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ESSENCE

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Dr. Prashant Singh, D.A.V. P.G. College, Dehradun, India Dr. Ajit Pratap Singh, BITS Pilani, India

Dr. Rajan Kumar Gupta, Banaras Hindu University, India Dr. Ajendra Kumar, GKV, Haridwar, India

Dr. D. P. Uniyal, UCOST, Dehradun, India Dr. Himanshu Gupta, GKV, Haridwar, India

Dr. Sanjeev Kumar Chadha, B. B. A. Central University, Lucknow, India

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Dr. Reetesh Shah, Kumaon university, Nainital, India

Content / Vol. VIII: Special Edition: 2: 2017

`

Content Page No.

1. Conifers and their association with the understory shrubs along a temperate

riparian ecosystem in Bhaderwah, Jammu and Kashmir

Sharma, Anu; Sharma, Neeraj and Sharma, Monika

1 – 7

2. Comparative account of impact of heavy metals cadmium and chromium on

haematological parameters of Channa punctatus

Arya, Shveta

8 – 14

3. Regression modelling of Traffic Noise Pollution at various crossings in Jammu city

(J&K)

Kaushal, Akanksha and Rampal, Rajkumar

15 – 21

4. Medicinal plants used to combat a major gynecological challenge: Ovarian cyst

Pracheta; Kulshrestha, Sunanda and Sharma, Arun Kumar

22 – 34

5. Assemblage composition and seasonal variations of waterbirds at Nav Talav,

Amgaon in Gondia District of Maharashtra

Jha, Ashish Kumar

35 – 44

6. Diversity of Spiders in Agricultural Fields from Partwada Tahsil, District

Amaravati (Maharashtra State)

Vairale, Amit B.

45 – 47

7. Quality Assessment of Physicochemical and Biological Parameters of bore well

water in Chisda Village, Dadra and Nagar Haveli in Surat region

Qureshi, Ikram and Gandhi, Devang

48 – 53

8. Evaluation of anti stress effects of Nardostachys Jatamansi Dc root extract on

Clinical Patients: A Psycological Estimation

Singh, Mamta; Saxena, Garima and Arya, Shveta

54 – 61

9. Biodiversity conservation, threats and their global impact

Maurya, Pradip K. and Yadav, Anuj Kumar

62 – 74

10. Cobalt(Ii)-Selective Potentiometric Electrode based on Salen-Base Chelate

Jain, Shalabh Kumar and Kaushik, R. D.

75 – 81

ESSENCE - International Journal for Environmental Rehabilitation and Conservation

Volume VIII: Special Edition: 2: 2017 [ISSN 0975 - 6272]

[www.essence-journal.com]

Content / Vol. VIII: Special Edition: 2: 2017

11. Toxicological effects of lead on certain enzymological and biochemical parameters

in Cirrhina Mrigala

Arya, Shveta; Singh, Jyotsna and Sharma, H.B.

82 – 87

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Sharma et al., Prasad/VIII: Special Edition: 2: 2017/1 - 7

1

Conifers and their association with the understory shrubs along a temperate riparian ecosystem in Bhaderwah, Jammu and Kashmir

Sharma, Anu; Sharma, Neeraj and Sharma, Monika

Received: June 15, 2016 Accepted: August 12, 2017 Online: December 31, 2017

Abstract

The present study has been conducted in the

slopes along Neeru stream, Bhaderwah

Jammu and Kashmir at an elevation ranging

from 850m (Puldoda) to 2200m (Thanalla).

The present study deals with the spatial

distribution of conifers along a riparian

gradient with that of understory component.

The findings revealed that dominant tree

species was Cedrus deodara followed by

Pinus wallichiana and Pinus roxburghii. In

two of the fifteen study sites namely

Galgander and Puldoda only Pinus

roxburghii was found and no associated

conifer species was observed. Other

associated trees found were broad-leaved

trees like Alnus nitida, Ficus palmata,

Quercus baloot, Quercus leucotrichophora,

Melia azedarach, Robinia pseudo-acacia,

Populus ciliata, Morus alba etc. The

associated shrub/lianas included Berberis

lycium, Daphne oledeoides, Prinsepia utilis,

Hedera helix, Rubus ellipticus, Rubus niveus,

Rosa brunoni, Jasminum humile, Indigofera

heterantha etc. Pearsons correlation has been

applied to species diversity, richness and soil

parameters like Nitrogen, phosphorus and

Potassium. Correlation has also been

established between elevation and other

abiotic factors (Environmental variables).

Keywords: Conifers | Distribution |

Phytosociology | Shrubs | Trees | Correlation

Introduction

Conifers belong to gymnosperms and family

Pinaceae. They are the needle-leaved trees

widely distributed at the uplands of the Neeru

stream. Pinus roxburghii and Pinus

wallichiana mostly grow in the dry upslope

stretch whereas Cedrus deodara is also

available near the stream in the moist belt.

These conifer forests have wide ecological

amplitude and are found all along the stream.

They are not only significant from the forests

point of view but also owe a lot of

significance because of their economic and

medicinal value. Pinus spp. are known for

the resin production. They are also

significant for the asthmatic patients. Cedrus

deodara is known for timber production. The

area is quite rich in the conifer forests.

Conifer forests are the evergreen trees of

temperate and subalpine and alpine climate.


Volume VIII: Special Edition: 2: 2017 [1 - 7] [ISSN 0975 - 6272]


For Correspondence: Institute of Mountain Environment, Bhaderwah Campus, University of Jammu, India Email: [email protected]


2

They are a source of timber and shade

throughout the year. Their wide canopy and

shape gives way to number of understory

growth of shrubs and herbs which are a

source of immense medical and aromatic

value. These are important natural resources

to sustain life in the Kashmir Himalayas. The

role of these forests lies in the maintenance

of biodiversity, watershed protection as well

as supplying timber, non-wood forest

products, grazing land and habitat for

threatened taxa (Ahmed et al., 2006 & 2010).

Natural population of conifers increases

exponentially in suitable conditions when

resources are freely available (Watkinson,

1997). Variation in species diversity along

environmental gradient is a major topic of

ecological investigation in latest years and

has been explained by reference to climate,

productivity, biotic interaction and habitat

heterogeneity (Givnish, 1999; Willig et al.,

2003 and Currie & Francis, 2004). Species

diversity and the functioning of the

ecosystem are the major areas of interest for

the ecologists worldwide. Species diversity

incorporates two components (Stirling &

Wilsey, 2001); evenness (how evenly

abundance or biomass is distributed among

species) and richness (number of species per

unit area). The composition of the understory

species beneath tree canopies often differs

with the canopy composition. Overstory

species can directly affect understory species

by altering the precipitation distribution

under their canopy, soil bulk density, soil

moisture, soil oxygen, soil surface

temperature, available sunlight, soil and leaf

litter accumulation, soil nutrient

concentration, and seed bank density

(Warnock et al., 2007). Interactions of

various biological and physical factors

determine the distribution of tree populations

in an area. Physiographic characteristics of

specific landforms such as slope, aspect,

parent materials and soils are used for

characterizing vegetation over the space

(Barnes et al., 1997).

Study area and Methodology

False color composite image of study area

Map 1: Map of the study area

The study area includes the linear

hydromorphological unit of Neeru stream

(from near its origin at Thanalla and its

confluence with river Chenab at Pul Doda) as

a corridor 35 km long and 1.5 Km wide

including the river bed and flood plain and

the edge upslopes ( 200 m on either sides).

The area forms the south-western part of

Chenab catchment with an altitudinal range

of 850 m (tail at Pul Doda) to 2200m

(Thanalla) lying between 32o5532 to

33o0826N and 75o3241 to 75o4650 E.

The field surveys were conducted during the

years 2015 and 2016. Fifteen sites were

identified for the analysis of various

phytosociological parameters along both

banks of the stream using species area curve

approach. 24 quadrats of 100 m2 were laid

each containing two quadrats of 25 m2 in


3

opposite corners for different parameters

frequency, density and abundance, basal area

following Curtis & McIntosh, 1950 and

diversity by Shannon-Weiner, 1963. The

geo-coordinates and elevation were recorded

with the help of GPS (Garmin- Montana

650). For soil analysis, the samples were

collected by removing litter from the surface

and digging out from 0-30 cm randomly from

each sub-site. Indices of dominance

(Simpson index), diversity (Shannon-weiner)

and richness (Margalefs and Menhinick)

were calculated for the trees and the

associated shrubs. About 200 g of soil

samples were collected in polyethylene bags

and were sealed and labeled properly and

then pooled together. Then the samples were

air dried at 20 to 250C, and passed through a

2 mm sieve. Soil analysis was performed for

different parameters i.e., pH (Philips make

digital pH meter), moisture content,

Electrical conductivity, Nitrogen (Subbiah &

Asija, 1956), Phosphorus (Jackson, 1958)

and Potassium (Pratt, 1965, Jackson 1958

and Peech & English, 1944).

Results and Discussion

The visual representation of conifers along

the elevational gradient is substantiated with

our findings wherein a clear trend of conifer

distribution is obtained along the rising

elevation. Abies pindrow, Picea smithiana,

Cedrus deodara, Pinus wallichiana and

Pinus roxburghii. Abies pindrow and Picea

smithiana were found only at site 15

(Thanalla). An analysis of table 1 shows that

Cedrus deodara was found to be dominant at

6 sites followed by Pinus wallichiana found

at 5 sites and Pinus roxburghii found at 4

sites. Table 1 also reveals that maximum

density (9.666) was found at site 5 (Seri) and

minimum (2.333) at site 1 (Puldoda). Basal

area was found to be maximum (0.09963) at

site 6 (Drudu) and minimum (0.01435) at site

2 (Galgander). Out of the fifteen study sites

Cedrus deodara was found at 11 sites, Pinus

wallichiana at 12 sites and Pinus roxburghii

at all the 4 study sites. Thus the range of

Cedrus deodara in the study area was from

1200m to 2200m, Pinus wallichiana was

950m to 2200m and Pinus roxburghii from

850m to 1240m. Variety of Shrubs have been

found growing under these conifer tree

species. The dominance was shown by

Berberis lycium and Daphne oloedoides

found at all the 15 sites followed by

Prinsepia utilis found at 13 out of 15 sites.

The various other shrub species which

followed the list were Rubus ellipticus,

Rubus niveus, Hedera helix, Ficus

sarmentosa, Jasminum officinale, Jasminium

humile, Indigofera heterantha, Vibubrnum

grandiflorum, Rosa brunoni, Rosa webbiana.

It was observed that the conifers showed a

linear trend with the shrubs. Shrubs were

observed to follow the similar trend as the

conifers followed. Conifers and shrubs

showed an increase in diversity and richness

from the lower to higher elevation and

Simpson index showed a decrease from

lower to higher elevation. Conifers showed

the highest diversity (1.382) and richness

(1.057, 0.7538) at 2200m and shrubs showed

the highest diversity (2.096) and richness

(2.0247, 2.867) and 1804m and 2200m

respectively (Table 2). All the soil

parameters (Organic carbon, organic matter,

Nitrogen, Phosphorus) showed and increase

with the rising elevation except pH and

Phosphate. Soil texture was loamy at all the


4

sites. Electrical conductivity and water holding capaity showed an irregular trend

Site Ele (m)

Dominant specie

Density Basal IVI Other conifers in terms of IVI

Dominant broad-leaved trees in terms

of IVI

Understory shrubs in terms of decreasing IVI

Puldoda 850 Pinus roxburghii

2.33 0.014 75.26 nil Melia azedarach, (86.7)

Berberis, lycium, Daphne oledeoides, Prinsepia utilis, Rubus ellipticus, Justicia adhatoda, Cactus

Galgander 950 Pinus roxburghii

2.66 0.014 82.59 nil Q.baloot, (206.6)

Berberis, lycium, Daphne oledeoides, Prinsepia utilis

Pranoo 1000 Pinus roxburghii

4.83 0.022 46.64 nil Alnus nitida, (153.6)

Berberis, lycium, Daphne oledeoides, Prinsepia utilis, , Rubus ellipticus, Rosa brunoni

Bhalla 1240 Pinus roxburghii

4.16 0.033 97.54 Pinus wallichiana Alnus nitida, (222.8)

Berberis, lycium, Daphne oledeoides, Prinsepia utilis, , Rubus ellipticus, Rosa brunoni,

Seri 1295 Cedrus deodara

9.66 0.083 116.38 Pinus roxburghii and Pinus wallichiana

Alnus nitida, (150.6)

Berberis, lycium, Daphne oledeoides, Hedera helix, Prinsepia utilis, , Rubus ellipticus, Jasminum officinale, Clematis montana, Ficus sarmentosa, Rosa brunoni, Rubus ellipticus

Drudu 1372 Cedrus deodara


Berbris, lycium, Daphne oledeoides, Prinsepia utilis, , Rosa brunoni, Rubus ellipticus, Jasminum humile

Dranga 1410 Cedrus deodara


Berberis, lycium, Daphne oledeoides, Hedera helix, Prinsepia utilis, , Rubus ellipticus, Jasminum officinale, clematis Montana

Amiranagar 1465 Pinus wallichiana

6.00 0.040 114.78 Cedrus deodara Alnus nitida, (179.3)

Berbris, lycium, Daphne oledeoides, Ficus sarmentosa, Hedera helix, Prinsepia utilis, , Rosa brunoni, Rubus ellipticus

Gatha 1480 Cedrus deodara


Berberis, lycium, Daphne oledeoides, Hedera helix, , Prinsepia utilis

Renda 1563 Pinus wallichiana


Berberis, lycium, Daphne oledeoides, Prinsepia utilis, Rubia manjith, Rubus niveus, Clematis montana

Guptganga 1610 Pinus wallichiana


Berberis, lycium, Daphne oledeoides, Hedera helix, Indigofera heterantha, Jasminum humile, Prinsepia utilis, Rubus niveus, Spiraea canescens

Dareja 1667 Pinus wallichiana


Berberis, lycium, Daphne oledeoides, Ficus sarmentosa, Jasminum humile, Prinsepia utilis. Eleaegnus umbellate, Rhamnus triquater

Bheja 1804 Pinus wallichiana


Artemisia maritima, Berberis, lycium, Daphne oledeoides,Indigofera heterantha, Rosa brunoni, Rosa webbiana, Rubus niveus, Sarcococca saligna, Prinsepia utilis

Thanthera 2160 Cedrus deodara


Artemisia maritima, Berberis, lycium, Daphne oledeoides, Eleagnus parviflora,Indigofera heterantha, Jasminum humile, Prinsepia utilis

Thanalla 2200 Cedrus deodara

2.5 0.015 187.45 Pinus wallichiana, Abies pindrow and Picea smithiana

Q.leucotrichophora, (82.0)

Berberis, lycium, Daphne oledeoides, Ficus sarmentosa, Hedera helix, Indigofera heterantha, Viburnum grandiflorum

Table 1: Phytosociological analysis of conifer species and associated trees and shrubs in the study area

Trees Shrubs Site Simpson’s

index Shannon-weiner

index Margalefs

index Menhinick

index Simpson’s

index Shannon-weiner

index Margalefs

index Menhinick

index Puldoda 1 0 0 0.2672 0.1931 1.281 1.144 0.8703

Galgander 1 0 0 0.25 0.3099 1.08 0.6792 0.6882

Pranoo 0.7415 0.418 0.2836 0.404 0.1951 1.579 1.0389 0.7293

Bhalla 0.5376 0.642 0.2749 0.3244 0.1746 1.725 1.2022 0.75

Seri 0.3919 0.995 0.4215 0.2797 0.1228 2.029 1.6895 1.007

Drudu 0.4010 0.967 0.4215 0.2797 0.1718 1.719 1.3377 0.925 Dranga 0.4115 0.943 0.4708 0.3585 0.1374 1.903 1.5499 1.01

Amiranagar 0.4149 0.94 0.4708 0.3585 0.1338 1.968 1.7894 1.131

Gatha 0.4523 0.883 0.5195 0.4375 0.2405 1.359 0.858 0.6963 Renda 0.3513 1.046 0.5498 0.4866 0.1655 1.709 1.456 1.0777

Guptganga 0.4421 0.872 0.4478 0.3219 0.1213 1.011 1.7028 1.0243

Dareja 0.4663 0.828 0.4757 0.0016 0.1515 0.859 1.5762 1.0435 Bheja 0.4687 0.799 0.4708 0.3585 0.1176 2.096 2.0247 1.248

Thanthera 0.4543 0.841 0.4423 0.3127 0.1254 1.983 1.8728 1.234 Thanalla 0.2642 1.382 1.057 0.7538 0.1833 1.678 1.3554 2.2867

Table 2: Describing the various indices of diversity and richness of conifers and associated shrubs


5

(Table 3). The composition of conifers in the

study area included the five main species

namely Abies pindrow, Picea smithiana,

Cedrus deodara Pinus wallichiana and Pinus

roxburghii. The distribution of conifers in

relation with the shrubs in the present study

revealed that where diversity and richness of

trees and shrubs followed the similar trend.

Abies pindrow and Picea smithiana was

restricted in the highest elevation (2200 m).

Cedrus deodara was widely distributed in the

region. Cedrus deodara and Pinus

wallichiana was found at higher elevations

and towards the NW aspect which was facing

the lesser sunshine. Pinus roxburghii was

found to be dense towards the SW aspect

which was sun facing side of the stream.

Similar studies have been conducted by the

various researchers like Barbier et al. (2008)

who studied the influence of tree species on

understory vegetation diversity. Understory

light is closely dependent on the canopy

structure. Air temperature and air humidity in

the understory are also dependent on canopy

structure, particularly canopy density (Sharpe

et al., 1996). Variations of these factors

among tree species have been observed

(Hunter, 1990 & Porte´ et al., 2004) and are

sometimes discussed as affecting understory

flora (Nihlgard, 1969). Tree species may

affect soil water availability by changing (i)

amounts of non-intercepted water, (ii)

quantity of water absorbed by tree roots, and

(iii) spatial distribution of water at the tree

scale (trunk and crown) (Barbier et al.,

2008). P. roxburghii has a wide ecological

amplitude and considerable economic

importance, providing large stretches of

grazing lands due to its typically well-

developed grass layer (Wahab, 2011) and

valuable timber-wood and resin.

Conclusion

From the following study it has been

observed that the canopy of the trees do have

influence on the understory shrubs as they

influence the availability of sunlight, stem

flow, rainfall, air temperature and also the

availability of soil water and nutrients to the

understory shrubs and species. It is therefore

concluded that the diversity and richness of

S. No. Site name Soil pH

EC Texture WHC Soil

moisture (%)

Organic carbon

(%)

Organic matter

(%)

Nitrogen (Kg/ha)

Phosphorus (Kg/ha)

Potassium (Kg/ha)

1. Puldoda 7.47 0.65 Loamy 15.77 4.62 2.0 5.17 20.85 26.28 225.62

2. Galgander 6.95 0.65 Loamy 18.58 4.23 8.75 15.09 59.08 33.77 132.46

3. Pranoo 6.90 0.78 Loamy 15.63 4.74 54.0 93.1 375.3 37.54 80.82

4. Bhalla 6.75 0.81 Loamy 15.28 4.55 38.0 65.51 264.1 37.54 199.8

5. Seri 6.65 0.79 Loamy 19.71 4.21 35.0. 60.34 243.25 11.26 90.92

6. Drudu 6.60 0.66 Loamy 19.03 4.40 33.0 56.89 229.35 30.03 69.03

7. Dranga 6.55 0.68 Loamy 17.67 4.45 68.0 117.23 472.6 26.28 332.82

8. Amiranagar 6.25 0.69 Loamy 15.7 4.31 56.0 96.54 389.2 11.26 125.16

9. Gatha 6.25 0.71 Loamy 17.56 4.56 33.0 56.89 229.35 26.27 85.31

10. Renda 6.20 0.71 Loamy 21.81 4.31 87.5 150.5 608.13 3.75 76.33

11. Guptganga 6.15 0.77 Loamy 17.42 4.37 135.5 233.6 441.73 37.54 103.83

12. Dareja 6.10 0.66 Loamy 17.66 4.22 112 193.08 778.33 30.03 124.03

13. Bheja 6.10 0.57 Loamy 14.05 3.90 99.5 171.54 691.53 22.52 91.48

14. Thanthera 6.10 0.76 Loamy 19.08 3.85 148 255.15 1028.6 33.78 136.94

15. Thanalla 6.10 0.79 Loamy 18.52 3.73 209.5 361.18 1178 41.28 182.96

Table 3: Physico-chemical characteristics of the soil of

the study sites


6

conifer species lead to the growth of diverse

shrub species. Conifers support the

understory shrub species. The probable

reasons are

1. The conifer trees provide the understory

shrubs the shade and protect them from

the intense sunlight thereby regulating the

air and soil temperature and moisture as

well.

2. The droppings of the trees help in

modifying the soil conditions which

includes the soil pH and water holding

capacity, organic matter, organic carbon

and NPK and make them available to the

shrubs for their better growth and

nourishment.

Acknowledgement

The authors are highly thankful to the Rector

Bhaderwah Campus and Institute of

Mountain Environment for providing every

necessary facility for the accomplishment of

this study. The authors are also thankful to

Dinesh Singh, Muzaffar Ahmed Kichloo and

Adil Najeeb for the help rendered during the

field investigations.

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Willig, M. R.; Kaufman, D. M.; Stevens, R.

D. (2003): Latitudinal gradients of

biodiversity: pattern, process, scale,

and synthesis. Ann. Rev. Ecol. Evol.

Systematics, 34: 273-309.

Wahab M. (2011): Population dynamics and

dendrochronological potential of pine

tree species of District Dir Pakistan.

Ph.D dissertation. Department Botany

Federal Urdu University Karachi

Pakistan.

Arya et al./VIII: Special Edition: 2: 2017/8 - 14

8

Comparative account of impact of heavy metals cadmium and chromium on haematological parameters of Channa punctatus

Arya, Shveta

Received: August 12, 2016 Accepted: September 12, 2017 Online: December 31, 2017

Abstract

The aim of present investigation was to

determine the effect of heavy metal pollutants

such as cadmium and chromium on

haematological parameters of fresh water fish,

Channa punctatus. The experimental group of

fish was exposed to a sublethal concentrations

of cadmium and chromium for a period of 45

days and observations were made on

Haemoglobin (Hb), Packed Cell Volume(PCV),

Total Erythrocyte Count(TEC), Mean Cell

Volume (MCV), Mean Cell Hemoglobin

Concentration (MCHC), Total Leucocytes Count

(TLC), Serum Glutamate Oxaloacetate

Transaminase (SGOT) and Serum Glutamate

Pyruvate Transaminase (SGPT). It was found that

there was significant decrease in Haemoglobin

Content, PCV and TEC in majority of results. This

was accompanied by decrease in MCV and MCH

and increase in MCHC in both cases of cadmium

and chromium exposure. TLC, SGOT increased

on exposure to cadmium while a significant

decrease was observed in TLC, SGPT and SGOT

on exposure to chromium. The study suggested

that the presence of toxic heavy metals in

aquatic environment has strong influence on

the hematological parameters in the fresh

water fish.

Keywords: Cadmium | Chromium |

haematological parameters | Channa

punctatus

Introduction

The heightened concern for reduction of

environmental pollutants that has been

occurring over the past half century has

stimulated active and continuing research on

toxicology of heavy metals. Among the

various heavy metals, cadmium merits special

attention due to its potential hazard to aquatic

biota (Mayer et al. 1991; Barber and Sharma

1998) as well as to human beings (Groten and

Van Bladeron 1994; Vanderpools and Reeves

2001). This heavy metal which is a common

aquatic pollutant is known to be highly toxic

to most organisms even at low concentration

in natural waters (Lovert et al. 1972).

Cadmium is present as impurity in several

products including phosphate fertilizers,

detergents, and refined petroleum products




For Correspondence: 1Department of Zoology, K.L.Mehta Dyanand College for Women, Faridabad. Email: [email protected]


9

and as wastes from many industrial processes

like tanning, dyeing, paper pulp, batteries,

electroplating etc. Cadmium enters water

bodies and from there it gets incorporated in

the food chain of aquatic biota including fish

which eventually ends up on dinner table of

human beings. Aquatic organisms take up

heavy metals and concentrate them to a higher

level than that found in the surroundings. Fish

is a high potential diet that is invariably

consumed by masses as staple food and

suffers metabolically due to heavy metal

toxicity. Its productivity and nutritive value is

depleted.

Chromium exists primarily in Cr (III) and Cr

(VI) oxidation states; the later, hexavalent

species, being considered as more toxic in the

environment due to its higher solubility and

mobility (R. Vinodhini et al., 2009).

Chromium is used in metal alloys and

pigments for paints, cement, paper, rubber and

other materials. Effluents from these

industries enter the streams, rivers, lakes etc.

Chromium often accumulates in aquatic life,

adding to the danger of eating fish that may

have been exposed to high levels of

chromium.

In humans breathing high levels chromium

(VI) can cause irritation to the nose,

nosebleeds, and ulcers and holes in the nasal

septum. Ingesting large amounts of chromium

(VI) can cause stomach upsets and ulcers,

convulsions, kidney and liver damage, and

even death. Skin contact with certain

chromium (VI) compounds can cause skin

ulcers. Allergic reactions consisting of severe

redness and swelling of the skin have been

noted. (Irwin R.J., 1997)

The effects of acute cadmium poisoning in

humans are very serious. They include severe

abdominal pain associated with nausea,

vomiting and diarrhoea, headache and vertigo.

Chronic symptoms include high blood

pressure, kidney damage, destruction of

testicular tissue and destruction of red blood

cells (Ferard et al. 1983).

It is known that physiological and

biochemical parameters in fish blood can

change when exposed to heavy metals.

Several biochemical and physiological

parameters in tissue and fish blood could be

used as indicators of heavy metal toxicity.

Haematological indices are very important

parameters for the evaluation of fish

physiological status. The present study

undertakes the effects of cadmium and

chromium exposure on various blood

parameters of local fish Channa punctatus.

Material and Methods

The Fresh water Murrel, Channa punctatus

were purchased from fish market and

disinfected with 0.1% KMnO4. The fish were

then kept in different aquaria for conduction

of various experiments. The fish were

acclimatized to laboratory conditions in

aquaria for a few days. In one aquarium the

fish were kept as control specimens given the

same food and environment as that of the

experimental fish except that they were not

given the dose of heavy metal compound.

Inorganic salt of the heavy metal Cadmium

namely Cadmium nitrate (CdNO3)2 4H20 and

an inorganic salt of the heavy metal chromium

namely potassium chromate (K2CrO4) were

selected as the experimental toxicants. To

observe the chronic effects of heavy metals,

sublethal dose (1/10 concentration of 96 hr


10

LC50) of the heavy metal compounds were

given for 45 days. Fish were fed on pellet diet

(Prawn powder-fish powder and minced liver

in the ratio of 2:2:1) at the rate of 2% body

weight.

Blood from the caudal vessel of control and

experimental fish was drawn with help of

heparinized needles. Haemoglobin,

haematocrit, erythrocyte and leucocyte count

were made in whole blood. The activities of

glutamate oxaloacetate transaminase and

glutamate- pyruvate transaminase were

determined in the serum.

Haemoglobin content of the blood was

estimated by the Acid haematin method.

Haematocrit, erythrocyte and leucocytes

count were determined by the method given

by Dacie and Lewis (1977). The activities of

the SGPT and SGOT were estimated by the

method of Bergmeyer (1974).

Fig. 1: Haematological alterations produced by the toxicants after 45 day

Observation and Results

Haemoglobin contents decreased on exposure

to cadmium for 45 days by 9.49% while

decrease was more marked after exposure to

chromium by 18.71%. A significant decrease

was observed in PCV on exposure to cadmium

and chromium by 32.5% and 43.3%

respectively. TEC decreased by 6.13% in case

PARAMETERS CONTROL CADMIUM CHROMIUM Haemoglobin(g/100ml) 11.06 ± 0.04 10.01 ± 0.05*** 8.99 ± 0.05*** PCV (%) 31.87 ± 0.06 21.51 ± 0.04*** 18.04 ± 0.06*** TEC(×10 6/mm3 ) 03.26 ± 0.05 03.06 ± 0.06* 2.98 ± 0.04*** MCV(fl) 97.72 ± 0.03 70.29 ±0.03*** 60.52 ± 0.03*** MCH(pg) 33.72 ± 0.02 32.66 ±0.05** 30.21± 0.05*** MCHC(g/dl) 34.51± 0.04 46.49 ± 0.03*** 49.89 ± 0.04*** TLC(×104/mm3) 06.91 ± 0.02 8.79 ± 0.04*** 5.39 ± 0.03***

SGPT (units/mlserum/hr) 21.6 ± 1.32 20.9 ± 0.84NS 10.6 ± 0.76*** SGOT (units/ml serum/hr) 40.50 ± 0.14 50.16 ± 3.51** 12.08± 0.25*** Values are mean± SD; N=6, NS= Not Significant {Significant *p<0.05 **P< 0.01, ***P<.001}

Table-1: Alteration in haematological parameters in Channa punctatus exposed to cadmium and chromium for 45 days


11

of cadmium and 8.58% in case of chromium.

A significant decrease was recorded in MCV

values on exposure to cadmium (28.06%) and

chromium (38.06%). MCH value decreased

by 3.14% on exposure to cadmium and

10.40% on exposure to chromium. A

significant increase was observed in MCHC

values on exposure to cadmium and

chromium by 34.71% and 44.56%

respectively. TLC increased after exposure to

cadmium (27.2%) while decreased after

exposure to chromium (21.90%). A

significant decrease by 50.92% was recorded

in SGPT after exposure to chromium while in

cadmium change was observed that was not

significant. SGOT value increased by 23.85%

on exposure to cadmium while decrease was

drastic after exposure to chromium by

70.17%.

Discussion

The present observations on chronic

experimentation clearly show significant

decrease in Hb content, PCV and TEC in

majority of results obtained in both cases of

exposure to cadmium and chromium. These

results are in agreement with Nasser A. Al-

Asgah et al. (2015) who found a significant

reduction in RBCs, Hb and Hct in comparison

with the control in Oreochromis niloticus

exposed to Cd. Shaheen and Akhtar (2012)

also reported significant decline in Hb content

and TEC counts of fish Cyprinus carpio when

exposed to Cr(VI). Reduction in the number

of red blood cells may be due to decreased rate

of erythropoeisis and/or accelerated

destruction of red blood cells (Mc Leay 1973).

On the other hand, loss of erythrocytes due to

toxicant induced hemorrhages in internal

organs cannot be excluded (Johansson-

Sjobeck and Larsson 1978). Inadequate

haemoglobinization of RBC`s can also be a

reason. This is particularly indicated in

Heteropneustes fossilis exposed to Sewage

and potash because haemoglobin deficient red

blood corpuscles are observed in the

circulation of these individuals (Narain and

Srivastava, 1979). M. Javed et al. (2012)

suggested that heavy metal exposure

decreases the TEC count, Hb content due to

impaired intestinal absorption of iron.

In the present study decrease in PCV, Hb and

TEC is accompanied by decrease in MCV and

MCH and increase in MCHC values. Increase

in MCHC despite decrease in TEC, Hb

content and PCV cannot be attributed to cell

shrinkage or swelling but rather to

disproportional decrease in the red blood cells

and the related values (Wepener et al. 1992).

Increase in TLC is observed on exposure to

cadmium while decrease is observed after

exposure to chromium. An increased response

may be the result of direct stimulation of

immunological defense due to the presence of

foreign substances or may be associated with

metal induced tissue damage (Ellis 1981).

According to McLeay and Brown (1974)

increase in leucocytes under chemical stress in

Onchorhynchus kistush is, probably for quick

removal of cellular debris of necrosed tissue.

Metal induced leucocytosis, as an adaptive

value to fish has been reported by Goel and

sharma (1987). M. Javed et al. (2016) also

observed leukocytosis in Channa punctatus

exposed to thermal power effluents. Other

workers have also reported similar

observation in C. punctatus exposed to Pb

(Hymavathi and Rao 2000), Clarias

batrachus exposed to HgCl2 (Joshi et al.


12

2002). Decrease in TLC on exposure to

chromium may be due to fragility of cells and

consequent breakdown. Decrease in white

blood corpuscles suggests that the fish is

losing its capacity to defend microbial or

bacterial infection and also autolysis caused

by the activity of certain hydrolytic enzymes

like phosphatases, lipases, e.t.c. released into

plasma under conditions of

stress(Wright,1960). The significant decrease

in WBC may also be the result of increased

secretion of corticosteroid hormones (Ellis

1981). Secretion of these hormones is a non

specific response to any environmental stress

and is a fundamental mechanism in the

increased susceptibility of fish to disease

when exposed to a pollutant. Reduction in

leucocytes (leucocytopenia) could further be

aggravated by necrosis of leucopoietic tissue

(Wepener 1992).

Enzymological observations in the present

study show elevation of serum glutamate

oxaloacetate transaminase (SGOT) activity on

exposure to cadmium while a decrease is

observed after exposure to chromium.

Decrease in the activity of serum glutamate

pyruvate transaminase (SGPT) is observed

after exposure to chromium. However, on

exposure to cadmium, change was observed

but that was not significant. Increase in the

activity of SGOT exposed to cadmium may be

due to chemostress. The blood enzyme pattern

is generally believed to indicate the

physiological state of the tissue in an organism

and is altered after slight injury to cells of an

organ in general and liver in particular. In this

light when the aforesaid enzyme patterns of

exposed and controlled fishes were

objectively compared, it becomes evident that

heavy metal cadmium cause injury to fish

tissues. Structural damage of the liver of

Channa punctatus exposed to HgCl2 has been

reported by Sastry and Gupta (1978). Elevated

level of transaminases is also indicative of

alteration in the cell membrane properties and

therefore permitting rapid leeching of

enzymes under hepatotoxic conditions (Slater

1979; Sastry and Sharma 1980). Increase in

the activity of SGOT after exposure to

cadmium in the present study is supported by

findings of Vineeta Shukla and Sastry (1994)

in Channa punctatus exposed to cadmium.

Decrease in the activity of transaminase on

exposure to chromium in the present study is

evidenced by the findings of Christensen et al.

(1972) in the blood of brown bullhead

(Iclaluros nebulosus) on exposure to copper

which resulted in decrease in transaminases.

Conclusion

After an analysis of the results obtained in the

present study, it can be concluded that anemic

state prevails in the fish and the effect of

chromium is more pronounced as compared to

cadmium.

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12:119–122

Lovert, R.J., Gutermann W. H., Pakkala I. S.,

Youngs W. D., Lisk D.J.,. Burdick G.E.

and Harris E. J. (1972): A Survey of total

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Mayer, W.M., Kretschmer, A., Hoffmann, G.,

and Harish (1991): Biochemical and

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the fresh water crayfish, Astacus astacus

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Kaushal and Rampal/VIII: Special Edition: 2: 2017/15 - 21

15

Regression modelling of Traffic Noise Pollution at various crossings in Jammu

City (J&K)

Kaushal, Akanksha and Rampal, Rajkumar

Received: August 19, 2017 Accepted: October 21, 2017 Online: December 31, 2017

Abstract

The present study has been carried out to

assess the traffic noise levels and prepare a

mathematical model for prediction of traffic

noise at various crossings of Jammu city

during the rainy, winter and summer seasons.

The study area was divided into three Zones:

Zone I (Crossings on NH1A Highway), Zone

II (Crossings on the main roads connecting the

Highway) and Zone III (Crossings within old

Jammu city with light vehicular traffic).

Regression equation was developed using

SPSS software. The validity of the prepared

model was assessed by comparing calculated

noise levels and observed noise levels during

different seasons of two-year study period.

Further the goodness of fit of the model was

tested using chi square test which revealed

insignificant difference (p>0.05) between the

observed and calculated values of Leq.

Keywords: Regression modeling | Noise

pollution | Jammu

Modern day life is facing huge problems and

one of such issues is that of noise. Noise is

actually unnecessary sound that is dumped

into the environment from various sources.

The term ‘Noise’ is derived from Latin word

“nausea” which means unwanted sound or

sound that is loud, unpleasant or unexpected.

EU Directive 49/EC (2002) defined

Environmental noise as “an unwanted or

harmful outdoor sound created by human

activities including noise emitted by means of

transport, road traffic, rail traffic, air traffic

and from sites of industrial activity, to which

humans are exposed in particular in built-up

areas, in public parks or other quiet areas in an

agglomeration, in quiet areas in open country,

near schools, hospitals, and other noise

sensitive buildings and areas”. Road vehicles

form an important part of our urban

environment and constitute about 55% of total

urban noise (Banerjee et al. 2008).

Various noise surveys conducted by workers

(Robinson, 1971; Roy et al., 1984;

Ravindranath et al., 1989; Thakur, 2006) at

different sites considered road traffic as the

dominant source of annoyance. Prabat and




For Correspondence:

Department of Environmental Sciences, University of

Jammu, Jammu (J&K), India

Email: [email protected]


16

Nagarnaik (2007) reported that the Indian

cities face road transport crisis characterized

by ill-planning, noise pollution, improper

traffic facilities, injuries, congestion and

inequality as compared to contemporary cities

in most of Europe and North America.

Bhattacharya et al. (2001) also supported to

the similar view stating that Indian cities have

heterogeneous vehicular traffic flow on the

same right-of-way with interrupted traffic

flow conditions.

Noise is considered a serious threat to the

environmental health having adverse effects

(O¨hrstro¨m, 1993; Stansfeld et al. 1985 and

Bluhm et al., 2007). Noise interferes with

speech. The learning of children is also affected

by noise (Evans, 1990; Hygge, 1993). It leads to

emotional and behavioural stress and a person

feels disturbed and annoyed in the presence of

loud noise. Noise may permanently damage

hearing. Noise increases the chances of

occurrence of diseases such as headache, blood

pressure, heart failure, etc. It affects the

sleeping there by inducing the people to become

restless (Nagai et al. 1989) and lose

concentration and presence of mind during their

activities.

Noise being one of the pollutants of the

environment needs to be controlled so some

measures need to be suggested to overcome

this problem. Traffic noise prediction models

are one of such measures to curb the ever

increasing effects of noise pollution. These

models help in predicting sound pressure

levels, specified in terms of Leq, L10, etc. They

are useful for designing of road structures,

predicting effects of various traffic light

cycles, traffic routings, pedestrian crossing

locations and other controls and preparing the

acoustic section of Environmental Impact

Statements (Steele 2001).

The present study was made to prepare a

traffic noise model for various crossings of

Jammu city. Jammu district is located between

74º 24ʺ and 75º 18ʺ longitude and 32º 50ʺ and

33 º 30ʺ North latitude and has a population of

15.264 lacs as per 2011 census. The study was

conducted on the different sites including

crossings located on NH1A highway passing

through the Jammu city (from Satwari chowk

to Amphalla chowk) and the major roads

connecting to it. The traffic noise prediction

was prepared taking into account various

physical parameters like atmospheric

temperature, surface temperature, relative

humidity and Leq at the sampling sites as well

as speed of the vehicles and vehicle count at

the sampling time.

Material and methods

To develop mathematical model for predicting

traffic noise, the study area was categorised

into three zones:

Zone I. Crossings on NH1A Highway viz.

Site I. Satwari chowk

Site II. Vikram chowk

Site III. Jewel chowk

Site IV. Rehari chowk

Site V. Amphalla chowk

Zone II. Crossings on the main roads

connecting the Highway viz.

Site VI. Panama chowk

Site VII. Canal Road chowk

Site VIII. Patta Bohri chowk

Site IX. Shakti Nagar chowk

Site X. Maheshpura chowk

Site XI. Kachi chawni chowk

Site XII. Gole market

Site XIII. Zorawar singh chowk


17

Site XIV. Bantalab chowk

Site XV. Shalamar chowk

Site XVI. Indira chowk

Site XVII. Panjtirthi chowk

Site XVIII. Paloura chowk

Site XIX. Kunjwani chowk

Site XX. Janipur chowk

Site XXI. Narwal Byepass chowk

Zone III. Crossings within old Jammu city

with light vehicular traffic viz.

Site XXII. Gujjar Nagar chowk

Site XXIII. Chowk chabutra

Site XXIV. City chowk

Site XXV. Shaheedi chowk

Atmospheric data like air temperature, surface

temperature, and relative humidity were

measured thrice i.e. Morning period (0800-

1000hrs.), Noon Period (1200-1400hrs.) and

Evening period (1800-2000hrs.) a day at the

selected sites using handheld Thermometer,

Soil Thermometer and Psychrometer

respectively. The numbers of vehicles (to and

fro) per ten-minute passing through the

different crossings were counted manually

thrice i.e. Morning period (0800-1000hrs.),

Noon Period (1200-1400hrs.) and Evening

period (1800-2000hrs.) a day at the selected

sites. The speed of the vehicle (Km/hr) passing

through the roads were determined using

handheld speed radar gun (Model M10P)

thrice for the above said time periods and the

noise levels were also recorded using Digital

Sound level meter (Data Logger Model:

407764A) at the selected sites. Leq was

calculated as:

Leq..=10 log(∑ 𝑓𝑖10𝐿𝑖 10⁄𝑛𝑖=1 )dB(A)

Where Li = sound intensity

fi = fraction of time for which sound pressure

level persists

i = time interval

n = number of observations

The data was compiled for three seasons of

two years and Traffic noise equation for noise

prediction was developed by calculating the

constants for Total vehicle count in both

directions, Average speed of vehicles in kmph,

Average atmospheric temperature in ⁰c,

Average surface temperature in ⁰c, Relative

humidity in %, by taking these as independent

variables and Leq as dependable variable by

regression method using SPSS software

(version 24) to obtain best form of following

regression equation.

Leq = X + aTa + bTs + cR.H + dV.C + eV.S

(R2 = Y)

Where,

Ta = Average atmospheric temperature in ⁰c,

Ts = Average surface temperature in ⁰c,

R. H = Relative humidity in %,

V.C = Total vehicle count in both directions,

V.S = Speed of vehicles in Km/hr

R2 = Coefficient of correlation.

X, Y, a, b, c, d and e are constants which vary

for different road conditions.

Coefficient of Correlation (R2) existing among

the variables was also determined. This

equation can be used for predicting Traffic

Noise in Jammu city. Further the validity of

the prepared model was determined by

comparing calculated noise levels and

observed noise levels during different seasons

of two year study period and the goodness of

fit of the model was tested using chi square

test.


18

Observations and Discussion

The analysis of the compiled data for two

years revealed that during rainy season of first

year study period average Observed Leq

ranged from 74.7 dB at Zone III (Site XXII-

XXV) to 88.7 dB at Zone I (Site I-V) with

average value of 80.9 dB at study area and it

varied from 77.2 dB at Zone III (Site XXII-

XXV) to 89.4 dB at Zone I (Site I-V) with

average value of 81.8 dB at study area during

second year study period. The analysis of data

of observed Leq revealed that it varied from

74.0 dB at Zone III (Site XXII-XXV) to 78.6

dB at Zone I (Site I-V) with average value of

76.0 dB at study area during winter season of

first year study period and 74.6 dB at Zone III

(Site XXII-XXV) to 81.4 dB at Zone I (Site I-

V) with average value of 77.0 dB at study area

during winter season of second year study

period. Further the Leq ranged from 75.7 dB at

Zone III (Site XXII-XXV) to 83.1 dB at Zone

I (Site I-V) with average value of 78.5 dB at

study area during first year study period of

Summer season and from 75.9 dB at Zone III

(Site XXII-XXV) to 84.1 dB at Zone I (Site I-

V) with average value of 78.9 dB at study area

during second year study period of summer

season. (Table I)

Overall survey of data revealed that Zone I

(Site I-V) exhibited maximum observed Leq

during all the seasons of two-year study

period. The observed Leq in rainy, winter and

summer season of second year study period

exhibited higher values as compared to that of

rainy season of first year except for Zone II

(Site VI- XXI) in rainy and winter season.

Traffic Noise equation (Leq=52.89+0.244Ta-

0.102Ts+ 0.182R.H+0.03V.C-.043V.S) for

noise prediction was developed by calculating

the constants for Total vehicle count in both

directions, Average speed of vehicles in

kmph, Average atmospheric temperature in

ºC, Average surface temperature in ºC,

Average relative humidity in %, by taking

these as independent variables and Leq as

dependable variable by regression method

using SPSS software (Version 24).

Coefficient of correlation for the model

prepared was observed to be 0.56. The

calculated Leq using the above equation was

observed to be:

The calculated Leq varied from 75.7 dB(A) at

Zone III (Site XXII-XXV) to 86.8 dB(A) at

Zone I (Site I- V) with average value of 80.3

dB(A) at study area during rainy season of

first year study period and 78.5 dB (A ) at

Zone III (Site XXII-XXV) to 87.4 dB(A ) at

Zone I (Site I-V) with average value of 81.9

dB(A) at study area during rainy season of

second year study period. During winter

season of first year study period average

calculated Leq ranged from 72.3 dB(A) at

Zone III (Site XXII-XXV) to 82.5 dB(A) at

Zone I (Site I-V) with average value of 76.6

dB(A) at study area and it varied from 72.7

dB(A) at Zone III (Site XXII-XXV) to 83.6

dB(A) at Zone I (Site I-V) with average value

of 77.1 dB(A) at study area during second

year. The study further revealed that the

average calculated Leq ranged from 77.4

dB(A) at Zone II (Site VI-XXI) to 81.4 dB(A)

at Zone I (Site I-V) with average value of 78.8

dB(A) at study area during first year study

period of Summer season and from 76.3

dB(A) at Zone III (Site XXII-XXV) to 80.7

dB(A) at Zone I (Site I-V) with average value

of 77.8 dB(A) at study area during second year

study period of summer season. (Table II).


19

Overall survey of data revealed that Zone I

(Site I-V) exhibited maximum calculated Leq

during all the seasons of two-year study

period. The calculated Leq in rainy, season of

both the years of study period exhibited higher

values as compared to that of winter and

summer seasons (except for Zone III in first

year).

Further the analysis of data of both the years

of study period showed the observed Leq

ranging from 76.0 dB to 89.1 dB with an

average of 81.3 dB for the study area and the

calculated Leq ranging from 77.1 dB to 87.1 dB

with an average of 81.1 dB for the study area

during rainy season. During winter season the

observed Leq varied from 74.3 dB to 80.0 dB

with an average of 76.5 dB for the study area

and the calculated Leq varied from 72.5 dB to

83.0 dB with an average of 76.8 dB for the

study area and for summer season the values

varied from 75.8 dB to 83.6 dB with an

average of 78.7 dB for the study area for

observed Leq and from 76.9 dB to 81.1 dB with

an average of 78.3 dB for the study area for

calculated Leq. Overall analysis of data

showed that Zone I exhibited maximum Leq for

all the seasons of two-year study period. On

compilation of the data the study revealed chi

sq. p-value ranging from 0.99 to 1 signifying

insignificant difference (p>0.05) between

observed and calculated Leq for all the Zones

and the study area. (Table III).

Subramani et al. (2012) constructed a similar

mathematical model for traffic noise of

Coimbatore city (Tamilnadu) by using the

parameters like traffic flow rate, speed of

vehicle, atmospheric temperature, surface

temperature, and relative humidity. R2 value

for the developed model was 0.523. The

Federal Highway Administration (FHWA)

model used by Govind and Soni (2012)

considering traffic volume and speed data

proved to be a successful tool for predicting

the noise levels along National Highway near

Gorakhpur city. Suksaard (1999) noise

prediction model for EIA in Thailand was

accurate with ±3 dB when predicted noise

levels were compared with that of measured

traffic noise levels.

Average Observed Leq (dB A)

Zone Ist year 2nd Year

Rainy Winter Summer Rainy Winter Summer

I 88.7 78.6 83.1 89.4 81.4 84.1

II 79.2 75.2 76.6 78.8 75.0 76.7

III 74.7 74.0 75.7 77.2 74.6 75.9

Study area 80.9 76.0 78.5 81.8 77.0 78.9

Zone I: Site I- V

Zone II: Site VI- XXI

Zone III: Site XXII-XXV

Table I: Zone wise average Observed Leq during

Rainy, winter and summer season in two

year study period

Average Calculated Leq (dB)

Zone Ist year 2nd Year

Rainy Winter Summer Rainy Winter Summer

I 86.8 82.5 81.4 87.4 83.6 80.7

II 78.3 74.9 77.4 79.8 75.0 76.5

III 75.7 72.3 77.6 78.5 72.7 76.3

Study area 80.3 76.6 78.8 81.9 77.1 77.8

Zone I: Site I- V



Table II: Zone wise average Calculated Leq during

Rainy, winter and summer season in

two year study period


20

Observed Vs Calculated Leq (dB)

Chi sq.

(p-value)

Zone

Rainy Winter Summer

Observed Calculated Observed Calculated Observed Calculated

I 89.1 87.1 80.0 83.0 83.6 81.1 0.999983

II 79.0 79.1 75.1 75.0 76.7 76.9 1

III 76.0 77.1 74.3 72.5 75.8 76.9 0.999977

Study area 81.3 81.1 76.5 76.8 78.7 78.3 1.0

Zone I: Site I- V



Table III: Observed Vs Calculated Leq of Zones

during Rainy, winter and summer season

in two year study period

Conclusion

The collected data of various parameters like

Atmospheric temperature (Ta), Surface

temperature (Ts), Relative humidity (R.H),

vehicle count (V.C.) and vehicular speed

(V.S.) was used to determine the calculated

noise level with the help of regression

analysis. The comparison test was made in

order to examine the goodness of fit, between

the observed and calculated noise level from

the collected data. From the present study it

was concluded that there was insignificant

difference between the observed and

calculated noise levels and R2 value for the

equation was found to be 0.56.

References

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Bhattacharya, S. and Gangopadhyay,

A. (2008): Evaluation and analysis of

road traffic noise in Asansol: An

industrial town of eastern India.

International Journal of

Environmental Research, Public

Health, 5(3):165-171.

Bhattacharya, C. C.; Jain, S. S.; Singh, S. P.;

Parida, M. and Mittal, N. (2001):

Development of comprehensive

highway noise model for Indian

condition. Journal of Indian Roads

Congress, 62:453-487.

EU Directive 2002/49/EC. (2002): Directive

of the European parliament and of the

council of 25 June 2002 relating to the

assessment and management of

environmental noise. Official journal

of the European Community’s L189:

12-25.

Evans, G. W. (1990): The nonauditory effects

of noise on child development. Noise

as a Public Health Problem, edited by

B. Berglund, U.4:421-468.

Govind, P. and Soni, D. (2012): Traffic noise

prediction using FHWA model on

National Highway-28 in India. Journal

of Environmental Research and

Development, 7(1): 107-115.

Hygge, S. (1993): Classroom experiments on

the effects of aircraft, traffic, train, and

verbal noise on long-term recall and

recognition in children aged 12–14

years. Noise as a Public Health

Problem, edited by M. Vallet~Arcueil

Cedex, France, INRETS, 2: 531–534.

Nagai, N.; Matsumoto, M.; Yamasumi, Y.;

Shiraishi, T.; Nishimura, K.;

Matsumoto, K.; Miyashita, K. and

Takeda, S. (1989): Process and

emergence on the effects of infrasonic

and low frequency noise on


21

inhabitants. Journal of Low Frequency

Noise Vibration, 8: 87–99.

Ohrstro¨m, E. (1993): Research on noise and

sleep since 1988: Present state. Noise

as a Public Health Problem, edited by

M. Vallet ~ Arcueil Cedex, France,

INRETS, 3:331–338.

Parbat, D. K. and Nagarnaik, P. B. (2007):

Assessment and ANN Modelling of

Noise Levels at Major Road

Intersections in an Indian Intermediate

City. Journal of Research in Science,

Computing and Engineering, 4(3): 39-

49.

Ravindranath, G.; Sankaralah, N. and Khan,

V. H. (1989): Study of traffic noise at

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Robinson, D. W. (1971): Towards a unified

system of noise assessment. Journal of

Sound and Vibration, 14: 279-298.

Roy, B.; Santra, S. C. and Mitra, B. (1984):

Traffic noise level in Calcutta.

Scientific Culture, 50: 62-64.

Stansfeld, S. A.; Clark, C. R.; Jenkins, L. M.

and Tarnopolsky, A. (1985):

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sample: I. The measurement of

psychiatric disorder and personality.

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Steele, C. (2001): A critical review of some

traffic noise prediction models.

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(2012): Modelling of Traffic noise

pollution. International Journal of

Engineering Research and

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Pracheta et al./VIII: Special Edition: 2: 2017/22 - 34

22

Medicinal plants used to combat a major gynecological challenge: Ovarian cyst

Pracheta; Kulshrestha, Sunanda and Sharma, Arun Kumar


Abstract

Documented data shows that ovarian cysts are

prevalent between 8%-18% in women of all

ages with high frequency in premenopausal

and reproductive age. The exact etiology

behind occurrence of ovarian cysts is still

unknown however can be categorized as

benign, borderline or malignant. Ovarian

Hyper-stimulation Syndrome (OHSS),

Ectopic pregnancy, Dermoid cysts, Poly

cystic ovaries and Endometriosis appears to

be some of the potent causes of it. Mutation of

BRCA 1 gene and BRCA 2 gene increases the

risk dramatically. Trans-vaginal grey scan

sonography, CT scan, PET, MRI, Chest X Ray

are some commonly used techniques

determine ovarian cysts. Cyst usually targets

right ovary and contains only fluid surrounded

by thin wall. India being a biodiversity rich

country have a large variety of abundant

medically essential plants are, thereby paving

a way for treatment of many women oriented

illness. Ayurveda is an ancient medical

therapy practiced successfully in a country

like India and has an advantage over other

treatments of being safe from any after effects

and hence proves to be a boon for females

suffering from ovarian cyst. Trifolium

paratense, Vitexagnus castus, Caulophyllum

thalictroid, Achillea millefolium,

Chamaelirium luteum and Piper nigrum are

some important medicinal plants used

extensively to treat ovarian cyst. Fibrous food,

zinc, mineral selenium, vitamin A and E are

also advantageous to the patient.

Keywords: Ayurveda | Ovarian Cyst |

Ovarian Syndrome | Medicinal Plants

Introduction

The incidence of ovarian cysts has increased

exponentially in past recent years making it a

problem of genealogical concern Present day

data shows that ovarian cysts are prevalent

between 8% to 18% in women of all ages

(Greenlee et al., 2010; Paixao, 2017). Around

5% to 10% of total women adopt surgery due

to ovarian cysts (Hilger et al., 2006). The




For Correspondence: Department of Bioscience and Biotechnology, Banasthali University, Banasthali, Rajasthan, India Email: [email protected]; [email protected]


23

development of simple benign ovarian cyst is

found to be more common in premenopausal

women especially during menstruation when

compared with postmenopausal women

(Neelgund and Hiremath, 2016). Poly cystic

ovaries are found in 70% women of

reproductive age leading to Poly Cystic

Ovarian Syndrome (Siriwardene et al., 2016).

Ovarian cysts are fluid filled ovarian follicles

which fail to release ovum at the time of

ovulation and continues to grow in size either

in or on the surface of ovary (Helm, 2015).

Cysts can be simple or complex and they may

be small as well as large and could be multiple

in number. Small cysts are usually

asymptomatic while a large cyst may

manifests as urinary tract obstruction,

pulmonary hypoplasia, and sometimes as

sudden death (Jedrzejewski et al., 2008).

Ovarian cysts can be categorized as either

benign, or borderline or malignant (Zalaudek

et al., 2001). They are also classified in

physiological and pathological type (Grimes

et al., 2014). Physiological cyst (benign)

includes follicular cyst, corpus luteum cysts,

dermoid cyst, theca-luteal cyst,

cystadenomas, endometrioma cyst while

pathological cysts include polycystic ovaries

and neoplastic cyst. Some categorized ovarian

cyst as uncomplicated and complicated

structures (Nussbaum et al., 1988). When

corpus luteum fails to dissolve and continue to

grow upto few centimeters, development of

corpus luteum cysts occurs. It causes ovaries

to twist resulting in acute pain and blood loss.

Follicular cysts arise due to hormonal

imbalance during ovulation. In this follicles

do not break but continue to grow leading to

irregularities in menstruation (Pal et al., 2015;

Williams, 2015). Theca luteal cyst is caused

by increased amount of human chorionic

gonadotropin serum levels and ovarian

hyperstimulation syndrome.

Ectopic pregnancy, Ovarian Hyperstimulation

Syndrome (OHSS), Dermoid cysts, Poly

cystic ovaries and Edometriosis are found to

be some of the potent causes of it.

Development and progression of ovarian cysts

is promoted by number of risk factors like age,

previous family history or personal history of

breast cancer etc. Infertility, hypothyroidism,

tubal ligation, nulliparity, maternal

gonadotropins also work as promoting agents

of ovarian cysts (Grabosch and Helm, 2016).

Other than this mutation of BRCA 1 gene and

BRCA 2 gene increases the risk dramatically

(American College of Obstetricians and

Gynecologists, 2010). Maternal diabetes, rh-

isoimmune hemolytic disease, toxemia,

deficiency of iodine or excess of bromide in

the body, early periods, early menopause, also

increases the risk 30-60 fold (Sehgal et al.,

2011). Elevated level of CA-125, uterine

fibroids, endometriosis, pregnancy, ectopic

pregnancy, ovarian cancer, anovulation,

PCOS also greatly contribute to ovarian cyst

(Hilger et al., 2006).

Ovarian cysts generally mimic the ectopic

pregnancy and other adnexal masses during

diagnosis, so great care should be taken while

diagnosing ovarian cyst in a patient (Tehrani

et al., 2014). Trans-vaginal Ultrasound,

wedge biopsy, CA-125 Blood Test, HE4 gene

expression along with CA-125 serum

concentration is very helpful in distinguishing

epithelial ovarian cancer from benign ovarian

tumors (Fawzey et al., 2016). Neutrophil

lymphocyte ratio and platelet lymphocyte

ratio along with serum cancer antigen 125 is

also very useful in distinguish malignant


24

ovarian cysts from benign ovarian cysts.

Proteomics is used extensively in women with

elevated C-125 in which several blood

proteins especially apo-lipoprotein is also

used as a marker in differentiating malignant

and benign ovarian masses, highly efficient

intra-operative frozen section, Trans-vaginal

grey scan sonography, CT Scan, PET, MRI,

Chest X Ray are some commonly used

techniques and methods used to determine

ovarian cysts. On an ultrasound image,

ovarian cysts may resemble bubbles. The cyst

usually contains only fluid and it is

surrounded by a very thin wall. Cyst usually

targets right ovary.

Women of 22 -30 has a higher risk of ovarian

cyst. Smoking habit increases the chances of

ovarian cancer (Greenlee et al., 2010). In the

U.S. nearly all premenopausal women are

affected with ovarian cyst but the chances of

conversion of ovarian cyst into ovarian

carcinoma are found to be 15 cases per

100,000 women. Optimal and effective

clinical management of ovarian cystic lesions

require knowledge of differential diagnosis,

imaging features, and management trends

(Wasnik et al., 2013). Though ovarian cysts

are not very deadliest but it poses various

great threats to the women’s reproductive

health, if ignored for prolonged time. Public

awareness regarding women oriented disease

like ovarian cyst is very important in

combating this disease completely.

Several treatments are given for the curing this

disease but inculcating habits like avoiding

caffeine, egg, refined food, red meat, white

sugar and alcohol can help the patients to

control or limit the ovarian cyst (Yasothai,

2014). Mixture of triphala churna and trikatu

churna, and kanchnaar guggul with aloe vera

juice and Chandraprabhavat, sharangdhar

samhita, Madhya khand, kachnaar guggul and

manibhadra churna have therapeutic effects.

They limit the size of cyst and cure

dismenorrhoea, gynecological syndromes and

hormonal imbalance (Sehgal et al., 2011).

Surgical methods are also very useful during

emergency but major treatment depends on

the medicines. Different type of surgery is

used to treat large size ovarian cysts including

cystectomy, oopherectomy, hysterectomy,

laproscopy and laparotomy (Garg, 2011). In

allopathic treatment, oral contraceptive pill,

progestins, antiandrogens and ovulation

induction agents are successfully used to cure

or limit the growth of ovarian cancer.

Medicines like morphine sulfate, oxycodone,

ibuprofen, indomethacin, naproxen,

diclofenac, ketoprofen, ketorolac, metformin,

sprintec and provera are commonly used to

overcome from cyst (Grunkemeier et al.,

2007). Allopathic medication is commonly

and effectively used to reduce the symptoms

of ovarian cyst. Yoga proves to be

inexpensive, natural and effective stress

buster in treating and managing ovarian cyst,

particularly PCOS. Several asanas are proved

to be very useful for PCOS patients such as

bhramri pranayama, bhadrasana, sun

salutation, bhujangasana, naukasana,

dhanurasana, warrior pose, sputa

badhakonasana, chakki chalanasana,

shavasana, padmasana, and kapalbhati etc.

since stress and unhealthy lifestyle are one of

the factors responsible for ovarian cyst.

Consequences of Ovarian Cyst

Ovarian cyst in most cases manifest in the

form of Polycystic Ovarian Syndrome (PCOS)

which in turn elicit other conditions to


25

develop. PCOS is the chronic metabolic

endocrinal disease affecting every one out of

five woman. It is a common heterogeneous

condition, manifested as diverse implications

including reproductive (hyper-androgenism,

hirutism, anovulation, multiple immature

follicles, ovulatory dysfunction, menorrhagia,

dysfunctional uterine bleeding, chronic pelvic

pain during menses, sub-fertility, infertility,

miscarriage, gestational diabetes, pregnancy

induced hypertensive disorders, neonatal

complications, endometrial hyperplasia),

phenetic (persistent acne, facial hair growth,

hair loss, acanthosis nigricans, obesity, male

pattern alopecia, oily skin, dandruff),

metabolic (insulin resistance, metabolic

syndrome, dislipidaemia, high blood pressure,

impaired glucose tolerance, diabetes mellitus

type-2, cardiovascular disease) and

pshycological (anxiety, depression, lack of

confidence, deteriorate life) features (Deeks et

al., 2010; Conisha, 2017). Benign breast

disease, benign ovarian tumors, fibroids,

ovarian and endometrial cancers are one of the

most consistent epidemiological

consequences of ovarian cyst (Ness et al.,

2000; Tabarrai and Kasraei, 2017). Different

interlinked consequences of ovarian cyst are

shown in figure 1.

Mechanism of Ovarian Cyst

An FSH surge stimulates the emergence of a

new follicle formation, from which a single

dominant follicle is selected at the time of

deviation. Through a positive feedback loop

oestradiol stimulates GnRH and LH

pulsatility, which in turn supports growth and

development of the dominant follicle. Upon

reaching preovulatory size, follicular

steroidogenic activity reaches a peak and

produces a preovulatory oestradiol surge. This

surge either fails to elicit a GnRH and

subsequent LH surge or the GnRH/LH surge

is delayed. The dominant follicle, therefore,

does not ovulate but, due to the ongoing LH

pulsatility, continues to grow and becomes a

cyst.

The disruption of the hypothalamic-pituitary-

gonadal axis can be caused by factors affecting

the oestradiol feedback mechanism and

GnRH/LH release at the hypothalamic-

pituitary level (1) and/or by an aberrant follicle

growth and development with alterations in

receptor expression and steroidogenesis (2),

leading to an altered oestradiol surge and

feedback (3). Hypothalamic-pituitary function

and follicular growth/development may be

affected by NEB through metabolic/hormonal

adaptations. In addition, in the situation of

NEB, the expression of genetic hereditary

factor(s) associated with COF may be

promoted or the functional importance may

increase, which in turn may affect follicle

growth and hypothalamic-pituitary function.

Figure 2 shows the schematic representation

of the pathogenesis and the possible pathways

involved in the formation of ovarian cysts

(Vanholder, 2006).

Ethno-medicinal plants used to treat

ovarian cancer

Use of oral contraceptive, progestins,

antiandrogens, and ovulation inducing agents

are standard therapies in allopathic

medication. Oral contraceptives prevent the

re-occurrence of cyst. But the repeated use of

Metformin, sprintec, YAZ, Tri-Sprintec,

TriNessa, Provera, spironolactone,

GeneressFe, and Seasonique, and Mononessa

drugs interferes in fertility of a patient (Rofe et


26

al., 2013). Excessive painful or irregular

periods was reduced by 25–50% among

women using the combined pill compared

with nonusers (Grimes et al., 2006). Hence, to

prevent a patient from all these types of side

effects use of natural treatment and methods

from Ayurveda is highly recommended.

Several important medicinal herbs and plants

in Ayurveda used for curing ovarian cysts are

listed in table 1.

Ayurveda is the ancient method to treat

number of diseases and is also popular in

demand and practice nowadays (Samy et al.,

2008). Hundreds of plant species are

extensively used by practitioners to cure

ovarian pathology and other related issues.

Kanchnar guggulu is good for PCOS (poly

cystic ovaries). Poly cystic ovaries results in

number of gynecological problems including

anaemia and sub-fertility which can be treated

by another ayurvedic regimen, Kalyalakagrita.

Kalyanakagrita and Kanchnarguggulu are

made of several natural ingredients like

Haritaki, Daru, Bhibhitaka and many more

(Siriward et al., 2010).

In conclusion, from the above discussion in

present day scenario, ovarian cyst has emerged

as the most life threatening problem in the

women all over the world. In most of the cases

lack of awareness, illiteracy, poverty and

unavailability of the treatments is responsible

for the spread and growth of the cysts in the

female’s body. Hence, the required facilities

and awareness about cysts should be made

available for the common people who are not

able to approach those kind modern and

affective treatments. Ayurveda is the ancient

medical therapy having greater curative

effects in ovarian cysts. This treatment

regimen prevents reoccurrence of ovarian

cysts.

Fig 1: Different interlinked consequences of ovarian cyst

ovarian cyst

Adenomyosis Endometrial hyperplasia

Increased LH secretion and decreased FSH

secretionAnovulation Breast cancer

Aromatisation of androgens into

estrogen

Infertility

Endometrial carcinoma

Damaged development follicle

Obesity Hyperandrogenism

Type -2 diabetes

Hirsutism, Acne, Alopecia and dandruff

Myocardial infraction

Gestational diabetes

Cardiovascular dysfunction

Insulin resistance


27

Fig 2: Mechanism of ovarian cyst formation

S. No. Name of The Plant Common Name

Family Parts Used Benefits References

1. Achillea millefolium

Yarrow Asteraceae Whole plant Relieve pain and useful in endometriosis and regulates irregular flow

Aggarwal et al., 2011; Vahide et al., 2014

2. Acorus calamus Vacha/ sweet flag

Acoraceae Whole plant Has anti inflammatory activities and relieves pain

Kim et al., 2009; Wu et al., 2009

3. Actaea racemosa

Black cohosh

Ranunculaceae

Whole plant Useful in treating amenorrhea, dysmenorrhea, uterine

Newton et al., 2006; Sakineh et al., 2010

4. Albezzia lebbock Shirish/Flee tree

Fabaceae Bark Is anti-inflammatory Faisal et al., 2012

5. Aloe vera Aloe Asphodelaceae Leaves Used to resolve ovarian problems and others related to cyst

Maharajan et al., 2010; Mahor et al., 2016

6. Asparagus racemosus

Shatavari

Asparagaceae Herb Improves stamina and helpful in carrying women’s reproductive cycle without any problems.

Visavadiy and Narasimhacharya, 2009; Mitra et al., 2012

7. Asphaltum

Shilajit

Pedaliaceae

Flower

Boon for menorrhagia, dysmenorrhea

Carlos et al., 2012; Wanjari et al., 2016

8. Baliospermum montanum

Danti

Euphorbiaceae

Leaves Balances female hormones

Panda et al., 2014; Chaganti and Prasad, 2015

9. Bauhinia variegata Kachnar/orchid tree

Leguminosae Whole plants including leaves, flower, bud and fruits

Contains alkaloids, tannins and ascorbic acid that help in resolving ovarian cyst.

Balakrishnan and Bhat, 2015

10. Beta vulgaris

Beet root Amaranthaceae Fruit Eradicates problems produced by ovarian cyst

Restuccia et al., 2012

Damage of ovarian

follicles itself

Elevated progesterone

level

Indirect factors like stress and infections

Lesser no of LH and FSH receptor in

granulose cells

Low surge of GnRHand LH but

increased LH pulse frequency

Low surge of positive

feedback of estradiol

Dysfunctional Hypothalamus-Pituitary axis

Anovulation

Aberrant follicle growth and development of cystic ovarian follicles

Formation of ovarian cysts


28

11. Caulophyllum thalictroid

Blue cohosh Berberidaceae Flower Useful in curing endometriosis, and ovarian cyst. Help in toning the ovaries, and prevent the bloating, inflammation and uneasiness due to ovarian cyst

Xia et al., 2014

12. Chamaelirium luteum

False Unicorn Root

Liliaceae

Root

Reduces menstrual cramps, growth of cyst and chances of infertility.

Victoria et al., 2012

13 Cinnamomum camphora

Karpoor

Lauraceae

Flowers and fruits

Has anti inflammatory activity on the ovaries

Lee et al.,2006; Mishra, 2016

14. Cinnamomum tamala

Tejpatta

Lauraceae

Bark and leaves

Is anti inflammatory and helps in pain

Chakraborty et al., 2010; Mishra et al., 2010

15. Cinnamomum zeylanicum

Cinnamon/ Dalchini

Lauraceae Bark and leaves

Reduces the risk of insulin resistance, irregular periods and diabetes

Arentz et al., 2014

16. Citrullus colocynthis

Bitter apple

Cucurbitaceae Fruit Has purgative, anti-inflammatory, antidiabetic, hair growth–promoting, abortifacient, and antioxidant properties an dhelp in reproductive

Ostovan et al., 2014

17. Clerodandrum serratum

Bharangi

Lamiaceae

Flower

Reduce fibroids and help in increasing appetite that mitigate weakness

Singh et al., 2012; Dave et al., 2015

18. Commiphora wightii

Kachnar gugglu

Burseraceae Resin Reduces cyst and tumors. Purify blood

Dev, 1997; Sharma and Sharma, 2014

19. Crataeva religiosa

Varuna

Capparaceae Leaf

Maintain fertility

Lagnika et al.,2011

20. Curcuma longa Haridra/ haldi

Zingiberaceae

Root

Induce proper ovulation and is anti-inflammatory

Bharti et al., 2007; Reddy et al., 2016

21. Cyprus rotundus

Mustak/ Java grass

Cyperaceae

Whole plant

Helps to dissolve ovarian fibroids

Natrajan et al., 2006; Kum et al.,2017

22. Dioscorea villosa Wild Yam Dioscoreaceae Root Useful for menstrual spasm, premenopausal complaints, miscarriage, painful labor and inflammation in ovaries

Roy et al., 2011

23. Elettaria cardamomum

Cardamomum

Zingiberaceae Seeds Dissolves fibroids Verma et al., 2009

24. Emblilica officinalis

Amala/Indian gooseberry

Euphorbiacea Seeds, leaves, bark, root, flower and dried fruits

Antioxidant, anti-cancerous, anti-inflammatory functions

Bhandari et al., 2012; Jain et al., 2015

25. Glycyrrhiza glabra

Mulethi

Fabaceae

Roots

Is anti-inflammatory in action and anti-oxidant

Dhingra et al., 2006

26. Grifola frondosa

Maitake Mushroom

Meripilaceae Fruit Remedy for polycystic syndrome by normaliz9999ing hormonal imbalance. Controls body weight and insulin resistance

Ding et al.,2016; Pachiappan et al., 2017

27. Linum usitatissimum

Flax/linseed

Linaceae Seeds Dissolves cyst Nowak et al., 2007; Grant, 2010


29

28. Momordica charantia

Karela

Cucurbitaceae

Fruit

Is anti-diabetic and anti-oxidant and helps in relieving symptoms of poly cystic syndrome

Chaggan, 2015

29. Piper chaba Chavya Piperaceae Root and fruit Relieves in painful menses

Kumar et al., 2008;

30. Piper longum Pippali Piperaceae Fruits and seeds

Treats menstrual pain and pain in ovaries

Lakshmi et al., 2006; Nabi et al.,2013

31. Piper nigrum

Maricha/ Black Pepper

Piperaceae

Fruit

Help in dissolving the cyst. Helps absorb nutrients in body

Lu et al. 2012; Sireeratawong et al., 2012; Tan et al., 2013

32. Ricinus communis

Castor

Ricininae

Seeds

Oil helps in limiting size of ovarian cyst

Tunaru et al., 2012; Abulrasheed et al., 2015

33. Saraca indica Ashok Caesalpinioideae Leaves Contains anti- inflammatory properties and help in reducing pain from cyst

Bhalerao et al., 2014

34. Symplocas racemosa

Lodhra

Symplocaceae Flower Potent herb in maintaining female reproductive health and relief pain

Rao et al., 2011; Jadhav et al., 2015

35. Taraxacm officinale

Dendalion

Asteraceae Leaf and roots Provide potassium, of which deficiency appears to contribute in the growth of the cyst. Reduces pain and swelling

Bussmann and Glenn, 2010; Nasri et al., 2015

36. Terminalia bellerica

Bhibhitaki combrataceae Fruit, bark Maintain female hormones and the regular menstruation, controls excess blod flow and pain

Patil et al., 2010; Deb et al., 2016

37. Terminlia chebula Haritaki Combrataceae Fruit Contains phytochemicals that limit the size of ovarian cyst

Jayweera, 1981; Bag et al.,2013

38. Tinospora cordifolia

Guduchi/ Giloy

Menispermaceae

Leaves

Antioxidants which fight free-radicals, giloy helps remove toxins, purifies blood

Nair et al.,1996; Dhanasekaran et al., 2009

39. Trifolium paratense

Red clover Fabaceae Whole plant Reduces menstrual cramp, helps in treating infertility

Ghazanfarpour et al., 2015

40. Tylphora indica Anantmool Apocynaceae Flower Helps in regulating menstrual cycle

Bhatia et al., 2013; Pratheesh et al., 2014

41. Vitexagnus castus Chaste berry

Lamiaceae Roots

Prevent ovarian cyst by lowering estrogen level and help in menstrual complaints

Die et al., 2009; Salehi et al., 2016

42. Withania somnifera Ashwagandha, Indian gingsen

Solanaceae

Whole plant

It provides strength to deal with the negative health impacts and is antispasmodic.

Karakava, 2004; Dhiman, 2014

43. Zingiber officinale Ginger Zingiberaceae Roots Remove out tiny cysts completely and prevent ovarian cancer. Balances hormonal level

Rhode et al., 2007

Table 1: Important Medicinal Plants used to combat ovarian cyst


30

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Ashish Kumar Jha/VIII: Special Edition: 2: 2017/35 - 44

35

Assemblage composition and seasonal variations of waterbirds at Nav Talav, Amgaon in Gondia District of Maharashtra

Jha, Ashish Kumar


Abstract

Among various biological components of

freshwater, the waterbirds play an important

role not only in trophic dynamics of

ecosystem but also in the indication of

changes in the quality of water due to

pollution or degradation because of their

ability to respond quickly to such changes.

Thirty waterbird species, mainly Anatidae

(26.57%) and Ardeidae (20.50%), were

recorded at Nav Talav, Amgaon in Gondia

district of Maharashtra using monthly point

counts. Little Egret, Red-crested Pochard and

Cotton Pygmy-goose were common, while the

globally-near threatened Black headed Ibis

and 15 species of local or regional

conservation concern were also observed.

Seasonal variation in waterbird assemblage

structure reflected the arrival and/or passage

of migrants, and few species were present

between May and August. Further studies are

required to uncover the factors contributing to

the observed seasonal variations in bird

assemblage within Nav Talav, Amgaon.

Keywords: Waterbirds | Anatidae | Ardeidae |

Seasonal variation

Introduction

Wetland ecosystems harbor loads of

biodiversity as it is being rich in nutritional

value and productivity (Gibbs, 1993;

Paracuellos, 2006) and are one of ecologically

important conservation concern sites owing to

its trophic dynamics. The waterbirds uses

wetland habitats either throughout their life or

during certain part of their life for nesting,

breeding, feeding, sheltering and migration

stopovers (Weller, 1999; Getzner, 2002).

They are beneficial not only in trophic

dynamics of ecosystem but also in the

indication of changes in the quality of water

due to pollution or degradation because of

their ability to respond quickly to such

changes. The spatial and seasonal

distributions of waterbirds are influenced by

conditions such as habitat types, climatic

conditions, resource stability and immediate




For Correspondence: Department of Bioscience and Biotechnology, Banasthali University, Banasthali, Rajasthan, India Email: [email protected]; [email protected]


36

human impact. Different workers like,

Osmatston (1922), Singh (1929), Ali (1932),

Ghazi (1962), Kannon (1980), Mujumdar

(1984), Davidar (1985), Newton et al., (1986),

Jhingram (1988), Ghosal (1995), Wadatkar

and Kasambe (2002), Yardi et al., (2004),

Kulkarni et al., (2005) and Harney (2014)

have examined waterbird communities for

annual variations in abundance and species

composition from different freshwater bodies

of India.

The present study was carried to investigate to

overall assemblage composition and seasonal

variations of waterbirds at Nav Talav,

Amgaon in Gondia district of Maharashtra.

The results would be useful in suggesting

strategies for the conservation of Nav Talav

and the waterbird diversity that it contains.

Materials and Methods

Study sites

Nav Talav at Amgaon in Gondia district is

situated in eastern region of Maharashtra state

at the geographical coordinate of 20°359′0″N

latitude and 80°384′0″E longitude (Fig-1).

Waterbird Survey

Waterbird surveys were carried out in Nav

Talav from January 2013 to December 2013

on a monthly basis covering a complete wet

season (June to September) and dry season

(March to June); rainy days were avoided

because rain interfere with visibility (Ralph et

al., 1993). Birds were identified by sight using

binoculars (Olympus 10×50). During field

studies, guidebooks were used to identify the

birds (Ali, 2002, Grimmett et al., 2011 and

Manakadan et al., 2011).

Obligate wetland users waterbirds from the

avian fauna were selected and all other species

were excluded from the analysis (Weller,

1999). All individuals of each waterbird

species were counted in each visit within

several parts of Nav Talav following the bird

census techniques (Bibby et. al., 2000).

Since preliminary observations showed that

waterbirds started to enter the study site for

resting and foraging when the water level was

high, all waterbird surveys were conducted

when the water level was high. Waterbirds

were defined as comprising species of

cormorants, herons, egrets, ibises, ducks, rails

and jacanas normally associated with

wetlands and also birds species in the families

Charadriidae (Lapwing) and Scolopacidae

(Sandpipers) in the present study.

The relative diversity (RDi) of families was

calculated by using following formula (Koli,

2014):

RDi = (No. of birds species in the family

/Total no. of species)*100


A total of 824 waterbirds in 30 species were

recorded in Nav Talav at Amgaon in Gondia

district during the study period. The species

belong to 6 orders: Pelecaniformes,

Ciconiiformes, Anseriformes, Gruiformes,

Charadriiformes, and Coraciiformes (Fig. 2).

Based on foraging behavior and habitat use

the waterbird families were grouped as

follows: 1) surface and aerial diving birds

(Phalacrocoracidae, Alcedinidae, Meropidae),

2) wading birds (Ardeidae, Ciconiidae,

Threskiornithidae, Recurvirostridae,

Charadriidae, Scolopacidae), 3) ducks

(Anatidae), and 4) marsh birds. Most were


37

Anatidae (26.57%), while Ardeidae (20.50%)

ranked the second among families in terms of

relative diversity (Table- 1). This agreed with

the finding of Kumar (2006), who reported

that, Ardeidae to be the most dominant family

in Bharathpuzha river basin in Kerala and

Surana et al., (2007) according to them

Anatidae to be the most dominant family in

Chimdi lake Nepal.

Fig. 1: A satellite view of Nav Talav, Amgaon in

Gondia district of Maharashtra

Fig. 2: Abundance (no./sq.m) of waterbirds Order at

Nav Talav, Amgaon in Gondia district of Maharashtra

(January, 2013 to December 2013): (expressed in %)

The most common species (making up > 7%

of total counts over the study period) were

Red-crested Pochard (Netta rufina), Cotton

Pygmy-goose (Nettapus coromandelianus),

Little Egret (Egretta garzetta) and Little

Cormorant (Phalacrocorax niger) (Table - 3).

21 species are very common, 03 are common,

04 are uncommon, 01 are occasional and 01

were rare to Nav Talav (Table - 2). Black-

headed Ibis (Threskiornis melanocephalus),

Blue-tailed Bee-eater (Merops philippinus),

Marsh Sandpiper (Tringa stagnatilis) and

Great Cormorant (Phalacrocorax carbo), all

of these species were relatively rare (< 0.5%

of total counts). Similar observation was also

recorded from Shrungarbandh lake

(Bhandarkar and Paliwal, 2014), Zaliya lake

(Puri, 2015) and Bothalkasa lake (Puri and

Virani, 2016) in Gondia District of

Maharastra.

Of the 30 waterbird species recorded during

the present study, 50% are of local or regional

conservation concern (Kulkarni 2005,

Kulkarni et. al., (2006 (a), 2006 (b), 2006 (c)),

among which Black-headed Ibis (Threskiornis

melanocephalus), the globally-near

threatened species (NT; Bird Life

International, 2006), had the highest

conservation value. Black-headed Ibis were

recorded in Nav Talav in December and

January, 2013 but the present study provides

the first record of this species in Amgaon

tehsil. Although only few individuals were

recorded during the study period, 3 juveniles

were seen roosting within the Nav Talav.

These sighting records of Black-headed Ibis

suggest that Nav Talav might be another

location in Gondia District.

The Red-crested Pochard (Netta rufina)

occupied first position in order of dominance

in respect to total number of wetland birds

(Table - 3). The population maximum of this

species was in the month November. The

second predominant species was Cotton

Pygmy-goose (Netta puscoromandelianus).

This species exhibited its population peak in

the month of November (Table-3). The third

numerical dominant species was Little Egret

26.58

25.9516.95

8.56

11.34

10.62 Anseriformes

Ciconiformes

Coraciiformes

Charadriformes


38

(Egretta garzetta). It showed its maxima in

February (Table - 3). The other numerically

important species observed were Spot-billed

Duck (Anas poecilorhyncha), Tufted Duck

(Aythya fuligula), Cattle Egret (Bubulcus

ibis), Red-wattled Lapwing (Vanellus

indicus), Asian Openbill (Anastomus

oscitans) and Green Bee-eater (Merops

orientalis). Great Cormorant (Phalacrocorax

carbo), Black-headed Ibis (Threskiornis

melanocephalus), Marsh Sandpiper (Tringa

stagnatilis) and Blue-tailed Bee-eater

(Merops philippinus) occured in individual in

very poor number in relation to total wetland

bird community (Table - 3).

There was an obvious reduction in waterbird

species richness in Nav Talav from March to

September 2013 (when no more than 20

species were recorded in any month), while

the number of species seen from October 2013

to February 2013 was greater (24 to 30). Nav

Talav is an important stopover and wintering

site for migrating birds, and the majority of

birds in Gondia are either passage migrants or

winter visitors. From mid-November to early

January (winter migration), migrants birds

pass through Gondia District from the

northern breeding sites to wintering grounds

near the equator and they fly back to the

breeding sites, passing through and staying in

Gondia district in April and May (Summer

migration) (Viney et al., 2005). All winter

visitors and passage migrants leave Gondia

district to their summer breeding grounds by

the begining of autumn (Dudgeon & Corlett,

2004). Since few of the waterbirds (20% of

total) recorded in Nav Talav were either

passage migrants or winter visitors, it is not

surprising that the number of waterbird

species recorded in Nav Talav dropped from

30 species in December to 19 species in

February 2013, followed by a summer-low

where only resident species including all

egrets except the Intermediate Egret, and

summer visitors were recorded.

Prey abundance, distribution and composition

among areas have a direct influence on habitat

use by waterbirds (Murkin & Kadlec, 1986;

Colwell & Landrum, 1993; Sánchez et al.,

2006a; Sánchez et al., 2006b). Aquatic

invertebrates are an important food of

wetlandbirds (Skagen & Oman, 1996; Pérez-

Hurtado et al., 1997; Gammonley & Laubhan,

2002), and positive correlations between

waterbird and benthic invertebrates is

common (Colwell & Landrum, 1993; Yates et

al., 1993; Safran et al., 1997; Placyk &

Harrington, 2004; Sánchez et al., 2006a;

Sánchez et al., 2006b). Since the assemblage

structures of aquatic macroinvertebrates in

different areas have yet to be studied in Nav

Talav, it is not possible to relate the

distribution of wetland birds to their prey.

The greater counts of Little Egret, Red-crested

Pochard and Cotton Pygmy-goose recorded in

Nav Talav may be due to the fact that Nav

Talav harbor loads of mollusca, crustacean

and insects. The greater number of Cattle

Egrets, which prey on insects, frogs and

lizards (Strange, 2000), in Nav Talav may be

explained by the presence of grazing cattle

which helped to flush out preys (MacKilligan,

2005). Habitat selection by waterbirds has

been shown to be affected by predation risk

(Caldwell, 1986; Yasué, 2006). For instance,

waders may avoid profitable feedings sites in

the proximity of trees and shrub cover from

which raptors launch surprise attacks

(Cresswell & Whitfield, 1994).


39

Since the bird survey was conducted only

once a month, it is inevitable that some

waterbird species were missed, especially

during the spring passage and autumn

passage. In order to have a better

understanding of the relationship between the

distributions of prey and waterbirds in Nav

Talav, further studies on the invertebrate

assemblage structure and sediment

characteristics, which affects the type and

abundance of invertebrates (Yates et al., 1993;

Bolduc & Afton, 2003; Sarkar et al., 2005)

and waterbirds (Grant, 1984; Mouritsen &

Jensen, 1992; Granadeiro et al., 2007) will be

needed.

S. No Order Family Scientific name Common name Population IUCN status

Residential status

1

Anseriformes Anatide

Nettapus coromandelianus Cotton Pygmy-goose LC RVc Netta rufina Red-crested Pochard LC WVc Anas poecilorhyncha Spot-billed Duck LC RC Aythya fuligula Tufted Duck LC RO

2

Ciconiformes

Ardeidae

Bubulcus ibis Cattle Egret LC RVc Ardeola grayii Indian Pond Heron LC RVc Egretta garzetta Little Egret LC RVc Ardea purpurea Purple Heron LC RVc Mesophoyx intermedia Intermediate Egret LC RVc

Threskiornithidae Threskiornis melanocephalus

Black-headed Ibis (White Ibis)

NT SVUc

Pseudibis papillosa Black Ibis LC RVc Ciconiidae Anastomus oscitans Asian Openbill LC RVc

3

Coraciiformes Alcedinidae

Alcedo attthis Common Kingfisher LC RVc Ceryle rudis Pied Kingfisher LC RVc Halcyon smyrnensis White-throated Kingfisher LC RVc

Meropidae Merops orientalis Green Bee-eater LC RVc Merops philippinus Blue-tailed Bee-eater LC WVUc

4

Charadriformes

Charadriidae Vanellus indicus Red-wattled Lapwing LC RVc Recurvirostridae Himantopus himantopus Black winged Stilt LC RC

Scolopacidae Tringa stagnatilis Marsh Sandpiper LC WVUc

Jacanidae Metopidius indicus Bronze-winged Jacana LC RC Hydrophasianus chairurgus Pheasant-tailed Jacana LC RUc

5 Pelecaniformes Phalacrocoracidae

Phalacrocorax carbo Great Cormorant LC WVc Phalacrocorax fuscicollis Indian Cormorant LC WVc Phalacrocorax niger Little Cormorant LC RVc

6

Gruiiformes Rallidae

Porzana pusilla Baillon’s Crake LC WVRr

Gallinula chloropus Common Moorhen LC RVc Porphyrio porphyrio Purple Swamphen RVc Amaurornis phoenicurus White – breasted

Waterhen LC RVc

Fulica atra Common Coot LC RVc

NT-Nearly Threatened, LC- Least Concern, R-Resident, WV- Winter Visitor, SV- Summer visitor Rr-Rare (<5%), O – Occasional (5-24%), Uc-Uncommon (25-49%), C- Common (50-74%), Vc- Very common (75-100%)

Table 2: Systematic List of Bird Species at Nav Talav, Amgaon in Gondia district of Maharashtra (January, 2013 to December 2013)

Anatide 26.58 Charadriidae 3.28

Ardeidae 20.51 Recurvirostridae 2.18

Threskiornithidae 2.67 Scolopacidae 0.49

Ciconiidae 2.79 Jacanidae 2.55

Alcedinidae 13.23 Phalacrocoracidae 11.41

Meropidae 3.76 Rallidae 10.56 Table 1: Relative diversity (RDi) of various families at Nav Talav, Amgaon in Gondia district of

Maharashtra (January, 2013 to December2013)


40

Species Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Nettapus

coromandelianus 0.196 0.26 0.11 0.036 0.03 0.02 N.C. N.C. 0.25 0.12 0.55 0.49

Netta rufina 0.52 0 0 0 0 0 N.C. N.C. 0 0 0.64 0.203 Anas poecilorhyncha 0.08 0.07 0.12 0 0 0 N.C. N.C. 0 0.4 0.39 0.345

Aythya fuligula 0.196 0 0 0 0 0 N.C. N.C. 0 0 0.175 0.155

Bubulcus ibis 0.203 2.57 0.64 0.52 0.226 0.26 N.C. N.C. 0.345 0.4 0.39 0.08 Ardeola grayii 0.47 0.04 0.003 0.04 0.01 0.04 N.C. N.C. 0.035 0.045 0.04 0.04

Egretta garzetta 0.323 0.556 0.37 0.145 0.117 N.C. N.C. 0.02 0.12 0.55 0.33 Ardea purpurea 0.053 0.016 0.023 0 0 0 N.C. N.C. 0.045 0.004 0.035 0.056

Mesophoyx intermedia 0.023 0 0 0 0 N.C. N.C. 0 0 0.11 0.115 Threskiornis

melanocephalus 0 0 0 0 0 0 N.C. N.C. 0 0 0.045 0.035

Pseudibis papillosa 0.06 0.006 0 0.01 0.004 0 N.C. N.C. 0.12 0.004 0.04 0.12 Anastomus oscitans 0.07 0.22 0.086 0.018 0.03 0.6 N.C. N.C. 0.5 0.04 0.19 0.155

Alcedo attthis 0.03 0.03 0.016 0.035 0.035 0 N.C. N.C. 0.045 0.04 0.045 0.5 Ceryle rudis 0.023 0.02 0.013 0.005 0.02 0.11 N.C. N.C. 0.02 0.03 0.175 0.115

Halcyon smyrnensis 0.07 1.04 1.71 0.213 0.18 0.05 N.C. N.C. 0.23 0.4 0.59 0.35 Merops orientalis 0.153 0.14 0.043 0.026 0.017 0.13 N.C. N.C. 0.125 0.045 0.08 0.065

Merops philippinus 0.047 0.04 0.003 0.04 0.01 0.04 N.C. N.C. 0.005 0.035 0.04 0.04 Vanellus indicus 0.023 0.02 0.0133 0.005 0.003 0.05 N.C. N.C. 0.29 0.05 0.01 0.25

Himantopus himantopus

0.06 0.006 0 0 0 0 N.C. N.C. 0 0.03 0.04 0.12

Tringa stagnatilis 0.023 0 0 0 0 0 N.C. N.C. 0 0 0.023 0.02

Metopidius indicus 0.12 0.03 0.03 0.015 0.003 0.05 N.C. N.C. 0.29 0.05 0.01 0.025 Hydrophasianus

chairurgus 0.023 0.02 0.013 0.005 0.02 0.11 N.C. N.C. 0.02 0.005 0.035 0.04

Phalacrocorax carbo 0.005 0 0 0 0 0 N.C. N.C. 0.013 0.01 0.004 0.007 Phalacrocorax

fuscicollis 0.01 0.03 0 0 0 0 N.C. N.C. 0.045 0.045 0.35 0.35

Phalacrocorax niger 0.23 0.12 0.18 0.08 0.03 0.145 N.C. N.C. 0.117 0.12 0.55 0.33 Porzana pusilla 0.047 0.04 0.003 0.04 0.01 0.04 N.C. N.C. 0.005 0.035 0.04 0.035

Gallinula chloropus 0.153 0.14 0.043 0.026 0.016 0.13 N.C. N.C. 0.115 0.01 0.065 0.01 Porphyrio porphyrio 0.28 0.29 0.08 0.05 0.003 0.017 N.C. N.C. 0.05 0.055 0.03 0.065

Amaurorni sphoenicurus

0.07 0.22 0.086 0.018 0.03 0.6 N.C. N.C. 0.5 0.04 0.1 0.125

Fulica atra 0.053 0.016 0.023 0.056 0.01 0.004 N.C. N.C. 0.04 0.045 0.004 0.035

Table 3: Abundance of waterbirds obtained per month from all sampling sites at Nav Talav, Amgaon in Gondia district of Maharashtra (January, 2013 to December2013): (expressed in % of total population)

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Amit B. Vairale/VIII: Special Edition: 2: 2017/45 – 47

45

Diversity of Spiders in Agricultural Fields from Partwada Tahsil, District Amaravati (Maharashtra State)

Vairale, Amit B.

Received: September 02, 2017 Accepted: October 11, 2017 Online: December 31, 2017

Abstract

Spiders are one of the most important

predators in the terrestrial ecosystem. The

current study was conducted in the

agricultural fields of district Amravati.

Keywords: Diversity | Spiders | Agricultural

Fields

Introduction

Spiders are among the most abundant

insectivorous predators of Terrestrial

ecosystem. Order Araneae is a large group of

animals, which is commonly called as

Spiders. They are one of the most diverse

animals group in the world. Spiders play an

important role in insect pest control without

any harm to agricultural field. Recently in

agricultural fields reduced pesticide use and

ecological sustainability have lead to

increased interest in spiders as potential

biological pest control agents. Considerably

insect populations increase when release from

predations by spiders. They are widespread

and found in all types of habitats. Regularly

use of pesticides in agricultural fields which

decreases the spider populations.

Spider species abundance in agricultural

Fields can be high as undisturbed natural

ecosystem. Spiders act as pest control

creature, which feeds on crop destructive

insects. Spiders are beneficial bio-control

agent of insect pest in agricultural fields. The

Spiders varies greatly in their shape and size.

A survey of Spiders was carried out in




For Correspondence: Department of Zoology, Ghulam Nabi Azad Arts, Commerce and Science College, Barshitakli, District Akola, Maharashtra Email: [email protected]


46

Agricultural fields of Partwada Tahsil,

District Amaravati during 2016-17.

Material and Method


Agricultural Fields of Partwada Tahsil,

District Amaravati during 2016-17. Spiders

specimens were collected from 2016-2017.

Spiders were collected from different areas of

Agricultural Fields of Partwada region. For

collection of spiders direct searching, Pit fall

trapping, collected by Insect nets, beating

steak and umbrellas were used. The Spiders

Specimens were put in 75% alcohol, labeled

and identified according to Kaston spider

book and Tikader 1962. Before preservation

the photographs were taken in different views,

to get the clear eye position, pattern and

shades of cephalothorax, abdomen, spines and

hairs pattern.

Observation and Result


Agricultural Fields of Partwada Tahsil,

District Amaravati during 2016-17. During

the present study survey we have reported 94

species of Spiders belonging to 14 Families

and 32 genera. Spiders of Families Araneidae,

Clubionidae, Eresidae, Gnaphosidae,

Lycosidae, Miturgidae, Oxyopidae,

Philodromidae, Salticidae, Scytodidae,

Tetragnathidae, Theridiidae, Thomisidae,

Uloboridae were recorded during the

investigation.

Conclusion

Spider’s predatory capacity can have an

effect in decreasing densities of insect pests.

Aphids are important pests of Agricultural

Fields, which are destroyed by spiders.

Spiders are used to balance the effect of

Insecticides and Pesticides. Due to destroying

pest, spiders act as natural biological control

agent in agricultural fields. Spiders act as

natural biological controller, so spiders are

good friends of farmer.

Sr. No. Family Genera Species 1 Araneidae 08 15 2 Clubionidae 01 04 3 Eresidae 01 02 4 Gnaphosidae 02 09 5 Lycosidae 03 12 6 Miturgidae 01 03 7 Oxyopidae 04 10 8 Philodromidae 01 02 9 Saltisidae 05 14 10 Scytodidae 01 04 11 Tetragnathidae 01 02 12 Theridiidae 01 04 13 Thomisidae 02 11 14 Uloboridae 01 02

Total 32 94

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584.


47

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.

Qureshi and Gandhi/VIII: Special Edition: 2: 2017/48–53

48

Quality Assessment of Physicochemical and Biological Parameters of bore well water in Chisda Village, Dadra and Nagar Haveli in Surat region

Qureshi, Ikram and Gandhi, Devang

Received: August 05, 2017Accepted: October1, 2017Online: December 31, 2017

Abstract

In order to determine the bore well quality of

Chisda village, bore water samples were

collected from Khoripada, Patel pada,

Rabadpada, Mulgam and Dadripa locations

in Chidsa village. Water sample were

analyzed for the chemical parameters such

as, pH, Colour, Taste, Odor, TDS, Turbidity,

Calcium, Magnesium, Phenolic compounds

and Biological parameters which are E.Coli

and Total coliform bacteria. Test results of

Khoripada bore well water were observed

within the specification limits. In Patelpada

bore well water sample, Average turbidity

value was found 1.55 NTU which is high

than the specification limit. Total coliform

bacteria test of bore well water was found in

month of March and October. Rabadpada

bore water test results shows high average

turbidity 1.57 NTU. Other chemical and

biological test parameters found within

specification limits.

Bore well water sample in Mulgam location

found satisfactory results for human

consumption. Dadripada bore water sample

observed high average turbidity value 1.16

NTU, also total coliform bacteria found

present in month of March and October.

Keywords: pH | Colour | Taste | Odor | TDS |

Turbidity | Calcium | Magnesium | Phenolic

compounds

Introduction

Atmosphere, Lithosphere and Hydrosphere

are the three assents of earth’s ecosystem.

Those are the vital for all creatures and plants

for living. Water is the one of the important

factor for earth’s eco cycle, around 71 % part

of earth is covered with water (PA. Paritha

2016, K. Saravana Kumar and R. Ranjith

Kumar 2011). 1.7% water sources are found

in ground water and of North and South Pole

ice glaciers occupy 1.7 % of water sources.

Human body contains 60% of water. Water is

used for various applications by human,




For Correspondence: Bioscience Department, Shri Jagdish prasad Jhabarmal Tibrewala University. Vidyanagari, Jhunjhunu, Rajasthan. India Email: [email protected]


49

animals and plants (Maurya, P. M. and

Singh, V. K. 2016).

Humans are used water in a huge quantity for

many applications such as, drinking, cooking,

washing, bathing etc. Hence, water quality

affects the human health directly. It is

important to consume pure water which is

free from any contaminants and pollutants.

WHO (2007), USEPA and BIS guidelines are

established by the regulatory bodies for the

evaluation of water quality and measurement

of contaminants and pollutants in water

(Maurya, P. M 2016, Maurya, P. M. and

Qureshi, I. 2016).

In the present research paper, Bore well

water of Chisda village Dadra & Nagar

haveli was evaluated by sampling and

analyzing bore water from five locations.

Bore water sample were collected from

Khoripada, Patel pada, Radadapada, Mulgam

and Dadripada locations and analyzed for the

pH, Colour, taste, Odor, Total Dissolved

Solids, Calcium, Magnesium, Phenolic

compounds, E.Coli and total coliform

bacteria parameters. Samples were collected

and analyzed in the month of March -2016,

July-2016 and October-2016. Water quality

of selected locations was evaluated on the

basis of seasonal sampling and analysis

reports.

Methodology

Total 15 bore water samples were collected

from five locations during the month of

March-2016, july-2016 and October-2016.

Sampling site was tracked with GPS

identifications. Water sample were collected

and analyzed as per IS 10500(2012)

specifications. Water quality was evaluated

as per specifications given in Indian Standard

guideline IS 10500 (2012) and APHA.

Sampling Time: Water sampling was done in

the first week of March 2016, July 2016 and

October 2016. Sampling time was, from

morning 10.00 am to evening 5.00 pm.

Sampling Process: In order to achieve

accurate test results, sample preservation

method is highly required. Sampling

container and sampling technique also have

an impact on Analytical test results. Indian

standard method (IS) and APHA method was

followed for sampling and preservation of

water sample.


Analysis of water sample of Chisda village

shows significant variations in chemical and

biological parameters. Water sample were

analyzed for nine chemical and two

biological parameters. Five bore well sample

locations were identified in Chidsa village. In

the khoripada location average ph value was

observed 6.9, obtained colour value was 1.3

Hazen. Taste and odor was found suitable for

human consumption. TDS value was

observed 248 mg/l, average turbidity was

observed 0.98NTU, average value of calcium

and magnesium was observed 35.33 and

13.67 mg/l respectively. Result of Phenolic

compound, E.Coliand Total coliform bacteria

parameters found within the specification

limit.

Water analysis of patelpada location reported

average pH value 7.0, average colour value

obtained 1.7 Hazen, Taste and odor of water

sample found suitable for drinking purpose,

average value of TDS,Turbidity, Calcium,

and Magnesium observed 259 mg/l, 1.55


50

NTU, 35.7 mg/l and 14.93 mg/l respectively.

Phenolic compounds and E.Coli found absent

in water sample. Total coliform bacteria test

found positive in the March and October

month, In July month it found negative.

Water sample analysis results of Rabdapada

location shows the average pH value 7.07,

average colour value was 1.33 Hazen, Taste

and odour of water sample compliance with

the required specification for during purpose.

Evaluated average value of TDS, Turbidity,

Calcium and Magnesium were 244 mg/l, 1.57

NTU, 37.03 mg/l, 17.47 mg/l respectively.

Phenolic compound was found absent in

water sample. E.Coli and Total coliform

bacteria found absent in water sample.

In Mulgam bore well water sample shows the

average pH value 7.71, average colour value

3.3 Hazen, Taste and odor of water sample

were found suitable for drinking purpose.

Average Calcium and Magnesium value were

observed 46.17 mg/l and 2.7 mg/l

respectively. Phenolic compound test, E.Coli

and coliform test parameters found within the

limit.

Bore well water sample of Dadripada

location shows the pH value 6.8 and average

colour value 3.7 Hazen. Average value of

TDS, Turbidity, Calcium and Magnesium

were observed 213 mg/l, 1.16 NTU, 29.3

mg/l, 13.93 mg/l respectively. Phenolic

compounds were found absent in water

sample.

S. No Test Parameter Unit Specification Limit Method 1 pH Value NA 6.5 to 8.5 IS3025(Part-11) 2 Colour Hazen 5 Max. IS3025(Part-4) 3 Taste NA Agreeable IS3025(Part-7&8) 4 Odor NA Agreeable IS3025(Part-5) 5 Total Dissolved Solids mg/L 500 Max. IS3025(Part-16) 6 Turbidity NTU 1 Max. APHA 2130 B 7 Calcium as Ca mg/L 75 Max. IS3025(Part-40) 8 Magnesium as Mg mg/L 30 Max. IS3025(Part-46) 9 Phenolic Compound

mg/L 0.001 Max. IS3025(Part-43) 10 E.Coli /100 ml Absent IS1622:1981Edi.2.4(2003-05) 11 Total Coliform Bacteria /100 ml Absent APHA(22ndEdi) 9221-D Table 2: Specification and Test Method S. No Test Parameter Unit March 2016 July 2016 October 2016 Average Result 1 pH Value NA 7.1 6.7 6.9 6.90 2 Colour Hazen 2 1 1 1.33 3 Taste NA Agreeable Agreeable Agreeable Agreeable 4 Odor NA Agreeable Agreeable Agreeable Agreeable 5 Total Dissolved Solids mg/L 306 193 244 247.67 6 Turbidity NTU 0.91 0.82 0.98 0.90 7 Calcium as Ca mg/L 42 30 34 35.33 8 Magnesium as Mg mg/L 19 10 12 13.67 9 Phenolic Compound mg/L ND ND ND ND 10 E.Coli /100 ml Absent Absent Absent Absent 11 Total Coliform Bacteria /100 ml Present Absent Present Present Table 3: Bore water analysis results of Khoripada Location

Location No Name of Location GPS 1 Khoripada N 20° 6' 39" E 73° 7' 20" 2 Patelpada N 20° 6' 46" E 73° 7' 22" 3 Rabadpada N 20° 6' 37" E 73° 7' 29" 4 Mulgam N 20° 6' 53" E 73° 7' 37" 5 Dadripada N 20° 6' 35" E 73° 8' 4" Table 1: Sampling location and GPS Identification


51

S. No Test Parameter Unit March 2016 July 2016 October 2016 Average Result 1 pH Value NA 7.1 6.9 7.01 7.00 2 Colour Hazen 2 1 2 1.67 3 Taste NA Agreeable Agreeable Agreeable Agreeable 4 Odor NA Agreeable Agreeable Agreeable Agreeable 5 Total Dissolved Solids mg/L 291 227 258 258.7 6 Turbidity NTU 1.71 1.28 1.67 1.55 7 Calcium as Ca mg/L 42.6 30.1 34.4 35.70 8 Magnesium as Mg mg/L 19.25 10.66 14.88 14.93 9 Phenolic Compound mg/L ND ND ND ND 10 E.Coli /100 ml Absent Absent Absent Absent 11 Total Coliform Bacteria /100 ml Present Absent Present Present Table 4: Bore water analysis results of Patelpada Location S. No Test Parameter Unit March 2016 July 2016 October 2016 Average Result 1 pH Value NA 7.3 6.9 7 7.07 2 Colour Hazen 2 1 1 1.33 3 Taste NA Agreeable Agreeable Agreeable Agreeable 4 Odor NA Agreeable Agreeable Agreeable Agreeable 5 Total Dissolved Solids mg/L 273 207 253 244.33 6 Turbidity NTU 1.67 1.41 1.62 1.57 7 Calcium as Ca mg/L 46.2 30.1 34.8 37.03 8 Magnesium as Mg mg/L 22.6 13.5 16.32 17.47 9 Phenolic Compound mg/L ND ND ND ND 10 E.Coli /100 ml Absent Absent Absent Absent 11 Total Coliform Bacteria /100 ml Absent Absent Absent Absent Table 5: Bore water analysis results of Rabadpada Location S. No Test Parameter Unit March 2016 July 2016 October 2016 Average Result 1 pH Value NA 7.9 7.4 7.83 7.71 2 Colour Hazen 4 2 4 3.33 3 Taste NA Agreeable Agreeable Agreeable Agreeable 4 Odor NA Agreeable Agreeable Agreeable Agreeable 5 Total Dissolved Solids mg/L 277 232 258 255.67 6 Turbidity NTU 0.54 0.39 0.48 0.47 7 Calcium as Ca mg/L 52.3 39.4 46.8 46.167 8 Magnesium as Mg mg/L 3.21 2.01 2.88 2.7 9 Phenolic Compound mg/L ND ND ND ND 10 E.Coli /100 ml Absent Absent Absent Absent 11 Total Coliform Bacteria /100 ml Absent Absent Absent Absent Table 6: Bore water analysis results of Mulgam Location S. No Test Parameter Unit March 2016 July 2016 October 2016 Average Result 1 pH Value NA 6.8 6.9 6.6 6.767 2 Colour Hazen 4 3 4 3.67 3 Taste NA Agreeable Agreeable Agreeable Agreeable 4 Odor NA Agreeable Agreeable Agreeable Agreeable 5 Total Dissolved Solids mg/L 236 196 208 213.33 6 Turbidity NTU 1.28 0.96 1.23 1.16 7 Calcium as Ca mg/L 36.9 21.4 29.6 29.3 8 Magnesium as Mg mg/L 17.62 10.72 13.44 13.93 9 Phenolic Compound mg/L ND ND ND ND 10 E.Coli /100 ml Absent Absent Absent Absent 11 Total Coliform Bacteria /100 ml Present Absent Present Present Table 7: Bore water analysis results of Dadripada Location

Conclusion

All the test parameters of Khoripada bore

well water found within the limit, Hence, it

can be concluded that bore well water at this

location can be used for human consumption

but more water parameters need to be study

for determination for other contamination.

Water sample of Patel pada location obtained

high Turbidity value and it is above

specification limits. Total coliform bacteria


52

test was found positive in water sample.

Hence, it is advisable that water treatment is

needed at this location to reduce the turbidity

and biological contamination in water. Bore

well water sample of Rabadapada location

shows the high turbidity value and it is found

higher than the specification limit and other

chemical and biological parameters found

within the specification limits. Bore well

water sample test results of Mulgam location

compliance with the specification limit and it

can be used for human consumption. Bore

well sample of Dadripada location shows the

high Turbidity value, also Total coliform

bacteria test was found positive. Hence, it is

highly recommended to obtain a water

treatment before human consumption.

References

APHA, Standard method for Examination of

Water and waste water, American

Public Health Association, 22nd

Edition.

Indian Standard, IS-3025 (Part 10) (1984):

Reaffirmed 2002, Methods of

Sampling and test for water and waste

water Turbidity analysis.




water for pH value analysis.

Indian standard, IS-3025 (Part 4) (1983):



water for Color analysis.




water for Odor analysis.




water for Taste Rating.


Reaffirmed 2003, Water and waste

water Method of Sampling and Test

For Calcium analysis.


Methods of Sampling and test for

water and waste water for iron

analysis.

Kumar, S. K. and Kumar, R. R. (2011):

Analysis of water quality parameters

of ground water near Ambattur

industrial area, Tamilndu, Indian

Journal of Science and Technology,

4: 5.

Maurya, P. M. and Qureshi, I. (2016): An

Analysis of Physico-chemical

parameters and water quality of

borewell from UdpurGelhawa

Village, Badlapurtehhsil, Jaunpur

(U.P.) International Journal of

Education and Science Research,

3(2): 29-32.

Maurya, P. M. and Singh, V. K. (2016): An

Analysis of Physico-chemical

parameters and water quality index of

Suraila Tall in Summer Season.

Centum Multi-Disciplinary Bi-

Annual Reasearch Journal, 9(2): 300-

308.

Maurya, P. M. (2016): Degradation of Water

Quality Due to Heavy Pollution in

Industrial Area of Sultanpur, Uttar

Pradesh. International Journal of

Chemical Studies, 4(4): 139-141.


53

ParithaBhanu, P.A. (2016): Characterization

of physic chemical parameters in

water samples of river Bhavani,

Biolife, 4(2): 333-336.

WHO. (2007): Water for Pharmaceutical

Use. In: Quality Assurance of

Pharmaceuticals: A compendium of

Guidelines and Related Material. 2nd

edn. World Health Organization,

Geneva. 2: 170-187.

.

Singh et al./VIII: Special Edition: 2: 2017/54 – 61

54

Evaluation of Anti Stress Effects of Nardostachys Jatamansi Dc Root Extract On Clinical Patients: A Psycological Estimation

Singh, Mamta1; Saxena, Garima2 and Arya, Shveta1


Abstract

Nardostachys Jatamansi DC is a reputed

ayurvedic herb and has been used in various

formulations. It is an effective drug and used

in the treatment of many diseases. The anti-

stress properties of Nardostachys jatamansi

extract were studied on male and female

registered patients of approximately similar

age groups, for cognitive disorders at local

ayurvedic hospital. Volunteers were grouped

according to their age, sex and educational

status (including both illiterate and literate).

These patient were divided into Control (C1)

Stress group (E1), Dose group (E2) and Stress

+Dose group (E3) and were subjected to

memory retention and recall test. Student t’

test was used to analyze the results. The study

demonstrated that N.jatamansi root extract

showed its anti-stress effect on drug treated

volunteers as compared to stressed.

This study was conducted to understand the

mechanism of Nardostachys jatamansi DC in

protection on loss of memory and cognition

deficit.

Keywords: Nardostachys jatamansi | Nervine

tonic | clinical study | brain monoamines

Introduction

Human body and its psychological personality

depend upon the various behavioural patterns.

Composite parameters of evaluating

personality, such as memory retention, also

include hyperactivity, short attention span,

impulsive and explosive behaviour, erratic

task performance and poor social adjustments,

lack of concentration etc. are very important

variables for a developed personality.

Memory is a cognitive process involving

collection, analysis, encoding, storage and

retrieval of information as and when required

(Kaplan and Sadock, 1995). The key role

Played by hippocampus in memory functions

has been well documented in the past




For Correspondence: 1Deptt. of Zoology, K. L. Mehta Dayanand College for Women, Faridabad H 2Deptt. of Zoology, Pacific College of Basic and Applied sciences, PAHER University, Udaipur, Rajasthan Email: [email protected]


55

(Rawlins, 1985; Squire, 1987 and Sutherland

and Rodriguez, 1989).

A reduction in the hippocampal volume and

cell numbers has been reported in animal

models of aging, depression, and

alcoholism—conditions that have all been

associated with memory loss in humans(Heine

et al., 2004; Herrera et al., 2003).Chronic

stress is associated with hippocampus-

dependent learning and memory impairments

have been reported in animals subjected to 6 h

of physical restraint each day for 21

days(Sunanda, et al., 2000). Behavioral

deficits in animal models of chronic stress

have been associated with loss of

hippocampal neurons, dendritic atrophy, and

increase in dendritic spines and excrescences

(Sunanda, et al., 1995; Uno et al., 1989) and

Such models therefore provide an useful

approach to study the pharmacological

potential of agents in preventing/reversing

stress-related cognitive impairments.

In Ayurveda there are so many drugs that are

used as memory and intelligence enhancers.

Some of them are as Guduchi (Tinospora

cordifolia), Jyothishmati (Celastrus

panniculata), Shankhapushpi (Evolvulus

alsenoids), Brahmi (Baccopa monniera),

Ashwagandha (Withania somnifera).

The plant Nardostachys jatamansi DC of

family Valerianaceae is a well-known plant in

the Indian traditional medicinal system and

has historically been used in Ayurveda as

Medhya (Brain tonic), Rasayana

(Rejuvenative to the mind), Nidrajnana

(Promotes sleep) and Manasrogaghna

(Alleviates mental diseases) (Pandey, 1991;

Sharma et al., 2001). N. jatamansi DC quickly

relieves from psychosis, maniac psychosis,

syncope and hysteria (Hamied et al., 1962),

antiparkinsonism (Ahmad et al., 2006), it

improves memory and reduces forgetfulness.

It acts as memory restorative agent in people

with memory loss. (Vinutha, 2007; Joshi and

Parle, 2006) and antidepressant (Habibur

Rahman and Muralidharan, 2010).

Neuropharmacological profile of jatamansone

was studied by Arora, (1965b), which is the

active ingredient of Nardostachys jatamansi,

reduced aggressiveness, restlessness.

Children with marked mental retardation

showed corresponding improvement in I.Q.

were also noted (Gupta and Virmani 1968).

Habibur Rahman et al. (2010) have also

concluded that methanolic extract of N.

jatamansi DC possesses protective activity

from the loss of memory and cognition

deficits. Karkada et al. (2012) also described

efficacy of Nardostachys jatamansi in the

prevention of stress induced memory deficit.

Although these studies give significant results

that N.jatamansi work as nervine tonic and

have memory enhancing properties but there

is no evidence of human testing. This study

was conducted to understand the mechanism

of Nardostachys jatamansi DC in protection

on loss of memory and cognition deficit

clinically.

Material and Methods

The study was conducted on 16 clinical out

patients for known stress related disorders

such as proved and known cases of cognitive

disorders, depression etc, at local Ayurveda

hospital with prior permission of the

authorities. For control studies, a group of

fifteen persons were selected by a pre-test for

the study and as per practitioner’s selection.

Healthy persons of both sexes and similar age

group were allowed to volunteer for the study.


56

Both control and patient volunteers were

explained related aspects of drug and test to be

conducted without explaining them the real

purpose of the tests prior to start of the

experiment. Test was conducted everyday

between 10 AM to 12 PM after one hour of

drug intake in all the four groups. Drug (N

Jatamansi) was given as per ayurvedic

practitioner’s prescription depending on the

patient’s condition. Persons of both sexes

were separately divided into Control (C1),

Diseased persons (stress group; E1), only

Dose group (E2) and Diseased persons

supplemented with dose (Stress + Dose group,

E3). Volunteers were grouped according to

their age, sex and educational status

(including both literate and illiterate). In each

group at least five volunteers selected were

illiterate.

Tests were designed keeping the educational

status of the volunteers with the help of

Department of Psychology, M.L.S.

University, Udaipur. Literate persons in all

groups were given test papers in Hindi or

English language according to their

preference. Illiterate persons in all groups

were given pictures of articles, animals, birds,

and specific scenes for testing memory status.

English and Hindi word list including nouns

were taken from paper of Yuile and Madigan,

(1969).

Mode of Test

Each volunteer in all control and experimental

groups was given a list of 20 words and made

to read aloud for 30 seconds one by one. The

person recalled these words in 1-minute time

period. Numbers of words recalled were

noted. After each trial a set of 10-15 words or

pictures were given as distracters, to avoid

learning word list. 5 trials were made to find

out the effect of repetition on long-term

retention. Percent value of word recall is

calculated for each person and each trial.

Mean value, standard deviation, standard error

of mean was calculated for all groups in both

sexes. Student‘t’ test was used to find out the

significance of the results. All values are

represented Mean ± SEM (N = 4).

Group Male Female

Control 77 ± 8.16 84 ± 7.40

Stressor Diseased (E1)

43 ± 4.18 +++

45 ± 3.14 +++

Only Dose (E2)

1st Week 72 ± 4.73 ***

75 ± 4.54 ***

2nd Week 72 ± 5.83 ***

80 ± 4.78 ***

3rd Week 78 ± 7.27 ***

80 ± 3.16 **

Stress or Disease + Dose (E3)

1st Week 53 ± 3.87 49± 4.10

2nd Week 58 ± 3.74 57 ± 3.46

3rd Week 67 ± 4.42 ***

66 ± 6.05 ***

Results

In control group, 4 volunteers of both sexes

were taken of near about same age groups. In

1st trail 50-60 % word recall was observed. In

2nd to 5th trial word recall percentage

increased gradually. First Trial in Males word

list recall was 50% it increased with increased

number of trails. In both male and female

word list recall was increased with each

successes trail.

In stressed or diseased group E1, Patient

suffering from varied disease and other

psychosocial stress related disorders including

epilepsy and depression. The word recall was

significant less (P< 0.05 in male and P<0.05

in Female) as Compared to Control group.


57

In E2 or only dose group the effect of drug

extract was evaluated to find out any

discrepancies due to drug intake. The

observed values suggest that after every week

of drug intake word recall values were found

to be significant in both male and female as

compared to stressed male and female.

In stress or disease + dose group (E3) same

patients were tested for the word list recall for

3 weeks. Analysis of the word recall

percentage for all the three weeks was done.

This observation showed that after first and

second week of drug intake both male and

female groups did not show any significant

increase in word recall, but after third day of

drug intake both male and female showed

significant increase (P<. 05) in memory level

as compared to stressed group. Data are

summarized in figure 1-3.

FIGURE 1: Effect of various treatments on word recall percentage after 1st week. Values are expressed as mean ±SEM and significance is obtained from student t-test showing p<0.01=++; p<0.05=+++ as compared to stress; p<0.01=**;p<0.05=*** as compared to stress group.

FIGURE 2: Effect of various treatments on word recall percentage after 2nd week. Values are expressed as mean ±SEM and significance is obtained from student t-test showing p<0.01=++;p<0.05=+++ as compared to stress;p<0.01=**;p<0.05=*** as compared to stress group.

0102030405060708090

100

Control Stress orDiseased

Only Dose Stress orDiseased + Dose

% o

f W

ord

s R

ecal

l

Groups

Male

Female

+++

***

+++

***

0102030405060708090

100



% o

f W

ord

s R

ecal

l

Groups

Male

Female

+++

***

+++

***


58

FIGURE 3: Effect of various treatments on word recall percentage after 3rd week. Values are expressed as mean ±SEM and significance is obtained from student t-test showing p<0.01=++; p<0.05=+++ as compared to stress; p<0.01=**;p<0.05=*** as compared to stress group.

Discussion

Clinical study in patients clearly demonstrates

neuroprotective effects of Nardostachys

jatamansi root extract. Study performed in

patients under strict control of a ayurvedic

physician has also shown significant

improvement in retention and recall of the

memory after treatment with the drug extract.

This inference is based on significant changes

observed i.e., gradual increase in score of the

drug treated diseased as compared to only

diseased and control volunteers from first

week to third week of the treatment with the

extract. Results of the study thus suggest

that N.Jatamansi root extract could be an

alternative treatment for cognitive

disturbances.

This study first time describes the

influence of Nardostachys jatamansi root

extract on a subjective trial in human

volunteers suffering from various cognitive

disorders such as memory deficit and memory

disturbances. Stress influences cognition in

both animals and humans is well established.

One of the symptoms of stress is

release of glucocorticoids (GC's;

Corticosterone in rats, Cortisol in humans).

Study in rodents has revealed that GCs

enhance or impairs performance dependent on

the specific memory type tested and on the

timing of the stress exposure, respectively

(Diamond et al., 1996; Lupien and McEwen,

1997; de Quervain et al., 1998; De Kloet et al.,

1999; Roozendaal, 2000). Experimental

studies in humans have repeatedly shown that

GC administration can interfere with

performance in working memory as well as

declarative memory tasks, (Newcomer et al.,

1999; Wolf et al., 2001) and delayed

recall of declarative material (De Quervain et

al., 2000). Memory impairing effects have

been also observed in young and elderly

subjects after exposure to psychosocial

laboratory stressors (de Quervain et al., 1998;

Kirschbaum et al., 1996; Wolf et al., 1999).

Prolonged treatment (several days) seems to

be needed in order for declarative learning

deficits to occur (Young et al., 1999; Wood

and Shors, 1998). Gender plays an important

role in the effects on cognition (Carlson and

Sherwin, 1999).

Other than GCs, stress decreases the level of

certain chemicals such as brain monoamines

(serotonin, norepinephrine, dopamine

0102030405060708090

100



% o

f W

ord

s R

ecal

l

Groups

Male Female

+++

******

+++

*****


59

noradrenaline, catecholamine, and GABA).

Scientific studies have found that N.jatamansi

increases the cerebral levels of GABA

(GAMMA-AMINOBUTYRIC ACID). N.

Jatamansi is likely to reduce depression by

increasing the levels of monoamines in the

brain (Dhingra and Goyal, 2008). In their

study Prabhu V et al., (1994) also Studied the

effect of acute and sub chronic administration

of alcoholic extract of the roots of N.

jatamansi DC on nor epinephrine (NE),

dopamine (DA), serotonin (5-HT),

5hydroxyindoleacetic acid (5-HIAA),

gamma-amino butyric acid (GABA) on male

albino Wistar rats. A significant increase in

the level of GABA was observed in the drug-

treated groups when compared to the controls.

A 15-day treatment resulted in a significant

increase in the levels of NE, DA, 5-HT, 5-

HIAA, and GABA. Jai Prakash and Md

Nazmul (2015) also demonstrated that

probably the active ingredients Jatamansone

from Nardostachys jatamansi regulates the

metabolic degradation of serotonin,

norepinephrine, dopamine and other

endogenous amines in CNS through

interaction with GABA ergic receptors and

help to increase their level.

These data indicate that alcoholic extract of

the roots of Nardostachys jatamansi causes an

overall increase in the levels of central

monoamines. In the present study it was also

observed that the females show less

effect of stress on short term memory than

males.

Conclusion

From the above results of the present study, it

is concluded that Nardostachys Jatamansi act

as nervine tonic, its de-stressing effects

stimulates the healthy nervine functions and

help combating cognitive performance,

learning, memory and other age related

neurodegenerative disorders.

References

Ahmad, M.; Yousuf, S.; Khan, Badruzzaman;

Hoda, N.; Ahmad, M.A.; Ishrat, T.;

Agarwal, A.K.; and Islam, F. (2006):

Attenuation by Nardostachys jatamansi

of 6-hydroxydopamine-induced

parkinsonism in rats: behavioral,

neurochemical, and immune-

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62

Biodiversity conservation, threats and their global impact

Maurya, Pradip K. and Yadav, Anuj Kumar

Received: September 11, 2017Accepted: October20, 2017Online: December 31, 2017

Introduction

Biodiversity means variation in different

species found within the world. The word

biodiversity is commonly used by scientist,

environmental organizations, industrialist and

economist. The term biodiversity was

discovered by W.G. Rosen in 1985.

According to Rio Convention on

Biodiversity, 1992 (Article-2), it can be

defined as: “Biological diversity means the

variability among living organisms from all

sources, including, interalias, terrestrial,

marine and other aquatic ecosystems and the

ecological complexes of which they are part;

this includes diversity within the species,

between species and ecosystem”. The

different groups of biodiversity are classified

as: grassland biodiversity, wetland

biodiversity, agrobiodiversity, desert

biodiversity, microbial diversity, genetic

biodiversity, and species biodiversity.

The concept of biodiversity (synonyms with

biological diversity) has been known to man

ever since he began to minutely observe the

living being around him. The term biological

diversity was used by Robert E. Jenkins and

Thomas Lovejoy in 1980. The word

biodiversity itself may have been coined by

W. G. Rosen in 1985. The term biodiversity

was used as the title for a symposium

organized by national Research council in

Washington in 1986. At about that time, as

people became more aware of the extinction

crisis, biodiversity emerged as a significant

issue. It was given concrete expression in the

World Resources Institute (WRI), World

Bank (WB), International Union of Nature

and Natural Resources (IUCN) and World

Wide Fund for Nature (WWF) publications

concerned with conservation of world’s

biological diversity. However, biodiversity

did not became a familiar term to general

public until the United Nations Conference

on the Environmental and Development

(UNCED) held at Rio de Janerio (Brazil) in

1992. The Conference laid immense stress on




For Correspondence: Department of Zoology& Environmental Science, GurukulaKangriVishwavidyalaya, Haridwar, Uttarakhand Email: [email protected]


63

the biological diversity of our earth planet

and the need to preserve it for posterity. It

defined the biodiversity: ‘Biodiversity means

the variability among living organisms from

all sources including, inter alia, terrestrial,

marine and other aquatic ecosystems and the

ecological complexes of which they are part;

this includes diversity within species,

between species and of ecosystems.’This is

the single legally accepted definition of

biodiversity adopted by the UN convention

on Biological Diversity.

The most straight forward definition of

biodiversity is the variation of life at all

levels of biological organization. It includes

diversity of forms right from the molecular

unit to the individual organism, and then on

to the population, community, ecosystem,

landscape and biosphere levels. In the

simplest sense, biodiversity may be defined

as the sum total of species richness, i.e. the

number of species of plants, animals and

microorganisms occurring in a given region,

country, continent of the entire globe.

Broadly speaking, the term biodiversity

includes genetic diversity, species diversity,

ecosystem diversity and habit diversity.

Grassland biodiversity: Those areas that

contain flowers and non-woody plants.

Grasslands have high nutrient rich soil,

support perennial grasses and various types

of shrubs. In the soil, mites, insect,

nematodes, termites survive well. In the

savanna, grazing animals like antelope,

gazelles, zebra and rhinoceros but other

animals such as monkey, porcupine and

hedgehogs are also found (Freedman, 2009).

Wetland biodiversity: Wetlands are shallow

lakes, water meadows, marshes and bogs.

These are rich in animal diversity,

amphibians (e.g. leopard, frogs), reptiles

(alligator), birds (gees, muskrat). The plants

diversity classified in submerged plants

(bladderworts), floating leaved plants (water

lilies), and floating plants (water hyacinth),

emergent plants (common reed, shrubs and

trees)

Agrobiodiversity: Agrobiodiversity is

component of variety of animals and plants

and microorganisms. It includes diverse

crops, livestock, pests, parasites, predators

(Qualsetet al., 1995).

Biodiversity: Desert ecosystem

characterized by very low low rainfall (less

than 50 cm), aridity and low vegetation. The

animals in desrt biodiversity include insect,

snake, lizards, birds and rodents and in plants

contain cactus, with having small and thick

leaves and extensive root systems (Rechtman

2007).

Microbial diversity: Microbial diversity is

important aspect in the field of

biotechnology; it denotes the species of

bacteria, archea, eukarya. Microbes like

bacteria and fungus play significant role in

soil fertility.

Genetic diversity: Genetic diversity is

diversity among same species with having

variation in gene level. This diversity arises

due to mutation in genes in response of

environmental changes. The variety of

common wheat (Triticumaestivam) is grown,

but there can be different species of wheat on

the bases of colour, size, nutrient content and

grain quality.

Species diversity: This diversity is also

named as community diversity. This diversity


64

is the variety of species found within an area

(Newmark, 2013). Species diversity

measured at the single species (alpha

diversity), regional (gamma diversity),

intermediate alpha and gamma (beta

diversity). Species are important component

of diversity and each species play a vital role

in the ecosystem. The number of the species

is greater in that area will represent greater

species diversity.

Importance of Biodiversity

Biodiversity is importance for the survival of

the living creature on earth. It not supports

only human life but also maintain ecosystem

channels.

1) Ecological balance: The biodiversity

helps in the nitrogen, carbon, sulphur,

oxygen and water cycle. The interaction

among different communities and

organisms play a key role in the

stabilization of ecosystem. The species

diversity change affects the functioning of

ecosystem.

2) Raw material source: The rich

biodiversity provides variety of different

raw materials for manufacturing

industries. Essential oils, gum and resins,

tans and dyes, fibers, bamboos, saw dust,

bidi leaves, lac, silk and honey are

important raw material bases for small,

cottage and large industries.

3) Ecotourism: Biodiversity is a source of

recreational activities. The biodiversity is

also important as economical sources such

as national parks, wildlife sanctuaries,

biosphere reserves etc. Boating, fishing,

nature watching, and photography are

other opportunism in ecotourism growth

4) Climate change: Forest diversity (herb,

shrub and tree) absorb greenhouse gases

like carbon dioxide, methane, nitrous

oxide which helps in the regulation of

climate change. Some other pollutant

gases such as sulphur dioxide, ozone,

chlorinated gases are also sequestering by

forest biomass.

5) Aesthetic value: The aesthetic value is

also important because it is related with

cultural and natural beauty of plants. The

people pay money for visit spiritual and

religious plants.

Biodiversity on Earth:

There are 80,000 species of edible plants

known on Earth, but 90% of the world’s food

comes from a mere 20 of these species.

Edible plant species, both those we know of

and those we don’t, offer a tremendous

resource of possibilities that could greatly

add to the security of our food. How many of

these have high potential for commercial

exploitation and for feeding the hungry?

Certainly a great many. Breeding cultivars

with their wild counterparts can also confer

resistance to diseases and increase crop yield.

Medicines originating from wild species,

including penicillin, aspirin, taxol, and

quinine, have saved millions of lives and

alleviated tremendous suffering. 40% of all

prescriptions are for medicines that

originated from plants and animals. No one

knows how many more cures await

discovery, hidden in Earth’s poorly studied

species.

Types of biodiversity

Genetic diversity (Diversity of genes within

a species). Genetic diversity refers to the


65

variation of genes among the population and

the individuals of the same species. There are

about 1.7 million known species of living

forms on the earth. Each one stores an

immense amount of genetic information. For

example, the number of genes is ~35,000 in

Homo sapiens. Genetic variation within

species constitutes distinct populations of the

same species or genetic variation within

population or varieties. Genetic variations

represent the differences in the sequence of

bases in nucleotides, which constitutes the

genetic code. Genetic variations are due to

gene mutations, and in an organism with

sexual reproduction these can spread by

crossing-over and recombination. Other

kinds of genetic diversity can be seen at all

levels of organization, including the amount

of DNA per cell, chromosome structure and

their number. Genetic diversity provides the

raw materials for adaptation to changing

environment and for the natural selection to

act upon. If a species has more genetic

variability, it can adapt better off to the

changed environment. The amount of genetic

variation is the basis of the evolution of new

life forms (speciation). It has a key role in the

maintenance of biodiversity at species levels.

Species diversity (Diversity among species).

It refers to the variety of species within a

region, i.e. the number of species per unit

area at the site (species richness). An

estimated 1.7 million species have been

described to date. Species are the primary

focus of evolutionary mechanisms and

therefore the origin and evolution of species

are principle agents in maintenance of global

biodiversity.

Ecosystem diversity (Diversity at the level

of community/ecosystem).

In an ecosystem there may exist different

landforms, each of which supports different

but specific vegetation’s. Ecosystem

diversity in contrast to genetic and specific

diversity is difficult to assess quantitatively

since the boundaries of the communities,

which constitute the various sub-ecosystems

are elusive. Ecosystem diversity could best

have understood if one studies the

communities in various ecological niches

within the given ecosystem; each ecosystem

is associated with defined species complexes.

These complexes are related to composition

and structure of the ecosystem.

Habitat diversity

It involves more than just the kind of

communities and species- it depends on the

spatial arrangement of habitats across a large

and on the fluxes of energy, nutrients,

disturbances and organisms across the area.

Ecological use three different terms for

various practical measures of biodiversity:

Alpha diversity It refers to diversity within a

particular area, community or ecosystem, and

is measured by counting the number of texa

within the ecosystem (usually species).

Beta diversity It refers to species diversity

between ecosystems and is measured by

comparing the number of taxa that are unique

to each of the ecosystems.

Gamma diversity It is a measure of overall

diversity for different ecosystems within a

region. Species diversity in natural habitats is

a high in warm areas and decreases with

increasing latitude and altitude. On land,

diversity is higher in areas of higher rainfall


66

and lower in drier areas. Tropical moist

forests undoubtedly, are the richer areas.

These comprise only 7% of the world surface

area, but contain over 90% of all species. In

India we are endowed with a rich diversity of

biogeographically distinct regions due to

varying physical conditions and species

groupings.

Ecological role of biodiversity

All species provide some kind of function to

an ecosystem. They can capture and store

energy, produce organic material, decompose

organic material, help to recycle water and

nutrients throughout the ecosystem, control

erosion or pests, fix atmospheric gases, and

help regulate climate. These physiologically

processes are important for ecosystem

function and human survival. Diverse is the

ecosystem better able to withstand

environmental stress and consequently is

more productive. The loss of a species is thus

likely to decrease the ability of the system to

maintain itself or to recover from damage or

disturbance. Just like a species with high

genetic diversity, an ecosystem with high

biodiversity may have a greater chance of

adapting to environmental change. In other

words, the more species comprising an

ecosystem, the more stable the ecosystem is

likely to be.

Economic role of biodiversity

For all humans, biodiversity is first a

resource for daily life. One important part of

biodiversity is crop diversity, which is also

called agro biodiversity. Most people see

biodiversity as a reservoir of resources to be

drawn upon for the manufacture of food,

pharmaceutical, and cosmetic products. Some

of the important economic commodities that

biodiversity supplies to humankind are:

Modern agriculture: Biodiversity is used as

a source of material for breeding improved

varieties, and as bio pesticides, bio fertilizers

etc.

Food: Crops, livestock, forestry and fish.

Mangroves and coral reefs in coastal zone

support fisheries.

Medical drugs

Wild plant species have been used for

medicinal purposes since before the

beginning of recorded history. For example,

quinine comes from the cinchona tree (used

to treat malaria), digitalis from the foxglove

plant (chronic heart trouble), and morphine

from the poppy plant (pain relief). According

to the National Cancer Institute, over 70% of

the promising anticancer drugs come from

plants in the tropical rainforests. It is

estimated that of the 2,50,000 known plants

species, only 5,000 have been investigated

for possible medical applications.

Industry: Fibbers are used for clothing,

wood for shelter, energy and various other

uses. Biodiversity may be a source of energy

(such as biomass). Other industrial products

are oils, fragrances, dyes paper, waxes,

rubber, latexes, resins, poisons, and cork,

which all can be derived from various plant

species. Supplies from animal origin include

wool, silk, fur, leather, lubricants and waxes.

Animals may also be used as a mode of

transport.

Aesthetic and cultural benefits

Biodiversity has great aesthetic value.

Examples of aesthetic value include eco-


67

tourism, bird watching, wildlife, gardening,

etc. Eco-tourism is a source of economical

wealth for many areas, such as many parks

and forests, where wild nature and animals

are a source of beauty and joy for many

people. Biodiversity is also part of many

cultural and religious beliefs. In many Indian

villages and towns, plants like

Ocimumsanctum (Tulsi), Ficusreligiosa

(Pipal), and Prosopis cineraria (Khejri) and

various other trees are considered sacred and

worshipped by the people. Several birds,

animals and even snake have been considered

sacred. Also, we recognize several animals as

symbols of national and heritage.

Scientific role of biodiversity

Biodiversity is important because each

species can give scientists some clue as to

how the life evolved and will continue to

evolve on Earth. In addition, biodiversity

helps scientists understand how life functions

and the role of each species in sustaining

ecosystems. From above it is clear that the

survival and well being of the present day

human population, depends on several

substances obtained from plants and animals.

The nutritional needs of mankind are also

met by wild and domesticated animal and

plant species. Indeed, the biodiversity in wild

and domesticated form is the source for most

of humanity’s food, medicine, clothing and

housing, much of the cultural diversity, and

most of the intellectual and spiritual

inspiration. It is, without doubt, the very

basis of man’s being. It is believed that 1/4th

of the known biodiversity, which might be

useful to mankind in one way or the other, is

in serious risk of extinction. This calls for an

integrated approach for conserving global

biodiversity.

The threatened biodiversity

The loss of biological diversity is a global

crisis. There is hardly any region on the Earth

that is not facing ecological catastrophes. Of

the 1.7 million species known to inhibit the

Earth (human are just one of them), one third

to one fourth is likely to extinct within the

next few decades. Biological extinction has

been a natural phenomenon in geological

history. But the rate of extinction was

perhaps one species every 1000 years. But

man’s intervention has speeded up extinction

rates all the more. Between 1600 and 1500,

the rate of extinction went up to one species

every 10 years. It is estimated that about 50

species are being driven to extinction every

year, bulk of them in tropical forest, due to

human interference.

Listing of threatened biodiversity

To highlight the legal status of rare species

for the purpose of conservation, the

International Union for Conservation of

Nature and Natural Resources (IUCN) has

established the following five main

conservation categories:

Extinct species that are no longer known to

exist in the wild. Searches of localities where

they were once found and of other possible

sites have failed to detect the species.

Endangered species that have a high

likelihood of going extinct in the near future.

Vulnerable species that may become

endangered in the near future because

populations of the species are decreasing in

size throughout its range.


68

Rare species that have small total numbers

of individuals often due to limited

geographical ranges or low population

densities.

Insufficiently known species that probably

belong to one of the conservation categories

but are not sufficiently well known to be

assigned to a specific category. These

categories were named as Red list

categories. The IUCN Red List is the

catalogue of texa that are facing the risk of

extinction. This list aims to impart

information about the urgency and scale of

conservation problems to the public,

environmentalists and policy makers. On the

global level, the IUCN published Red Data

Book, name given to the book dealing with

threatened pants and animals of any region.

The IUCN, now known as World

Conservation Union (WCU), in 2001

recognized nine Red List Categories as

Extinct (Ex), Extinct in wild (EW), Critically

Endangered (CR), Endangered (EN),

Vulnerable (VU), Near Threatened (NT),

Least Concern (LC), Data Deficient (DD)

and Not Evaluated (NE). The main purpose

of the IUCN RED List is to catalogue and

highlight those texa that are facing a higher

risk of global extinction (i.e. those listed

Critically Endangered, Endangered and

Vulnerable.

Reasons for extinction of Biodiversity

Destruction of habitat

The natural habitat may be destroyed by man

for his settlement, grazing grounds,

agriculture, mining, industries, highway

construction, drainage, dam building, etc. as

a consequence of this, the species must adapt

to the changes, move elsewhere or may

succumb to predation, starvation or disease

and eventually die. This is the most pervasive

threat to birds, mammals and plants affecting

89% of all threatened birds, 83% of the

threatened animals assessed. In our country,

several rare butterfly species are facing

extinction with the uncannily swift habitat

destruction of the Western Ghats. Of the 370

butterfly species available in the Ghats, up to

70 are at the brink of extinction.

Hunting

From time immemorial, man has hunted for

food. Commercially, wild animals are hunted

for their products such as hide and skin, tusk,

antlers, fur meat, pharmaceuticals, perfumes,

cosmetics and decoration purposes. For

example, in India, rhino is hunted for its

horns, tigers for bones and skin, musk deer

for musk (have medicinal value), elephant for

ivory, gharial and crocodile for their skin,

and jackal for thriving fur trade in Kashmir.

One of the most publicized commercial hunts

in that of whale. The whalebone or ‘baleen’

is used to make combs and other products.

Poaching of the Indian tiger has been risen

because of the increasing demand from

pharmaceutical industries, which consume

the bones of 100 tigers per year. Such huge

demand has been mat by poachers from

India. Even the Project tiger Programme

failed to check poaching and resultantly the

tigers have been almost disappeared from

Ranthambore and Keoladeo national parks.

Smuggling of tiger bones and skins is a

lucrative business. Hunting for sport is also a

factor for loss of wild animals.


69

Over exploitation

This is one of the main cause of the loss of

not only economic species but also biological

ciriosities like the insectivorous and primitive

species and other taxa needed for teaching or

laboratory (like Nepenthes, Gnetum,

Psilotum, etc.). commercial exploitation of

wild plants has invariably causes their

overuse and eventual destruction. This has

been true in case of Indian wild mango trees,

which were turned into plywood as of the

whales that were hunted for tallow. Plants of

medicinal value like Podophyllum

hexandrum, Coptisteeta, Aconitum,

Disocoreadeltoidea, Rauwolfiaserpentine,

Paphiopedilumdruryi, etc., and horticultural

plants like orchids and rhododendrons come

under the over-exploited category. Faunal

losses have been mainly because of over-

exploitation. For instance, excessive

harvesting of marine organisms such as fish,

mollusks, sea cows and sea turtles has

resulted in extinction of these animals.

Collection for zoo and research

Animals and plants are collected throughout

the world for zoo and biological laboratories

for study and research in science and

medicine. For example, primates such as

monkey and chimpanzees are sacrificed for

research as they have anatomical, genetic and

physiological similarities to human being.

Introduction of exotic species

Native species are subjected to competition

for food and space due to competition for

food and space due to introduction of exotic

species. For example, introduction of goats

and rabbits in the Pacific and Indian regions

has resulted in destruction of habitats of

several plants, birds and reptiles.

Control of pest and predators

Predator and pest control measures, generally

kill predators that are a component of

balanced ecosystem and may also

indiscriminately poison non-target species.

Pollution

The water pollution especially injurious to

the biotic components of estuary and coastal

ecosystem. Toxic wastes entering the water

bodies disturb the food chain, and so to the

aquatic ecosystems. Insecticides, pesticides,

sulphur dioxide, nitrogen oxides, acid rain,

ozone depletion and global warming too,

affect adversely the plant and animal species.

The impact of coastal pollution is also very

important, it is seen that coral reefs are being

threatened by pollution from industrialization

along the coast, oil transport and offshore

mining. Noise pollution is also the cause of

wildlife extinction. According to a study

Arctic whales are seen on the verge of

extinction as a result of increasing noise of

ships, particularly ice breakers and tankers.

Radiation effect on biodiversity

Radiation coming from solar rays like UV-B

is harmful for living organisms on earth.

There may be somatic and genetic damage

changes. UV-B radiation affects on crop,

aquatic and terrestrial organisms, human,

microorganisms and plants. Thinning of

ozone layer in stratosphere leads to radiation

hazards on earth. Terrestrial and aquatic both

organisms seriously exposed with eyes and

skin diseases. Planktons (Phytoplankton and

Zooplankton) are threatened due to over

exposure of UV radiation. Planktons are


70

source of food in aquatic ecosystem, food

chain in aquatic ecosystem.

Urban sprawl and biodiversity

Rapid rate of urbanization reduces forest land

cover which ultimately leads to lower species

richness at local and regional level. Urban

developments are often responsible for

introduce of exotic species like Parthenium

spp. Parthenium is toxic weed with having

allelopathic nature which reduce the growth

of nearby plants and affects biodiversity.

Construction of roads, river valley projects,

industrialization and railway network

expansion in forest areas disturb the forest

ecosystem. Human settlement promotes the

developments of exotic species due to high

disturbance. The demand of food raw

materials and recreational activities increases

the pressure on ecosystems.

Global worming

Climate change affects the living organisms

(plants and animals) respiration,

photosynthetic activity, metabolic rate, and

reproduction cycle and water consumption.

Behaviour changes like reproduction cycle,

nesting, and feeding habits are suspected to

alter. Rise in temperature makes many

biological communities habitat

defragmented. The climate change effects

rainfall patters, species diversity, and plants

growth. Rise in temperature and CO2

concentration is harmful for coral reef

diversity.

Deforestation

One of the main causes for the loss of

wildlife is population explosion and the

resultant deforestation. Deforestation mainly

results from population settlement, shifting

cultivation, development projects, demand

for fuel wood, demand of wood as a raw

material for many industries such as paper

and pulp, match, veneer and plywood,

furniture etc. In the Country, the current rate

of deforestation is 13,000 sq. km annually. If

this rate of deforestation continues, one can

imagine the ultimate fate of our forest and

biological richness. It is presumed that in

coming years, the global loss of biodiversity

from deforestation alone would be 100

species every day.

Other factors: Other ecological factors that

may also contribute to the extinction of

wildlife are as follows:

i. Distribution range – The smaller the range

of distribution, the greater the threat of

extinction.

ii. Degree of specialization – The more

specialized an organism is, the more

vulnerable it isto extinction.

iii. Position of the organism in the food chain

– The higher the position of the organism is

in food chain, the more susceptibility it

becomes.

iv. Reproductive rate – Large organisms tend

to produce fewer offspring at widely spaced

intervals.

v. Outbreaks of diseases – it is also one of the

major causes for the decline in wildlife

species.

vi. Loss of gene flow – The individuals of

plant and animal life may decline to the

significant levels as a result of loss of gene

flow.

vii. Substitution – During the process of

evolution an existing species may be replaced


71

by ecologically another one. In developing

counties like India, the development policies

and projects have rarely been sensitive to the

need for biodiversity conservation, and that

of the local communities. The government’s

failure to remove poverty and curb middle-

class consumerism has led conditions in

which sensible natural resources management

assumes low priority.

Biodiversity conservation

A) In-situ Conservation

This type of conservation includes

conservation of plant and animals in their

native ecosystems or in manmade ecosystem

where they naturally occur. This type of

conservation applies only to wild fauna and

flora and not to the domesticated animals and

plants because conservation is possible by

protection of population in nature. In situ

conservation aims at either enhancement of

existing populations or creation of self-

supported new populations via

reintroductions and translocations, using

sampled or propagated material. In-situ

conservation includes a system of protected

areas of different categories, e.g., National

Park, Sanctuaries, Biosphere Reserves,

Cultural Landscapes, and National

Monument etc. According to the World

Conservation Union, protected area is

defined as: "An area of land and/or sea

specially. dedicated to the protection and

maintenance of biological diversity and of

natural and associated cultural resources and

managed through legal or other effective

mean.

B) Ex-situ Conservation

Ex-situ conservation means conservation of

species (sample of genetic diversity),

particularly of endangered species away from

their natural habitat. It is done through

establishment of 'gene banks', which include

genetic resource centres, botanical gardens,

cultural collection and zoos etc. Ex situ

conservation needs biologically effective,

financially realistic and easy-to-use

guidelines that can be applied to a wide range

of situations. The development of such

guidelines must take into consideration basic

issues of conservation biology. Traditionally,

the germplasm sampled for ex situ collection

is supposed to represent potential adaptive

variation within a species.

C) Wild Life Conservation in India

The shocking death of many tigers and lions

due to a mysterious disease in our sanctuaries

has brought wildlife conservation policies

and their implementation into public focus.

India has a wide variety of wildlife, many of

them endangered, ranging from the snow

leopard in the Himalayas to the giant

Malabar squirrel in the rain forests of Kerala.

Wildlife conservation has been very much in

forefront of government policy and India is a

signatory to the Convention on International

Trade in Endangered Species (CITES).

Enforcement of wildlife protection is done

under the Wildlife Protection Act, 1972. The

Indian Board for Wildlife (IBWL) is the apex

advisory body in the field of wildlife

conservation in the country and is headed by

the Prime Minister. Indian wildlife is

protected in 107 zoos, 49 deer parks, 16

safari parks, 6 snake parks, 24 breeding


72

centres and 6 aquariums, besides of course 95

national parks and 500 sanctuaries. Forest

staff looks after anti-poaching activities,

habitat management and improvement.

Besides, there are also projects for the

flagship species like Project Tiger and

Project Elephant where the habitats are

maintained according to the requirements of

the flagships species like tiger or elephant.

What are some methods to conserve

biodiversity?

Biodiversity banking places a monetary value

on biodiversity. One example is the

Australian Native Vegetation Management

Framework. Gene banks are collections of

specimens and genetic material. Some banks

intend to reintroduce banked species to the

ecosystem (e.g. via tree nurseries). Reducing

and better targeting of pesticides allows more

species to survive in agricultural and

urbanized areas. Location-specific

approaches are less useful for protecting

migratory species. One approach is to create

wildlife corridors that correspond to the

animals' movements. National and other

boundaries can complicate corridor creation.

Strategies for Protection of Biodiversity at

the Landscape Level

Two major strategies for conservation of

biodiversity at the landscape level of analysis

have emerged over the last 20 years. One

strategy promoted by Conservation

International, International Union for the

Conservation of Nature and some other

international conservation organizations, is to

focus on “world hotspots of biodiversity”

defined as areas with larger numbers of

species and high species diversity.

Some Wild Life Projects In India

1) Project Tiger: 50 years ago, there were over

40,000 tigers in India. But poaching and

destruction of habitat had reduced the

number to just 1827 by 1972, making them

an endangered species. To protect the tigers

from extinction, the Government of India

started "Project Tiger" on April 1st, 1973.

Due to the success of the project the tiger

population has now grown to over 4,000.

2) Lion Project: In Gir forest of Gujarat started

in 1972.

3) Yak Research Centre 9 In Arunachal

Pradesh

4) Crocodile Breeding Project Started in 1975.

5) Himalayan Musk Deer Project AtKedarnath

in Uttar Pradesh.

6) Project Elephant Started in 1991

7) Snow Leopard Project at 12 reserves

throughout the Himalayas. Project Hanghul

at Dachigan sanctuary, Jammu and

Kashmir, started in 1970.

8) Project Hanghul at Dachigan sanctuary,

Jammu and Kashmir, started in 1970.

9) Rhino conservation Project at Dudhwal

National Park.

10) Manipur Brow Antlered Deer Project at

Keibul Lamjoa, since 1977.

Floral and Faunal diversity

India accounts about 2.4 % of world’s total

geographical area, India occupies 6.7 % of

the faunal diversity of the world. In faunal

diversity 96,423 insect species (Table 2)

India holds about 29,105 species of algae,

bryophytes, pteridophytes, gymnosperms. It


73

occupies 9.13 % of the world’s floral diversity in these groups (Table 1).

Plant group Number of species described World (estimated) India Percentage in India

Algae 40,800 7,244 17.75 Bryophytes 14,500 2,504 17.27

Pteridophytes 12,000 1,267 10.56 Gymnosperms 650 74 11.38 Angiosperms 250,000 17,926 7.17

Total 317,950 29,015 9.13 Source: BSI (2013) Table 1: Floral diversity of India

Taxonomic group Number of species World India Percentage in India Protista (Protozoa) 31,250 3500 11.20 Animalia 1,53,122` 13,033 8.51 Mesozoa 71 10 14.08 Porifera 5000 500 10.00 Cnidaria 10,105 1042 10.31 Ctenophora 100 12 12.00 Platyhelminthes 17,511 1,650 9.42 Rotifera 2500 330 13.20 Gastrotricha 3000 100 3.33 Kinorhyncha 100 10 10.00 Nematoda 30,028 2902 9.66 Acanthocephala 800 229 28.63 Sipuncula 145 35 24.14 Mollusca 66,535 5169 7.77 Echiura 127 43 33.86 Annelida 17,000 1000 5.88 Onychophora 100 1 1.00 Arthopoda 11,81,398 74,175 6.28 Crustacea 60,000 3549 5.91 Insecta 10,20,007 63,423 6.22 Arachnida 73,451 5850 7.96 Pycnogonida 600 17 2.83 Chilopoda 8000 101 1.26 Diplopoda 73,451 5850 7.96 Symphyla 120 4 3.33 Merostomata 4 2 50.00 Phoronida 11 3 27.27 Bryozoa (Ectoprocta) 4000 200 5.00 Entoprocta 60 10 16.67 Brachiopoda 300 3 1.00 Chaetognatha 111 30 27.03 Tardigrada 514 30 5.84 Echinodermata 6600 779 11.80 Hemichordata 120 12 10.00 Chordata 64,669 5,665 8.76 Protochordata 2106 119 5.65 Pisces 32,120 3,022 9.41 Amphibia 6771 342 5.05 Reptilia 9230 526 5.70 Aves 9026 1,233 13.66 Mammalia 5416 423 7.81 Total (Animalia) 13,99,189 92,873 6.64 Grand total (Protista+Animalia) 14,30,439 96,373 6.74 Source: ZSI (2014) Table. 2: Faunal diversity of India


74

Forest

The forests in India are spread over an area

of 692,027 km2, covering 21.05% of the

geographical area of the country. There are

16 major forest types and 251 sub-types (FSI

2011). According to National Forest Policy

(1952) about 33 percent of geographical area

should be under forest cover. However, the

present situation not fulfilled the criteria. The

forest cover of India is 21.05 percent which

is much below the average of 30.4 percent of

the world. The areas of different types of

forest found in India are shown in Table 3.

Class Area (Km)2 Percent of geographical area Forest cover

a) Very Dense forest 83,471 2.54 b) Moderately Dense Forest 3,20,736 9.76 c) Open forest 2,87,820 8.75

Total Forest Cover 6,92,027 21.05 Scrub 42,176 1.28 Total geographical area 3,2,87,263 100.00 Table. 3: Forest cover of India

Protected areas

The conservation of wide range of diversity

is necessary for ecological balance. National

Parks, Sanctuaries, Biosphere reserves and

other protected areas have been established.

At present in India, 103 National parks, 536

wildlife Sanctuaries, 67 conservation reserves

and 26 community reserves (Table 4)

Number Area (Km2) % Geographical area of India National Parks (NPs) 103 40500.13 1.23 Wildlife Sanctuaries 536 118005.33 3.59 Conservation Reserves 67 2349.38 0.07 Community Reserves 26 46.93 0.001

Source: www.wiienvis.nic.in/Database/Protected-Area-854.aspx.Wiienvis.nic.in /Database/ html /pages /forest cover map.html

Table. 4: Protected areas of India (As on 14 June, 2016)

References

Convention on Biological Diversity, June,

(1992): UNCED art 8(j) 1992,

UN.Doc. UNEP/Bio.

Div./N7/NC.514.

Freedman, J. (2009): Grasslands:sweeping

savannas, The rosen Publishing

group, 24.

Newmark, W. D. (2013): Conserving

biodiversity in East African Forests:

A study of the Eastern Arc

Mountains. Springer Sciences and

Business media, 200.

Rechtman, M. (2007): Cliff Study Solver:

Biology, chapter-15, 311-312.

Qualset, C. O.; McGuire, P. E. and Wargyrton,

M. L. (1995): Agrobiodiversity Key

to agriculture productivity. Calif

Agric 49(6): 46-49.

BSI (2013): Official communication from

Botanical Survey of India, Kolkata,

India.

ZSI (2014): Official communication from

Zoological Survey of India, Kolkata,

India.

Jain and Kaushik/VIII:Special Edition: 2: 2017/75–81

75

Cobalt(ii)-selective potentiometric electrode based on salen-base chelate

Jain, Shalabh Kumar and Kaushik, R.D.

Received: August 30, 2017Accepted: November 05, 2017Online: December 31, 2017

Abstract

A poly (vinyl chloride) based membrane

using N-(2-Hydroxybenzylidene)-N'-(2-

picolyl) ethylenediamine Cobalt (II)as an

ionophore(I) has been developed and explored

as selective potentiometric electrode for Co (II).

Various membranes have been prepared with

and without plasticizer viz., tris(2-ethylhexyl)

phosphate (TEHP), tri-n-butylphosphate

(TBP), di-octylphthalate (DOP),

chloronapthalene (CN) and anion excluder,

sodiumtetraphenylborate (NaTPB) were

studied in detail. The best performance was

observed for the membrane electrode having

a composition of I:PVC:NaTPB:CN as

3:140:2:80 (w/w, mg). The electrode exhibits

Nernstian response over a wide concentration

range of 7.9 × 10-6 to 1.0 × 10-1 M with a

slope of 30.0 mV per decade of concentration

between pH range 3.0-9.0. The slope and

working concentration range of membrane

electrode remain constant in a partially non-

aqueous medium having up to 25% (v/v) of

methanol and ethanol water mixtures. The

response time of the electrode is about 12 sec

and can be used for a period of up to ~3

month without any drift in potential. The

electrode revealed good selectivity over a

range of cations including alkali, alkaline

earth, heavy and transition metal ions. This

electrode was successfully applied for the

determination of cobalt ion in various

samples and could also be used as indicator

electrode in potentiometric titration.

Introduction

Cobalt is a natural and essential element for

plants, animals and human body having

relatively high concentration in liver, bone

and kidney [Singh, et al. (2009)]. It plays an

important role in the metabolism of iron and

synthesis of hemoglobin. In the field of

industrial chemistry, cobalt is widely used in

the paint and varnish industries, in ink as

drying agent, in the preparation of pigments

like cobalt blue and cobalt green in ground

coats for porcelain enamels, in lithium ion

battery electrodes and in electroplating

industry. Cobalt has both a beneficial and




For Correspondence: Department of Chemistry, GurukulaKangriVishwavidyalaya, Haridwar, Uttarakhand E-mail: [email protected]


76

harmful effects on human health. It is

beneficial in the form of vitamin B12 which

is required for the biosynthesis of porphyrins

ring and treatment of anaemia [Bianci, et al.

(1989)] while long term exposure of cobalt

may cause lung cancer [Mur, et al. (1987)].

The maximum dietary tolerable level of

cobalt is 10 mg L-1 [Venugopal, and Luckey,

(1979)] and higher consumption level could

causes heart effects, thyroid damage,

vomiting, diarrhea, high blood pressure, and

slow respiration. In view of toxicity and

widespread use of cobalt, it is important to

determine its concentration in environmental

samples.

A number of sophisticated methods have

been reported for cobalt ion detection

including atomic absorption spectrometry

(AAS), inductively coupled plasma atomic

emission spectrometry (ICP-AES),

inductively coupled plasmamass

spectrometry (ICP-MS), neutron activation

analysis, flow injection chemiluminscence

and fluorescence spectroscopy [Souza, and

Tarley, (2009); Steffan, and Vujicic, (1993);

Lagerström, et al. (2013); Garg, et al. (1993);

Li, et al. (2006); Yu, et al. (2014)]. These

methods provide accurate determination of

cobalt ion in sample but required large

infrastructure backup, are time consuming

and also required complicated sample

preparation. Ion selective electrodes (ISEs)

provides analytical procedures that overcome

the above drawbacks since they are fast,

convenient and require no sample

pretreatment and also suitable for online

analysis [Cosofret, and Buck, (1993)]. A

number of ISEs for estimation of cobalt

concentration have also been reported

[Gupta, et al. (2008); Kumar, and Shim,

(2009); Singh, et al. (2009); Singh, et al.

(2006); Ghiaci, et al. (2010); Isa, et al.

(2011); Mashhadizadeh, et al. (2002)]

however most of them electrode exhibit

limitation with regard to narrow working

concentration range, non-Nernstian potential

response, narrow pH range, and high response

time. Keeping this in view, in this article Co

(II) salen-based complex was immobilized in

PVC matrix to prepare the electrode for

sensing the cobalt ion. The results showed that

the electrode is optimal selective and sensitive

for cobalt ion.

Experimental: Reagent

N-(2-Hydroxybenzylidene)-N'-(2-picolyl)

ethylenediamine Cobalt (II) (I) fig. 1 was

synthesized using a reported method [Adam

and Moeller, (2004)]. High molecular weight

poly vinyl chloride (PVC), Aldrich (USA);

sodiumtetraphenylborate (NaTPB) and tri-n-

butylphosphate (TPB), BDH (England);

dioctylphthalate (DOP); Riedel (New Delhi,

India); tris-(2-ethylhexyl)phosphate (TEHP) and

chloronaphthalene (CN) from E-Merck

(Germeny) were used as obtained. Analytical

reagent grade tetrahydrofuran (THF), nitric acid

and sodium hydroxide were obtained from

Ranbaxy, India. The nitrate salts of all the cations

used were of analytical grade and used without

any further purification. Double distilled water

was used for the preparation of working

solutions of different concentration by diluting

stock solution of 0.1M concentration.

N

C N N CH

O

co

O

Fig. 1: Structure of N-(2-Hydroxybenzylidene)-N'-(2-picolyl) ethylenediamine Cobalt (II)


77

Preparation of membranes

The membrane was prepared using a reported

method [Craggs, et al. (1993)]. Homogenous

membranes of the metal salen-base complex

were prepared by mixing ionophore (I), anion

excluder (NaTPB), solvent mediators (TEHP,

TBP, DOP and CN) and PVC in THF (5-10 mL)

in their appropriate amount. The resulting

mixture was vigorously stirred with a glass rod

and after removing all air bubbles it was poured

in acrylic ring placed on a smooth glass plate.

THF was allowed to evaporate for 48h at room

temperature. A transparent membrane of 5mm

thickness was obtained which was then cut in to

circular disc and glued to one end of a pyrex

glass tube with araldite. The membrane thus

prepared was equilibrated for 2 days in

0.1MCo2+ solutions.

Apparatus and potential measurement

The potential measurement were carried out at

25±0.10C with a digital potentiometer (Model

5652A, ECIL, India) and Century Micro

Voltmeter (Model CVM 301, India) by setting

up the following cell assembly, employing

saturated calomel electrodes (SCE) as a reference

electrode

SCE / test solution/ membrane/ internal

solution, (0.1 M, Co2+) / SCE

The potentials were measured by varying the

concentration of Co (NO3)2 in test solution in the

range 1.0 × 10-6 to 1.0 × 10-1M. Standard Co

(NO3)2 solutions were obtained by gradual

dilution of 0.1 M Co (NO3)2 solution.

Result and discussion

Working concentration range and slope

Before starting any potentiometric study the

membrane was equilibrated with 0.1 M Co2+

solutions. It was found that equilibrium time

of two days was optimum as equilibrated

membranes gave reproducible results and no

drift in potential was observed. The potential

of the electrode set up having membrane

with or without plasticizers was determined

in the concentration range 1.0 × 10-6 to 1.0 ×

10-1M of Co2+ solution and plotted in fig.2.

The working concentration ranges and slopes

from the plots of different electrodes have been

determined and given in table.1. The electrode

no.1 having membrane without plasticizer

exhibits working concentration range of 3.0 ×

10-5 - 1.0 × 10-1M with a slope of 40 mV per

decade of concentration. The response time of

the electrode was found to be 35 s. The slope for

the membrane is non-Nernstain and the working

concentration range is narrow. An improvement

in the performance was attempted by the addition

of plasticizers to the membranes. The addition of

the plasticizers not only improves the workability

of the membranes but also contribute

significantly towards the improvement in the

working concentration range, stability and shelf

life of the electrode. Thus, different plasticizers

viz; TEHP, TBP, DOP and CN were added to

the membrane and the results obtained are also

given in Table.1. It was observed that the

addition of different plasticizers to the membrane

has affected the performance to different extent.

Of all the used plasticized membranes, the one

with CN plasticizer (electrode no.5) and having a

composition of (mg) of I: PVC: NaTPB: CN as

3:140:2:80 which gave the best performance

with respect to wide concentration range,

response time and near Nernstain slope, was

used for further studies.

Response and life time

The response time has been measured as the


78

time taken by the electrode to attain a steady

potential, and their values are included in

table 1. It can be seen from the table that the

response time of the electrode no.1 without a

plasticizer is very high (40s). However, with

the addition of plasticizers to the membranes

(numbers 2-5), the response time is

sufficiently reduced. Among all the electrode

prepared with different plasticizers, electrode

number 5 with the CN plasticizer improved

the response time to a maximum extent. This

electrode generates a stable and reproducible

potential within 12 s. To enhance stabilized

the membrane electrode was stored in 0.1 M

Co2+ solution. Since the electrode no.5

exhibited the best performance characteristic,

the same was chosen for further studies.

pH and solvent effect

In order to investigated the pH effect on the

potential response of the electrode, the

potential was measured at Co2+ concentration

of 1.0 × 10-3 and 1.0 × 10-4 M in working pH

range of 1.0-12.0 and the potential variation

as a function of pH is plotted in figure 4. It

can be seen that the potential is independent

in the pH range of 3.0-9.0. The performance

of the electrode was also investigated in a

partially non-aqueous media using 10%, 20%,

25% and 30% (v/v) in methanol/water and

ethanol/water (v/v) mixtures, and the results

are given in table 2. It can be seen from the

table that the electrode worked satisfactorily

up to 25% (v/v) non-aqueous content as the

working concentration range and slope remain

almost constant. However, above a 25% non-

aqueous content, the slope and working

concentration range are appreciably decreased

due to the leaching of ion at higher non-

aqueous content.

Fig.3: Effect of pH on cell potential; [Co2+] =1.0

10-3 and 1.0 10-4 M

Selectivity

The most important characteristic of any

electrode is its response to the primary ion in

the presence of other ions present in solution,

which is expressed in terms of the

potentiometric selectivity coefficients. In this

work, the selectivity coefficients of the

electrode towards different cationic species

(Mn+) were evaluated by using fixed

interference method (FIM) [Sa’ez de Viteri,

and Diamond, (1994)]. The selectivity

coefficients so calculated by this method are

summarized in Table 3. It showed that the

electrode exhibits selective response toward

Co2+. A value of selectivity coefficient equal

to 1.0 indicates that the membrane responds

equally to primary as well as interfering ion.

A value smaller than 1.0 indicates that it

responds more to primary ion than interfering

ion and in such a case the electrode is said to

be selective to primary ion over interfering

0 2 4 6 8 10 12

120

100

80

60

40 1x 10-3

1X 10-4

Cell p

ote

ntial (m

V)

pH

Non-aqueous content(% v/v)

Working activity range (M)

Slope (mV/decade of activity)

0 Methanol

10 20 25 30

Ethanol 10 20 25 30

7.9 × 10-6 - 1.0 × 10-1

7.9 × 10-6 - 1.0 × 10-1

7.9 × 10-6 - 1.0 × 10-1

8.1 × 10-6 - 1.0 × 10-1

1.0 × 10-5 - 1.0 × 10-1

7.9 × 10-6 - 1.0 × 10-1

7.8 × 10-6 - 1.0 × 10-1

8.3 × 10-6 - 1.0 × 10-1

1.6 × 10-5 - 1.0 × 10-1

30.0

30.0 29.8 29.0 25.7

30.0 30.0 29.5 25.0

Table 2:Performance of Co2+ -selective electrode no. 5 in non-aqueous media


79

ion. Further, smaller selectivity coefficient

value reflects higher selectivity order. From

table 3. revealed that the selectivity

coefficients are in order of 10-4 or lower for

almost all diverse ions tested. Thus, these ions

would not cause significant interference in the

estimation of Co2+ ions by this electrode

unless present in large amounts. Thus the

electrode can be used for the determination of

Co2+ by direct potentiometric method even in

presence of foreign ions.

Interfering ion (B)

Selectivity coefficient

(KPot

BCo ,2 )

Li+ 7.4 × 10-4

Na+ 7.6 × 10-4

K+ 8.3 × 10-4

Ca2+ 8.1 × 10-4

Ba2+ 8.5 × 10-4

Mg2+ 8.4 × 10-4

Sr2+ 8.2 × 10-4

Hg2+ 2.7 × 10-3

Cu2+ 9.2 × 10-4

Zn2+ 1.4 × 10-3

Cd2+ 1.2 × 10-3

Ni2+ 4.4 × 10-3

Pb2+ 2.4 × 10-3

Mn2+ 8.6 × 10-4 Table 3. Selectivity coefficients of electrode no. 5

as determined by fixed interference method (FIM)

Potentiometric titration

The utility of electrode has also been used

successfully to determine the end-point in the

potentiometric titration of Co2+ with EDTA.

A 25-mL sample of 1.0 × 10-3 M Co2+ was

titrated against EDTA (1.0 × 10-3 M) at pH

4.0 and the change in cell potential was noted

and plotted in fig.7. The titration plot is of

sigmoid shape, thus indicates that the

electrode is sufficiently selective for Co2+

and the end point corresponds to 1:1

stoichiometry of Co-EDTA complex. Thus,

the electrode can be used to determine cobalt

by potentiometric titration.

Fig. 4:Potentiometric titration curve of 25 mL of Co2+(1.0 10-

3M)with EDTASolution

Potentiometric titration

The utility of electrode has also been used

successfully to determine the end-point in the

potentiometric titration of Co2+ with EDTA.

A 25-mL sample of 1.0 × 10-3 M Co2+ was

titrated against EDTA (1.0 × 10-3 M) at pH

4.0 and the change in cell potential was noted

and plotted in fig.7. The titration plot is of

sigmoid shape, thus indicates that the

electrode is sufficiently selective for Co2+

and the end point corresponds to 1:1

stoichiometry of Co-EDTA complex. Thus,

the electrode can be used to determine cobalt

by potentiometric titration.

Conclusion

The result indicates that the membrane of

salen-base complex is sufficiently selective

for the Co2+ ion over a number of other ions.

Among membranes examined, the electrode

no.5 having membrane with composition of

I:PVC:NaTPB:CN as 3:140:2:80 exhibited

wide working concentration range of 7.9 ×

10-6 to 1.0 × 10-1M with near-Nernstian

slope of 30.0 mV per decade of

concentration. The electrode showed a lowest

response time of 12 s and operates in a pH


80

range 3.0-9.0 and also satisfactorily works in

partially non-aqueous media. The selectivity

studies of the electrode, evaluated with the

fixed interference method showed that the

electrode under consideration possesses

excellent selectively for Co2+over a large

number of alkali, alkaline earth and heavy

metals. Thus, the electrode can be used for

the determination of Co2+ in the presence of

various interferences ions by direct

potentiometry.

References

Singh, A. K. and Singh Bhattacharjee, P. G.

(2009): Determination of cobalt ions

at nano-level based on newly

synthesised pendant armed

macrocycle by polymeric membrane

and coated graphite electrode,

Talanta. 80: 685–693.

Bianci, C.; Bertanza, L.; Pietre, R. and

Sabbioni, E. (1989): Cobalt-induced

hypothyroidism, cardiomyopathy,

polycythemia and hypertricosis in an

infant, J. Trace Elem. Exp. Med. 2:

311-319.

Mur, J. M.; Moulin, J. J.; Charruyer-Seinerra,

M. P. and Lafitte, J. (1987): A cohort

mortality study among cobalt and

sodium workers in an electrochemical

plant, Am. J. Ind. Med. 11: 75-81.

Venugopal, B. T. and Luckey, D. (1979): A

Metal Toxicity in Mammals, Plenum

Press, New York.

Souza, J. M. O. and Tarley, C. R. T. (2009):

Sorbent separation and enrichment

method for cobalt ions determination

by graphite furnace atomic absorption

spectrometry in water and urine

samples using multiwall carbon

nanotubes, Int. J. Environ. Anal.

Chem. 89: 489–502.

Steffan, I. and Vujicic, G. (1993):

Determination of cobalt,

molybdenum and vanadium in

Austrian mineral waters by ICP-

AES after ion-exchange separation

and preconcentration, Mikrochim.

Acta. 110: 89–94.

Lagerström, M. E.; Field, M. P.; Séguret, M.;

Fischer, L.; Hann, S. and Sherrell,

R. M. (2013): Automated on-line

flow-injection ICP-MS

determination of trace metals (Mn,

Fe, Co, Ni, Cu and Zn) in open

ocean seawater: application to the

GEOTRACES program, Mar.

Chem. 155: 71–80.

Garg, A. N.; Weginwar, R. G. and Chutke, N.

L. (1993): Radiochemical neutron

activation analysis of Fe, Co, Zn, Sb

and Se in biomedical and

environmental samples, Sci. Total

Environ. 139: 421–430.

Li, B.; Wang, D.; Lv, J. and Zhang, Z.

(2006): Chemometrics-assisted

simultaneous determination of

cobalt(II) and chromium(III) with

flow-injection chemiluminescence

method, Spectrochim. Acta A 65:

67–72.

Zhang, X. P.; Liu, Q.; Li, Y.; Zhen, X.;

Zhang, Y. and Ma, Z. (2014): An

“off–on” fluorescent and

colorimetric probe bearing

fluorescein moiety for

Mg2+andCa2+ via a controlled


81

supramolecular approach, Mater.

Sci. Eng. C 39: 73–77.

Cosofret, V. V. and Buck, R. P. (1993):

Recent advances in pharmaceutical

analysis with potentiometric

membrane sensors, Crit. Rev. Anal.

Chem. 24: 1.

Gupta, V. K.; Jain, A. K.; AlKhayat, M. S.;

Bhargava, K. and Raisoni, J. R.

(2008): Electroanalytical studies on

cobalt(II) selective potentiometric

sensor based on bridge modified

calixarene in poly(vinyl chloride),

Electrochim. Acta 53: 5409–5414.

Kumar, P. and Shim, Y. B. (2009): A novel

cobalt(II)-selective potentiometric

sensor based on p- (4-n-

butylphenylazo)calix[4]arene,

Talanta 77: 1057–1062.

Singh, A. K.; Singh, P. and Bhattacharjee, G.

(2009): Determination of cobalt ions

at nano-level based on newly

synthesised pendant armed

macrocycle by polymeric membrane

and coated graphite electrode,

Talanta. 80: 685–693.

Singh, A. K.; Singh, R. P. and Saxena, P.

(2006): Cobalt(II)-selective

electrode based on a newly

synthesized macrocyclic compound,

Sensors Actuators B 114: 578–583.

Ghiaci, M.; Rezaei, B.; Sadeghi, Z. and

Safaei-Ghomi, J. (2010): A Co2+-

selective poly(vinyl chloride)

membrane electrode based on a

newly synthesized 3,3′-

(dodecylazanediyl)bis(N-(2-(2-

aminoethylamino)ethyl)propanamid

e) compound, J. Chem. Eng. Data

55: 2792–2798.

Isa, I. M.; Mustafar, S.; Ahmad, M.; Hashim,

N. and Ghani, S. A. (2011): Cobalt

(II) selective membrane electrode

based on palladium(II)

dichloroacetylthiophenefenchoneazi

ne,Talanta 87: 230–234.

Mashhadizadeh, M. H.; Momeni, A. and

Razavi, R. (2002): Cobalt (II)-

selective membrane electrode using a

recently synthesized mercapto

compound, Anal. Chim. Acta.

462:45–252.

Adam, W. and Moeller, C. R. (2004): A

convenient synthesis of nickel(II)

and cobalt(II) complexes of

unsymmetrical salen-type ligands

and their application as catalysts for

the oxidation of 2,6-dimethylphenol

and 1,5-dihydroxynaphthalene by

molecular oxygen, Ind. J. Chem. 43:

56-62.

Craggs, A.; Moody, G. J. and Thomas, J. D.

R. (1993): PVC matrix membrane

ion-selective electrodes.

Construction and laboratory

experiments, Pure Appl. Chem.

65: 1849-1858.

Sa’ez de Viteri, F. J. and Diamond, D.

(1994): Determination and application

of ion-selective electrode model

parameters using flow injection and

simplex optimization, Analyst 119:

749-758.

Arya et al./VIII: Special Edition: 2: 2017/82 – 87

82

Toxicological Effects of Lead On Certain Enzymological and Biochemical Parameters in Cirrhina Mrigala

Arya, Shveta1; Singh, Jyotsna1 and Sharma, H. B.2

Received: August 31, 2017 Accepted: November 10, 2017 Online: December 31, 2017

Abstract

Lead is found to be the most toxic of heavy

metals so the present research is designed to

study toxicological effects of lead on certain

enzymological and biochemical parameters in

tissue and blood. Fish were exposed to

sublethal dose of lead chloride (PbCl2) for 40

days. The tissue extracts were taken and tested

for the activity of key enzymes of glycolysis

and Kreb’s cycle along with their biochemical

components. In blood, the level of glucose

increased by 115.38%. Pyruvic acid and lactic

acid decreased by 25.97% and 37.5%

respectively. In liver, the glycogen content

increased by 104.2%. The activity of glucose-

6- phosphatase increased in liver and gills.

Inhibition in the activity of LDH in liver and

muscle indicates decreased rate of glycolysis.

Elevation in the activity of PDH and SDH in

liver, gill and muscle shows that in fish the

rate of oxidative metabolism is increased to

withstand the toxic stress.

Introduction

Heavy metals are common persistent

pollutants of aquatic ecosystem entering them

through numerous diverse anthropogenic and

natural sources (Moore, 1991). Aquatic

systems are very sensitive to heavy metal

pollutants and the gradual increase in the level

of such metals in aquatic environment, mainly

due to anthropogenic sources, became a

problem of primary concern (Thirumvalan. R,

2010). This is due to their persistence, as they

are not usually eliminated either by

biodegradation or by chemical means, in

contrast to most organic pollutants.

Lead (Pb) is one of the most toxic of heavy

metals and its compounds are included in the

grey list of international conventions (Taylor

et al., 1985). Lead enters the aquatic

environment through erosion and leaching

from soil, lead-dust fall out, combustion of

gasoline, municipal and industrial waste

discharges, run-off water deposits from streets

and other surfaces as well as precipitation

(Department of Water and Forestry

(D.W.A.F.), 1996). Lead that is emitted into

the atmosphere can be inhaled, or it can be




For Correspondence: 1Department of Zoology, K. L. Mehta Dayanand College for Women, Faridabad, Haryana 2HOD, Department of Zoology, B.S.A.(PG) College, Mathura (UP); India Email: [email protected]


83

ingested after it settles out of the air. It is

rapidly absorbed into the bloodstream and is

believed to have adverse effects on the central

nervous system, cardiovascular system,

kidneys, and the immune system (Bergeson,

Lynn L, 2008).

Chemical changes disturb the equilibrium

(homeostasis) of ecosystems and thus prevent

their normal functioning. In polluted water

bodies, concentrations of compounds

containing both essential metals and those

playing no part in an organism’s functioning

(lead) may increase to toxic levels (Jezierska

and Witeska 2001). The above affect

individual development of plants and animals.

Among animal species, fish are the inhabitants

that cannot escape from the detrimental

effects of these pollutants (Olaifa et al., 2004;

Clarkson, 1998; Dickman and Leung, 1998).

Fish are widely used to evaluate the health of

aquatic ecosystems because pollutants build

up in the food chain and are responsible for

adverse effects and death in the aquatic

systems (Farkas et al., 2002; Yousuf and El-

Shahawi, 1999). So an attempt has been made

in the present research to study the effect of

heavy metal lead (Pb) on certain physiological

activities and biochemical parameters in tissue

and blood.

Materials and Method

The fish, Cirrhina mrigala were purchased

from the local fish market having an average

length 12±3 cm and wt 200±2 gms The fish

were then kept in different aquaria for

conduction of various experiments. The fish

were acclimatized to laboratory conditions in

aquaria for a few days. In one aquarium the

fish were kept as control specimens given the

same food and environment as that of the

experimental fish except that they were not

given the dose of heavy metal compound.

Inorganic salt of heavy metal lead namely lead

chloride anhydrous (PbCl2) was the

experimental toxicant. To observe the chronic

effects of lead, sublethal dose (1/10

concentration of 96 hr LC50) of the heavy

metal compound was given for 40 days.

During exposure period the fish were

conditioned to feeding on packed fish food at

the rate of 2% of body weight. The fish were

fed once daily at 11am.

For the estimation of activities of enzymes of

glycolysis and kreb’s cycle, 10% of W/V

homogenates were prepared in 0.25 M sucrose

solution for tissues namely liver, gills and

muscles. The homogenates were refrigerated

in cold at 1000x g for 20 min. and clear

supernatant fluids were used as the source of

enzymes. The activities of Lactate

Dehydrogenase (LDH), Pyruvate

Dehydrogenase (PDH), Succinate

Dehydrogenase (SDH) enzymes were

estimated by the triphenyltetrazolium chloride

method of Srikantan and Krishnamoorthi

(1995). The activity of Glucose-6-

Phosphatase was determined by adopting the

method of Swanson (1955). Glycogen in liver

was estimated by the method of Hassid and

Abrahim (1957). The method of Lowery et al.

(1951) was adopted for the estimation of total

proteins.

Blood from the caudal vessel of both control

and experimental fish was drawn with the help

of heparinized needles for the estimation of

the levels of glucose, lactic acid and pyruvic

acid. Glucose was determined by the method

of Folin and Wu (1929). Pyruvic acid was

determined by the method of Friedmann and

Haugen (1944). Lactic acid in blood was


84

estimated according to the method of Barker

(1963). The significance of the difference

between control and experimental means was

calculated by Students‘t’ test (Wardlaw,

1985)

Observation and Results

Enzymological Studies

LIVER

The activity of Glucose-6-Phosphatase

increased by 38.91% after 40 days of

exposure. The activity of lactate

dehydrogenase (LDH) decreased after 40 days

of exposure (39.45%). Increase was observed

in the activity of pyruvate dehydrogenase

(PDH) and succinate dehydrogenase (SDH)

after 40 days of exposure by 103.5% and

40.47% respectively.

GILLS

Increase in the activity of Glucose-6-

phosphatase was observed after 40 days

(9.27%). The activity of lactate

dehydrogenase decreased after 40 days of

exposure. However, increase of 27.78% was

recorded in the activity of pyruvate

dehydrogenase (PDH) after 40 days of

exposure. The activity of succinate

dehydrogenase (SDH) increased after 40 days

(41.49%).

Muscle

Decrease was observed in the activity of

lactate dehydrogenase (LDH) after 40 days of

exposure. However, no significant change was

observed in the activity of PDH during

exposure period. Increase by 40.13% was

observed in the activity of Succinate

dehydrogenase (SDH) after 40 days of

exposure.

Biochemical Studies

Blood

The blood glucose level increased after 40

days of exposure by 115.38%. The fish were

hypolectemic after chronic exposure to lead.

Pyruvic acid level decreased in the blood after

40 days of exposure by 25.97%.

Liver

On chronic exposure to lead, glycogen content

of liver increased by 104.2%.

ENZYMES CONTROL 40 DAYS GLUCOSE-6- PHOSPHATASE a 27.72±0.05 30.29±0.01*** LACTATE DEHYDROGENASE b 4.87±0.02 4.73±0.08*

PYRUVATE DEHYDROGENASE b 3.73±0.04 5.60±0.04*** SUCCINATE DEHYDROGENASE b 6.49±0.01 8.66±0.05***

Values are mean±SD; n=6, NS= not significant *significant, *p<0.05, **p<0.01, ***p<0.001 aµg inorganic phosphate/mg Protein/hr bµg formazon/mg Protein/hr

Table-2: Alterations in Gill Enzyme Activities In Cirrhina Mrigala Exposed To Lead (Pb) For 40 Days

ENZYMES CONTROL 40 DAYS GLUCOSE-6- PHOSPHATASE a 233.39±0.02 324.22±0.04*** LACTATE DEHYDROGENASE b 4.79±0.02 2.90±0.03***

PYRUVATE DEHYDROGENASE b 3.14±0.04 6.39±0.02*** SUCCINATE DEHYDROGENASE b 5.09±0.01 7.15±0.04***

Values are mean±SD; n=6, NS= not significant *significant, *p<0.05, **p<0.01, ***p<0.001 aµg inorganic phosphate/mg Protein/hr bµg formazon/mg Protein/hr

Table-1: Alterations In Liver Enzyme Activities In Cirrhina Mrigala Exposed To Lead (Pb) For 40 Days


85

ENZYMES CONTROL 40 DAYS LACTATE

DEHYDROGENASE b 8.03±0.02 6.69±0.01***

PYRUVATE DEHYDROGENASE b 11.05±0.04 10.97 ± 0.07NS

SUCCINATE DEHYDROGENASE b 4.51±0.03 6.32±0.03*** Values are mean±SD; n=6, NS= not significant *significant, *p<0.05, **p<0.01, ***p<0.001

bµg formazon/mg Protein/hr

Table-3: Alterations In Muscle Enzyme Activities In Cirrhina Mrigala Exposed To Lead (Pb) For 40 Days

PARAMETERS CONTROL 40 DAYS BLOOD Glucose a 0.13±0.01 0.28±0.01*** Lactic Acid a 0.08±0.01 0.05±0.05** Pyruvic Acid a 0.77±0.03 0.57±0.03***

LIVER Glycogen b 0.70±0.05 1.43±0.07*** Values are mean±SD; n=6, NS= not significant *significant, *p<0.05, **p<0.01, ***p<0.001 a mg/ml of blood

b mg/gm wet weight of tissue

Table-4: Alterations In Biochemical Parameters In Cirrhina Mrigala Exposed To Lead (Pb) For 40 Days

Discussion

The toxic effects of heavy metals arise from

their action on biological systems. ‘Enzymes’

are the common targets of toxicants and

undergo marked alterations in their activities

with the period of exposure and tissue.

After 40 days of exposure, blood sugar level

increased showing increased rate of

glycogenolysis as the activity of G-6-Pase

also increases after 40 days but simultaneous

increase in the liver glycogen may be due to

the increased rate of glycogensis. Hinston et

al. (1983) reported an increase in the blood

glucose level in the fish Channa punctatus

exposed to pollutants. R. Vinodhini et al.

(2008) also observed increase in blood

glucose level in Cyprinus carpio after

exposure to mixture of heavy metals, lead

(Pb), cadmium (Cd), chromium (Cr) and

nickel (Ni) for 32 days. Patil and Dhande

(2000) also reported increased blood glucose

level in Channa punctatus exposed to

mercuric chloride. Almeida et al. (2001) also

found increase in blood glucose level in

Oreochromis mossambicus exposed to

cadmium.

Decrease in lactic acid content and increase in

the liver glycogen shows increased rate of

glycogenesis i.e. formation of liver glycogen

from lactic acid. Sastry et al. (1987), Sastry

and Sunita (1983) and Lowe Jinde and Niimi

(1984) reported decrease in lactic acid in the

liver of Heteropneustes fossilis and Channa

punctatus exposed to lead (Pb) and chromium

(Cr) respectively.

Decrease in the activity of lactate

dehydrogenase (LDH) in liver, gills and

muscle on chronic exposure to lead (Pb)

indicates decreased rate of glycolysis. Similar

decrease in LDH activity was noted in the

early developmental stages of African Catfish,

Clarias graiepines on exposure of lead nitrate

(Pb(NO3)2) (Alla G. M. Osman et al. 2006).

Singhal (1994) also observed decrease in

activity of lactate dehydrogenase (LDH) in

liver, gill, muscle and kidney of catfish

Hetropneustes fossilis on exposure to

sublethal concentrations of lead nitrate

(Pb(NO3)2).

Inhibition in the activity of LDH is evidenced

by decrease in the pyruvic acid level of blood

showing decreased rate of glycolysis. Similar


86

decrease in pyruvic acid was recorded by

Rajeshwari et al. (1990) in Clarias batrachus

on exposure to cadmium.

Oxidative metabolism prevailed in liver and

gills and muscles after 40 days of lead

exposure. The evidence in favour of this

comes from the increase noted in the activity

of PDH and SDH in liver, gill and muscle (40

days). Impairment of glycolysis and favouring

of oxidative metabolism shows that pyruvic

acid utilized in the oxidative metabolism was

derived from an alternative source. Similar

increase in the PDH and SDH activities was

observed in different tissues of Channa

punctatus exposed to chromium (Sastry and

Sunita, 1983). Increase in SDH activity was

noted on exposure of cadmium for 30 days in

Channa punctatus (Sastry et al., 1997).

Conclusion

Increase in the activity of SDH and decrease

in LDH in different tissues clearly shows that

in fish, the rate of oxidative metabolism is

increased to withstand the toxic stress.

Elevation in the activity of enzymes of Kreb’s

cycle in different tissues suggest that

metabolic rate of fish exposed to lead(Pb) was

higher than control fish,

References

Adam, W. and Moeller, C. R. (2004): A

convenient synthesis of nickel(II) and

cobalt(II) complexes of unsymmetrical

salen-type ligands and their

application as catalysts for the

oxidation of 2,6-dimethylphenol and

1,5-dihydroxynaphthalene by

molecular oxygen, Ind. J. Chem. 4356-

62.

Bianci, C.; Bertanza, L.; Pietre, R. and Sabbioni,

E. (1989): Cobalt-induced

hypothyroidism, cardiomyopathy,

polycythemia and hypertricosis in an

infant, J. Trace Elem. Exp. Med. 2:

311-319.

Cosofret, V. V. and Buck, R. P. (1993): Recent

advances in pharmaceutical analysis

with potentiometric membrane

sensors, Crit. Rev. Anal. Chem. 24: 1.

Craggs, A.; Moody, G. J.; Thomas, J. D. R.

(1993): PVC matrix membrane ion-

selective electrodes. Construction and

laboratory experiments, Pure Appl.

Chem. 65: 1849-1858.

Garg, A. N.; Weginwar, R.G. and Chutke, N. L.

(1993): Radiochemical neutron

activation analysis of Fe, Co, Zn, Sb

and Se in biomedical and

environmental samples, Sci. Total

Environ. 139: 421–430.

Ghiaci, M.; Rezaei, B.; Sadeghi, Z.; Safaei-

Ghomi, J. (2010): A Co2+-selective

poly (vinyl chloride) membrane

electrode based on a newly

synthesized 3,3′-(dodecylazanediyl)

bis(N-(2-(2-aminoethylamino) ethyl)

propanamide) compound, J. Chem.

Eng. Data 55: 2792–2798.

Gupta, V. K.; Jain, A. K.; AlKhayat, M.;

Bhargava, S. K. and Raisoni, J. R.

(2008): Electroanalytical studies on

cobalt(II) selective potentiometric

sensor based on bridge modified

calixarene in poly (vinyl chloride),

Electrochim. Acta 53: 5409–5414.

Isa, I. M.; Mustafar, S.; Ahmad, M.; Hashim, N.

and Ghani, S. A. (2011): Cobalt (II)

selective membrane electrode based on

palladium(II) dichloro acetylthiophene

fenchone azine,Talanta 87: 230–234.


87

Kumar, P. and Shim, Y. B. (2009): A novel

cobalt(II)-selective potentiometric

sensor based on p- (4-n-

butylphenylazo) calix[4]arene, Talanta

77: 1057–1062.

Lagerström, M. E.; Field, M. P.; Séguret, M.;

Fischer, L.; Hann, S. and Sherrell, R.

M. (2013): Automated on-line flow-

injection ICP-MS determination of

trace metals (Mn, Fe, Co, Ni, Cu and

Zn) in open ocean seawater:

application to the GEOTRACES

program, Mar. Chem. 155: 71–80.

Li, B.; Wang, D.; Lv, J. and Zhang, Z. (2006):

Chemometrics-assisted simultaneous

determination of cobalt(II) and

chromium(III) with flow-injection

chemiluminescence method,

Spectrochim. Acta A 65: 67–72.

Mashhadizadeh, M. H.; Momeni, A.; Razavi, R.

(2002): Cobalt (II)-selective membrane

electrode using a recently synthesized

mercapto compound, Anal. Chim. Acta.

462: 245–252.

Mur, J. M.; Moulin, J. J.; Charruyer-Seinerra, M.

P. and Lafitte, J. (1987): A cohort

mortality study among cobalt and

sodium workers in an electrochemical

plant, Am. J. Ind. Med. 11: 75-81.

Sa’ez de Viteri, F. J. and Diamond, D. (1994):

Determination and application of ion-

selective electrode model parameters

using flow injection and simplex

optimization, Analyst 119: 749-758.


(2009): Determination of cobalt ions at

nano-level based on newly synthesised

pendant armed macrocycle by

polymeric membrane and coated

graphite electrode, Talanta 80: 685–

693.


(2009): Determination of cobalt ions at

nano-level based on newly synthesised

pendant armed macrocycle by

polymeric membrane and coated

graphite electrode, Talanta 80: 685–

693.

Singh, A. K.; Singh, R. P. and Saxena, P.

(2006): Cobalt(II)-selective electrode

based on a newly synthesized

macrocyclic compound, Sensors

Actuators B 114: 578–583.

Souza, J. M. O. and Tarley, C. R. T. (2009):

Sorbent separation and enrichment

method for cobalt ions determination by

graphite furnace atomic absorption

spectrometry in water and urine samples

using multiwall carbon nanotubes, Int. J.

Environ. Anal. Chem. 89: 489–502.

Steffan, I. and Vujicic, G. (1993):

Determination of cobalt, molybdenum

and vanadium in Austrian mineral

waters by ICP-AES after ion-exchange

separation and preconcentration,

Mikrochim. Acta 110: 89–94.

Venugopal, B. and Luckey, T. D. (1979): A Metal

Toxicity in Mammals, Plenum Press,

New York.

Yu, X.; Zhang, P.; Liu, Q.; Li, Y.; Zhen, X.;

Zhang, Y. and Ma, Z. (2014): An “off–

on” fluorescent and colorimetric probe

bearing fluorescein moiety for

Mg2+and Ca2+ via a controlled

supramolecular approach, Mater. Sci.

Eng. C 39: 73–77.

Documents

ESSENCEessence-journal.com/wp-content/uploads/Full_Issues/Special_Edition_2_2017.pdf · Institute of Mountain Environment, Bhaderwah Campus, University of Jammu, India Email: [email protected]