical parameters was studied by Murugan and Prabaharan (2012). The species

diversity is peak in post monsoon, coinciding with favourable conditions such as

sufficient water and ample food resources. The diversity was low in pre-monsoon

probably due to the shrinkage of the water spread of the reservoir. The anthropogenic

factors were held responsible for declining population of fish species in study area.

The survey done by Shivashankar and Venkataramana (2012) synthesized

information on fish biodiversity in the freshwater body in selected parts of Bhadra

river, Karnataka. Correlation between species diversity and physico-chemical

parameters shows positive correlation with temperature and dissolved oxygen and

negatively correlated with pH, conductivity, alkalinity and hardness.

Attempts have been made to collect, classify and identify fish of river Narmada

in its Western Zone by Bakawale and Kanhere (2013) The survey indicated that 51

species of fish were found in this zone of the river viz. major carps, minor carps and cat

fishes and noted that he fish species diversity was decreasing. The main reasons

behind the decline of species are habitat destruction, introduction of exotic species,

pollution and over fishing.

Eknath (2013) highlighted pollution status and impact on fish diversity in

Mula-Mutha river. The polluted areas with heavy influx of organic and innumerable

industrial waste has been drastically reduced the biodiversity in city area and

downstream of river. Pollution resulted into disappearance of fish fauna during winter

35

and summer. In the polluted stretch of this river, tolerant species as Oreochromis

mossambicus and Gambusia affinis were found at many places.

The downstream zone of river Mahisagar was surveyed by Mahendrasinh and

Pradeep (2013) for diversity of fish fauna during post monsoon season. Total 26

species were reported from 03 orders and 12 families having diverse food habits and

ecosystem. Industrial wastes, sewage pollutants etc. released in river at many places,

changes the natural water quality and thus affected the diversity of ichthyofauna.

Mohite and Samant (2013) reported impact of environmental change on fish

and fisheries in Warna river basin, Western Ghats. In Warna basin riverine ecosystems

and fishing activity in the basin is increasingly influenced by anthropogenic activities.

The major environmental impacts on fisheries are due to change in land use pattern,

transformation in river flow regime, riparian habitat loss, invasion of exotic species,

over fishing and agricultural expansion.

The fish diversity of the Aami river in relation to physico-chemical parameters

was studied by Shukla and Singh (2013). The results of present investigation reveal

the occurrence of 18 fish species belonging to 6 order, 11 family and 17genera. The

study findings showed that fish diversity of the study area is reducing with the increase

of water pollution.

Ichthyofaunal diversity of Jammer river was studied by Vyas and

Vishwakarma (2013). The results of the study revealed that Jammer river harbours a

rich and diversified fish fauna. The total 27 fish species were recorded under four

orders, nine families and 16 genera. Due to some anthropogenic activities fish diversity

of the river is in declining mode. They documented strict management measures with

large scale public awareness would be essential to save the fish fauna of this river.

Ayandiran and Fawole (2014) studied the seasonal distribution and condition

factor of Clarias gariepinus from polluted Oluwa river, Nigeria. Seasonally, there were

more fish specimens harvested during the dry season than the rainy season. However,

more fish species were sampled during the first year of the while there was a reduction

in the population of the fish sampled in the second year. They recorded higher mean

condition factor (r) for males fish species indicating that males were in good condition,

and their general well-being was better than the females fish species examined.

Freshwater fish diversity, abundance and richness status of Anjanapura

reservoir, Karnataka was studied by Basavaraja et al. (2014). Habitat loss and

36

environmental degradation has seriously affected the fish fauna. Conservation of fish

diversity was suggested as top most priority under changing circumstances of gradual

habitat degradation

East Kolkata wetlands, an ecologically important Ramsar site in West Bengal,

India include a rich floral and faunal diversity. The study of Dasgupta and Panigrahi

(2014) reported that these sewage fed wetlands are threatened by aquatic pollution and

declining ichthyofaunal diversity is destructive to the entire food chain of the wet land

was a major concern.

Pollution and fish growth:

The study of length-weight relationship is of great importance in fishery

science, as It is a powerful tool in understanding the general well- being and growth

patterns in a fish population and it also throws light on the environmental conditions of

the aquatic ecosystem in which the fish is residing as Basheer et al. (1993) opined that

length- weight relationship of fish varies depending upon the condition of life in

aquatic environment.

Pandey and Shukla (1982) observed declining growth rate of certain tropical

freshwater fishes viz. Colisa fasciatus and Channa punctatus fingerlings under

pollutional stress of arsenic, zinc and malathion.

Variations in condition factor with seasons and pollution has also been

documented by Khallaf et al. (2003) in Shanawan drainage canal in Egypt. They

reported differences in L-W relationships of Oreochromis niloticus in a polluted canal

compared with those of other authors in different localities and times. These

differences were attributed to the effect of eutrophication and pollution on growth and

other biological aspects of O. niloticus.

Chandra and Jhan (2010) analysed the length weight relationship of Channa

punctatus with relation to physico-chemical parameters. They observed that in the clear

water the growth was good i.e. 1.89% in comparasion to polluted water i.e.1.05%.

The research was conducted on different life stages (90, 120, 150 and 180-day

age) of Indian major carps to check the growth responses under sub-lethal chronic

toxicity of iron by Hussain et al. (2011). All the fish species showed significantly

(p<0.05) negative growth in terms of weight, fork length and total length. Condition

37

factor of control fish reveals that weight gain was maximum in relation to length of fish

as compared to fish kept under chronic exposure of iron.

Rajput (2011) showed allometric growth (b = 2.7) of Tor tor in relation to high

fluoride concentration in lake Bhimtal. Adult fish with low value of condition factor

indicated the effects of pollution on growth of fish.

Shakir and Qazi (2013) investigated the impact of industrial and municipal

discharges on growth coefficient and condition factor of major carps (Catla catla,

Cirrhinus mrigala and Labeo rohita ) from Lahore stretch of river Ravi. Reduction in

„b‟ value in downstream polluted sites indicated adverse effect of aquatic pollution on

fish growth.

Pollution and fish reproduction:

Fish reproduction is affected by the direct or indirect exposure to aquatic

pollution (Kime, 1995). It is understood that the reproduction is basic to the survival of

the maximum number of young and, hence the success of the fish species.

Long-term exposure to environmental stressors causes detrimental effects on

important features such as metabolism, growth, reproduction and, ultimately, the

condition and survival of fish (Barton et al., 2002; Benejam et al., 2008).

Aquatic organisms chronically exposed to environmental pollutant may

eventually suffer serious physiological damage. This damage could result in the

impairment of reproductive processes of the affected organism. Reduced regeneration

or recruitment of these fish could lead to the eventual decline of their population.

Pollutants such as heavy metals, PAHs, and chlorinated hydrocarbons have

been linked to a range of physiological problems in fishes, including reproductive

impairment. These include delayed or inhibited onset of spawning, reduced number of

spawning females, reduced fecundity and reductions in the fertilization and hatchability

of the eggs. One form of reproductive impairment linked to pollution is atresia, the

abnormal reabsorption of oocytes destined to be spawned. Atresia has been used as an

indicator of pollutant-related reproductive impairment in fish. This could have

deleterious effects on the population and survivability of aquatic organisms, thereby

causing a disruption in the aquatic ecosystem. (Cross et al., 1984; Hunter and

Macewicz, 1985; Cross and Hose, 1988; Hose et al., 1989; Thomas, 1989; Nath and

Kumar, 1990; Murugesan and Haniffa, 1992; Hill and Janz, 2003; Van den Belt et

38

al., 2003; Pollino et al., 2007; Sherwood et al., 2007; Pierron et al., 2008; Verma

and Srivastava, 2008; Love and Goldberg, 2009 & Akande et al., 2010).

A chronic exposure study performed by Hermanutz (1978) tested the toxicity

of Endrin and Malathion on Florida Flag Fish (Jordanella floridae). Both of these

compounds affected the fish‟s reproductive potential, first by decreasing the number of

eggs produced by females and second by reducing the number of fish that matured into

reproductive adults.

Exposure of heavy metals to white sucker (Catostomus commersoni) was

studied by Mc Farlane and Franzin in 1978 showing lowered catch per unit effort,

lowered spawning success and small eggs.

Decreased egg production was also reported by Homing and Neiheisel in 1979

while studying copper toxicity on blunt nose minnow (Pimephales notatus).

Buckler et al. (1981) observed the impact of Mirex and Kepone contaminants

on fat head minnow (Pimephales promelas) and suggested decreased number of

spawns, egg production and decreased egg hatchability.

Cross et al. (1984) had observed high level of atresia-reabsorption of mature

eggs in ovaries of fish from polluted sites of the southern California coast.

Freeman and Sangalang (1985) observed the impact of lowered pH on Salmo

salar and reported reduced egg production and increased egg mortality.

The effects of an increasing downriver pollution gradient on the reproductive

system of Astyanax fasciatus were investigated in the Rio dos Sinos by Schulz and

Martins (2000). The comparison of mean oocyte diameters, gonadal indices and

gonado-somatic relationships of specimens captured in polluted areas with individuals

from unpolluted reference sites revealed a significant decrease of these parameters with

increasing water pollution.

Gupta and Guha (2006) reported less yolk in developing oocytes, follicle cells

were deformed and necrosis in the ovary of Heteropneustes fossilis under mycrocystin

toxicity.

Olfat and El-Greisy (2007) observed extensive necrosis of oolema,

hypertrophy and hyperplasia of the follicular cells of oocytes, atresia in the large

vacuolated mature follicles of the ovary in Siganus rivulatus after exposure to different

waste sources containing Zn.

39

Different sources of pollutants (industrial, mixed and domestic) were studied by

Wahbi and El-Greisy (2007) and found to affect fish by variable degrees. Exposure to

pollutant diluted levels (1.5% industrial, 3.0% mixed and 5.0% domestic) for 42 days

led to a decrease in GSI, more smaller and less developed oocytes, fewer mature

oocytes and an increase in number of atretic follicles. They concluded that low levels

of pollutants by time will have an inhibitory effect on the reproduction, decreasing the

fecundity of fish. After a long-term decline, it may lead to eventual extinction.

The histopathological changes in gonads due to exposure to different pollutants

have been studied by Mazrouh and Mahmoud (2009). It was concluded that fish

exposed to higher concentrations of pollutants showed higher incidence of gonadal

abnormalities in the form of deformed oocyte and spermatocyte with reduction in their

numbers and lack of active oogensis and spermatogenesis.

Akande et al. (2010) studied the effect of sewage effluents on reproductive

parameters in Zebra fish (Danio rerio). They reported delayed or inhibited onset of

spawning, reduced number of spawning females, reduced fecundity and reductions in

the fertilization and hatchability of the eggs.

Benejam et al. (2010) detected a significant negative decrease in condition and

reproductive traits at the polluted area for several fish species. Compared with

upstream control sites, low values of fitness-related traits were also observed far

downstream of the polluted reservoir. The responses to the pollutants were species-

specific, and common carp (C. carpio) was the species with the clearest effects on

fitness-related traits at the impacted area, despite also being among the most tolerant to

pollution.

Ebrahimi and Taherianfard (2011) analyzed the effects of heavy metals

exposure on reproductive systems of cyprinid fish from Kor river. This study showed

that heavy metal contamination not only directly affects fish health, but it can also

disrupt the normal steroidogenesis pattern in fish, leading to impaired hormone

production in both male and female fish, and decrease the quality and quantity of

sperm and ova production.

Deka and Mahanta (2012) reported adhesion of primary follicle, cytoplasmic

retraction & clumping, cytoplasmic degeneration, increased atretic oocytes, partial

destruction of ovigerous lamellae and vitellogenic membrane in the ovary of

Heteropneustes fossilis treated with Malathion.

40

The study was conducted by Gaber et al. (2013) to assess the effect of the

water quality of El-Rahawy drain on the African catfish, Clarias gariepinus. They

observed histopathological alterations in the testis and ovaries of the studied fish which

may reduce the ability of fish to reproduce. Also, the mean number of yolky oocytes of

C. gariepinus was affected by the polluted environment.

Significant histopathological alterations were observed in the ovaries of Puntius

ticto under dimethoate toxicity by Marutirao (2013). The prominent changes are

occurrence of atretic oocytes and increase in interfollicular spaces.

Shobikhuliatul (2013) visualised the effect of pollution on the histopathology

of gonads in Puntius javanicus and concluded that endocrine disrupting effect on

reproductive organ has been occurred in the Mas river, Surabaya. These effects were

indicated by the presence of defects in fish gonads. The defects include atretic oocyte

on the ovary, degenerative, collapsing and necrotic changes in both the wall and the

cellular elements of the seminiferous tubules with focal areas of fibrosis on the testis

and edema.

Ortiz-Zarragoitia et al. (2014) while studying the effect of endocrine

disrupting compounds in coastal and estuarine environments on Mugilid fish observed

that mullet populations inhabiting waters polluted with dopamine antagonist drugs,

such as domperidone, can show altered reproductive and gametogenic cycle.

41

Chapter – 3

Material and Methods

Selected Spots:

In order to assess the anthropogenic influences on the water quality and fishes of

river Tawi, two sampling sites were selected along the longitudinal profile of the

river.

• Upstream tation-I --- near ainik chool, Nagrota, just before the entrance

of the river into city area . Latitude 32 47 N, Longitude 74 55 E.

• Downstream (Station-II --- near Gujjar Nagar after the influence of city

garbage into the river). Latitude 32 43 N, Longitude 74 51 E. tation-II

receives heavy pollution load and organic matter in the form of sewage and

garbage from city area. The distance between the two sites was more than 10

kms.

42

Monitoring of water quality of river Tawi involved different parameters viz.

physico-chemical analysis, heavy metal analysis and bacteriological analysis.

Physicochemical Analysis of water:

During period of investigation (2009-11), water samples were collected

monthly from different selected stations in plastic containers. For DO, however, water

samples were collected in oxygen fixing bottles and fixed on the spot. At the time of

water sampling, air and water temperature were also recorded. Analysis of water was

done within few hours of sampling.

The methods applied for determination of various physico-chemical

parameters of water, during the course of study, are as under:

Physico-chemical parameters:

Atmospheric temperature: Air temperature was recorded with the help

of a mercury thermometer, avoiding direct exposure of the mercury bulb to

sunlight.

Water temperature: Water temperature was recorded with the help of a

mercury centigrade thermometer. This was done by vertically dipping the

thermometer into water.

pH: pH of water samples were determined with the help of portable field

pH meter (Hanna).

DO: Dissolved oxygen was measured by using modified Winkler‟s

method (APHA, 1985).

FCO2: Titrimetric method recommended by APHA (1985) was used for

free carbon dioxide estimation.

Carbonates and Bicarbonates: Carbonates and bicarbonates were

estimated according to Indian standard methods (1982) and APHA (1985).

Chloride: Argentometric method using potassium chromate as indicator

was used for determination of chlorides (APHA 1985).

Calcium and Magnesium: The estimation of calcium and magnesium

was done by the method suggested by Indian Standard Institution Methods

(I.S.I. 1982) and APHA (1985).

43

Electrical Conductivity: Estimation of electrical conductivity was done

by IIIM, Jammu.

Total Dissolved solids: Estimation was done by IIIM, Jammu.

Fluoride: Estimation was done by IIIM, Jammu.

.

Heavy metal analysis:

For heavy metal analysis, the water samples were collected in sterilized

containers. Heavy metals present in samples were estimated with acid digestion

method using Atomic absorption spectrophotometer (APHA, 1985) in Quality

Analysis Lab, IIIM, Jammu.

Bacteriological analysis:

For bacteriological studies, water samples were collected from selected spots

in pre- sterilized bottles. Fish samples were collected by trapping the fish in a net or

trap. Bacteria were isolated from the water samples by streaking the loopful of sample

on sterile nutrient agar plate and then incubating at 370 C for 24 hours. Bacteria from

fish were isolated from the skin, gills and intestine of the fish. Fishes were brought to

the laboratory and bacteria were isolated by first bathing the fish in UV treated

distilled water and with the help of sterile loop, bacteria were picked up from the skin

and gills. The loaded loop was directly streaked on nutrient agar plates. For taking the

sample from intestine, the intestine was removed first by opening the body with

sterilized scissors and then intestine was cut open and with the help of red hot loop,

the samples were taken in by inserting the loop into the intestine and directly

streaking the loaded loop on nutrient agar plates and incubating the plates at 370C for

24 hours. The isolated colonies were further sub cultured on nutrient agar slants and

their colony characterizations were noted down.

Quantitative estimation of bacteria:

The microbiological examination of water included both harmful and

harmless bacteria. The counting of different types of bacteria was done by the

following methods:

Standard plate count (SPC cfu/ml)

MPN of coliform bacteria (MPN/100ml):

44

SPC count:

This method provides density of aerobic and facultative bacteria in water that

can grow at 370C. The method involved inoculating water samples, its dilution on

suitable media and counting the colonies on the Petri dish.

Total viable count (SPC cfu/ml) was done by serially diluting the water

sample in sterile saline solution. Aliquots (0.1ml) of different dilutions were spread on

nutrient agar plates and then incubated at 370C for 24hrs. The total number of

colonies in each dilution was counted which gave the standard plate count of aerobic

and facultative bacteria. Usually, 1 - 0.1 ml water or its appropriate dilution is plated

to get 30-300 colony forming units (cfu) of bacteria per plate after incubation at

required temperature for 24-48 hours. The result was expressed as cfu of bacteria/ ml

of sample.

MPN count:

The Most Probable Number will depict the coliform count in the water

samples. The multiple tube technique is used due to its applicability to all kinds of

water. Estimation of number of coliform bacilli in water is made by adding varying

quantities of water (0.1ml to 10ml) to peptone water.

Measured amount of single and double strength Mac-Conkey medium was

sterilized in test tubes containing Durham's tubes for indicating gas production.10 ml

of sample was pipetted into each of five tubes of double strength medium, 1.0 ml

amount into each of five tubes of single strength and 0.1 ml into each of five tubes of

single strength medium.

The indicator was added to see the acid production. The tubes were incubated

at 370C and examined after 48 hrs. The tubes were examined for gas formation by

coliform organisms after the incubation period. Those that showed acid and sufficient

gas to fill at the top of Durham's tube are considered to be "presumptive positive" as a

result of coliform bacilli. The density of bacteria was calculated on the basis of

positive and negative combinations of the tubes using MPN table. MPN count was

calculated from McCrady‟s probability table. The table gives only the most probable

number of bacteria and not the actual density. The results were expressed as MPN of

organisms per 100 ml of water. The culture tubes, positive for presumptive test, were

subjected to confirmatory test.

45

The MPN for combinations, not appearing in the table or for other

combinations of tubes, may be estimated by Thomas‟s formula given as:

The accuracy of this method is influenced by the number of tubes per dilution.

Table 1 Table of Most Probable Number (MPN) per 100ml of Sample (Using 5 Tubes With

10,1and 0.1ml Volumes)

Postive

MPN/100ml

Positive

MPN/100ml

10 1 0.1

10 1 0.1

0 0 0

0 0 1

0 0 2

0 0 3

0 0 4

0 0 5

0 1 0

0 1 1

0 1 2

0 1 3

0 1 4

0 1 5

0 2 0

0 2 1

0 2 2

0 2 3

0 2 4

0 2 5

0 3 0

0 3 1

0 3 2

0 3 3

0 3 4

0 3 5

0 4 0

0 4 1

0 4 2

0 4 3

0 4 4

0 4 5

0 5 0

0 5 1

0 5 2

0 5 3

0 5 4

0 5 5

0

1.8

3.6

5.4

7.2

9

1.8

3.6

5.5

7.3

9.1

11

3.7

5.5

7.4

9.2

11

13

5.6

7.4

9.3

11

13

15

7.5

9.4

11

13

15

17

9.4

11

13

15

17

19

1 0 0

1 0 1

1 0 2

1 0 3

1 0 4

1 0 5

1 1 0

1 1 1

1 1 2

1 1 3

1 1 4

1 1 5

1 2 0

1 2 1

1 2 2

1 2 3

1 2 4

1 2 5

1 3 0

1 3 1

1 3 2

1 3 3

1 3 4

1 3 5

1 4 0

1 4 1

1 4 2

1 4 3

1 4 4

1 4 5

1 5 0

1 5 1

1 5 2

1 5 3

1 5 4

1 5 5

2

4

6

8

10

12

4

6.1

8.1

10

12

14

6.1

8.2

10

12

15

17

8.3

10

13

15

17

19

11

13

15

17

19

22

13

15

17

19

22

24

46

Postive

MPN/100ml

Positive

MPN/100ml

10 1 0.1

10 1 0.1

2 0 0

2 0 1

2 0 2

2 0 3

2 0 4

2 0 5

2 1 0

2 1 1

2 1 2

2 1 3

2 1 4

2 1 5

2 2 0

2 2 1

2 2 2

2 2 3

2 2 4

2 2 5

2 3 0

2 3 1

2 3 2

2 3 3

2 3 4

2 3 5

2 4 0

2 4 1

2 4 2

2 4 3

2 4 4

2 4 5

2 5 0

2 5 1

2 5 2

2 5 3

2 5 4

2 5 5

4.5

6.8

9.1

12

14

16

6.8

9.2

12

14

17

19

9.3

12

14

17

19

22

12

14

17

20

22

25

15

17

20

23

25

28

17

20

23

26

29

32

3 0 0

3 0 1

3 0 2

3 0 3

3 0 2

3 0 5

3 1 0

3 1 1

3 1 2

3 1 3

3 1 4

3 1 5

3 2 0

3 2 1

3 2 2

3 2 3

3 2 4

3 2 5

3 3 0

3 3 1

3 3 2

3 3 3

3 3 4

3 3 5

3 4 0

3 4 1

3 4 2

3 4 3

3 4 4

3 4 5

3 5 0

3 5 1

3 5 2

3 5 3

3 5 4

3 5 5

7.8

11

13

16

20

23

11

14

17

20

23

27

14

17

20

24

27

31

17

21

24

28

31

35

21

24

28

32

36

40

25

29

32

37

40

41

Positive

MPN/100ml

Positive

MPN/100ml

10 1 0.1

10 1 0.1

4 0 0

4 0 1

4 0 2

4 0 3

4 0 4

4 0 5

4 1 0

4 1 1

13

17

21

25

30

36

17

21

5 0 0

5 0 1

5 0 2

5 0 3

5 0 4

5 0 5

5 1 0

5 1 1

23

31

43

58

76

95

33

46

47

4 1 2

4 1 3

4 1 4

4 1 5

4 2 0

4 2 1

4 2 2

4 2 3

4 2 4

4 2 5

4 3 0

4 3 1

4 3 2

4 3 3

4 3 4

4 3 5

4 4 0

4 4 1

4 4 2

4 4 3

4 4 4

4 4 5

4 5 0

4 5 1

4 5 2

4 5 3

4 5 4

4 5 5

26

31

26

42

22

26

32

38

44

50

27

33

39

45

52

59

34

40

47

54

62

69

41

48

56

64

72

81

5 1 2

5 1 3

5 1 4

5 1 5

5 2 0

5 2 1

5 2 2

5 2 3

5 2 4

5 2 5

5 3 0

5 3 1

5 3 2

5 3 3

5 3 4

5 3 5

5 4 0

5 4 1

5 4 2

5 4 3

5 4 4

5 4 5

5 5 0

5 5 1

5 5 2

5 5 3

5 5 4

5 5 5

64

84

110

130

49

70

95

120

150

180

79

110

140

180

210

250

130

170

220

280

350

430

240

350

540

920

600

2400+

Qualitative estimation of bacteria

Isolation and growth of bacteria:

The bacteria obtained as samples from any source are rarely pure. Therefore,

the isolation is the first important step to obtain pure culture. The culture was

repeatedly plated on nutrient agar plates in various dilutions to get single colony.

Spreader method:

The water sample was diluted in 0.1% peptone water. 1 drop of dilution was

placed in the center of the nutrient agar plates and spreaded it by pushing the glass

spreader backward and forward while rotating the plate. The plates were incubated at

48

370C for 48 hrs to get the isolated colonies. These colonies were then picked up with

the help of sterilized inoculating needle tip and then again streaked on nutrient agar

slants to get pure culture of particular isolate.

Looping out method:

The dilution of water was done in distilled water before streaking. A loopful

of 1:10 dilution was placed on the medium near the rim of the plate and spreaded over

the segment area A and B in parallel streaks taking care not to let the streak

overlapped. The loop was flamed and again repeated with the area C and so on. The

plate was inverted and incubated at 370C for 24 hrs. to get the isolated colonies.

These colonies were then picked up and streaked on nutrient agar slants for isolation

of pure culture.

Methods for identification of bacteria:

Various methods have been explained for identification of bacteria but

identification on the basis of minimum number of tests has been recommended by

Bergey's Manual, 8th

edition. There are many tests for identification of bacteria but

only few conventional tests were done during the study period. Following were the

methods used for identification of bacteria isolated from water as well as from fish:

Culture characteristics

Motility

Morphology and staining reactions

Biochemical reactions

Colony and cultural appearances:

Colonies were examined with hand lens. Observed pigment formation both on

top of and under the surface of the colony and noted if pigment diffused into the

medium.

Motility (Hanging Drop Method):

Inoculated the culture in nutrient broth and incubated at 370C for 24 hrs.

Placed a loop full of culture of microorganism in the center of the cover slip. Inverted

the depression slide over the cover slip with well or depression over the drop. Pressed

over the drop gently. The hanging drop suspended in depression was examined under

49

microscope for motility of bacteria.

Staining:

Smears were made by removing small amount of surface growth and mixed

with a drop of distilled water on the slide. The smear was fixed after passing through

a Bunsen flame two or three times and then stained.

Solution 'A':

Crystal Violet (90% dye content)- 2g

Ethyl alcohol (95%) - 20ml

Solution 'B':

Ammonium Oxalate - 0.8g

Distilled water - 80ml

Sol.A and Sol.B were mixed, kept for 24 hr and then filtered.

2 g of Potassium Iodide and 1g of Iodine in 300 ml of distilled water.

0.25 g of safranin was dissolved with 10 ml of 95% alcohol. Distilled water was

added to make 100ml.

Procedure:

A loop full of overnight grown culture of bacterial isolates was smeared on

clean (non greasy) slide and fixed by passing the slide in the flame after

drying the smear.

The smear was stained with crystal violet for 1 min.

Washing was done with water.

Then slide was immersed in iodine solution for 1 min.

Excess of stain was decolorized by putting 95% ethyl alcohol on the slide

until the excess of crystal violet was removed.

Counter stained the slide with safranin solution for 1 min.

Finally the slide was washed, dried and examined.

Results:

Gram positive - Violet

Gram negative - Red

BIOCHEMICAL REACTIONS:

Organisms, which are similar in microscopic and cultural characteristics, are

50

often differentiable by their reactions in various biochemical tests.

Triple Sugar-Iron Agar (TSI) Test:

Aim: To test the ability of the bacteria to ferment different sugars. Basically this test

is to differentiate among the different group or genera of Enterobacteriaceae (Gram-

negative bacilli) capable of fermenting glucose and production of acid: and to

distinguish Enterobacteriaceae from other Gram- negative intestinal bacilli.

Principle: Some bacteria have the property of fermenting some of the carbohydrates

with the product in of gas or acid or both.

Materials used:

(i) Triple Sugar-Iron Agar slants-6.5g/100ml distilled water

(ii) Sterile platinum loop.

Procedure:

The TSI slants were inoculated by means of the stab and streak procedure. This

required the insertion of a straight needle from the base of the slant into the butt.

Incubated for 18-24 hours at 37˚C.

Results:

Some bacteria have the property of fermenting some of the carbohydrates with the

production of gas or acid or both.

Fermentative activities of organisms as described below:

Alkaline slant (red) and acid butt (yellow) with /without gas production

(breaks in agar butt)- only glucose fermentation.

Acid slant (yellow) and acid butt (yellow) with/without gas production-

lactose and/ or sucrose fermentation.

Alkaline slant (red) and alkaline butt (red) or no change (orange-red) butt- No

carbohydrate fermentation

Hydrogen Sulphide Production Test:

Aim: To test the production of hydrogen sulphide by the bacteria.

Principle: Certain bacteria produce hydrogen sulphide in the presence of lead acetate,

which turns to black color due to the formation of lead sulphide.

Triple Sugar- Iron Agar test can also be used to detect the H2S production .Its because

of the formation of insoluble precipitate of ferrous sulphide.

51

Materials used:

(i) Nutrient broth

(ii) Bacteria to be tested for H2S production

(iii) Filter paper strips impregnated with 10 % solution of lead acetate, which is dried.

Procedure: Took 5ml of nutrient broth with the bacterial culture into a test tube.

Placed a strip impregnated with lead acetate on the top of the inoculated broth tube.

Incubated the tube at 26-280C for 24-72 hrs.

Result:

Positive: Blackening of the filter paper strip

Negative: No change

Indole Production Test:

Aim: To determine the ability of bacteria to degrade the amino acid- Tryptophan.

Principle: Indole is a nitrogen-containing compound formed from the degradation of

the amino acid tryptophan, which is not a characteristic of all bacteria. Therefore it

serves as a biochemical marker.

Materials used:

(i) SIM Agar slants- 3.62 g SIM Agar medium / 100 ml of distilled water.

(ii) Kovac reagent

Procedure: Using sterile technique, inoculated the culture into the test tube of SIM

Agar by means of a stab inoculation by means of a stab inoculation. Incubated tubes

for 24-48 hrs at 370C. Add 0.3 ml of Kovac‟s reagent to the cultured IM Agar slants.

Result:

Positive: Cherry red reagent layer

Negative: No change

Catalase Test:

Aim: To determine the catalase activity of bacteria.

Principle: Some bacteria are capable of splitting hydrogen peroxide to release free

oxygen by producing enzyme catalase.

Material used:

(i) Agar slant culture

(ii) 3 % hydrogen peroxide

52

(iii) Glass slides

Procedure: Made a thin film of the cultured bacteria from the agar slant on the glass

slide and added to it a few drops of 3 % hydrogen peroxide.

Result:

Positive: Surface frothing (bubbles)

Negative: No bubbles

Cytochrome Oxidase Test:

Aim: To detect the presence of cytochrome oxidase enzyme in the bacteria.

Materials used:

(i) Bacterial culture in the agar plates

(ii) Freshly prepared 1% aqueous tetra methyl-p-phenylene-diamine-dichoride

solution

Procedure: On a filter paper strip in a petridish added few drops of freshly prepared

1% aqueous tetra methyl-p-phenylene-diamine-dichloride solution and subsequently

smeared a bacterial colony over the moistened paper by means of a loop.

Result:

Positive: Dark purple coloration within 30 seconds.

Negative: No change.

Methyl Red Test and Voges Proskauer Test:

Aim: To determine the ability of microorganisms to oxidize glucose with the

production of acidic or non-acidic end products.

To distinguish between Escherchia coli and Enterobacter aerogens.

Principle: Hexose monosaccharide glucose is the major substrate oxidized by all

enteric organisms for energy production. The end products of this process will vary

depending on the specific enzymatic pathways present in the bacteria. Methyl red acts

as pH indicator, which detects the presence of large concentration of acidic end

products. Production and detection of non-acidic end products from glucose

fermentation is done by voges proskauer test.

Materials used:

(i) MR-VP broth

(ii) Methyl Red

iii Baritt‟reagent

53

Procedure: Inoculated the broth and incubated at 26-280C for 24 hrs. Distributed

broth into two equal test tubes.

For Methyl Red Test:

Added 2 to 4 drops of methyl red solution. Recorded the immediate reaction.

For V.P Test:

Added few drops of Baritt reagent.

Result:

For M.R. Test:

Positive: Red color (immediately)

Negative: Yellow color

For V.P. Test:

Positive: Bright pink color appearing in 5 min.

Negative: No colors change

Citrate Utilization Test:

Aim: To determine the utilization of citrate by bacteria.

Principle: Some bacteria are capable of utilizing citrate as the sole source of carbon

and mono ammonium phosphate as the sole source of nitrogen. As a result the pH of

the medium changes which is indicated by the indicator present.

Material used:

(i) Simmons Citrate Agar-24.28g

(ii) Distilled water.

Procedure: Inoculated Simmons Citrate Agar slants using sterile technique by means

of stab and streak method. Incubated all cultures for 24 to 48 hrs. at 370C.

Result:

Positive: Green color of medium changes to blue.

Negative: Green color (No change and no growth).

Nitrate Reduction Test:

Aim: To determine the ability of some bacteria to reduce nitrates (NO3-) to nitrites

(NO2-)

Principle: The reduction of nitrates by aerobic and anaerobic microorganisms occurs

in the absence of molecular oxygen, an anaerobic process. In these organisms

54

anaerobic respiration is an oxidative process whereby the cell uses inorganic

substances such as nitrates or sulphates to supply oxygen that is subsequently utilized

as final hydrogen acceptor during energy formation.

Material used:

(i) Nitrate broth

(ii) Solution A (Sulfanilic acid)

(iii) Solution B (Alpha naphthylamine)

(iv) Zinc powder

Procedure: Inoculated nitrate broth with a drop of a light suspension of the organism

and incubated at 370C for 24-48 hrs. Added 1 ml of Solution A followed by 1ml of

Solution B.

Result:

A positive reaction was indicated by the appearance of deep red color. To tubes not

showing red coloration, added pinch of zinc dust and allowed them to stand.

A negative reaction was shown by the appearance of red color after the addition of

zinc indicated the presence of nitrate in the medium i.e. the organism did not reduce

nitrate. In case when there was absence of red coloration in both the conditions,

indicated that organism reduced both nitrate and nitrite and this was also a positive

reaction.

Urease Test:

Aim: To determine the ability of microorganisms to degrade urea by means of the

enzyme urease.

Principle: Urease, which is produced by some microorganisms, is an enzyme that is

helpful in the identification of Proteus vulgaris. Therefore this test serves to rapidly

distinguish members of this genus from other lactose-fermenting enteric organisms.

Material used:

(i) Urea broth

(ii) Distilled water

Procedure: Using sterile technique, each experimental organism was inoculated into

tube containing urea broth by means of loop inoculation. Incubated cultures at 370C

for 24-48 hrs.

55

Results:

Positive: Deep red color

Negative: Red color (no change)

Starch Hydrolysis:

Aim: To determine the ability of microorganisms to hydrolyze starch.

Material used:

(i) Starch Agar

(ii) Lugol's iodine

Procedure: Cultures were spot inoculated on starch agar plates and plates were

incubated 300 C for 5 days. Plates were flooded with Lugol's iodine solution.

Results:

Positive: Hydrolysis was indicated by clear colorless zone around the inoculated

colonies

Negative: Medium turns blue where starch had not been hydrolyzed.

Lipid hydrolysis:

Aim: To determine the ability of microorganisms to hydrolyze lipid.

Procedure: Spot inoculation of culture was done on tributyrin agar containing 1%

tributyrin. Plates were incubated at 25 to 300 C for 48hrs.

Result:

Positive: A slight colorless zone around the colony.

Negative: No colorless zone.

Gelatin hydrolysis:

Aim: To determine the ability of microorganisms to hydrolyze Gelatin.

Procedure: Inoculated Gelatin Agar plates with a drop of light suspension and

incubated for 5 days at 370 C. Plates were flooded with Frazier reagent.

Result:

Positive: Clear zone around inoculated colony.

Negative: No colorless zone

56

Carbohydrate Utilization:

This test is for oxidation or fermentation of carbohydrate with production of

acid or acid and gas. Some bacteria are very active and able to transform number of

sugars; others (Pseudomonas) are able to transform very less number of sugars

(Carbon Compounds).

In this test the bacterium was inoculated into peptone broth containing 1 % of

a particular carbon compound (sugars to be tested). Production of acid following the

breakdown of carbon source (sugars) was detected by the use of indicator such as

Phenol red or Bromocresol purple. Gas evolution during fermentation was estimated

by inserting Durham's tubes.

10 ml of Peptone water was taken in tube and Durham tube was inserted in

each and then sterilized. Then 1 ml of sterilized 1% sugar solution (to be tested) and

indicator were added. Before inoculating, the Durham tubes were checked to be filled

with fluid. Then inoculated and incubated for 48 hrs at 370 C. If acid produced the

indicator will change color due to change in pH (Bromocresol is purple in alkali,

yellow in acid). Phenol red is red in alkali, yellow in acid. If gas is produced it will

collect in the Durham's tube. If there is no change then the results are negative.

Types of Media Used: (Different media used for this study were Hi-media products).

Nutrient Agar

Nutrient Broth

Nitrate Broth

Triple Sugar Iron Agar (TSI)

Mac Conkey Broth (Double Strength)

Mac Conkey Broth (Single Strength)

Simmons Citrate Agar

Starch Agar

Gelatin Agar

SIM Medium

Urea Broth

MR-VP Medium

Tryptone Soya Agar (TSA)

Eosine Methylene Blue (EMB) Agar

57

Deoxycholate Citrate Agar (DCA)

Cetrimide Agar Base (CAB)

Tributyrin Agar

Mac Conkey Agar Medium

Peptone Water

Baird Parker Agar Base (BPA)

Mannitol Salt Agar

Bacillus cereus agar

Bile Esculin Agar

Thiosulphate Citrate Bile Salt Sucrose (TCBS) agar

Xylose Lysine Deoxycholate (XLD) Agar

Salmonella shigella agar etc.

Indicators Used:

(i) Bromocresol Purple

(ii) Methyl Red

Reagents Used:

(i) alpha-Naphthylamine

(ii) Lugol's Iodine

(iii) Sulphanilic Acid

(iv) Kovac's Indole Reagent

(v) Barritt's Reagent

(vi) Frazier Reagent

COLLECTION OF FISH SPECIMENS:

For the present study, fishes were captured monthly from their natural habitat

in upstream (S-I) and downstream (S-II) sections of river Tawi with the help of cast

net, drag net, gill net etc.

CATCH PER UNIT EFFORT (CPUE):

Catch Per Unit Effort (CPUE) were calculated on the basis of numbers of

individuals per net per hour. The CPUE values of different months were then

averaged to arrive at the seasonal averages viz. Pre-monsoon, Monsoon and Post-

58

monsoon season catch statistics.

LENGTH-WEIGHT RELATIONSHIP:

It is a general expectation in fishes that the weight of fish would vary as the

cube of its length. Theoretically, it is expressed by the formula W = KL3, where „W‟

is weight in gm. and „L‟ is length in cm. „K‟ as a constant. In many cases, the value of

exponent in the formula may vary considerably from „3‟. o, a more satisfactory

formula adopted by Le Cren (1951) was used presently. According to this formula

W = aLb,

where „W’ & ‘L’ are weight & length respectively and „a’ & ‘b’ are constants.

For the practical purpose this relationship is usually expressed in its logarithmic form

as:

Log W = Log a + b Log L (Le Cren, 1951)

CONDITION FACTOR (K):

The variation in weight for a particular length of a fish is called as Condition

factor or Ponderal Index. It is an indicator of the well being of the fish. It was

calculated by the following formula:

K = W x 100

L3

Histological study:

Ovaries of the fish were carefully removed, excessive moisture was blotted

and quickly weighed on an electronic balance, these were then fixed in Bouin‟s

fixative (freshly prepared from picric acid 75%, formalin 20% and acetic acid 5%) for

24 hrs, washed, dehydrated and embedded in paraffin wax (melting point 52°C-53°C).

Transverse sections of ovaries were stained in Haemotoxylin-Eosin stain.

Microphotography:

The illustration of the microscopic materials were photographed with the help

of Olympus photographic camera.

Fecundity:

Fecundity = Weight of ovary × No. of ova in the sub-sample

Weight of the sub-sample

59

Statistical analysis of data:

Standard Deviation (SD):

Standard Deviation was calculated by using the formula:

where,

d deviation from the mean –

n = total number of observations.

Regression analysis: It was calculated by using Microsoft Excel 2010.

Diversity index: Species diversity was calculated by Shannon- Weiner Index using

SPSS software and Microsoft Excel 2010.

√∑d2

n SD =

60

Chapter – 4

Results and Discussion

4.1 Water Quality Analysis: During the present investigation, water quality analysis

involved

Physico-Chemical Analysis

Heavy Metal Analysis

Bacteriological Analysis

4.1.1 Physico-Chemical Analysis:

Seasonal fluctuations in physico-chemical factors in different stations at river Tawi:

Temperature:

Temperature is one of the most important factors, as it is responsible for the

metabolic and physiological behaviour of an aquatic ecosystem and it directly or

indirectly influences its physico-chemical parameters and biological processes.

61

During present investigation, the mean value of air temperature at the sampling

stations (I and II) fluctuated between 16.5ºC to 37.7ºC in the year 2009-10 and values

showed its range from 15.7ºC to 40.5ºC in the year 2010-11. In both the years of study,

the maxima was seen in the month of June, whereas the minimum value was observed in

January during first year of study and during second year of study, it was seen in Feb.

(Table 1 and Fig. 1).

The mean value of water temperature closely followed the air temperature (due to

shallowness and continuous flow) and varied from 15.0ºC to 34.5ºC in the first year of

study and from 13.0ºC to 35.0ºC in the second year of study (Table 2, Fig. 2). The

maximum value of water temperature recorded during June was 34.5ºC during 2009-10

and 35.5ºC in 2010-11 whereas the minima of water temperature was recorded during

January i.e. 15ºC (2009-10), 13ºC (2010-11) at both the stations. Earlier many workers

also reported positive relationship between air and water temperature in different lotic

water bodies (Reed, 1962; Singh, 1965; Reid and Wood, 1976; Dutta, 1978; Qadri and

Yousuf, 1980; Raina et al., 1982; Walia, 1983; Bhatt et al., 1984; Anatharaj et al.,1987;

Pandey et al.,1992; Joshi et al., 1993; Verma and Nasar, 1995; Ghezta, 1998; Pandey and

Sharma, 1998; Kaur and Joshi, 2003; Kumar et al., 2004; Siraj et al., 2006; Shvetambri,

2007, Hina, 2010, Yadav and Srivastava, 2011 and Deshkar et al., 2014).

Increased solar radiation and longer day length during summer resulted in

comparatively higher temperature range in both air and water as seen during the present

study. Similarly, fall in insulation and shorter photoperiod may explain winter decline in

air and water temperature. The results are in line with Dutta, 1978; Puri, 1989; Kumar,

1990; Sharma, 1992; Sawhney, 2004; Andotra, 2007; Hina, 2010 and Murugan and

Prabaharan, 2012.

pH:

pH, the measure of hydrogen-ion concentration describes the acid-base

equilibrium achieved by different dissolved nutrients and is the suitability index for

assessment of chemical conditions prevailing in any aquatic ecosystem.

During the period of investigation, pH fluctuated from 7.5 to 8.5 at both the

sampling stations in both the years. Seasonal changes in pH revealed maxima in the

month of January and minima in the month of July (Table3, Fig. 3). The studies further

revealed that pH showed less variation and remained alkaline throughout the year.

62

Singhal et al., 1986; Ghosh and George, 1989; Shastree et al., 1991 also reported similar

findings in different inland waters of India.

Seasonal studies of pH clearly indicate that it was in low regime during summer

and rainy season and in high regime during winter season. Similar findings were also

reported by Bhanja and Patra (2000), Prasannakumari et al. (2003) and Murugan and

Prabaharan (2012).

Stationwise picture of pH shows a decreasing trend as we move from Station I to

Station II i.e. Minimum values were observed during rainy season i.e. 8.0 (Aug.) at S-I

and 6.9 (July) at S-II in first year of study whereas at Station-II, minima at both the

stations was observed in the month of July. However, maximum pH was recorded during

winters. pH showed a declining trend as we move from upstream to downstream sections

of river Tawi i.e. from 8.6 at Station I to 8.4 at Station II (2009-10) and from 8.7 (Station

I) to 8.4 (Station II) in the year 2010-11. Therefore, at sewage polluted station (Station

II), pH was observed low as compared to less polluted station (Station I). Low record of

pH at the pollutant mixing stations has also been reported by Motwani et al., 1956; David

and Ray, 1966; Rajagopalan et al., 1970; Singh and Bhowmick, 1985; Malviya et al.,

1990; Shrotriya and Dubey, 1990; Zutshi, 1992; Israili and Ahmad, 1993; Chopra and

Patrick, 1994; Andotra, 2007; Hina, 2010 and Deshkar et al., 2014.

Free carbon-dioxide:

Presence of free carbon-dioxide in natural waters acts as a buffer against rapid

shifts in alkalinity or acidity of an aqueous system. Free CO2 enters aquatic systems

through diffusion from atmosphere, respiration by aquatic organisms and as a by-product

of decomposing organic matter. On the contrary, some factors are responsible for its

reduction, e.g.

a) Carbonates, when present do not allow CO2 to be produced in bottom and

column so that it could reach up to the surface.

b) Algae that utilizes CO2 during photosynthesis.

c) Low ions retaining capacity of water for gases at high temperature.

d) Lower microbial activity.

During present course of investigation, Free CO2 showed a mean variation from 0

to 3.2 mg/l in the sampling stations. Highest record of Free CO2 was noticed during the

month of Feb. (2009-10) and Jan. and Feb. (2010-11). A comparative stationwise study

63

further indicated that at Station I (Nagrota) Free CO2 marked its complete absence in both

the sampling years except in the month of Apr. and May (2009-10) and in Sep. (2010-11)

whereas, at Station II (Gujjar Nagar), presence was noticed in all seasons except in the

month of June and July & July (2009-10). (Table 4, Fig. 4)

Absence of FCO2 in water bodies, has often been linked with its consumption in

photosynthesis and for formation of carbonates as suggested by Hutchinson (1967),

Wetzel (1975), Jhingran (1982), Patil et al. (1985), Shardendu and Ambasht (1988), Puri

(1989), Khajuria (1992), Dalpatia (1998), Sharma (1999), Sharma (2002), Akhtar

(2003), Sawhney (2004), Siraj et al. (2006) and Shvetambri (2007).

From the present scenario, it can be safely inferred that there is an inverse

relationship between DO and Free CO2. An inverse relationship between DO and FCO2

is already on record and is in accordance with the findings of Welch (1952), Odum

(1956), Saha et al. (1959), Sreenivasan (1964), Hutchinson (1967), Unni (1972), Wetzel

(1975), Reid and Wood (1976), Sehgal (1980), Jhingran (1982), Goldman and Horne

(1983), Zutshi (1992), Pandey (1997), Ghezta (1998), Sawhney (2007), Hina (2010)

and Mishra and Hasan (2013).

Dissolved oxygen:

Dissolved oxygen is an exceptionally important factor because it determines the

survival of species as well as limits their distribution within an aquatic ecosystem

(Francis et al., 2007). The presence of DO in an aquatic ecosystem is due to its diffusion

from the atmosphere and because of photosynthetic activity of aquatic plants. The rate at

which oxygen diffuses in water generally depends upon various concomitantly operating

factors like, temperature, concentration of dissolved salts, relative solubility, pollution

and respiration by bacteria, plants and animals while its consumption and reciprocal

depletion is caused by respiration of flora and fauna, reduction due to other gases and

decomposition of organic matter.

Persuals of Table 5 and Fig. 5 evidently revealed that during the present

investigation, DO recorded its highest mean value during the month of February i.e. 7.1

and lowest during June i.e. 5.0 in the first year of study (2009-10) whereas in the second

year, DO showed its maxima in the month of December i.e. 6.9 and minima in the month

of May i.e. 5.2. An overall study revealed highest DO during winter and lowest value

during summer in both the sampling stations studied presently.

64

Comparative data of DO in sampling stations revealed that the maximum DO

(during Jan.) was at Station I ((9.1 mg/ l) and (9.4 mg/l) as compared to Station II (5.3

mg/l). Similar trend was also noticed during June in 2009-10 (minimum DO value), when

it was 7.0 mg/l at Station I and 3.1 mg/l at Station II. In the second year of study (2010-

11), the mean value of DO recorded its minima in May. The study also revealed a

negative correlation between dissolved oxygen and pollution level in the present aquatic

system. Low values of DO were estimated at sewage mixing station (Station II) as

compared to less polluted station (Station I). Low DO at the sewage polluted sites has

already been explained by Hynes (1960), Saxena et al. (1966), Wilber (1970), Agarwal et

al. (1976), Singh and Bhowmick (1985), Trivedy et al. (1990), Goel and Chavan (1991),

Baruah et al. (1993), Chopra and Patrick (1994), Chopra and Rehman (1995), Khanna et

al. (1997), Fatoki et al. (2002), Sawhney (2007), Hina (2010) and Deshkar et al. (2014)

for different study areas.

According to the general trend, the temperature showed an inverse relationship

with the dissolved oxygen as presently revealed from the Table 1 and Table 5. This

higher level of DO in winter as compared to summer could possibly be linked with its

miscibility with water at lower temperature because of enhancement in gas retaining

capacity of oxygen at lower temperature and rise in temperature lowers the oxygen

retention capacity of water, resulting in low values of DO during summer and monsoon

season. Findings of Khalaf and MacDonald (1975), Badola and Singh (1981), Joshi and

Bisht (1993), Kataria et al. (1995), Khaiwal et al. (2003), Thilaga et al. (2004), Yadav

and Srivastava (2011), Murugan and Prabaharan (2012), Eknath (2013) and Kumari et al.

(2013) also corroborate the present results.

Chloride:

The presence of chloride in riverine water may be due to the result of washing out

salts from ground surface, organic decomposition, runoff water from the catchment areas,

domestic sewage contamination and public use of soaps and detergents for bathing and

washing. Greater source of chlorides in fresh water is sewage and detection of which is

done for assessing the amount of sewage.

Chloride showed a mean fluctuation between 14.0 mg/l (July) and 37.4 mg/l

(Oct.) at the selected stations in first year of study (2009-10) and during the second year

(2010-11), chloride showed its fluctuation between 13.7 (July) and 39.1 (Oct.) mg/l.

65

Perusal of Table 6 and Fig. 6 showed that chloride recorded its highest value during the

month of October and lowest during July.

Monsoon fall in chloride concentration is due to dilution effect caused by direct

rainfall and inundation of water from catchment area. The observed fall in chloride

concentration during July, with rise in water level due to monsoon, supports the earlier

observations of Saha et al. (1971), Saad (1973), Mitchell (1975), Kumar (1990), Sharma

(2004) and Mishra and Hasan (2013).

As per the tablular data (Table 6), chloride was reported comparatively high at

Station II (56.5 mg/l and 58.7 mg/l) as compared to Station I (18.4 mg/l and 19.6mg/l) in

2009-10, 2010-11 in the month of October respectively. Again, during July, highest value

of chloride i.e. 22.8 mg/ and 22.6 mg/l was recorded from Station II as compared to

5.3mg/l and 4.8 mg/l reported from Station I during first year and second year of study

respectively. Thus the present scenario clearly depicts that maximum value of chloride

was found at Station II (Gujjar Nagar) which can be attributed to increased pollution level

due to addition of sewage at this station. Rise in chloride due to sewage discharge is

already recorded by various workers viz. Ganpati (1942), Roy (1955), Chakraborty et al.

(1959), Ghosh et al. (1974), Mudgal et al. (1990), Shrotriya and Dubey (1990), Trivedy

et al. (1990), Sharma (1991), Jebanesan et al. (1992), Chopra and Rehman (1995),

Sawhney (2004), Singh and Singh (2007), Khan and Hazarika (2012), Mishra and Hasan

(2013) and Gupta et al. (2014).

Calcium and Magnesium:

Calcium being a micronutrient is required by aquatic organisms for their growth.

Its quantitative concentration in water strongly influences seasonal and dial dynamics of

aquatic fauna. Magnesium is a cation used in enzymatic transformations by algae, fungi

and bacteria. Magnesium compounds are more soluble as compared to their calcium

counterparts. As a cation, it is present in a water body as bicarbonate.

A perusal of Table 7 and Fig. 7 revealed that the mean annual variation of calcium

content in the present study area (Station I & II) varied from 36.9 mg/l (July) to 61.8 mg/l

(Oct.) during 2009-10 and from 40.7 mg/l (July) to 62.1 mg/l (Oct.) during 2010-11.

The mean value of magnesium (Table 8, Fig. 8) varied from 11.7 mg/l (April,

May) to 26.0 mg/l (Oct.) during the year 2009-10 and from 12.5 mg/l (April) to 26.0 mg/l

(Oct.) during the year 2010-11. Both the cations recorded their highest record during

66

October whereas lowest value for calcium is noticed in the month of July and that of

magnesium in the month of April and May. Place to place variation in the composition of

rocks may account for the present variability in the concentration of these water

parameters as advocated earlier by Reid and Wood (1976), Jhingran (1991), Wetzel

(2000), Joseph and Claramma (2010).

Carbonates and Bicarbonates:

Alkalinity of water is due to the presence of hydroxyl ions (OH-) which combines

with H+ ions in solution. Both carbonates and bicarbonates form the components of

alkalinity. Carbonate is the principal anion and in most fresh water bodies it occurs as

calcium bicarbonate.

In the presently selected stations (I-II) for physicochemical analysis of water,

mean value of carbonate varied from 0 mg/l (Feb.) to 12.5 mg/l (June) in 2009-10 and

from 3.3 mg/l (Feb.) to 10.2 mg/l (July) in 2010-11. Bicarbonates showed a mean

fluctuation between 201.5 mg/l (Nov.) to 370.0 mg/l (April) in 2009-10 and from 194.5

mg/l (Dec.) to 372.0 mg/l (April) in 2010-11. (Table 9, Fig. 9 and Table 10, Fig. 10).

A look at the table of FCO2 and Carbonates (Table 4 and Table 9) revealed that

carbonates showed their presence at all the stations and in every month wherever FCO2

marked its absence. Therefore, an inverse relation was noticed between FCO2 and

Carbonates. Such an inverse relationship between Carbonates and FCO2 found during

present finding, has earlier been reported by Welch (1952), Cole (1975), Reid and Wood

(1976), Jhingran (1991), Wetzel (2000), Hutchinson (2004) and Mishra and Hasan

(2013).

The high values of Carbonates and Bicarbonates recorded at Station II may be due

to addition of more sewage at this particular site. These findings are in conformity with

those of Philipose (1959), Gochhait (1991), Singh and Rai (2003), Andotra (2007) and

Zubair and Ahrar (2013) who have also observed similar type of urban influences

responsible for high carbonate and bicarbonates content in the water bodies investigated

by them.

Electrical conductivity:

Electrical conductivity (EC) in natural waters is the normalized measure of the

water‟s ability to conduct electric current. This is mostly influenced by dissolved salts

present in water body. It is also an indirect measure of the presence of ions, such as

67

nitrate, sulphate, phosphate, sodium, magnesium, calcium, and iron. These substances

conduct electricity because they are negatively or positively charged when dissolved in

water. Therefore, the variation of conductivity indicates the uneven occurrence of un-

ionized chemical substances. The basic unit of measurement of conductivity is the mho or

siemens. Conductivity is measured in micromhos per centimeter (µmhos/cm) or

microsiemens per centimeter (µs/cm).

The concentration of dissolved solids, or the conductivity, is affected by the

bedrock and soil in the watershed. It is also affected by human influences. For example,

agricultural runoff can raise conductivity because of the presence of phosphate and

nitrate. It is important to measure the conductivity of water because aquatic organisms

require a relatively constant concentration of the major dissolved ions in the water.

Levels too high or too low may limit survival, growth or reproduction.

As per Table 11 and Fig. 11, the water conductivity reflected a mean range of

125–197.5 (µs/cm2) during first year of study whereas during second year, electrical

conductivity showed a mean range of 135-253.5 (µs/cm2).

Seasonal analysis of mean values of conductivity showed maxima in premonsoon

period during both year of study. Similar to present findings, Saksena et al. (2008) and

Verma and Saxena (2010) also observed EC maxima in summers.

Stationwise picture of EC showed an increasing trend as we move from upstream

(S-I) to downstream (S-II) section of river Tawi due to more anthropogenic influences at

Station-II. The present observations are in line with Sharma (2003), Zaimoglu et al.

(2006), Marchese et al. (2008), Tripathi et al. (2008), Begum et al. (2009), Khan and

Hazarika (2012) and Yahya et al. (2012), who also observed higher electrical

conductivity due to various anthropgenic activities.

Fluoride:

Fluorides are the pollutants with immense significance as they have considerable

potential for ecological damage. Fluoride ions have dual significance in water supplies.

High concentration of Fluoride ion causes dental fluorisis (Disfigurement of the teeth). At

the same time a concentration less than 0.8mg/l results in dental caries. Hence it is

essential to maintain the fluoride ion concentration between 0.8 to 1.0 mg/l in drinking

water.

68

During present course of investigation, mean fluoride concentration varied from

0.021 to 0.089 mg/l during first year of study and from 0.01 to 0.0805 mg/l during the

second year of investigation (Table 12, Fig. 12).

Seasonal analysis of data revealed fluoride maxima in monsoon period. Monsoon

maxima of fluoride values may be attributed to leaching of fluoride rich pesticides from

agricultural fields, inflow of sewage and other solid wastes especially dead decaying

organic matter etc. Present findings coincide with the findings of Masuda, 1964; Bovay,

1969; Marrier, 1972; Jacks, 1973; Groth, 1975; Mc Laughlin et al., 2001 and Dutt et al.,

2011.

Total Dissolved Solids:

Total dissolved solids (TDS) is the term used to describe the inorganic salts and

small amounts of organic matter present in solution in water. The principal constituents

are usually calcium, magnesium, sodium, and potassium cations and carbonate, hydrogen

carbonate, chloride, sulphate, and nitrate anions.

During the present course of investigation, total dissolved solids (TDS) recorded

maxima in monsoon (Table 13, Fig. 13). Monsoon maxima of TDS (Total dissolved

solids) was also reported by Aggarwal and Arora (2012), Das et al. (2014) and Santhi

and Rajan (2014) in their findings.

Increase in conductivity during present study indicated increased

concentrations of TDS which may be attributed to development activities, leaking

septic systems, salt run-off from highways, fertilizers, or other pollutants.

Positive relation of EC and TDS was also advocated by Joseph and Claramma

(2010), Kar et al. (2010), Patra et al. (2011), Surana et al. (2013) and

Das et al. (2014).

69

4.1.2 Heavy Metal Analysis:

Heavy metals are among the most common environmental pollutants, and their

occurrence in waters and biota indicate the presence of natural or anthropogenic sources.

The main natural sources of metals in waters are chemical weathering of minerals and

soil leaching. The anthropogenic sources are associated mainly with industrial and

domestic effluents, urban storm, water runoff, landfill leachate, mining of coal and ore,

atmospheric sources and inputs rural areas. (Kabata-Pendias and Pendias, 1992; Biney et

al., 1994 and Zarazua et al., 2006).

Heavy metals have been used as indices of pollution because of their high toxicity

to human and aquatic life. Trace amounts of metals are common in water, and these are

normally not harmful to our health. In fact, some metals are essential to sustain life.

Calcium, magnesium, potassium, and sodium must be present for normal body functions.

Cobalt, copper, iron, manganese, molybdenum, selenium, and zinc are needed at low

levels as catalysts for enzyme activities. Drinking water containing high levels of these

essential metals, or toxic metals such as Al, As, Ba, Cd, Cr, Pb, Hg, Se, Zn, Cu, Mn, Co,

Ni, and Mo consistently found as contaminants in human drinking water supplies in many

areas around the world and prove hazardous to our health. (Groopman et al., 1985).

The term heavy metal is used for metals with a density more than 5 g/cm3. Heavy

metals important in environmental and health issues include arsenic, lead, cadmium,

copper, chromium, mercury, zinc, cobalt, nickel, tin and vanadium (WHO, 1993). Those

are not normally a part of the human body and are more poisonous to us than other

metals.

In the present study, the heavy metals detected in water at the two stations of river

Tawi include Iron (Fe), Copper (Cu), Zinc (Zn), Arsenic (As) and Lead (Pb). Their

monthly concentrations have been presented in Table 14 (2009-10) and Table 16 (2010-

11) and annual mean values have been depicted in the Table 15, 17 and Fig. 14, 15.

Perusals of Tables revealed that the relative dominance of the heavy metals in all

the water samples from both the stations was observed in the following sequence:

Fe > Zn > As > Cu > Pb.

In the first year of study (2009-10) on heavy metal detection- At Station-I, Iron

(Fe) recorded the highest mean value i.e. 0.0263 mg/1 and lead showed lowest mean

value i.e. 0.0001 mg/l whereas mean conc. of Copper (Cu), Zinc (Zn) and Arsenic (As)

was observed to be 0.0009, 0.0105 and 0.0011 mg/l respectively. Station-II showed

70

highest record for iron i.e. 0.1042 mg/l and lowest for copper i.e. 0.0087 mg/l. (Table 15,

Fig. 14)

During second year of study (2010-11), the heavy metals at Station-I showed

mean value of 0.0662 mg/l for Fe, 0.0022 mg/l for Cu, 0.0153 mg/l for Zn, 0.0006 mg/l

for Pb and 0.0028 mg/l for Arsenic and the mean conc. of heavy metals in Station-II

follows the following pattern i.e. Fe (0.1683 mg/l) > As (0.0285 mg/l) > Zn 0.0255

mg/l) > Cu (0.0048 mg/l) > Pb (0.0025 mg/l). (Table 17, Fig. 15)

Seasonal analysis of tabulated data (Table 14) for the first year of study (2009-

10) at both the stations revealed that among the detected heavy metals (Fe, Cu, Zn. Pb

and As), Iron recorded its peak (0.1000 mg/l) in the month of Jan. and minima (0.0010

mg/l) in the month of March and July at Station-I whereas at Station-II, Fe was maximum

in June (0.3000 mg/l) and minimum in September (0.0200 mg/l). Zinc showed its

maximum value (0.0800 mg/l) in the month of March and minimum in the month of May

at S-I whereas at S-II, Zn showed its peak in July and minima in April.

Similarly, the higher value (0.0060 mg/l) for Arsenic was recorded in the month

of December and was observed below detectable limit in the month of Sep., Oct and Jan.

at S-I and at S-II, As was maximum in Nov. and in the month of May, Sep., Oct. (2009)

and Jan. (2010), Arsenic (As) was below detectable limit of 0.0001 mg/l. However, Lead

and Copper was recorded in negligible amounts throughout the year.

During the second year of seasonal analysis of heavy metal detection in the

study stations, it was observed that iron recorded its peak in the month of July (0.2000

mg/l) and minima in the month of Nov. (0.0040 mg/l) at S-I whereas at S-II, Fe was high

(0.3000 mg/l) in June and August and minimum value was recorded in the month of April

(0.0500 mg/l). Maximum value for Lead (Pb) was observed in October and minimum

was recorded in April and September at S-I. At S-II, Pb was high (0.0100 mg/l) in the

month of June whereas in the month of April, it was recorded in negligible amounts.

However, at S-I Arsenic (As) showed its maxima in the month of June-10 and Feb-11

and minimum in the month of Sep.-10 and at S-II, concentration of As was high in Nov.-

10 and below detectable limit in May-10 and Jan.-11. At Station-I, Copper (Cu) showed

its maxima in the month of Oct.-10 and Jan.-11 and was below detectable limit in the

months of June, July, Dec. and Feb. However at S-II, Cu was observed in higher amount

in the month of Sep. and Dec. and absent in June. Zn was observed maximum in March

71

and August at S-I and II respectively whereas at both the study stations, it was below

detectable limit in the month of December. (Table 16)

During the present observations, variation in zinc concentration may be caused

due to the natural contribution of earth, human activity (use of chemicals and zinc based

fertilizers by farmers) and weathering of soil (Egila and Nimyel, 2002). Zinc level in the

study area could also be attributed to the high concentrations of Iron as Zn occurs in

nature along with other metals such as Iron and Cadmium (Dallars and Day, 1993).

The presence of Iron in the present study area could be attributed to high organic

matter and low dissolved oxygen content, in that Iron can easily be absorbed on

particulate organic matter or complexed with colloidal organic matter in aquatic

environment. This view point is also advocated by Bryan, 1976; Sanders, 1997;

Ataikiru, 1997; Akporido, 2000; Emoyan et al., 2006 and Odoemelam et al., 2013.

Lead is distributed in surface waters due to weathering of minerals and

atmospheric deposition (Merian, 1991; Robinson, 1996; Emoyan et al., 2006). The

level of Pb in Tawi river could be attributed to the industrial and agricultural discharge.

Also, dust which holds a huge amount of lead from the combustion of petrol in

automobile cars led to increase in Pb content (Hardman et al., 1994).

Similarly, the presence of copper may be due to high alkalinity of water and low

levels of Cu in the surface water during the monsoon could be caused due its adsorption

on the particulate matter and consequent settlement to the bottom. Marathea et al., 2011

also opined similar view.

Thus, the present results revealed that the seasonal mean levels of all the heavy

metals in water at Station-I (upstream) were lower than those of Station-II (downstream).

This view point gets quite an impressive support from the observations of Yousafzai et

al. (2008) who assessed heavy metal pollution in river Kabul concluded that the

concentration of heavy metals increases along the downstream of the river.

Present findings are also in line with works of Raju et al. (2013), who studied

spatio-temporal variation of heavy metals in Cauvery river basin revealed that

downstream stations accumulate metals at significant level as compared to upstream

stations. Shanbehzadeh et al. (2014) also confirmed the present results while evaluating

heavy metals in water and sediment of Tembi river that the average concentration of the

metals in water and sediment in downstream was more than that of upstream.

72

The higher concentration of heavy metals at Station-II could be attributed to the

number of sewerage inlets, various untreated/partially treated industrial effluents and

other anthropogenic activities. Similar findings were also reported by Lichtfuss and

Gerbard, 1981; Anatharaj et al., 1987; Pardon et al., 1990; Boughriet et al., 1992;

Ankley et al., 1996, Sathish, 1998, Sharma et al., 1999; Yu et al., 2001; Papafilippaki

et al., 2008; Christopher et al., 2012; Kalaivani and Dheenadayalan, 2013 and

Rahman et al., 2013.

Further, from the seasonal studies, it became apparent that the metal

concentrations were mostly higher during the monsoon and post monsoon season as

compared to the values of premonsoon season. This can be attributed to runoff from land

into the rivers during the wet season. (Okonkwo and Mothiba, 2005; Ntakirutimana et

al., 2013).

Contrary to the present findings, more heavy metal concentration in dry season as

compared to wet season was reported by Papafilippaki et al., (2008); Kikuchia et al.,

(2009); Dan’azumi and Bichi, (2010); Mondol et al., (2011) and Kaur and Mehra,

(2012).

However, Ntakirutimana et al. (2013) reported that As, Cu and Cd were present

at high concentrations levels in winter (Dec to Feb), Cr and Pb attained their highest

concentration in spring (March to May) and Zinc in summer (June to august).

Higher concentration of the dissolved metals during premonsoon season was also

reported by Patil and Sawant (2013). More accumulation of metals during low flow

condition of river may be attributed to high evaporation rate of surface water followed by

elevated temp, whereas low values of few metals such as copper, Zinc, Iron, Lead during

post monsoon season may be due to effect of rains. Gummadavelli et al., (2013);

Kalaivani and Dheenadayalan, (2013) and Jayadev and Puttaih, (2013) also observed

similar results of summer maxima and monsoon minima. The low concentration of heavy

metals during monsoon could be attributed to the heavy rainfall. The rise in temperature

and evaporation in summers cause the rise in heavy metals concentration in water as

suggested by various workers. (Murthy and Rao, 1987; Padmini and Geetha, 2007

and Baheerathi and Revathi, 2013).

As per the above results, all the values of presently studied heavy metals were

found within the acceptable limits of WHO (2006).

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4.1.3 Bacteriological Analysis

4.1.3.1 Quantitative Estimation of Bacterial load:

Bacterial load during the present study was assessed by two methods:

Standard plate count method

Most probable number method

Standard Plate Count (SPC/ml):

Station-I:

During the present course of investigation, in the first year of study (2009-10),

the peak (2.9 × 103/ ml) of SPC count was observed in the month of July and the minima

(1.0 × 102/ ml) was observed in the month of August. During the second year of study

(2010-11), the SPC count showed maxima (3.4 × 104/ ml) in the month of August and the

average minima (2.9 × 102 / ml) was observed in the month of January.

Station-II:

In the year 2009-10, the SPC count varied from 2.2 × 104/ ml (May) to 9.4 ×

104/ ml (June). During the second year of investigation (2010-11), the peak (9.5 × 10

4/

ml) of SPC count was observed in the month of June and the minima (3.4 × 103/ ml) was

observed in the month of December (Table 18).

Most probable number (MPN/100ml):

The most probable number depicts the coliform count in the water samples. The

mean value of MPN count of bacteria ranged from 42/100 ml (Jan.) to 1229 +

/100 ml

(May) in the first year of study (2009-10). During the study, the count registered highest

records in summers (till September). After that, a decline was observed from October and

continued till January.

The average minima of MPN at the selected stations was observed in the month of

January in both the years of study. In first year, the mean value of MPN count was 42/

100 ml and in the second year, it was 56.5/ 100 ml. The average maxima in first year was

recorded in May i.e. 1229.0+ and in second year studies, it was found to 1247.5

+ (June)

(Table 19).

Stationwise study of MPN count during first year showed the minima at Station-I

i.e. 15/100 ml (Sep.). Value registered for the minima at Station-II was in the month of

January i.e. 58/100 ml. At Station-I, maxima (84/100ml) was observed during June

whereas at Station-II, maxima (2400+/100 ml) was seen during May.

74

The perusal of MPN Table (Table 19) further revealed that during the second

year of microbial study at Station-I, the minima (22/100 ml) was observed in the

month of September and at Station II, the minima was recorded as 84/100 ml in January.

The maximum MPN values varied from 95/100 ml (Station-I) to 2400+/100 ml (Station-

II). At Station-II, the maxima showed two peaks (2400+/100 ml) i.e. in May and June.

Thus, the present investigation clearly reveals that maximum coliform count was

estimated in water samples from Station-II as compared to Station-I, perhaps due to

higher quantity of sewage disposal at this station.

The present observations coincided with the studies of Austin and Austin,

(1985); Rees, (1993); Gyananath et al., (2000); Crowther et al., (2001); Lasut et al.,

(2005), Agbogu et al., (2006); Sabae and Rabeh, (2007); Das et al., (2010); Srivastava

and Srivastava, (2011); Tahir et al., (2011) and Nath, (2012) who also associated the

high levels of bacterial count in river water samples with the amount of sewage

contamination.

From the present scenario of seasonal microbial studies on water samples, it

becomes apparent that the microbial load estimated through MPN and SPC count seems

to vary according to the water temperature, showing highest records during peak values

of temperature (summer) and low counts at low values of temperature (winter). Support

for this can be drawn from the findings of Gocke et al., (1990); Sharma, (1993); Sabae,

(1999); Young and Thackstonz, (1999); El-Fadaly et al., (2001); Al-Harbi, (2003);

Parashar et al., (2003); Sabae, (2004) and Abo-elela et al., (2005) who have also

reported enhancement of microbial activities due to high temperature.

Similarly, Sood et al. (2008), while assessing bacterial indicators and

physicochemical parameters of Gangetic river system of Uttarakhand, observed high

bacterial growth in summer and low in rainy and winter season. Sharma et al. (2010)

while assessing pollution status of North Indian lakes, Tahir et al. (2011) of Lahore canal

and Mishra et al. (2013) that of Bhojtal lake reported similar trend in results.

On monitoring data related to bacterial load of Devolli river, Albania, Hamzaraj

and Krecl (2013) recorded high bacterial load during summer. However, they also

observed another peak of bacterial load during Feb., despite the low temperatures. They

attributed this abnormal peak to heavy rain conditions during this month by accumulating

more sewage in the river.

75

The highest number of bacterial count was recorded during spring and summer by

Salman et al. (2013) from Al- Hilla river, which might be the consequence of the high

level of suspended solid and nutrients in the drainage water or due to the positive

relationship between temperature and bacterial levels suggests that heat induced growth

may be a contributing factor to seasonally high bacteria levels. Similarly, Banoo et al.

(2014) correlated high bacterial load to river out fall or land run off and optimum

temperature.

On the contrary, Srivastava and Srivastava (2011) while assessing physico-

chemical properties and sewage pollution indicator bacteria in surface water of river

Gomti reported higher bacterial population during monsoon due to increased land run off

and higher faecal inputs in to river from various sources. An increase in the Faecal

Coliform level after rainfall was also reported by Szewzyk et al., 2000; Shehane et al.,

2005.

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4.1.3.2 Qualitative Analysis of Bacteria:

Bacterial Isolates from water and fish samples:

During the present investigation at polluted and non- polluted stations, about 189

bacteria were isolated as pure cultures. Out of them, 126 belonging to seven genera were

isolated from water and 63 were isolated from fish samples. (Table 20, Fig. 16)

Among 126 isolates (from water), 18 were unidentified. Out of 108 identified

isolates of water, 28 were E. coli, 22 were Salmonella sp., 13 were Pseudomonas sp., 19

were Vibrio sp., 10 were Staphylococcus sp., 9 were Bacillus sp. and 7 were

Enterococcus sp.

From 63 isolates of fish samples, 28 were unidentified. Among 35 identified

isolates of fish, 18 were E. coli, 9 were Salmonella sp., 3 were Pseudomonas sp., 4 were

Vibrio sp. and 1 belonged to genus Bacillus (Table 20). The biochemical tests of bacterial

isolates are given in Table 23 (Plates 5-10).

A comparative study of the sampling stations (Table 21, Fig. 17) clearly depicted

that the highest number of bacteria were present in Station II (polluted site). This highest

number of bacteria may be due to addition of untreated sewage, drainage etc. Present

results can be correlated with Musaddiq (2000) who also attributed untreated sewage as

a causative factor for high microbial concentration during analysis of surface water

quality of Morna river at Akola. Works of Andotra (2007) and Hina (2010) also support

the present observations.

Stationwise tabulated data revealed that at Station II, 101 bacterial isolates were

recorded out of 126 pure bacterial isolates whereas only 25 were isolated from Station I.

At Station II (Gujjar Nagar), 89 bacterial isolates were identified and 12 remained

unidentified. Identified bacteria belong to seven genera viz. E. coli, Salmonella,

Pseudomonas, Vibrio, Staphylococcus, Bacillus and Enterococcus. However, at Station I

(Nagrota), only 19 bacterial isolates were identified belonging to four genera (E. coli,

Salmonella, Pseudomonas and Bacillus) and 6 remained unidentified. (Table 21, Fig. 17).

During the present period of study, pathogenic bacterial isolates viz. Salmonella

sp., Vibrio sp. etc. were recorded from both the stations (S-I and S-II). The data further

revealed that their number was highest in Station II (polluted station) as compared to

Station I (Table 21). These findings are in accordance with Bailey and Scott, 1966;

Efstratiou et al., 1998; Polo et al. 1999; Andotra, 2007 and Hina 2010, who also

suggested predominance of pathogenic bacterial isolates at sewage polluted stations.

77

Similarly, bacteria were isolated from different organs (Skin, Gills and Intestine)

of fish samples. The organwise distribution of bacterial isolates was given in Table 22.

Organ wise study of bacterial load depicts the following trend: Skin > Gills > Intestine.

18 E. coli, 9 Salmonella sp., 3 Pseudomonas sp., 4 Vibrio sp. and 1 Bacillus sp. were

detected in fish samples.

Present findings get support from the works of Adedeji et al. (2011) who

obtained highest microbial load from the skin as compared to stomach of Oreochromis

niloticus. The high microbial load of the tilapia in this study was attributed to mass

pollution of the environments where the fish were caught.

Bacterial load as a reflection of aquatic environment:

The pathogenic bacterial species i.e. E. coli, Salmonella (paratyphyi B),

Pseudomonas aeruginosa., Vibrio (cholera and parahaeolyticus) and Bacillus cereus

were isolated from water sample of river Tawi as well as from resident fishes from

selected spots.

There appeared to be a strong correlation between the bacterial species present in

the water and in the fish samples, regardless of the type of fish. This is an indication that

the surrounding water has an influence on the composition of the microflora of the fish.

Similar observations are in line with Geldreich and Clarke (1966), Shewan and Hobbs

(1967), Silva and Widanapathirana (1984), Buras et al. (1987), Ogbondeminu (1993),

Sivakami et al. (1996) and Apun et al. (1999).

Presently, the faecal coliforms were recorded from fish samples. As the normal

flora of fish does not include coliforms, thus these faecal coliforms in fish reflect the

level of pollution of their environment. Cohen and Shuval (1973) also supported this

view point.

Additionaly, during the present investigation, diseased fish with skin lesions and

fin rot had been encountered but their number was very few. The causative organism

isolated for skin lesions and fin rot was Pseudomonas sp. Many workers also reported

Pseudomonas sp. as causative pathogen for skin lesions (Manohar et al., 1976; Kumar

et al., 1986; Karunasagar et al., 1988; Sahu et al., 1996; Hina, 2010).

The present author therefore is of the view point that these bacterial isolates

mainly act as opportunistic pathogen of fish and can be safely considered as pollution

indicators. Further, the higher count of these pathogens in downstream spot (Station II)

fed with sewage made the water unfit for human consumption and hence suggested that

78

the water from these spots should not be considered as potable unless proper pretreatment

is given.

Description of identified bacterial isolates:

During the present investigation, bacteria isolated were identified on the basis of

their characteristics and biochemical features for proper description. Morphological and

biochemical tests were done for the identification of particular bacteria as per methods

given by Bergey‟s manual 8th

edition, 1974; Austin and Austin, 1987; Collins and Lyne,

1985).

Staphylococcus aureus:

Enriched samples were streaked on Baird Parker Agar (BPA) and the plate was

incubated at 37°C for 24-48 hours. Appearances of jet black colonies surrounded by

white halo were considered to be presumptive Staphylococcus aureus followed by

confirmation via various biochemical tests. S. aureus gram positive cocci in clusters,

showed yellow coloured colonies due to mannitol fermentation on Mannitol salt agar.

Biochemical characteristics of S. aureus were given in Table 25; Plate 15.

Escherichia coli:

They produced smooth colonies on nutrient agar media. Enrichment medium used

was Mac Conkey agar and detection media was EMB (Eosine Methylene Blue) agar on

which E. coli showed green metallic sheen. Biochemical characteristics of E. coli were

presented in Table 26; Plate 14.

Pseudomonas aeruginosa:

They were gram-negative rods. Enrichment medium used was Soyabean casein

digest broth and detection media was CAB (Cetrimide Agar Base) on which

Pseudomonas aeruginosa showed fluorescent growth. Biochemical characteristics of E.

coli were presented in Table 27, Plate 13.

Salmonella paratyphi B:

Appeared as Gram negative short straight motile rods. MacConkey media and

Selenite broth were used as enrichment medium and DCA (Deoxy cholate Citrate Agar)

was used as selective media on which Salmonella showed black growth. Apart from other

biochemical characteristics, Salomonella paratyphi B was peculiar among Salmonella

species to show Citrate utilization test positive. Biochemical characteristics were shown

in Table 28, Plate 12.

79

Bacillus cereus:

B. cereus is gram positive rod shaped motile bacterium. It is facultative anaerobe.

This bacteria is responsible for gastrointestinal problems viz. nausea, vomiting, diarrhoea

etc. Bacillus cereus Agar was used as selective medium and B. cereus showed peacock

blue colonies on it (Table 29, Plate 14).

Enterococcus faecalis:

Appeared as gram positive non-motile microbe, Enterococcus faecalis can cause

endocarditis and bacteremia, urinary tract infections (UTI), meningitis, and other

infections in humans. Detection media used for E. faecalis was Bile Esculin Agar. It

showed dark brownish growth on detection medium. Brown pigment diffusible in the

medium was noticed. Biochemical characteristics of E. faecalis were given in Table 30,

Plate 11.

Vibrio sp.:

Members of the genus Vibrio were motile, gram-negative rods. Two species of

Vibrio were isolated during the present study viz. Vibrio cholera and Vibrio

parahaemolyticus. Alkaline peptone water was used as an enrichment medium for

cultivation of Vibrio sp. TCBS (Thiosulphate Citrate Bile salt Sucrose Agar) was used as

detection medium. V. cholera showed yellow round smooth glistening and slightly

flattened colonies on TCBS agar whereas V. parahaemolyticus showed green colonies on

TCBS after 24-28 hrs incubation at 370C. Biochemical characteristics were given in

Table 31; Plate 16.

80

4.2 Impact of pollution on some biological characters of fish:

4.2.1 Fish Diversity and Abundance:

Riverine fish communities show seasonal changes in the composition and

relative abundance of species, which may be influenced by constant fluctuations in

environmental factors. A perfect understanding of the ichthyo-faunal diversity of a

system is prerequisite for successful implementation of fisheries development,

sustainable utilization of fishery resources and for adopting suitable conservation

measures. Documentation of biodiversity has become a very important aspect to

understand different ecosystems and factors influencing them.

Owing to callous anthropogenic activities, freshwater ecosystems are stressed.

As compared to the terrestrial ecosystem fresh water species are at higher risk of

extinction (Naiman et al., 1993; Weijters et al., 2009).

During the last several decades, the water quality of the Indian rivers has been

deteriorating due to continuous discharge of industrial wastes and domestic sewage

(Dyniel and Wood, 1980; Unni, 1984; Shaw et al., 1991; Sivakumar et al., 2000;

Sachidanandamurthy and Yajurvedi, 2006; Krishna et al., 2007; Duran and Suicmez,

2007; Smitha et al., 2007; Raja et al., 2008).

Fish have been regarded as an effective biological indicator of environmental

quality and anthropogenic stress in aquatic ecosystems (Fausch et al., 1990; Simon,

1991; Simon and Lyons, 1995; Scott and Hall, 1997; Smith et al., 1999; Arunachalam,

2000; Siligato and Bohmer, 2001; Basa and Rani, 2003; Ibarra et al., 2003; Rashleigh,

2004; Roset et al., 2007; Almroth et al., 2008; Ael et al., 2014).

The composition and distribution of fish species in the study stations during the

present investigation are shown in Table 32-55. Analysis of fish diversity showed the

presence of twenty one fish species belonging to 13 genera, 4 orders (Cypriniformes,

Symbranchiformes, Channiformes and Siluriformes) and 5 families (Cyprinidae,

Balitoridae, Bagridae, Mastacembelidae and Channidae) from both the study stations

i.e. Station-I (Nagrota) and Station-II (Gujjar Nagar) (Table 56, Plate 17).

Tor tor, Tor putitora, Labeo boga, Labeo dero, Barilius bendelisis, Barilius

vagra, Puntius ticto, Puntius sophore, Puntius conchonius, Channa punctatus,

Channa striatus , Garra gotyla, Garra lamta, Aspidoparia morar, Crossocheilus

latius, Mastacembelus panculus, Mastacembelus armatus, Mystus seenghla,

81

Nemacheilus botia, Schistura sp., Shizothorax richardsonii were the 21 different fish

species observed in both the sampling stations.

The order Cypriniformes was found to be dominant (76.2%) as it constituted

highest number (16) of species. The order Channiformes and Symbranchiformes

included two species (9.5% each) whereas the order Siluriformes included single

species (4.7%).

Relative abundance of the major families of fish in the study stations revealed that

Cyprinidae was the most abundant family contributing 66.7% (15 sp.) of the fish fauna

followed by Balitoridae, Mastacembelidae and Channidae contributing 9.5% (two

sp. each) whereas family Bagridae showed least contribution of 4.7% (single sp.)

(Table 56).

Dominance of Cypriniformes in the assemblage structure, as seen during the

present study, is in accordance with the observations of Dass and Nath, (1966); Tilak,

(1971); Malhotra et al., (1975); Dutta and Malhotra, (1984); Dutta and Kour, (1999);

Dutta et al. (2002); Dutta (2003); Kaur, (2006); Johnson and Arunachalam, (2009);

Kantaraj et al., (2011); Johnson et al,. (2012); Murugan and Prabaharan, (2012); Mishra

et al., (2013) who attributed it to their high adaptive variability to occupy all possible

habitats.

During the present study, fish diversity was subjected to diversity analysis using the

Shannon-Wiener index. SW index of diversity in the present study had shown a variation

range of 2.58 to 5.99 at S-I and 3.35 to 6.06 at S-II during first year of study (Table 32-

43). During second year of investgation, SW varied from 1.64 to 5.52 at S-I whereas at S-

II, it varied from 3.16-6.59 (Table 44-55, Fig. 19, 20).

Johnson and Brinkhurst (1971) observed the SW values ranging from 1.00 to

3.66, Mackey et al. (1973) reported that in their study the Shannon-Wiener index ranged

from 1.3 to 2.5 whereas Osborne et al. (1976) and Godfrey (1978) observed values

ranging from 0.14 to 2.69 and from 1.938 to 5.34 respectively.

In Central Amazonian lakes, Barthem (1981) found variation in the Shannon index

from 2.2 to 3.2. Pereira (2000) used this same index to evaluate the diversity of

Camaleao Lake, finding values varying from 3.9 to 4.1. Fish diversity studies of two

rivers (Kathani and Adan) of the northeastern Godavari basin, India was evaluated by

Heda in 2009 and reported Shannon index for Kathani as 2.58 and for Adan as 2.1.

82

While studying the fish diversity in Bhadra reservoir (Karnataka), Kantaraj et al.

(2011) calculated Shannon-Weiner index whose values ranged from 2.2 to 4.10. Fish

diversity and assemblage structure in Ken river of Panna landscape was studied by

Johnson et al. (2012). In their study, they noted that the Madla area of Ken had a high

Shannon diversity index (3.48), whereas the Mahuapani stream registered a low Shannon

diversity index (0.99).

Similar results were reported by Sarkar et al. (2013) who observed fish species

diversity (H-) ranging from 3.8-5.2. Shannon Weiner Index (H) 2.4-3.0 was also

advocated by Basavaraja et al. (2014) while studying fish diversity and abundance in

relation to water quality of Anjanapura reservoir, Karnataka, India.

Data analysis of seasonal catch per unit effort (CPUE) revealed that these values

were higher (Table 57, 58 and Fig. 21, 22) mostly during Post-monsoon season which

could be attributed to better environmental conditions during that period viz. high DO,

Low BOD, low microbial load, optimum temperature etc. Similar to the present findings,

Whitefield and Blaber (1979), Offem et al. (2009), Hina (2010), Khajuria et al. (2013)

also suggested similar environment factors responsible for high catch during post-

monsoon period.

Stationwise seasonal fish catch statistics (Table 32-55) showed comparatively

more diversity (as revealed by higher Shannon-Weiner index i.e. H values) at

downstream section (S-II) because of diversified organic material available as food to the

fish, better breeding areas, less competition etc. The present results are in agreement with

Welcomme, 1985; Bayley and Li, 1994; Granado, 2000; Slavik and Bartos, 2001; Offem

et al., 2009; Hina, 2010 and Patra et al., 2011. Comparative study of both the stations

depicts that there was more abundance of cold water fishes in upstream section (S-I)

where as downstream section of river Tawi showed more abundance of warm water and

hardy species. Similar abundance pattern was also advocated by Sharma and Dutta

(2012) while studying icthyofaunal diversity of river Basantar, an important tributary of

river Ravi.

A decline in diversity during the present investigation as revealed by seasonal catch

statistics was attributable to more anthropogenic impact. Several investigations have been

carried out on similar effects of pollution in aquatic ecosystems. (Tsai, 1968; Williams

and Harcup, 1974; Victor and Tetteh, 1988; Harrel and Hall, 1991; Grall and Glemarec,

1997; Chow-Fraser et al., 1998; Dean et al., 1998, Boet et al., 1999; Gafny et al., 2000;

83

Koul, 2000; Martin et al., 2000, Idodo-Umeh, 2002; Guyonnet et al., 2003; Oguzie,

2003; Habit et al., 2006; Das and Chakrabarty, 2007; Heda, 2009; Kantaraj et al., 2011;

Patra et al. (2011); Murugan and Prabaharan, 2012; Mohite and Samant, 2013). Thus, the

present study derives support from earlier studies on the effect of human induced

influences on the diversity and abundance of the fish.

Therefore, the present investigation revealed that the physical habitat variables play

key role in the distribution of fishes in river Tawi. The study findings showed that fish

diversity and fish abundance of the study area is reducing with the increase of water

pollution. In the polluted stretch of the river hardy and tolerant species are thriving well

and other species are considered to be threatened by increasing water pollution due to

their gradual decrease year after year. This investigation would be used as a basis for

formulating a strategy of controlling water pollution and conserving the fish species in

the river Tawi.

84

4.2.2 Impact of pollution on fish growth:

Fish growth is considered as biomarker for riverine pollution because it integrates

all effects within fish. Understanding of the growth in fish is very important for more

specific fishery management. Deteriorating habitat quality has become a debatable

question for ecologists and a significant research has been done on the relationship

between deteriorating environmental quality and fish health.

The length-weight relationship of fish is an important fishery management tool.

Its importance is pronounced in estimating the average weight at a given length group

and in assessing the relative well being of a fish population. It also throws light on the

environmental conditions of the aquatic ecosystem in which the fish is residing as

Basheer et al. (1993) opined that length- weight relationship of fish varies depending

upon the condition of life in aquatic environment.

Perusal of Table 59 depicts the total annual catch statistics, where younger size

range i.e. between 30.0-39.0 mm was found to be insignificant at polluted station (S-II).

However, only 10.3% i.e. 31 individuals of size range between 40.0-49.0 mm were

collected at this station. Comparatively at non-polluted site (S-I) about 20 no. of juveniles

in the size range of 30.0-39.0 mm were collected followed by about 82 no. in between

size range of 40.0-49.0 mm were caught. Also, tabular data of agewise percental catch

composition of B. vagra revealed that in 0+

age group there was 12.5 % difference in

catch composition in polluted station (S-II) as compared to non-polluted one (S-I) (Table

61). However in 1+ and 2

+ age groups, the percental difference was less as compared to 0

+

age group (Table 60).

The data clearly reveals that there is drastic depletion in young stages/population

at polluted station (S-II) as compared to non-polluted one (S-I). From observed figures of

fish catch, one can have a clear idea that perhaps high pollution load at S-II might have

severely affected the younger stages more than the older stages i.e.> 50 mm in length.

Similar to the present observations, Balik et al. (2006) and Jones et al. (2002) also

postulated that the pollution stress either chronic or acute could affect the population size

if it occurs immediately after recruitment or in a nursery area thereby resulting in lower

age at maturity, reduced reproductive capacity and reduction of year class strength.

In order to evaluate the impact of pollution on growth of fishes of river Tawi, a

comparative study was conducted on selective sized fish of different genera viz. Puntius,

Nemacheilus and Barilius from both the study stations. The Linear regression equation

85

describing length-weight relationship in different selected fish was computed and

described in Tables 61, 62 and Fig. 23, 24. The value of correlation coefficient (r) at

Station I for Nemacheilus botia, Puntius ticto and Barilius vagra was 0.95, 0.97 and 0.93

respectively Table 61 whereas at tation II, values of “r” recorded were 0.95 for N.

botia, 0.96 for P. ticto and 0.94 for B. vagra (Table 62). At both the stations the value of

correlation coefficient „r‟ is positively correlated. imilar positive correlation with r

0.92 was also discussed by Kanwal and Pathani (2011) for Garra lamta.

Ideally, the regression coefficient “b” of a fish should be very close to 3.0 Allen,

1938), however the cube law does not hold good throughout the life period and the

weight gain in a fish may not be always cube of its length gain (Rounsefell and Everhart

1953 . Hile 1936 and Martin 1949 opined that the value of “b” may range between 2.5

and 4.0.

During the present investigation, the value of regression coefficient „b‟ was found

to be 0.59 (N. Botia), 1.33 (P. ticto), 1.11 (B. vagra at tation I whereas at tation II, “b”

value varied from 0.53 (N. Botia) to 1.27 (P. ticto) and 1.03 (B. vagra) (Table 61, 62).

The present work revealed that studied fish species did not followed the cube law

completely. Similar departure from cube law has been advocated by LeCren, (1951);

Antony, (1967); Subla and Sunder, (1981); Sunder et al., (1984); Mitra, (2001);

Raizada et al., (2005); Devi et al., (2008); Mir and Mir, (2012) and Patel et al., (2014)

who attributed the variation in “b” value to environmental factors, season, food

availability, sex, life stage and other physiological factors.

Similarly, the condition factor ‘k’ is an indicator of general well-being of the

fish. „k‟ was greater than one 1 expressed as indicative of the general well being of fish,

whereas its value less than one (1) indicated that fish is not in a good condition. The

nearness of “k” value to 1 indicates the suitability of environment for fish growth. “k”

fluctuates between fish species and within fish species due to feeding differences, climate

and environmental conditions (Lizama et al., 2002).

At Station I, the values of condition factor (k) for N. botia, P. ticto and B. vagra

was observed to be 0.95, 1.84 and 1.50 respectively. However, at Station II, the value of

“k” showed a declining trend as compared to -I i.e. 0.88 (N. Botia), 1.73 (P. ticto) and

1.45 (B. vagra). The results of the condition factors (Table 61, 62) in the present study

thus revealed that fishes at tation II have comparatively low “k” value than those at

Station I which clearly reflected poor environmental conditions at Station II.

86

Similar to present findings, low “k” values were also reported by Ajayi (1982) (k

= 0.77-0.81) for Cynolossus canariensis, Nwadiaro and Okorie (1985) (k=0.49-1.48)

for Chrychthys filamentosus, Dars et al., 2010 (k=0.94-1.03) for Labeo gonius, Mir and

Mir (2012) for P. conchonius (0.57-0.98 . Low value of “k” due to anthropogenic stress

was also discussed by Eastwood and Counture, (2002); Anene, (2005); Kumolu-

Johnson and Ndimele, (2010); Uttah et al., (2012) and Patel et al., (2014) in different

fishes studied by them and they attributed low “k” value to unfavourable environmental

conditions and aquatic pollution causing adverse conditions for fish growth.

Contrary to the present observations, the higher “k” values at polluted sites in

comparison with non-polluted was reported by Hedayati and Safahieh (2011) who

suggested that toxic substance in yellowfin seabream caused several changes in the

enzymatic, biochemical and hormonal parameters of the experimental fish.

87

4.2.3 Impact of pollution on reproductive potential of Barilius vagra

Due to less availability of mature male fish (Barilius vagra) during the study

period, the investigation on gonadal maturation stages were restricted on female fish

only.

In this study, it was observed that young females attain maturity while they were in

in 1+

age group (TL-70 mm to 80 mm).

4.2.3.1 Ovarian Maturity Stages:

Depending upon the frequency distribution of different ova, the ovary of Barilius

vagra was classified into following stages (Table 63, Plate 18):

Results observed at Station I (Non-Polluted):

Immature/Resting Phase (January-February): In resting phase, 72.62 % immature

oocytes were at Stage-I and 27.38 % in Stage-II.

Early Maturing Phase (March-April): In this phase, the ovary was found to contain

50.79 % of oocyte in Stage-I, 33. 43 % in Stage-II and 15.78 % in Stage-III.

Developing Phase (May-June): In this phase, oocyte in Stage-III contributed 42.61 %,

followed by 33.83 % Stage-IV and 14.62 % Stage-V in the ovary. A few Stage-II (8.94

%) oocytes were also seen in the ovary.

Developed/Pre-Spawning Phase (July-August): In developed phase, the oocyte in

Stage-VI contributed 62.94 % followed by Stage-V oocyte 33.13 % and few Stage-IV

(3.93 %) oocyte.

Spawning Phase (August-September): In Spawning phase, 73.29 % of oocytes in

Stage-VI followed by 24.44 % atretic oocytes, 2.27 % Stage-V oocytes were observed in

the ovary.

Results observed at Station II (Polluted):

Immature/Resting Phase: In this phase, ovary was found to contain 89.34 % of oocyte

in Stage-I and 10.66 % of oocyte in Stage-II.

Early Maturing Phase: In this phase, the ovary was observed to contain oocytes in

different stages of development. Higher percentage of oocytes in Stage-I (75.21 %),

followed by Stage-II (20.12 %) and Stage-III (4.67 %).

88

Developing Phase: In developing phase, Stage-II (57.89%) followed by Stage-III

(36.46%) were found to be present in large proportion. A few Stage-IV (5.65%) oocytes

were also seen in the ovary.

Developed/Pre-Spawning Phase: In developed phase, the oocytes in Stage-IV

contributed 37.88% followed by Stage-V oocytes 26.22%, Stage-III oocytes 15.23% and

Stage-VI 2%. Atretic oocytes (18.67%) were also seen during this phase.

Spawning Phase: In Spawning phase, 41.74% of atretic oocytes followed by 36.12%

oocytes in Stage-VI, 13.14% Stage-V and 8.90% oocytes in Stage-IV were observed in

the ovary.

The results thus clearly depicts that the ovarian development and spawning is

drastically effected by environmental pollution as revealed from the ovarian samples

taken from Station II as compared to Station I. However, the effect was more pronounced

during pre-spawning and spawning phase of maturation.

During pre-spawning season (July-Aug.), ovarian samples from non-polluted

station (S-I) showed 33.13% and 62.94% V and VI maturity stages respectively whereas

samples from polluted station (S-II) revealed only about 26.22 % V stage of ovarian

maturation and negligible amount of VI stage. However, about 18.67% of atretic oocytes

were also shown in S-II.

During spawning season (Aug.-Sep.) samples from non-polluted station showed

73.29% of Stage VI (last stage of maturation) and about 24.44% of atretic oocytes

whereas in polluted samples, the observed atretic oocytes were 41.74% with only 36.12%

of Stage VI oocyte (Table 63).

In tune with present observations, Cross et al. (1984) also observed high level of

atresia-reabsorption of mature eggs in ovaries of fish from polluted sites of the southern

California coast. Similarly, Tylor and Sumpter (1996), advocated that atresia at later

stages of oocyte development was largely considered to be the result of environmental

stress. Ankely et al. 2005 and Van der Ven et al. 2007 also reported that additional

exposure of adult fish to various chemicals in laboratory conditions has been seen to

result in increased rate of ovarian atresia. Similar view point has also advocated by El-

Morshedi et al. (2014) who observed more atretic follicles while studying the effect of

heavy metal pollutants in thin lipped grey mullet (Liza ramada) and the sea bass

(Dicentrarchus labrax) in Egyptian lakes.

89

4.2.3.2 Fecundity potential:

Further, data on the number of mature ova before spawning (July-Aug.)

revealed that fishes from Station II were less fecund than those at Station I (non-

polluted). At Station I, average fecundity of 1+ age group (70.0-99.0 mm) was 905.33 ±

152 and 1310 ± 150 in 2+ age group (100.0-109.0 mm) whereas at Station II, it was

771.66 ± 110 in 1+ age group and 1190 ± 177 in 2

+ age group. The data further revealed

that ovarian weight of fish samples from Station I was comparatively more than Station

II. (Table 64).

Stationwise percental difference in fecundity from both the stations at 1+ age

group and 2+ age group showed 17.38 % and 10.08 % more number of ova in mature

ovary in fishes at S-I than at S-II respectively. Stationwise percental difference in

ovary weight (Plate 19) in fishes from both the stations also followed the same trend i.e.

it was 9.83 % and 10.2 % more at S-I in 1+ and 2

+ age group respectively (Table 64).

Decreased number of spawned eggs would solely affect the number of larvae and

therefore result in a proportional decrease in the number of individuals in later age

classes. The present results gets support from findings of Homing and Neiheisel in 1979

who reported decreased egg production while studying copper toxicity on blunt nose

minnow (Pimephales notatus).

Thus, the following stationwise studies clearly depicts that aquatic pollution

exerts a direct effect on the reproductive physiology of Barilius vagra, a minor carp

resulted in reduced ovarian weight, lesser number of mature ova and increased atresia in

fishes inhabiting polluted aquatic environment. The present findings get further

authenticated by various workers viz. Thomas, 1989; Nath and Kumar, 1990; Kamel,

1990; Hill and Janz, 2003; Love and Goldberg, 2009; Akande et al., 2010 and

Marutirao, 2013 who studied impact of pollution on reproduction of fishes.

Thus, the present study adds valuable insight to our understanding of the potential

ecological effects of hazardous pollutants in the aquatic environment by incorporating

data on individual Barilius vagra responses to pollution. Survival in early life stages was

identified to be the most important factor affecting population growth and structure. It

could therefore be inferred that the documented reproductive impairment (reduced

fecundity and delayed maturation) due to pollution has serious consequences not only for

individuals but also at population level.

90

Chapter - 5

Summary and Conclusion

As the water pollution is deleterious for both aquatic and terrestrial life, so it is need

of the hour to assess the water quality of rivers as this is a very important issue related to

human health and environment. Keeping this in mind, the present research work has been

carried out to evaluate the degree of pollution level at upstream and downstream regions of

river Tawi so as to assess its potability and the impact of pollution on fishes.

For present investigation, two sampling stations were selected along the

longitudinal profile of River Tawi, viz; S-1 and S-II. At upstream section of river, Station-I

(S-1) was selected near Nagrota, where water was comparatively clean with the bottom

composed of stones, sand and boulders. Whereas Station-II (Gujjar Nagar) was selected in

downstream section of river Tawi at a distance of about 10 kilometers from Station-I. S-II

received heavy pollution load and organic matter in the form of sewage and garbage etc

from heavily populated city area. Monitoring of water quality of river Tawi involved

91

different parameters viz. physico-chemical analysis, heavy metal analysis and

bacteriological analysis.

Physico-chemical analysis:

All the values of presently studied physico-chemical parameters viz. Temp., pH, DO,

FCO2, Calcium, Magnesium, Chloride, Carbonates, Bicarbonates, etc. at both the

study stations were mostly within the permissible limits (Table 1-13).

However, stationwise data analysis revealed that low values of pH, DO and high

values of FCO2, chloride etc. at Station-II can be attributed to increased pollution

level due to the addition of sewage from the city at this station.

Heavy metal analysis:

The study data revealed that the relative dominance of the heavy metals in all the

water samples from both the stations was observed in the following sequence:

Fe > Zn > As > Cu > Pb.

In the first year of study (2009-10), at Station-I, Iron (Fe) recorded the highest mean

value i.e. 0.0263 mg/1 and lead showed lowest mean value i.e. 0.0001 mg/l whereas

Station-II showed highest record for iron i.e. 0.1042 mg/l and lowest for copper i.e.

0.0087 mg/l.

During second year of study (2010-11), the heavy metals at Station-I showed mean

value of 0.0662 mg/l for Fe, 0.0022 mg/l for Cu, 0.0153 mg/l for Zn, 0.0006 mg/l for

Pb and 0.0028 mg/l for Arsenic and the mean conc. of heavy metals in Station-II

follows the following pattern i.e. Fe (0.1683 mg/l) > As (0.0285 mg/l) > Zn 0.0255

mg/l) > Cu (0.0048 mg/l) > Pb (0.0025 mg/l).

An increasing trend in the mean conc. of heavy metals was observed as we move

from upstream to downstream i.e. Station-I to Station-II. (Table 14-18)

Bacteriological analysis:

[

Quantitative analysis of bacterial load:

The maximum MPN values varied from 95/100 ml (Station-I) to 2400+/100 ml

(Station-II). However, the SPC count varied from 1.0 × 102/ ml to 3.4 × 10

4/ ml at

S-I and from 2.2 × 104/ ml to 9.5 × 10

4/ ml at S-II.

92

Tabular data on quantitative estimation of bacteria revealed that microbial load

estimated through MPN and SPC count seemed to vary according to temperature,

showing highest record during summers and low values during winters.

Stationwise maximum count was observed in water samples from Station-II, owing

to more anthropogenic influences, which was an alarming signal for us. (Table 19-

20)

Qualitative analysis of bacterial load:

A comparative study of sampling stations elucidated that the highest number of

bacteria were present in Station-II (polluted site) as compared to Station-I.

Bacterial isolates identified during the study were Escherichia coli, Bacillus cereus,

Vibrio cholera, Vibrio parahaemolyticus, Staphylococcus aureus, Salmonella

(paratyphi B), Enterococcus faecalis and Pseudomonas aeruginosa.

Number of bacterial isolates viz., E. coli, P. aeruginosa, Bacillus cereus, Vibrio

cholera etc. showed their dominance at downstream section of river Tawi (S-II).

Organwise bacterial study of fish indicated higher prevalence of bacterial isloates in

fish muscles as compared to other organs (Gills & intestine). (Table 21-22)

After studying various water quality parameters (physico-chemical, heavy

metal and bacteriological parameters), an attempt was undertaken to study the impact

of pollution on fish population, growth and reproduction.

Impact of pollution on Fish diversity:

A total of 21 species of fish belonging to 14 genera, 4 orders (Cypriniformes,

Symbranchiformes, Channiformes and Siluriformes) and 5 families (Cyprinidae,

Balitoridae, Bagridae, Mastacembelidae and Channidae) were recorded. The order

Cypriniformes is found to be dominant (76.2%) as it constitutes highest number (16) of

species.

Seasonal fish catch statistics show comparatively more diversity (as revealed by

higher Shannon-Weiner index….H values at downstream section -II) because of

diversified organic material available as food to the fish. (Table 32-55)

Year wise study indicated that there was gradual decrease in fish catch due to

continuous increase in pollution level.

93

Impact of pollution on fish growth:

The length-weight relationship of fish is an important fishery management tool.

This relationship of fish varies depending upon the condition of life in aquatic

environment. In order to evaluate the impact of pollution on growth of fishes of river

Tawi, a comparative study was conducted on selective sized fish of different genera viz.

Puntius, Nemacheilus and Barilius from both the study stations.

During the present investigation, the value of regression coefficient „b‟ was found to be

0.59 (N. Botia), 1.33 (P. ticto), 1.11 (B. vagra) at Station-I whereas at Station-II, b value

varied from 0.53 (N. Botia) to 1.27 (P. ticto) and 1.03 (B. vagra).

tationwise comparative low values of „b‟ as recorded in present study reflects

reduction in general fish health, growth and fatness.

Similarly, the condition factor ‘k’ is an indicator of general well-being of the fish. At

Station-I, the values of condition factor (k) for N. botia, P. ticto and B. vagra was

observed to be 0.95, 1.84 and 1.50 respectively. However, at Station-II, the value of

„k‟ showed following fluctuations as compared to station-I i.e. 0.88 (N. Botia), 1.73 (P.

ticto) and 1.45 (B. vagra).

tation wise analysis of regression coefficient “b” and condition factor “k” showed

declining trend from S-I to S-II suggesting thereby that the fishes of upstream section of

river (S-I) were in better condition than those of downstream (S-II).

Further, studies of impact of pollution on various biological parameters of fish

were conducted on a minor carp, Barilius vagra that predominated both the study

stations throughout the year.

Impact of pollution on population structure and growth of Barilius vagra:

Perusal of total annual catch statistics revealed that younger size range i.e. between

30.0-39.0 mm was found to be insignificant at polluted station (S-II). However, only

10.3% i.e. 31 individuals of size range between 40.0-49.0 mm were collected from this

station. Comparatively at non-polluted site (S-I) about 20 no. of juveniles in the size

range of 30.0-39.0 mm were collected followed by about 82 no. in between size range

of 40.0-49.0 mm were caught.

94

The data of length size and age-wise percental catch composition of Barilius vagra

from different stations clearly depicted significant difference in percental catch

composition in early length group and age group. However in higher length group/age

group, the difference noted was comparatively insignificant. Thus the present findings

revealed that there was drastic depletion of young stages/population at polluted station

(S-II) as compared to non-polluted one (S-I).

Impact of pollution on reproductive potential of Barilius vagra:

Due to less availability of mature male fish (Barilius vagra) during the study period,

the investigation on gonadal maturation stages were restricted on female fish only. In this

study, it was observed that young females attained maturity while they were in 1+

age group

(TL-70 mm to 80 mm).

Oocyte maturity stages:

The percental data of ovarian maturation depicts that the ovarian development and

spawning was drastically effected by environmental pollution as revealed from the

ovarian samples taken from S-II as compared to S-I. However, the effect was more

pronounced during pre-spawning and spawning phase of maturation in the form of

atretic oocytes.

During pre-spawning season (July-Aug.), ovarian samples from non-polluted station

(S-I) showed 33.13% & 62.94% V and VI maturity stages respectively whereas samples

from polluted station (S-II) revealed only about 26.22 % V stage of ovarian maturation

and negligible amount of VI stage. However, about 18.67% of atretic oocytes were also

shown in S-II.

During spawning season (Aug.-Sep.) samples from non-polluted station showed

73.29% of Stage VI (last stage of maturation) and about 24.44% of atretic oocytes

whereas in polluted samples, the observed atretic oocytes were 41.74% with only

36.12% of Stage VI oocyte. (Table 63).

Fecundity:

Data on the number of mature ova before spawning (July-Aug.) revealed that fishes

from Station-II were less fecund than those at Station-I (non-polluted). This is attributed

95

to more anthropogenic influences at S-II. At Station-I, average fecundity of 1+ age

group (70.0-99.0 mm) was 905.33 ± 152 and 1310 ± 150 in 2+ age group (100-109.0

mm) whereas at Station-II, it was 771.66 ± 110 in 1+ age group and 1190 ± 177 in 2

+

age group.

Stationwise percental difference in fecundity from both the stations at 1+ age group

and 2+ age group showed 17.38 % and 10.08 % more number of ova in mature ovary in

fishes at S-I than at S-II respectively. Stationwise percental difference in ovary

weight in fishes from both the stations also follow the same trend i.e. it was 9.83 % and

10.2 % more at S-I in 1+ and 2

+ age group respectively.

Thus, the present study adds valuable insight into our understanding of the potential

ecological effects of hazardous pollutants in the aquatic environment on various biological

parameters of fishes inhabiting river Tawi. It could therefore be inferred that the

documented reproductive impairment (reduced fecundity, delayed maturation and atretic

oocytes) due to pollution has serious consequences not only for individuals but also at

population level. Similar types of studies should be thoroughly conducted on some other

economically important fish species in and around the study area.

Proposed Conservation and Management Plan

It is quite evident that the rivers are receiving huge amount of organic waste when it passes

through an urban sitting. This has been caused due to even increasing population. The

solution of this problem lies in the treatment of sewage and disposable of fully or partially

treated sewage waste. Through the processing is quite expensive but will have to be

followed in order to save river.

Short Term Measures

These include following:

1) Discouraging stagnation of domestic sewage on the ground surface.

2) Provide proper garbage collection system to prevent citizens from dumping the same into

the river.

3) Provide lavatories to prevent open defecation.

Long Term Measures

Long term measures to minimize pollution in river include the following:

1) Plan for establishing more septic tanks and soak pits and for sewage disposal.

96

2) Dredge the entire length of river bed to improve its carrying capacity.

3) Provide proper garbage collection stations.

4) Proper treatment of effluents from various sources to acceptable levels and Sewage

treatment plant can be established in the out skirt of city.

5) Dry sanitation or the sanitation which uses less or almost no water for the waste disposal

is what which we should adopt for.

6) Mass awareness among the people against consequences of pollution.

Steps to control decline in fishery:

Damage to fish breeding and feeding grounds due to collection of stones, sand for

construction purposes and other anthropogenic activities should be checked.

Intensive fishing during breeding season results in loss of future fish stock and should

be banned.

Laws framed should be strictly implemented to punish the guilty.

People, particularly those living along the banks of the Tawi river, should realize that

the river is not a waste disposable site. People have to change their mindset and regard the

river as nature‟s gifts to us. Any plan to control pollution and conservation of fishery

resources can succeed only if the people feel motivated and involved in its working and are

able to participate in its implementation.

The decline in aquatic fish diversity is likely to increase further unless proper

conservation measures are implemented. The best approach to the conservation of the

species is to disseminate information, education and practices among fishermen and other

stakeholders and inform them about the danger of extinction of the species and the need for

its conservation. This will go a long way towards protecting and preserving the species.

Prevention now is not only better, but also cheaper than looking for ways of recalling the

lost species. Once extinction occurs, it cannot be recalled.

The need of the hour is to rationalize the use of domestic, agricultural and industrial

effluents on one hand and to minimize their interferences with biotic components including

fish. Required guidelines for restoration of aquatic environment, rehabilitation and

conservation of fisheries would have to be evolved by pursuing appropriate research.

97

Chapter – 6

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