Bishop Heber College (Autonomous)

URBANIZATION AND ITS IMPACT ON
ENVIRONMENT IN PUDUKKOTTAI, TAMILNADU,
INDIA.
Thesis submitted to
Bharathidasan University
for the award of the Degree of
DOCTOR OF PHILOSOPHY
IN
ENVIRONMENTAL SCIENCES
By
J.MULLAI MANI MOZHI, M.Sc., P.G.D.J.PR
P.G & Research Department of Environmental
Sciences
Bishop Heber College (Autonomous)
Nationally Accredited with A+ by NAAC
Tiruchirapalli – 620 017. Tamil Nadu, India.
MAY 2010.
CERTIFICATE
This is to certify that the thesis entitled, Urbanization and its Impact
on Environment in Pudukkottai,Tamilnadu,India, submitted to the
Bharathidasan University for the Award of the Degree of Doctor of
Philosophy in Environmental Sciences, is a record of the original research
work done by Ms. J.Mullai mani mozhi, during the period of her study in
the Department of Environmental Sciences, Bishop Heber College
(Autonomous), Tiruchirapalli – 620 017 under my supervision and guidance,
and the thesis has not previously formed the basis for the award of any
degree, diploma, fellowship, associateship or any other similar title to any
candidate of the university.
Guide/Supervisor
J.Mullai Mani Mozhi, M.Sc.,
Research Scholar,
Department of Environmental Sciences,
Bishop Heber College (Autonomous),
Tiruchirapalli – 620 017.
Tamil Nadu, India.
Date : ……………
Declaration
I hereby declare that this work has been originally carried out by me under
the guidance and supervision of Dr. C.Ravichandran, Associate Professor ,
Department of Environmental Sciences, Bishop Heber College (Autonomous),
Tiruchirapalli – 620 017, Tamil Nadu and that this work has not been submitted
elsewhere for any other degree, diploma, associate ship, etc., for any other
university.
J.Mullai Mani Mozhi.
S. No.
Contents
Page No.
1.
Introduction
01
2.
Review of Literature
38
3.
Materials and Methods
91
4.
Results and Discussion
111
5.
Tables and Figures
141
6.
References
7.
Appendix
1.0 INTRODUCTION
1.1. DEFINITION-ENVIRONMENT
1.2 .URBANIZATION
1.2.1 URBAN ECOSYSTEM
1.2.2. URBANIZATION IN WORLD
1.2.3 .URBANIZATION IN BIG CITIES
1.3 .URBANIZATION IN INDIA
1.3.1 .URBANIZATION IN INDIA AND METROPOLITAN
CITIES
1.3.2. IMPACT OF URBANIZATION ON THE
ENVIRONMENTAL QUALITY IN THE
METROPOLITAN
CITIES OF INDIA
1.3.3. LIVING CONDITION IN THE METROPOLITAN
CITIES
IN INDIA
1.4 .URBANIZATION IN TAMIL NADU
1.4.1. SLUM POPULATION: A PROFILE
1.4.2.DECADAL GROWTH
1.4.3. DENSITY
1.5 .PUDUKKOTTAI TOWN –THE STUDY AREA:
1.5.1. HISTORY OF PUDUKOTTAI
1.5.1.1. EARLY HISTORY
1.5.1.2 .MEDIEVAL HISTORY
1.5.1.3 .MODERN HISTORY
1.5.2 . MYTHOLOGICAL STORY OF ORIGIN
1.5.3 .PUDUKKOTTAI TOWN PAST AND PRESENT
1.6 .GEOGRAPHY OF PUDUKKOTTAI DISTRICT
1.6.1. LOCATION AND AREA
1.6.1.1. TERRAIN
1.6.1.2 .HILLS
1.6.1.3 .PLAINS
1.6.1.4 .TANKS
1.6.1.5 .RIVERS
1.6.1.6 .SEACOAST
1.6.1.7 .CLIMATE
1.7 .TRANSPORTATION
1.8 .INDUSTRIES
1.8.1. IMPORTANT INDUSTRIES
1.9 .TOURISM
1.10. EDUCATION
1.10.1. UNIVERSITY AND COLLEGES
1.11.POPULATION TRENDS
1.11.1. TREND IN BIRTH/DEATH RATE AND INFANT
MORTALITY RATE
1.12 .RESOURCE
1.12.1. LAND RESOURCE
1.12.1.1. SOILS
1.12.1.2 . CROPS CULTIVATED
1.12.2 .TRENDS IN PRODUCTION AND PRODUCTIVITY
1.12.3 .HORTICULTURE
1.12.3.1. VEGETABLES
1.12.3.2. PLANTATION CROPS-CASHEW:
1.12.4 .FOREST RESOURCES
1.12.4.1. FLORA
1.12.4.2 .FAUNA
1.12.4.3 .MAN MADE FOREST
1.12.4.4 .RARE AND THREATENED SPECIES
1.12.5 .SURFACE WATER
1.12.6 .HERITAGE RESOURCES
1.13 .TOURIST ARRIVALS
1.14 .GROWTH OF VEHICLE POPULATION
1.15 .DENSITY OF POPULATION
1.15.1. URBAN SLUM POPULATION
1.16 .URBAN SERVICES
1.16.1. WATER SUPPLY
1.16.2 .MUNICIPAL SOLID WASTE GENERATION
1.17 .POVERTY LINE
1.18 .INDUSTRIAL DEVELOPMENT AND ENVIRONMENTAL
STATUS
1.18.1. SIPCOT COMPLEX
1.19 .ACQA CULTURE ACTIVITIES
1.20 .ENVIRONMENTAL INSTITUTIONS
AIM AND OBJECTIVES
2.0 Review of Literature
2.1. Impact of Urbanization
2.2 .AIR POLLUTION
2.3. NOISE POLLUTION
2.4. WATER POLLUTION
2.4.1. Groundwater and its contamination
2.4.2. Pesticides
2.4.3. Sewage
2.4.4. Nutrients
2.4.5. SYNTHETIC ORGANICS
2.4.6. The effects of Water pollution
2.4.7. Plankton
2.5. SOIL POLLUTION
2.5.1. Evolutionary nature of soil
2.5.1.1. SOIL PROFILE
2.5.1.2. Horizons of soil
2.5.1.3. Components of soil
2.5.2. Soil nutrients
2.5.2.1. Macronutrients
2.5.2.2. Micronutrients
2.5.3. Life in the soil
2.5.4. Soil deterioration
2.5.5. Soil Degradation
2.5.6. Soil erosion
2.5.7. Salinity and alkalinity
2.5.8. Mining and Environmental degradation
2.5.9. Water logging and marshy land
2.5.10. Agriculture progress, problems and constraints
2.5.10.1. Depletion of water resources
2.5.10.2. Decline in soil organic matter
2.5.11. Soil treatment
2.5.12. Agriculture and Horticulture Waste Land
2.6. WASTE WATER TREATMENT
2.6.1. Aquatic Macrophyte
2.6.2. WASTE WATER USAGE
2.7. SOLID WASTE MANAGEMENT
2.7.1. GENERATION OF MUNICIPAL SOLID WASTES
2.7.2. WASTE COMPOSITION
2.7.3. Trace Elements and MSW Compost
2.7.4. Effects on Water Quality
2.7.5. Effects on Soil Organisms
2.7.6. Long-term Concerns
2.7.7. Potential Benefits of Trace Elements in MSW Compost
2.7.8. Related Regulatory Issues
2.8. SOCIO-CULTURAL DETERMINANTS OF URBAN OCCUPATION
2.8.1. BELTS OF VEGETATION OF IRREGULAR SHAPE
3.0 MATERIALS AND METHODS
3.1. General Profile of Pudukkottai
3.1.1. CURRENT STATE OF ENVIRONMENT IN
PUDUKKOTTAI
3.1.1.1.TOPOGRAPHY
3.1.1.2.CLIMATE
3.2 .AIR SAMPLING and ANALYSIS
3.3 .NOISE ASSESSMENT
3.3.1. Selected zones
3.3.1.1.Residential zone
3.3.1.2 .Commercial zone
3.3.1.3 .Silence zone
3.3.1.4 .Industrial zone
3.4 .WATER SAMPLING and ANALYSIS
3.5 .SOIL SAMPLING , ANALYSIS and TREATMENT
3.5.1. Selection and Location of the study area
3.5.1.1. Terrain Evaluation
3.5.1.2 .Soil profile
3.5.1.3 .Soil Fertility Studies
3.5.2 .COLLECTION OF SAMPLE
3.5.3 .Soil Treatment
3.5.3.1. Cultivation of Palmarosa
3.5.3.2 .Cultivation study
3.5.4 .Plant description
3.5.4.1.Botanical Classification
3.5.4.2 .Description of the herb palmarosa
3.5.4.3 .Properties
3.5.4.4 .Therapeutic uses
3.6 .MUNICIPAL WASTE WATER ANALYSIS ANS TREATMENT
3.6.1. Floating Aquatic Plants
3.6.1.1 .Growth Characteristics
3.6.2 .BUFFALO GRASS: KANSAS WILDFLOWERS AND
GRASSES
3.7 .SOLID WASTE ASSESSMENT AND MANAGEMENT
3.7.1 .Composting
3.7.2.Process description of Composting
3.7.3 .Nutrient contents of Sugar industry effluent
3.7.4.Preparation of Bio-compost
3.7.5 .SITE SELECTION FOR COMPOSTING PLANT
3.7.5.1. WINDROW METHOD
3.8 .BIODIVERSITY
3.8.1 .Flora
3.8.1.1. Sample survey
3.8.2 .Fauna assessment
3.8.2.1. Insects
3.8.2.2 .Birds
3.8.2.3 .Vertebrate species
3.8.2.3.1. POINT SURVEY METHOD
3.8.2.3.2 .ROADSIDE COUNTS
3.8.2.3.3 .Pellet and track counts
3.8.2.4 .Reptiles
3.8.2.5.AQUATIC BIOLOGICAL ENVIRONMENT
3.8.2.5.1. Planktons
3.9. SOCIO-ECONOMIC STUDY
3.9.1. Questionnaire
4.0.Results and Discussion
4.1. Population Distribution
4.2. Air Quality Status in Pudukkottai
4.2.1. Suspended Particulate Matter
4.2.2. Sulphur di Oxide
4.2.3. Nitrogen Oxides
4.3. Noise Assessment in Pudukkottai
4.4. Water Quality in Pudukkottai
4.4.1. Ground Water Status
4.4.2. Surface Water Analysis
4.5. Soil Analysis
4.6. Waste Water Characteristics and its Treatment
4.7. Solid Waste
4.7.1. Characteristics of Solid Wastes
4.7.2. Solid Waste Management and Soil Quality Improvement
4.7.3. Nutrient components in bio compost
4.7.4. Soil treatment with Bio-Compost
4.8. Biodiversity
4.9. Socio Economic Status
ACKNOWLEDGEMENT
I have great pleasure in writing this page in my dissertation to express my
happiness which I feel to acknowledge all those who helped me to complete this
work successfully.
It gives me great pleasure expressing my deep sense of indebtedness and
gratitude to my Research Guide Dr. C. Ravichandran, Associate Professor, P.G.
and Research Department of Environmental Sciences, Bishop Heber College, for all
the encouragement and valuable guidance received by me at every stage of the work.
The useful discussions I had with him, both inside and outside the College, were
always a source of inspiration to me.
I owe my sincere thanks to Prof. D. Swamiraj, former Director, Bishop
Heber College, for having given me this opportunity to do my Ph.D through this
institution.
I sincerely thank Prof. Dr. Marcus Diepen Boominathan, Principal Bishop
Heber College, for having given me this wonderful opportunity to do my Ph.D work,
in Bishop Heber College.
I also record my gratitude to Dr.Susila Appadurai, Vice Principal ,Bishop
Heber College, for his valuable suggestions and encouragement.
My Special heartfelt thanks to Dr. (Mrs) Kalavathy , Associate Professor,
Department of Botany, Bishop Heber College for her encouragement throughout the
course of this work.
My thanks are due to the Doctoral Committee member Dr.George John,
Associate Professor , Department of Zoology, E.V.R.College, Trichy for his help
during the period of my research work
I extent my sincere gratitude to Prof. AlagappaMoses, HEAD, Department
of Environmental Sciences, Bishop Heber College, for their keen interest and
valuable suggestions in the progress of my work.
I record my sincere thanks to Ms.V.Jeya Shobana and N.Mahalakshmi,
Faculty members, Department of Environmental Science, J.J.College of Arts and
Science College, for their timely help and encouragement.
I would like to record my sincere thanks to Mr.Carter Perem Raj, Dept .of
Social Work, B.H.C for the motivation.
I record my sincere thanks to Ms. A.Karthigaiveni and Sivagami for their
valuable suggestions.
I deeply appreciate the help I received from my former students
Madava Krishnan, Varun and so many other students who helped me in the
experimentations.
I also extent my thanks to Mr.Srinivasan, Mr. Mahalingam and
Mr. Selvakumar, Department of Environmental Sciences for their help while
conducting my studies in the laboratory.
I express my special thanks to Dr. N. Saraswathy, Lecturer, Dept. of
Microbiology,
Srimathi
Indhira
Gandhi
College,
Dr.P.Sampath
Kumar,
Lecturer,Dept.of Biochemistry, SASTRA,Kumbakonam.At a personal level, I would
like to thank Mrs.M.Tamizharsi, P.G.Asst, St.Josephs Girls Higher Sec School,
Vadugarpet and their family, without whom I would not have been what I am.
I owe a special word of indebtedness to my father Mr. P.Jothivel, mother
Mrs. Amutha Jothivel, sister Mrs. J. Mullai mani malar, brother-in -law
Mr. N. Rengarajan and Grand fathers, mothers for their good wishes in the
successful completion of this work.
I express my sincere thanks to Mrs. Rani Ravichandran for their constant
encouragement during the course of my work.My special thanks to Maruti systems.
ABBREVATIONS
BOD-Biochemical Oxygen Demand
CO-Carbon Monoxide
COD-Chemical Oxygen Demand
Cr-Chromium
Cu-Copper
EC-Electrical conductivity.
EPA-Environmental Protection Agency
Fe-Iron
HC-Hydrocarbon
HCl -Hydro Chloric acid
L10-A noise value level that is exceeded for 10% of the time during every hour of
sampling
L50- A noise value level that is exceeded for 50% of the time during every hour of
sampling
L90- A noise value level that is exceeded for 90% of the time during every hour of
sampling
Leq-Equivalent continuous energy level of fluctuating noise level.
Lmax-Maximum noise level value observed during the study period.
Lmin-Minimum noise level value observed during the study period.
Mn-Manganese
MSW-Municipal Solid Waste
NO2 –Nitrogen di oxide
NOx –Oxides of nitrogen
Pb-Lead
PFT-Pulmonary Function Test
SO2-Sulphur di oxide
SOx- Oxides of Sulphur
SPM-Suspended Particulate Matter
TDS-Total Dissolved Solids
TOC-Total Organic Carbon
TOM-Total Organic Matter
TSS-Total Suspended Solids
TS-Total Solids.
WHO-World Health Organization
Zn-Zinc
ABSTRACT
Urbanization takes place rapidly all over the world due to many reasons.
Population increases in urban areas due to two major reasons: migration of rural
population to urban areas and increase in population by birth. As a result ,the
boundaries of any urban area expands by encroaching the nearby rural areas.
Pudukkottai is no exception to this urban growth. Urbanization without proper
planning, leads to environmental degradation in many forms.
Pudukkottai was the capital of the only princely state of Tamilnadu during
the British time (1686 to 1948) and presently is district headquarters. It is one of
the planned towns of India, home of one among the earliest cave temples (about
1300 years old) with a continuous traditions till date. It was a notable centre for
arts and temple architecture during the period of royalty.
The Government Museum, the Palace and impressive public buildings are a
few other attractions. This town is located on Tiruchirappalli - Rameswaram NH
210, about 50 km south-east of Tiruchirappalli and about 60 km south of
Thanjavur. It is situated in the valley of the Vellaru - 6½ km to the north of the
river. It stands on a ridge that slopes gradually towards the south.
In this present study the impact of urbanization in Pudukkottai on air
environment, water environment, soil environment, biotic environment and socioeconomic environment was determined and assessed.
In order to assess the impact on air environment, air samples were collected
at selected places for one year at different seasons and the concentration of SPM,
SO2 and NO2 were estimated.
Urban growth in Pudukkottai has deteriorated air quality to a reasonable
extent. SPM, SO2 and NO2 and noise levels exceeded the standards. Increased
vehicular traffic due to urbanization was attributed to the deterioration of air
quality.
In order to assess the impact on water environment, surface water and
ground water samples were collected at selected places for one year at different
seasons. In surface water turbidity and fluoride levels exceeded the standards. The
discharge of domestic wastes and sewage was found to be the major cause for
deterioration of surface water quality. Ground water was found to be unpolluted
except with E.Coli. Poor sanitation facilities and open defecation were attributed for
this.
In order to assess the impact on soil environment, soil samples were
collected at selected places for one year at different seasons. The results suggested
that urbanization did not pose any effect on soil quality.
In order to assess the impact on biotic environment, flora and fauna were
identified and quantified at selected places for one year at different seasons. The
biotic assessment revealed that diversity of flora and fauna was less in urban area
when compared to the surrounding suburban area of Pudukkottai. It suggests that
urban growth in Pudukkottai had cast out several organisms hence poor in
biodiversity.
Biodegradable wastes constitutes more than 50% in MSW generated in
Pudukkottai. On average 30-35 tonnes of MSW has been generated in Pudukkottai.
The present population of Pudukkottai is about 1 lakh. As the town is expanding in
its area and in its population, the amount of MSW is likely to increase accordingly.
In this present study, samples of biodegradable solid wastes were subjected to
composting using micro organisms.
The biocompost thus produced were used for te growth of Palmarosa plant.
Biocompost had positive effect on the growth of the plant. That is, the biocompost is
rich in nutrients. Hence, it is suggested that, Municipality can adopt composting for
disposal of biodegradable waste, which can reduce the amounts of MSW
considerably.
Waste water generated was found rich in nutrients. Hence the waste water
was used for plant growth after treatment with Lemna sp. The treated water was
used for the growth of Buffalo grass in a separate field. Positive improvements were
seen in plant growth with treated waste water.
In order to assess the impact on socio-economic environment random
sampling was carried out .The results revealed that urbanization had improved the
quality of life of people in terms of education, employment and income.
In nutshell, it may be stated that urban growth in Pudukkottai had caused
deterioration of air quality and decrease in biodiversity, while improving the socioeconomic status of the people.
INTRODUCTION
1.1. Definition-Environment
The word Environment is derived from the French word “Environ” which
means “surrounding”. Our surrounding includes biotic factors like human beings,
Plants, animals, microbes, etc and abiotic factors such as light, air, water, soil, etc.
Environment is a complex of many variables, which surrounds man as well
as the living organisms. Environment includes water, air and land and the interrelation ships which exist among and between water, air and land and human beings
and other living creatures such as plants, animals and micro organisms (Kalavathy,
2004).She suggested that environment consists of an inseparable whole system
constituted by physical, chemical, biological, social and cultural elements, which are
interlinked individually and collectively in myriad ways.
The natural environment consist of four interlinking systems namely, the
atmosphere, the hydrosphere, the lithosphere and the biosphere. These four systems
are in constant change and such changes are affected by human activities and vice
versa (Kumarasamy et al., 2004).
Components of Environment
Our environment has been classified into four major components:
1.Hydrosphere, 2.Lithosphere, 3.Atmosphere, 4.Biosphere.
Hydrosphere
Hydrosphere includes all water bodies such as lakes, ponds, rivers, streams
and ocean etc. Hydrosphere functions in a cyclic nature, which is termed as
hydrological cycle or water cycle.
Lithosphere
Lithosphere means the mantle of rocks constituting the earth’s crust. The
earth is a cold spherical solid planet of the solar system, which spins in its axis and
revolves around the sun at a certain constant distance .Lithosphere mainly, contains
soil, earth rocks, mountain etc. Lithosphere is divided into three layers-crusts,
mantle and core (outer and inner).
Atmosphere
The cover of the air, that envelopes the earth is known as the atmosphere.
Atmosphere is a thin layer which contains gases like oxygen, carbon dioxide etc. and
which protects the solid earth and human beings from the harmful radiations of the
sun. There are five concentric layers within the atmosphere, which can be
differentiated on the basis of temperature and each layer has its own characteristics.
These include the troposphere, the stratosphere, the mesosphere, the thermosphere
and the exosphere (Kalavathy, 2004).
Biosphere
It is otherwise known as the life layer, it refers to all organisms on the earth’s
surface and their interaction with water and air. It consists of plants, animals and
micro-organisms, ranging from the tiniest microscopic organism to the largest
whales in the sea. Biology is concerned with how millions of species of animals,
plants and other organisms grow, feed, move, reproduce and evolve over long
periods of time in different environments. Its subject matter is useful to other
sciences and professions that deal with life, such as agriculture, forestry and
medicine. The richness of biosphere depends upon a number of factors like rainfall,
temperature, geographical reference etc. Apart from the physical environmental
factors, the man made environment includes human groups, the material
infrastructures built by man, the production relationships and institutional systems
that he has devised. The social environment shows the way in which human
societies have organized themselves and how they function in order to satisfy their
needs (Kumarasamy et al., 2004).
1.2. Urbanization
Urbanization is the process of population moving towards towns and cities
from rural areas, and taking up the culture and work prevailing in the urban areas.
The country’s population is spread over villages and also towards their nativity with
formal occupation, mostly agricultural or its allied ones, making their living with or
without ancestral property like lands or houses. An analysis of distribution of
population between rural and urban areas of country will reveal the extent of
urbanization. Deteriorating quality of urban and suburban environment is to a great
extent the result of injudicious land use and is a threat to the whole socio-economic
system. Thus planned cities are as necessary as planned farms (Tyler Miller, 1992).
1.2.1 Urban Ecosystem
Ecology is simply the study of organisms and their surroundings. Most
urbanites are unaware of the connection between their livelihood, quality of life and
their dependence on the processes and cycles of the natural world. For those living
in urban areas, many of the processes that explain the relationships between plants,
animals and their natural habitats appear unfamiliar or inappropriate in a city. Urban
ecology shows how these processes are the same ones that affect the urban
communities’ humans inhabit (Nadine Anne Bopp, 2006).
1.2.2. Urbanization in World
The arguments of Kelly and William (1984) that the slow growth of
agricultural land stock and high growth of population of labour force in developing
countries are factors that presumably push rural population toward urban areas are
not correct for the recent past. The sluggish performance of manufacturing (as
compared to agriculture) remains largely responsible for the observed slower pace of
urban growth in developing countries, and may have decelerated urban growth from
what other wise would have been higher rates in the 1980s and 1990s by curbing net
rural to urban migration. Even though manufacturing is performing well but cannot
generate adequate employment being capital intensive is unlikely to accelerate rural
to urban migration. The likely deceleration of rural to urban migration could be the
important reason for the slowing down of urbanization in the developing countries in
recent times.
The push factors like population growth and unemployment etc., and pull
factors like opportunities in the urban areas are debated in the studies of India’s
urbanization. The National Commission on Urbanisation (1988) has termed them as
factors of demographic and economic momentum respectively.
Census is the main source of data on urban population for not only India but
also most of the countries of the world. Census defines urban areas based on certain
criteria. In India since 1961, two important criteria namely: i.) statutory
administration and ii. ) economic and demographic aspects have been adopted to
declare certain settlements as towns. The former includes civic status of towns such
as municipal corporations, municipality, cantonment board, notified area committee,
etc., and the later includes criteria like population size, density of population and
percentage of work force in non-agricultural sector. The former is also known as
statutory town and the latter as census town. These two types of town based on two
different criteria have added complexity to the urbanisation process in India. For
example, the predominance of non-agricultural activities is expected to be found in
urban areas, but surprisingly we have significant number of towns in the country
which are predominantly agriculture oriented. Such paradoxical development creates
doubts about the quality of urbanisation in India (Bhagat, 1992).
The United Nations estimates indicated that at mid 1990s, about 43 per cent
of the world population lived in urban areas. With the urban population growing two
and a half times faster than its rural counterpart, the level of urbanisation was
projected to cross the 50 per cent mark in 2005. United Nations projections further
showed that by 2025, more than three- fifth of the world population would live in
urban areas (U. N. 1993).
The fertility decline could also be the another important factor for lower
urban growth in several parts of the developing world particularly in Latin America
where total fertility rate declined from 6 in the early 1960s to 3 in the early 1990s
( United Nations. 1993 ).
The growth rate of urban population of developing regions has been
declining recently. It was estimated to be 3.9 per cent per annum during 1980-85,
which declined to 3.79 per cent per annum during 1985-90, 3.62, and 3.43 during
1990- 95 and 1995-2000 respectively. The decline in the rate of urbanisation is also
continuing in developed regions of the world. As a result, some of the European
countries have experienced negative urbanisation during 80s (U. N. 1993).
The continued absence, namely, adequate data on rural to urban migration in
most developing countries as well as on natural increase in rural and urban areas
separately precludes attribution of the slowing down of urban growth in most of the
countries to any single demographic process. It reflects the effects the host of factors
like the relatively week expansion of urban industries and price shifts unfavourable
to manufactured goods, population aging, policies to alter migration and spatial
distribution patterns in some countries, and no doubt other forces (Brockerhoff and
Brennam, 1998 ).
Scientists suggest that there is over population when organisms (humans in
this case) become so numerous that they degrade the ability of the environment to
support their kind of animal in the future. The number of people Earth can support in
the long term (without degrading the environment) given existing socioeconomic
systems, consumption patterns, and technological capabilities is called the human
carrying capacity of the planet at that time. This indicates that the study of
population is not simply about population density, but also about the number of
people in an area relative to its resources and the capacity of the natural environment
to sustain human activities the area's carrying capacity. The biophysical aspect of the
carrying capacity can be defined as the maximum population size that could be
sustained under given technological capabilities. Likewise, social carrying capacity
of a system can be described as the maximum population that could be sustained
under a given social system and its associated pattern of resource consumption. It
can thus be concluded that the critical difference between the terms overpopulation
and population density lies in the amount of resources available and the number of
human beings consuming them. Population growth and its environmental and social
impact know no national boundaries. Environmental degradation is compounded by
lack of food security, soil losses, uneven distribution of the water supply,
consumptive lifestyles, and many other socioeconomic factors leading to loss of
biodiversity and natural resources (Sabiha Daudi, 2002).
The City is a relatively recent form of social organization. Homo sapiens, the
present human form has existed on earth for about 40,000 years, but cities have
existed for less than 10,000 years. Jericho in about 7000 B.C. grew from village to a
"city" of about 3,000. 3,500-4,000 B.C. first large city (population of about 25,000)
were established in Mesopotamia. A "city" refers to a place of relatively dense
settlement dense enough so that city residents can not grow their own food. A city
population, therefore, is always dependent upon its "hinterlands" to provide it with
food.
Upuntil very recently about 200 years ago that proportion was limited to
about 5% of an entire population. So cities existed, but there was no urbanization.
Urbanization refers to a process in which an increasing proportion of an entire
population lives in cities and the suburbs of cities. Historically, it has been closely
connected with industrialization. When more and more inanimate sources of energy
were used to enhance human productivity (industrialization), surpluses increased in
both agriculture and industry. Larger and larger proportions of a population could
live in cities. Economic forces were such that cities became the ideal places to
locate factories and their workers, At mid-century only 17.8% of the population of
Developing Country societies lived in cities, but in the fifty years since 1950 that
percent has increased to over 40%. By the year 2030, almost 60% of Developing
country populations will live in cities. In just a few years the World will become
predominately urban about 80-85 years after that happened in the United States
(Fig1.1) (www.urbanization.com.2005).
Rapid economic development “industrialization, population growth and
unplanned urbanization “were determined to be the main causes of these
environmental problems. Some recommendations are also made for mitigating and
managing these problems in the sustainable urban development perspective.
According to current estimates, cities occupy 4% or less of the world’s
terrestrial surface, yet they are home to almost half the global population, consume
close to three-quarters of the world’s natural resources, and generate three-quarters
of its pollution and wastes. Moreover, the UN estimates that virtually all net global
population and economic growth over the next 30 years will occur in cities, leading
to a doubling of current populations. This growth will require unprecedented
investment in new infrastructure and create undreamed challenges for political and
social institutions.
Nowhere are the opportunities more promising or challenges to sustainability
more daunting than in the rapidly urbanizing regions of the world. These
transforming cities represent the engines of growth for the developing world and, in
all regions, will continue to be the centres of innovation, culture, and the arts. These
same cities, however, are the loci of increasing poverty, pollution, disease, political
instability, and social inequality. The transformation of surrounding land due to
urban expansion and urban dwellers ever-increasing demand for energy, food,
goods, and other resources is behind the degradation of local and regional
environments, threatening basic ecosystem services and global biodiversity.
Although the growth of urbanized regions will be a major challenge in the
coming decades, the rate of urbanization is not accelerating. In fact, urbanization
rates were higher in the past decades than projected for in the coming years yet,
because of their increasing population base, the absolute numbers of new urbanites
is enormous (Cohen, 2004).
1.2.3. Urbanization in Big Cities
Virtually all the population growth expected at the world level during the
next thirty years will be concentrated in urban areas. Also, for the first time, the
number of urban dwellers will equal that of rural dwellers in 2007. These findings
are from official estimates and projections of urban, rural and city populations
prepared by the Population Division of the UN Department of Economic and Social
Affairs. The “World Urbanization Prospects: The 2001 Revision” presents estimates
and projections of urban and rural populations for major areas, regions and countries
for the period 1950-2030. It also provides population estimates and projections of
urban agglomerations with 7, 50,000 or more inhabitants in 2000 for 1950-2015, and
the population of all capitals in 2001. Major findings of the study, are:
•
Half the world population is expected to live in urban areas in 2007. The
urban population reached 2.9 billion in 2000 and 3 billion in 2010.It is
expected to rise to 5 billion by 2030, whereas 30 per cent of the world
population lived in urban areas in 1950 and the proportion of urban dwellers
rose to 47 per cent by 2000 and is projected to attain 60 per cent by 2030.
•
Almost all of the population increase expected during 2000-2030 will be
absorbed by the urban areas of the less developed regions. During that
period, the urban population of these regions is expected to increase by 2
billion persons, nearly as much as will be added to the world population, 2.2
billion.
•
In 1995-2000, the world’s urban population grew at a rate of 2.2 per cent per
year. During 2000-2030, it is projected to grow at an average annual rate of
1.8 per cent; at that rate, the world’s urban population will double in 38
years.
•
The urban growth rate of less developed regions reached 3.0 per cent per
year in 1995-2000, compared to 0.5 per cent in more developed regions. This
rate will continue to be particularly rapid in the urban areas of less developed
regions, averaging 2.4 per cent per year during 2000-2030, consistent with a
doubling time of 29 years.
•
In contrast, the rural population of the less developed regions is expected to
grow very slowly at just 0.2 per cent per year during 2000-2030. The world
rural population will remain nearly stable during 2000-2030, varying
between 3.2 billion and 3.3 billion.
•
The process of urbanization is already very advanced in the more developed
regions, where 75 per cent of the population lived in urban areas in 2000.
Nevertheless, the concentration of population in cities is expected to continue
so that by 2030, 84 per cent of the inhabitants of more developed countries
will be urban dwellers.
•
There are marked differences in the level and pace of urbanization among the
major areas constituting the less developed regions of the world. Latin
America and the Caribbean, as a whole, are highly urbanized, with 75 per
cent of the population living in urban settlements in 2000 - a proportion
higher than that of Europe and twice as high as estimated for Africa or Asia.
With 37 per cent of their respective populations living in urban areas in
2000, Africa and Asia are considerably less urbanized and, consequently, are
expected to experience rapid rates of urbanization during 2000-2030. By
2030, 53 per cent and 54 per cent, respectively, of their inhabitants are
expected to live in urban areas. At that time, 84 per cent of the population of
Latin America and the Caribbean will be urban, a level similar to that of
North America, the most highly urbanized area of the world.
•
The proportion of people living in very large urban agglomerations or mega
cities is small. In 2000, 3.7 per cent of the world population resided in cities
of 10 million inhabitants or more, and by 2015 that proportion is expected to
rise to 4.7 per cent. In 2000, 24.8 per cent of the world population lived in
urban settlements with fewer than 500,000 inhabitants, and by 2015 that
proportion will likely rise to 27.1 per cent. In 2000, 41.8 per cent of the
population in developed countries lived in urban settlements with fewer than
500,000 inhabitants and by 2015, that proportion is expected to rise to 43.0
per cent. In less developed regions, where the majority of the population still
resides in rural areas, the proportion of people living in small cities was 20.7
per cent in 2000 and will rise to 23.8 per cent by 2015.
•
In 2000, 52.5 per cent of all urban dwellers lived in settlements with fewer
than 500,000 inhabitants, a proportion that is expected to decline slightly by
2015, but still remain over 50 per cent. Consequently, the trend towards
concentration of the population in larger urban settlements has not yet
resulted in a marked decline of either the proportion or the number of
persons living in smaller urban settlements.
•
Large urban agglomerations do not necessarily experience fast population
growth. In fact, some of the fastest growing cities have small populations
and, as population size increases, the growth rate of a city’s population tends
to decline.
With 26.5 million inhabitants, Tokyo is the most populous urban
agglomeration in the world, followed by Sao Paulo (18.3), Mexico City (18.3), New
York (16.8) and Mumbai (16.5). By 2015, Tokyo will remain the largest urban
agglomeration with 27.2 million inhabitants, followed by Dhaka, Mumbai, Sao
Paulo, Delhi and Mexico City, all of which are expected to have more than 20
million inhabitants (www.emeraldinsight.com).
1.3. Urbanization in India
The population is growing at the rate of about 17 million annually which
means a staggering 45, 000 births per day and 31 births per minute. If the current
trend continues, by the year 2050, India would have 1620 million populations.
Population explosion is one of the most threatening issues facing contemporary
India particularly by the Indian cities. One of the most important reasons for
population explosion in the cities of India is the large scale rural to urban migration
and rapid urbanization (Kamal Raj, 2005).
Due to uncontrolled urbanization in India, environmental degradation has
been occurring very rapidly and causing shortages of housing, worsening water
quality, excessive air pollution, noise, dust and heat, and the problems of disposal of
solid wastes and hazardous wastes. The large and metropolitan cities present a
particularly depressing picture today. The situations in metropolises like Mumbai,
Kolkata, Chennai, Delhi, Bangalore, Kanpur, Hyderabad etc., are becoming worse
year by year. The problems of finding space and housing for all have been
intensified. Slums have become an inevitable part of the major Indian metropolises.
Environmental pollution in India can broadly be attributed to rapid industrialization,
energy production, urbanization, commercialization, and an increase in the number
of motorized vehicles (Maitra, 1993). Vehicles are a major source of pollution in
cities and towns. The concentration of ambient air pollutants in the metropolitan
cities of India as well as many of the Indian cities is high enough to cause increased
mortality. The rate of generation of solid waste in urban centres has outpaced
population growth in recent years with the wastes normally disposed in low-lying
areas of the city’s outskirts (State of the Environment, 1995).
1.3.1. Urbanization in India and Metropolitan Cities
Urbanization is a process whereby increasing proportions of the population
of a region or a country live in urban areas. Urbanization has become a major
demographic issue in the 21st Century not only in India but also all over the world.
There has always been great academic interest in the Indian urbanization process
and a number of scholars have analysed India’s urban experience, particularly in the
post independence period (Bose, 1978; NIUA, 1988; Mohan, 1996). The level of
urbanization in terms of the proportion of urban population to the total is low in
India but the urban population in absolute terms is high. Since the first regular
census of India was taken in 1881, almost all census reports have commented on the
urban growth. During the last three decades in India, the link between urbanization
and environment and the threat to the quality of life have emerged as a major issue.
(i) Pattern and Trend of Urbanization in India during 1901-2001
The pattern and trend of urban population and number of towns in India
during 1901 to 2001 shows that (Table 1.1 )total urban population has increased
more than ten times from 26 million to 285 million whereas total population has
increased less than five times from 2387 million to 10270 million from 1901 to
2001. A continuous increase has been noticed in the percentage of urban population
from 11% in 1901 to 17% in 1951 to further 28% in 2001. In the same fashion the
number of towns had also increased from 1916 in 1901 to 2422 in 1951 and then to
4689 in 1991. This reveals the rapid urbanization process in India (COI, 2001).
(ii) Percentage of Urban Population in India by Size-Class of Urban Centres,
1961-1991
Table 1.2 shows percentage growth of urban population in India by size class
of town during 1901 to 1991. The process of urbanization in India reflects a certain
degree of abnormality because of the fact that more than 60% of the urban
population of the country lives in Class I town alone and remaining below 40%
urban population lives in the smaller sized towns. An unremitting increase has been
noticed for percentage of total urban population in Class-I city over the decades
(1901 to 1991), while class IV, V and VI towns have experienced a continuous
decline. However, class II and III towns have almost constant percent of total urban
population over the decades. About three-fold increase has been found in the
percentage of total urban population in class one city, from 23% in 1901 to 65% in
1991. This depicts a huge concentration of urban population in large cities. The
urbanization in India shows the pattern of ‘inverted triangle’ where majority of the
urban population resides in the Class I cities (COI, 2001).
(iii) Growth in the Number of Million Plus (1,000,000 Population or More) Cities in
India during 1901-2001
Table 1.3 shows the growth in the number and population of million plus
cities in India during 1991 to 2001. There was only one million plus city (Kolkata)
in 1901 in India. It became two in 1911 (Mumbai added) and was constant during
1911 to 1941. Million plus cities increased to five in 1951 and continuously
increased after this decade and became 23 in 1991 and currently it is 35 in 2001
census. Total population also increased in the million plus cities from 1.51 million in
1901 to 107.88 million in 2001, almost a fifty fold increase. The percentage decadal
growth rate in the total population of million plus city was noticed highest during
1941 to 1951, because of the incidence of partition. After independence also, the
decadal growth rate was more than 50% in each decades. This illustrates the realistic
situation of exhausted growth in the million plus cities. Looking at the percentage of
total population of India residing in million plus cities, it reveals that it has increased
drastically from less than 1% in 1901 to 3% in 1951 and further to 8% in 1991.
Again, the percentage of total urban population of India residing in million plus
cities has also increased drastically from 6% in 1901 to 19% in 1951 and further to
33% in 1991(COI, 2001).
(iv) Trend in Total Population and Annual Growth Rate in the Four Metropolitan
Cities of India during 1901-2001
More than thirty fold increase has been noticed in the population of Delhi in
100 years, from 0.41 million in 1901 to 12.8 million in 2001, whereas, there has
been 20 fold increase in Mumbai’s population, from 0.8 million to 16.4 million from
1901 to 2001. However, Chennai has experienced more than 10 fold increase (0.59
million to 6.4 million) in its total population during last 100 years whereas, Kolkata
has experienced the lowest increase (less than 9 fold) in its total population among
the metropolitan cities in last ten decades. The maximum growth rate has been
noticed during 1941 to 1951, highest in Delhi (90%) followed by Mumbai (76%)
and Chennai (66%). However, Kolkata has noticed comparative low growth rate
(29%) during the same period. This was the era of partition in India when a huge
influx of migration has taken place to big cities because of the Hindu Muslims
communal riot. A large numbers of population joined the big cities after the
insurrection. After independence, Delhi experienced the highest decadal growth rate
(close to 50%) in its total population in all the censuses (1951 to 2001), followed by
Mumbai where growth rate was about 40% during those Census years. However,
Kolkata experienced continuous declining decadal growth rate from 1951 to 2001.
On the other hand, Chennai has experienced a mixed pattern of high and low decadal
growth rate during last 50 years. Initially Kolkata was the most populous city of
India till 1981, but Mumbai surpassed it in 1991 Census. Again, Delhi is expected to
cross the population of Kolkata in the next Census of 2011 if both cities will
experience same growth rate pattern. Thus it is evident with the table that Mumbai
and Delhi metropolis are experiencing profuse growth in their population
(Table 1. 4) (COI, 2001).
1.3.2. Impact of Urbanization on the Environmental Quality in the
Metropolitan Cities of India
Urbanization and its allied process have made a profound impact on the
environment of the metropolitan cities of India. It has been accepted by the United
Nations that it is quite impossible for developing countries to provide in advance the
urban planning and design because it is not possible to accurately project the urban
growth.
(i) Slum Situation in India and its Metropolitan Cities
The Govt. of India Slum Areas (Improvement and Clearance) Act of 1954
defines a slum ‘as any predominantly residential areas, in which light or sanitary
facilities or any combination of these factors are detrimental to the safety, health or
morals’. The vast majority of the people who migrated to the city were attracted by
opportunity and comforts offered by modernization. They belonged to the working
class and found it difficult to secure accommodation within their means. So, they
squatted on every open space available, as near their workplaces as possible and put
up huts with cheap building materials. In this way slums grew in number and
population.
Total and slum population in India according to size/class of towns during
1991 shows that 41% of the total slum population was residing in million plus cities,
where 27% of total population of India resides (Table 1.5). However, cities with
population between 0.5 million to 1 million have only 9% of total slum population
where 20% of total population was residing. Further, cities with population between
0.3 million to 0.5 million have only 6% of total slum population where 19% of total
population was residing. This shows that cities with population between 0.5 to 1
million and city with population between 0.3to0.5 million have very less percentage
of slum population whereas million plus cities have more percentage of slum
population. It reveals that the opportunity in the medium city is less than the million
cities. Therefore the unskilled population is more attracted towards the million cities
and thus joins the slums for their residence. On the other hand, the towns with
population less than 50,000 show little more percentage of total slum population
(21%) than its share of total population (18%). It shows the poor housing quality in
the small towns and also may be because the semi-pucca and kachcha houses may
be identified as slum. Slum population is a serious problem of the mega-cities of
India. A large population of Mumbai, Kolkata and Delhi lives in slum, despite of
several Government housing policies.
Table 1.6 shows the percentage of slum population in the four metropolitan
cities of India during 1981 to 2001. A continuous increase has been found in the
percentage of slum population over the last three decades in the four metropolitan
cities of India in which Mumbai was highest. In 1981, 31% populations of Mumbai
were residing in slum, and in 2001 nearly half of Mumbai’s population (49%) was
living in slums. However, Kolkata and Delhi had not shown as severe condition as
Mumbai. The proportion of slum population was 30% and 18% in 1981 in Kolkata
and Delhi, which increased to 36% and 23% respectively. On the other hand, it is
little bit comfortable sign for Kolkata and Delhi that in 2001 the proportion of slum
population has decreased to 33% and 19%, respectively. Although Chennai has
lowest slum population among the four metropolitan cities yet it has experienced
continuous increase in the slum population over the three decades. There was 14%
slum population in Chennai in1981, which increased to 15% in 1991 and further
18% in 2001(COI, 2001).
(ii) Composition of Solid Wastes in the Four Metropolitan Cities of India
Delhi exhibits the highest percentage of ash, which is about 52% of the
weight of all the solid waste, followed by Mumbai, Kolkata and Chennai. The
reason that Delhi has the highest percentage of ash as solid waste may lie in the fact
that Delhi is a large industrial centre with mainly metal industry, which uses coal as
a source of power and the number of industries is growing day by day because of the
growing urbanization. About 10% of all the solid waste generated in the
metropolitan cities is paper. Textile waste generation ranges between 3 to 5%.
Leather waste is generated mostly by Chennai and generated lowest in Mumbai.
Kolkata generates the largest amount of plastics among all the metros, which
accounts for 8% of all the weight of the solid waste materials. This is a serious
problem and will increase in future because of increase in packaging of consumer’s
goods, if proper management will be not available. Also this has an irreversible
health hazards (CPCB, 1998).
(iii) Status of Municipal solid waste generation and collection in Metropolitan Cities
of India
Mumbai generated the largest amount of Municipal solid waste in 1996,
which was 5355 tones/day followed by Delhi (4000tonnes/day), Kolkata (tones/day)
and Chennai (3124 tones/day) (Table 1.8). But if we consider the per capita
generation of solid waste, it was largest in Chennai. The lowest per capita waste
generation was in Kolkata, which is about 350gms/day. Again about 90% of the
generated Municipal solid waste in Mumbai and Chennai were being collected.
However, in Delhi there was not adequate system of collection as only 77% of the
generated Municipal solid wastes were collected (CPCB, 1998).
(iv) Growth in motor vehicles in India and in Metropolitan Cities
Motor vehicles, which are the main source of vehicular pollution, are
constantly increasing in number since the year 1990 (Table 1.9). With in 10 years
from 1990 to 2000 there has been almost a three-fold increase in the number motor
vehicles in India. On an average 10% increase has been found in each year, which is
a serious concern for air pollution. Again, the number of vehicles in Delhi has
increased from 1813 thousand in 1991 to 2630 thousand in 1996, a one and half
times increase in 6 years followed by Chennai. This is because of lack of the suburban train facility in Delhi with a huge number of commuting population. On the
other hand, increase in the number of vehicles was quite less in Mumbai and
Calcutta compared to Delhi and Chennai (Table 1.10).The 15.15 kiolmeter long
Inderlok-Mundka line,the first gauge(4 ft 8.5 inches) Metro line of India,is now a
part of the metro net work.Thos line is expected to benefit over one lakh commuters
residing on west delhi localities (www.transport in Delhi.com).
(v) Vehicular Emission Load
Table 1.11 shows the estimated vehicular emission load in tonnes per day in
the three metropolitan cities of Delhi, Kolkata and Chennai in 1994. Among all the
vehicular emission loads, the amount of Carbon monoxide (CO) was found highest
followed by Hydro Carbon and Nitrogen Oxide in all the three metropolitan cities.
The total amount of all type of vehicular emission load was found highest in the
atmosphere of Delhi (1046 tonnes / day) followed by Mumbai (660 tonnes /day) and
Calcutta (294 tonnes /day). Carbon monoxide contributes to more than 65% in all
the three metro cities, which was 651 tonnes/day in Delhi followed by Mumbai (497
tonnes/day) and Kolkata (188 tonnes/ day). The amount of Suspended Particulate
Matter (SPM) in the air was highest in Delhi (10.3 tonnes/day) followed by Mumbai
(5.6 tonnes/day) and Kolkata (3.3 tonnes/day). As the previous table shows that in
Delhi the numbers of registered vehicles are highest, the vehicular emission load
also substantiates it, as all the elements were found highest in Delhi. The ingredient
of vehicular emission load affects the health of the people and deteriorates the
quality of life of the residence of metro cities (Transport research wing, 1997).
(vi) State of Ambient Air Quality in Four Metropolitan Cities of India
Although air pollution is only one of the many environmental hazards in
urban centers of the world, along with water contamination, hazardous wastes,
overcrowding, congestion, and so on, it is a unique problem as it affects every
resident, it is seen by every resident, and is caused by every resident (Maitra, 2000).
Table 1.12 shows the state of ambient air quality in the four metropolitan
cities of India during 1991 to 1995. Here air quality has been measured by the
presence of SO2 (Sulphur dioxide), NO2 (Nitrogen dioxide) and SPM (Suspended
Particulate Matters) in µg/cu.m in the air, which causes air pollution. One good sign
is that the presence of SPM in the air of the metro cities has been declining over the
years but only exception is Delhi where the presence of suspended particulate matter
has been increased from 390 to 410 mg/cu.m from 1991-95. The concentration of
SO2 has increased in Mumbai over the five years but in Kolkata it has declined
significantly.
(vii) Waste Water Generation, Collection and Treatment in Metropolitan Cities
Like air pollution, water pollution is also one of the increasing problems due
to the growing population. Water resources are diminishing not just because of large
population numbers but also because of wasteful consumption and neglect of
conservation. With rapid urbanization and industrialization, huge quantities of
wastewater enter rivers. Table 1.13 shows the volume of wastewater generated (in
millilitre per day) from different domestic and industrial sources, the volume
ultimately collected and the amount of waste water treated before it is ultimately
disposed off, in the four metropolitan cities of India.
The volume of domestic wastewater generation was highest in the
metropolitan city of Mumbai, which was 2228.1 ml/d followed by Kolkata (1383
ml/d) and Delhi (1270 ml/d) and the lowest was in Chennai (only 276 ml/d). The
generation of industrial wastewater was also highest in Mumbai. Again looking at
the percentage of waste water collection from the four metropolitan cities, Chennai
and Mumbai performed better than Delhi and Kolkata. Regarding the treatment of
the collected waste water in all the metro cities, the water is disposed only after
primary and secondary treatment. Again the collected wastewater in Mumbai was
mainly disposed off in the Arabian Sea and in Kolkata some amount was disposed in
the Hugli river and the rest was used in the fish farming. However, in Delhi and
Chennai the waste water was mainly used for agricultural works and the remaining
water was disposed in the Yamuna river in Delhi and in the Bay of Bengal in
Chennai (Anon, 1997).
(viii) Noise Levels in the Metropolitan Cities
Table 1.14 shows the average noise levels in various metropolitan cities of
India both during the day and night in the industrial area, commercial area,
residential area and as well as in the silence area during 1997. The noise pollution
was noticed above than the prescribed standard in all the metro cities. Kolkata
experienced the highest noise pollution level in all the areas like residential,
commercial, and Industrial in both during day and night. Mumbai was in better
situation than Kolkata but worse than Delhi in respect of noise pollution in all areas.
In 2010 Mumbai had highest noise level both day and night.
1.3.3. Living Condition in the Metropolitan Cities in India
Table 1.15 shows housing characteristics of the four Metropolitan Cities and
urban India in 1998-99. In Mumbai 34% of the household lived in semi-pucca and
3% in kachcha houses followed by 33% and 9% respectively in Chennai. However,
in Delhi, 11% household resides in semi-pucca and less than 1% in kachcha houses.
It is a good sign for Kolkata that there were only 5% semi-pucca houses and almost
negligible kachcha houses. This shows that in Mumbai and Chennai housing
situation was poorer than Kolkata and Delhi. On the other hand, the houses in these
metros are very much overcrowded. More than three people residing in a single
room, is condition for 56% of the population of Mumbai followed by 43%
population of Kolkata, 30% population of Chennai and one-fourth population of
Delhi. Further, five and more person residing in a room, such miserable condition,
was faced by 28% population of Mumbai followed by 17% of the population of
Kolkata and about 10% population of Delhi and Chennai both. Looking at the
sanitation condition of the metro cities, it is apparent that, almost universal flush
toilet facility is available in Mumbai followed by 90% in Kolkata and 89% in Delhi.
However, the matter-of-fact is that more than half of this facility in Mumbai is
available in public place and not in house premises. Kolkata and Delhi might have
the similar situation. Again it is unfortunate to note that about 9% population of
Kolkata and Delhi uses pit toilet. Further what is the worst situation that 9% of
Chennai’s population does not have toilet facility at all followed by 6% in Delhi.
This shows the inadequate planning of Municipal Corporation because of
unprecedented population pressure.
As regard to the sources of safe drinking water, the situation was best in
Mumbai where almost the entire population had access to piped drinking water.
However, a substantial population was dependent on hand pump in Kolkata (35%)
followed by Chennai (31%) and Delhi (13%). On the other hand, in Chennai, 6% of
population was dependent on the sources other than hand pump and taped/piped
water. Considering the methods of purification of drinking water, again it is a very
deplorable fact that, half of the urban population in India does not purify drinking
water at all. In Kolkata three fourth populations did not purify drinking water
followed by 62% of population of Delhi. However, the situation was little bit better
in Mumbai and Chennai where 27% and 43% population respectively, did not
purified drinking water. But at the same time, majority of the Mumbai population
purified drinking water by straining by cloths only. The situation reveals the danger
of diseases related to water-borne. This may cause serious health problems
especially to the slum dwellers and low-income population.
Electricity facility was almost universal to Mumbai’s population whereas
10% population of Chennai and 6% population of Kolkata did not have the
electricity facility. Main type of fuel used for cooking in urban India was LPG
followed by biomass fuel and kerosene. However, in Kolkata and Chennai more
than 50% population used kerosene. There was very less percentage (less than 9%)
of user of biomass fuel and others in all the four metro cities, except Kolkata where
15% population uses it. This enhances the problem of indoor pollution in the metro
cities (Kudesia and Tiwari, 1993).
1.4. Urbanization in Tamil Nadu
As per the 2001 Population Census, Tamil Nadu’s urban population is placed
at 27.2 million accounting for 43.79 per cent of the State population. While
urbanization in Chennai was cent per cent, in the districts of Dindigul and
Coimbatore 66 per cent each and more than half of the population in Kanyakumari,
Nilgiris, Thirunelveli, Madurai, Thiruvallur, Theni and Kancheepuram in Ariyalur,
the least urbanized district, urban population accounted for only 11 per cent of the
total population. Urban population accounted for less than 20 per cent in the districts
of Thiruvannamalai (18%), Pudukkottai (17%), Dharmapuri (16%), Perambalur
(15%) and Villupuram (14%).
1.4.1 Slum Population: A Profile
The salient features of slum population as per the Census 2001 are given below:
i.
The number of persons living in slums was placed at 28.38 lakhs (14.32 lakh
Males and 14.06 lakh Females).
ii.
Among the six Corporations in the State, the relative share of slum
population was the highest in Chennai (25.6%) followed by Tiruchi (21.7%),
Salem (20.0%), Madurai (19.1%), Tirunelveli (13.8%) and Coimbatore
(6.5%).
iii.
In the concentration of slum population Chennai district tops with a total
slum population of 10.79 lakhs followed by Madurai (1.76 lakhs) and
Tiruchi (1.62 lakhs).
iv.
Regarding literacy rate among slum population, slums in Kanyakumari
district had the highest literacy rate of 90.0 per cent followed by Dindigul
with 87.91 per cent and Thiruvallur district (85.77%). In terms of malefemale literacy rate of the slums male literacy rate was highest in Dindigul
district (93.55%) and the female literacy in Nagercoil (86.66%).
Mushrooming growth of slum population in the State exerts increased
pressure on provision of minimum basic services such as education, health,
water supply, housing and other basic infrastructure including sanitation.
(Population dynamics, 2001).
Urbanization status in Tamilnadu was represented in tables (Table1.16 to
Table1.18). Due to uncontrolled urbanization in India, environmental degradation
has been occurring very rapidly and causing shortages of housing, worsening water
quality, excessive air pollution, noise, dust and heat, and the problems of disposal of
solid wastes and hazardous wastes.
Recently released provisional Census 2001 results place the population of
Tamil Nadu at 62.1 million comprising of 31.3 million males and 30.8 million
females (Table 1.19). The rural and urban population are 34.9 million and 27.2
million. The density of population is placed at 478 per sq.km. and the sex ratio 986
per 1000 males. The total working population is estimated at 27.8 million
comprising 23.7 million main workers and 4.1 million marginal workers. The
number of non-workers has been placed at 34.3 million. In total population, 0-6 age
group accounted for 10.98 per cent. The literacy rate increased to 73.47 per cent
from 63 per cent in 1991; 82.33 per cent for males and 64.55 per cent for females.
The proportion of urban population rose to 43.9 per cent from 34.2 per cent.
Among the 15 major States in India, Tamil Nadu is the sixth largest populous
State and Tamil Nadu’s population accounted for 6.0 per cent share of national
population at 1027.02 million. Among the districts, Coimbatore (42.24 lakhs) has
emerged as the most populous district, followed by Chennai (42.16 lakhs). The
districts of Nilgiris (7.65 lakhs), Perambalur (4.87 lakhs), Karur (9.34 lakhs) and
Ariyalur (6.94 lakhs) had a population of less than one million.
1.4.2. Decadal Growth
There is a drastic deceleration in growth of population during 1991-2001
compared to the preceding decade (1981-91). The growth rate obtaining at 11.19 per
cent during 1991-01 is much lower than the 15.39 per cent recorded during the
previous decade. Tamil Nadu has the second lowest growth of population, next only
to Kerala (9.42%), among the 15 major States in India.
1.4.3. Density
With Tamil Nadu’s geographical area of 1.3 lakh sq.km. being constant, the
increase in population from 55.9 million in 1991 to 62.11 million in 2001 had
pushed up the density of population from 429 in 1991 to 478 in 2001 per sq.km.
Similarly, at the all-India level, the density had increased at a faster rate from 267 to
324 in 2001 per sq.km. West Bengal (904) was found to be the most densely
populated State and Rajasthan the least dense (165). Tamil Nadu took the sixth
position in this regard. Among the districts, excluding Chennai, with 24231 persons
per sq.km, Kanyakumari had the highest density of 992 and Sivagangai the lowest
with 275 persons per sq.km.
1.5. Pudukkottai Town –The Study Area
Pudukkottai was the capital of the only princely state of Tamilnadu during
the British time (1686 to 1948) and presently is district headquarters. It is one of the
planned towns of India; Home of one among the earliest cave temples (about 1300
years old) with a continuous tradition till date; A notable centre for arts and temple
architecture during the period of royalty. The Government Museum, the Palace and
impressive public buildings are a few other attractions to this town.It is located on
Tiruchirappalli - Rameswaram NH 210, about 50 km south-east of Tiruchirappalli
and about 60 km south of Thanjavur. Pudukkottai is connected with Tiruchi,
Madurai, Thanjavur, Karaikkudi with Regular bus service. It has a notable station
of southern railways which connects Pudukkottai with Chennai, Chidambaram,
Thanjavur, Tiruchi and Rameswaram. It is situated in the valley of the Vellaru - 6½
km to the north of the river. It stands on a ridge that slopes gradually towards the
south.
1.5.1 History of Pudukottai
™ In 1784 Pudukkottai was a thick forest(It is a MARUTHAM).In 1799
Veerapandia Kattabomman entered into this thick forest for their
protection.(i.e)Collector office and Town RC church were situated in the
centre of the forest.
™ In 1826 Kings destroyed the forest trees and practiced agriculture. Then
Vellalar people changed the Vellar basin environment and stagnated for
irrigation. They converted forests into fields. North of Vellaru is called
Konadu and South area is Kanadu.
™ During 1680-1730 Regunatharaja Thomdaimon constructed New Kottai. So
this was named as “PUDUKKOTTAI”. Old name was Thondaimon Nadu.
™ In TamilNadu in1901, the first Car was purchased by Pudukkottai King.
In1904-Pudukkottai King donated one steam bus. It ran between Trichy and
Pudukkottai. Before the introduction of buses, people travelled from
Pudukkottai to Trichy and Thanjavur people by Judka.
™ In 1912 Pudukkottai Corporation was started.
™ In 1929 Train transport was started.
1.5.1.1. Early History
Of the founding and early history of the town, there is very little hard
evidence. 'Pre-historic' burial sites in Sadaiyap-parai, west of Thirugokarnam and on
either sides of Thirukkattalai ‘cart-track’ indicate that this region of the town, as
other parts of this tract, was the home of early men. When and how such a
megalithic settlement crystallized into a populous town mangalam or nagaram, is not
quite clear.
According to ‘A Manual of the Pudukkottai State (2004)’, the megalithic
settlements may have grown into a populous town of Kalasa-mangalam, which
became an important settlement of the Chettiyars and Karala-Vellalar communities.
The mercantile part of this town grew into a nagaram, called Senikula-manikkapuram with a merchant-guild. With the accession to power of the Pallava-rayars of
Vaiththur, Kalasa-mangalam became the capital of a Palayam. To the west of
Kalasa-mangalam, was Singa-mangalam. Parts of these two mangalams became the
eastern and western halves of the modern Pudukkottai town. Near them grew up
another nagaram, Desabala-manikka-puram by name.
1.5.2. Mythological Story of Origin
There is also mythological story about the origin. A General History of the
Pudukkottai state (1916) recounts the following story. According to this, one
Muchu-kunda-chakravarti, a Chozha king, who had his capital, Thiruvarur in the
Thanjavur district, in one of his tours through his dominions was so struck with the
beauty of the tract to the north of the Vellaru that he thought of building a town
there. The Rishi Parasara fixed an auspicious hour for commencing operations, and
Kalasa-mangalam, consisting of 'nine cities', (blocks) was brought into existence.
The king Muchukunda applied for inhabitants to the God Kubera, who sent him
1,500 families. The story was probably invented, after the town had become rich and
its merchants were found to be very wealthy. In this account fact and fiction are
inextricably mixed (Venkatarama Ayyar,2004).
1.5.3. Pudukkottai Town Past and Present
Pudukkottai may be considered as divided into the following blocks: The
town proper, a densely populated block, consists of wide straight streets running
east to west and north to south, and intersecting one another at right angles. In the
centre are now the ruins of the 'fort' with thick and high ramparts (only part of the
western wall remains.). Within it at the centre stood what was called the 'old palace'
containing a shrine to Dakshina-moorthi, a Durbar Hall that was used on state
occasions by the former Rajas of Pudukkottai, and the palace stable. State functions
and ceremonies, including the Dassara, were conducted here. Abutting on the inner
fort on its eastern side are situated the temple of Santha-natha-swami, and the
picturesque Sivaganga tank), popularly known as Pallavan-kulam, with its central
mandapam, flights of steps and substantial parapets.
Outside these run the four main streets, called Raja Veedhi-s in Tamil. Thus
there are four main streets (Raja Veedhi-s); East Main Street (Keezha Raja Veedhi),
West Main Street (Mela Raja Veedhi), North Main Street (Vadakku Raja Veedhi)
and South Main Street (Therku Raja Veedhi). Beyond these the naming of the street
is regular, like East Second Street, East Third Street, etc. South Main Street is the
bazaar street, and is the commercial centre of the town.
Machuvadi,
Rama-chandra-puram,
Ganesh
Nagar,
Gandhi
Nagar,
Marthanda-puram, Santha-natha-puram and Lakshmi-puram in the south and
Rajagopala-puram
near
the
railway
station
were
residential
suburbs.
Sandhaippettai, to the west of the town proper, was and is, as its name implies, the
market place. The market, which was formerly held on the roadside, has been shifted
to an open space to the south of the road where permanent sheds have been erected
for the sale of commodities. The market, which is held every Friday, is the largest in
the district. Also there is a ‘farmer’s market’ where the farmers sell their produce
without the middlemen, in the west fourth street.
To the west of the town lies Thirugokarnam at the foot of a rock. Here is the
famous temple of Gokarnesvara and Brahadambal. The Goddess was the tutelary
deity of the former Rajas of Pudukkottai, who consequently styled themselves ‘Sri
Brahdamba-dasa’ or the 'servants of Sri Brahadambal'. They were ceremonially
installed on the gadi and anointed at this shrine. It is in the name of this deity that
the coin called the Amman-kasu was struck. Thiruvappur is another suburb. This
suburb was once a centre of silk weaving and was mostly inhabited by the silkweaving Sourashtrian community called Patnool. According to the Statistical
Account of Pudukkottai (1813) there were 30 looms in the place in 1813, and
according to Pharaoh's Gazetteer, it was an emporium with an 'extensive weekly
market', and 'numerous bazaars in which cloths of various qualities and the best in
the province' were sold. The weekly market referred to here, was subsequently
transferred to Sandhaippettai. The dyers of the place prepared pink dhotis which
had a wide reputation, but at present their craft is moribund. Near is the Kavinattukkanmai, the largest tank, in the district.
There is a Government Museum in Tirugokarnam. It was opened in 1910. It
consists of different sections like
•
Arts and Industries-representing local arts and industries with specimens
from outside the State for comparison and study
•
Economic section containing a representative collection of local cereals,
fibres etc.,
•
The Natural History section
•
Ethnology-with a fine selection of arms and armour and of musical
instruments
•
Numismatics-a fairly representative collection of Indian coins
•
Archaeology-illustrative of the large field of ancient monuments and
sculpture for which the State is famous
•
Paintings
•
Zoology
1.6. Geography of Pudukkottai District
The original princely state of Pudukkottai was a land-locked territory, with
Tiruchirappalli, Thanjavur and Ramanathapuram as its neighbours. At the time of
being made as a separate district in 1974 the coastal strip of Aranthangi was added
to it. Presently, the boundaries of the Pudukkottai District are the Bay-of-Bengal in
the east, Thanjavur and Tiruchirappalli in the north, Tiruchirappalli in the west and
Sivaganga and Ramanathapuram in the south. It is having a 36 km. of seashore in
the east. Area: 4661 square kilometres.
1.6.1. Location and Area
Pudukkottai is one of the new districts formed after the 1971 census, on 14th
January, 1974. It is one of the small districts of Tamil Nadu with an area of 4661
sq.Kms. The district lies between 78 degrees 25' to 79 degrees 15' of the eastern
longitude and 9 degrees 50' to 10 degrees 40' of the northern latitude. This district is
bounded by Tiruchirappalli in the north, Thanjavur in the north-east, Bay of Bengal
in the east and Ramanthapuram in the south. It has a coastline of about 36 Kms.
Total area of the district is 4651 sp. Kms. Headquarters of the district is Pudukkottai.
1.6.1.1. Terrain
The terrain of the district is generally flat; Dry open lands with cultivation
as well as semi-barren wastelands form the basic Pudukkottai country. On the
western surface of the plain emerge rocks of low and middle elevation. The scrub
jungle, once plentiful, is to be met with now in a few pockets only. The terrain is
divisible into two broad portions with distinctive physical aspects, eastern and
western. The dividing line may be taken as a north-south line passing through the
town of Pudukkottai. The lands west of this line comprise the greater portion of
Kolattur and Thirumayam taluk-s and are rocky. In the east are Alangudi
Pudukkottai, Aranthangi and part of Thirumayam taluk-s, and are bereft of hard
rocks. Alluvia and soft rock are found here.
1.6.1.2. Hills
Though the Tamil word used for the hills of Pudukkottai is malai, that is
mountain, none of the outcrops would meet the requirement of the definition. There
are numerous hills and lofty rocks are to be found in Pudukkottai. The important
among them are the Narttamalai hills, Sevalur hills and Annavasal hills. Fine
quality granite is available in plenty. Names of a number of places bear malai as
suffix or prefix like Narttamalai, Viralimalai, Malayadippatti ,Malaiyakkoil, etc.,
1.6.1.3. Plains
The Pudukkottai terrain studded with hills in the west of the district gently
slopes towards the flatland, estuaries and seacoast in the east. The plains of east
Pudukkottai consist of miles of open country, ploughed fields and tidal mudflats. The
presence of alluvial soil on the east Pudukkottai surface makes it fertile and suitable
for agriculture.
1.6.1.4. Tanks
The district s tanks are ubiquitous. Irrespective of the geology, tanks, called
kanmai in Tamil, can be seen distributed over the entire district. These tanks irrigate
the district s agricultural fields.
1.6.1.5. Rivers
Rivers in Pudukkottai are only jungle streams that themselves take their rise
from tanks. Since the tanks have surplus only for a short period around the monsoon
time, most rivers are dry for most part of the year. The most significant stream is
Vellaru .The other streams or rivers are the Pambaru('Snake-river'), the
Agniyaru(Fire-river ), the Ambuliyaru etc.,
1.6.1.6. Seacoast
The length of seacoast in the district is about 36 kilometers. Where the rivers
of the district enter the sea, estuarine islets have been formed. The point off Mimisal,
where Kolavanaru joins the sea, is one such islet. The Pudukkottai seaboard, like
the rest of the Coromandal coast, has a simple structure.
1.6.1.7. Climate
The district has a hot tropical climate, humid near the coast. The summer
season is from March to May, May being the hottest (Temperature about 37 °C).
South-west monsoon lasts from June to September. October and November
constitute the retreating monsoon season. The north-east monsoon is over by the
second-half of December.
The relative humidity is between 50 and 80 per cent, but during FebruaryJuly the air is drier. The annual rainfall is 950 mm. The sky is generally cloudy
during the monsoon. In the rest of the year it is mostly clear. Recorded history of
Pudukkottai lists a succession of years that have witnessed drought and the
consequent famine.
1.7 Transportation
There are no national highways passing through the district. The total length
of roads in this district is 3243 Kms. Comprising of 78.10 Kms. of state highways,
434.30 Kms. of major district roads and 2730.60 Kms. of panchayat roads. The total
length of metre-guage railway line in the district is about 84 Kms. with 12 railway
stations connecting Pudukkottai town with Tiruchirappalli as also Karaikkudi and
Manamadurai in the adjacent Ramanathapuram district, meter guage is now
converted into broad -guage. Arantangi is connected with Thiruvarur in the adjacent
Thanjavur district. The railway line from Chennai to Rameswaram passes through
this district. The transport handled by the railways in the district is very meagre on
account of the low route length and limited potential for transportation in the
hinterland.
1.8. Industries
Pudukkottai district is not gifted with wealth. There are no mineral deposits
worth mentioning in the entire area of the district. However, a narrow belt of good
grade feldspar and quartz is reported to be available in Kulattur taluk ; pink granite
deposit is reported to be available in Ponnamaravati area of Tirumayam taluk. The
reserves of limestone reported to be available in Adanakottai area of alangudi taluk
is estimated at about 8230 tonnes and the present level of exploitation is only 200
tonnes. The district is industrially backwards and the three taluks, viz. Alangudi,
Tirumayam and Kulattur had already been declared by the State government as
backward area entitling industrial units to be set up there for a central subsidy of 15
per cent on fixed capital investment. There are six large scale industries in the
district as given below: (1) M/s. Cauvery Spinning and Weaving Mills Ltd., Cauvery
Nagar. (2) M/s. Pudukkottai Textile Mills Ltd., Pudukkottai. (3) M/s. Sri Nadiambal
Textile Mills Ltd., Arantangi. (4) M/s. Ramachandran Chemicals (P) Ltd., Kiranur.
(5) M/s. Sundaram Industries Ltd., Pudukkottai. (6) The State Government Printing
Press, Pudukkottai. Among the six large scale industries mentioned above, three are
located in Pudukkottai itself. There are 392 small scale units. The main industries
are engaged in wood based industries, tinkering, fabrication of metal products,
printing and binding, manufacture of agricultural implements, manufacture of
agricultural implements, manufacture of cement tiles and other cement products,
automobile servicing and repairing and safety matches.In addition to the small scale
industries, there are a number of village and cottage industries. Prominent among
them are pottery, blacksmithy, carpentry, small lime kilns, small brick kilns, basket
making, rope making and synthetic gem cutting.
1.8.1. Important Industries
1. National Oxygen Ltd.: Trichy Pudukkottai Road, Mathur Village,
Pudukkottai, Tamil Nadu.It is a manufacture and traders of industrial
gases such as Oxgen gas, dissolved acetylene gas, medical Oxygen,
Nitrogen gas, liquid Oxygen, liquid Nitrogen high purity Nitrogen.
2. SRF Ltd.: (Formerly known as Shriram Fibers Ltd.) Viralimalai, Dist.
Pudukkottai, Tamil Nadu.It is a manufacture of Nylon industrial yarn
tyre cord/fabrics leather auxiliaries, fluro carbon refrigerant gases and
hydrofluoric acid, besides nylon moulding powder in technical
collaboration with chemtex fibres INC. USA.
3. M/s. SRF Nippondenso Ltd. is a joint venture with Nippon Denso Co.
Ltd. of Japan for manufacture of automotive electricals.
4. M/s. SRF Transnational Holdings Ltd. is a subsidiary company.
1.9. Tourism
List of Tourist places are,
•
Sri Kokaraneswarar temple
•
Government Museum
•
Sittannavasal
•
Kudumiyamalai
•
Kodumbalur
•
Viralimalai
•
Narthamalai
•
Tirumayam
•
Avadaiyarkovil
1.10. Education
In the urban areas Pudukkottai, there are 59 Higher Secondary Schools, 85
Secondary Schools, 124 Middle Schools and 202 Primary Schools per every 10000
population. Kiranur, Alagapuri and Alangudi have the highest proportion of Higher
Secondary Schools (151), Secondary Schools (327) and middle Schools (347)
respectively per 10,000 urban population. But the case of primary schools the
highest proportion of 993 schools per 10000 population is found in Kadiapatti.
1.10.1 University and Colleges
V.SSivalingam Govt. Arts College, Pulankurichi, Pudukkottai. Ganesar
Senthamil Kalloori Melaisivapuri, Pudukkottai. Government Art College for
Women, Pudukkottai. Government College for Education, Pudukkottai. HH The
Rajah's
College,
Pudukkottai.
K
B
Y
S
College
of
Physiotherapy,
Pudukkottai.J.J.College of Arts and Science, Sivapuram, Pudukkottai.
1.11. Population Trends
The story of population growth in Pudukottai is fairly in tune with the
classical theory of demographic transition.The total population for the town in 1901
censes was only 20,347 whereas it has grown upto 1, 01,723 in 2001. The absolute
term , the population of Pudukkottai increased by whopping 2,86,382during 19912010. Although the net addition in population during each decade has increased
consistently.
ƒ
In 1901-1921 had stagnant population
ƒ
In 1921-1951 had steady growth
ƒ
In 1951-1991 had rapid hith growth
ƒ
In 1991-2010 has high growth.
1.11.1 Trend in Birth/Death rate and infant mortality rate
The birth rate for Pudukkottai is21.7 in 1991 which is nearly half of the rate
as compared to the birth rate in 1951.The first three decades showed a significant
decline in birth rate (Table 1.24). In respect of death rate the decline is gradual every
year and the death is only 7.3 in 1991, which is below the state average. The infant
mortality rate has also come down from 76.3(1951) to 30.9(1991). In 2010 mortality
rate is reduced to 14.5%
1.12. Resource
1.12.1. Land Resource
The total geographical area of the district is 4657.24 Sq.Km. The biggest
taluk area wise being Kulathur and smallest Pudukkottai (Table 1.25).
The utilization of land area in Pudukkottai is up to 66%.About 29.4% land
are not available for cultivation. About 22% of the soil is reported to be suffering
from salinity/alkalinity (Venkatarama Ayyar, 2004).
1.12.1.1. Soils
The major soil types, on the order of their extent, are laterite, mixed and red
loamy types.About one fourth of the soils suffer from one problem or other, the
main problems being salinity/alkalinity (Venkatarama Ayyar, 2004).
1.12.1.2. Crops cultivated
The important cultivated crops are shown in Table 1.26.Cereals such as Rice,
Cholam, Varagu, Ragi, Maize and Cumbu were cultivated.Pulses such as Red
gram,Cow Pea,Horse gram,Black gram and green gram were cultivated.Oil mseeds ,
Condiments,sugars and fiber crops were also cultivated.
1.12.2. Trends in Production and Productivity
Though agriculture is the main source of sustenance for a majority of the
population, the scenario is not quite encouraging. Dry land farming which is
predominant suffers badly due to frequent poor monsoons affecting agricultural
production. Cereals have shown fluctuations both in area cultivated and production
from 1980-81 to 1995-1996 (Venkatarama Ayyar, 2004).
1.12.3. Horticulture
An interesting feature in the farm sector is the development of orchards using
dry farming techniques and minimum irrigation in the formation stage. Banana is the
main fruit crop under irrigation. The major fruit crops are,
Jack, Guava and Acid lime are raised only on a very limited scale. Except for
banana, the rest are raised on the red or latereritic soil belts (Table 1.27).
1.12.3.1. Vegetables
Brinjal (Solanum melongena) and ladies finger (Hibiscus esculentus) are the
two major vegetables cultivated here.
1.12.3.2. Plantation Crops-Cashew
A noteworthy feature of this area is the cultivation of cashew as a rainfed
crop over extensive areas in the lateritic belt. However no cashew processing unit
has been established locally. The nuts are taken to numerous processing units that
have sprung around Panruti in Cuddalore district (Venkatarama Ayyar, 2004).
1.12.4. Forest Resources
Major portion of the forests of this area was the personal preserve of the
kings of Pudukkottai. Large forest areas were presesrved as the hunting grounds for
the rulers, their families and friends.The control of the forests were transferred
initially to the Revenue Department in 1948 and subsequently to the forests
department in 1950.
1.12.4.1. Flora
Much of the natural forests have been converted into plantations.Only
isolated patches of natural forests like Narthamalai R.F are being managed by the
forest department and these forests support the following forest types:
Tropical dry evergreen forests
These forests are unique in nature and the floristic compositions are as
follows.
Characteristic species
•
Manilkara hexandra.
•
Mimusops elengi.
•
Albizia amara.
•
Memecylon umbellatum.
•
Diospyros ferrea syn maba buxifolia.
Top Canopy
•
Mimusops elengi.
•
Diospyros ebenum(Occasional)
•
Strychnos nux vomica(Occasional)
•
Strychnos potatorum (Occasional)
•
Diospyros chloroxylon (Occasional)
•
Drypetes sepiarea(Rare)
•
Syzygium cumini.
•
Canthrium decoccum(frequent)
•
Zizipus glaberrima(frequent)
•
Acacia leucophloea(frequent)
•
Catunaregam spinosa(frequent)
•
Buchanania lanzan(Occasional)
•
Sapinda emarginatus(Occasional)
•
Albizia amara.
•
Albizia lebbek.
•
Tamarindus indica.
•
Azadirachta indica.
•
Borassus flabellifer.
Under wood
•
Cassia carandas(abundant)
•
Flacourtia indica(locally abundant)
•
Diospyros ferrea(frequent)
•
Grewia sp(abundant)
•
Gymnosporia spp(frequent)
•
Ixora arborea(frequent)
•
Tarenna ascatica(frequent)
•
Memecylon umbellatum.
•
Garcinia spicata.
Shrubs
•
Strobilanthus
•
Dononaea viscosa(abundant)
•
Glycosmis pentaphylla.
•
Ochna asiatica.
Herbs
•
Hemidesmus indicus.
Southern Carnatic umbrella thorn forests.This is an economically important
forest type supporting many valuable fuel wood species (Table 1.28) (www.National
Information System.com)
1.12.4.2 Fauna
Eventhough forests of this area were the game resources of the former rulers
and supported a variety of fauna, degradation have reduced the wildlife wealth.The
animal commonly found are catalogued in table 1.29.
Around Viralimalai murugan temple, Peafowls are seen in large numbers in
tank bed plantations, private fields and a top trees.
1.12.4.3. Man made Forest
The entire Pudukkottai area abounds in cashew and Eucalyptus tereticornis
plantations and the Arimalam series of Eucalyptus plantations is justly famous.
Casuarina on a limited scale.
1.12.4.4. Rare and Threatened species
Rhyncosia velutina and Santapura madurensis are the two plant species
which have become vulnerable and endangered, respectively.
1.12.5. Surface water
The district is one of the good rainfall regions with an average monthly
rainfall of 77.13 mm(Table 1.30).This is ensured a high percent of water table in the
district as indicated by the following data for 1995-1996.
Agniyar basin is the main source of surface water in Pudukkottai. An
important point to be noted in this basin is that there are no reservoirs across any of
the rivers of this basin, the main reason being none of the rivers has copious flow.
No drought, flood or cyclone has been reported between 1985 and 1996.
1.12.6. Heritage Resources
The rare collection is the sections of Geology, Zoology, Painting,
Anthropology, Epigraphy and Historical records are very interesting and
informative. The beautiful bronze sculptures of various periods are really attractive
pieces of this museum.
1.13. Tourist Arrivals
Fig 1.2 shows the information on tourist flow indicates that there has been a
steady increase in number in the district between 1990 and 1994.In the later period
the flow declined (1994-96) (ENVIS,2005).
1.14. Growth of Vehicle population
The vehicle population in general has increased over eight times in
Pudukkottai town. Four wheelers have registered a three fold increase in their
population. Similarly two wheelers have recorded a manifold increase in numbers
from 1,472 to 1, 93,479(1986-96) (ENVIS, 2005).
1.15 Density of Population
The overall density of the population has registered an increase from
246 persons/ sq.km to 317persons/sq.km. The density of population was 3000
persons per sq.km in urban sites during 1996.Table 3.1 show the population status in
the year 2001 (ENVIS, 2005).
1.15.1. Urban slum population
The recorded slum population in this area was around 30,000 during
1991.No relevant data is available for comparative analysis of slum population over
the period 1981-96.
1.16. Urban Services
1.16.1. Water supply
Ground water is the major sources of supply in the district and the designed
capacity is 126.95 lakh litres. The averages per capita water supply is 60lpcd (Litres
pr capita per day) with per capita water availability of 67 lpcd at Pudukkottai. Over
85% of the town population is covered by protected drinking water supply. The
estimated sewage generation is 51.8 lakh liters. There is no under ground sewage
system. There is no treatment plant in the town and therefore there is no organized
disposal of sewage (ENVIS, 2005).
1.16.2. Municipal Solid waste generation
The total daily solid waste in urban areas of Pudukkottai district is 45.5
tonnes with collection efficiency of 89%.Of these 25 tonnes are generated in
Pudukkottai town itself. Primary component of the waste is compostable matter and
accounts for 85% of the total waste.
1.16.3. Health and Hygiene
Over a period of 10 years (1987-96), largely reported water borne diseases
have been gastroentitis, Diarrhoea and malaria. The reported incidence of death
occurred only due to diarrhea (Table 1.31 and 1.32).
Under Indian Medicine systems hospital and bed facilities are available for
Siddha and Homeopathy. There are totally 314 registered practitioners in various
form of medicines.
1.17. Poverty Line
96,733 families are reported to be below poverty line (ENVIS, 2005).
1.18. Industrial Development and Environmental Status
There is not much industrial activity in this area. There are only 29 large and
medium units operating while as many as 3000 units are reportedly working in the
small scale sector. A major facility available to industrial enterprises in the district is
the developed plots and built-up sheds provided by SIPCOT and SIDCO
respectively.
1.18.1. SIPCOT Complex
The SIPCOT complex is situated 7km.from Pudukkottai on the PudukkottaiTrichy road in an extent of 412 acres. Of this area, around 51 acres are allotted to
SIDCO Industrial Estates where build up sheds are made available to entrepreneurs
(ENVIS, 2005).
1.19. Acqa Culture activities
The acqua culture activities practiced in Pudukkottai have been of semi
intensive type.There are 50 such units covering 231 hectares of area (ENVIS, 2005).
1.20 Environmental Institutions
There are 16 NGOs rendering their services for creating environmental
awareness and campaigns (Venkatarama Ayyar, 2004).
AIM AND OBJECTIVES
The aim of the study is to evaluate the impact on environment that has
occurred already with the following objectives:
¾ To assess the existing air quality.
¾ To assess the extent of pollution of water bodies due to developmental
activities.
¾ To assess the quality of soil and extent of soil pollution and soil degradation.
¾ To assess the extent of noise pollution.
¾ To assess the quantities and types of solid wastes generated, assess the
efficacy
of present disposal method and to propose suitable methods of
disposal.
¾ To assess the amounts of sewerage generated and its quality.
¾ To assess the efficiency of existing sewage system and propose sewage
treatment facilities.
¾ To assess the existing biotic components in Pudukkottai town (flora and
fauna).
¾ To assess the socio-economic impact of urbanization in Pudukkottai.
REVIEW OF LITERATURE
This section reviews the various spects (Urbanization, Air, Noise, Water,
Soil, Waste water, Solid waste, Flora, Fauna and Socio-economic status) that are
related to the research work.
2.1. Impact of Urbanization
Maiti and Agrawal (2005) reported some of the important environmental
problems caused by over population growth and rapid urbanization process in the
metropolitan cities of India. Total urban population in India has increased more than
ten times surpassing India’s total population growth, which has increased less than
five times during 1901 to 2001. Also, there was about three-fold increase in the
percentage of total urban population in Class-I city followed by almost a fifty-fold
increase in the total population in the million plus cities in India from 1901 to 2001.
Despite several Government housing policies, 41% of the total slum population of
India is residing in million plus city alone.
A three-fold increase in the number of motor vehicles has been found in
India in the last decade. In all the four metro cities SPM was found highest along
with the problem of solid wastes. The noise pollution was noticed more than the
prescribed standard in all the four metro cities. Five and more person residing in
single room was faced by more than one fourth population of Mumbai followed by a
little less than one fifth population of Kolkata and about 10% population of Delhi
and Chennai both. Also there is an acute shortage of piped drinking water in these
metro cities. India’s urban future is grave. Therefore there is an urgent need to tackle
the urban environmental problem in a rational manner giving attention to the need
for improving urban strategies.
Between 2005 and 2030, the world’s population is expected to increase by
1.7 billion people, from 6.5 billion in 2005 to 8.2 billion in 2030. Almost all growth
of the world’s population between 2005 and 2030 is expected to occur in less
developed regions. In particular, the projected population growth at the world level
will be primarily accounted for by the growth in the urban areas of the less
developed regions.
That is, while the world population is projected to grow by 1.8 billion people
between 2005 and 2030, the urban population is projected to increase by 1.7 billion.
The absolute growth in the total population is lower than that of the urban
population because of a declining rural population over the next 25years (U.N.
1993).
2.2 Air Pollution
The main source of air pollution are industrial plants, power stations,
automobiles, locomotives, aeroplanes, jets, missiles, domestic furnaces, dead bodies
burning, burning of oils, sewers ,refuse burning ,etc. The emissions from these
sources mainly consist of aerosols, odour, and gases. These air pollutants affect man,
animals, vegetation and also having economical, sociological and psychological
impact. It causes irritation of the mucous linings of the eyes, nose and throat,
headaches, nausea, chronic bronchitis, bronchial asthma, asthmatic bronchitis,
pulmonary emphysema, cancer, death etc.
Nowadays, acid rain has become the talks of the day. Today every body has
a craze for having own vehicles and in most of the cities automobiles are rapidly
becoming the main source of air pollution. Although the number of vehicles, plying
in Indian cities including metropolitan is still insignificant as compared to the
number of USA, Europe and Japan, due to the inferior maintenance of vehicles in
combination with lower combustion efficiency is making the vehicular exhausts a
menance to the city dwellers. The auto mobiles are mostly driven by petrol or diesel.
The petro-burning vehicles emit carbon monoxide, hydrocarbons and oxides of
nitrogen. Diesel engines emit relatively little of these but produce more particulates
and smoke. Oxides of nitrogen and hydrocarbons interact in the presence of sunlight
to produce oxidant smog which irritates the eyes and lungs and damage sensitive
plants (Trivedy and Goel, 1995).
Djen (1992) concluded that urban heat island effect is large and has
enhanced with time. During recent decades, the urban centre of Shanghai has
experienced lower wind speeds, lower humidity, fewer fog days, fewer sunny days,
increased low cloudiness and increased overcast days. Concurrent variations at
nearby rural stations were dissimilar. Solar radiation in urban Shanghai shows
accelerating decreases of both direct solar radiation (S) and global radiation, but
increase of both diffuse radiation (D) and average turbidity (D/S).
Air pollution is a major issue amongst many environmental problems of
Calcutta, especially from the health perspective. The air quality of the city becomes
worse during winter due to frequent thermal inversion and low wind speed. When
present condition of air pollution in the city was found to be a cumulative effective
of many factors .The environmental problem was found acute in core-Calcutta
whose area is 104 sq km and night time population of 3.4 millions and that of day
time 6.0 million (Gautam,1998).
Robert and Douglas (1977) reported that urban areas affect the wind flow
pattern and hence the transport of contaminants in the atmosphere. The central park
station (Singapore) was initially taken to be the most urban location and deviations
between the wind speed at the park and at each of the other locations along the
stream flow line were determined. The deviation of wind direction along each
available stream flow line was determined relative to the wind direction at the first
upwind rural site. During both day time and night time hours there exists a critical
rural wind speed below which air is accelerated as it flows over the rough, warm
city.
Maccarrone (1989) monitored three heavily trafficked roads in Australia with
different traffic volumes and speeds for air quality volumes and wind patterns
coinciding with pollutant measurements were monitored. The level of reduction of
air lead level with increasing distance from the road way was determined by
simultaneously monitoring its level at distances 20.50 and 80m from the road way.
Sabbak (1990), conducted a comprehensive field study of atmospheric nitrogenous
pollutants in Jissah, Saudi Arabia for the period of 1984-1987. The decrease in NO
concentration from 1984-87 was mainly due to two reasons(i) Phasing out of many
construction and industrial projects.(ii)Enforcement of the Motor Vehicle Periodic
Inspection(MVPI).The analysed data showed lower mean than International air
quality standard.
Alam etal (1999) suggested that by introducing mass transportation system
like rail or monorail it may be possible to reduce the number of motor vehicles on
the road. In the developing countries like India, urbanization is quite revolution that
is engulfing the country. This urbanization is intricately linked with the process of
economic development and hence it is considered inevitable. Urbanization while
having a positive impact on income levels employment and other various
developmental factors has also brought about certain negative impacts on the
environment of the area. It is found that the overall quality of urban environment is
fast deteriorating over the years. The problem of air pollution came into existence
when man first learnt to start fire in his cave for cooking and providing light. Now
harmful gases in large quantities get released into the atmosphere due to various
activities of man.
Marsh and Foster (1967) reported that due to increased concern about the
effects of air pollution on both people and materials, many countries have introduced
legislation designed to control the amount of pollution in the air. The annual average
concentration of sulphur dioxide at individual sites is strongly correlated with the
consumption of local installations emitting their effluents from chimneys less than
21m high.
Ghosh and Seth (1994) reported that atmospheric pollutants get deposited on
the earth’s surface through various physical and chemical process. Precipitation
pathway for deposition of atmospheric aerosols and anthropogenic materials contain
pollutants of varying nature, causing deterioration of physical ,chemical and
biological characteristics of waters.
Bitan (1992) reported that the future most of the world's population will live
in urban areas and there also most economic activities will be concentrated. This
will lead to enormous environmental and climatological problems, unless urban
planners and architects develop a new urban planning strategy and building design
methods, which will enable the continuation of the growth of urban areas and also
enable its population to live and work in a good climatic environment. To achieve
this goal the combination of using alternative energy sources together with
integrating climatological factors in all urban planning levels should be designed to
achieve an expected improved climatic and environmental quality of the urban area.
Chary Srinivas (1992), estimated the concentration levels of CO, HC, NOx, SOx, Pb
across five dense roads of Delhi.
The ambient air quality data in Delhi showed that the annual and 24hr mean
value of SOx and NOx did not exceed the stipulated standards during 1989 where as
the annual mean values for SPM exceeded 200mg/m3 at the monitoring stations.
Luria (1986), analysed the data obtained at the Jerusalem municipal air
monitoring station, during the years 1979-1983.Seasonal and long term trends in air
quality were determined. The results indicated that ambient air quality levels in
Jerusalem were influenced not only by local forces but also by transport of air
pollutants from Israis’s coastal areas. It was found that in 1981 concentration of
pollutants including the total suspended particulate were high. Hence it was
concluded that air pollutants level in the city were influenced more by multi annual
change in dispersion conditions than by the combination of all local anthropogenic
sources.
Prasanthi and Rajeswari (2003) conducted a survey at major traffic points in
Kurnool town to investigate the effect of vehicular emissions on the health of 53
traffic policemen. It was found that these personnel were directly exposed to
vehicular emissions for nearly 8 hours per day. The main symptoms observed were
cough 80%, breathlessness 20%, headache and dizziness 30% and passage of black
sputum in the morning 3%and also conducted pulmonary function test (PFT) on
these personnel. Some of them exhibited normal pulmonary function test. About
60% showed mild to moderate obstruction, out of which 65% were non-smokers and
35% were smokers. In case of 20% of smokers the obstruction was severe .It was
concluded that traffic policemen were suffering from respiratory disorders due to
exposure to vehicular pollution.
Diesels engine exhausts have significantly higher particulate and gas phase
pollutants. The chemicals associated with the paticles may interact with the lung
cells and cause damage, inflammation and excess mucus production (Santondilonata
et al., 1978).
Pedro et al (2007) study applied a methodology for discriminating local and
external contributions of atmospheric particulate matter (PM) at a rural background
station in the North-western coast of Spain. The main inputs at the nearest scale had
come from soil dust, marine aerosol and road traffic. At a larger scale, the highest
contributions had come from fossil-fuel combustion sources, giving rise to relatively
high ammonium sulphate background levels, mainly in summer. External
contributions from long-range transport processes of African dust and nitrate had
been detected. Morocco and Western Sahara were identified as the main potential
source regions of African dust, with a higher content of Al and Ti than other crustal
components. Geographical areas from central and Eastern Europe were identified as
potential sources of particulate nitrate.
Infante et al (1990) reported that the concentration of particles < 2 µm in
diameter remained constant during the sampling period while the concentration of
particles > 7 µm showed time variations. Aerosol from Ponce, Puerto Rico was area
is greatly composed of particles > 7 µm in diameter, they accounted for over 45% of
the Total Suspended Particulate (TSP) of this area. Over 75% of the aerosol
concentration was from particles > 3.3 µm, approximately only 20% of the aerosol
concentration was from particles < 2 µm in diameter. A linear relationship was
observed between the different particle size and the TSP. The size distribution and
its time variation were explained in terms of local sources such as agricultural
burning, natural contributions and industrial activities, as well as contribution from
the Sahara haze that crossed the Atlantic from Africa and reached the Caribbean
region during the summer.
Gautam et al (1998) reported that air pollution became acute in Calcutta
during winter. Pollutants couldnot disperse easily, mainly due to inversion, low wind
speed and high concentration. Calcutta was known to be one of the world’s most
polluted cities. The average SPM concentrations during the winter in 1992, 1993 and
1994 were 982mg/m3, 1007mg/m3 and 1181mg/m3 respectively. The anthropogenic
SPM was more toxic than the SPM of natural origin. Various factors like use of
kerosene and coal as cooking fuel by a large portion of the city dwellers, large
number of registered and unregistered factories, poorly maintained cars, poor quality
of fuel, bad condition of the city streets, small road area compared to the total city
area, high population density, miserable slum conditions of habitation and overall
poor socio-economic status of city dwellers were together responsible for the serious
air pollution in the city.
Panda and Kar (2003) reported that in Rajesthan the mine area the maximum
SPM in winter H-block was 146mg /m3 as per standard. Air pollution was controlled
by afforestation. The silica content in the respirable dust was within permissible
limits. The noise levels recorded was around 50dB during daytime and <40dB in
night time. The solid waste generated was in the form of overburden at a rate of 2.54
lakh tones per annum. Topological advantages of the hilly terrain were taken to
facilitate drainage.
Karue et al (1992) , analysed suspended particulate matter in air at three
different sites in Nairobi. The values were well within the WHO standard but when
compared to the values in some European countries they were found to be high.
Zannetti et al (1977) reported that Sulphur dioxide concentration in the
historical centre Venice and its surroundings are related to meteorological
parameters.
Alison and William (1985) examined national trends in Sulphur dioxide
concentrations from 1975 to 1982 in USA. From the analysis it was found that SO2
levels were (statistically) scientifically lower in the later years of their study than in
the earlier years, due to various control measures taken by the Government.
Marshall et al (1986), reported that in Atlanta, U.S. both SPM and sulphur
compounds increased in summer from winter values probably due to enhanced
production
of
particulate
sulphur
from
gaseous
precursors.
Kuntasal
(1987),conducted air quality trend analysis for hydro carbons(HC),NO and CO from
1968 to 1984 at South Coast air basin of California.Emissions and air quality trends
were compared. It was found that the ambient HC,NO trends were some what
different from estimated emission trends of HC and NO whereas there was a definite
downward trend of ambient CO was consistent with vehicular emission control
measures.
Capannelli et al., (1977) reported that NO is found in all the high
temperature combustion processes. Later part of the NO reacts with the atmospheric
O2 to form NO2.
Bower et al (1991) reported that no site in the U.K. breached the NO2
Directive Limit Value during the year 1987, though the closest approaches were at
the two London stations. Annual average NO2 concentrations, which varied from 23
to 39 ppb, were consistent with the top five percentile of long-term measurements
from a national survey of over 360 U.K. urban areas carried out in 1986.The
temporal variability of NO2 concentrations was substantially lower over all time
scales than that for NO: winter/summer ratios for all sites averaged 2.9 for NO and
1.3 for NO2. Most sites showed strong diurnal variations for NO which were
primarily influenced by traffic emissions during rush hours, although these
variations were less marked for NO2.
Pandey et al., (1992) reported the diurnal patterns in the concentrations of
ozone (O3), nitrogen dioxide (NO2), sulphur dioxide (SO2) and total suspended
particulate matter (TSP) in the urban atmosphere of Varanasi city in India during
1989. The city was divided into five zones and three monitoring stations were
selected in each zone. Ambient concentrations of NO2 and SO2 were maximum
during winter but ozone and TSP concentrations were highest during summer. NO2
and SO2 concentrations peaked in the morning and evening. Peak concentrations of
O3 occurred in the afternoon, generally between noon and 4 p.m.
Mrinal et al (2005) reported that the public health implications of vehicular
emissions were substantial. The particulate matter, particularly that less than 10 µ in
size, can pass through the natural protective mechanism of human respiratory system
and plays an important role in genesis and augmentation of allergic disorders. They
discussed the approach for the selection of air monitoring stations, the methodology
adopted for sampling and subsequent analysis. The results of SPM, RPM, NOx, SO2,
CO and Pb levels indicated that they were at levels dangerous to human health. In
order to mitigate air pollution in the city a strategic air pollution management plan
was proposed and the possible different measures that could be adopted to maintain
the balance between sustainable development and environmental management have
been discussed.
Fung et al (2005) studied the role that ambient air pollution plays in
exacerbating cardiovascular and respiratory disease hospitalization in London,
Ontario from 1 November 1995 to 31 December 2000. The number of daily cardiac
and respiratory admissions was linked to concentrations of air pollutants (sulphur
dioxide, nitrogen dioxide, ozone, carbon monoxide, coefficient of haze, PM10) and
weather variables (maximum and minimum of temperature and humidity). Results
showed that current day carbon monoxide and coefficient of haze produced
significant percentage increase in daily cardiac admissions of 8.0% and 5.7% for
people < 65 years old. PM10 was found to be significantly associated with asthma
admission in the > 65 group, with percentage increase in cardiac admission of 25%
and 26.0% for current day and 2day means, respectively.
Shangedanova and Burt (1994) estimated the pollutant emissions and air
quality in MOSCOW .The concentration of NO2 was the major aspect of air
pollution. But during recent years of considerable reduction of SPM and SO2 levels
were achieved due to increased use of natural gas. Ostria Sergis and Lawrence
Michel (1994), believed that intelligent highway systems (IVHS) improved and
increased the operation and efficiency of the transport system in USA. Air quality
problems associated with congestion, poor vehicle manitanance, wasted travel and
too many vehicle trips were alleviated by IVHS.
Prakasa Rao et al (1992) studied all the wet and dry deposition samples for
major cations and anions along with pH. Dry depositions were minimum in the
monsoon season and maximum in the winter season though there was no significant
difference in pH values. The wet deposition of all ionic components was found to be
higher than the dry deposition. The depositions of the ionic components from natural
sources (soil and sea) were higher than those from anthropogenic sources. The dry
deposition velocities of the aerosols were found to be increasing with increase of
their mass median diameters.
The chemical composition of the dry deposition at Pune indicated maximum
depositions of the alkaline substances, which are the main cause for the alkaline pH
of rain water. Their results further suggested that the atmospheric composition in the
city was strongly influenced by natural sources rather than anthropogenic.
Air quality in major cities has deteriorated to a large extent because of the
rapid growth on the number of motor vehicles every year. Inhalation of diesel
exhaust components such as particulate, SO2, NO2 and Ozone are associated with
health effects ranging form increased mortality and hospital admission to subtles
changes in lung functions at low to very low concentration (Brunekreef et al., 1995).
Lutmer et al (1967) reported that O3, NO and NO2 were potential enhancing
agents for the formation of carboxy haemoglobin during short-term exposure to
concentrations of CO in experimental animals.
Marsh and Foster (1967) reported that in recent years there had been
increased concern about the effects of air pollution on both people and materials,
and many countries have introduced legislation designed to control the amount of
pollution in the air. This control had two main aims. The first was to reduce the
amount of pollution emitted at a great enough height to give sufficient dilution of the
pollutions by the time they reach ground level. The second aim was that the height
of chimney must be defined so that it would allow the required dilution of its
effluents-ideally, in all weather conditions.
Daniel and Bytnerourniz (1993) measured ambient levels of the nitrogenous
pollutants NO, NO2, HNO3, ammonia particulates at a Southern California mountain
forest location severly imparted by urban photochemical smog. Air quality at the
location was characerised by high levels of nitric acid and
per oxyacyl nitrates
PAN.
Quin and Chen (1993), measured the traffic related air pollutant
concentration, wind speed, traffic volumes and vehicle speed in street canyons at
Guangzhou city of China during winter and summer of 1988.It was found that the
ground level air pollution in Guangzhou had changed from coal combustion
emission type into traffic source emission type. The average contribution of this
source to the concentration of CO and NO2 was about 87% and 67% respectively.
Michel hall and Juli (2003) suggested that the climate and health connection
is at best, complex .Climate changes across time-scales, influencing ecological
systems through direct and indirect events in turn affecting diseases conditions.
The vehicular emission load of the major metropolitan cities in India exceedd
more than 3596.8 tons/day and contained more than 450 different organic chemical
compounds either in gaseous or particulate or in the combined form. Many of these
substances have been shown to be genotoxic, cytotoxic, fibrogenic and carcinogenic
(Chellan and Jackson, 1999).
The first episode of effect of sulphur dioxide on human health occurred in
Belgium’s Meuse Valley on December 1, 1930, which took a toll of 60 lives. The
incident occurred due to the accumulation of pollutants emitted by sulphuric acid
plants, steel works, zinc works. Sulphur dioxide concentrations were estimated to be
as high as 9.338ppm. It was found that sulphur dioxide increased the incidence of
respiratory diseases. Sulphur dioxide was also found to increase and promote
bronchitis (Perkins, 1974).
It was found that mild doses of NO2 suppresses growth in plants and causes
leaf bleaching. It was observed that 0.5ppm of NO2 for 10-12 days suppressed the
growth of beans and tomatoes. Yellowing of leaves (chlorosis) was also found due
to damages caused by smog (Rao et al., 1989).
The effects of realistic mixture of nitrogen dioxide with other pollutants on
the cuttings of Populates nigra were studied. It was found that the total main non
structural carbohydrates in the leaves were reduced but those in the roots were
unaffected. The gases were found to cause more damage on older leaves than
younger ones (Bucker and Ballach, 1992).
In lesser concentrations nitrogen dioxide causes eye irritation. Nitrogen
dioxide has adverse effects on health. It has been found that 24 hour NO2
concentration between 0.062- 0.019ppm or greater causes acute respiratory diseases
(Rao and Rao, 1983).
Khan (2005) suggested that the freshness of the air in one's environment has
a most fundamental and direct impact on the quality and length of one's life. Air is
more a necessity of life than either food or water.
Inhalable particulate present in urban air frequently co-exist with other
respiratory irritants such as oxidant gases like ozone or acidic aerosols. Exposure to
NO2 at concentrations of 500ppm or greater for short periods of time can results in
pulmonary edema with broncho pneumonia and finally death.1ppm of SO2 exposure
caused consistent changes in pulmonary compliance, breathing frequency etc.
Extrinsic nerve reflexes and direct action on smooth muscles may mediate broncho
constriction (Hine et al., 1970; Koening and Luchtel, 1997).
The adverse effect of this complex mixture on lungs include increased
incidence of respiratory infections, bronchitis, asthma, pneumonia, emphysema,
cancer etc (Bofetta et al., 1990).
Anon (1989), found that emission from automobile sources comprise about
three quarters of gross NOx emissions in Sydney.
2.3. Noise Pollution
Professor Gunther Lehman, President of International Association Against
Noise has observed, “Noise is not a measure of the progress of technology but a sign
of regression” (Encyclopedia Americana, 1991).
Noise pollution, as it affects humans, has been a recognized problem for
decades, but the effect of noise on wildlife has only recently been considered a
potential threat to animal health and long-term survival. Research into the effects of
noise on wildlife, which has been growing rapidly since the 1970s, often presents
conflicting results because of the variety of factors and variables that can affect
and/or interfere with the determination of the actual effects that human-produced
noise is having on any given creature. Both land and marine wildlife have been
studied, especially in regards to noise in the National Parks System and the
onslaught of human- made cacophony in the oceans from military, commercial and
scientific endeavours. Most researchers agree that noise can affect an animal's
physiology and behaviour, and if it becomes a chronic stress, noise can be injurious
to an animal's energy budget, reproductive success and long-term survival. Armed
with this understanding it should follow that humans would attempt to minimize the
threat to wildlife by reducing the amount of noise that they are exposed to in natural
areas; but this has not been the situation.
Natural areas continue to be degraded by human-made noise, wildlife
continues to suffer from these disturbances, and to date the majority of the debate
revolves around the egocentric demands of people to either produce more noise in
nature (through motorized recreation, scientific research, military exercises etc.) or
experience natural areas in the absence of anthropogenic noise. Neither side has
adequately addressed the issue from the bio centric view of wildlife and the known,
or as yet undiscovered, damage that our increasingly noisy human-altered
environment is inflicting upon them (Sharma and Khur, 1994).
Noise is a disturbance to the human environment that is escalating at such a
high rate that it will become a major threat to the quality of human lives. In the past
thirty years, noises in all areas, especially in urban areas, have been increasing
rapidly. There are numerous effects on the human environment due to the increase in
noise pollution. Slowly, insensibly, we seem to accept noise and the physiological
and psychological deterioration that accompanies it as an inevitable part of our lives.
Although we attempt to set standards for some of the most major sources of noise,
we often are unable to monitor them. Major sources of noise can be airplanes at
takeoff and landing, and a truck just off the assembly line, yet we seem accept and
enjoy countless other sounds, from hard rock music to loud Harley Davidson motor
cycles( Nunez, 2000).
Sudden and unexpected noise has been observed to produce marked changes
in the body, such as increased blood pressure, increased heart rate, and muscular
contractions. Moreover, digestion, stomach contractions, and the flow of saliva and
gastric juices all stop. Because the changes are so marked, repeated exposure to
unexpected noise should obviously be kept to a minimum. These changes
fortunately wear off as a person becomes accustomed to the noise. However, even
when a person is accustomed to an environment where the noise level is high,
physiological changes occur (Broadbent, 1957).
Airplane noise can be a much greater disturbance to sleep than other noises.
Research indicates that near a major airport-London (Heathrow) Airport- the number
of people awakened by airplanes was about 50% greater than the number awakened
by other noises (Wegman, 1967).
In the United States airport noise has been hit the hardest, than any other
developed country due to the large geographic area. In 1966, in the United States
there were 500 commercial air passengers per 1000 inhabitants, versus 106 for the
United Kingdom, 85 for West Germany, and 36 for France (Alexandre, 1970).
Obviously, with the number and variety of factors known to contribute to
these events, there is good reason for contradictory results. Even with the relatively
ambitious steps currently being taken or envisioned to control noise in most
countries, sound levels and exposure to noise will remain high, and possibly
increase. At the same time rising living standards will bring about demands for
better environmental quality and probably lead to more vigorous and more organized
protests against noise. These protest may even be triggered by lower noise levels
than in the past, for it is highly likely that as the public acquires more amenities it
will want to be exposed to "comfortable" rather than merely tolerable levels of
sound (Bauer, 1970).
Other researchers have found the same kind of relationship. For example
Cohen et al (1973) determined that elementary school students living for at least 4
years in the lower floors of an apartment complex near heavy traffic show greater
impairment of reading ability than children living on higher floors away from the
traffic. In the studies, indoors sound levels varied form 66-dB on the lower floors of
an apartment to 55-dB on the higher floors. In a recent U.S. EPA classification,
"noisy residential areas" averaged 58-dB and were rated low socioeconomic, while
"quiet residential" averaged 38-dB and were rated affluent neighborhoods. These, of
course, were outdoors sound levels. With indoor levels of 55-66-dB, concentration,
the ability to pay attention, may well be difficult to nonexistent.
Almost everyone has had one experience of being temporarily "deafened" by
a loud noise. This "deafness" in not permanent, although it is often accompanied by
a ringing in the ears, and one can hear another person if he raises his voice.
Likewise, normal hearing comes back within a few hours at most. This sort of partial
hearing loss is called Temporary Threshold Shift (TTS) (Bugliarello, 1976). A TTS
may be experienced after firing a gun or after a long drive in the car with the
windows open. This type of exposure to noise does not have to be as loud as a gun
being fired; it can be as simple as a person shouting across the room. The type of
hearing loss is any degree from partial to complete hearing loss. This loss, usually, is
permanent and is not satisfactorily corrected by any devices such as, hearing aids.
The loss is caused by the destruction of the delicate hair cells and their
auditory nerve connections in the Organ of Corti, which is contained in the cochlea
(Bugliarello, 1976). Every exposure to loud noise destroys some cells, but prolonged
exposure damages a larger amount of cells, and ultimately collapses the Organ of
Corti, which causes deafness.
Most of society is now aware that noise can damage hearing. However, short
of a threat that disaster would overtake the human race if nothing is done about
noise, it is unlikely that many people today would become strongly motivated to do
something about the problem. Yet, the evidence about the ill effects of noise does
not allow for complacency or neglect. For instance, researchers working with
children with hearing disorders are constantly reminded of the crucial importance of
hearing to children. In the early years the child cannot learn to speak without special
training if he has enough hearing loss to interfere effectively with the hearing of
words in context (Bugliarello et al., 1976). In this respect, there is a clear need for
parents to protect their children’s hearing as they try to protect their eyesight. If no
steps are taken to lessen the effects of noise, we may expect a significant percentage
of future generations to have hearing damage. It would be difficult to predict the
total outcome if total population would suffer hearing loss. Conceivably, the loss
could even be detrimental to our survival if it were ever necessary for us to be able
to hear high frequencies. Colavita has consistently been unable to find among
university students in his classes any one could hear 20 kHz, although the classical
results of Fletcher and Munson show 20 kHz as an audible frequency (Fletcher,
1953).
There are two types of hearing loss: conductive and sensorineural. In
conductive deafness sound-pressure waves never reach the cochlea, most often as a
consequence of a ruptured eardrum or a defect in the ossicles of the middle ear
(Bugliarello, 1976).
The three bones form a system of levers linked together, hammer pushing
anvil, anvil-pushing stirrup. Working together, the bones amplify the force of sound
vibrations. Taken together, the bones double, often triple the force of the vibrations
reaching the eardrum (Bugliarello, 1976).
Mitigation of potentially harmful amplification occurs via muscles of the
middle ear. These muscles act as safety device protection of the ear against
excessive vibrations from very loud noises, very much like an automatic damper or
volume control. When jarring sounds with their rapid vibrations strike the eardrum;
the muscles twist the bones slightly, allowing the stirrup to rotate in a different
direction. With this directional shift, less force is transmitted to the inner ear: less,
not all (Bugliarello, 1976).
The human ear is a delicate and fragile anatomical structure on the other
hand it’s a fairly powerful physical force. These muscles act quickly but not always
as in examples of when the ear catches the sound of gun being shot unexpectedly.
The muscles of the ear were relaxed and were unprepared for such a blast, because
of this damage were done.
Conductive hearing loss can be minimized, even overcome by use of the
familiar hearing aids. The most common is worn over the mastoid bone behind the
pinna. It picks up sound waves and transmits them through the skull to the cochlea.
Sensor neural hearing loss, the most common form in the United States,
occurs as a result of advancing age as well as exposure to loud noises. In both
instances there is a disruption of the organ of Corti. The organ serves two functions:
converting mechanical energy to electrical and dispatching to the brain a coded
version of the original sound with information about frequency, intensity, and
timbre. The hair cells of the organ of Corti send their electrochemical signals into
the central nervous system, where the signals are picked up by thousands of auditory
nerve fibers and transmitted to the brain. It is the decoding of all the information that
enables a person to distinguish the unique and separate sounds of a violin, trumpet,
and clarinet, even all three are playing the same note. The organ of corti, a
gelatinous mass, is one of the best protected parts of the body, encased as it is within
the cochlea which in turn is deeply embedded in the temporal bone, perhaps the
hardest of the 206 bones (Bugliarello, 1976). Nonetheless, loud noise can damage
the hair cells and the auditory nerve, producing at times, depending on the type of
noise, sudden and often total deafness.
Sustained noise over a period of time can also engender sensorineural
deafness in the form of gradual losses in hearing. This is the most common loss in
teenagers today listening to loud rock music (Bugliarello, 1976). Until a few years
ago, sensor neural deafness could not be helped by hearing aids. However, with
advances in electronic wizardry and miniaturization, devices for insertion into the
auditory canal are available.
Kenichi Ohsakas, a Yamato City official who keeps track of noise levels, has
been reported as saying, "it’s just like living inside a subway car." Yamato holds
regular weekly takeoff and landing exercises to keep its pilots skills honed, and
night sessions are particularly important. Be that as it may, the residents are
unimpressed, cannot sleep, and prefer the training sessions to be moved elsewhere.
Aircraft noise began to be a major problem with the great surge in air transportation
that followed World War II. The introduction of jet airplanes, which came into
widespread use by the end of the 1950 led to a second revolution in aviation, as well
as to an escalation of the noise level from aircraft’s. Since then, annoyance to people
living near airports caused by the noise of jet takeoffs and landings has become a
psycho physiological and economic problem of enormous magnitude and
complexity. Still a third escalation in aircraft noise will occur when supersonic
transports come into commercial operation, and if general aviation and, above all,
vertical take off and landing (Bugliarello, 1976).
Determining the effect of noise on wildlife is complicated however because
responses vary between species and between individuals of a single population.
These variable responses are due to the characteristics of the noise and its duration,
the life history characteristics of the species, habitat type, season, activity at the time
of exposure, sex and age of the individual, level of previous exposure, and whether
other physical stresses such as drought are occurring around the time of exposure
(Busnel and Fletcher, 1978).
Aircraft noise is not simply a problem for those trying to sleep. Welldesigned, well-controlled studies have demonstrated that exposure to high levels of
aircraft and environmental noise can adversely affect reading ability in school-age
children.
Maser et al (1978) reported that children who attended school beneath the
Seattle-Tacoma airport in-flight paths showed a deficit on standardized tests of
scholastic achievement compared to students in quiet schools.
One example of psychological trauma is the research of Jenkins and his
group at the London Institute of Psychiatry (Jenkins et al., 1979). It reviewed the
findings of two studies conducted in the area of London Heathrow Airport. These
studies had compared rates of admission at Springfield Psychiatric Hospital among
residents living near Heathrow. Findings suggested that areas closest to the airport,
with presumably higher levels of noise, also had the highest rates of hospital
admission.
The problem of aircraft noise is complicated by the great economic
significance that the aviation industry holds to the economies of developed
countries. For instance, at the end of 1971 the U.S. scheduled airlines alone had
revenues of close to $10 billion, and employed almost 300,000 employees. Without
airlines, a number of economic activities of great importance to national economies
from business and tourism, to the transportation of mail, would be severely affected.
Sleep disturbances are probably the most widespread source of annoyance caused by
noise, if anecdotal responses are any criteria. Recently, French investigators (Vallet,
1979) studied the problem under real-life conditions in bedrooms of people living
close to freeways and airports. Using miniaturized electronic units; they recorded
ECG, eye movements, muscular activity, and heart rhythm with remote-reading
equipment. Noise inside the rooms was recorded continuously. With the noise from
the highways, subjects took longer to fall asleep and had less deep sleep so that the
young to middle-aged group became more like the 50-60-year old group in their
depth of sleep. Rapid eye movement (REM) sleep was also reduced. If both deep
and REM sleep are physiologically and psychologically important, this type of
alteration may well be damaging. But this remains to be substantiated by further
study.
Given the concern over noise, one wonders just how desirable a quiet town
would be. Darlington, near Newcastle, England, was almost such a place. Between
1976-1978, Darlington was designated a "quiet town experiment" (Gloag, 1980).
Noise abatement zones and better traffic management was instituted, as were vehicle
noise testing and stricter enforcement of noise regulations.
According to the investigations of Cohen (1969) reading and math scores of
third grade students in noise abated classrooms were higher than those in classrooms
without those qualities were.
Peterson and Northwood (1981) to demonstrate in rhesus monkeys that
moderate levels of realistic noise can produce sustained elevations in blood pressure
without significant alterations in the auditory mechanism.
Noise undoubtly has psychological effects. The question is how these effects
can be assessed and whether they lead to damage. No clear case has been made thus
far for psychological damage caused by moderately high levels of noise, the levels
that would cause hearing damage to only a small fraction of the people exposed.
Indeed, fears have been expressed that over emphasis on damage may backfire
when people come to realize that the truth of the matter seems to be simply that
people can express violently their dislike about being disturbed by noises. This is
recounted vividly by Connell (1972). A middle-aged woman living in Soho became
affected by the incessant noise from a newly open discotheque. She complained to
the management, the Police, the Local Authority but nothing was done to reduce the
noise. Her action took the form of suicide. In Italy a 44 year old man took an
overdose of drugs because his eleven children made too much noise while he was
watching the Olympic Games on television. In a quiet part of Middlesex with an
ambient noise level of 30 to 40 decibels lived Fred, a lusty, healthy builder’s
labourer. The M4 Motorway was built within a few feet of his cottage home. The
resultant traffic caused the noise level to rise to 80 and 90 decibels so this poor man
suffered an increase of 100,000 times in the noise level. He took it for some weeks.
Discovered there was nothing he could do about it and his action was also directed
against the self.
Hearing loss can be entrapping in onset. Years of traumatic exposure to high
levels can occur before symptoms become manifest. The popularity for portable
sound equipment such as Walkman-type radios and tape players has already
produced a sharp increase in clinically verified hearing loss, especially among rock
music addicts who prefer their music very loud (EPA,1971). Obviously, the
Walkman-radio industry believes it is not their products that are the problem; rather
it is improper use. If, they say, the volume is kept down, there would be no problem,
which is equivalent to saying that if we all drove cautiously there would be no
accidents. Considering that earphone listening has been around for some 20 years,
why has the problem only recently surfaced. Apparently the pattern of listening has
changed. Currently, earphones are used while walking or running on noisy easy
streets rather than in the privacy of the home or other relatively quiet area where the
listener did not wish to disturb others. Now the volume must be turned up to
overcome the noise of city traffic. The listener wants the Walkman to blot out the
"noises of the city."
Yet it has been argued that because noise produces no dramatic ill effects, the
public has been largely uninterested in its suppression. It may be more to the point to
say that the degree of annoyance and discomfort that people will endure is
astonishing. Although noise is an integral part of civilization, it would appear that
unless some definite steps are taken to reduce the present inordinate levels in both
industry and community generally, more people will become auditory cripples.
Noise is one of the most pervasive problems ,penetrating all areas of human
activity .All symbols of civilization from jet planes, vehicles and railway engines to
factories ,generators, demolition and construction, television and radio ,public
address system and public voice have one thing in common –it is noise. The man
made activities are responsible for the increase in the ambient noise level
particularly in the urban areas (Sapru, 1987; Sharma, 1990).
As the urban centres grow in size both in population and in area , they too
develop a number of environmental problems. Hence it is of immense importance to
study the various aspects of pollution, especially noise, because of its adverse effect
on health (Gopikrishna, 1978).
Noise does not kill, there is no evidence even of hearing loss due to
environmental noise, but it does produce stress. It is a matter of quality of life and
quiet can be considered as a luxury (Brownz, 1998).
Researchers at the University of South Ampton are investigating the scope
for neutralizing low frequency brass notes by generating “antiphase” sound with an
ordinary Hifi system (Environmental resources abstracts, 1985).
According to Anand Shah, an ENT specialist, though all individuals are
exposed to high sound levels, the damage varies from individual to individual. The
recovery from temporary threshold shift depends on the type of exposure, the
severity of the hearing shift, person-to- person susceptibility (The Sunday observer,
1987).
In Britain it is an offence to play noisy instruments or to sing in streets near
offices and homes. People who regularly play their radios, sterios very loudly are
liable to be prosecuted under British law (The Hindu, 1988).
A study done at Central Institute for the Deaf in St.Louis, Chichillas exposed
Guinea pigs to brief intermittent periods of above normal but supposedly tolerable
noise levels.It developed swollen cochlear membranes and obliteration of inner ear
hair cells (The Encyclopedia Americana, 1991).
Bombay is considered to be the third noisiest city in the world. New Delhi is
said to be closely following Bombay in noise pollution. The World Health
Organisation has fixed 45 dB as the safe noise level for a city. Bombay, New Delhi,
Calcutta and Madras usually register more than 90 dB. Table 2.3 gives the noise
exposure limit specified by WHO- 1980(EPA, 1971).
The persons living along the sides of London’s Hearthrow airport that have
significantly higher rate of admission to mental hospitals than persons living in
otherwise comparable localities(Kudesia and Tiwari, 1993). The din and noise of
crackers during Diwali (which can more correctly be called the ‘festival of sound’
instead of ‘festival of light’) are so loud and unbearable that it is necessary to think
seriously about the health hazards associated with it(Sharma and Kaur, 1995).Los
Angeles airport have implemented a scheme requiring all aircrafts not complying the
federal noise regulations to take off and approach over the sea between 11 pm to 6
am (Singal,1995).
Harshavardhan et al (2003) reported that acoustic pollution is a significant
environmental system problem. It can be defined as a sound without agreeable
quality or as unwanted sound. The problem of noise is likely to increase in coming
years as mines become larger and more mechanized employing bigger and more
powerful machines in greater number. Hindrance in vocal communication in an
environment of high noise may cause accidents. Masking of warning signals as in
case of roof falls may lead to serious consequences. Also a person becomes irritable
and quarrelsome and loses concentration. These results in decreased efficiency and
increased incidents of errors.The most serious effects of exposure to high noise
levels are deafness, which is initially temporary but with prolonged exposure to high
levels, gradually becomes permanent. Hence the noise of the levels higher than the
standards laid down by the Ministry of Environment and Forest must be abated not
only to achieve greater percentage production, but also to restore physical health of
workers at work place.
Bhatnagar and Srinivas (1992) has studied Shopping is an important activity
of a common urbanite in any town. The shopping complexes have several problems
like distance from residential areas, over crowding and increased vehicular traffic in
the vicinity. The air in these shopping centres is humid and lacks freshness .Further
high level of noise are observed to be a prominent environmental parameter in these
area. A worker in South India is never free from noise at ant part of the aspects. He
wakes up to noise from transistors works in a noisy industry, goes to his work-place
through noisy streets lives with loud speakers and returns to a noisy home. By all
standards, he is exposed to noise levels which exceed the permissible noise levels
(Kameswaran, 1992).
Panday and Ravi Verma (1997) repoted that rapid urbanization,
industrialization, transportation and mushrooming of settlement along the highways
have contributed significantly to increase in noise levels. For assessing the noise
pollution in an urban
centre, a systematic study is required involving objective
measurement and supportive study involving subjective reaction of the people
affected by the environmental noise.
Naik and Purohit (2003) reported that the noise levels were measured at ten
residential locations at Bondamunda both during day and night time exceeded the
noise standards recommended by CPCB. The noise generally came from many
sources such as radio, TV, VCR, Music system, Coolers, motar cycle, Chattering
among people, Children playing, traffic noise, use of loud speakers at the religious,
cultural and social functions etc.
Ravindran and Sriram (2004) carried out ambient noise level at various
locations of the Kanchipuram town during March 2003. The comparision of the data
showed that the noise levels at various locations (residential, commercial and silent
zones) of the town were more than the permissible limits.Vehicular traffic and air
horn were found to be the main reasons for high noise levels.
A study by USEPA studied the magnitude of the U.S. population exposed to
noise, and the percentage expressing annoyance with specific sources of noise.
Considering that 60-dB is akin to the sound of an air conditioner at a distance of
20ft, it was evident that with a population in excess of 280 million approximately
7%, or 17+ million people were exposed to noise levels, from traffic alone, of from
70 to over 80 dB (U.S. EPA,1980).
Pal et al (1992) undertook a study on the work place noise problems in coal
washery industry. The study revealed that the workers had been affected by noise
with auditory effects.
Extent of noise pollution from house hold equipment and appliances was
conducted in two colonies of Ludhiana and two villages of Ludhiana district. The
noise levels produced by the use of house hold equipment and appliances ranged
from 40dB (A) to 97dB (A) which were quite high and intolerable as compared to
acceptable noise level of 45 dB (A)(Nagi Gurupret et al.,1993).It revealed that the
urban families experienced more noise nuisance from interior sources as compared
to the rural folks. The excessive noise was found to cause multi farious ill effects
and reduced working efficiency among people.
Bansal (1996) reported noise level status of Bhopal city during 1994.Noise
level in the sensitive areas of Bhopal was in the range of 32 dB(A) to 78dB(A)
during daytime, while during night time it was in the range of 30dB(A) to 60
dB(A).In these areas about 43.3% values were found exceeding the limit of dB(A)
during night time.
Bhattacharya et al (1996) examined the presence of potential noise hazard to
health and safety in the working areas of two drilling sites of petroleum in the open
field by evaluating and analyzing noise levels and characteristics. the results showed
that the sound pressure levels ranged from 96 to102dB(A). All exceeding the
standard noise exposure limits of 90dB(A).
Padmanabhamurthy and Satapathy (1996) assessed the efficacy of different
types of screens by conductivity controlled noise migration experiment in an open
site at Jawaharlal Nehru University. They suggested two screens, namely plywood
and aluminium were most effective. Sound attenuation was found to be more in case
of ply wood screen compared to aluminium. To assess the efficacy of bushes and
hedges the experiments were also conducted at two localities. Sound pressure level
attenuation was found to be higher in case of hedges compared to other vegetation.
The status of noise pollution in Tiruchirappalli was studied
by
Ravichandran et al (1997).The result revealed that in all the commercial, residential
and silence zones, the noise exceeded the limit prescribed by Central Pollution
Control Board.
Edison Raja et al (1999) assessed noise pollution due to automobiles in
Cuddalore, which revealed that the traffic policemen were exposed to high noise
levels during peak hours.
Alagappa Moses et al (2000) carried out noise pollution assessment in
Chidambaramm, Erode and Thanjavur in Tamilnadu. Noises levels exceeded the
ambient air quality standards for noise in all these places.The vehicular traffic was
found to be the major cause for noise pollution in these places.
Varshney (2003) reported that the Diwali may be an unsafe one, with
authorities turning a deaf ear to the pleas of health experts and the masses. Study
conducted that noise generated by fire crackers was much higher than the prescribed
levels. The permitted noise level is 125 decibels as per the Environment (protection)
Rules, 1999.
Kalyankar et al (2004) reported that the noise levels recorded in places under
silence zone, residential zone and commercial zone were higher than the prescribed
limits. None of the place was found to be calm. The residents in these areas were
exposed to high noise levels and as a result they would develop auditory and nonauditory effects in due course of time.
Robert et al., (2004) reported that the noise was the most pervasive pollution
in America.
2.4. Water Pollution
As our communities grow, we notice many visible changes, including
housing developments, road networks, expansion of services, and more. These
changes impact our precious water resources, with pollution of water resources
being one potential impact. To understand how our water supplies can become
polluted, it’s important to understand the oldest solar-powered “recycling” system:
the water cycle, also called the hydrologic cycle.The hydrologic cycle transports
water between earth’s land, atmosphere, and oceans. The major processes moving
water are evaporation, transpiration, condensation, and precipitation. Evaporation
occurs when the sun’s energy turns liquid water on the earth’s surface into water
vapor, which enters the atmosphere. Water vapor leaves plants in a process called
transpiration. Collectively, these two processes are called evapotranspiration. The
water vapor in the atmosphere cools to form clouds (condensation).Through
precipitation in the form of rain or snow, the water returns to earth. Snow
accumulates in the mountains, providing storage in the form of a snow pack that will
slowly melt and release water in the spring and summer. Some of the rain runs off
the land, into rivers or lakes. While it’s hard to believe, rivers contain only about
0.0001 percent and fresh water lakes only about 0.009 percent of all water on earth!
Rain also soaks into the ground, or infiltrates, and replenishes.
The increase in impervious or hard surfaces, including rooftops and
pavement (roads, driveways, and parking lots), decreases the amount of water that
soaks into the ground, or infiltrates. This increases the amount of surface runoff. The
impervious surfaces collect and accumulate pollutants, such as those leaked from
vehicles, or deposited from the atmosphere through rain or snowmelt. The runoff
water carries pollutants directly into water bodies. Because there is less infiltration,
peak flows of storm water runoff are larger and arrive earlier, increasing the
magnitude of urban floods. Paving may alter the location of recharge, or
replenishment, of groundwater supplies, restricting it to the remaining unpaved
areas. If infiltration is decreased sufficiently, groundwater levels may decline,
affecting stream flows during dry weather periods. Lowered groundwater levels can
result in subsequent well failures. While the effects of urbanization on the water
cycle can be major, if wise choices are made during the development process, the
impacts can be minimized and our future water supply protected (ENVIS, 2005).
Freshwater resources all over the world are threatened not only by over
exploitation and poor management but also by ecological degradation. The main
source of freshwater pollution can be attributed to discharge of untreated waste,
dumping of industrial effluent, and run-off from agricultural fields. Industrial
growth, urbanization and the increasing use of synthetic organic substances have
serious and adverse impacts on freshwater bodies. It is a generally accepted fact that
the developed countries suffer from problems of chemical discharge into the water
sources mainly groundwater, while developing countries face problems of
agricultural run-off in water sources. Polluted water like chemicals in drinking water
causes problem to health and leads to water-borne diseases which can be prevented
by
taking
measures
even
at
the
household
level.
(http://www.vyh.fi/eng/environ/sustdev/indicat/rehevoit.htm)
In a survey conducted by the Central Pollution Control Board, there were
2000 large and medium scale industries in the country which polluted the ground
water. Of these only 27% had adequate treatment plants 14% of the industries the
treatment units were still under construction. Of the 17% sugar industries, only 6%
had effluent treatment plants. The remaining 42% industries were simply disposing
the wastes without any sort of prior treatment into the aquatic bodies which were the
potential sources of public water supply. They generated enormous problems of
water pollution (Trivedy and Goel, 1984). Now 50% of industries simply disposing
the waste water without treatment (www. industrial effluents.com).
Studies have revealed that some of our major rivers are polluted far beyond
the permissible limit prescribed for human use and consumption. The mighty Ganga
in the North and Cauvery in the South are also heavily polluted that the once life
giving forms have now become a menace to aquatic life and human population.
India suffers from water, air and soil pollution contributing to the overall
degradation of the environment.Indiscriminate water pollution is a phenomenon
particularly in densely populated industrial cities at India (Babacar et al., 2005).
Schueler and Holland (2000) suggested that the effects of urbanization on the
water cycle can be major; if wise choices were made during the development
process, the impacts could be minimized and our future water supply be protected.
Purandara et al (2003) reported that with the rapid growth of population and
industrialization in the country, pollution of natural water by municipal and
industrial wastes had increased tremendously.
Danilo (1993) reported that the impact on urban areas, with their extensive
hardened surfaces and inadequate storm water infrastructure to manage urban runoff,
could be significant.
Sheridan et al (1996) reviewed the implications of inadequate provision of
water and sanitation on children’s health and general development, especially in
urban areas. Research into health differentials showed that child mortality and
morbidity rates in poor urban settlements was equal or exceed those in rural areas.
The chemical composition of ground water depends upon the soluble products of
rock weathering and decomposition and changes with respect to time and space in
addition to the external pollution agents (Mariappan et al., 2000).
Groundwater is a precious natural resource for several vital functions such as
for public, industrial and agricultural water supply. It provides drinking water to
almost a third of the population and irrigates about 17% of the crop land. Due to the
increased demand of water the groundwater is excessively exploited. Now a days
,the increasing effects of pollution on and overexploitation of ground water have
become a serious threat.Many workers such as Kaza et al(1991),Ravichandran and
Pundarikanthan (1991), Dayal (1992), Ali Akram and Iqbaluddin (1992),Mittal et al
(1994),Prasad and Ramesh Chandra (1997), Sambasivarao (1997), Dhembare et al
(1998), Tripathi (2003) have been carried exhaustive study on ground water quality.
Activities such as indiscriminate disposal of human and agricultural waste,
manure spreading over the vicinity of human habitation, housing of livestock, onsite
human waste disposal system, septic systems and open defecation etc, are
responsible for fecal contamination of ground water in the rural areas of the country.
The American academy of microbiology has opined that the quality of drinking
water is declining all over the world mainly because of bacteriological
contamination, a significant cause of gastro-intestinal diseases. As a consequence
immunity to gastro-intestinal disease following exposure to contaminated water is
slowly disappearing. Eric Minz of the US, centre for disease control estimated more
than 3 million cases of diarrohea in all over the world per year leading to 10million
deaths caused by water borne micro organisms(Conboy and Goss, 2001).
2.4.1. Groundwater and its contamination
Many areas of groundwater and surface water are now contaminated with
heavy metals, POPs (persistent organic pollutants), and nutrients that have an
adverse affect on health. Water-borne diseases and water-caused health problems are
mostly due to inadequate and incompetent management of water resources. Safe
water for all can only be assured when access, sustainability, and equity can be
guaranteed. Access can be defined as the number of people who are guaranteed safe
drinking water and sufficient quantities of it. Urban water generally have a higher
coverage of safe water than the rural areas (Allen et al., 1980).
In the urban areas water gets contaminated in many different ways, some of
the most common reasons being leaky water pipe joints in areas where the water
pipe and sewage line pass close together. Sometimes the water gets polluted at
source due to various reasons and mainly due to inflow of sewage into the source.
Ground water can be contaminated through various sources and some of these are
mentioned below (Allen et al., 1980).
2.4.2. Pesticides
Run-off from farms, backyards, and golf courses contain pesticides such as
DDT that in turn contaminate the water. Leechate from landfill sites is another major
contaminating source. Its effects on the ecosystems and health are endocrine and
reproductive damage in wildlife. Groundwater is susceptible to contamination, as
pesticides are mobile in the soil. It is a matter of concern as these chemicals are
persistent in the soil and water (Joshi et al., 2004).
2.4.3. Sewage
Untreated or inadequately treated municipal sewage is a major source of
groundwater and surface water pollution in the developing countries. The organic
material that is discharged with municipal waste into the watercourses uses
substantial oxygen for biological degradation thereby upsetting the ecological
balance of rivers and lakes. Sewage also carries microbial pathogens that are the
cause of the spread of disease (Tyagi, 1998).
2.4.4. Nutrients
Domestic waste water, agricultural run-off, and industrial effluents contain
phosphorus and nitrogen, fertilizer run-off, manure from livestock operations, which
increase the level of nutrients in water bodies and can cause eutrophication in the
lakes and rivers and continue on to the coastal areas. The nitrates come mainly from
the fertilizer that is added to the fields. Excessive use of fertilizers cause nitrate
contamination of groundwater, with the result that nitrate levels in drinking water is
far above the safety levels recommended. Good agricultural practices can help in
reducing the amount of nitrates in the soil and thereby lower its content in the water
(Achuthan Nair et al., 2005).
2.4.5. Synthetic organics
Many of the 100 000 synthetic compounds in use today are found in the
aquatic environment and accumulate in the food chain. POPs or Persistent organic
pollutants represent the most harmful element for the ecosystem and for human
health, for example, industrial chemicals and agricultural pesticides. These
chemicals can accumulate in fish and cause serious damage to human health. Where
pesticides are used on a large-scale, groundwater gets contaminated and this leads to
the chemical contamination of drinking water. Acidification of surface water, mainly
lakes and reservoirs, is one of the major environmental impacts of transport over
long distance of air pollutants such as sulphur dioxide from power plants, other
heavy industry such as steel plants, and motor vehicles. This problem is more severe
in the US and in parts of Europe (Kataria, 1994).
Ramakrishnan et al (1991) had studied the physico-chemical parameters of
five drinking water sources at Tiruvannamalai. All parameters except DO Calcium
and magnesium were found to be in the permissible limit.
Gupta Hari Om and Sharma Brijmohan (1993) had analysed quality of water
at Laliltpur, an industrial area of Donnvalley. Calcium, magnicium and PO4 were
found above the permissible limit in natural waters.The river and canal water
confirmed the increased pollution due to industrial development.
Vaithuyanathan et al (1993) studied the transport and distribution of heavy
metals in Cauvery River. Tributaries Hemavathi and Kabini draining highly
mineralized areas contribute significantly to the heavy metal load of the Cauvery
River. Particulate metal transport is influenced by the presence of major dams built
across the river.
Prakash et al (2004) studied on E.Coli bacterial contamination of drinking
water , a common problem in many rural areas, In that areas about 18.85% hand
pumps,40% of pipeline water supply and 46.43%mini water supply sources were
affected by E.Coli.
Radha (2003) suggested that micro organisms are widely distributed in
nature and are found in most natural waters. Their abundance and diversity used as a
guide to the suitability of water for fish, animals or recreational and amenity
purposes (African Technical review,1986).With increasing urbanization and
industrialization ,water sources have been
adultered with industrial as well as
animal and human wastes. As a result, water has become a formidable factor in
disease transmission. The presence of non pathogenic organisms is not of major
concern, but intestinal contaminants of fecal origin are important .These pathogens
are responsible for intestinal infections such as bacillary dysentery, typhoid, fever,
cholera and paratyphoid fever etc.
Panday and Soni (1993) had analysed physico chemical quality of
Naukuchiyatal lake water in Kumaun Himalaya. The lake was highly polluted and
contained high amount of free CO2, total alkalinity and pH except DO during 1991
as compared to study report of 1978.
2.4.6. The effects of Water pollution
Water pollution is the acceleration of the eutrophication processes of waters.
Eutrophication is the aging of a lake by biological enrichment of its water. In a
young lake the water is cold and clear, supporting little life. With time, streams
draining into the lake introduce nutrients such as nitrogen and phosphorus, which
encourage the growth of aquatic organisms. As the lake's fertility increases, plant
and animal life burgeons, and organic remains begin to be deposited on the lake
bottom. Over the centuries, as silt and organic debris pile up, the lake grows
shallower and warmer, with warm-water organisms supplanting those that thrive in a
cold environment. Marsh plants take root in the shallows and begin to fill in the
original lake basin. Eventually the lake gives way to bog, finally disappearing into
land. Depending on climate, size of the lake, and other factors, the natural aging of a
lake may span thousands of years.
However, pollutants from man's activities can radically accelerate the aging
process. During the past century, lakes in many parts of the earth have been severely
eutrophied by sewage and agricultural and industrial wastes. The prime
contaminants are nitrates and phosphates, which act as plant nutrients. They over
stimulate the growth of algae, causing unsightly scum and unpleasant odors, and
robbing the water of dissolved oxygen vital to other aquatic life. At the same time,
other pollutants flowing into a lake may poison whole populations of fish, whose
decomposing remains further deplete the water's dissolved oxygen content. In such
fashion, a lake can literally choke to death. Moreover in the case of lake and
reservoirs with a long time of water turnover phosphorus will accumulate in the
aquatic ecosystem determining periodic cycles of algal proliferation with inorganic
P being organized in the algae cells followed by microbial decomposition of algal
residues with the organic P being remineralized. Only the removal of organic
substance from the lake either as sludge accumulated on the bottom or as living
organism (e.g. fish) can reduce the water body eutrophication.
The cities among the coastal areas are discharging their effluents in sea and
oceans. The coastal area of Bombay has become slightly acidic and polluted. The
main areas of old and New Delhi are on the West Bank of Yamuna, while the old
Shahdara is located on the left bank. At Wazirabad, where the river enters the Union
territory of Delhi, it is tapped for the water supply. The river leaves the union
territory at Okhla, where the city waste water is discharged after treatment .During
48km of its length a large number of drains meet the river and carry into its sullage
and other waste waters from various parts of the city. As for sewage system is
concerned there is only partially sewered. Even in the sewered area all sources of
waste water are not connected to the sewerage system. As a result a significant
volume of waste water is not connected to the sewerage system. As a result a
significant volume of waste water generated, finds its way into the open
drains.Usually, 80% of the water supplied to the community returns as waste water
(Babacar et al., 2005).
Pondicherry has large and medium industries and 1108 small scale industries
out of which 101 units are responsible for water pollution. The city has plan for
laying down sewers to collect the pump most of the sewage on sewage farms after
marginal treatment through settling process. But at present sewage and sullage flows
through a number of open drains into sea through various backwaters
(Bandopadhyaya, 1986).
Except in big cities no testing of ground water is dones although, ground
water is generally bacteriologically free but it gets contaminated with sewage or
industrial seepage. Most of the sewer and water supply lines are found parallel to
each other. Sometimes tap water, wells water area found contaminated with sewer
water. Many water borne diseases like encephalitis, schistosomiasis, malaria,
diarrhea are increasing. To these may be added typhoid, cholera, dysentery,
gastroenteritis and hepatitis which are spread by contaminated water or dirty hands
as well as scabies, yaws, leprosy and conjunctivitis diseases which are aggravated by
insufficient water for washing purposes (Sharma and Khar,1995).
Kshipra (1991) analysed trace metals of the textile mill effluents and
sediments in water of river Khan. Higher Concentration were usually found in the
upstream regions.
Ruparelloa et al (1993) reported that the pollution of river Bhadar is caused
by dyeing and printing industries in the belt of Jetpur Dhoraji taluka of Saurashtra
region.
Manzoor (1993) studied the water quality of a mining area in Keonjhar
district for drinking and agriculture. Examination of agricultural parameters revealed
that the water flowing down the mines was suitable for irrigation purposes with most
of the irrigation water quality parameters except in case of salinity and residual
carbonate.
Singh et al., (1994) analyzed the effect of effluent from the Sindri fertilizer
factory in the river Damodar. The range of pollutants discharged included inert
deoxygenators, nitrogen and phosphorus compounds and poisonous substances.
These compounds increased the BOD and COD load of the water body, leading to
anoxia conditions.
2.4.7. Plankton
The term plankton refers to unattached organisms that are dispersed
individually or in colonies in water. Phytoplanktons are plant plant plankton, and
zoo planktons are animal plankton. The plankton is specific for a particular
environmental condition and they are considered to be the best indicators of
environmental quality. The enrichment of water body by the supply of nutrients
through various sources leads to a condition of eutrophication. However the general
impact of environmental stress, both external and internal to the aquatic
environment, is manifested on these organisms. The presence or absence of certain
organisms in the aquatic environment shows the extent of contamination of water
bodies. The identification and quantification of these organisms serve as inexpensive
and efficient early warning and control system to check the effectiveness of the
measures undertaken to prevent damages to ecosystem.
2.5. Soil Pollution
Soil is the natural medium for the growth of land plants. Soil covers land as a
continuum except on rocky slopes and in regions of continuous cold. Its
characteristics in any one place results from the combined influence of climate and
living matter, acting upon rock material as conditioned by relief over periods of
time. Soil is a dynamic three dimensional piece of landscape that supports plants. It
has a unique combination of both internal and external characteristics. Its upper
surface is the land; its lower surface is defined by the lower limits of soil forming
processes; and its sides are boundaries with other kinds of soil. In short, each soil is
a natural body which is surrounded by other soils with different properties (Sharma
and Khar, 1995).
2.5.1. Evolutionary nature of soil
The soils undergo continual change. Each soil has a life cycle in terms of
geological time. The soil properties have been influenced by the integrated effects of
climate and living matter acting upon parent material over a period of time.
Weathering of bedrock provides the debris which is the parent material for the
evolution of soil profiles. Over a period of time a soil horizon will come into
existence.
2.5.1.1. The soil profile
When soil is examined vertically it shows the presence of more or less
distinct horizontal layers. Such a section is called a “profile” and the individual
layers are called as horizons. The horizons found above the parent material are
collectively called as solum. The word “solum” is a latin word meaning soil, land or
a piece of land (Sharma and Khar, 1995).
2.5.1.2. Horizons of soil
The upper layers of soil contain large amounts of organic matter. These layers
are the major zone of organic matter accumulation. The underlying subsoil contains
lesser organic matter. In mature humid regions of the soil the subsoil layers may be
a) an upper zone of transition b) a lower zone containing sufficient amounts of
compounds like iron, aluminium oxides, clays, gypsum and calcium carbonate.
2.5.1.3. Components of soil
Soil is a mixture of mineral matter, organic matter water and air. The
approximations are as follows – mineral matter45%, organic matter5%, water 25%,
air 25%. These proportions vary from time to time. The volume of air and water bear
a reciprocal relationship. Half the volume is pore space.
2.5.2. Soil nutrients
For the growth of plants certain elements are definitely essential. The
capacity of soils to supply the essential elements and crop residues are often
amended in order to enhance plant growth and crop returns. The soil nutrients can be
divided into two classes-Macronutrients and Micronutrients.
2.5.2.1. Macronutrients
Six elements are used in large quantities and they are “nitrogen, phosphorus,
potassium, calcium, magnesium and sulphur”. Growth of plants may become
retarded if these are available too slowly or if they are in adequately balanced by
other nutrients. Nitrogen, Phosphorus and potassium are commonly supplied to the
soil as farm manure and commercial fertilizer.
2.5.2.2. Micronutrients
The other nutrient elements like iron, manganese, copper, zinc, boron,
molybdenum chlorine and cobalt are required by the plants in very small amounts.
These are called micronutrients or trace elements.
2.5.3. Life in the soil
Living organisms in the soil, both fauna and flora are very essential in the
process of degradation and synthesis of humus. These organisms are essential for the
numerous biochemical changes, help to stabilize the structure of the soil. The soil is
not complete without the living components (flora and fauna) and can not function.
Of all the microbes, bacteria are by for the most numerous (one thousand million in
single gram of soil) followed by viruses, fungi, actinomycetes, algae, protozoa. They
create air ways within the soil that are essential to plant roots.
2.5.4. Soil deterioration
1. Fragilitry: - Man’s influence severely upsets the natural balance.
2. Progressiveness: - The vicious circle of “cause and effect” can also damage
the soil.
3. Irreversibility: - Loss of animal and plant species.
Due to these reasons erosion and deterioration of soil can lead to
desertification. Looking after soil which was the original meaning of cultivation is
literally the basis of human culture. Yet man’s many activities is increasing
converting the fertility and productivity of soil into an irreversible situation of
unproductivity. Yet arable land is being thoughtlessly consumed for infrastructural
facilities.
2.5.5. Soil Degradation
Agriculture plays a key role in the development of any economy. Agriculture
provides basic sustenance to all living beings. It is very important that ecologically,
socially and economically sustainable agriculture should become the backbone of
the development process of the State. Agriculture should be sustainable so that the
natural resources such as soil, water and biodiversity are used efficiently and
equitably. It should be economically viable and lead to increasing employment
opportunity, socially feasible, strengthening the role of women and other
marginalized sections of the people. Equity in sharing benefits is vital for
community participation in the conservation and enhancement of natural resources.
Agriculture continues to be the prime mover of the State economy supporting 62
percent of the population and contributing 13 percent of the State income as of
2004-05. The Government is aiming to achieve 100% food security in the State and
also to create avenue for export of agricultural produce for economic enlistment of
the farming community. During the Tenth Plan period, the State is aiming an annual
growth rate of 4% in agriculture and 8% in horticulture crops for sustainable
agricultural development, employment generation and poverty alleviation. The
Government is focusing its policies towards overall development of agriculture
sector in terms of increasing the cropping intensity by bringing every piece of land
under cultivation, productivity increase, maximizing natural resources with parallel
efforts to conserve them (Anon, 2005).
In Tamil Nadu 94 soil families were identified and classified according to
soil taxonomy into six orders. Among the six orders inceptisol formed 50% of the
total geographical area followed by alfisols (30%). Soil depth is not a limiting factor
for crop growth in Tamil Nadu except shallow soils which occur in 14 percent of the
total geographical area of the State. The texture of surface soil of the State shows
that 18 percent area has sandy surface soil, 53 percent has loamy surface soils and
22 percent has clayey surface soil (Anon, 2004).
2.5.6. Soil erosion
Soil erosion is caused by wind or water. Erosion causes depletion of fertility
through the removal of the valuable and fertile surface soil. In Tamil Nadu erosion
was observed about 13 lakh ha (Anon, 2004).
2.5.7. Salinity and alkalinity
Salinity in soil hinders crop growth and results in reduction in crop yield.
The estimated extent of soils affected by salinity and alkalinity was estimated at 2.48
L.ha. besides 1.23 L.ha. suffering from acidic soils (Anon, 2004).
2.5.8. Mining and Environmental degradation
It has been estimated that 16250ha were under mining in Tamil Nadu of
which 3285 ha were in the district of Salem followed by 3155 ha in Cuddalore
district. The other districts which had fairly substantial area under this category
include Namakkal, Perambalur, Tirunelveli and Sivagangai (Tonapi, 1980).
2.5.9. Water logging and marshy land
Excess water hinders plant growth by reducing aeration, which in turn
decreases the water absorption and nutrient uptake by roots. The coastal regions of
Tamil Nadu face heavy damages due to water logging. The command areas in major
irrigation projects experience waterlogging problem. In TamilNadu 44,820 ha. was
estimated as marshy lands. About 14 percent of the area in Tamil Nadu was under
very poorly drained soils. Another 16 percent was under moderately well drained to
well drained soils and 15 percent was somewhat excessively drained soil (Anon,
2004).
2.5.10. Agriculture progress, problems and constraints
2.5.10.1. Depletion of water resources
Tamil Nadu's geographic area consists of 17 river basins, a majority of which
are water-stressed. There are 61 major reservoirs; about 40,000 tanks and about 3
million wells that heavily utilize the available surface water (17.5 BCM) and
groundwater (15.3 BCM). Agriculture is the single largest consumer of water in the
State, using 75% of the State's water. A recent World Bank report has shown that the
agriculture sector faces major constraints due to dilapidated irrigation infrastructure
coupled with water scarcity due largely to growing demands from industry and
domestic users and intensifying interstate competition for surface water resources. In
some parts of the state, the rate of extraction of groundwater has exceeded recharge
rates, resulting in falling water tables. Water quality is also a growing concern.
Effluents discharged from tanneries and textile industries and heavy use of
pesticides and fertilizers have had a major impact on surface water quality, soils and
groundwater. The State Government has taken a number of progressive actions on
water resources and irrigation management, particularly through the Bank-assisted
Tamil Nadu Water Resources Consolidation Project (WRCP). Tamil Nadu was one
of the first states to pass a groundwater bill.The State had prepared a planning
framework for water resources management, and a State Water Policy (Anon, 2004).
Hundal etal (1988) reported that soil moisture content influenced the
presence of most organic pesticides in soil. Combined with adsorption and biomass
measurements, the experiments were used to describe the mechanisms by which soil
water influenced the rates of microbial degradation of the herbicides. The size of the
microbial biomass in the soil was found to have a direct influence on the rates of
degradation of diallatae and triallate and since the water content influenced both the
size and survival of the biomass, it indirectly influenced the degradation rates.
Paul et al (1985) reported heat and soil moisture function together in the
inactivation of soil microbes. Sensitivity of micro organisms to heat is affected by
soil moisture. The role of heat in micro organism inactivation is better understood
than that of soil moisture. Heat directly affects survival since cellular components
such as protein, membrane lipids and nucleic acids are unstable at elevated
temperatures. The effect of soil moisture is not as readily apparent. Water probably
acts as a catalyst in the heat denaturing process. It lowers the amount of heat
required to reach the activated state, denature bio molecules and subsequently
inactivate cells.
Sharma and Gupta (1989) reported that trees improved soil fertility. The
organic carbon increased from 0.03 to 0.47 and total nitrogen from 0.07 to 0.43%
under Prosopis, whereas P2O5 increased from 14.95 to 33.68 Kg/ha.
2.5.10.2. Decline in soil organic matter
The soil health is deteriorating. The organic matter content in the soil has
gone down from 1.20% in 1971 to 0.68% in 2002 in Tamil Nadu, because of less
use of organic inputs (Anon, 2004).
Olaniya et al (1992) reported that organic matter increased when soil was
amended with compost ans sewage sludge contraining heavy metals. But water
percolating from such soil adds chlorides, sulphates and nitrates to ground and sub
soil waters.
The pattern of land ownership is unfavourable for agricultural development.
The average size of holdings has declined from 1.25 ha in 1976-77 to 0.95 ha in
1995-96. The all India figure for average area owned per household is 1.59 ha. This
reflects the pressure of population on land. The share of total land operated by small
and marginal farmers has increased from 42 percent to 52 percent during the same
period. The growth in number and extent of small and marginal farmers is a major
hurdle in promoting capital investment in agricultural sector and modernizing
agriculture sector. Fragmentation of land results in uneconomic land holdings
(Anon, 2004).
2.5.11. Soil Treatment
The demand of land for various productive propose is on an increase. The
degradation of land resources is taking place at an alarming rate. This is due to the
diversion of lands in a fragile ecosystem for various purposes like – dams and roads,
the reckless destruction of forest wealth, expansion of irrigation without adequate
concern for the treatment of the catchments, danger of water logging, salinity,
desertification, floods and droughts, improper agricultural practices, toxic effects of
agricultural chemicals and industrial effluents.
2.5.12. Agriculture and Horticulture Waste Land
A recent estimate showed that in 20 districts of Tamil Nadu, there was waste
land to the extent of 36.28 lakh ha (Table 2.9). Special schemes have been drawn to
put these lands to productive use by suitable reclamation of land and cultivation of
select crops, with the technical and financial support of the Government of Tamil
Nadu. If the landless agricultural labours were the target beneficiaries of the scheme,
it will generate employment opportunities to at least 20 lakh farm workers.
Mobilisation of required resources and economically viable operational strategy
would make the scheme a success. Emphasis must be on participatory development
through collective community based efforts, because individual tiny farms are
economically not viable on such marginal and sub-marginal lands (Anon, 2005).
Hundal et al (1988) have said that after incubation with green manures the
phosphorus absorption characteristics of the soil was changed.
Totey (1989) have reported that green manure adds organic matter in surface
soil treated with moong ,of soil nitrogen and phosphorus in surface and subsurface
layers.
2.6. Waste Water Treatment
Importance of waste water
According to world health organization (WHO) almost 80% of the diseases
in the world are attributable to inadequate water and the related problems of
sanitation .It is reported from various parts of our country that 50 to 70% of the
pollution load of rivers and streams is from domestic sewage (Acheson, 1983).
The waste water usually contains numerous pathogenic micro organisms that
enter into human body and dwell in the intestinal tract.
Chatterjee et al (1967) studied the utilization of sewage for fish culture on
oxidation ponds. The ponds were designed to treat 720,000 gallons of sewage per
day, the percapita water supply of the town ship, being in the order of 100 gallons
per day.
Alagarswamy et al (1967) studied the succession of different micro fauna
and their correlation to BOD reduction in a high rate deep stabilization pond.It was
shown that the stalked ciliates existed in great numbers in the later stages which
incidentally coincided with the higher BOD reduction.
Bopardikar (1967) discussed on the microbiology of waste stabilization pond
for sewage treatment. Sehgal and Siddiqi (1969) analysed the characteristics of the
waste waters of Kanpur city and concluded that the waste wateres from the city
could be utilized for irrigation after making suitable dilution with water from river
Ganges.
Bokil amd Agarwal (1976), Studied the performance characteristics of high
rate shallow stabilization ponds .It was found that overall BOD removal efficiency
was about 85%.
The establishment of waste water management scheme for a small
community is highly essential since waste water discharges disrupt the ecosystem.
The domestic sewage has its own effects on the human health as well as on other
organisms. The purpose of sewage treatment is to remove at lowest cost,
contaminants of the waste water , so that the final effluent can be discharged to a
receiving water with minimal effect on the natural flora and fauna and on the
subsequent use of the water. Thus waste water analysis and their treatment have
become increasingly important and were worked out by many authors.
Bokil and John (1981) described the feasibility of developing the
flocculating algal bacterial system under (Natural) sunlight. It was found that the
optimum removal efficiencies were 80% for COD, 72% for total nitrogen and 58%
for total phosphorous.
Kankal et al (1987) studied the relationship of detention period and dissolved
oxygen concentration on the efficiency of treatment by aerated lagoons. Longer
detention period resulted in higher DO which improved the quality of effluent.
Katyal and Sataka (1989) have discussed the stages of sewage treatment and
have designated as primary, secondary and tertiary treatment. Primary treatment was
able to remove organic material responsible for 25-35% of BOD of the sewage.
Arceivala (1990) have studied the importance and use of aquatic plant ponds.
The growth of algae, vascular aquatic plants, water hyacinths, hydrilla etc have been
cultured in ponds for waste water treatment to remove heavy metals such as Hg, Pb,
Cd, phenols, Ni, pesticides, nutrients etc from waste water.
Tripathi and Shukla (1991) conducted a study on laboratory scale to evaluate
the potential role of Eichhornia crassipes, Chlorella and Chlamydomonas mirabilis
in waste water treatment.Sewage of Varanasi city, mixed with the effluents of about
1200 small scale industries was used for the tests. Water hyacinths were grown in
tanks of waste water with 15days retention time. This resulted in very high reduction
of BOD (96.9), suspended solids (78.1%) and COD (77.9%).
The researchers reported that human waste mainly contains undigested food
comprising of organic matter such as carbohydrates, proteins and lipids. Bacteria in
conventional sewage treatment systems use enzymes to oxidize the organic matter,
in this process electron are released. Normally electrons power respiratory reactions
of the bacteria’s cells and eventually combine with oxygen molecules (Microbial
fuel cell, 2004).
Lakshmi and Sundara Moorthy (2004) investigated the germination and
seedling growth behaviour of Paddy and Groundnut seeds grown with tannery
effluent. The germination percentage, seedling growth, fresh weight and dry weight
of seedlings showed better performance upto 10% effluent concentrations when
compared with control.
2.6.1. Aquatic Macrophyte
Duckweed and water velvet both have shown bioaccumulation co efficient of
about 1000 for Cu and Cr (Rutiner, 1953). Of all the aquatic plants used for waste
water remediation more basic biology and remediation application work has been
described
for
species
of
family
lemnaceal
commonly
called
duckweed(Tarver,1986).The plant is easy to grow under simple laboratory condition
(Hillman, 1961).
2.7. Solid Waste Management
The United States Environmental Protection Agency (USEPA) projects that
the annual production of municipal solid waste in India will climb to about 200
million tons by the year 2000 and to 230 million tons by 2010. These projections
have prompted interest in composting municipal solid waste as an alternative to
landfills and incineration. Municipal solid waste (MSW) is composted to reduce
waste volume and disease-causing organisms, and to cycle nutrients. While
municipal solid waste can be converted into compost, the question arises about what
to do with the compost once it is produced. Since there are limited markets and few
standards on how to utilize MSW compost on land, only 30% of all such compost is
used for agriculture, landscape, and horticulture, while 70% of the compost is land
filled. Agricultural lands are excellent sites for beneficially using municipal solid
waste compost as an organic soil amendment. The organic matter present in many
soils throughout Minnesota and the U.S. has gradually decreased over the past 100
to 200 years. Most agricultural cropping systems result in the depletion of organic
matter. Soil organic matter acts as a sink and source of nutrients in the soil system
because it has a high nutrient-holding capacity. It also acts as a large pool for the
storage of nitrogen, phosphorus, and sulfur, and has the capacity to supply these and
other nutrients for plant growth. Soil organic matter interacts with trace metals, often
reducing their toxicity to plants. The physical benefits of organic matter on soil
include improved soil structure, increased aeration, reduced bulk density, increased
water-holding capacity, enhanced soil aggregation, and reduced soil erosion. The
application of municipal solid waste compost to agricultural soil can be a means to
return the organic matter to agricultural soil and in some cases reduce the cost of
municipal solid waste disposal.
The weight of solid waste generated per person per day usually lies between
250 and 1000 gm world wide and the main constituents of domestic waste are
vegetable putrescible matter, inert matter, paper glass and metals (Flintoff and
Millard, 1969).
Solid waste may be defined as municipal solid waste resulting from
commercial, institutional operations or Industrial solid waste and that generated in
effluent treatment facilities. Therefore the term “Solid Waste/ Refuse” encompasses
a wide variety of material such as discarded food, paper, plastic, metal, glass and
others. These wastes results from diverse societal operations. Such wastes are
collected by municipality for disposal in a common treatment facility. In some
locations, these wastes are handled along with liquid wastes also (Britoon, 1972).
According to Becker (1979), yield was higher in soil amended with
municipal solid waste compost compared to soil with no compost, except for the
first year on a sandy loam soil. Compost carry-over effect was observed on corn
yields three to four years after compost application. On the other hand, annual
compost application (40tonnes/acre) resulted in consistent yields for the three years
of compost application. Supplementing the 40 tonnes/acre compost rate with half the
Nitrgen needs for corn was sufficient to give optimum yield. Generally, the percent
of compost N available to the crop ranged from 5% to 11%.
Deborah (1989) reported that Japanese incinerate 23 percent waste and US
9% . As population wealth, and the ability and willingness to produce disposable
packaging and products increase, waste volumes also increase. This generation of
waste is expected to continue to increase. Incineration is the fastest growing option
in waste disposal management. The disadvantages of incineration include a large
amount of money required for construction, the special need for skilled employees
and high maintenance of repair costs.
Solid waste management in class I cities in India (1999) gives the
information about composting process and the various types of composting. It gives
the waste management by all means of treatment and has considered corresponding
financial aspects (Shekdar et al., 1989).
Solid waste disposal has become a major problem because of the increase in
the quantity of waste materials. Water and air pollution can result from poor disposal
practice of solid waste. Other types of solid waste like hazardous waste can also
become a part of municipal solid wastes. The important aspects of SWM are
protection of public health, economical handling, collection, storage and disposal
and resource recovery with due consideration to acceptability and conservation
(Flintoff, 1976).
Giovanni Vallini and Antonio Pera (1988) suggested that the vegetable waste
can be composted to green compost. He has given the performance of the
composting system adopted together with physico-chemical characteristic of the
starting material and the final product. Some microbiological and phytotoxicological
details concerning the green compost products is also given
Municipal solid waste management systems, as they exist in India, consist of
collection, transportation and disposal, occasionally with material recovery on
processing (Shekdar et al., 1989).
2.7.1. Generation of Municipal Solid Wastes
Municipal bodies have to manage the solid wastes arising from residential,
commercial and institutional activities along with waste from street sweepings.
Normally the municipal bodies handle all the waste, deposited in the community
bins located at different places in the city. The municipal solid waste is transported
to processing / disposal facilities. Majority of the municipalities do not weigh their
solid waste vehicles but estimate the quantities on the basis of the number of trips
made by the vehicles. Since the density of waste is considerably less as compared to
the material for which these vehicles are designed to carry, such data on quantity can
not be relied upon. In a number of studies carried out by NEERI the waste quantity
was measured. The data indicates that the quantity varies between 0.2- 0.4 kg per
capita per day depending upon the population of the urban centre. In metropolitan
cities quantities upto 0.5kg/ capita/day have been recorded (Table 2.11). The
percapita waste quantity tends to increase with the passage of time due to various
factors like increased commercial activities, standard of living, etc. Increase in
percapita waste quantity is also known to occur at a slightly lesser rate than the
increase in GDP / Capita. This increase is estimated to occur in India at a rate of 11.33% per year (Gaikwad et al., 1985).
2.7.2. Waste Composition
The organic content is high due to the practice of the common use of fresh
vegetables and fruits in the food. The high organic content also necessitates frequent
collection and removal of the waste.The paper, glass and plastic content is small;
these materials are sold by the citizens to hawkers, who collect and sell them for
reuse or recycling. Hence it is only that fraction which does not have a resale value
and is in a non usable form, remains in the waste. The waste contains a high
percentage of ash and fine earth. This is due to the common practice of depositing
street sweepings in community bins. Similarly in many case the surfaces adjoining
the roads are uncovered and a large amount of earth materials are swept away and
mixed with the waste materials. The calorific value of Indian solid waste varies
between 300- 500 kg/ m3 (Bhide and Sundaresan, 1980).
2.7.3. Trace Elements and MSW Compost
Many metals and metalloids are present in minute ("trace") amounts in the
soil and water. These trace elements occur naturally as a result of the weathering of
rocks. They can be leached into surface water or groundwater, taken up by plants,
released as gases into the atmosphere, or bound semi-permanently by soil
components such as clay or organic matter.
Metals appear in the municipal solid waste stream from a variety of sources.
Batteries, consumer electronics, ceramics, light bulbs, house dust and paint chips,
lead foils such as wine bottle closures, used motor oils, plastics, and some inks and
glass can all introduce metal contaminants into the solid waste stream. Composts
made from the organic material in solid waste will inevitably contain these elements,
although at low concentrations after most contaminants have been removed.
In small amounts, many of these trace elements (e.g., boron, zinc, copper,
and nickel) are essential for plant growth. However, in higher amounts they may
decrease plant growth. Other trace elements (e.g., arsenic, cadmium, lead, and
mercury) are of concern primarily because of their potential to harm soil organisms
and animals and humans who may eat contaminated plants or soil. The impact of
metals on plants grown in compost amended soils depends not only on the
concentration of metals, but also on soil properties such as pH, organic content and
cation exchange capacity. Different types of plants also react very differently to
metals which may be present (Linsay, 1973).
2.7.4. Effects on Water Quality
In addition to affecting plant and animal health, trace elements contained in
MSW composts may be leached (carried by water) from the soil and enter either
ground or surface water. As with plant uptake, soil pH, organic matter content, and
other soil characteristics affect the amount of leaching.
While other data on leaching from MSW composts is scarce, the evidence
from long-term applications of sewage sludge suggests that the rate of leaching is
low. Leaching of metals into groundwater is only likely to occur with heavy,
repeated applications of MSW composts over many years in areas with sandy soils
or other conditions that limit the opportunity for adsorption of metals by soil (Sinha
et al., 1977).
2.7.5. Effects on Soil Organisms
Little is known about the effect of trace elements in MSW composts on soil
organisms such as invertebrates (e.g., earthworms) and micro-organisms (e.g.,
nitrogen-fixing bacteria). When sewage sludge is applied to land, the concentration
of some trace metals (e.g., cadmium) in earthworms is increased, but this increase
does not pose a significant risk to the worms or to wildlife that consumes them
based on the risk assessment performed to establish the new APL (Acceptable
Permissible Limit) values for sewage sludge. The average values of lead, copper,
and zinc in MSW composts exceed soil limits proposed by a group of European
researchers to protect soil invertebrates. Those limits may be conservative, however,
since metals are often less biologically available in composts than in mineral
soils.There is contradictory evidence as to whether metals in MSW composts may
harm soil micro-organisms, including nitrogen-fixing bacteria (Stevenson, 1982).
2.7.6. Long-term Concerns
As organic matter decomposes, the concentration of metals in compostand
thus, in the soil to which it has been applied may increase. The available data
suggest that if large amounts of MSW composts are applied to agricultural soils, half
of the organic matter may decompose within one or two decades. Metal
concentrations in soil are unlikely to exceed the concentration present in the original
compost, unless very large amounts of compost high in organic matter are applied.
Over time, metals generally become less available to plants and other organisms
unless soil pH decreases greatly or the soil is flooded for a long period of time
(Sinha et al., 1977).
2.7.7. Potential Benefits of Trace Elements in MSW Compost
Singh et al (1994) reported the potential adverse effects of heavy metals and
metalloids in MSW compost. There are also potential beneficial effects for
agriculture and horticulture. Soils that have been cropped for many years may be
deficient in nutrients such as boron, zinc and copper, and MSW compost could
mitigate such deficiencies. Other benefits include improved soil physical
characteristics such as increased water-holding capacity, improved chemical
characteristics such as nutrient retention capacity, and stimulation of microbial
activity that can improve plant growth and decrease the leaching of pollutants into
water supplies. MSW compost may also limit harm to plants by tying up trace
pollutants and toxic organic compounds (Linsay, 1973).
2.7.8. Related Regulatory Issues
For most heavy metals and metalloids, the levels in MSW compost are low
relative to proposed standards for sewage sludges, such as the newly established
APL values for sewage sludge. With the significant exception of lead, MSW
composts can usually meet these limits. However, it is essential to remember that
these values, developed for sewage sludge, may need to be adjusted for MSW
compost. In addition, some toxicologists and policy makers are concerned that the
risk assessment methodology used to develop such standards is based on incomplete
knowledge and are advocating a more conservative approach (Woodbury, 2005).
Almitra Patel (2001) suggested that City compost helped to improve the soil
condition, which is depleted due to the excessive use of fertilizers. Compost contains
useful microbes and humus that aerates soil, and improve water retention and
resistance to both drought and water logging, thereby reducing irrigation
requirements. Organic manure invariably increased crop productivity compared to
synthetic fertilizers.
Mamo et al (2002) reported that the conditions for efficient biological
decomposition of organic waste depened on optimum temperatures, moisture,
oxygen, pH levels and carbon to nitrogen ratios of the feedstock.If conditions
deviated fromthese optimum levels, the composting process was slowed and
chemically unstable compost may be produced.
Khambe and Bamane (2003) suggested that Garbage was an unavoidable
consequence of prosperous high technology. Hospitality industry as it is called has
increased multi fold during the last two decades in most of the urban centres in
India. In big and medium cities due to various socio-economic factors, there is
sudden increases in number of big, small and road side eateries around every nook
and corner of the city. This has contributed to a large quantum of solid waste
generated in big cities. These waste can be grouped as dry waste and wet waste.
Amongest the various methodologies for treatment of the solid waste from hotels the
reuse and recycling methodology of dry waste whereas vermicomposting method for
treatment of wet biodegradable solid waste found to most suitable, feasible and
economical method because it is pollution free and purely natural process.
Indra and Sarangthem (2003) reported that increasing use of sewage on soils
of sewage farms is a common practice. However a heavy load of heavy metal in
organic waste may inflate the concentration of the hazardous metal ions in soil plant
ecosystem and thus may find their way into the food chain of human beings and
animals (Novel and Lindsay, 1972).
Zehnder et al (2007) reported that Municipal solid waste compost (MSW)
results from the process of breaking down the organic components of garbage, such
as paper, food scraps, and yard waste. This process helps reduce the load on landfills
while enhancing utilization of normal household trash. More than 50% of normal
household trash consists of organic waste (vegetable and food scraps, paper, straw,
coffee grounds, egg shells, leaves, sawdust, weeds, wood, ash, and plant trimmings)
that can be composted. Currently, MSW compost can be used as a soil amendment
for farm fields, roadsides, lawns, nurseries, and golf courses. Alternative uses for
MSW compost are possible because some MSW compost contains low
concentrations of regulated elements and can be provided in large and consistent
quantities.
One such alternative is to use MSW compost as bedding in cattle feedlots.
Currently, paper or crop residues are widely used for cattle feedlot bedding
materials. However, because of increased interest in recycling paper products in
recent years, paper is no longer affordable for cattle feedlots. Similarly, some costs
associated with harvesting, storing, and processing cornstalks or other crop residues
for bedding may be eliminated if MSW compost emerges as an acceptable bedding
alternative.
A study was conducted by the University of Minnesota to determine the
impact of using MSW compost as bedding on cattle health, tissue element residues,
and the environment. It was also intended to help formulate general guidelines for
the use of MSW compost as a cattle-bedding alternative. The study involved
bedding two pens over two consecutive cattle feeding periods (summer and winter)
with either MSW compost or cornstalks. Bedding use, feed intake, and manure
output were measured and sampled to gauge flow of nitrogen and phosphorous and
concentrations of regulated elements. A selected sample of cattle were also
monitored for regulated element concentrations in their blood and feces (throughout
the feeding period) and in their kidneys and liver (at harvesting), as well as for
polychlorinated biphenyl (PCBs) concentrations in their perirenal fat (at harvesting).
In the trial conducted by the University of Minnesota, a veterinarian examined the
cattle at the start and ends of each feeding period, and reported no abnormal
observations. Nor did bedding cattle with MSW compost affect blood concentrations
of macro-elements, electrolytes, glucose, or liver and kidney enzymes.
Concentrations of some regulated elements found in kidneys and livers harvested
from cattle in a selected sample at the end of each feeding period. Kidneys of cattle
bedded with MSW compost had greater concentrations of copper and lead. Lead
concentration in the livers of these cattle was greater than those of cattle bedded on
cornstalks. In spite of these differences, tissue concentrations of both copper and
lead fell within the normal ranges observed in healthy cattle Accumulation of copper
and lead in tissue likely resulted from MSW compost being inhaled or consumed.
Further evaluation of concentrations of these elements in feces indicated that some
cattle within the selected sample were deliberately consuming some MSW compost.
This resulted in concentrations of lead and copper in feces that were similar
for cattle on either bedding system before the study began, but increased more for
cattle on MSW compost with extended exposure to MSW compost. PCBs were not
detectable in fat samples from cattle bedded with either material. All these
observations, taken together, supported the conclusion that cattle bedded on MSW
compost were healthy throughout the study. Also, although some MSW compost
may be consumed or inhaled, concentrations of regulated elements in kidneys and
livers did not increase beyond normal concentrations (Zehnder et al., 2007).
2.8. Socio-Cultural Determinants of Urban Occupation
All the human elements that determine or affect man’s occupation or
livelihood can conveniently be grouped into three broad categories (a)Social
elements-comprising a number of elements related with society,(b)Cultural
elements-referring to the process and stage of development of a society which
includes the level and trends of urbanization, economic advancement-agricultural
and industrial development, transportation and communication net work, public
health and education system etc and(c)personal elements-denoting the individual
characteristics of man such as his age, sex, health and education together with his
attitude and psychology. Age and sex are very important aspects of personal
environment in determining ones capacity and ability to adopt an occupation.
The quantum strength of labour force is determined by age-structural of
population .The age structure influences the economic and social interactions, social
attitudes and social and occupational mobility. Education and training are most
influential factors to determine man’s occupation. It is education and training that
encourage rural to urban migration motivated by the objective of acquiring
prestigious non-agricultural occupations and also accelerate the pace of occupational
mobility from agriculture to diversified non-agricultural fields. Education
strengthens the capacity of the social group s to respond to them and there by
promotes the process of development. It is a fact that high ratio of educated and
trained persons make the task of economic development much easier. Further
education level is higher in urban areas than in rural areas where most of the
population is illiterate and uneducated.
2.8.1. Belts of Vegetation of Irregular Shape
Several kilometres around urban areas have come to be called green belts.
Their purpose is to prevent further expansion of city, reduce heat and pollution and
to preserve the special characteristics of some historic cities. The development of
green belt s was conceived long ago. This idea is almost a precursor of the present
day emphasis on ecological development and conservation .The green belt, for its
success, requires that there is no grazing in the area. In the absence of grazing,
forests and grass lands grow undisturbed and act effectively against dust storm.
Kedir (2005) reviewed the quantitative and qualitative evidence on urban
poverty in Ethiopia. The review covered the discussion of key correlates/dimensions
of poverty, such as livelihood insecurity, gender, household income, prices and
HIV/AIDS.
Cobus de Swardt (2005) reported that urban sprawl decreased the amount of
open space, agricultural land, and natural habitats in regions surrounding cities.
These regions were affected by the waste and pollution produced by the city, and
were also depleted natural resources used by the city. As people move out of the city
into surrounding regions, the cities expanded, and further pollution and resource
depletion occurred as people traveled longer distances from home to work. Ruralurban migration also has a strong impact on the demography of rural areas. There
was often a pattern in such migration with respect to age and gender, and this
migration can act as a sort of "brain drain", whereby rural areas were left with the
least educated people, placing them in a position of even lower social and political
power.
Cities have strong socio-cultural impacts on their surrounding rural areas.
The mass media depicts city life as superior to rural life, the "standard" language is
deemed that of the national capital, and better services are received in the city due to
its wealth. National symbols and values are generally more evident in urban than
rural areas, since they attempt to bind otherwise isolated city dwellers. The fertility
rate in cities are often lower than in rural areas due to the absence of agriculture, the
cost of children, food and living space in cities, and family planning(Zehnder etal.,
2007).
In Urban areas, there is more population density, shortage of houses,
congestion, more automobiles, more crimes, prostitution, juvenile delinquencies,
social tensions, riots, shortage of parks, playgrounds and open spaces, cattle
problem, air, water and noise pollution, traffic hazards, industrialization. Besides
this there are more contaminants, dust, more cloudiness, more fog in winter, high
temperature ,less humidity, less radiation, less wind speed, more unemployment in
comparison to rural areas. The ecology of urban India is on the verge of collapse.
One can find haphazard and chaotic growth of cities and towns, misuse of land,
slums, jhuggis and jhonparies in all parts of cities. Industries are established on
political ground without consideration of pollutants. Most of the residential areas are
having obnoxious and noise creating industries. Heavy traffic passes through
residential colonies. There are also problems for energy, electricity and water
supply, health and hygine facilities, etc. The crisis in urban areas is due to wrong
orientation of science and technology ,misuse of political
and administrative
powers, rampant corruption, nepotism, favouritism in most of the departments,
misuse of funds, lack of interest in welfare activities ,deterioration in morality of the
people ;lack of national character; implementation of the plan by untrained
personnel; escapism from responsibilities ,division of society in the basis of caste,
creed, class, religion, etc.(Vanden Berg,1997).
MATERIALS AND METHODS
STUDY AREA
3.1. General Profile of Pudukkottai
The modern town of Pudukkottai is now about 200 years old. It originally
consisted of irregular streets and narrow lanes of mud-built thatched houses. Just a
century after its origin, it was almost entirely destroyed by fire. The new town that
was built partly from private funds and partly with the help of a state subsidy of
3,000 pagodas distributed to the poor was well laid out with broad streets. Again, in
course of time deterioration set in; encroachments marred the rectilinear layout. The
municipality was brought into existence in April 1912 under Regulation I of 1912.
Pudukkottai town has an area of 284.06 sq.Km. It lies between the parallels of 9°
50’ and 10°40’ North Latitude and between the meridians of 78°25’ and 79°15’ East
Longitude .The total population of Pudukkottai in 1901 census was only 20,347.In
1931 census was 28,776; in 1981 it was 70,952; in 1991 it was 76,657 whereas, it
has grown up to 1, 00,723 in 2005.Now these municipality is divided into 42 wards.
This present study is aimed at analysing the detail impact of the Urbanization on
Environment in Pudukkottai (Fig 2.1).
3.1.1. Current State of Environment in Pudukkottai
Pudukkottai stands on a sandy plain, and has tropical maritime, monsoon
type of climate. The temperature is very high throughout the year. The mean
maximum and minimum temperature are 37° C and 30° C respectively in summer.
The mean minimum and maximum temperature in winter are 20.6° C and 21.3° C.
The mean annual rain fall at Pudukkottai is 83.5 cm and mean number of rainy days
are 89 days. The city spreads over an area of 23.26 square kilometres.
3.1.1.1. Topography
Pudukkottai town has got a peculiarity. The town from the centre point leads
to Thanjavur on North, Aranthangi and Pattukkottai on east, Trichy on west and
Karaikudi etc, on the south. The roads and streets are parallel and perpendicular. The
main offices like Government departments, collector’s office, and public head
quarters are from within the stone’s throw away distance from the centre of the
town.
Though there is no specific industry around Pudukkottai except SIPCOT
industrial complex. This cannot be stamped as a main business centre.
3.1.1.2. Climate
The climate of the Pudukkottai naturally resembles closely that of the
surrounding districts of the presidency. It is one of the drier areas in Southern India.
The year may be divided into four distinct seasons. The first period January to
March is relatively dry and cool. In the second, April to May, though more rain is to
be expected, the heat steadily increases. The second half of the year comprises the
two monsoons. Practically, the hot season extends from March to October, with
occasional intervals of rain, while the rainy season properly so-called extends over
October, November and December and sometimes into January. Such “cold weather
“as the sets in December and lasts till March. The rainfall varies remarkably from
year to year. More rain is generally expected from the North –east than from the
South-west monsoon, but statistics show that this expectation is by no means always
realized (ENVIS, 2005).
The Temperature is officially recorded daily only at the observatory at
Pudukkottai Town. For the major portion of the year the mean daily temperature is
generally about its mean annual temperature. The range of temperature during the
course of a day varies very greatly during the different seasons of the year. The
range of daily variations is greatest in April or May and least in November or
December.
The South –west monsoon wind, popularly known as the west wind, blows
steadily from the middle of June to August. The northerly breeze of SeptemberOctober shifts to the east when the North –east monsoon breaks. In January and
February the wind blows from the east, and from March to June a southerly wind
prevails till it again shifts round to the west with the setting in of the South-west
monsoon.
Work Plan
The present study was carried out to determine the impact of urbanization in
Pudukkottai on its Environment. In order to assess the impact, the following aspects
were studied in detail.
™ Air quality with reference to SPM,SO2 , NOx and noise levels
™ Water quality-surface and ground water
™ Soil quality
™ Flora and faunal status
™ Socio-economic status
™ Waste water characteristics and
™ Solid waste –characteristics, amount and disposal.
3.2. Air Sampling and Analysis
According to Air (Prevention and Control of Pollution) Act 1981, Air
pollution is defined as “the presence of solids, liquids and/or gaseous substances in
the atmosphere, in concentrations which may cause injuries to human beings or
other living organisms or property or environment”.
For the sampling of air, high volume air sampler (Model VFC-PM10) was
used (10 meter above and 5 meter away from road) and the particulates were
collected on whatt man GFA glass fibre filters dried in a hot air oven at 105°C for
1hr and weighed. The average flow rate was about 1.1 cubic meters.
This study was undertaken to investigate the quality of air in Pudukkottai
town and in sub urban. These selected sites were residential zone, commercial zone,
silence zone and Industrial Zones. To study the quality of air, three common
pollutants were taken into consideration. They were suspended particulate matter,
sulphur dioxide and oxides of nitrogen. The study was conducted from June 2005March 2006. Samples were collected from urban and sub urban areas in four
different seasons (Season I -June, July; Season II-September, October; Season IIIDecember, January; Season IV-March, April).The sampling was done for 6 hours
intervals at different stations, samples were taken 3 times in the day, morning,
afternoon and night . Weekly two days sampling was done in a same place of
different zones. Urban and sub-urban sampling stations were selected, which
represent 7 different zones. ( I- Urban residential zone, II-Sub urban residential
zone, e III- Urban commercial zone, IV- Sub urban commercial zone, V-Urban
sensitive zone, VI-Sub urban sensitive zone and VII-Industrial zone).
The sampling procedures are as follows
1. The fibre glass filters were checked for any pin holes. Particulates or other
imperfections. The filter was dried in a hot air oven at 105°C for one hour
and the initial weight of the filter was noted (w1).The filter was not folded
and it was carried in a polythene bag to the sampling site.
2. The filter was fixed on the filter holder in position (rough side up), the face
plate was replaced and the nuts were fastened securely. A very thin
application of talcum powder was used on the sponge rubber of the face plate
to prevent it from sticking. The instrument was placed at approximately 10m
above and 5m away from the road.
3. At the beginning and at the end of the sampling period, the flow rates were
noted and the average flow rate was calculated. The closing time was also
noted.
4. For the collection of gas sample, the gas impinger was filled with 30 ml of
the absorbing solution. The impinger was checked to make sure there was no
leakage. The gases were absorbed at the rate of 1 lts/min.
5. After the sampling was completed, the face plate was removed and the filter
was carefully removed from the holder.
6. The impinger was carefully removed. The volume of absorbing reagent was
checked in the tube. It was less due to evaporation of water and it was
compensated by adding distilled water.
7. The filter and the solution in the impinger were taken to the laboratory.
8. The filter was kept in a hot air oven for 2 hours and then cooled. The filter
with sample was weighed (w2).
3.3. Noise Assessment
In order to assess the extent of noise pollution due to vehicular traffic
different zones viz., Silence zone, Residential Zone, Commercial zone, Traffic
signals and Industrial zones were identified in urban and suburban areas of
Pudukkottai. Adequate number of observations were made in all the selected sites by
using the sound level meter (LT Lutron SL-4001) .The data were analysed and the
noise levels were computed. Noise observations were made in all the selected places
during day time, night time and Lmin, Lmax, L10, L50, L90, Leq values were
determined. The selected sites and the number of readings were mainly focused to
improve the sampling technique and to get better representation and in turn reduce
the error involved in sampling technique.
The observations were made during three different occasional days (holiday,
working day and festivals).Day and night studies were done. 6 different zones were
selected: (I- Urban residential zone, II-Sub urban residential zone, III- Urban
commercial zone,
IV- Sub urban commercial zone, V-Urban sensitive zone and
VI-Sub urban sensitive zone).
3.3.1. Selected zones
3.3.1.1. Residential zone: This zone included the residential areas of upper middle
class, middle class and lower middle class people.
3.3.1.2. Commercial zone: Busy streets, bus stops, bazaars etc., fall under
commercial zone in Pudukkottai.
3.3.1.3. Silence zone: Educational institutes, Hospitals, Temples, Courts etc. fall
under the silence zone. Silence zone is defined as the area in and around 100m of
educational institutions, hospitals, parks etc., Use of vehicle’s horns, loudspeakers
and bursting of crackers etc ., are banned in these zone (Sharma and Kaur,1994).
3.3.1.4. Industrial zone: In Pudukkottai SIPCOT is the only place which represents
the Industrial zone.
3.4. Water Sampling and Analysis
For collection of sample from surface water plastic jug was used. Samples
from the bottom of shallow water were collected by lowering a closed plastic bottle
to the bottom, opening and closing it there by hands and taking out. Parameters
like temperature, pH were immediately recorded. Dissolved oxygen was
immediately fixed by manganous sulphate and alkaline iodide solutions. Other
samples in well labelled and tightly capped containers were brought to the
laboratory in ice-box.
Samples were collected from urban and sub urban areas in three different
seasons (Season I –Monsoon (June-Sep), Season II-North east Monsoon (Oct-Jan),
Season III-Pre monsoon (Feb-May).
Ground water Samples were collected from urban and sub urban areas in
three different seasons (Season I –Monsoon, Season II-North east Monsoon, Season
III-Pre monsoon).Samples were collected from six different zones (I- Urban
residential zone, II-Sub urban residential zone, III- Urban commercial zone, IVSub urban commercial zone, V-Urban sensitive zone, and VI-Sub urban sensitive
zone). In each zone 10 samples were collected. This study was conducted from Jan
2004-Dec 2004.
Water samples were collected and analysed as per standard methods (APHA,
2005). The following parameters were analysed: Turbidity,
Dissolved Oxygen,
Nitrate, Acidity, Alkalinity, Hardness, Electrical Conductivity, Total Solids, Total
suspended solids, Total Dissolved solids,
Magnesium,
Nickel, Fluoride, pH,
Temperature, Iron, Lead,
Nitrite, Chloride,
Calcium,
Biochemical Oxygen
Demand, Chemical Oxygen Demand, Coli form, Plankton.
3.5. Soil Sampling, Analysis and Treatment
Surface soil samples were collected using spade. For collection of soil from
deeper profiles special borer samplers were used, collected samples were put in thick
quality polythene bags and immediately brought to the laboratory. Soil was ground
using mortar and pestle and sieved through a 2mm mesh sized sieve. Soil samples
were collected from urban and sub urban areas in three different seasons (Season I –
Monsoon, Season II-North east Monsoon, Season III-Pre monsoon). Random
samples were collected from six different zones (I- Urban residential zone, II-Sub
urban residential zone, III- Urban commercial zone, IV- Sub urban commercial
zone, V-Urban sensitive zone, and VI-Sub urban sensitive zone). In each zone 10
random samples were collected.
3.5.1. Selection and Location of the study area
Pudukkottai urban and suburban areas are predominantly a dry tract in
Pudukkottai district .The district as well as town have more than half the area under
wastelands. Pichathanpatti village is a small village. The people of this village are
mostly agricultural labourers with small and fragmented land holdings, lying fallow.
Agriculture is mostly rain fed and failure of rains would mean socio-economic
suffering. The holdings that are lying as wastelands are not cultivated for various
reasons and if cultivated with crops like groundnut, pulses and fodder maize the
returns are not guaranteed if rains fail. Under these circumstances any creative effort
in wasteland development, economically affordable would supplement income
towards socio economic development. More than that would indeed contribute to
ecological conservation and enrichment of environment.
3.5.1.1. Terrain Evaluation
Terrain Evaluation is used to assess the inherent suitability of lands for the
range of possible uses. It is a process involving analysis, classification and appraisal
of a tract of country with regard to its natural features and configuration. Terrain
evaluation of wasteland is based on classification and subdivision of wastelands on
the basis of selected attribute values and their evaluation for certain pragmatic use.
Certain parameters of the prevailing natural conditions have to be left out because
under certain condition they may be unimportant.
3.5.1.2. Soil profile
To study the soil profile, a 3×3 pit to a depth where bed rock was dug. The
various horizons that were distinctly visible were demarked by their boundaries. The
texture and structure of the soil in each layer were studied and noted. Lime status
was indicated by effervescence or its absence with diluted HCl. The pH of the soil at
each layer was also noted using colour charts. The horizon depth or layer was noted
in cm. The description of each layer in the profile in a comprehensive manner
constitutes the soil profile.
3.5.1.3. Soil Fertility Studies
Soil fertility studies were carried out by drawing out random samples and
testing the nutrient values (a) Before planting, (b) After first harvest and (c) After
second harvest. The procedures for soil samplings and procedures used for testing
the nutrients are outlined below.
3.5.2. Collection of Sample
The surface was scraped away and sampling auger was inserted to plough a
desirable depth, and the sample was collected in sufficient amount. Samples were
taken randomly over a distributed area and placed in a clean bucket or basin (Metal
containers were avoided). The soil samples taken from 15 spots were mixed
thoroughly and only about 1kg was taken after discarding the rest. Discarding was
carried out by quartering. Quartering was done by dividing thoroughly mixed soil
into 4 equal parts and 2 opposite quarters were discarded. The remaining 2 quarters
were to mixed and again divided into 4 equal parts. The opposite quarters were
rejected and remaining 2 quarters were mixed. This process was repeated until about
1kg of soil was left taken to the laboratory in a clean labelled container. Soil samples
were collected and analysed as per standard methods (Trivedy and Goel, 1986). The
following parameters were analysed:
pH, Electrical Conductivity, Total Organic Carbon, Total Organic matter,
Total nitrogen, Total Phosphorus, Total Potassium, Total Sodium, Total Calcium,
Total magnesium.
3.5.3. Soil Treatment
3.5.3.1. Cultivation of Palmarosa
Cultivation of palmarosa was done in 50 cents in a farm belonging to a
private industrial group based at Pudukkottai. The farm has a palmarosa oil
distillation plant. Urban and sub-urban sampling stations were selected.
3.5.3.2. Cultivation study
The ½ acre (50 cent) of land was divided into two equal blocks of 25 cents
each (1cent = 40 sq.m). In one block palmarosa samplings raised from a prior
nursery were planted as such at a spacing of 30cm × 30cm (Block I). In the other
block (block II) 50 baskets of Humus (500kg) and 10kg of powdered Neem cake
were added to the soil before planting and saplings were planted as like in other
block. In both the blocks stones were removed manually and 3 ploughings was done
by country plough. The last ploughing was done in such a way to form regular
ridges 30 cm spacing. The quality of oil was estimated by procedures laid out by IS:
526-1986. The yield of oil, the economics of cultivation, the monetary returns of the
grass and oil at current market prices were estimated.
3.5.4. Plant description
Palmarosa (East Indian geranium)
Cymbopogon martini
It is used as a herb in alternative herbal treatments to treat ailments and
problems. It is used to correct production of skin sebum, stimulates cell rejuvenation
and has a hydrating effect on the skin.
3.5.4.1. Botanical Classification
Family
-
Gramineae.
Genus and species
-
Cymbopogon martini.
Other names
rosha, as
-
Andropogon martinii, East Indian geranium,
well as geranium grass.
3.5.4.2. Description of the herb palmarosa
It is not generally cultivated, the herb has long slim stems and flowering
tops, the leaves resemble grass and have a noticeable fragrance. The fresh and dried
grass is used.
3.5.4.3. Properties
It has anti-viral, antiseptic, cytophylactic as well as febrifuge properties.
3.5.4.4. Therapeutic uses
• Internal use
o Internally it may be used to aid the digestion and to lower fevers, but
is not commonly used as a herbal compound taken internally.
•
External use
o
•
Externally it helps to reduce oiliness of the skin.
Aromatherapy and essential oil use
o
Palmarosa essential oil uplifts and calms the emotions; reduces fever.
It is used as a digestive tonic to stimulate appetite.
o
On the skin, it has a moisturizing and hydrating effect. It stimulates
cell rejuvenation and encourages the correct production of sebum.
o
It has antiseptic, antiviral, bactericide, cytophylactic, hydrating and
febrifuge properties.
Sewage water were collected from sewerage system and examined by the
standard APHA (2005) methods. Then it was treated by lemna aquatic plant in a big
cement tank at five days interval the change was recorded up to 25th day. Floating
aquatic treatment systems have been used for a variety of treatment purposed
including secondary treatment, advanced secondary treatment and nutrient removal.
3.6.1. Growth Characteristics
Lemna grows on quiet of sluggishly moving waters of ponds, pools, lakes,
swamps, streams, drainage ditches, canals, bayous and sloughs. Plants reproduce
vegetatively by a process called budding, where new plants grow from within
marginal cavities or pouches along the basal portion of the frond. The daughter
plants may remain attached to the parent plant for a period of time or repeat the
budding process before breaking off. Although rarely seen, duckweed may
occasionally flower and produce seeds. The treated water was utilized for the growth
of buffalo grass. After harvesting, the plant parameters were analysed and compared
with control.
3.6.2. Buffalo Grass: Kansas Wildflowers and Grasses
Buchloe dactyloides (Nutt.) Engelm
Perennial
2 - 12 inches tall
Flowers: May – June
Sewage Water samples were collected and analysed as per standard methods
(APHA.2005). The following parameters were analysed.
Turbidity,
Dissolved Oxygen,
Electrical Conductivity, Total Solids,
Nitrate, Acidity,
Alkalinity, Hardness,
Total suspended solids, Total Dissolved
solids, Temperature, Iron, Calcium, Magnesium, Fluoride, pH, Nitrite, Chloride,
Sulphate, Phosphorus, Biological Oxygen Demand, Chemical Oxygen Demand,
Nitrate and Potassium.
Treated sewage utilized plant samples were collected and analysed as per
standard methods (Harborne, 2005). The following parameters were analysed.
Dry weight, Fresh weight, Total chlorophyll, Phenol content, Free sugar,
Leaf length.
3.7. Solid Waste Assessment and Management
Solid waste management is an obligatory function of urban local bodies
(ULBs) in India. However this service is poorly performed and that resulted in
problems of health, sanitation and environmental degradation with over 3.6% annual
growth in urban population and the rapid pace of urbanization. The situation is
becoming more critical with the passage of time. It is estimated that every human
being release 500-1000g of Solid waste per day.
3.7.1. Solid Waste
The samples of refuse from each of the sampling points were collected. The
15 composite samples thus obtained were brought to the lab, where they were
physically sorted out and analysed to determine their physical composition. The
samples were collected from urban and sub urban areas in 6 different zones ( IUrban residential zone, II-Sub urban residential zone, III- Urban commercial zone,
IV- Sub urban commercial zone, V-Urban sensitive zone and VI-Sub urban sensitive
zone)during working day, holiday and festival days. This study was conducted from
June2005-March 2006.
3.7.1. Composting
One of the problems of solid waste disposal is its safe disposal .Although
there are many ways of solid waste disposal only few are safe. Composting is one of
the best ways of managing the solid waste. Composting is a process by which
organic waste are converted into organic manure by means of biological activity
under controlled conditions. Composting also provides stable humus like product,
which act as soil conditioners.
Composting is the biological decomposition of the organic constituent of
waste under controlled condition.
Biological process
Organic solid ----------------------› Humus
Presence of air
(Usage as soil condition)
3.7.2. Process description of Composting
1. Preparation of the solid waste.
2. Decomposition of the solid waste.
3. Product preparation and utilised in the field.
3.7.3. Nutrient contents of Sugar industry effluent
One cubic meter of primary treated effluent (sugar industry) contains 1.5Kg
N, 0.25Kg phosphate and 10Kg potash and 15Kg of digested organic matter. The
nutrients can be effectively trapped and used for sustainable agriculture purpose.
3.7.4. Preparation of Bio-compost
Composting of organic waste may be carried out in two different methods
viz aerobic and anaerobic composting. The sugar industry effluent from Aranthangi
Sugar Mill) is being converted into fine compost by aerobic method.
3.7.5. Site Selection for Composting Plant
The Sufficient area was selected for composting. Organic wastes were
collected from Municipal dump yard. This study attempted to use new sugar
industry effluent instead of water as a moistening material. Solid waste from
Pudukkottai municipality was taken as a raw material and effluent from Pudukkottai
sugar industry was used as moistening agent. Pleurotus species are inoculated for
degradation of lignocellulosic substances present in the solid waste and Bacillus
species was inoculated for solubilizing phosphate. Finally the Lemna sp was
introduced (it is screened after waste water treatment). It contains more nutrients.
3.7.5.1. Windrow Method
Procedure
The waste was allowed to dry for one week under sunlight. Phospho bacteria
species was added and mixed to the raw material, which gives first bed. Similarly
another bed was made with the help of Azotobacter species and which was added
above the first bed. The bed was made with the phosphobacteria and which was
added above the second bed. The beds gave the 1.5mts of height before the addition
of inoculum; sugar industry effluent was added to each bed and filled.
Urea,
Gypsum and Rock Phosphate were added in the ratio of 5:4 after several days of
addition of inoculum. The temperature was observed everyday and raised gradually
up to 65oc in 15 days period .The waste were constantly filled and moisture content
was maintained by spraying. The sugar industry effluent was added twice a week.
Composting of the materials was completed in 45-60 days.
Regarding solid waste it was treated, and the bio-compost was utilized for
the same plant growth which was used in soil treatment and their growth
comparisons were studied.
3.8. Biodiversity
An attempt to obtain a fairly comprehensive picture of biological resources
of Pudukkottai, the study was made on diversity of Plants, Birds, Insects, Reptiles,
Amphibians, Mammals and Invertebrates.
3.8.1. Flora
The plant species diversity estimates were divided into five components. The
Herb layer estimation was carried out in a total of 8 quadrates on four different sites
(east, west, north and south) and one centre transect for trees estimation. The
localities of quadrates and transect were plotted in the Pudukkottai map. Samples
were collected from urban and sub urban areas in four different seasons (Season I June, July; Season II-September, October; Season III-December, January; Season
IV-March, April). The study of flora involved intensive sample survey of vegetation
in the urban and suburban location applying standard methods (Greig-smith 1983,
Caustan 1988).
3.8.1.1 Sample survey
To examine the trees and shrubs quadrates of 25×25m and for herbs 2×2m
were laid. In each of the larger quadrates species and their number were noted. In the
sample quadrants the shrubs were also enlisted and enumerated, examined and the
average was computed. In the smaller quadrates the shrubs were also enlisted and
enumerated. At each location 8 quadrants were examined and the average was
computed. In the smaller quadrate (2×2m) herbs were enlisted and enumerated.
Specimens of the plants whose identity couldn’t be confirmed in the field were
collected and preserved following standard methods (Santapau, 1955) and identified
subsequently using regional and district floras.
3.8.2. Fauna assessment
The animal life of an area is dependent upon the vegetation and there are
countless relationships between the species composing an animal community. Fauna
assessment involves more problems than flora assessment by virtue of the greater
variety of animal types, their mobility and behaviour. Faunal assessment provides a
basis for determining relative abundance and evaluating commons or rarity of each
species encountered. In the study area, the animal survey was conducted in all the
sampling sites along with the plants.
The study of fauna involved intensive sample survey along Pudukkottai and
its suburban areas. To assess the animals, the area was covered intensively on foot.
Both direct and indirect observation methods were used to survey the fauna. Visual
encounter (search) method was employed to record vertebrate species. Additionally
survey of relevant literature was also done to consolidate the list of vertebrate fauna
distributed in the area (Smith 1933-43).
3.8.2.1. Insects
In the urban and sub urban places seven zones were selected for study of
insects (I- Urban residential zone, II-Sub urban residential zone, III- Urban
commercial zone, IV- Sub urban commercial zone, V-Urban sensitive zone VI-Sub
urban sensitive zone and VII-Industrial zone) during the year of 2005.
(1)
Sweep net method
Insect nets designed to collect sweep samples from vegetation were used in
systematically sweeping the ground level vegetation. Roughly a square plot was
chosen where 20 steps of walk on each side were made to collect insects by net. The
insects were collected and transferred to a plastic container containing cotton dipped
in ethyl acetate and is properly labelled; the insects were preserved in alcohol till
sorting.
(2)
Pitfall trap
Tree pitfall traps were placed in each locality. The trap consisted of plastic
cup, which was buried at ground level and collected after 3 days time. The pitfall
trap was used to collect ground dwelling insects.
(3)
Shake method
A sheet of size 5m ×3m was spread under the thick shrub or small trees. The
shrub was shaken or beaten vigorously for 10 minutes. Insects were collected from
the sheet and preserved in alcohol till sorting.
(4)
Light trap
A portable light operating on batteries was placed in the white sheet spread
in the middle of the plot for1 hour at night in each locality. Insects were removed
from the spread sheet and preserved in alcohol till identification.
(5)
All out search method:
This method was used only to collect butterflies. The butterflies were
collected between 10-11am. Within the allotted time an attempt was made to collect
representative individuals of as many species as possible.
The sampling was done in seven different zones of urban and sub urban
areas. The insects were preserved either as dry specimen if large or in alcohol if
small. The specimen collected from each locality was being preserved separately.
All the collections were being carefully labelled. The number of species were
counted and not the number of the individual species.
3.8.2.2. Birds
Samplings for birds were done by walking along fixed predetermined path.
While walking along a path, a range of 10 meters on either side of the observer was
the zone of actual counting. Thus the entire path was covered without any overlap.
Birds were identified based on sightings, calls and overhead flight. For flying birds
to avoid including those far above, the criterion used was to include the birds flying
at a height at which even a small bird may be recognized without the aid of field
glasses. Thus the samplings were done in urban and sub urban areas for 2 hours in
the morning for four seasons (Season I -June, July; Season II-September, October;
Season III-December, January; Season IV-March, April)in seven different zones ( IUrban residential zone, II-Sub urban residential zone, III- Urban commercial zone,
IV- Sub urban commercial zone, V-Urban sensitive zone, VI-Sub urban sensitive
zone and VII-Industrial zone).
3.8.2.3. Vertebrate species
Visual encounter methods, Pellet and track method were used to identifying
other vertebrates.
3.8.2.3.1. Point Survey Method: Observation was made in each site for 15 minutes
duration.
3.8.2.3.2. Roadside Counts: The observer travelled by motor vehicles from site to
site, all sightings were recorded (this was done both day and night).
3.8.2.3.3. Pellet and track counts: All Possible animal tracks and pellets were
identified and recorded (Southwood, 1978).
3.8.2.4. Reptiles
Reptiles were recorded based on sightings and previous records. The number
of species were counted and not the number of the individual species.
3.8.2.5. Aquatic Biological environment
Water samples were collected from lentic and lotic water system of the
study area and Plankton was identified and listed.
3.8.2.5.1. Planktons
Planktons particularly phytoplankton have been used as indicators of water
quality. The species assemblage of phytoplankton and zooplankton also may be
useful in assessing water quality. Sampling locations, depths and frequency have
been determined, field sampling were prepared (Phytoplankton 0.5 to 1lit;
Zooplankton 0.5 to 5 lit), sample containers were labelled. In the field record note
book temperature, time, turbidity, salinity also were recorded.
3.9 Socio-Economic Study
In general, socioeconomic factors that can be considered in the assessment of
environmental impact range from social impact such as population growth, density,
aesthetics, standards of living, congestion, incompatibility with surrounding
community, increase in recreational recruitments, and conflict in lifestyles.(The
Sunday Observer,1987).
3.9.1 Questionnaire
In order to obtain the reaction of general public regarding socio-economic
status of Pudukkottai, a questionnaire was prepared and was got answered by people
from 11 sampling sites (urban and sub urban area). It included different age groups
of both sexes, belonging to different social strata and of different walks of life
pursuing different profession for their livelihood.
Questioner:1
SOCIO-ECONOMIC STATUS OF PUDUKKOTTAI
1.NAME OF THE PLACE
:
2.LOCATION
:
3.NAME OF THE PERSON :
4.GENDER
: MALE FEMALE
5.LIST OF FAMILY MEMBERS
NAME
AGE GROUP
:
SEX
INCOME
EDUCATION
STATUS
6.SOURCES OF DRINKING WATER:
BOREWELL
HAND PUMP
CORPORATION WATER
SURFACE WATER
7.TYPE OF HOUSE:
OWN
-HUT
RENT
-HOW MUCH-
NEAREST MAINPLACE
-RAILWAY STATION
8.ELECTRICITY AVAILABILITY
YES
NO
INDIVIDUAL CEMENT HOUSE FLAT
BUS STAND MARKET
9.OCCUPATION
DAILYWAGE
PRIVATE
GOVT
UNEMPLOYEE
10.STATE OF WATER SUPPLY
REGULAR
IRREGULAR
11.AMOUNT OF WATER SUPPLY
SUFFICIENT
IN-SUFFICIENT
12.MODE OF WATER STORAGE:
OPEN CONTAINER
CLOSED CONTAINER
13.BATHROOM &TOILET FACILITY:
YES
-PRIVATE
PUBLIC
ROADSIDE
NO
14.SOLIDWASTE COLLECTION METHOD:
BIN-YES
NO
VEHICLE
15.SOLID WASTE COLLECTED FREQUENCY DAYS:
DAILY
TWO DAYS ONCE
WEEKLY ONCE
OTHERS
16.SOLIDWASTE DISPOSAL METHOD
OPEN DUMPING
OTHER METHODS-
17.DISPOSAL OF WASTE WATER(DRAINAGE FACILITY)
YES
-OPEN
CLOSED
NO
-WATER LOGGING
-YES
NO
18.VEHICLE FACILITY
BUS
AUTO
TWO WHEELER
19.LAND
OWN
-YIELD
LEASE
20.LIVESTOCKS
OWN
-BENEFITS
21.HOME APPLIANCES
TV
MIXY
GRINDER
WASHING MACHINE
GAS STOVE
OTHERS
22.DISEASE
WATER BORN
AIR BORN
OTHERS 23.RECREATIONAL AREA
CINEMA
PARK
OTHERS
24.QUALITY OF WATER SUPPLY
GOOD
BAD
25.EDUCATION STATUS OF CHILDREN
DISTANCE CROSSED-
FOUR WHEELER
RESULTS AND DISCUSSION
4.1. Population Distribution
The population of Pudukkottai has increased substantially over the years
(Fig 3.1). In 1901 the total population in Pudukkottai town was around 20,347, it
increased up to 28,776 in 1931.But 2001 population was 1,01,723, it had increased
up to 1,08,341 in 2005. Population stabilization is an essential pre-requisite for
sustainable human and social development with more equitable distribution. This
rapid growth is mainly due to the urbanization.
4.2. Air Quality Status in Pudukkottai
The air quality has been determined with reference to SPM, SO2 and NO2 at
selected places in Pudukkottai for four seasons separately and the results are
presented in figures 4.1a to 4.9g.
4.2.1. Suspended Particulate Matter
Of all the four zones, commercial zone was found to have the highest SPM
concentration. Commercial zone in “Urban Pudukkottai” exceeded the NAAQS
(National Ambient Air Quality Standards).The prescribed level is 500mg/m3 for
industrial zone and 200 for residential /other zones. Commercial areas cannot be
considered as industrial zone; even if considered, the SPM value in commercial zone
of urban Pudukkottai exceeded the value prescribed for industrial zone.
The SPM values in commercial zone of suburban Pudukkottai were above
300mg/m3 which were also in excess than the prescribed level for residential /other
zone.
Of the four seasons, season IV (March –April) was found to have the
maximum concentration of SPM and season I with minimum concentration in
commercial area of urban Pudukkottai. Whereas, there were only slight variation in
SPM concentration among the seasons in “Suburban Commercial”. As the March
and April being the dry period (summer), the soil particles become loosened and
wind-borne. Hence there was an increase in SPM in season IV. SPM concentration
in industrial zone in all the seasons did not exceed the standard. It suggests that the
industrial activities are not intense to pollute the environment in Pudukkottai.
SPM concentration in residential zone during all the seasons, both in urban
and suburban did not exceed the standard.
In sensitive zone the SPM concentration exceeded the standard in all the
seasons.
Quite surprisingly, the SPM concentration was found to be slightly higher in
suburban areas of residential and sensitive zones. Wind-blown dust from open space
and “kutcha” road in suburban areas may be attributed to this.
Based on statistical analysis the following conclusions have been arrived for
SPM.
1. The SPM values among various zones during season I (June-July) didnot
differ significantly.
2. The SPM values among various zones in all other seasons differ significantly
(Table 4.1).
4.2.2. Sulphur di Oxide
Of all the four zones, commercial zone was found to have the highest SO2
concentration. Commercial zone in ‘Urban Pudukkottai” exceeded the NAAQS. The
prescribed level is 120mg/m3 for industrial zone, 80 mg/m3 for residential and 30
mg/m3 for sensitive zones. Commercial areas cannot be considered as industrial
zone; even if considered, the SO2 value in commercial zone of urban Pudukkottai
exceeded the value prescribed for industrial zone.
The SO2 values in commercial zone of suburban Pudukkottai did not
exceeded the prescribed level.
SO2 concentration in Industrial zone in all the seasons did not exceed the
standard. It suggests that the industrial activities are not intense to pollute the
environment in Pudukkottai.
SO2 concentration in residential zone during all the seasons both in urban
and suburban did not exceed the standard.
In sensitive zone, the SO2 concentration exceeded the standard in all the
seasons.
Quite a few number of shops, medium and small hotels are present in this
area. Emission from these shops can be attributed to the presence of SO2 in this area.
Night time SO2 values were generally found to be higher than that of other
times.
Statistical analysis revealed that SO2 concentration did not differ
significantly among the different zones (Table 4.2).
4.2.3. Nitrogen Oxides
Of all the four zones commercial zone was found to have the highest NO2
concentration. Commercial zone in ‘Urban Pudukkottai’ exceeded the NAAQS. The
prescribed level is 120mg/m3 for industrial zone, 80 mg/m3 for residential and 30
mg/m3 for sensitive zone. Commercial areas cannot be considered as industrial zone;
even if considered, the NO2 value in commercial zone urban Pudukkottai exceeded
the value prescribed for industrial zone.
The NO2 values in commercial zone of suburban Pudukkottai did not
exceeded the prescribed level. The results showed that there was no variation in NO2
concentration among seasons.
Commercial zone is a busy area with number of medium to heavy vehicles.
This can be attributed to the excessive nitrogen oxides in this area.
NO2 concentration in industrial zone in all the seasons did not exceed the
standard. It suggests that the industrial activities are not intense to pollute the
environment in Pudukkottai.
NO2 concentration in residential zone during all the seasons both in urban
and suburban did not exceed the standard.
In sensitive zone, the NO2 values exceeded the standard value in all the
seasons in urban area and only in season II and III in suburban areas.
Statistical analysis reveals that NO2 concentration varied significantly
between urban and suburban areas. From the air quality determination it is found
that SPM, SO2 and NO2 exceeded the standard in commercial zone and in sensitive
zone (Table 4.3). It reveals that the urban growth has adverse effects on air quality
of Pudukkottai.
4.3. Noise Assessment in Pudukkottai
Noise levels were observed for commercial zone, residential zone and silence
zone in Pudukkottai for day time and night time separately on working days,
holidays and festival days separately. From the observationsLeq, L50, L90, Lmin and
Lmax were computed and the results are presented in figures 4.10a to 4.16c.
¾ In all the zones, the noise levels were always higher in Urban Pudukkottai
than that of sub urban Pudukkottai.
¾ On holidays, the Leq values exceeded the standard both during day and night
times in all the zones in “Urban Pudukkotttai”. Leq exceeded the standard in
sub urban Pudukkottai at residential and commercial zones in day time and at
commercial zone in night time.
¾ Of three zones the highest Leq values were found in commercial zone during
holidays. This suggests that the commercial activities take place in
commercial zone even during holidays.
¾ On working days, the Leq exceeded the standard on all the zones both during
day and night times both in urban and suburban areas. It suggests that the
day-to-day activities in these places contribute to high noise levels.
¾ On festival days, the Leq noise levels exceeded the standard in all the zones
both during daytime and night time and both in urban and suburban
Pudukkottai.
¾ Interestingly in all the zones, both during day and night times the Leq values
were the lowest on holidays and the hightest on festival days. They are in the
following order. Leq on Holidyas<Leq on working days< Leq on Festival
days.
This suggests that the people generate more noise during the occasion of
festivals. Loud speakers, crakers, musical display etc. on festival days could
contribute additional noise to the exsiting background noise levels during festival
celebration.
Statistical analysis (Anova) carried out to compare the Leq values among
various zones of urban and sub urban Pudukkottai revealed that the Leq values vary
significantly (Table 4.4a to 4.4c). This could be attributed to variation in intensity
of activities etc.
From the above results, it is concluded that the urban growth certainly
increased the noise levels in Pudukkottai. That is the urbanization of Pudukkottai
have adverse environment impact due to noise pollution.
4.4. Water Quality in Pudukkottai
4.4.1. Ground Water Status
Ground water samples were collected during three seasons viz season I
Monsoon (June – Sep), season II North east monsoon (Oct – Jan) and season III Pre
monsoon (Feb – May). From selected places and analysed for physio–chemical
characters. The results are presented in figures 4.17a to 4.37c.
pH
The standard for pH is 7.0 to 8.5. All the samples from both urban and
suburban places had the pH values within the prescribed level there was a slight
increase in commercial zone during season II and season III. In general the pH was
low during season I (Monsoon) when compared with other season. The results
further reveal that, the samples from urban zone in all the places had slightly high
pH. During Northeast monsoon period (season I), the pH of ground water slightly
increased. This may be due to the seepage of ions that increase the pH.
EC
EC values ranged from 0.7 mmho cm-1 to 1.3 mmho cm-1 in samples of
urban area and
0.69 to 1.1mmho cm-1 in samples of sub urban area .The results
revealed that that EC is high in urban commercial zone and low in suburban
commercial zone.
This may be due to street runoff from various shops. Of the three seasons,
season-II (North east Monsoon) recorded high value and recorded low value in
season I (Monsoon) of urban samples. This may be due to the dissolution of ions
while rain water infiltrated. No seasonal change was observed in suburban samples.
Temperature
The temperature of the samples ranged from 27 to 29.5°C.Temperature of
samples collected from suburban areas of all zones seem to have slightly higher
temperature .The samples collected in season III-Premonsoon season (Feb-May)had
the highest temperatures. As this season is summer, the water samples also had the
maximum temperature.
Turbidity
Turbidity in all the samples were well below the prescribed limit of 5 NTUs.
Of the seasonal variations, the turbidity of urban samples were high in season II
(Northeast Monsoon) and no seasonal change was observed in sub urban samples.
TSS
In Season III (Pre monsoon) high TSS values were observed in urban
residential zone and silence zone. The presence of TSS in water bodies indicates
contamination either by sewage or some industrial waste.
Total Dissolved Solids
The results showed that urban commercial zone had high TDS and sub urban
sensitive zone had less TDS. The standard for total dissolved solids is 500mg/l. All
the samples in the study conducted were having TDS concentration below the
permissible limit.
Samples of season I (Monsoon) had high value and samples of season III
(Pre Monsoon) had less in urban area. In sub urban samples there was no seasonal
change.
Total Solids
The results showed that urban commercial zone had high TS and urban
sensitive zone had less TS. Samples of season II (Northeast Monsoon) had high
solids.
Total Hardness
300mg/l is the standard value for total hardness as CaCO3.The water samples
from urban areas ranged from 139.6 to 184.7 mg/l .Whereas in suburban areas it
ranged from 121 to 146mg/l.Results further revealed that season II (Northeast
Monsoon) had less hardness in urban samples. During northeast monsoon,
Tamilnadu receives its maximum rainfall. The dilution effect of rainwater maybe
attributed for low hardness during North-east monsoon season.
In all the zones, the hardness values were higher in urban areas. The
percolation of water contaminants with hardness-causing substances as wastes from
urban areas may be attributed for this. These substances may include, wasted
drugs/medicines, lime etc. However, the total hardness did not exceed the standard
in any of the samples.
Calcium
The result showed that calcium was high in urban commercial zone and low
in sub urban sensitive zone. The calcium content ranged from 79.9 mg/l to 96.7 mg/l
in urban areas and 75 mg/l to 88.8 mg/l in suburban areas. Of all the seasons, season
I (Monsoon) had high calcium and season II (North-east Monsoon) had low value.
This seasonal variation may be due to change in the amount of the percolation of
washing and bathing waste water to the ground water.
Magnesium
Magnesium ranged from 55.8 to 93.2 in urban areas and 46.5 mg/l to 58.3
mg/l in suburban areas. Of the seasons magnesium was less in season II (North-east
Monsoon) of urban samples, whereas in sub urban samples there was no seasonal
change.
The seasonal variation in total hardness, calcium and magnesium was mainly
due to variation in rainfall.
Alkalinity
The results showed that alkalinity was high in urban commercial zone and
less in urban sensitive zone samples. Of the seasons, season II (North-east Monsoon)
had high value and season III (Pre Monsoon) had low value.
This may be due to carbon dioxide and water attacking sedimentary
carbonate rocks and dissolving out some of the carbonate to form bicarbonate
solution .Most of the natural alkalinity in waters is due to HCO3-which is produced
by the action of ground water on lime stone .As more water percolates during northeast monsoon, these are chances that more dissolution of carbonates/bicarbonates
(Mehta, 2003).
Acidity
The results showed that it was high in urban commercial zone and low in
urban residential zone. Urban samples had high acidity in season I (Monsoon). This
may be due to the percolation of strong minerals acids, weak acids such as carbonic
and acetic and hydrolyzing salts such as iron or aluminium sulphates from various
type of waste water (Ramakrishnan etal., 1991).
Dissolved Oxygen
The DO content ranged from 5.9 mg/l to 6.1 mg/l in urban samples and from
5.1 mg/l to 6.2 mg/l in suburban samples. Of all the seasons, season II (North-east
Monsoon) had high DO.DO is an index of physical and biological processes going
on in water. In general, the DO values in suburban samples were higher than that in
urban samples. In urban areas, seepage of waste water in urban areas into ground
water will reduce the DO content .Higher BOD and COD in urban areas also
substantiate this.
Biological Oxygen Demand
The BOD values were around 1.2 mg/l in urban and 1.18 mg/l in sub urban
samples. However the BOD observed were well below the prescribed limit. Of the
seasons, season II (North-east Monsoon) had less BOD.
Chemical Oxygen Demand
In urban samples it ranged from 9.3 mg/l to 10.2 mg/l whereas in sub urban
areas from 8.7 mg/l to 10.1 mg/l. The results further revealed that it was high in
season III (Pre Monsoon) and season I (Monsoon) had less COD .This is an
indication of pollution due to chemically oxidisable organic matter in the ground
water due to seepage.
Nitrite
Nitrite values were higher in suburban samples in residential and commercial
zones while the opposite was noticed in sensitive. Urban sensitive zone had high
value and urban residential zone had less value of nitrite. This may be due to
variation in the biological action of in the soil.
Nitrate
In general, nitrate was higher in suburban samples in all zones. It was high in
season II (North-east Monsoon) and low in season I (Monsoon). The presence of
nitrates indicates that the organic matter present in water is fully oxidised and the
water is no longer harmful. Use of nitrogen fertilizers may also seep into the ground
water to increase the nitrate content.
Chloride
It ranged from 23.1 mg/l to 27.9 mg/l. In urban samples it is high in season II
(North-east Monsoon).This may be due to overland flow from various sources.
Sewage contains more amount of chloride due to the fact that salt consumed in food
is excreted by body.
Fluoride
In urban samples it was high in season II (North-east Monsoon), where as in
season I (Monsoon) sub urban samples had high value. This may be due to the
presence of fluoroapatite in water naturally associated with phosphate deposits.
Fluoride in all the samples was well below the standard values.
Sulphate
Results showed that sulphate was high in sub urban commercial zone. Of all
the three seasons, season II (North-east Monsoon) had high amount of sulphate and
season I (Monsoon) had less amount. The sulphate ion is usually second to
carbonate as the principal anion in water. This variation might be due to the
principal anion present in water. This element can combine with metal and non
metals to form many compounds.
E.Coli
E.Coli count ranged from 47 /100ml to 81.6/100ml in urban sample whereas
from 36 /100ml to 53 /100ml in suburban samples. Season II (North-east Monsoon)
had high E.Coli count in samples and season III (Pre Monsoon) had less count .This
might be due to organic waste water pollution during season II. Improper drainage
system and improper disposal of house-hold wastes may be attributed for high
E.Coli.
Based on the water quality determination of ground water in Pudukkottai, it
may be stated that ground water is not polluted except for E.Coli. Hence, it can be
concluded that the urbanization of Pudukkottai have not posed any serious threat to
the ground water quality.
Statistical analyses revealed that EC, temperature, TDS, Total hardness, DO,
BOD, COD, Nitrate and Chloride values of samples did not differ significantly
among the various zones while other parameters differed signicantly in some
seasons (Table 4.5a to 4.5u).
4.4.2. Surface Water Analysis
The results are presented in figures 4.38 to 4.53 .Surface water were
collected during three seasons viz monsoon (season I), Northeast monsoon (season
II) and premonsoon (season III) from selected places and analysed for physiochemical characters.
pH
pH value was high in season I(Monsoon) in urban samples. Urban pH value
ranged from 8.06 to 8.5.Whereas in suburban areas it ranged from 7.76 to 8.5 pH.
There was no seasonal change within sub urban samples.
EC
Urban sample EC value was 1.6 mmho cm-1 and sub urban EC ranged from
1.5 mmho cm-1 to 4.7 mmho cm-1 . It was high in season I (Monsoon) in sub urban
samples.
Temperature
Urban samples, temperature ranged from 27°C to 30°C, in sub urban it
ranged from 27°C to 30°C. It was high in season III (Pre Monsoon) and low in
season I (Monsoon) in urban samples. Variation in water temperature was due to
seasonal variation.
Turbidity
It was high in season II (Northeast Monsoon) and low in season I
(Monsoon). Urban values ranged from 28NTU to 82NTU.Sub urban values varied
from 36NTU to 94NTU.During Northeast monsoon, Tamil Nadu receives the
maximum rainfall. Falling raindrops and subsequent runoff would have caused
increased turbidity values. Open areas in sub-urban could be attributed to higher
turbidity values relativity.
Total Solids
It was high in season II (Northeast Monsoon) and low in season I
(Monsoon). Urban sample values ranged from 132mg/l to 346mg/l and suburban
values ranged from 144mg/l to 336mg/l. Higher values of TS were observed in pond
water, which was probably due to the waste disposal around the pond and dust also
mixed with runoff. Rainfall during north-east monsoon and subsequent runoff would
have brought more solids (both dissolved and suspended solids) to the water body
and hence the high TS values during season II(Northeast monsoon).
Total Hardness
It was high in season III (Pre Monsoon) and less in season I (Monsoon).
Urban samples had comparatively higher values such as 24mg/l to 43mg/l.Sub urban
values ranged from 22mg/l to 31mg/l.But all samples are within permissible limit
according to WHO, ICMR.
Total Alkalinity
It was high in season II (Northeast Monsoon) and low in season I(Monsoon)
.In urban samples it ranged form 106mg/l to 176mg/l and in sub urban from 114mg/l
to 157mg/l.This may be due to runoff, which dissolves the carbonates and
bicarbonates from soil/rocks.
Total Acidity
It was high in season I (Monsoon) and low in season III (Pre Monsoon). In
urban samples it ranged from 1.8mg/l to 2.1mg/l; in suburban samples ranged from
1.4 mg/l to 2mg/l. This may be due to the percolation of strong minerals acids, weak
acids such as carbonic and acetic and hydrolyzing salts such as iron or aluminium
sulphates from various type of waste water. But all samples were within permissible
limit according to WHO , ICMR.
Dissolved Oxygen
Urban samples values ranged from 6.2mg/l to 6.4mg/l. In sub urban samples
it ranged from 5.2 to 6.3 Low DO 5.2mg/l in sub urban sample in season III may due
to the waste water pollution. All samples were within prescribed level according to
WHO , ICMR.
Biochemical Oxygen Demand
It was high in season I (Monsoon) and low in season II (Northeast
Monsoon). Urban values ranged from 3.8 to 4.8mg/l.Sub urban ranged from 4.2 to
4.8mg/l. It was found that all waters had the BOD values within the limit.
Nitrite
It was high in season III (Pre Monsoon) and low in season I (Monsoon) in
sub urban samples (2.6mg/l and 2.1mg/l). Whereas in urban samples it was high in
season II (Northeast Monsoon) and low in season I (2.8mg/l and 2.2 mg/l). All
samples were within permissible limit. Lower nitrite content during all the three
seasons may due to biological oxidation of nitrites. But all samples are within
permissible limit according to WHO , ICMR.
Total Chloride
There was no difference between urban and sub urban samples (16mg/l to
21.4mg/l). It was high in season III (Premonsoon) and low in season I (Monsoon).
Chloride levels in all the samples were well within the permissible limits.
Fluoride
It was high in season II (Northeast Monsoon) and low in season I (Monsoon)
in urban samples.
Where as in sub urban samples it was high in season II (Northeast Monsoon)
and season III (Pre Monsoon) shows low value .This may be due to the dissolved
salts form rocks. All samples slightly exceeded the permissible limit according to
WHO, ICMR. High fluoride content is not desirable as it may cause dental/skeletal
fluorosis.
Sulphate
It was high in season III (Pre Monsoon) and less in season I (Monsoon).The
values ranged from 4.6 to 8.2mg/l. Sub urban sample values ranged from 4.9 to
4.2mg/l .Metal and non-metal elements combine to form sulphate ions in the water
resource may be the reason for variation in the range (Tonapi,1980).
Phosphate
It was high in season III (Pre Monsoon) and low in season II (Northeast
Monsoon) in sub urban samples (0.13mg/l and 0.04mg/l); where as in urban samples
it was high (0.1mg/l) in season II (Post Monsoon).Phosphate fertilizer dissolved
from surface runoff to water resource may be the reason of variation. There is no
specific permissible limit for phosphates. Natural waters generally contain total
phosphorous compounds less than 0.1mg/l.
E.Coli
It was high in season III (Pre Monsoon) and low in season I (Monsoon) in
sub urban samples. Sub urban values ranged from 1800 to 3200/100ml, urban values
ranged from 2000 to 3000/100ml.Washing and bathing activities of people and
animals may be the reason for this high E.Coli.
Statistical analyses revealed that turbidity and BOD values of samples differ
significantly among seasons (Table 4.6a).
Planktons
Plankton study were done in four seasons .It wais high in season I(June-July)
and low in season IV(Sep-Oct) .This is mainly due to eutrophication in water
bodies.More number of species were available in sub urban water samples(13
species). Within this seven species were Phytoplanktons; six were Zooplanktons.but
in uraban water samples totally six species were available.
There was no seasonal variation. Phytoplanktons like Diatoma sp, Navicula
closterium were present in all the seasons of both urban and sub urban samples.
Zooplanktons like Filinia, Notholca and Branchionus quadridentatus were present
in only one season but Keratella sp was present in all the four seasons (Table 4.6b
and 4.6c).
4.5. Soil Analysis
Soil samples were collected during three seasons viz., monsoon (June-Sep),
North-east monsoon (Oct-Jan) and Premonsoon (Feb-May) from selected places and
analysed for physico-chemical characters. The results are presented in figure 4.54a
to 4.63c.
pH
The pH of the samples collected in urban area ranged from 7.72 to 8.4; in
suburban area it ranged from 7.74 to 8.6.Season I(Monsoon) showed high pH and
season III(Pre monsoon) showed low pH in all the zones. But in sub urban samples
there was no seasonal change.
EC
A season wise representation result showed that urban commercial zone had
high EC value. The samples of the urban areas had EC values ranging from 0.1
mmho cm-1 to 0.3mmho cm-1. In suburban areas the range was 0.19mmho cm-1 to
0.9mmho cm-1 .Sub urban values were higher than urban values. Of the three
seasons, season III (Pre monsoon) had high EC value and season I (Monsoon) had
low value.
This variation might be due to the leaf decomposition .It changes soil pH and
EC. Indiscriminate disposal of solid waste, discharges of sewage or waste water on
land and use of chemical fertilizers, insecticides and pesticides also maybe the
reason for this change during various seasons. The rain water will have dilution
effect and hence variation.
Total Organic Carbon
The soil samples collected form urban had the organic carbon ranged from
0.3 %to 0.43%, where as in suburban areas the range was from 0.36% to 0.62%.
Season wise results depict that season III (Pre monsoon) had high TOC
content in soil and season I (Monsoon) had less content.
This might be due to the high temperature in soil. Due to high temperatures
organic carbon couldn’t accumulate. Soil temperature made interaction of soil micro
organisms. Strong interaction may be possible between organic and inorganic
portion (Anon, 2004).
Total Organic matter
Total organic matter values in urban area ranged from 0.68% to 0.9%.In
suburban areas it ranged from 0.9% to 1.09%. Among seasons, season III (Pre
monsoon) had high total organic matter in soil and season I (Monsoon) had less
content.
This may be influenced by the availability of oxygen in the soil. In some
cases there occurs strong interaction between the organic and inorganic portions of
soil. Nitrogen fertilization and clipping management is also practiced here. This may
be influenced to have more organic matter in soil (Badrinath, 1994).Disposal of
garbage, street sweeping and market waste at some places may also play a major
role in the concentration of total organic matter in these places.
Total Nitrogen
The total nitrogen content in the urban sample ranged from 0.81mg/g to
1.7mg/g.In suburban samples it ranged from 0.58 mg/g to 1.78 mg/g. Of all the
seasons, season III (Premonsoon) had high nitrogen content and season I
(Premonsoon) had low nitrogen content. However in urban commercial zone less
nitrogen content in season III (Premonsoon) was noticed.
After rain during North east monsoon, nitrogen fertilizers washed out and
reaches to the soil may be the reason for high amount of total nitrogen in the
Premonsoon period. In the commercial zone there is no garden or field. This may the
reason for less amount of nitrogen. The decay of dead plants (biomass) and animals,
plant residues and faeces, urine of animals getting hydrolysed may be also reason for
high nitrogen in this soil (Hundal et al., 1988).
Total Phosphorus
The phosphorus content in the urban sample ranged from 0.05 mg/g to
0.06mg/g whereas in suburban sample, it ranged from 0.05 mg/g to0.07 mg/g. Of the
seasons, season III (Pre monsoon) had high content and season I (Premonsoon) had
less phosphorus content.
Usage of phosphate containing detergents, soaps etc. could be the reason for
high total phosphorous content at some places.
Total Potassium
Season wise representation results reveal that suburban commercial zone had
high potassium content and sub urban sensitive zone had less amounts. the
potassium content present in the urban samples ranged from 1.1 mg/g to 1.3 mg/g.
In suburban sample it ranged from 0.9 mg/g to 1.7 mg/g. Of all the seasons, season
III (Pre monsoon) had high potassium and season I (Monsoon) had less potassium.
After rain some soil fungi produce chelating organic acids like citric acid
which react with silicate minerals and release potassium. When pesticides undergo
photochemical reactions they may also produce more amount of potassium in the
soil (Rajamannar, 1994).
Total Sodium
Season wise representation results exhibit that sub urban commercial zone
had high value of sodium. Total sodium ranged from 0.03 mg/g to 0.4 mg/g in urban
sample and 0.05 mg/g to 1.4 mg/g in suburban sample. Of all seasons the Sodium
content did not vary much seasonally in all the places except in sub urban residential
zone and urban commercial zone
Total Calcium
Seasonal representation results exhibit that sub urban residential zone had
high calcium content. In urban sample total calcium content ranged from 0.69mg/g
to 1.07 mg/g. Where as in suburban sample it ranged from 0.66 mg/g to 2.3 mg/g.
Sub urban soil samples had high calcium content than urban samples. There was no
seasonal change in calcium content.
Total Magnesium
Total
Magnesium
in
urban
sample
ranged
from
0.5
mg/g
to
0.64mg/g.Whereas in suburban sample it ranged from 0.4 mg/g to 0.63 mg/g. Of all
the seasons, season I (Monsoon) had high magnesium content and season III (Pre
monsoon) had less magnesium content in soil samples.This slight increase might be
due to weathering of rocks, new chemicals introduced for more crop yield (Sharma
and Khar,1995) .
Based on the statistical analysis the following observations were made
1. Most of the parameters had high values in Pre monsoon period (seasonIII).
Premonsoon season follows the north-east monsoon in Tamilnadu. The
runoff water of rainfall may bring the ions and deposits over soil. This could
be the possible reason for higher value of certain parameters.
2. Variations in concentration of certain parameters in sub urban soil samples
were noticed after rainy season.
3. Sodium, Calcium and Magnesium contents were high. NPK was found to be
very less in all the urban soil samples.
ANOVA tests reveal that the concentration of many parameters did not vary
significantly (Table 4.7a to 4.7j).
From the soil quality, it may be concluded that the urbanization /urban
growth in Pudukkottai did not have much adverse impact on soil quality as of now.
4.6. Waste Water Characteristics and its Treatment
By the natural formation of the drainage system water pouring due to heavy
rain will flow from east to west. The planning of the town has been laid in such a
way that water will flow through the open drainage system. This drainage is
available on both sides of the streets. To ensure the normal flow these canals are
cleaned regularly. The water finally gets collected at the Kattuputhukulam that is on
the western part of the town. The composition of the sewage is complex and hence it
leads to some toxicity to live stocks. The characteristic feature of the sewage was
studied and also simple biological treatment was attempted.
Collected waste water quality was analysed. After the analysis the nutrients
were determined. As the waste water is rich in nutrients, the waste water was used
for plant growth, as well as for treatment of waste water. Certain unwanted
parameters should be brought down before the usage. So the aquatic weed lemna
was selected and grown for 25days for treating the waste water. Results were
tabulated every 5days interval. pH was reduced from 9.11 to 8.55.The determination
of pH value of sewage is important due to the fact that certain treatment methods
depend on proper pH value of sewage for their efficient working. Electrical
conductivity had increased from 1.2 mmho cm-1 to 5.2 mmho cm-1. Hardness was
reduced from 915 mg/l to 415 mg/l. Calcium was reduced from 106.2 mg/l to 4.008
mg/l. Magnesium also reduced from 219.9 mg/l to 99.87 mg/l. Dissolved oxygen has
increased from 0 mg/l to 3 mg/l. Sewage has generally no dissolved oxygen .Its
presence in the effluent after treatment indicates that considerable oxidation has
been accomplished by the sewage treatment by Lemna. COD had decreased from
515.2 mg/l to 420.21 mg/l. BOD decreased from 32.23 mg/l to 30.4 mg/l. Chloride
decreased from 303.9 mg/l to 199.98 mg/l. Sulphate decreased from 7 mg/l to
3.5mg/l.Phospherous became nil.Iron decreased from 0.98mg/l to 0.68mg/l. Nitrate
decreased from 108.2mg/l to 82mg/l. Potassium decreased from 212mg/l to 130mg/l
(Tables 4.8 and 4.9).
Thus treated water was used for the growth of Buffalo grass in a separate
field. It is having the nature of absorbing more amount of water. It is a type of
fodder grass. After the use of water for this fodder grass, the plant sample was also
analysed. For the comparison purpose control plant was maintained with bore well
water. Leaf length, fresh weight, dry weight, free sugar, phenol and total chlorophyll
contents were analysed both in control plant sample and treated plant sample
(Figure 4.64a to 4.64f).
Leaf length
Control plant leaf length was 7.16cm.But the treated waste water plant
sample had 10.1cm leaf length.
Fresh weight of plant
Borewell water irrigated grass weight was 0.9mg/g.But waste water treated
grass weight was 1.09mg/g.
Dry weight of plant sample
Control plant dry weight is 0.63mg/g, treated plant sample weight was
0.72mg/g.
Free Sugar
The control plant sample contained 0.9% free sugar , but the treated plant
tissue had 1.28% of sugar. This may be due to the impact of high nutrient content of
waste water.
Phenol
Control plant tissue contained 0.17% of phenol content. But the treated plant
tissue had 0.23%.
Total Chlorophyll content
In control plant tissue total chlorophyll content was 82.8%, whereas in waste
water treated plant it had increased to 99%.
Positive improvements were seen in treated plant growth. Statistical results
revealed that leaf length and dry weight had significance increase (Table 4.10).
4.7. Solid Waste
In Pudukkottai district the total amount of solid waste collected around 45.5
tonnes/day which includes the solid wastes from
Pudukkottai town.The town
generates about 25 tonnes/day and it comes to be 6,2220.028 tonnes/year.
0.482kg/day was generated per day/person. Solid waste samples were collected from
15 sites consisting of residential, commercial and litter free zones. The samples were
brought to the laboratory, sorted out, and analysed to determine their physical
composition. The samples from each zone were collected during holidays, working
days and festival days separately. Biodegradable wastes were found to be the highest
in Zone I (residential zone).Biodegradable waste constituted more than 54% in
residential zone. The percentage of biodegradable wastes did not vary much among
holidays, working days and festival days in residential zone.
In Commercial zone, biodegradable wastes exceeded 50% during working
days reached 52% during festival days and recorded 37.7% during holidays. In Litter
free zone (zone III), they were 44.66%during festival days, 41.38% during working
days and 39.86% during holidays (Table 4.11).
These results revealed the following
™ Biodegradable wastes constitute the major portion of the MSW in
Pudukkottai.
™ Biodegradable wastes were maximum during festival days and minimum
during holidays both in Zone II (commercial zone) and in Zone III (Litter
free zone).
™ Biodegradable wastes were maximum during holidays and minimum during
working days in Zone I (Residential zone).
™ Of all the three zones, residential zone recorded the maximum amount of
biodegradable wastes.
™ Papers and rags were found to be maximum during holidays and festival
days in all the three zones.
™ In general plastics were found to be maximum during festival days followed
by holidays and then by working days.
™ Glass wastes were found to be the highest (approximately three times)in
Zone III during all the days.
™ Other wastes varied differently.
From the above observations, it may be concluded that during holidays and
festival days people generate more amounts of plastics and paper wastes .Perhaps,
during these occasions, people buy the packed food/articles/items and the removal of
wrappers, plastic bags might have contributed them more to MSW in Pudukkottai.
4.7.1. Characteristics of Solid Wastes
Chemical properties of solid waste
Collected solid wastes from residential, commercial and litter free zones
were dried and powered for chemical analysis. The results are presented in table 4.
pH
It ranges from 7.06 to 7.22 in working days.7.06 to 7.24 in holidays .7.04 to
7.14 in festival days. There is no significance in urban zone (I), (III) samples. But in
sub urban zone (II) showed significance in working day and festival day wastes.
Moisture content
Moisture content in the solid waste ranged from 42.6% to 49% in working
days.34% to 49% in holidays. 46.9 to 50% in festival days.
Ash
It ranged from 38.7% to 43% in working days; 38% in holidays collection
sample.36% to 45% in festival days sample.
Organic matter
It ranged from 57% to 61% in working day sample.48% to 62% in holidays
sample.55% to 64% in festival days sample.
Carbon
It ranged from 34% to 35% in working days; 285 to 36% in holidays; and
32% to 37% in festival days.
Nitrogen
It ranged from 0.8% to 1.3% in working days; 0.6% to 1.2% in holidays;
0.6% to 1.3% in festival days.
C/N
It ranged from 27% to 47% in working days; 26% to 57% in holidays ;29%
to 56% in festival days.
4.7.2. Solid Waste Management and Soil Quality Improvement
Biodegradable wastes were subjected for decomposition and bio compost
was prepared. The properties of bio compost were analysed. The results are
presented in figures 4.65a to 4.66d.
pH
On zero day pH was 6.7, then it had increased up to 6.9 during partial
decomposition. On 20th day it declined to pH6.7.Then after that again gradually
increased and finally reached the 7.1 pH (On 45th day).
Moisture content
On the zero day moisture percentage was 45% .At the time of decomposition
it raised up to 52%.
Temperature
It ranged from 29ºC to 27ºC .The graph showed the increase and decrease of
temperature.
C/N ratio
On the zero day, it was 32%.After the decomposition period it reduced to
15%.
Total solids
It reduced from 60% to 55% during decomposition period.
Volatile Solids
It had reduced from 62% to 58% during decomposition.
Ash content
It had increased from 50% to 60% after decomposition.
4.7.3. Nutrient components in bio compost
Carbon content
It was 26% in the partially decomposed solid waste. Then it reduced to
12.2% in the complete bio compost.
Sulphate content
It was 0.02 mg/g in the compost.
Micro Nutrients
Calcium increased from 0.4% to 0.5%, magnesium decreased from0.3% to
0.2%, chlorides increased from 0.5% to 0.9%.
Macro nutrients
NPK amounts increased substantially in the final compost.
Based on the results during decomposition period pH increased from 6.7 ºC
to 7.1 ºC. Temperature decreased from 28 ºC to 27 ºC .C/N ratio, ash, total solids
and volatile solids were also decreased.
The bio compost obtained from the solid wastes was used for soil treatment.
Bio compost played a major role in the improvement of soil quality and raises the
yield of the plant. In this present study , Palmarosa plant were raised using the
compost (Block-II).For comparision the Palmarosa plant were raised in control
(Block-I).The results are presented in figure 4.67a to 4.70c.
4.7.4. Soil treatment with Bio-Compost
After the Palmarosa plant growth in block I the available nitrogen in soil
ranged from 94 mg/g to 104 mg/g; in block II it ranged from 97 mg/g to 131
mg/g.Potassium content value ranged from 49 mg/g to 57 mg/g but in block II it
ranged from 48 mg/g to 60 mg/g . Phosphate content ranged from 1.3 mg/g to 3.1
mg/g in block I. Whereas in block II it ranged from 1.1 mg/g to 4.2 mg/g.Zinc
content ranged from 2.01 mg/g to 2.1 mg/g.whereas in block II it ranged from 2.1
mg/g to 2.3 mg/g.Iron content ranged from7.1 mg/g to 7.6 mg/g in block I whereas
in block II it ranged from 7.6 mg/g to 7.9 mg/g. Manganese ranged from 6.5 mg/g to
6.8 mg/g in block I whereas in block II it ranged from 6.8 mg/g to 6.9 mg/g. Copper
content ranged from 0.8 mg/g to 0.9 mg/g, whereas in block II it ranged from 0.9
mg/g to 0.95 mg/g . Bacterial content ranged from 40 mg/g to 68 mg/g in block I in
block II it was from 40 mg/g to 68mg/g. Actinomycetes content in soil was ranged
from 28 mg/g to 45mg/g in block I in block II it ranged from 28 mg/g to
45mg/g.This positive increase may be due to the addition of bio compost.
After the first and second harvest the yield also calculated as grass and oil.
Palmarosa grass yield ranged from 420Kg to 455Kg at block-I in two harvests.536
to 618 at block –II in both harvests. Palmarosa oil yield increased from 2.1 to 2.2 kg
at block I whereas 2.4 to 3 at block-II in both harvests. This may be attributed to the
bio compost .After I and II harvest the treated soil had high nutritive value,
increased micro flora as well as better yield. Oil of Palmorosa is one of the most
important essential oils of India.
Based on the statistical analysis the following observations were made:
1. After the first harvest macro nutrients N, P, K showed significant increase in
the soil.
2. Micro nutrients also increased significantly.
3. Microbial count increased significantly.
4. Palmarosa grass and oil yield significantly increased.
This may be due to the impact of biocompost (Table 4.13 to 4.16).
4.8. Biodiversity
Flora and fauna present in Pudukkottai were observed and recorded for
Urban zone and Sub urban zone separately. The total numbers are presented in
tables 4.18 to 4.23.
Total numbers of species are comparatively less in urban area. This is mainly
due to the anthropogenic disturbance.
Urban Flora
Mollugo verticillata species is abundant in eastside of the urban area.
Citrullus colocynthis, Morus alba, Hemidesmus indicus ,Casuarina equissetifolia,
Cucumis sativas are frequent in Westside of the urban sampling site. Eucalyptus
species is abundant in north side of the plot. Musa paradisiaca is abundant in south
side sampling area. But 69 species are in rare condition within 126 species.
Seasonal Variation
Cyanodon sp, Nelumbo nucifera, Acalipha indica are frequent in season
II(Sep-Oct) in eastside. Cauarina is abundant in season IV (Mar-April)in Westside.
Ervattamia coranaria, Bryophyllum, Hibiscus rosasinensis are frequent in all the
four seasons of northside. Capsicum frustescens is frequent in all the four seasons of
the south side.
Sub urban Flora
Oryza sativa, Calatropis gigantean, Pistia stratiotes are abundant in
Westside of the suburban area.Eleucine coracana is abundant in eastside sampling
site. Eclipta alba, oryza sativa, Tridax procumbens are abundant in north side.
Phaseolus mungo, Phaseolus radiatus, Solanum melangena are abundant in south
side. But 83 species are rare condition within 172 species.
Seasonal Variation
Chrysanthimum sp, Coriandram sp, Crossandra sp, Lausonia sp are frequent
in season II (Sep-Oct) in eastside. Calendula officinalis, Eucalyptus globules,
Saccharum sp, Zea maize are frequent in season IV (Mar-April)in the Westside.
Cymbapogan citrates is abundant in all the four seasons in the north side.
Amaranthus blitum and Amaranthus viridis are frequently present in all the
four seasons of southside.
Based on the results suburban sampling area show rich in species. The
maximum species occurrence in spring could be attributed to favourable climatic
conditions while the prevalence of unfavourable climatic conditions during winter
resulted in less floristic richness.
Fauna
The fauna present in residential zone, commercial zone, silence zones are
presented in Table 4.24a to 4.25g.
Urban fauna
Insects such as giant water bug, giant water scorpion, small termites are
common insects present only in urban sampling area. In the residential zone 21
species occurred. In commercial zone 14 species were observed.
In the industrial zone only 6 species were observed. This may be due to high
noise and less flowering plants.
House crow, hen, small sun bird, Indian robin are common birds. This may
be due to urbanization. In this urban area most of the plants are destroyed. So that
the dependent insects, birds and other animals would have migrated to other places.
This study area consists of Hen, House crow that are high in individual count.
This is widely distributed in all zones. In all sampling areas 268 of Hen
species were recorded.
Seasonal Variation in Avifauna
Season I (June-July) and season IV (March-April) had more number of
species.
Sub urban fauna
Insects such as paddy bug, plant lice and white termites were present only in
sub urban zone. Residential zone sample contained 56 species within that 14 species
were butterflies. The highest number of species was observed in this area. In the
commercial zone it was observed up to 26 species of insects. In silence zone 12
species of insects were observed. Parrot, Koel and Duck were common sub urban
birds. In the residential zone 23species were observed. In Commercial zone 14
species were observed. In the silence zone 12 species were observed. This may due
to the proper maintenance of dense forest near inside the old palace area.
Seasonal Variation
Season II (Sep-Oct) and season III (Dec-Jan) had maximum species.
Comparatively urban residential zone had more number of species. Urban
sampling recorded 20 different insect orders and more than 56 species were present.
But in sub urban sampling area number of species was also more and number of
orders was also high.
From the above results, it may be concluded that urbanization certainly limits
the number of species greatly. In other words, it may be said that biodiversity loss
has occurred due to urbanization in Pudukkottai.
4.9. Socio Economic Status
Socio-economic status of Pudukkottai has been determined by random
sampling using questionnaire. The results are presented in figures 4.71 to 4.75.
80% of the families in sub urban area and 60% of the families in urban area
had small family size (4 or below).
More interestingly the urban families comprised the people of all ages while
the ages between 20 and 30 and 40 and 50 were found to be predominant on sub
urban families’ (Table 4.26).The reason for this variation is not known.
25% of the families in urban area and 50% of the families in sub urban area
people had education up to bachelor level.75% of urban family members and 25% of
the sub urban people had still higher education(more than P.G. Degree).This may be
due to the availability of transport and Institutions in the urban areas(Table 4.27).
90%of the people from sub urban areas are employed and earning less than
Rs.5000 but 7% are earning more than Rs.5000/- from agriculture and 1% people
involved in the family business such as general stores. In the urban area only 50%
were employed members. Of this 25% people earn less than Rs.5000/- and
remaining earn more than Rs.5000/. The rest 20% people undertake family business
such as Jewellary shops, Bakery, Fruit shops, Private bank etc. The rest are
unemployed (Table 4.28).
When land use pattern is considered, the sub urban 25% was cultivated area
and 75% was left as barren land. But in urban area 100% are utilized by building
construction and other facilities such as roads, offices, bus stand etc. In urban
residential zone 75% of the families reside in their own houses while others reside in
rented houses. This implies that the economical status in reasonably high of
Pudukkottai people to own a house. All type of home appliances was used by these
people. They use public transport as well as private vehicles.
In sub urban area 90% of the families reside in rented houses and 10% only
own their houses. They depend more on public transport. This may also be one of
the reasons for less air pollution in sub urban areas.
Municipal water supply facility is available for about 80% of urban
population; the remaining 20%depend on common water supply and bore well
water. In sub urban area municipal water supply facility is available only for 20% of
the population. Other people depend on bore well water.
Drinking water supply was regular and sufficient during rainy season. But it
was irregular and insufficient during summer in urban areas .It is obvious that in
summer season, the availability of water would be scarce. In addition to this,
population increases every year and this leads to increasing demand for water which
ultimately manifests as insufficiency (Table 4.29).
25% of the rented houses in urban area had common toilets. In sub urban
area 27% houses had common toilets. The remaining houses had individual toilet
and bathroom facility. Solid waste collection was done periodically and safely
disposed in the urban areas. But in sub urban areas it was not done periodically.
There is no proper sewerage system for disposal of waste water in sub urban area. In
urban areas sewage water was properly channelised and sent through open sewerage
system.
The houses are constructed in sub urban areas without proper planning. This
has resulted in poor solid waste collection system and poor sewerage system.
CONCLUSION
The present study was carried out to determine the impact of urbanization in
Pudukkottai on its Environment. In order to assess the impact, the air quality with
reference to SPM,SO2 , NOx and noise levels, water quality-surface and ground
water, soil quality, flora and faunal status, socio-economic status, waste water
characteristics and solid waste –characteristics, amount and disposal were studied in
detail.
The results revealed the following
™ Urban growth in Pudukkottai has deteriorated air quality to a reasonable
extent. SPM, SO2 and NOx and noise levels exceeded the standards.
vehicular traffic can attributed to the deterioration of air quality.
™ Surface water from all the sources in Pudukkottai was found to be
polluted.
™ The discharge of domestic wastes and sewage was major cause for
deterioration of surface water quality.
™ Ground water was found to be unpolluted except with E.Coli. Poor
sanitation facilities and open defecation may be attributed for this.
™ Urbanization did not pose any adverse effect on soil quality.
™ Biodegradable wastes constitutes more than 50% in MSW generated in
Pudukkottai. On average 30-35 tonnes of MSW has been generated in
Pudukkottai. The present population of Pudukkottai is about 1 lakh. As
the town is expanding in its area and in its population, the amount of
MSW is likely to increase accordingly. In this present study, samples of
biodegradable solid wastes were subjected to composting using micro
organisms. The biocompost thus produced were used for te growth of
Palmarosa plant. Biocompost had positive effect on the growth of the
plant. That is, the biocompost is rich in nutrients.Hence, it is suggested
that, Municipality can adopt composting for disposal of biodegradable
waste, which can reduce the amounts of MSW considerably.
™ Waste water generated was found rich in nutrients. Hence the waste
water was used for plant growth after treatment with Lemna sp.The
treated water was used for the growth of Buffalo grass in a separate field.
Positive improvements were seen in plant growth with treated waste
water.
™ Biotic assessment revealed that diversity of flora and fauna is less in
urban area when compared to surrounding suburban area of pudukkottai.
It reveals that urbanization certainly limits the existence of several
organisms.
™ In order to assess the impact on socio-economic environment random
sampling was carried out .The results revealed that urbanization had
improved the quality of life of people in terms of education, employment
and income.
Table 1.1
Urbanization Trends in India
Urban
Census
Number
Population
Percent
Years
of Towns
(in
Urban
millions)
Annual
Exponential
Growth Rate
Rate of
Urbanization
1901
1916
25.9
10.8
-
1911
1908
25.9
10.3
0.0
1921
2048
28.1
11.2
0.8
1931
2220
33.5
12.0
1.7
1941
2422
44.2
13.8
2.8
1951
3060
62.4
17.3
3.5
1961
2700
78.9
18.0
2.3
0.40
1971
3126
109.1
19.9
3.2
1.06
1981
4029
159.5
23.3
3.8
1.72
1991
4689
217.6
25.7
3.1
1.02
2001
5161
284.53
27.8
2.7
0.82
(COI,2001)
-0.46
0.87
0.71
1.50
2.54
Table 1.2
Percentage of urban population in India by size-class of urban centres,
1961-1991
Size Class
Class I
(100000+)
1961
1971
1981
1991
51.4 (102)
57.2 (148)
60.4 (216)
65.2 (296)
Class II
(50000-100000)
11.2 (129)
10.9 (173)
11.6 (270)
11.0 (341)
Class III
(20 000-50 000)
16.9 (437)
18.0 (558)
14.4 (738)
13.2 (927)
Class IV
(10000- 20 000)
12.8 (719)
10.9 (827)
9.5 (1053)
7.8 (1135)
Class V (5000-10 000)
6.9 (711)
4.5 (623)
3.6 (739)
2.6 (725)
Class VI
(< 5000)
0.8 (172)
0.4 (147)
0.5 (229)
0.3 (185)
Total
100. (2270)
100. (2476)
100. (3245)
100. (3690)
Table 1.3
Growth in the number of million plus (1,000,000population or more) cities in
India 1901-2001
Population of million
cities as percent of
India’s
Total
Urban
Population Population
0.6
5.8
1901
Number of
cities with
population
more than
one million
1
1911
2
2.76
82.8
1.1
10.7
1921
2
3.13
13.4
1.3
11.1
1931
1941
2
2
3.41
5.31
8.95
5.71
1.2
1.7
10.2
12.0
1951
5
11.75
21.3
3.3
18.8
1961
7
18.10
54.0
4.1
22.9
1971
9
27.83
53.8
5.1
25.5
1981
12
42.12
51.3
5.2
26.4
1991
23
70.67
67.8
8.4
32.5
2001
35
107.88
52.8
10.50
37.8
Census
years
Population
(in million)
Percent
increase
1.51
-
Excludes Assam in 1981 and Jammu and Kashmir in 1991(COI, 1991)
Table 1.4
Trend in total population (in 10,000s) and annual growth rate (in percent) in
rate
Growth
India
rate
Growth
Chennai
rate
Growth
Delhi
rate
Growth
Kolkata
rate
Growth
Mumbai
years
Census
the four Metropolitan Cities of India, 1901-2001
1901
81.3
-
151.0
-
40.6
-
59.4
-
2384
-
1911
101.8
25.2
174.5
15.6
41.4
2.0
60.4
1.7
2521
5.7
1921
124.5
22.3
188.5
8.0
48.8
17.9
62.8
4.0
2513
-0.3
1931
126.8
1.8
213.9
13.5
63.6
30.3
77.5
23.4
2786
11.0
1941
168.6
33.0
362.1
69.3
91.8
44.3
92.1
18.8
3187
14.2
1951
296.7
76.0
467.0
29.0
174.4
90.0
153.1
66.2
3611
13.3
1961
415.2
39.9
598.4
28.1
265.9
52.5
192.4
25.7
4392
21.6
1971
597.1
43.8
742.0
24.0
406.6
52.9
305.8
58.9
5482
24.8
1981
891.7
49.3
919.4
23.9
622.0
53.0
428.9
40.3
6833
24.7
1991
1259.6
41.3
1102.2
19.9
942.1
51.5
542.2
26.4
8463
23.8
2001
1636.8
29.9
1321.7
19.9
1297.1
37.7
642.5
18.5
10270
21.4
(COI, 2001)
Table 1.5
Total slum population in India according to size/class of towns, 1991
Size class
Percentage to
No of
Total
Percentage
Slum
population
to total
population(in
(in 00000)
population
00000)
23
710
26.6
189
41.3
31
215
19.8
43
9.3
3,00000-4,99999
39
151
18.9
29
6.3
1,00000-2,99999
207
325
16.8
54
11.9
50,000 to 99,999
345
236
20
47
10.3
Less than 50,000
3052
521
18.3
95
20.9
Total
3697
2158
21.2
457
100.0
category of
cities /towns
(population)
population
cities
total slum
and
towns
More than
10,00000
5,0000010,00000
(A Compendium on Indian Slums, 1996)
Table 1.6
Percentage of slum population in the four Metropolitan Cities of India,
1981-2001
Metropolitan
Cities
Greater Mumbai
(UA)
Kolkata (UA)
Delhi Municipal
Corp. (UA)
Chennai (UA)
1981
1991
2001
30.8
43.2
48.9
30.3
36.3
32.6
18.0
22.5
18.9
13.8
15.3
17.7
(COI, 2001)
Table 1.7
Composition of solid wastes in the four Metropolitan cities of India, 1998
Metropolitan
Characteristics (in percent by weight)
cities
Paper Textile Leather Plastic Metal Glass
Ash Compostable
etc.
matter
Mumbai
10.0
3.6
0.2
2.00
-
0.2
44.0
40.00
Kolkata
10.0
3.0
1.0
8.00
-
3.0
35.0
40.00
Delhi
06.6
4.0
0.6
1.50
2.50
1.2
51.5
31.78
Chennai
10.0
5.0
5.0
3.00
-
-
33.0
44.00
(Central Pollution Control Board, 1998)
Table 1.8
Status of Municipal solid waste generation and collection in Metropolitan Cities
of India, 1996
Metropolitan
Municipal Solid
Per capita
Collection in
cities
Waste(tones/day)
Generation(Kg/day)
percent
Mumbai
5355
0.436
90
Kolkata
3692
0.347
-
Delhi
4000
0.475
77
Chennai
3124
0.657
90
(CPCB, 1998)
Table 1.9
Growth in motor vehicles in India, 1990-2000
Years
Number of vehicles
(in thousands)
Percent increase
1990
19152
-
1991
21374
11.6
1992
23507
10.0
1993
25505
8.5
1994
27660
8.4
1995
30287
9.5
1996
33850
11.8
1997
37231
10.0
1998
43159
15.9
1999
48240
11.8
2000
53100
10.1
(CPCB, 2000)
Table 1.10
Growth in Motor Vehicles in Metropolitan Cities in India during 1991-96 as on
31st March (in 000s)
Metropolitan
1991
1992
1993
1994
1995
Mumbai
629
647
546
608
667
Calcutta
475
497
517
545
561
Delhi
1813
1963
2097
2239
2432
Chennai
544
604
641
689
768
cities
1996
724
588
2630
812
(Transport Research Wing, 1997)
Table 1.11
Estimated vehicular emission load in 1994(tones per day)
Emission load
Delhi
Mumbai
Kolkata
Particulates
10.30
5.59
3.25
8.96
4.03
3.65
126.46
70.82
54.69
249.57
108.20
43.88
651.01
496.6
188.24
1046.3
659.57
293.71
Sulphur
Dioxide (SO2)
Nitrogen
Oxide (NO2)
Hydro
Carbons (HC)
Carbon
Monoxide (CO)
Total
(Centre for Science and Environment, 1996)
Table 1.12
State of ambient air quality in four Metropolitan Cities of India, 1991-1995
Suspended
Sulphur
Nitrogen
dioxide(SO2)
dioxide(NO2)
(mg/cu.m)
(mg/cu.m)
1991
28
29
244
1992
18
33
238
1993
22
35
232
1994
33
34
231
1995
31
26
209
1991
63
40
391
1992
36
27
307
1993
40
40
460
1994
25
43
446
1995
35
29
354
1991
14
01
130
1992
18
30
364
1993
19
30
424
1994
25
43
446
1995
23
47
410
1991
14
01
130
1992
07
03
074
1993
14
00
100
1994
16
00
128
1995
21
00
127
Metropolitan
cities/ years
Particulate
matter(SPM)
(mg/cu.m)
Mumbai
Kolkata
Delhi
Chennai
(Anon, 1997)
Table 1.13
Waste water generation, collection and treatment in Metropolitan Cities of India, 1997-98
Metropolitan
cities
Volume of wastewater generated
Wastewater collected
Volume
Capacity
disposal
Primary
Secondary
109.0
YES
YES
75.1
-
-
-
1016.0
80.0
981.0
YES
YES
257.0
93.1
257.0
YES
YES
Percent
Domestic
Industrial
Total
Mumbai
2228.1
227.9
2456.0
2210.0
90.0
Kolkata
1383.8
48.4
1432.0
1074.9
Delhi
1270.0
-
1270.0
Chennai
276.0
-
276.0
(Vishwanathan, 1999)
Mode of
Treatment
(mld)
(mld)
Sea
Hugli river
Fish farm
Agriculture
Yamuna river
Agriculture Sea
Table 1.14
Average noise levels in the Metropolitan Cities, 1997
Metropolitan
cities
Day/night
Mumbai
Day night
Kolkata
Day night
Chennai
Day night
Silence
Industrial
Commercial
Residential
area
area
area
76
75
70
66
65
66
62
52
78
82
79
79
67
75
65
65
71
78
66
63
66
71
48
49
area
(State of the Environment, 1997)
TABLE 1.15 Housing characteristics of the four metropolitan cities and urban
India, 1988-99
Household
Mumbai
Kolkata
Delhi
Chennai
All India
Pucca
62.8
94.1
88.2
57.5
66.0
Semi-pucca
34.1
5.2
32.8
24.4
Kachcha
2.8
0.2
0.9
9.2
9.4
Flush Toilet
97.4
89.5
85.5
89.1
63.9
Owned
29.4
-
-
-
-
Shared Flush Toilet
15.2
-
-
-
-
Public Flush Toilet
52.8
-
-
-
-
Characteristics
Types of House
10.7
Sanitation Facilities
Flush Toilet
Pit Toilet
0.1
8.9
8.9
1.6
16.8
No Facility
2.5
1.6
5.6
9.3
19.3
Piped
99.6
64.0
86.7
63.3
74.5
Hand Pump
0.2
34.5
12.0
30.6
18.1
Mumbai
Kolkata
Delhi
Chennai
All India
0.2
1.5
1.3
6.1
7.4
Strains water by cloths
54.1
1.0
3.9
14.7
25.1
Uses water filter
10.2
17.0
18.8
15.1
14.8
Boils water
18.2
5.7
14.4
38.2
13.6
Uses electronic purifier
2.5
2.7
3.8
3.0
1.2
Other methods
0.7
1.5
1.0
0.7
2.0
Does not purify water
27.1
74.2
62.4
42.8
50.4
Yes
99.5
93.8
97.7
89.6
91.3
No
0.5
6.2
2.3
10.4
8.7
Kerosene
39.5
50.3
16.3
54.0
21.5
LPG
58.9
39.9
17.0
37.3
46.9
Biomass fuel and others
1.4
14.6
3.7
8.7
31.6
Sources of Drinking Water
Household
Characteristics
Others
Methods of Purifying
Drinking Water
Electricity
Main Type of Fuel Used
for Cooking
Persons Per Room
<3
43.9
57.4
75.2
69.9
68.6
3-4
27.6
25.3
15.1
19.8
19.5
5-6
20.1
11.9
6.7
8.8
8.3
7+
8.3
5.5
3.0
1.1
3.5
(State of the Environment, 1997)
Table 1.16
Population of urban agglomerations with 750,000 inhabitants or more in
2005(Tamil Nadu)
YEAR
Chennai
Coimbatore
Madurai
Salem
Tiruchirappalli
1950
1491
279
361
197
287
1955
1705
348
419
231
313
1960
1915
435
482
268
336
1965
2399
555
576
327
388
1970
3057
710
692
404
454
1975
3609
810
790
458
522
1980
4203
907
893
511
599
1985
4748
995
981
544
652
1990
5338
1088
1073
574
705
1995
5836
1239
1132
647
768
2000
6353
1420
1187
736
837
2005
6916
1618
1254
834
915
2010
7545
1806
1365
932
1009
2015
8280
2005
1514
1039
1123
(WPP, 2007)
Table 1.17
Percentage of the total population residing in each urban agglomeration with
750,000 inhabitants or more in 2005
TAMIL NADU URBAN AREAS
YEAR
Chennai Coimbatore
Madurai
Salem
Tiruchirappalli
1950
0.4
0.1
0.1
0.1
0.1
1955
0.4
0.1
0.1
0.1
0.1
1960
0.4
0.1
0.1
0.1
0.1
1965
0.5
0.1
0.1
0.1
0.1
1970
0.6
0.1
0.1
0.1
0.1
1975
0.6
0.1
0.1
0.1
0.1
1980
0.6
0.1
0.1
0.1
0.1
1985
0.6
0.1
0.1
0.1
0.1
1990
0.6
0.1
0.1
0.1
0.1
1995
0.6
0.1
0.1
0.1
0.1
2000
0.6
0.1
0.1
0.1
0.1
2005
0.6
0.1
0.1
0.1
0.1
2010
0.6
0.2
0.1
0.1
0.1
2015
0.7
0.2
0.1
0.1
0.1
(WPP, 2007)
Table 1.18
Percentage of the Urban Population residing in each Urban agglomeration with
750,000 inhabitants or more in 2005 (Tamilnadu)
YEAR
Chennai Coimbatore
Madurai
Salem
Tiruchirappalli
1950
2.4
0.5
0.6
0.3
0.5
1955
2.5
0.5
0.6
0.3
0.5
1960
2.4
0.5
0.6
0.3
0.4
1965
2.6
0.6
0.6
0.4
0.4
1970
2.8
0.6
0.6
0.4
0.4
1975
2.7
0.6
0.6
0.3
0.4
1980
2.6
0.6
0.6
0.3
0.4
1985
2.5
0.5
0.5
0.3
0.4
1990
2.5
0.5
0.5
0.3
0.3
1995
2.3
0.5
0.5
0.3
0.3
2000
2.2
0.5
0.4
0.3
0.3
2005
2.2
0.5
0.4
0.3
0.3
2010
2.1
0.5
0.4
0.3
0.3
2015
2.1
0.5
0.4
0.3
0.3
(WPP, 2007)
Table 1. 19
Salient Features (Persons in Lakhs)
Sl.
Details
Total
Rural
Urban
Persons
621.11
348.69
272.42
Male
312.69
175.09
137.60
Female
308.42
173.60
134.82
11.19
(-)5.20
42.79
986
992
980
Persons
73.47
66.66
82.07
Male
82.33
77.47
68.40
Female
64.55
55.84
75.64
175.72
102.40
142.90
93.94
32.82
8.45
172.97
170.02
No
1.
2.
3.
4.
Population
Decadal (199101)Growth
Sex Ratio
Literacy Rate (%)
5.
Total Workers
6.
Main Workers
7.
Marginal Workers
8.
Non-Workers
9.
Cultivators
10.
11.
12.
Agricultural
Labourers
Workers in Health
Industry
Other Workers
(Census, 2001)
278.12
(44.8)
236.85
(38.1)
41.27 (6.7)
342.99
(55.2)
3.88
51.14 (18.4)
47.26
86.65 (31.2)
75.65
11.00
14.59 (5.2)
8.15
6.44
44.66
81.08
125.74
(45.2)
Table 1.20
Literacy Rate for the State 1961-2001
Year
Persons
Males
Females
1961
36.39
51.59
21.06
1971
45.40
59.54
30.92
1981
54.39
68.05
40.43
1991
62.66
73.75
51.33
2001
73.47
82.33
64.55
(COI, 2001)
Table 1.21
Literacy Rates by Sex for State and Districts
Literacy Rate*
Sl.
Persons
State / District
Males
Females
No
TAMIL NADU
1991
2001
1991
2001
1991
2001
62.66
73.47
73.75
82.33
51.33
64.55
1
Thiruvallur
66.22
76.54
77.03
84.62
54.9
68.23
2
Chennai
81.6
80.14
87.86
84.71
74.87
75.32
3
Kancheepuram
66.53
77.61
77.11
84.82
55.51
70.21
4
Velour
60.87
73.07
72.94
82.67
48.58
63.53
5
Dharmapuri
46.02
59.23
57.21
68.82
34.23
49.1
6
Thiruvannamalai
53.07
68.22
66.71
80.14
39.25
56.31
7
Villupuram
48.36
64.68
60.92
76.02
35.38
53.16
8
Salem
52.76
65.72
63.51
75.25
41.31
55.61
9
Namakkal
54.37
67.66
66.65
78.02
41.71
57.04
10
Erode
53.8
65.51
65.54
75.49
41.58
55.26
11
The Nilgiris
71.7
81.44
81.79
89.63
61.47
73.39
12
Coimbatore
66.35
76.95
76.45
83.82
55.73
69.8
13
Dindigul
56.68
69.83
69.19
80.29
43.94
59.3
14
Karur
56.06
68.74
69.62
80.42
42.59
57.3
15
Tiruchirappalli
68.67
79.16
79.5
87.19
57.69
71.19
16
Perambalur
51.81
65.88
64.74
77.68
38.57
54.26
17
Ariyalur
48.98
64.88
63.19
77.92
34.47
52.03
18
Cuddalore
58.59
71.85
71.53
82.76
45.21
60.86
19
Nagapattinam
65.75
76.89
77.03
85.61
54.43
68.35
20
Thiruvarur
66.15
76.9
77.45
85.59
54.73
68.36
21
Thanjavur
66.13
76.07
77.26
85.45
55.01
66.95
22
Pudukkottai
57.63
71.96
71.78
83.22
43.62
60.94
23
Sivaganga
62.95
72.66
76.9
83.7
49.59
62.12
24
Madurai
69.08
78.65
79.93
87.24
57.9
69.93
25
Theni
60.26
72.01
72.7
82.5
47.51
61.41
26
Virudhunagar
62.91
74.23
75.67
84.56
50.17
64.09
27
Ramanathapuram 61.65
73.05
74.73
82.96
48.84
63.55
28
Thoothukudi
73.02
81.96
82.02
88.66
64.57
75.64
29
Tirunelveli
65.58
76.97
77.46
85.89
54.23
68.5
30
Kanyakumari
82.06
88.11
85.7
90.88
78.39
85.38
(COI, 2001)
Table 1.22
Population Distribution, Percentage Decadal Growth, Sex Ratio, Population Density and Literacy Rate (Major States)
India/
States/
UT*
Sl
No
India
Percentage
Decadal
Growth
Population 2001
Sex Ratio
(Females
per 1000 Males)
Population Density
(Per sq.km.)
Persons
Males
Females
81-91
91-01
91
01
91
01
Persons
Males
Females
1027015247
531277078
495738169
23.86
21.34
927
933
267
324
65.38
75.85
54.16
1.
Punjab
24289296
12963362
11325934
20.81
19.76
882
874
403
482
69.95
75.63
63.55
2.
Haryana
21082989
11327658
9755331
2741
28.06
865
861
372
477
68.59
79.25
56.31
3.
Rajasthan
56473122
29381657
27091465
28.44
28.33
910
922
129
165
61.03
76.46
4.34
4.
Uttar Pradesh
166052859
87466301
78586558
25.55
25.80
876
898
548
689
57.36
70.23
42.98
5.
Bihar
82878796
43153964
39724832
23.38
28.43
907
921
685
880
47.53
60.32
33.57
6.
Assam
26638407
13787799
12850608
24.24
18.85
923
932
286
340
64.28
71.93
56.03
7.
West Bengal
80221171
41457694
38733477
24.73
17.84
917
934
767
904
69.22
77.58
60.22
8.
Orissa
36706920
18612340
18094580
20.06
15.94
971
972
203
236
63.61
75.95
50.97
9.
Madhya Pradesh
60385118
31456873
28928245
27.24
24.34
912
920
158
196
64.11
76.80
50.28
10.
Gujarat
50596992
26344053
24252939
21.19
22.48
934
921
211
258
69.97
80.50
58.60
11.
Maharashtra
96752247
50334270
46417977
25.73
22.57
934
922
257
314
77.27
86.27
67.51
12.
Andhra Pradesh
75727541
38286811
37440730
24.20
13.86
972
978
242
275
61.11
70.85
51.17
13.
Karnataka
52733958
26856343
25877615
21.12
17.25
960
964
235
275
67.04
76.29
57.45
14.
Kerala
31838619
15468664
16369955
14.32
9.42
1036
1058
749
819
90.92
94.20
87.86
15.
Tamil Nadu
62110839
31268654
30842185
15.39
11.19
974
986
429
478
73.47
82.33
64.55
(COI, 2001)
Table 1.23
Population Distribution, Percentage decadal Growth Rate, Sex-Ratio and Population Density of Tamil Nadu and Districts.
Sex-Ratio (No.
of Females per
1000 males)
91
2001
Population
Density
per Sq.Km.
91
2001
Persons
Males
Females
% Decadal
Growth
Rate
81-91
91- 2000
TAMIL NADU
62110839
31268654
30842185
15.39
11.19
974
986
429
478
1
Thiruvallur
2738866
1390292
1348574
31.53
22.35
957
970
654
800
2
Chennai
4216268
2161605
2054663
17.24
9.76
934
951
22077
24231
3
Kancheepuram
2869920
1455302
1414618
26.14
18.84
962
972
545
647
4
Velour
3482970
1743871
1739099
15.14
15.09
978
997
498
573
5
Dharmapuri
2833252
1462136
1371116
21.61
16.66
942
938
252
294
6
Thiruvannamalai
2181853
1093191
1088662
14.4
6.8
983
996
330
352
7
Villupuram
2943917
1484573
1459344
16.08
6.83
969
983
380
406
8
Salem
2992754
1551357
1441397
13.43
16.28
925
929
493
573
9
Namakkal
1495661
760409
735252
12.79
13.08
960
967
386
436
10
Erode
2574067
1306039
1268028
12.17
10.94
958
971
283
314
11
The Nilgiris
764826
379610
385216
12.7
7.69
983
1015
279
300
12
Coimbatore
4224107
2156280
2067827
14.65
20.4
952
959
470
566
Sl.
No.
State/District
Population 2001
13
Dindigul
1918960
966201
952759
12.54
8.99
976
986
291
317
14
Karur
933791
464489
469302
12.87
9.32
999
1010
284
311
15
Tiruchirappalli
2388831
1194133
1194698
12.57
8.76
982
1000
499
542
16
Perambalur
486971
242664
244307
17.92
7.97
975
1007
258
278
17
Ariyalur
694058
345777
348281
11.16
9.06
975
1007
328
358
18
Cuddalore
2280530
1148729
1131801
16.13
7.43
967
985
582
626
19
Nagapattinam
1487055
738287
748768
11.68
7.95
993
1014
507
548
20
Thiruvarur
1165213
578870
586343
12.04
5.92
987
1013
508
538
21
Thanjavur
2205375
1091557
1113818
11.13
7.38
996
1020
605
649
22
Pudukkottai
1452269
720847
731422
14.72
9.43
1005
1015
285
312
23
Sivaganga
1150753
565594
585159
10.72
4.32
1033
1035
263
275
24
Madurai
2562279
1295124
1267155
17.51
6.75
964
978
686
733
25
Theni
1094724
553118
541606
12.98
4.33
964
979
342
357
26
Virudhunagar
1751548
870820
880728
16.71
11.92
994
1011
365
409
27
Ramanathapuram
1183321
582068
601253
12.11
5.73
1011
1033
271
287
28
Thoothukudi
1565743
764087
801656
7.8
7.54
1051
1049
315
339
29
Tirunelveli
2801194
1372082
1429112
12.53
11.97
1034
1042
367
411
30
Kanyakumari
1669763
829542
840221
12.43
4.34
991
1013
950
992
(COI, 2001)
Table 1.24
Trend in Birth/Death rate and infant mortality rate in Pudukkottai
YEAR
Birth Rate
Death Rate
Infant Mortality Rate
1951
42.6
20.2s
76.3
1961
35.3
16.8
61.7
1971
28.6
13.6
49.3
1981
24.4
9.2
37.5
1991
21.7
7.3
30.9
(ENVIS,2005)
Table 1.25
Land Resource
Taulk
Area sq.km
Pudukkottai
284.06
Kulathur
1322.94
(ENVIS, 2005)
Table 1.26
Crops cultivated
Category
Cereals
Pulses
Oil seeds
Condiments
Sugars
Fibers
Common name
Rice
Cholam
Varagu
Ragi
Maize
Cumbu
Red gram
Cow pea
Horse gram
Black gram
Green gram
Ground nut
Coconut
Gingelly
Soya bean
Chillies
Tamarind
Sugar cane
Palmyra
Cotton
Botanical name
Oryza sativa
Sorghum bicolot
Paspalum scrobiculation
Eleusine coracana
Zea mays
Pennisetum typhoides
Cajanus cajan
Vigna unguiculata
Dolicus biflorus
Phaseolus mungo
Phaseolus aureus
Arachis hypogaea
Cocus nucifera
Sesamum indicum
Glycine soya
Capsigum annum
Tamarindus indica
Saccharum officinarum
Borassus flabellifer
Gossypium hirsutum
Table 1.27
Horticulture fruit crops
Banana
Musa sp
Mango
Mangifera indica
Jack
Artocarpus heterophyllus
Guava
Psidium guajava
Acid lime
Citrus aurantifolia
(ENVIS, 2005)
Table 1.28
Fuel wood
Acacia planifrons
Albizia amara
Choloroxylon swietenia
Top canopy
Canthium dicoccum
Gyrocarpus jacquinu
Givotia rottleriforms
Sapindus trifoliatus
Acacia latronum
Under growth
Dichrostachys cinerea
Atlantia monophylla
Hemicyclia sepraria
Randia dymetorum
Carissa spinarum
Shrubs
Zizipus spp
Acalypha fruticosa
Barleria sp
Soleannum toroum
Climbers
(ENVIS, 2005)
Acacia pennata
Pterolobium hexapetalum
Table1.29
Fauna
Mammals
Bonnet macaque
Macaca radiata
Jungle cat
Felis chaus
Jackal
Canis aureus
Small Indian civet
Viverricula indica
Mongoose
Herpestes edwardsi
Black naped hare
Lepus nigrieollis
Reptiles
Green wipe snake
Ahaetulla nasutus
Cobra
Naja naja
Indian Krait
Bungarus caeruleus
Russel's viper
Vipera russelli
Aves
Peafowl
Pavo sp
Blank drongo
Dicrurus adsimitis
Jungle and House crow
Ergots
Patridges
(ENVIS, 2005)
Table 1.30
Water Resourse
Normal monthly rainfall
77.13m.m
Normal annual rainfall
925.6m.m
Actual annual rainfall
485.9m.m
Water table-low level
23.9
Water table-high level
1.2
(ENVIS, 2005)
Table 1.31
Reported cases of water born diseases
Reported Cases
Year
Gastroentitis Diarrhoea
Cholera
Typhoid Jaundice Malaria
1987-88
-
68
3
Nil
Nil
68
1988-89
-
101
3
Nil
4
140
1989-90
-
111
4
Nil
4
167
1990-91
-
9
Nil
Nil
12
171
1991-92
318
51
1
Nil
Nil
382
1992-93
197
385
Nil
Nil
1
292
1993-94
4
352
1
Nil
79
344
1994-95
391
81
Nil
Nil
50
176
1995-96
105
Nil
Nil
Nil
Nil
Nil
(ENVIS, 2005)
Table 1.32
Death rate of water born diseases
Deaths
Year
Gastroentitis Diarrhoea
Cholera
Typhoid Jaundice Malaria
1987-88
-
7
Nil
Nil
Nil
Nil
1988-89
-
8
Nil
Nil
Nil
Nil
1989-90
-
6
Nil
Nil
Nil
Nil
1990-91
-
2
Nil
Nil
Nil
Nil
1991-92
9
5
1
Nil
Nil
Nil
1992-93
6
16
Nil
Nil
Nil
Nil
1993-94
Nil
16
Nil
Nil
Nil
Nil
1994-95
5
2
1
Nil
Nil
Nil
1995-96
6
Nil
Nil
Nil
Nil
Nil
(ENVIS, 2005)
Table 2.1
District wise waste land in Tamil Nadu State
S.No.
District
Land (ha)
1.
Kancheepuram
183
2.
Cuddalore
276
3.
Vellore
149
4.
Thiruvannamalai
141
5.
Salem
262
6.
Dharmapuri
195
7.
Coimbatore
182
8.
Erode
180
9.
Thiruchirapalli
391
10.
Pudukottai
137
11.
Thanjavur
139
12.
Madurai
177
13.
Dindigal
209
14.
Ramanathapuram
144
15.
Virudhunagar
141
16.
Sivaganga
177
17.
Tirunelveli
284
18.
Thoothukkudi
180
19.
Nilgiris
47
20.
Kanyakumari
34
(Waste land development, TNAU, 2001)
Table 3.1
Total Population up to 2001 in Pudukkottai Dist
Male
7,20,847
Female
7,31,422
Total
14,52,269
Pudukkottai Town 2001
1 lakhs
APPENDIX
CPCB Ambient Air Quality Standard (2000)
Location
SPM
SO2
NO2
Industry
300
80
90
Residential and Commercial zone
140
60
60
Silence zone
70
20
20
No. of fatal accidents.
Year
Delhi
Bombay
Calcutta
1980
720
918
1156
1981
1021
1375
1732
1982
1172
1951
2156
1983
1559
2384
2983
1984
1956
2976
3413
(Vandana pandey, 1992)
Noise Exposure Limit World Health Organization 1980
Environment
Recommended
Max. level
Effects
Indoor/Domestic
Night time
35dB
Increased awaking at higher levels
Indoor/Domestic
Day time
45dB
Speech communication
Defeuorates at higher Levels
Community/Urban
Night time
55dB
Difficulties in falling Asleep at higher
levels
Community/Urban
Day time
55dB
Annoyance increases at
Higher levels
Industrial/Occupational
90dB
Predictable risk of hearing
Impairment at higher levels.
(Times of India, 1989)
Noise levels and Humans response
Sources of noise
Decibels
Response
1.Jet take off(near by)
150
Threshold of pain
2.Hydraulic press (1 meter)
130
Limit of amplified speech
3.Jet take off(60 meters)
120
4.construction noise (3 meters)
110
Very annoying
5.Heavy truck(15 meters)
90
Hearing damage(8 hours)
6.Alarm clock
80
Annoying
7.Hhigh way traffic(15 meters)
70
Telephone use difficult
60
Intrusive
9.Quiet residential area
50
Quiet
10.Living room
40
Very quiet
11.Soft music
30
Very quiet
12.Board casting studio
20
Very quiet
13.Rusing leaves in breeze
10
Just audible
8.Air conditioning unit
(6 meters)
Maximum vocal effort
possible
(Trivedi and Gurdeep raj, 1992)
Expose Level and Time Limit
Level(dB)
Dose time limit
90
8hr
93
4hr
100
48 minutes
110
4.8 minutes
120
28.8 seconds
130
2.88 seconds
(Trivedi and Gurdeep Raj, 1992)
Effect of high Intensity noise on human being
Noise(dB)
Effects Observed
0
Threshold of audibility
105
Significant change in pulse rate
110
Stimulation of reception in skin
120
Pain threshold
130-135
Nausea, Vomiting, Dizziness, Interference
with touch and muscle.
140
Pain in ear, prolonged exposure casing
insanity
145
Extreme limit of human noise tolerance
150
Prolonged exposure causing burning of the
skin.
160
Minor permanent damage if prolonged.
190
Major permanent damage in a short time.
(Trivedi and Gurdeep raj, 1992)
U.S. Population Exposed to Noise, by Level and Source, (1980)
(Million People exposed)
Decibels
Traffic
Aircraft
Construction
Rail
Industrial
More than 80
0.1
0.1
------------
------
--------
More than 75
1.1
0.3
0.1
-------
--------
More than 70
5.7
01.3
0.6
0.8
---------
More than 65
19.3
4.7
2.1
2.5
0.3
More than 60
46.6
11.5
7.7
3.5
1.9
More than 55
96.8
24.3
27.5
6.0
6.9
(US Environmental Protection Agency, 1980)
Population Reporting "Highly Annoying" Noise Sources Population per square mile
More than 20,000 3,000-20,000 Less than 3,000
Source
Rank
% of
Percentage of
Percentage of
Respondents
Respondents
Respondents
Highly
Rank
Highly
Rank
Highly
Annoyed by
Annoyed by
Annoyed by
Source
Source
Source
Motorcycles
1
12.7
1
13.2
1
9.4
Automobiles
2
9.4
3
7.4
3
4.2
Large trucks
3
7.3
2
10.0
7
2.6
Construction
4
6.5
4
7.2
4
3.7
Sport cars
5
5.9
5
7.0
6
3.1
Constant
6
4.7
6
5.5
10
1.5
Buses
7
4.7
8
3.5
11
1.1
Small trucks
8
4.1
7
4.1
9
1.5
Helicopters
9
3.9
10
3.1
2
5.3
Airplanes
10
3.6
9
3.4
5
3.2
Power garden
11
1.2
11
2.1
8
1.8
traffic
tools
Total
66.0
62.2
55.9
(Council on Environmental Quality, 1979)
Horticultural crops in Tamil Nadu
(Area: Lakh Ha., Production: Lakh MT., Productivity: MT/Ha.)
Crops
2004-2005 (Provisional)
2005-2006 (Estimated)
Area
Prdn.
Pdy
Area
Prdn.
Pdy
Fruits
2.39
39.08
16.37
2.58
42.31
16.41
Vegetable
2.06
50.59
24.53
2.23
54.78
24.59
Spices
1.67
7.50
4.50
1.80
8.12
4.51
2.53
8.68
3.44
2.73
9.40
3.44
0.22
1.75
7.99
0.34
1.89
8.01
0.04
0.08
1.90
0.05
0.09
1.90
8.91
107.68
12.09
9.73
116.59
12.12
Plantation
Crops
Flowers
Medicinal
Plants
Total
(All crops)
(Anon, 2004)
Percapita Quantity of Municipal Solid Wastes in Indian Urban Centres
Population Range (in million)
Average percapita value
Kg/capita/day
0.1-0.5
0.21
0.5-1.0
0.25
1.0-2.0
0.27
2.0-5.0
0.35
5.0
0.50
(Gaikwad et al., 1985)
Table 4.1.
ANOVA for SPM in various zones in Pudukkottai
Season
I
II
III
IV
Between Groups
Within Groups
Total
Sum of
squares
859169.3
1504311.62
2363480.9
6
28
34
Between Groups
Within Groups
Total
1834435.4
408614.5
2243050
6
28
34
SPM
Between Groups
Within Groups
Total
Between Groups
Within Groups
Total
1954558.3
416737.1
2372955
2240677
656457.4
2891134.4
G1=Urban residential zone
G2=Sub urban residential zone
G3=Urban commercial zone
G4=Sub urban commercial zone
G5=Urban sensitive zone
G6=Sub urban sensitive zone
G7=Urban industrial zone
Df
6
28
34
6
28
34
Mean
Square
143194.8
60172.46
305739.2
16344.5
325759.7
16669.4
373446.1
26018.2
Mean
G1=124.3
G2=153.7
G3 =600
G4 =405
G5= 269
G6= 319
G7= 285
G1=110
G2=133.7
G3 =785
G4 =355
G5= 180.8
G6= 317
G7= 240
G1=110
G2=130
G3 =793
G4 =331
G5= 182
G6= 171
G7= 225
G1=118.3
G2=141.5
G3 =854
G4 =371.2
G5= 132.5
G6= 373.6
G7= 235
Statistical
inference
F=2.38
P>0.05
Not
Significant
F=18.706
P<0.05
Significant
F=19.54
P<0.05
Significant
F=14.35
P<0.05
Significant
Table 4.2.
ANOVA for So2 In Various Zones in Pudukkottai
Season
I
Morning
SO2
Between Groups
Sum of
squares
df
Mean
Square
Mean
79958
6
13326.3
G1=30.53
Within Groups
320580.4
28
12823.2
G2=25.25
Total
400538.5
34
Statistical
inference
F=1.039
G3 =165.6 P>0.05
G4 =128.7 Not
G5= 80.51 Significant
G6= 69.75
G7= 75
I
After
Between Groups
153568.7
6
25594.7
G1=31
Within Groups
594225.9
28
23769
G2=24.7
Total
747794.7
34
F=1.07
G3 =225.6 P>0.05
noon
G4 =127
Not
G5= 87.4
Significant
G6= 72
G7=79
I
Night
Between Groups
222272.6
6
37045.4
G1=31.1
Within Groups
774334.3
28
30973.3
G2=30.2
Total
996607
34
F=1.196
G3 =263.8 P>0.05
G4 =135.6 Not
G5= 68
Significant
G6= 79
G7= 80
II
Morning
Between Groups
49609.3
6
8268.2
G1=31.8
Within Groups
222857.8
28
8914.3
G2=25
Total
272467
34
F=.928
G3 =141.7 P>0.05
G4 =87
Not
G5= 64.8
Significant
G6= 58
G7= 65
Season
II
After
noon
II
Night
SO2
Sum of
squares
df
Mean
Square
Mean
Statistical
inference
Between Groups
Within Groups
Total
76331.8
319703.9
396035.8
6
28
34
12721.9
12788.1
Between Groups
41937.4
6
6987.9
G1=28.83
G2=28.25
G3 =170.5 F=.995
G4 =80.75 P>0.05
G5= 71.7
G6= 69.25
G7= 75
G1=36.2
Within Groups
200244.3
28
8009.7
G2=37.7
Total
242171.8
34
G3 =139.5 F=.872
G4 =85.7
>0.05
G5= 58.3
G6= 67.7
G7= 83.5
III
Between Groups
15026.3
6
2504.3
G1=36.38
Morning
Within Groups
96937.9
28
3877.5
G2=26.5
Total
111964.2
34
G3 =92.1
F=.646
G4 =68.7
>0.05
G5= 66
G6= 48.7
G7= 64
III
Between Groups
15112.8
6
2518.8
G1=35.3
After
Within Groups
90145.3
28
3605.8
G2=26
noon
Total
105258.2
34
G3 =91.4
F=.699
G4 =66.7
>0.05
G5= 68.5
G6= 50.5
G7= 62.5
III
Night
Between Groups
24332.2
6
4055.3
Within Groups
132416.9
28
5296.6
Total
156749.1
34
G1=38.3
G2=29.7
G3 =112.4
G4 =78.7 F=.766
G5= 75
>0.05
G6= 57
G7= 75
Season
SO2
Sum of
squares
df
Mean
Square
Mean
IV
Between Groups
70785.5
6
11797.5
G1=26.3
Morning
Within Groups
290722.5
28
11628.9
G2=23.5
Total
361508
34
Statistical
inference
G3 =157.1 F=1.015
G4 =111.7 >0.05
G5= 65.2
G6= 81.3
G7= 72.5
IV
After
Between Groups
135283
6
22547.3
G1=24.8
Within Groups
508804
28
20352.1
G2=26.5
Total
644088
34
G3 =210.6 F=1.108
noon
G4 =115
>0.05
G5= 63.2
G6= 83.8
G7= 71
IV
Between Groups
158483.3
6
26413.8
G1=30.6
Night
Within Groups
602855.3
28
24114.2
G2=29.2
Total
761338.7
34
G3 =230.5 F=1.095
G4 =120.5 >0.05
G5= 67.7
G6= 91
G7= 75
G1=Urban residential zone
G2=Sub urban residential zone
G3=Urban commercial zone
G4=Sub urban commercial zone
G5=Urban sensitive zone
G6=Sub urban sensitive zone
G7=Urban industrial zone
Table 4.3.
ANOVA for No2 in Various Zones in Pudukkottai
Season
I
Morning
NO2
Sum of
squares
df
Mean
Square
Between Groups
96049.9
6
16008.3
Within Groups
105047.1
28
4201.8
Total
34
Mean
Statistical
inference
G1=23.1
G2=27
G3 =174.5
G4 =79
F=3.81
G5= 51.6
P<0.05
G6= 26.6
Significant
G7= 79
I
After
Between Groups
92784.6
6
15464.1
G1=20.2
Within Groups
104351.5
28
4174
G2=26.2
Total
197136.1
34
G3 =170.8
noon
G4 =72.5
F=3.705
G5= 50.9
P<0.05
G6=28
G7=83.5
I
Night
Between Groups
146883.5
6
24480
G1=23
Within Groups
179935
28
7197
G2=31.7
Total
326818.6
34
Significant
G3 =211
G4 =84.5
F=3.401
G5=51.9
P<0.05
G6= 29.4
G7= 91
II
Morning
Between Groups
34408
6
6413.4
Within Groups
24897
28
995.8
Total
63377.9
34
Significant
G1=19.5
G2=22
G3 =118.1 F=6.44
G4 =52.7
P<0.05
G5= 42
G6= 37.5
G7= 72.5
Significant
Season
II
After
NO2
Sum of
squares
df
Mean
Square
Mean
Between Groups
68505
6
11417.5
G1=18.5
Within Groups
73378.5
28
2935.1
G2=25.2
Total
141884
34
Statistical
inference
G3 =151.5 F=3.89
noon
G4 =57
P<0.05
G5= 45.5
G6= 45.2
Significant
G7= 82.5
II
Night
Between Groups
37549.2
6
6258.2
G1=19.8
Within Groups
25012.5
28
1000.5
G2=21.5
Total
62561.7
34
G3 =117.6 F=6.25
G4 =55.5
<0.05
G5= 40.1
Significant
G6= 47.5
G7= 72.5
III
Between Groups
18037.5
6
3006.2
G1=35.8
Morning
Within Groups
24749.9
28
989.9
G2=27.7
Total
42787.5
34
G3 =97.3
F=3.03
G4 =42.5
<0.05
G5= 48.5
Significant
G6= 35
G7= 62.5
III
Between Groups
17101.5
6
2850.2
G1=35.5
After
Within Groups
20217.9
28
808.7
G2=28.2
noon
Total
37319.5
34
G3 =95.8
F=3.52
G4 =47.5
<0.05
G5= 46.8
Significant
G6= 34.5
G7= 63
Season
III
NO2
Sum of
squares
df
Mean
Square
Mean
Statistical
inference
Between Groups
Within Groups
Total
22347.4
33481
55828
6
28
34
3724.5
1339.2
G1=39.3
G2=32.7
G3 =109.6 F=2.781
G4 =53.5 <0.05
G5= 51.6 Significant
G6= 40.7
G7= 67.5
IV
Morning
Between Groups
Within Groups
Total
78083
75652.8
153735.8
6
28
34
13013.8
3026
G1=21
G2=25.7
G3 =157.1 F=4.301
G4 =73.2 <0.05
G5= 44.5 Significant
G6= 29.5
G7= 75
IV
Between Groups
Within Groups
Total
84680.7
88364.1
173044.8
6
28
34
14113.4
3534.5
G1=20.3
G2=27.2
G3 =163.5 F=3.9
G4 =69.5 <0.05
G5= 44.7
G6= 30.8 Significant
G7= 76
Between Groups
Within Groups
Total
95287.8
101168.3
196456.2
6
28
34
15881.3
4046.7
G1=23
G2=29
G3 =175
G4 =69
G5= 47
G6= 35.1
G7= 79
Night
After
noon
IV
Night
G1=Urban residential zone
G2=Sub urban residential zone
G3=Urban commercial zone
G4=Sub urban commercial zone
G5=Urban sensitive zone
G6=Sub urban sensitive zone
G7=Urban industrial zone
F=3.9
<0.05
Significant
Table 4.4a.
ANOVA for Leq Noise Level on Holidays
Holiday
Leq
Sum of
df
squares
Day time Between Groups
Mean
Mean
Square
inference
3166.1
5
633.2
G1=70.8
Within Groups
325.2
23
21.68
G2=56.8
Total
3491.3
28
G3 =79.9
G4 =66.1
F=29.2
G5= 72.6
P<0.05
G6= 35.9
Significant
Night
Between Groups
3574.2
5
714.8
G1=60.04
time
Within Groups
847.4
23
56.4
G2=37.15
Total
4421.6
28
G3 =74.2
F=12.65
G4 =60.1
P<0.05
G5= 60.5
Significant
G6= 32.17
G1=Urban residential zone
G2=Sub urban residential zone
G3=Urban commercial zone
G4=Sub urban commercial zone
G5=Urban sensitive zone
G6=Sub urban sensitive zone
Statistical
Table 4.4b.
ANOVA for Leq Noise Level on Working Days
Working
Leq
Sum of
day
Daytime
df
squares
Between
Mean
Mean
Square
inference
1841.5
5
368.3
G1=81.13
Groups
714.8
23
47.6
G2=62.6
Within Groups
2556
28
G3 =86.07
Total
G4 =66.3
F=7.72
G5= 78
P<0.05
G6= 58
Significant
Night
Between
1676.7
5
335.3
G1=63.6
time
Groups
391.2
23
26.08
G2=57.3
Within Groups
2067
Total
28
G3 =77.6
F=12.8
G4 =62.7
P<0.05
G5= 66.7
Significant
G6= 45.9
G1=Urban residential zone
G2=Sub urban residential zone
G3=Urban commercial zone
G4=Sub urban commercial zone
G5=Urban sensitive zone
G6=Sub urban sensitive zone
Statistical
Table 4.4c.
ANOVA for Leq NOISE Level on Festival Days
Festiva
Leq
Sum of
l day
df
squares
Day
Between Groups
time
Within Groups
Mean
Mean
Square
inference
1808.8
5
361.7
G1=90.2
477.7
23
31.8
G2=70.4
Total
28
G3 =87.9
G4 =68
F=11.3
G5= 83.2
P<0.05
G6= 64.2
Significant
Night
Between Groups
2593.1
5
518.6
G1=72.5
time
Within Groups
808.5
23
53.9
G2=59
Total
3401.6
28
G3 =81.8
F=9.62
G4 =64.8
P<0.05
G5= 67.6
Significant
G6= 41.8
G1=Urban residential zone
G2=Sub urban residential zone
G3=Urban commercial zone
G4=Sub urban commercial zone
G5=Urban sensitive zone
G6=Sub urban sensitive zone
Statistical
Table 4.5a.
ANOVA for pH in various zones
haracteristics of Ground water in Pudukkottai
Season
I
Monsoon
pH
Sum of
df
squares
Mean
Square
Mean
Statistical
inference
Between Groups
.144
5
2.8E-02
G1=7.9
Within Groups
.241
25
2.6E-02
G2=7.7
F=1.075
Total
.385
34
G3 =7.9
P>0.05
G4 =7.7
Not
G5= 7.7
Significant
G6= 7.6
II
Between Groups
1.06
5
.213
G1=8.4
North-east
Within Groups
.445
29
4.9E-02
G2=8.2
Monsoon
Total
1.508
34
G3 =8.8
F=4.3
G4 =8.4
P<0.05
G5= 8.1
Significant
G6= 8.1
III
Between Groups
.788
5
.158
G1=8.3
Premonsoon
Within Groups
.325
29
3.6E-02
G2=8.1
Total
1.114
34
G3 =8.6
F=4.3
G4 =8.3
P<0.05
G5= 8.05
Significant
G6= 8.02
Table 4.5b.
ANOVA for Electrical Conductivity in Various Zones
Season
I
EC
Sum of
df
squares
Mean
Square
Mean
Statistical
inference
Between Groups
.540
5
.108
G1=.78
Within Groups
.310
29
3.4E-02
G2=.74
F=3.13
Total
.849
34
G3 =1.23
P>0.05
G4 =.95
Not
G5= .78
Significant
G6= .69
II
Between Groups
.717
5
.143
G1=.82
Within Groups
.228
29
2.53
G2=.805
F=5.6
Total
.945
34
G3 =1.37
P>0.05
G4 =1.02
Not
G5= .85
Significant
G6= .79
III
Between Groups
.378
5
7.5E-02
G1=.81
Within Groups
.258
29
2.8E-02
G2=.77
F=2.6
Total
.636
34
G3 =1.18
P>0.05
G4 =1.01
Not
G5= .84
Significant
G6= .75
Table 4.5c.
ANOVA for Temperature in Various Zones
Season
I
Sum of
Temperature
df
squares
Mean
Square
Mean
Statistical
inference
Between Groups
1.0
5
.2
G1=27.6
F=.415
Within Groups
4.3
29
.48
G2=27.5
P>0.05
Total
5.3
34
G3 =28
Not
G4 =28
Significant
G5=27.3
G6= 27.5
II
III
Between Groups
.733
5
.147
G1=27
Within Groups
1.66
29
.185
G2=27.5
Total
2.4
34
G3 =27
F=.792
G4 =27.5
P>0.05
G5=27.3
Not
G6= 27
Significant
Between Groups
1.1
5
.220
G1=28.6
Within Groups
3.8
29
.426
G2=29
Total
4.9
34
G3 =29
F=.517
G4 =29.5
P>0.05
G5= 28.6
Not
G6= 29
Significant
Table 4.5d.
ANOVA for Turbidity in various zones
Season
I
Turbidity
Sum of
squares
df
Mean
Square
Mean
Between Groups
.408
5
8.15E02
G1=2.63
Within Groups
.455
23
5.05E-02
G2=2.35
Total
.862
28
Statistical
inference
G3 =2.7
G4 =2.43
F=1.61
G5= 2.46
P>0.05
G6=2.25
Not
Significant
II
Between Groups
.555
5
.111
G1=2.8
Within Groups
.544
23
6.03E-02
G2=2.4
1.098
28
Total
G3 =2.8
F=1.83
G4 =2.4
P<0.05
G5= 2.59
Significant
G6=2.37
III
Between Groups
.407
5
8.15E02
G1=2.6
Within Groups
.456
23
5.05E-02
G2=2.3
Total
1.127
28
G3 =2.7
F=1.59
G4 =2.4
P<0.05
G5= 2.4
Significant
G6= 2.25
Table 4.5e.
ANOVA for TSS in various zones
Season
TSS
Sum of
df
squares
I
II
Mean
Mean
Square
Statistical
inference
Between Groups
2829.6
5
565.9
G1=64
Within Groups
6683.3
29
743.1
G2=59.5
Total
9518
34
G3 =62.6
G4 =67.5
F=.762
G5= 31.3
P<0.05
G6= 41
Significant
Between Groups
31379.2
5
6275.8
G1=202
Within Groups
15408
29
1712.03
G2=215
Total
46787.6
34
G3 =260
F=3.6
G4 =239
P<0.05
G5= 140.3 Significant
G6=145
III
Between Groups
3629.5
5
725.9
G1=74
Within Groups
3666.1
29
407.3
G2=46.5
Total
7275.7
34
G3 =72
F=1.782
G4 =52.5
P>0.05
G5= 32.3
Significant
G6= 54.5
Table 4.5f.
ANOVA for TDS in various zones
Season
I
TDS
Sum of
df
squares
Mean
Square
Mean
Between Groups
17664.4
5
3532.8
G1=133.6
Within Groups
5153.3
29
572.5
G2=153
Total
22817.7
34
Statistical
inference
G3 =193
G4 =165
F=6.17
G5= 103.3 P<0.05
G6= 98
II
Between Groups
14871
5
2974.3
G1=122.3
Within Groups
5132.5
29
570.2
G2=147.5
Total
20004.4
34
Significant
G3 =179.6 F=5.2
G4 =154
P<0.05
G5= 99.6
Significant
G6= 92
III
Between Groups
12322.5
5
2464.5
G1=117.3
Within Groups
4563.1
29
507
G2=137.5
Total
16885.7
34
G3 =164.5 F=4.86
G4 =144
P<0.05
G5= 94
Significant
G6= 82
Table 4.5g.
ANOVA for TS in Various Zones
Season
I
TS
Sum of
df
squares
Mean
Square
Mean
Between Groups
31676.7
5
6335.3
G1=197.6
Within Groups
15637
29
1739.6
G2=212.5
Total
47333.7
34
Statistical
inference
G3 =255.6
G4 =232.5 F=3.64
G5= 134.6 P<0.05
G6= 139
II
Between Groups
31379.26
5
6275.8
G1=202
Within Groups
15408.3
29
1712.03
G2=215
Total
46787.6
34
Significant
G3 =260
F=3.6
G4 =239
P<0.05
G5=140.3
Significant
G6= 145
III
Between Groups
22793
5
6101
G1=191.3
Within Groups
9936.3
29
1456.4
G2=184
Total
32725
34
G3 =236
F=4.129
G4 =196.5 P<0.05
G5= 126.3 Significant
G6= 136.5
Table 4.5h.
ANOVA for total hardness in various zones
Season
I
Total Hardness
Sum of
df
squares
Mean
Square
Mean
Between Groups
3988.7
5
797.7
G1=144.9
Within Groups
2855.9
29
317.3
G2=136
Total
6844.7
34
Statistical
inference
G3 =158.1
G4 =147.1 F=2.51
G5= 184.7 P>0.05
G6= 146.3 Not
Significant
II
Between Groups
3733.1
5
746.6
Within Groups
2673.1
29
297.01
Total
6406.2
34
G1=144.9
G2=136
F=2.5
G3 =158.1 P>0.05
G4 =147.1
G5= 184.7 Not
G6= 146.3 Significant
III
Between Groups
4539.5
5
907.9
G1=141.1
Within Groups
2631.16
29
292.3
G2=132.6
Total
7170.6
34
G3 =152.9 F=2.1
G4 =137.8 P>0.05
G5= 177.7
G6=125.5
Not
Significant
Table 4.5i.
ANOVA for calcium in various zones
Season
Calcium
Sum of
df
squares
I
Mean
Mean
Square
Statistical
inference
Between Groups
260.5
5
52.1
G1=86.3
Within Groups
203.1
29
22.5
G2=84.3
F=2.308
Total
463.3
34
G3 =96.7
P>0.05
G4 =88.8
Not
G5= 90.9
Significant
G6= 86.7
II
Between Groups
623.08
5
124.6
G1=79.9
Within Groups
330.7
29
36.7
G2=75.8
Total
953.8
34
G3 =92.7
F=3.39
G4 =80.6
P<0.05
G5= 87.7
Significant
G6=75.06
III
Between Groups
600.9
5
120.1
G1=81.1
Within Groups
274.1
29
300.4
G2=77.1
Total
875.04
34
G3 =93.6
F=3.94
G4 =82.8
P<0.05
G5= 88.3
G6=75.5
Significant
Table 4.5j.
ANOVA for magnesium in various zones
Season
I
II
Magnesium
Sum of
df
squares
Mean
Square
Mean
Between Groups
2069
5
413.8
G1=59.7
Within Groups
3647
29
405.3
G2=54.5
Total
5716
34
Statistical
inference
G3 =55.8
F=1.02
G4 =54.8
P>0.05
G5= 82.6
Not
G6= 46.5
Significant
Between Groups
3056.1
5
611.2
G1=58.5
Within Groups
3578.8
29
397.6
G2=51.6
Total
6634.9
34
G3 =61.4
F=1.53
G4 =58.3
P<0.05
G5= 93.2
G6= 59.6
III
Between Groups
2725.9
5
545.1
G1=60
Within Groups
3467.8
29
385.3
G2=55.5
Total
6193.7
34
Significant
G3 =59.3
F=1.41
G4 =55
P<0.05
G5= 89.3
G6= 50
Significant
Table 4.5k.
ANOVA for alkalinity in various zones
Season
I
Alkalinity
Sum of
df
squares
Mean
Square
Mean
Between Groups
444.6
5
88.9
G1=125.2
Within Groups
179
29
19.8
G2=122.5
Total
623.7
34
Statistical
inference
G3 =137.6
G4 =135
F=4.4
G5= 133.9 P<0.05
G6= 128.6 Significant
II
Between Groups
1177.9
5
235.5
G1=131
Within Groups
343.6
29
38.1
G2=128
Total
1521.6
34
G3 =141
F=6.17
G4 =141.5 P<0.05
G5= 117.3
G6=124.5
III
Between Groups
1641.1
5
328.2
G1=127
Within Groups
478.8
29
53.2
G2=121.5
Total
2120
34
Significant
G3 =136.3 F=6.16
G4 =135.5 P<0.05
G5=108.3
G6= 115.5
Significant
Table 4.5l.
ANOVA for acidity in various zones
Season
I
Sum of
Acidity
df
squares
Mean
Square
Mean
Statistical
inference
Between Groups
5.6
5
1.12E-02
G1=.83
Within Groups
.118
29
1.31E-02
G2=.7
F=0.858
Total
.174
34
G3 =.88
P>0.05
G4 =.84
Not
G5= .9
Significant
G6= .8
II
Between Groups
.416
5
8.3E-02
G1=.47
Within Groups
4.8E-02
29
5.3E-03
G2=.55
Total
34
G3 =.91
F=15.6
G4 =.84
P<0.05
G5=.8
Significant
G6= .79
III
Between Groups
.398
5
.080
G1=.47
Within Groups
.037
29
.004
G2=.55
Total
.434
34
G3 =.9167 F=19.52
G4 =.83
P<0.05
G5= .77
Significant
G6= .78
Table 4.5m.
ANOVA for DO in various zones
Season
I
DO
Sum of
df
squares
Mean
Square
Mean
Between Groups
.293
5
5.85E-02
G1=5.72
Within Groups
.362
29
4.02E-02
G2=6.1
Total
.655
34
Statistical
inference
G3 =5.8
G4 =6
F=1.67
G5= 5.9
P>0.05
G6=6.01
Not
Significant
II
III
Between Groups
.139
5
2.7E-02
Within Groups
.149
29
1.65E-02
Total
.288
34
G1=5.9
G2=6.2
G3 =6.06
F=1.45
G4 =6.17
P>0.05
G5= 6.12
Not
G6= 6.14
Significant
Between Groups
.151
5
3.01E-02
G1=5.7
Within Groups
.143
29
1.5E-02
G2=6
Total
.294
34
G3 =5.8
F=1.89
G4 =5.9
P>0.05
G5= 5.9
Not
G6= 5.8
Significant
Table 4.5n.
ANOVA for BOD in various zones
Season
I
Sum of
BOD
df
squares
Mean
Square
Mean
Between Groups
4.78
5
9.5
G1=1.2
Within Groups
9.69
29
1.07
G2=1.18
Total
1.4
34
Statistical
inference
G3 =1.19
G4 =1.19
F=0.889
G5= 1.18
P>0.05
G6= 1.16
Not
Significant
II
III
Between Groups
2.7E-02
5
5.4E-03
G1=1.147
Within Groups
2.1E-02
29
2.4E-03
G2=1.06
Total
4.9E-02
34
G3 =1.03
F=2.249
G4 =1.07
P>0.05
G5= 1.04
Not
G6= 1.02
Significant
Between Groups
4.62
5
9.2
G1=1.2
Within Groups
4.42
29
4.9
G2=1.17
Total
9.04
34
G3 =1.17
F=1.8
G4 =1.14
P<0.05
G5= 1.17
G6= 1.1
Significant
Table 4.5o.
ANOVA for COD in various zones
Season
I
Sum of
COD
df
squares
Mean
Square
Mean
Between Groups
1.83
5
.367
G1=9.34
Within Groups
4.51
29
.501
G2=8.7
Total
6.3
34
Statistical
inference
G3 =9.7
G4 =9.5
F=.733
G5= 9.7
P>0.05
G6= 9.16
Not
Significant
II
III
Between Groups
1.68
5
.338
G1=9.3
Within Groups
4.05
29
.451
G2=8.7
Total
5.74
34
G3 =9.7
F=.749
G4 =9.7
P>0.05
G5= 9.5
Not
G6= 9.1
Significant
Between Groups
3.09
5
.618
G1=9.43
Within Groups
2.52
29
.218
G2=8.91
Total
5.61
34
G3 =10.2
F=2.204
G4 =10.2
P>0.05
G5= 9.9
Not
G6= 9.7
Significant
Table 4.5p.
ANOVA for nitrite in various zones
Season
I
Nitrite
Sum of
squares
df
Mean
Square
Between
1.007E-02
5
2.01E-
G1=1.6E-02
Groups
1.500E-03
29
03
G2=2.5E-02
34
1.6E-04
G3 =2.6E-02
Within Groups
Total
II
Mean
F=12.08
G5= 8.0E-02
P<0.05
G6= 5.0E-02
Significant
9.1E-03
5
1.8
G1=1.6E-02
Groups
1.5E-03
29
1.7
G2=2.5E-02
34
Total
inference
G4 =7.0E-02
Between
Within Groups
Statistical
G3 =2.6E-02
F=10.4
G4 =7.0E-02
P<0.05
G5= 8.0E-02
Significant
G6= 5.0E-02
III
Between
9.17E-03
5
1.8E-03
G1=1.6E-02
Groups
1.58E-03
29
1.75E-
G2=2.5E-02
Within Groups
1.07E-03
34
02
G3 =2.6E-02
F=10.4
G4 =7.0E-02
P<0.05
G5= 8.0E-02
Significant
Total
G6= 5.0E-02
Table 4.5q.
ANOVA for nitrate in various zones
Season
Nitrate
Sum of
df
squares
I
Mean
Mean
Square
Statistical
inference
Between Groups
.604
5
.121
G1=4.24
Within Groups
.945
29
.105
G2=4.3
Total
1.549
34
G3 =4.7
G4 =4.6
F=1.15
G5= 4.6
P>0.05
G6= 4.7
Not
Significant
II
III
Between Groups
1.17
5
.235
G1=4.4
Within Groups
1.13
29
.150
G2=4.7
Total
2.52
34
G3 =4.8
F=1.5
G4 =5.1
P>0.05
G5= 4.7
Not
G6= 5.2
Significant
Between Groups
1.28
5
.257
G1=4.3
Within Groups
1.32
29
.147
G2=4.7
Total
2.6
34
G3 =4.7
F=1.74
G4 =5.2
P>0.05
G5= 4.7
Not
G6= 5.2
Significant
Table 4.5r.
ANOVA for chloride in various zones
Season
I
Chloride
Sum of
df
squares
Mean
Square
Mean
Between Groups
61.1
5
12.2
G1=24.9
Within Groups
144.6
29
16.06
G2=19.5
Total
205.7
34
Statistical
inference
G3 =24.5
G4 =20.2
F=1.36
G5= 23.1
P>0.05
G6= 24.2
Not
Significant
II
III
Between Groups
95.7
5
19.1
G1=27.3
Within Groups
126.7
29
14.8
G2=20.7
Total
222.5
34
G3 =27.9
F=.761
G4 =22.5
P>0.05
G5= 26.6
Not
G6= 262
Significant
Between Groups
82.7
5
16.5
G1=25.5
Within Groups
152.3
29
16.9
G2=19.7
Total
235.1
34
G3 =26.5
F=.978
G4 =21.6
P>0.05
G5= 25.8
Not
G6= 24.6
Significant
Table 4.5s.
ANOVA for Fluoride in various zones
Season
Fluoride
Sum of
df
squares
I
Mean
Mean
Square
Statistical
inference
Between
3.31E-05
5
6.6E-06
G1=3.0E-02
Groups
1.183E-05
29
1.3E-06
G2=3.0E-02
Within
4.493E-05
34
G3 =3.0E-02
Groups
G4 =3.0E-02
F=5.03
Total
G5= 3.0E-02
P<0.05
G6= 3.0E-02
Significant
II
Between
3.0E-05
5
6.06-
G1=3.0E-02
Groups
.000
29
E06
G2=3.0E-02
Within
3.0E-05
34
.000
G3 =3.0E-02
F=4.08
Groups
G4 =3.0E-02
P<0.05
Total
G5= 3.0E-02
G6= 3.0E-02
III
Significant
Between
0.000
5
0.000
Groups
0.000
29
0.000
G2=3.0E-02
Within
0.000
34
0.000
G3 =3.0E-02
F=5.08
Groups
G4 =3.0E-02
P<0.05
Total
G5= 3.0E-02
Significant
G1=3.0E-02
G6= 3.0E-02
Table 4.5t.
ANOVA for sulphate in various zones
Season
Sulphate
Sum of
df
squares
I
II
Mean
Mean
Square
Statistical
inference
Between Groups
15.5
5
3.1
G1=
Within Groups
7.1
29
.798
G2=
Total
22.7
34
G3 =
G4 =
F=3.8
G5=
P<0.05
G6=
Significant
Between Groups
19.5
5
3.9
G1=23.7
Within Groups
9.4
29
1.05
G2=23.1
Total
29.02
34
G3 =24
F=3.725
G4 =23.1
P<0.05
G5= 22.7
Significant
G6= 20.3
III
Between Groups
18.17
5
3.63
G1=23.7
Within Groups
10.3
29
1.14
G2=23.01
Total
28.4
34
G3 =23.8
F=3.16
G4 =23.1
P>0.05
G5= 22.8
Not
G6= 20.35 Significant
Table 4.5u.
ANOVA for E.COLI in various zones
Season
E.Coli
Sum of
df
squares
I
Mean
Mean
Square
Statistical
inference
Between Groups
2783
5
556.6
G1=61
Within Groups
2320.3
29
257.8
G2=38.5
F=2.15
Total
5103.3
34
G3 =76.6
P>0.05
G4 =53
Not
G5= 47.3
Significant
G6= 38.5
II
III
Between Groups
3237.9
5
647.5
G1=64.6
Within Groups
2501
29
277.8
G2=41.5
Total
5738.9
34
G3 =81.6
F=2.33
G4 =52
P>0.05
G5= 49.6
Not
G6= 40.5
Significant
Between Groups
2096
5
419.3
G1=47
Within Groups
1708
29
189.8
G2=36
Total
3804.9
34
G3 =67.6
F=2.209
G4 =42.5
G5= 42.3
P>0.05
G6= 31.5
Not
Significant
G1=Urban residential zone
G2=Sub urban residential zone
G3=Urban commercial zone
G4=Sub urban commercial zone
G5=Urban sensitive zone
G6=Sub urban sensitive zone
Table 4.6a.
ANOVA for characteristics of surface water in pudukkottai
S.NO
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Variable
Mean
G1=8.2
G2=7.9
EC
G1=1.6
G2=2.7
Temperature
G1=28.2
G2=28.3
Total Solids
G1=310
G2=313
Turbidity
G1=59.08
G2=47
Total Alkalinity G1=116.4
G2=130.7
Total Acidity
G1=2.1
G2=2.04
Chloride
G1=17.4
G2=17.6
Total Hardness G1=26.8
G2=23.7
SD
1.06
0.86
0.27
3.13
2.6
2.42
29.04
40.37
11.7
7.21
22.26
19.72
0.29
0.2
3.53
4.05
8.04
5.23
Statistical Inference
t=0.2
p>0.05 Not significant
t=0.71
p>0.05 Not significant
t=0.6
p>0.05 Not significant
t=0.72
p>0.05
Not significant
t=12.5
p<0.05
Significant
t=0.7
p>0.05
Not significant
t=0.62
p>0.05
Not significant
t=0.6
p>0.05
Not significant
t=0.29
p>0.05
Not significant
Fluoride
3.04
3.07
0.43
0.95
0.78
0.76
1.2
1.35
1.28
1.31
0.16
0.2
220
310
t=0.2
p>0.05
t=0.67
p>0.05
t=1.072
p<0.05
t=0.2
p>0.05
t=0.5
p>0.05
t=0.6
p>0.05
t=0.2
p>0.05
pH
11.
DO
12.
BOD
13.
Nitrite
14.
Sulphate
15.
Phosphate
16.
E.Coli
G1=3.15
G2=3.75
G1=6.2
G2=5.2
G1=3.8
G2=4.2
G1=2.6
G2=2.8
G1=5.62
G2=4.9
G1=0.1
G2=0.9
G1=2400
G2=2890
G1=Urban surface water
G2=Sub urban surface water
Not significant
Not significant
Significant
Not significant
Not significant
Not significant
Not significant
Table.4.6b.
Planktons in urban pond water
SEASON SEASON SEASON SEASON
%
S.NO
I
II
III
IV
Occurance
+
+
75
PHYTOPLANKTONS
Chlorophyceae
1
Scenedesmus quandricanda
+
BACILLARIOPHYCEAE
2
Diatoma sp
+
+
+
+
100
3
Navicula closterium
+
+
+
+
100
+
+
50
+
+
50
CYANOPHYCEAE
4
Anabaena variabilis
ZOOPLANKTON
Copepoda
5
Diaptomus
Rotifera
6
Filinia
+
+
TOTAL
6
3
4
5
100
50
66.66
100
PERCENTAGE
50
Table.4.6c.
Planktons in sub urban pond water
SEASON SEASON SEASON SEASON
%
S.NO
I
II
III
IV
Occurance
+
+
75
PHYTOPLANKTONS
Chlorophyceae
1 Scenedesmus quandricanda
+
2 Tetrastrum
+
25
3 Ankistrodesmus
+
25
BACILLARIOPHYCEAE
4 Diatoma sp
+
+
+
+
100
5 Navicula closterium
+
+
+
+
100
+
+
6 Nitzshia palea
50
CYANOPHYCEAE
7 Anabaena variabilis
+
+
50
ZOOPLANKTON
Rotifera
8 Filinia
+
9 Notholca
10 Keratella
+
11 Branchionus quadridentatus
+
+
25
+
25
+
100
+
25
Copepoda
12 Diaptomus
+
+
50
Cladocera
+
+
50
13 Alona
TOTAL
PERCENTAGE
7
7
7
7
53.8
53.8
53.8
53.8
ANOVA FOR SOIL CHARACTERISTICS IN VARIOUS ZONES IN
PUDUKKOTTAI
Table 4.7a. ANOVA for pH in various zones
Season
I
Monsoon
pH
Sum of
df
squares
Mean
Square
Mean
Statistical
inference
Between Groups
1.54
5
.309
G1=8.4
Within Groups
3.3
23
.377
G2=8.86
F=.820
Total
4.9
28
G3 =8.16
P>0.05
G4 =8.5
Not
G5= 7.9
Significant
G6= 7.9
II
Between Groups
1.3
5
.263
G1=8.2
North east
Within Groups
1.9
23
.211
G2=8.6
F=1.24
Monsoon
Total
3.2
28
G3 =8.01
P >0.05
G4 =8.5
Not
G5= 7.8
Significant
G6= 7.8
III
Between Groups
1.2
5
.241
G1=8.14
Premonsoon
Within Groups
1.6
23
.183
G2=8.5
F=1.31
Total
2.8
28
G3 =7.9
P >0.05
G4 =8.3
Not
G5= 7.7
Significant
G6= 7.7
Table 4.7b
ANOVA for EC in various zones
Season
EC
Sum of
df
squares
I
Mean
Mean
Square
Between Groups
1.01
5
Within Groups
0.0001
23
Total
1.0101
28
Statistical
inference
.204
G1=.27
0.0001
G2=.195
G3 =.143
F=24.52
G4 =.96
P<0.05
G5= .203
Significant
G6= .195
II
Between Groups
.847
5
.169
G1=.3167
Within Groups
.0001
23
.0001
G2=.1950
Total
.8471
28
G3
=.1967
F=22.3
G4 =.880
P<0.05
G5=
Significant
.1567
G6= .13
III
Between Groups
.838
5
.168
G1=.34
Within Groups
.0001
23
.0001
G2=.24
Total
.838
28
F=20.1
G3 =.23
G4 =.92
P<0.05
G5= .2
G6= .19
Significant
Table 4.7c.
ANOVA for total calcium in various zones
Season
Total Calcium
Sum of
df
squares
I
II
Mean
Mean
Square
Statistical
inference
Between Groups
3.17
5
.636
G1=.95
Within Groups
2.73
23
.304
G2=2.37
Total
5.9
28
G3 =1.39
F=2.09
G4 =1.55
P>0.05
G5= 1.07
Not
G6= .97
Significant
Between Groups
2.7
5
.556
G1=.88
Within Groups
2.4
23
.277
G2=2.06
F=2.007
Total
5.2
28
G3 =1.2
P>0.05
G4 =1.37
Not
G5= .69
Significant
G6= .86
III
Between Groups
1.86
5
.372
G1=.87
Within Groups
1.95
23
.217
G2=1.94
F=1.714
Total
3.8
28
G3 =1.2
P>0.05
G4 =1.35
Not
G5= .97
Significant
G6= .845
Table 4.7d.
ANOVA for total magnesium in various zones
Season
I
Total
Sum of
Magnesium
squares
df
Mean
Mean
Square
Statistical
inference
Between Groups
4.11
5
8.22
G1=.63
F=.521
Within Groups
.142
23
1.57
G2=.62
P>0.05
Total
.183
28
G3 =.58
Not
G4 =.5
Significant
G5= .64
G6= .51
II
III
Between Groups
4.72
5
9.4
G1=.57
Within Groups
.125
23
1.3
G2=.59
Total
.175
28
G3 =.516
F=.683
G4 =.44
P>0.05
G5= .59
Not
G6= .46
Significant
Between Groups
2.8E-02
5
5.6E-03
G1=.55
Within Groups
.116
23
1.28E-02
G2=.54
Total
.144
28
G3 =.51
G4 =.44
F=0.439
G5= .55
P>0.05
G6= .45
Not
Significant
Table 4.7e.
ANOVA for total sodium in various zones
Season
Total Sodium
Sum of
df
squares
I
Mean
Mean
Square
Statistical
inference
Between Groups
3.15
5
.63
G1=7.6
F=1.38
Within Groups
4.08
23
.453
G2=.4
P>0.05
Total
7.23
28
G3 =9
Not
G4 =1.4
Significant
G5= 5
G6= .16
II
III
Between Groups
2.8
5
.562
G1=6.3
Within Groups
3.6
23
.405
G2=.35
Total
6.4
28
G3 =7.6
F=1.3
G4 =1.36
P>0.05
G5= 5
Not
G6= .165
Significant
Between Groups
2.8
5
5.61
G1=6.3
Within Groups
3.5
23
.398
E-02
Total
6.3
28
G2=.36
G3 =7.5
F=1.41
E-02
P>0.05
G4 =1.35
Not
G5= 3.9
Significant
E-02
G6= .12
Table 4.7f.
ANOVA for total organic carbon in various zones
Season
I
Total Organic
Sum of
Carbon
squares
df
Mean
Mean
Square
Statistical
inference
Between Groups
4.01
5
8.02
G1=.386
F=.37
Within Groups
.19
23
2.16
G2=.42
P>0.05
Total
.235
28
G3 =.34
Not
G4 =.51
Significant
G5= .37
G6= .36
II
III
Between Groups
.0017
5
1.029
G1=.406
Within Groups
.25
23
2.78
G2=.49
Total
.302
28
G3 =.39
F=.37
G4 =.56
P>0.05
G5= .403
Not
G6= .425
Significant
Between Groups
.0001
5
1.2E-02
G1=.43
Within Groups
.264
23
2.9E-02
G2=.52
Total
.324
28
G3 =.43
F=.416
G4 =.62
P>0.05
G5= .44
Not
G6= .46
Significant
Table 4.7g.
ANOVA for total organic matter in various zones
Season
I
Total Organic
Sum of
Matter
squares
df
Mean
Mean
Square
Statistical
inference
Between Groups
.335
5
6.7
G1=.77
F=.93
Within Groups
.649
23
7.2
G2=.47
P>0.05
Total
.984
28
G3 =.68
Not
G4 =1.04
Significant
G5= .74
G6= .72
II
Between Groups
.0001
5
1.029
G1=.406
Within Groups
.25
23
2.78
G2=.49
F=.37
Total
.302
28
G3 =.39
P>0.05
G4 =.56
Not
G5= .403
Significant
G6= .425
III
Between Groups
.292
5
5.8E-02
G1=.43
Within Groups
.703
23
7.8E-02
G2=.52
Total
.995
28
G3 =.43
F=.747
G4 =.62
P>0.05
G5= .44
Not
G6= .46
Significant
Table 4.7h.
ANOVA for total nitrogen in various zones
Season
Total Nitrogen
Sum of
df
squares
I
Mean
Mean
Square
Statistical
inference
Between Groups
1.2
5
.24
G1=1.11
F=1.19
Within Groups
1.8
23
.201
G2=.65
P>0.05
Total
3.007
28
G3 =1
G4 =1.7
Not
G5= 1.05
Significant
G6= .97
II
Between Groups
1.14
5
.229
G1=1.18
Within Groups
1.86
23
.207
G2=.7
F=.1.1
G3 =1.06
P>0.05
Total
28
G4 =1.75
III
G5= 1.13
Not
G6= 1.11
Significant
Between Groups
1.49
5
.29
G1=.43
Within Groups
2.74.2
23
.3
G2=.52
Total
28
G3 =.43
G4 =.62
F=..973
G5= .44
P>0.05
G6= .46
Not
Significant
Table 4.7i.
ANOVA for total phosphate in various zones
Season
Total Phosphate
Sum of
df
Mean
squares
I
Mean
Square
Statistical
inference
Between Groups
2.5
5
5
G1=6
F=.511
Within Groups
8.8
23
9.8
G2=6.3
P>0.05
Total
11.3
28
G3 =5.7
G4 =5.5
Not
G5= 5
Significant
G6= 5.8
II
Between Groups
.407
5
8.15
G1=6.6
Within Groups
.93
23
1.037
G2=6.7
F=0.787
Total
1.3
28
G3 =6.2
P>0.05
G4 =6.05
III
G5= 5.2
Not
G6= 6
Significant
Between Groups
4.5E-04
5
9.1E-05
G1=7.0
Within Groups
6.6E-04
23
7.3E-05
E-02
Total
1.1E-03
28
G2=7.0
F=1.2
E-02
P>0.05
G3 =6.6
E-02
G4 =6.4
Not
E-02
Significant
G5= 5.4
E-02
G6= 6.6
E-02
Table 4.7j.
ANOVA for total potassium in various zones
Season
Total Potassium
Sum of
df
squares
I
Mean
Mean
Square
Statistical
inference
Between Groups
2.8
5
.57
G1=1.26
F=12.14
Within Groups
.422
23
4.69
G2=.99
P<0.05
Total
3.27
28
G3 =1.13
Significant
G4 =1.63
G5= 1.18
G6= 5.8
II
Between Groups
.517
5
.103
G1=1.2
F=1.94
Within Groups
.478
23
5.3
G2=.99
P>0.05
Total
.994
28
G3 =1.17
Not
G4 =1.65
Significant
G5= 1.23
G6= 1.41
III
Between Groups
0.573
5
.115
G1=1.35
F=1.127
Within Groups
0.915
23
.102
G2=1.09
P>0.05
Total
1.48
28
G3 = 1.2
Not
G4 =1.7
Significant
G5= 1.5
G6= 1.4
G1=Urban residential zone
G2=Sub urban residential zone
G3=Urban commercial zone
G4=Sub urban commercial zone
G5=Urban sensitive zone
G6=Sub urban sensitive zone
Table 4.8
Municipal Sewage Characteristics Before and After Treatment
0day
5day
10day
15day
20day
25day
pH
9.11
9.99
8.75
8.2
8.44
8.55
ElectricalConductivity
1.2
2.1
5.8
2
4.1
5.6
Temperature
37
29
29
30
29
29
Total Solids
9600
2000
2800
3600
2800
2800
Total dissolvedsolids
800
1200
2000
1200
2000
2800
Total suspendedsolids
8800
800
800
2400
800
Nil
Alkalinity
500
830
930
1320
1350
1350
Acidity
1375
1362.5
612.5
550
550
550
Hardness
1010
915
810
710
555
415
Calcium
106.212
100.2
98.196
6.01
4.008
4.008
219.6
197.9
172.96
171.06
133.89
99.87
Nil
Nil
1.654
2.101
2.621
3.01
COD
515.04
500.2
480.3
440.23
431.26
420.21
BOD
32.2
32.23
31.41
31.22
30.9
30.4
Flouride
2.24
2.22
2.2
2
2.2
2.2
Chloride
303.9
303.9
265.91
241.92
203.93
199.38
Phosphorus
0.07
0.06
Nil
Nil
Nil
Nil
7
5
5
3.5
3.5
3.5
Iron
0.98
0.98
0.83
0.73
0.72
0.68
Nitrate
108.2
98.2
92.2
90.8
89.2
82
212
142
135.1
135
132
130
Magnesium
Dissolved Oxygen
Sulphate
Potassium
Table 4.9
Nutrients in Waste water
Sl.No
Nutrients
Mean
1
Nitrate
108.2
2
Phosphorus
0.07
3
Potassium
212
4
Magnesium
219.6
Table 4.10
ANOVA for waste water treated plant growth
S.NO
1.
2.
3.
4.
5.
6.
Variable
Leaf length
Mean
SD
Statistical Inference
G1=7.16
1.55
t=20.2
G2=10.16
1.27
p<0.05
G1=0.98
0.08
t=0.2
G2=1.09
0.11
p>0.05
G1=0.63
0.08
t=2.6
G2=0.72
0.09
p<0.05
G1=0.99
0.54
t=0.8
G2=1.28
0.66
p>0.05
G1=0.17
0.13
t=0.7
G2=0.23
0.14
p>0.05
Total
G1=82.2
26.43
t=0.8
Chlorophyll
G2=99
39.4
p>0.05
Fresh weight
Dry weight
Free sugar
Phenol
G1=Contol plant
G2=Waste water treated plant
Significant
Not significant
Significant
Not significant
Not significant
Not significant
TABLE 4.11.
Physical composition of solid waste in Pudukkottai area
HOLIDAY Sample
ZONE I
ZONE II
ZONE III
Biodegradable waste
57.78
37.68
39.86
Paper and Rags
12.6
13.76
11.06
Plastics
13.38
11.74
11.26
Glass
2.64
2.46
6.18
Metals
2.34
4.6
2.44
Inert
10.94
34.58
26.44
ZONE I
ZONE II
ZONE III
Biodegradable waste
54.48
50.4
41.38
Paper and Rags
9.74
8.34
8.34
Plastics
10.14
8.16
11.28
Glass
1.36
2.04
7.1
Metals
0.52
1.2
1.06
Inert
23.64
33.68
31.04
ZONE I
ZONE II
ZONE III
Biodegradable waste
56.02
52.78
4.66
Paper and Rags
12.78
12.74
13.96
Plastics
14.18
13.46
15.36
Glass
2.96
3.52
5.96
Metals
2.82
2.18
1.76
Inert
11.94
19.16
19.98
WORKING DAY
Sample
FESTIVAL DAY
Sample
TABLE 4.12.
Properties of Solid Waste in Different Zones
Working day
sample
pH
ZONE I
ZONE II
ZONE III
7.22
7.2
7.06
%moisture
45.72
42.58
43.1
%ash
39.84
38.72
42.84
%organic matter
60.16
61.28
56.92
%carbon
34.84
35.5
34.18
1.04
0.82
1.26
36.96
46.78
27.28
%nitrogen
c/n
Holiday
sample
pH
ZONE I
ZONE II
ZONE III
7.24
7.08
7.06
%moisture
49.14
34.86
40.36
%ash
38.34
51.92
46.74
61.66
48.08
53.26
35.758
27.868
30.886
0.66
0.96
1.2
56.944
29.684
25.868
%organic
matter
%carbon
%nitrogen
c/n
Festival day sample
ZONE I
ZONE II
ZONE III
pH
7.14
7.06
7.04
%moisture
46.9
49.48
46.08
%ash
42.18
45.28
36.04
%organic matter
57.82
54.72
63.96
33.528
31.732
37.0916
0.64
1
1.3
55.562
34.886
28.676
%carbon
%nitrogen
c/n
Table 4.13.
ANOVA for macro nutrients in biocompost treated soil
Variable
Period
Before Cultivation
Available
After First Harvest
Nitrogen
After Second Harvest
Before Cultivation
Phosphate
After First Harvest
After Second Harvest
Before Cultivation
Potassium
After First Harvest
After Second Harvest
Mean
SD
Statistical inference
G1=94.1
2.8
t=0.62
G2=97.2
5.3
p>0.05 Not significant
G1=100
20.8
t=1.52
G2=117
12.3
p<0.05 Significant
G1=104
12.3
t=5.29
G2=131
20.9
p<0.05 Significant
G1=1.3
20.8
t=0.26
G2=1.1
25.3
p>0.05 Not significant
G1=2.2
2.8
t=2.8
G2=3.5
1.3
p<0.05 Significant
G1=3.1
1.3
t=16.2
G2=4.2
2.1
p<0.05 Significant
G1=49.4
8.2
t=0.38
G2=48
4.3
p>0.05 Not significant
G1=52
3.8
t=0.72
G2=55
4.9
p>0.05 Not significant
G1=104
12.3
t=0.8
G2=131
20.9
p>0.05 Not significant
Table 4.14.
ANOVA for micro nutrients in biocompost in treated soil
Variable
Period
Before Cultivation
Zinc
After First Harvest
After Second Harvest
Before Cultivation
Iron
After First Harvest
After Second Harvest
Before Cultivation
Manganese
After First Harvest
After Second Harvest
Before Cultivation
Copper
After First Harvest
After Second Harvest
Mean
SD
Statistical inference
G1=2.15
1.2
t=0.72
G2=2.15
0.8
p>0.05 Not significant
G1=2.01
1.02
t=2.4
G2=2.21
1.2
p<0.05 Significant
G1=2.18
1.2
t=6.2
G2=2.32
0.9
p<0.05 Significant
G1=7.6
1.28
t=0.22
G2=7.6
2.3
p>0.05 Not significant
G1=7.12
2.8
t=16.8
G2=7.9
1.3
p<0.05 Significant
G1=7.2
1.3
t=15.2
G2=7.9
2.1
p<0.05 Significant
G1=6.8
0.2
t=0.3
G2=6.8
0.9
p>0.05 Not significant
G1=6.93
0.8
t=3.2
G2=6.93
0.2
p<0.05 Significant
G1=6.62
0.2
t=3.1
G2=6.92
0.9
p<0.05 Significant
G1=0.94
0.2
t=0.2
G2=0.8
0.3
p>0.05 Not significant
G1=0.82
0.2
t=2.72
G2=0.94
0.3
p<0.05 Significant
G1=0.91
0.12
t=0.12
G2=0.95
0.2
p>0.05 Not significant
Table 4.15.
ANOVA for microbial content in biocompost treated soil
Variable
Period
Before Cultivation
Bacterial
After First Harvest
Content
After Second Harvest
Before Cultivation
Fungal content
After First Harvest
After Second Harvest
Before Cultivation
Actinomycetes
After First Harvest
content
After Second Harvest
Mean
SD
Statistical inference
G1=40
12.8
t=0.52
G2=40
15.3
p>0.05 Not significant
G1=52
20.1
t=2.52
G2=61
10.3
p<0.05 Significant
G1=58
10.3
t=6.29
G2=68
20.9
p<0.05 Significant
G1=17.3
2.8
t=0.6
G2=17.1
2.3
p>0.05 Not significant
G1=22.2
8
t=1.8
G2=23.5
13
p<0.05 Significant
G1=28
1.3
t=3.2
G2=31
2.1
p<0.05 Significant
G1=28
8.2
t=1.38
G2=30
4.3
p<0.05 Significant
G1=39
3.8
t=2.72
G2=43
4.9
p<0.05 Significant
G1=45
12.3
t=10.8
G2=52
20.9
p<0.05 Significant
Table 4.16.
ANOVA for productivity in biocompost treated soil
Variable
Period
Mean
SD
G1=420
12.8
t=12.52
G2=536
15.3
p<0.05 Significant
After Second
G1=455
19.3
t=30.29
Harvest
G2=618
28.9
p<0.05 Significant
After First Harvest
G1=2.1
18
t=0.6
G2=2.4
33
p>0.05 Not significant
After Second
G1=2.28
1.3
t=1.8
Harvest
G2=3.1
2.1
p<0.05 Significant
After First Harvest
Statistical inference
Palmarosa
Grass yield
Palmarosa Oil
yield
Table 4.17
Total Flora in Pudukkottai
Urban flora
Total number of species
Sub urban flora
:126
Total number of species
:172
Table 4.18
Flora in Pudukkottai
Urban flora
Sub urban flora
Herbs
:70
Herbs
:114
Shrubs
:16
Shrubs
:28
Trees
:40
Trees
:30
Table 4.21.
Fauna in Pudukkottai
Name of the Animal
Urban fauna
Suburban fauna
139
209
Annelids
2
2
Arthropods
2
2
Arachnids
2
2
Myriapods
2
2
Crustaceans
1
1
Molluses
5
2
Insects
52
94
Fishes
4
5
Ambhibians
2
2
Reptails
8
10
Birds
38
69
Mammals
13
18
Total number of species
Table 4.26.
Age Group in the Families
Age Group
Total % in urban
Total % in sub urban
0-10
14.5
10
10-20
14.8
10
20-30
14
30
30-40
14.5
10
40-50
14
25
50-60
14
10
60-80
14.2
5
Table 4.27.
Educational Status
URBAN
SUB URBAN
DEGREE
50%
50%
HIGHER STUDIES
75%
25%
Table 4.28.
Occupation
EARNING
URBAN
SUB URBAN
More than Rs.5000/-
25%
7%
Less than Rs.5000/-
50%
90%
Business
22%
1%
Unemployed
3%
2%
Table 4.29.
Water Facility
DRINKING WATER
NUMBER OF URBAN
NUMBER OF SUB URBAN
SUPPLY
RESPONDENT
RESPONDENT
REGULAR
10
40
IRREGULAR
40
10
SUFFICIENT
10
40
INSUFFICIENT
40
10
FIG 1.1
Trends of Urbanization (www.urbanization.com, 2005)
FIG 1.2
IN LAKHS
TOURIST ARRIVAL IN PUDUKKOTTAI
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
TOURIST
ARRIVAL
1991 1992 1993 1994 1995 1996
YEAR
(ENVIS, 2005)
FIG 2.1 PUDUKKOTTAI MAP
x
M
n
P
K
G
C
W
R
B-busstand.
C-collectoroffice.
E-educationcollege
G-GH.
K-pudukulam.
M-milkfarm.
P-policestation.
R-railwaystation.
W-womencollege.
x-machuvadi.
n-brindavan.
B
n
E
FIG 3.1
POPULATION DISTRIBUTION IN
PUDUKKOTTAI
450000
400000
300000
250000
Population
distribution
200000
150000
100000
50000
0
19
01
19
31
19
81
19
91
20
01
20
05
20
10
POPULATION
350000
YEAR
SPM concentration(mg/m3)
FIG 4.1a SPM CONCENTRATION IN RESIDENTIAL
ZONE
180
160
140
120
100
80
60
40
20
0
Urban
Sub-urban
Sampling Period
Urban
SEASON IV
SEASON III
Sub-urban
SEASON II
900
800
700
600
500
400
300
200
100
0
SEASON I
SPM concentration(mg/m 3)
FIG 4.1b SPM CONCENTRATION IN
COMMERCIAL ZONE
Sampling period
350
300
250
200
Urban
150
Sub-urban
100
SEASON IV
SEASON III
0
SEASON II
50
SEASON I
SPM concentration(mg/m3)
FIG 4.1c SPM CONCENTRATION IN SENSITIVE
ZONE
Sampling Period
FIG 4.1d SPM CONCENTRATION IN INDUSTRIAL
ZONE
3
SPM concentration(mg/m )
300
250
200
URBAN
150
100
Sam pling Period
SEASON IV
SEASON III
SEASON II
0
SEASON I
50
FIG 4.2b SO2 CONCENTRATION IN
COMMERCIAL ZONE
300
30
250
25
20
Urban
15
Sub-urban
10
200
Urban
150
Sub-urban
100
50
5
SEASON I
SEASON I
FIG 4.2c SO2 CONCENTRATION IN SENSITIVE
ZONE
Urban
SEASON I
NIGHT
AFTERNOON
Sub-urban
MORNING
100
90
80
70
60
50
40
30
20
10
0
NIGHT
AFTERNOON
M ORNING
NIGHT
AFTERNOO N
0
SO2 (mg/m 3)
0
SO 2 (m g/m 3 )
35
MO RNING
SO2 (mg/m 3 )
FIG 4.2a SO2 CONCENTRATION IN RESIDENTIAL
ZONE
FIG 4.3b SO2 CONCENTRATION IN
COMMERCIAL ZONE
180
35
160
30
140
Sub-urban
15
80
40
5
20
0
0
NIGHT
Sub-urban
60
10
AFTERNOON
Urban
100
SEASON II
SEASON II
Urban
SEASON II
NIGHT
Sub-urban
AFTERNOON
80
70
60
50
40
30
20
10
0
MORNING
SO2 (mg/m 3)
FIG 4.3c SO2 CONCENTRATION IN SENSITIVE
ZONE
NIGHT
20
120
AFTERNOON
Urban
MORNING
25
SO2 (mg/m 3)
40
MORNING
SO2 (mg/m 3)
FIG 4.3a SO2 CONCENTRATION IN
RESIDENTIAL ZONE
FIG 4.4a SO2 CONCENTRATION IN RESIDENTIAL
ZONE
FIG 4.4b SO2 CONCENTRATION IN
COMMERCIAL ZONE
45
120
40
100
25
Urban
20
Sub-urban
15
SO2 (mg/m 3 )
30
80
Urban
60
Sub-urban
40
10
SEASON III
SEASON III
Urban
NIGHT
Sub-urban
AFTERNOON
80
70
60
50
40
30
20
10
0
MORNING
SO2 (mg/m 3)
FIG 4.4c SO2 CONCENTRATION IN SENSITIVE
ZONE
SEASON III
NIG HT
MORNING
0
NIG HT
AFTERNO O N
0
AFTERNO ON
20
5
MO RNING
SO2 (mg/m 3 )
35
FIG 4.5a SO2 CONCENTRATION IN RESIDENTIAL
ZONE
35
SO2 (mg/m 3)
30
25
20
Urban
15
Sub-urban
10
NIGHT
MORNING
0
AFTERNOON
5
SEASON IV
FIG 4.5b SO2 CONCENTRATION IN
COMMERCIAL ZONE
250
Urban
150
Sub-urban
100
SEASON IV
NIGHT
0
AFTERNOON
50
MORNING
SO2 (mg/m 3)
200
FIG 4.5c SO2 CONCENTRATION IN SENSITIVE
ZONE
100
90
70
60
Urban
50
Sub-urban
40
30
20
NIGHT
MORNING
0
AFTERNOON
10
SEASON IV
FIG 4.5d SO2 IN INDUSTRIAL ZONE IN VARIOUS
SEASON
90
80
70
60
MORNING
50
AFTERNOON
40
NIGHT
30
20
SEASON IV
SEASON III
0
SEASON II
10
SEASON I
SO2 (mg/m 3)
SO2 (mg/m 3)
80
FIG 4.6a NO2 CONCENTRATION IN RESIDENTIAL
ZONE
FIG 4.6b NO2 CONCENTRATION IN
COMMERCIAL ZONE
35
250
30
Urban
15
Sub-urban
10
150
Urban
Sub-urban
100
SEASON I
SEASON I
FIG 4.6c NO2 CONCENTRATION IN SENSITIVE
ZONE
60
40
Urban
30
Sub-urban
20
SEASON I
NIGHT
0
AFTERNOON
10
MORNING
NO2 (mg/m 3)
50
NIGHT
MORNING
NIGHT
AFTERNOON
0
AFTERNOON
50
5
0
NO2 (mg/m 3)
20
MO RNING
NO2 (mg/m 3)
200
25
FIG 4.7b NO2 CONCENTRATION IN
COMMERCIAL ZONE
FIG 4.7a NO2 CONCENTRATION IN RESIDENTIAL
ZONE
160
30
140
25
NO2 (m g /m 3 )
Urban
15
Sub-urban
10
100
Urban
80
Sub-urban
60
40
5
SEASON II
SEASON II
FIG 4.7c NO2 CONCENTRATION IN SENSITIVE
ZONE
50
45
35
30
Urban
25
Sub-urban
20
15
10
SEASON II
NIG HT
0
AFTERNO O N
5
MO RNING
No2 (m g/m 3 )
40
NIG HT
AF TERNO O N
MO RNING
0
N IG H T
AF T ER N O O N
0
20
M O R N IN G
N O2 ( m g /m 3 )
120
20
FIG 4.8a NO2 CONCENTRATION IN
RESIDENTIAL ZONE
FIG 4.8b NO2 CONCENTRATION IN
COMMERCIAL ZONE
45
120
40
100
25
Urban
20
Sub-urban
15
10
80
Urban
60
Sub-urban
40
SEASON III
SEASON III
FIG 4.8c NO2 CONCENTRATION IN SENSITIVE
ZONE
60
40
Urban
30
Sub-urban
20
SEASON III
NIG HT
0
AFTERNO O N
10
MO RNING
NO2 (mg/m 3 )
50
NIG HT
MO R NING
NIG H T
AF T ER NO O N
0
AF T ER NO O N
20
5
0
N O2 (m g /m 3 )
30
MO R NIN G
N o2 (m g /m 3 )
35
FIG 4.9a NO2 CONCENTRATION IN RESIDENTIAL
ZONE
35
NO2 (mg/m 3)
30
25
20
Urban
15
Sub-urban
10
NIGHT
MORNING
0
AFTERNOON
5
SEASON IV
FIG 4.9b NO2 CONCENTRATION IN
COMMERCIAL ZONE
200
180
140
120
Urban
100
Sub-urban
80
60
40
SEASON IV
NIGHT
0
AFTERNOON
20
MORNING
NO2 (mg/m 3)
160
FIG 4.9c NO2 CONCENTRATION IN SENSITIVE
ZONE
60
NO2 (mg/m 3)
50
40
30
Urban
20
Sub-urban
NIGHT
AFTERNOON
0
MORNING
10
SEASON IV
FIG 4.9d NO2 IN INDUSTRIAL ZONE IN VARIOUS SEASONS
70
60
50
40
30
AFTERNOON
Season IV
Season III
NIGHT
Season II
20
10
0
MORNING
Season I
NO2(mg/m 3)
100
90
80
FIG 4.10b NOISE LEVEL IN
COMMERCIAL ZONE
FIG 4.10a NOISE LEVEL IN
RESIDENTIAL ZONE
90
60
50
Urban
40
Suburban
30
20
10
0
Urban
Suburban
HOLIDAY(DAY TIME)
HOLIDAY(DAY TIME)
FIG 4.10c NOISE LEVEL IN SILENCE
ZONE
90
80
Noise level in dB
Noise in dB
70
Noise level in dB
80
100
90
80
70
60
50
40
30
20
10
0
70
60
50
Urban
40
Suburban
30
20
10
0
HOLIDAY(DAY TIME)
FIG 4.11b NOISE LEVEL IN
COMMERCIAL ZONE
FIG 4.11a NOISE LEVEL IN
RESIDENTIAL ZONE
60
70
Noise level in dB
80
50
40
Urban
30
Suburban
20
60
50
Urban
40
Suburban
30
20
10
10
0
0
HOLIDAY
HOLIDAY(NIGHT TIME)
FIG 4.11c NOISE LEVEL IN SILENCE
ZONE
70
60
Noise level in dB
Noise level in dB
90
70
50
40
Urban
30
Suburban
20
10
0
HOLIDAY
FIG 4.12b NOISE LEVEL IN
COMMERCIAL ZONE
100
90
80
70
60
50
40
30
20
10
0
URBAN
SUBURBAN
Noise level in dB
120
100
80
URBAN
60
SUBURBAN
40
20
0
WORKING DAY(DAY
TIME )
WORKING DAY(DAY TIME)
FIG 4.12c NOISE LEVEL IN SILENCE
ZONE
Noise level in dB
Noise level in dB
FIG 4.12a NOISE LEVEL IN
RESIDENTIAL ZONE
100
90
80
70
60
50
40
30
20
10
0
URBAN
SUBURBAN
WORKING DAY(DAY
TIME)
FIG 4.13a NOISE LEVEL IN
RESIDENTIAL ZONE
FIG 4.13b NOISE LEVEL IN
COMMERCIAL ZONE
80
60
70
50
40
URBAN
30
SUBURBAN
20
Noise in dB
90
70
60
50
URBAN
40
SUBURBAN
30
10
20
0
10
0
WORKING DAY(NIGHT
TIME)
WORKING DAY(NIGHT
TIME)
FIG 4.13c NOISE LEVEL IN SILENCE
ZONE
80
Noise in dB
Noise level in dB
100
80
70
60
50
URBAN
40
30
SUBURBAN
20
10
0
WORKING DAY(NIGHT
TIME)
FIG 4.14b NOISE LEVEL IN
COMMERCIAL ZONE
120
100
100
80
URBAN
60
SUBURBAN
40
20
Noise level in dB
120
80
URBAN
60
SUBURBAN
40
20
0
0
FESTIVAL DAY(DAY
TIME)
FESTIVAL DAY(DAY
TIME)
FIG 4.14c NOISE LEVEL IN SILENCE
ZONE
100
Noise level in dB
Noise level in dB
FIG 4.14a NOISE LEVEL IN
RESIDENTIAL ZONE
80
60
URBAN
40
SUBURBAN
20
0
FESTIVAL DAY(DAY
TIME)
FIG 4.15b NOISE LEVEL IN
COMMERCIAL ZONE
100
80
80
60
URBAN
40
SUBURBAN
Noise in dB
100
60
URBAN
40
SUBURBAN
20
20
0
0
FESTIVAL DAY(NIGHT
TIME)
FESTIVAL DAY(NIGHT
TIME)
FIG 4.15c NOISE LEVEL IN SILENCE
ZONE
80
70
60
Noise in dB
Noise in dB
FIG 4.15a NOISE LEVEL IN
RESIDENTIAL ZONE
50
40
URBAN
30
SUBURBAN
20
10
0
FESTIVAL DAY(NIGHT
TIME)
FIG 4.16a NOISE LEVEL IN
INDUSTRIAL ZONE IN HOLIDAYS
FIG 4.16b NOISE LEVEL IN
INDUSTRIAL ZONE IN WORKING DAY
30
Night
Leq
20
10
Day
L90
L50
L10
FIG 4.16c NOISE LEVEL IN IN
INDUSTRIAL ZONE FESTIVAL DAY
120
100
80
DAY
60
NIGHT
40
20
L90
L50
L10
Lmax
Lmin
0
Leq
Noise level dB
Lmax
Lmin
0
L90
Night
L50
Day
50
40
L10
60
Lmin
Noise level in dB
70
Leq
Noise level in dB
90
80
100
90
80
70
60
50
40
30
20
10
0
Lmax
100
FIG 4.17b pH IN COMMERCIAL ZONE
FIG 4.17a pH IN RESIDENTIAL ZONE
9
8.8
8.6
8.6
8.4
pH value
8.2
Urban
8
suburban
7.8
8.2
Urban
8
suburban
7.8
7.6
7.6
7.4
7.4
SEASONIII
GROUND WATER SAMPLING
PERIOD
SEASON I
7
SEASON SEASON SEASON
I
II
III
SEASON II
7.2
7.2
GROUND WATER SAMPLING
PERIOD
FIG 4.17c pH IN SENSITIVE ZONE
8.2
8.1
8
7.9
Urban
7.8
suburban
7.7
7.6
SE ASON II
7.4
SE ASON II
7.5
SE ASON I
pH value
pH VALUE
8.4
GROUND WATER SAMPLING
PERIOD
FIG 4.18a EC IN RESIDENTIAL ZONE
FIG 4.18b EC IN COMMERCIAL ZONE
1.6
1.4
Urban
1.2
0.8
EC(mho-1 )
suburban
0.75
Urban
1
suburban
0.8
0.6
0.4
GROUND WATER SAMPLING PERIOD
SEASONIII
SEASON I
0
SEASON II
0.2
SEASO N
III
SEASO N
II
0.7
SEASO N
I
GROUND WATER SAMPLING
PERIOD
FIG 4.18c EC IN SENSITIVE ZONE
SEASO N
III
Urban
suburban
SEASO N
II
1
0.8
0.6
0.4
0.2
0
SEASO N
I
E C ( m h o -1 )
E C ( m h o -1 )
0.85
GROUND WATER SAMPLING PERIOD
FIG 4.19b TEMPERATURE IN COMMERCIAL
ZONE
FIG 4.19a TEMPERATURE IN RESIDENTIAL ZONE
29.5
30
29.5
29
Temperature(oC)
Urban
28
suburban
27.5
27
28.5
28
Urban
suburban
27.5
27
26.5
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
GROUND WATER SAMPLING
PERIOD
FIG 4.19c TEMPERATURE IN SENSITIVE ZONE
29.5
29
28.5
28
Urban
suburban
27.5
27
SEASON III
SEASON II
SEASON I
26.5
26
SEASONIII
26
SEASON II
25.5
SEASON I
26
26.5
Temperature( oC)
Temperature(oC)
29
28.5
GROUND WATER SAMPLING
PERIOD
FIG 4.20a TURBIDITY IN RESIDENTIAL ZONE
FIG 4.20b TURBIDITY IN COMMERCIAL ZONE
3
2.9
2.8
Turbidity(NTU)
2
Urban
suburban
1.5
2.7
Urban
2.6
suburban
2.5
2.4
1
0.5
SEASON I
SEASON II
2.2
0
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
GROUND WATER SAMPLING
PERIOD
FIG 4.20c TURBIDITY IN SENSITIVE ZONE
2.7
2.6
2.5
2.4
Urban
2.3
suburban
2.2
SEASON III
SEASON II
SEASON I
2.1
2
SEASONIII
2.3
Turbidity(NTU)
T urb id ity Valu e(NT U)
2.5
GROUND WATER SAMPLING
PERIOD
FIG 4.21b TSS IN COMMERCIAL ZONE
FIG 4.21a TSS IN RESIDENTIAL ZONE
90
90
80
80
70
70
TSS(mg/l)
60
50
Urban
suburban
40
50
Urban
40
suburban
30
20
30
0
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
GROUND WATER SAMPLING
PERIOD
FIG 4.21c TSS IN SENSITIVE ZONE
60
50
40
Urban
30
suburban
20
SEASON III
SEASON II
SEASON I
10
0
SEASONIII
10
SEASON I
0
SEASON II
10
20
TSS(mg/l)
TSS(mg/l)
60
GROUND WATER SAMPLING PERIOD
FIG 4.22a TDS IN RESIDENTIAL ZONE
FIG 4.22b TDS IN COMMERCIAL ZONE
180
250
160
200
100
Urban
suburban
80
60
150
Urban
suburban
100
50
SEASO N I
0
SEASON I SEASON II SEASON III
SEASO NIII
0
20
SEASO N II
40
GROUND WATER SAMPLING
PERIOD
GROUND WATER SAMPLING
PERIOD
FIG 4.22c TDS IN SENSITIVE ZONE
120
100
80
Urban
60
suburban
40
SEASO N III
0
SEASO N II
20
SEASO N I
TDS(m g/l)
T D S(m g /l)
120
TDS(m g/l)
140
GROUND WATER SAMPLING
PERIOD
FIG 4.23b TS IN COMMERCIAL ZONE
FIG 4.23a TS IN RESIDENTIAL ZONE
300
220
215
250
205
195
Urban
190
suburban
T S(m g /l)
200
200
Urban
150
suburban
100
185
180
50
SEASO N I
SEASO N II
0
170
165
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
GROUND WATER SAMPLING
PERIOD
FIG 4.23c TS IN SENSITIVE ZONE
150
145
140
135
Urban
130
suburban
125
SEASON III
SEASON II
SEASON I
120
115
SEASO N III
175
TS(mg/l)
T o tal So lid s(m g /l)
210
GROUND WATER SAMPLING
PERIOD
FIG 4.24a TOTAL HARDNESS IN RESIDENTIAL
ZONE
FIG 4.24b TOTAL HARDNESS IN COMMERCIAL
ZONE
150
160
155
140
Urban
135
suburban
130
h a rd n e s s ( m g /l)
150
145
Urban
140
suburban
135
130
SEASO N I
S E A S O N II
120
120
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
GROUND WATER SAMPLING
PERIOD
FIG 4.24c TOTAL HARDNESS IN SENSITIVE
ZONE
200
180
160
140
120
Urban
100
suburban
80
60
40
SEASON III
SEASON II
SEASON I
20
0
S E A S O N III
125
125
Hardness(mg/l)
H a rd n e s s ( m g /l)
145
GROUND WATER SAMPLING
PERIOD
FIG 4.25b CALCIUM IN COMMERCIAL ZONE
FIG 4.25a CALCIUM IN RESIDENTIAL ZONE
100
88
86
95
80
Urban
78
suburban
C alciu m (m g /l)
82
90
Urban
85
suburban
80
76
SEASO N I
70
70
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
SEASO N III
72
SEASO N II
75
74
GROUND WATER SAMPLING
PERIOD
FIG 4.25c CALCIUM IN SENSITIVE ZONE
100
90
80
70
60
Urban
50
suburban
40
30
20
SEASO N III
0
SEASO N II
10
SEASO N I
Calcium (m g/l)
C alciu m (m g /l)
84
GROUND WATER SAMPLING
PERIOD
FIG 4.26a MAGNESIUM N RESIDENTIAL ZONE
FIG 4.26b MAGNESIUM IN COMMERCIAL ZONE
62
62
60
60
Magnesium (mg/l)
56
Urban
54
suburban
52
58
Urban
56
suburban
54
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
SEASONIII
46
SEASON II
50
48
SEASON I
52
50
GROUND WATER SAMPLING
PERIOD
FIG 4.26c MAGNESIUM IN SENSITIVE ZONE
100
90
80
70
60
50
Urban
40
suburban
30
20
SEASO N III
0
SEASO N II
10
SEASO N I
Magnesium (m g/l)
Magnesium (mg/l)
58
GROUND WATER SAMPLING
PERIOD
FIG 4.27b ALKALINITY IN COMMERCIAL ZONE
FIG 4.27a ALKALINITY IN RESIDENTIAL ZONE
142
130
140
126
Urban
124
suburban
122
138
Urban
136
suburban
134
116
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
GROUND WATER SAMPLING
PERIOD
FIG 4.27c ALKALINITY IN SENSITIVE ZONE
160
140
120
Urban
100
suburban
80
60
40
20
0
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
SEASO NIII
SEASO N I
130
118
SEASO N II
132
120
Alkalinity(mg/l)
Alkalinity(mg/l)
128
Alkalin ity(m g /l)
132
FIG 4.28b ACIDITY IN COMMERCIAL ZONE
0.94
0.8
0.92
0.7
0.9
0.6
0.5
Urban
0.4
suburban
Acidity(mg/l)
0.9
0.88
Urban
0.86
suburban
0.84
0.82
0.3
SEASON I
0.1
SEASON II
0.78
0
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
SEASONIII
0.8
0.2
GROUND WATER SAMPLING
PERIOD
FIG 4.28c ACIDITY IN SENSITIVE ZONE
0.92
0.9
0.88
0.86
Acidity(mg/l)
Acidity(mg/l)
FIG 4.28a ACIDITY IN RESIDENTIAL ZONE
0.84
Urban
0.82
suburban
0.8
0.78
0.76
0.74
0.72
0.7
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
FIG 4.29b DO IN COMMERCIAL ZONE
FIG 4.29a DO IN RESIDENTIAL ZONE
6.2
6.4
6.3
6.1
6.2
Urban
5.9
suburban
Urban
5.9
suburban
5.8
5.8
SEASO N I
SEASO N II
5.6
5.6
5.5
5.4
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
SEASO NIII
5.7
5.7
GROUND WATER SAMPLING
PERIOD
FIG 4.29c DO IN SENSITIVE ZONE
6.2
6.15
6.1
6.05
6
DO(mg/l)
DO (m g/l)
6
DO (m g /l)
6
6.1
5.95
Urban
5.9
suburban
5.85
5.8
5.75
5.7
5.65
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
FIG 4.30a BOD IN RESIDENTIAL ZONE
FIR 4.30b BOD IN COMMERCIAL ZONE
1.4
1.25
1.2
0.8
Urban
suburban
0.6
Urban
1.15
suburban
1.1
1.05
1
0
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
GROUND WATER SAMPLING
PERIOD
FIG 4.30c BOD IN SENSITIVE ZONE
1.2
1.15
1.1
Urban
1.05
suburban
1
0.95
0.9
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
SEASONIII
SEASON I
0.95
0.2
SEASON II
0.4
BOD(mg/l)
BOD(mg/l)
1
BOD(mg/l)
1.2
FIG 4.31b COD IN COMMERCIAL ZONE
10.4
9.4
10.2
9.2
10
9
Urban
suburban
8.8
COD(mg/l)
9.6
9.8
Urban
suburban
9.6
9.4
8.6
9.2
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
SEASONIII
SEASON I
9
8.2
SEASON II
8.4
GROUND WATER SAMPLING
PERIOD
FIG 4.31c COD IN SENSITIVE ZONE
10.2
10
9.8
COD(mg/l)
COD(mg/l)
FIG 4.31a COD IN RESIDENTIAL ZONE
9.6
Urban
9.4
suburban
9.2
9
8.8
8.6
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
FIG 4.32b NITRITE IN COMMERCIAL ZONE
FIG 4.32a NITRITE IN RESIDENTIAL ZONE
0.03
0.08
0.07
0.025
suburban
0.05
Urban
0.04
suburban
0.03
0.01
0.02
0.005
0.01
GROUND WATER SAMPLING
PERIOD
SEASON I
SEASON SEASON SEASON
I
II
III
SEASON II
0
0
GROUND WATER SAMPLING
PERIOD
FIG 4.32c NITRITE IN SENSITIVE ZONE
0.09
0.08
0.07
0.06
0.05
Urban
0.04
suburban
0.03
0.02
0.01
0
SEASON I SEASON II SEASON
III
GROUND WATER SAMPLING
PERIOD
SEASONIII
0.015
Nitrite (mg/l)
Urban
Nitrite (mg/l)
Nitrite(m g/l)
0.06
0.02
FIG 4.33a NITRATE IN RESIDENTIAL ZONE
FIG 4.33b NITRATE IN COMMERCIAL ZONE
4.5
4.4
Urban
4.3
suburban
4.2
4.1
Urban
suburban
SEASO N I
4
3.9
SEASON I SEASON SEASON
II
III
GROUND WATER SAMPLING
PERIOD
GROUND WATER SAMPLING
PERIOD
FIG 4.33c NITRATE IN SENSITIVE ZONE
Urban
SEASON III
suburban
SEASON II
5.4
5.3
5.2
5.1
5
4.9
4.8
4.7
4.6
4.5
4.4
4.3
SEASON I
Nitrate (mg/l)
Nitrate(mg/l)
4.6
5.3
5.2
5.1
5
4.9
4.8
4.7
4.6
4.5
4.4
4.3
SEASO NIII
Nitrate content(m g/l)
4.7
SEASO N II
4.8
GROUND WATER SAMPLING
PERIOD
FIG 4.34b CHLORIDE IN COMMERCIAL ZONE
30
25
25
20
Urban
suburban
15
10
C h lo rid e(m g /l)
30
20
Urban
15
suburban
10
SE ASO N I
S EAS O N II
0
0
SEASON SEASON SEASON
I
II
III
GROUND WATER SAMPLING
PERIOD
SEA SO N III
5
5
GROUND WATER SAMPLING
PERIOD
FIG 4.34c CHLORIDE IN SENSITIVE ZONE
27
26
Chloride (mg/l)
Chloride (mg/l)
FIG 4.34a CHLORIDE IN RESIDENTIAL ZONE
25
Urban
24
suburban
23
22
21
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
FIG 4.35a FLUORIDE IN RESIDENTIAL ZONE
0.0326
0.0345
0.0324
0.034
0.0322
0.0335
Urban
0.033
suburban
0.0325
0.032
SEASO N II
0.031
SEASO NIII
0.0315
F lu oride co ntent(m g /l)
0.035
SEASO N I
0.032
0.0318
Urban
0.0316
suburban
0.0314
0.0312
0.031
0.0308
0.0306
SEASON I SEASON SEASON
II
III
GROUND WATER SAMPLING
PERIOD
GROUND WATER SAMPLING
PERIOD
FIG 4.35C FLUORIDE IN SENSITIVE ZONE
0.0321
0.032
0.0319
Flouride (mg/l)
Fluoride (m g//l)
FIG 4.35b FLUORIDE IN COMMERCIAL ZONE
0.0318
0.0317
Urban
0.0316
suburban
0.0315
0.0314
0.0313
0.0312
SEASON I SEASON SEASON
II
III
GROUND WATER SAMPLING
PERIOD
FIG 4.36b SULPHATE IN COMMERCIAL ZONE
24.5
23.5
24
23
23.5
22.5
Urban
suburban
22
21.5
Su lp h ate (m g /l)
24
23
22.5
22
21.5
21
SEASO N II
GROUND WATER SAMPLING
PERIOD
SEASO N I
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
FIG 4.36c SULPHATE IN SENSITIVE ZONE
30
25
20
Urban
15
suburban
10
5
0
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
SEASO N III
Urban
21
20.5
Sulphate (mg/l)
Su lp h ate co n ten t(m g /l)
FIG 4.36a SULPHATE IN RESIDENTIAL ZONE
suburban
FIG 4.37a E.Coli IN RESIDENTIAL ZONE
FIG 4.37b E.Coli IN COMMERCIAL ZONE
90
70
80
60
40
Urban
suburban
30
60
50
Urban
40
suburban
30
20
0
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
GROUND WATER SAMPLING
PERIOD
FIG 4.37c E.Coli IN SENSITIVE ZONE
60
50
40
Urban
30
suburban
20
10
0
SEASON I SEASON II SEASON III
GROUND WATER SAMPLING
PERIOD
SEASO NIII
SEASO N I
0
10
SEASO N II
10
20
E.Coli (/100ml)
E.C oli (1 0 0 /m l)
50
E.Co li(100/m l)
70
8.8
8.6
8.4
8.2
8
7.8
7.6
7.4
7.2
Urban
SEASON
III
SEASON
II
SEASON
I
Suburban
Sam pling Period
FIG 4.39 EC IN SURFACE WATER IN
VARIOUS SEASONS
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
Urban
SEASON
III
SEASON
II
Suburban
SEASON
I
EC(mho -1)
pH
FIG 4.38 pH IN SURFACE WATER IN
VARIOUS SEASONS
Sam pling season
SEASON
III
SEASON II
SEASON I
Temperature oC
FIG 4.40 TEMPERATURE IN SURFACE
WATER IN VARIOUS SEASONS
30.5
30
29.5
29
28.5
Urban
28
Suburban
27.5
27
26.5
26
25.5
Sam pling Period
100
90
80
70
60
50
40
30
20
10
0
Urban
SEASON
III
SEASON
II
Suburban
SEASON
I
Turbidity value(mg/l)
FIG 4.41 TURBIDITY IN SURFACE WATER
IN VARIOUS SEASONS
Sam pling Period
FIG 4.42 TS IN SURFACE WATER IN
VARIOUS SEASONS
400
300
250
Urban
200
Suburban
150
100
SEASON
II
SEASON I
0
SEASON
III
50
Sam pling Period
FIG 4.43 TOTAL HARDNESS IN SURFACE
WATER IN VARIOUS SEASONS
50
45
40
35
30
25
20
15
10
5
0
Urban
SEASON
III
SEASON
II
Suburban
SEASON
I
Hardness(mg/l)
Total solids(mg/l)
350
Sam pling Period
200
180
160
140
120
100
80
60
40
20
0
Urban
SEASO N
III
SEASO N
II
Suburban
SEASO N
I
Alkalinity(mg/l)
FIG 4.44 TOTAL ALKALINITY IN SURFACE
WATER IN VARIOUS SEASONS
Sam pling Period
FIG 4.45 TOTAL ACIDITY IN SURFACE
WATER IN VARIOUS SEASONS
2.5
1.5
Urban
Suburban
1
0.5
SEASO N
III
SEASO N
II
0
SEASO N
I
Acidity(m g/l)
2
Sam pling Period
FIG 4.46 DO IN SURFACE WATER IN
VARIOUS SEASONS
7
DO (m g/l)
6
5
4
Urban
3
Suburban
2
1
SEASO N
III
SEASO N
II
SEASO N
I
0
Sampling Period
FIG 4.47 BOD IN SURFACE WATER IN
VARIOUS SEASONS
6
4
Urban
3
Suburban
2
1
SEASON
III
SEASON
II
0
SEASON
I
BOD(mg/l)
5
Sam pling Period
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
Urban
SEASON
III
SEASON
II
SEASON I
Suburban
Sam pling Period
FIG 4.49 CHLORIDE IN SURFACE WATER IN
VARIOUS SEASONS
25
20
15
Urban
10
Suburban
SEASON
III
0
SEASON
II
5
SEASON I
Chloride(mg/l)
Nitrite content(mg/l)
FIG 4.48 NITRITE IN SURFACE WATER IN
VARIOUS SEASONS
Sam pling Period
FIG 4.50 FLUORIDE IN SURFACE WATER
IN VARIOUS SEASONS
4
Fluoride(mg/l)
3.5
3
2.5
Urban
2
Suburban
1.5
1
0.5
SEASON
III
SEASON
II
SEASON
I
0
Sam pling Period
9
8
7
6
5
4
3
2
1
0
Urban
SEASON
III
SEASON
II
Suburban
SEASON
I
Sulphate (mg/l)
FIG 4.51 SULPHATE IN SURFACE WATER IN
VARIOUS SEASONS
Sam pling Period
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
Urban
III
SEASON
II
SEASON
I
Suburban
SEASON
Phosphate(mg/l)
FIG 4.52PHOSPHATE IN SURFACE
WATER IN VARIOUS SEASONS
Sam pling Period
FIG 4.53 E.Coli IN SURFACE WATER IN
VARIOUS SEASONS
3500
2500
2000
Urban
1500
Suburban
1000
500
SEASON
III
SEASON
II
0
SEASON
I
E.coli (100/ml)
3000
Sam pling Period
9
8.8
8.6
8.4
8.2
8
7.8
7.6
FIG 4.54b pH IN COMMERCIAL ZONE
8.8
8.6
8.4
pH
Urban
suburban
8.2
Urban
8
suburban
7.8
SEASON
III
SEASON
III
SOIL SAMPLING
PERIOD
SEASON
II
7.4
SEASON
I
SEASON
II
7.6
SEASON
I
SOIL SAMPLING
PERIOD
FIG 4.54c pH IN LITTER FREE ZONE
8
7.95
7.9
7.85
7.8
7.75
7.7
7.65
7.6
SEASON
III
SEASON
II
Urban
suburban
SEASON
I
pH
pH
FIG 4.54a pH IN RESIDENTIAL ZONE
SOIL SAMPLING PERIOD
FIG 4.55b EC IN COMMERCIAL ZONE
FIG 4.55a EC IN RESIDENTIAL ZONE
Ecmho-1
1
Urban
suburban
0.8
Urban
0.6
suburban
0.4
0.2
SEASON
III
SEASON
II
SEASON
I
SEASON
III
SEASON
II
0
SEASON
I
SOIL SAMPLING PERIOD
SOIL SAMPLING PERIOD
FIG 4.55c EC IN LITTER FREE ZONE
0.3
0.25
0.2
0.15
0.1
0.05
SEASON
III
SEASON
II
0
SEASON
I
EC(mho -1)
EC(mho-1)
1.2
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
SOIL SAMPLING PERIOD
Urban
suburban
FIG 4.56a TOTAL ORGANIC CARBON
IN RESIDENTIAL ZONE
FIG 4.56b TOTAL ORGANIC CARBON
IN COMMERCIAL ZONE
Urban
0.3
suburban
0.2
Urban
suburban
SEASON
I
SEASON III
SOIL SAMPLING
PERIOD
SOIL SAMPLING
PERIOD
FIG 4.56c TOTAL ORGANIC CARBON
IN LITTER FREE ZONE
0.7
0.6
0.5
0.4
Urban
0.3
0.2
0.1
0
SEASON
III
SEASON
II
suburban
SEASON
I
% TOC
0
SEASON II
0.1
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
SEASON
III
% TOC
0.4
SEASON I
% TOC
0.5
SEASON
II
0.6
SOIL SAMPLING
PERIOD
FIG 4.57b TOTAL ORGANIC MATTER
IN COMMERCIAL ZONE
1.2
0.8
1
0.6
Urban
0.4
suburban
% TOM
1
0.2
0.8
Urban
0.6
suburban
0.4
0.2
SEASON
III
SOIL SAMPLING
PERIOD
FIG 4.57c TOTAL ORGANIC MATTER
IN LITTER FREE ZONE
1
0.8
0.6
Urban
0.4
suburban
0.2
SEASON
III
SEASON
II
0
SEASON
I
% TOM
SEASON
III
SOIL SAMPLING
PERIOD
SEASON
II
0
SEASON
I
SEASON
II
0
SEASON
I
% TOM
FIG 4.57a TOTAL ORGANIC MATTER
IN RESIDENTIAL ZONE
SOIL SAMPLING
PERIOD
FIG 4.58a TOTAL NITROGEN IN
RESIDENTIAL ZONE
FIG 4.58b TOTAL NITROGEN IN
COMMERCIAL ZONE
0.8
0.6
0.4
0.2
SEASON
I
suburban
SEASON
III
SEASON
II
0
Urban
SOIL SAMPLING PERIOD
SOIL SAMPLING PERRIOD
FIG 4.58c TOTAL NITROGEN IN
LITTER FREE ZONE
1.4
1.2
1
0.8
Urban
0.6
suburban
0.4
0.2
SEASON
III
SEASON
II
0
SEASON
I
Total Nitrogen(mg/g)
SEASON
III
Urban
suburban
1
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
SEASON
II
Total nitrogen(mg/g)
1.2
SEASON
I
Total Nitrogen(mg/g)
1.4
SOIL SAMPLING
PERIOD
Urban
SOIL SAMPLING PERIOD
SOIL SAMPLING PERIOD
FIG 4.59c TOTAL PHOSPHORUS IN
LITTER FREE ZONE
0.08
0.07
0.06
0.05
0.04
Urban
suburban
0.03
0.02
SEASON
III
SEASON
II
0.01
0
SEASON
I
Total Phosporus (mg/g)
SEASON III
suburban
SEASON II
0.068
0.066
0.064
0.062
0.06
0.058
0.056
0.054
0.052
0.05
SEASON I
SEASON III
suburban
Total Phosporus(mg/g)
FIG 4.59b TOTAL PHOSPHORUS IN
COMMERCIAL ZONE
Urban
SEASON II
0.072
0.07
0.068
0.066
0.064
0.062
0.06
0.058
0.056
0.054
SEASON I
Total Phosporus(mg/g)
FIG 4.59a TOTAL PHOSPHORUS IN
RESIDENTIAL ZONE
SOIL SAMPLING PERIOD
FIG 4.60b TOTAL POTASSIUM IN
COMMERCIAL ZONE
Urban
SOIL SAMPLING PERIOD
SOIL SAMPLING PERIOD
FIG 4.60c TOTAL POTASSIUM IN
LITTER FREE ZONE
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Urban
SEASON
III
SEASON
II
suburban
SEASON
I
Total Pottasium(mg/g)
SEASON
III
suburban
SEASON
I
SEASON
III
SEASON
II
suburban
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
SEASON
II
Ur ban
Total Pottasium(mg/g)
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
SEASON
I
Total Pottasium(mg/g)
FIG 4.60a TOTAL POTASSIUM IN
RESIDENTIAL ZONE
SOIL SAMPLING
PERIOD
FIG 4.61b TOTAL SODIUM IN
COMMERCIAL ZONE
Urban
SOIL SAMPLING
PERIOD
SOIL SAMPLING PERIOD
FIG 4.61c TOTAL SODIUM IN LITTER
FREE ZONE
0.6
0.5
0.4
Urban
0.3
suburban
0.2
0.1
SEASON
III
SEASON
II
0
SEASON
I
Total sodium(mg/g)
SEASON III
suburban
SEASON II
SEASON
III
SEASON
II
suburban
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
SEASON I
Urban
Total sodium(mg/g)
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
SEASON
I
Total Sodium(mg/g)
FIG 4.61a TOTAL SODIUM IN
RESIDENTIAL ZONE
SOIL SAMPLING PERIOD
FIG 4.62b TOTAL CALCIUM IN
COMMERCIAL ZONE
FIG 4.62a TOTAL CALCIUM IN
RESIDENTIAL ZONE
1
0.5
suburban
SEASON
I
SEASON
III
SEASON
II
0
Urban
SOIL SAMPLING PERIOD
SOIL SAMPLING PERIOD
FIG 4.62c TOTAL CALCIUM IN LITTER
FREE ZONE
1.2
1
0.8
Urban
0.6
suburban
0.4
0.2
SEASON
III
SEASON
II
0
SEASON
I
Total Calcium(mg/g)
SEASON
III
suburban
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
SEASON
II
Urban
1.5
Total calcium(mg/g)
2
SEASON
I
Total Calcium(mg/g)
2.5
SOIL SAMPLING PERIOD
FIG 4.63b TOTAL MAGNESIUM IN
COMMERCIAL ZONE
0.5
0.4
Urban
0.3
suburban
0.2
0.1
SOIL SAMPLING PERIOD
SOIL SAMPLING PERIOD
FIG 4.63c TOTAL MAGNESIUM IN
LITTER FREE ZONE
0.7
0.6
0.5
0.4
Urban
0.3
suburban
0.2
0.1
SEASON
III
SEASON
II
0
SEASON
I
Total Magnesium(mg/g)
SEASON
III
0
SEASON
I
SEASON III
suburban
0.6
SEASON
II
Urban
Total Magnesium(mg/g)
0.7
SEASON II
0.64
0.62
0.6
0.58
0.56
0.54
0.52
0.5
0.48
SEASON I
Total Magnesium(mg/g)
FIG 4.63a TOTAL MAGNESIUM IN
RESIDENTIAL ZONE
SOIL SAMPLING PERIOD
FIG 4.64b FRESH WEIGHT VARIATION IN
WASTE WATER USED PLANT
FIG 4.64a LEAF LENGTH VARIATION IN
WASTE WATER USED PLANT
1.1
1.08
8
1.06
1.04
Weight mg/g
10
Control
6
Treated
4
2
Control
1.02
1
Treated
0.98
0.96
0.94
0.92
0
Control
Control
Treated
Sampling Plant(Buchloe
dactyloids)
Treated
Sampling plant
FIG 4.64c DRY WEIGHT VARIATION IN
WASTE WATER USED PLANT
0.74
0.72
0.7
weight mg/g
length (cm)
12
0.68
Control
0.66
Treated
0.64
0.62
0.6
0.58
Control
Treated
Sampling plant
FIG 4.64d FREE SUGAR VARIATION IN
PLANT TISSUE
FIG 4.64e PHENOL VARIATION IN
PLANT TISSUE
1.4
0.25
1.2
Control
0.6
Treated
Phenol %
0.8
0.15
Control
Treated
0.1
0.4
0.05
0.2
0
0
Control
Control
Treated
Treated
Sampling plant
Sampling plant
FIG 4.64f TOTAL CHLOROPHYLL IN
PLANT TISSUE
105
100
Chlorophyll(mg/g)
Free sugar %
0.2
1
95
90
Control
85
Treated
80
75
70
Control
Treated
Sam pling plant
FIG 4.66a CARBON CONTENT IN
BIOCOMPOST
30
% of carbon
25
Partially
decomposed
MSW
Biocompost
20
15
10
5
Biocompost
Partially
decomposed
MSW
0
0.0205
0.02
0.0195
0.019
0.0185
0.018
0.0175
0.017
0.0165
0.016
0.0155
Biocompost
Partially
decomposed
MSW
Biocompost
Partially
decomposed
MSW
% of Sulphate
FIG 4.66b SULPHATE CONTENT IN
BIOCOMPOST
FIG 4.66c MICRO NUTRIENTS IN
BIOCOMPOST
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Chlorides
Magnisium
Calcium
Partially
decomposed
MSW
Biocompost
FIG 4.66d MACRO NUTRIENTS
IN BIOCOMPOST
1.4
1.2
0.8
Partially
decomposed
MSW
0.6
Biocompost
0.4
Phosphorus
0
Pottasium
0.2
Nitrogen
NPK in %
1
FIG 4.65b MOISTURE CONTENT
IN BIODEGRADABLE SOLID
WASTE
FIG 4.65a pH IN
BIODEGRADABLE SOLID
WASTE
52
Moisture content(%)
7.2
7.1
7
6.9
pH
6.8
6.7
6.6
50
48
Moisture
%
46
44
42
6.5
1
3
5
7
9
3
5
7
9
Decom position
Period
Decom position Period
FIG 4.65c TEMPERATURE IN
BIODEGRADABLE SOLID WASTE
30
29
T em p eratu re(o C )
1
28
27
Temperature
26
25
24
23
1
3
5
7
9
Decomposition Period
FIG 4.65d Carbon/Nitrogen
RATIO IN BIODEGRADABLE
SOLID WASTE
35
C/N ratio
30
25
20
C/N
15
10
5
0
1
3
5
7
9
Decom position pe riod
FIG 4.65e Solids and Ash IN BIODEGRADABLE
SOLID WASTE
70
Amount in%
60
50
Total Solids%
40
Volatile Solid%
30
Ash Content%
20
10
0
1
3
5
7
9
Decom position period
FIG 4.67a AVAILABLE NITROGEN IN
COMPOST TREATED SOIL
FIG 4.67b POTASSIUM IN COMPOST
TREATED SOIL
140
70
120
60
BLOCK-II
40
BLOCK-I
30
BLOCK-II
20
10
0
0
After II
harvest
Before
cultivation
20
FIG 4.67c PHOSPHATE IN COMPOST
TREATED SOIL
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
BLOCK-I
After II
harvest
BLOCK-II
Before
cultivation
mg/g
After I
harvest
40
After II
harvest
60
After I
harvest
BLOCK-I
mg/g
50
80
Before
cultivation
mg/g
100
2.35
2.3
2.25
2.2
2.15
2.1
2.05
2
1.95
1.9
1.85
BLOCK-I
After II
harvest
cultivation
BLOCK-II
Before
mg/g
FIG 4.68a ZINC IN COMPOST
TREATED SOIL
FIG 4.68b IRON CONTENT IN COMPOST
TRETED SOIL
8.2
8
7.6
BLOCK-I
7.4
BLOCK-II
7.2
7
6.8
After II
harvest
After I
harvest
6.6
Before
cultivation
mg/g
7.8
FIG 4.68c MANGANESE IN COMPOST
TREATED SOIL
7.1
7
6.9
mg/g
6.8
BLOCK-I
6.7
BLOCK-II
6.6
6.5
6.4
After II
harvest
After I
harvest
Before
cultivation
6.3
FIG 4.68d COPPER IN COMPOST TREATED
SOIL
1
0.9
BLOCK-I
BLOCK-II
0.85
0.8
After
second
harvest
After first
harvest
0.75
Before
cultivation
mg/g
0.95
FIG 4.69b ACTINOMYCETES IN COMPOST
TREATED SOIL
FIG 4.69a BACTERIAL CONTENT IN
COMPOST TREATED SOIL
60
80
70
50
40
50
40
10 2 /g
BLOCK-I
BLOCK-II
30
BLOCK-I
30
BLOCK-II
20
0
After II
harv es t
FIG 4.69c FUNGAL CONTENT IN COMPOST
TREATED SOIL
35
30
20
BLOCK-I
15
BLOCK-II
10
5
After II
harvest
After I
harvest
0
Before
cultivation
10 2/g
25
After II
harv es t
0
Before
c ultiv ation
10
After I
harv es t
10
After I
harv es t
20
Before
cultiv ation
10 4 /g
60
FIG 4.70a PALMAROSA GRASS YIELD
IN BIOCOMPOST TREATED SOIL
700
3.5
600
3
400
300
200
After I harvest
2.5
After II harvest
2
Kg
500
Kg
FIG 4.70b PALMAROSA OIL YIELD IN
BIOCOMPOST TREATED SOIL
After I harvest
After II harvest
1.5
1
100
0.5
0
BLOCK- BLOCKI
II
0
BLOCK-I BLOCK-II
SOCIO-ECONOMIC STATUS OF PUDUKKOTTAI
FIG 4.71 FAMILY SIZE
80%
60%
40%
URBAN
20%
SUBURBAN
LARGE FAMILY
SMALL FAMILY
FIG 4.72 LAND PATTERN
75%
CULTIVATED
BARREN LAND
25%
FIG 4.73 RESIDENCIAL STATUS
90%
75%
URBAN
SUBURBAN
25%
10%
OWN
HOUSE
RENT
HOUSE
FIG 4.74 WATER FACILITY
80%
80%
URBAN
SUBURBAN
20%
CORPORATION
SUPPLY
20%
BOREWELL SUPPLY
FIG 4.75 SANITARY FACILITY
75%
73%
URBAN
27%
SUBURBAN
25%
COMMON TOILET
INDIVIDUAL TOILETS
Table 4.19a
Sub Urban Flora in Season I for East Side
S .no
PLANT NAME
Density
Rel. density
Frequency Fre.class Rel. frequency Abandance
Aba. Class
0.875
0.61
62.5
D
3.70
1.4 Rare
5
3.53
50
C
2.96
10 Occasional
1
Euphorbia hirta
2
Azadiracta indica
3
Carica papaya
1.75
1.23
50
C
2.96
3.5 Rare
4
Abutilon indicum
2.25
1.58
75
D
4.44
3 Rare
5
Ocimum santum
6.75
4.76
87.5
E
5.18
7.71 Occasional
6
Zizipus jujuba
3.25
2.29
87.5
E
5.18
3.71 Rare
7
Cyperus corymbosus
1
0.70
50
C
2.96
2 Rare
8
Canna indica
2.75
1.94
87.5
E
5.18
3.14 Rare
9
Lycopersicum esculentum
9.62
6.79
62.5
D
3.70
15.4 Frequent
10
Eleucine coracana
11.5
8.12
37.5
B
2.22
30.66 Abundant
11
Acalypha indica
2.125
1.5
37.5
B
2.22
5.66 Occasional
12
Achyranthes aspera
6.62
4.67
87.5
E
5.18
7.57 Occasional
13
Aloe vera
8.87
6.26
87.5
E
5.18
10.14 Occasional
14
Crossandra undulaefolia
5.25
3.706
87.5
E
5.18
6 Occasional
15
Cleome gynandra
0.75
0.52
37.5
B
2.22
2 Rare
16
Bombox malabaricum
4.875
3.44
50
C
2.96
17
Dolicus lablab vor lignosus
0.75
0.52
37.5
B
2.22
2
Rare
18
Dolicus lablab var typeus
0.875
0.61
50
C
2.96
1.75
Rare
19
Ferinia limonia
1.625
1.14
50
C
2.96
3.25
Rare
20
Cassia auriculata
5.75
4.06
50
C
2.96
11.5
Occasional
21
Ocimum canum
6.375
4.5
62.5
D
3.70
10.2
Occasional
22
Chryasnthimum cinerarifolium
11.625
8.2
50
C
2.96
23.25
Frequent
23
Coccinea indica
5.875
4.14
87.5
E
5.18
6.71
Occasional
24
Cocos nucifera
4.375
3.08
50
C
2.96
8.75
Occasional
25
Coriandram sativam
8.625
6.09
37.5
B
2.22
23
Frequent
26
Cucumis sativas
8.625
6.09
75
D
4.44
11.5
Occasional
27
Lausonia inermis
0.75
0.52
37.5
B
2.22
2
Rare
28
Jatropha glandulifera
6.75
4.76
75
D
4.44
9
Occasional
29
Murraya koenigii
7.125
5.03
62.5
D
3.70
11.4
Occasional
9.75 Occasional
Table 4.19b.
Sub Urban Flora in Season II For East Side
S .No
PLANT NAME
Density
Rel. density
Frequency Fre.class Rel. frequency Abandance
Aba. Class
1
Euphorbia hirta
4
2.6
62.5
D
3.59
6.4
Occasional
2
Azadiracta indica
5
3.25
50
C
2.87
10
Occasional
3
Carica papaya
1.75
1.145
50
C
2.87
3.5
Rare
4
Abutilon indicum
2.25
1.46
75
D
4.31
3
Rare
5
Ocimum santum
6.75
4.39
87.5
E
5.03
7.71
Occasional
6
Zizipus jujuba
3.25
2.11
87.5
E
5.03
3.71
Rare
7
Cyperus corymbosus
1
0.65
50
C
2.87
2
Rare
8
Canna indica
2.75
1.79
87.5
E
5.03
3.14
Rare
9
Lycopersicum esculentum
9.625
6.27
62.5
D
3.59
15.4
Frequent
10
Eleucine coracana
11.5
7.49
37.5
B
2.15
30.66
Abundant
11
Acalypha indica
2.125
1.38
37.5
B
2.15
5.66
Occasional
12
Achyranthes aspera
5.375
3.5
87.5
E
5.03
6.14
Occasional
13
Aloe vera
8.875
5.78
87.5
E
5.03
10.14
Occasional
14
Crossandra undulaefolia
4
2.6
87.5
E
5.03
4.57
Frequent
15
Cleome gynandra
0.75
0.48
50
C
2.87
1.5
Rare
16
Bombox malabaricum
4.25
2.76
50
C
2.87
8.5
Occasional
17
Dolicus lablab vor lignosus
0.75
0.48
37.5
B
2.15
2
Rare
18
Dolicus lablab var typeus
0.87
0.57
50
C
2.87
1.75
Rare
19
Ferinia limonia
1.62
C
2.87
3.25
Rare
20
Cassia auriculata
5.75
3.74
50
C
2.87
11.5
Occasional
21
Ocimum canum
6.37
4.156
62.5
D
3.59
10.2
Occasional
22
Chryasnthimum cinerarifolium
11.62
7.57
50
C
2.87
23.25
Frequent
23
Coccinea indica
5.87
3.82
87.5
E
5.03
6.71
Occasional
24
Cocos nucifera
4.37
2.85
50
C
2.87
8.75
Occasional
25
Coriandram sativam
8.62
5.611
37.5
B
2.15
23
Frequent
26
Cucumis sativas
8.62
5.61
75
D
4.31
11.5
Occasional
27
Lausonia inermis
11.87
7.73
50
C
2.87
23.75
Frequent
28
Jatropha glandulifera
6.75
4.39
75
D
4.31
9
Occasional
29
Murraya koenigii
7.125
4.64
62.5
D
3.59
11.4
Occasional
Table 4.19c.
Sub Urban Flora in Season III for East Side
S .no
PLANT NAME
Density
Rel. density
Frequency Fre.class Rel. frequency Abandance
Aba. Class
1
Euphorbia hirta
4
2.60
62.5
D
3.59
6.4
occasional
2
Azadiracta indica
5
3.25
50
C
2.87
10
occasional
3
Carica papaya
1.75
1.14
50
C
2.87
3.5
Rare
4
Abutilon indicum
2.25
1.46
75
D
4.31
3
Rare
5
Ocimum santum
6.75
4.39
87.5
E
5.035
7.71
occasional
6
Zizipus jujuba
3.25
2.11
87.5
E
5.03
3.71
Rare
7
Cyperus corymbosus
1
0.65
50
C
2.87
2
Rare
8
Canna indica
2.75
1.79
87.5
E
5.035
3.14
Rare
9
Lycopersicum esculentum
9.625
6.27
62.5
D
3.59
15.4
Frequent
10
Eleucine coracana
11.5
7.49
37.5
B
2.15
30.66
Abundant
11
Acalypha indica
2.125
1.38
37.5
B
2.158
5.66
occasional
12
Achyranthes aspera
5.375
3.501
87.5
E
5.035
6.14
occasional
13
Aloe vera
8.875
5.78
87.5
E
5.035
10.14
occasional
14
Crossandra undulaefolia
4
2.60
87.5
E
5.035
4.57
occasional
15
Cleome gynandra
0.75
0.48
50
C
2.87
1.5
Rare
16
Bombox malabaricum
4.25
2.76
50
C
2.87
8.5
occasional
17
Dolicus lablab vor lignosus
0.75
0.48
37.5
B
2.158
2
Rare
18
Dolicus lablab var typeus
0.875
0.57
50
C
2.87
1.75
Rare
19
Ferinia limonia
1.625
1.058
50
C
2.87
3.25
Rare
20
Cassia auriculata
5.75
3.74
50
C
2.87
11.5
occasional
21
Ocimum canum
6.375
4.15
62.5
D
3.59
10.2
occasional
22
Chryasnthimum cinerarifolium
11.625
7.57
50
C
2.87
23.25
Frequent
23
Coccinea indica
5.875
3.82
87.5
E
5.03
6.71
occasional
24
Cocos nucifera
4.375
2.85
50
C
2.87
8.75
occasional
25
Coriandram sativam
8.625
5.61
37.5
B
2.15
23
Frequent
26
Cucumis sativas
8.625
5.61
75
D
4.31
11.5
occasional
27
Lausonia inermis
11.875
7.73
50
C
2.87
23.75
Frequent
28
Jatropha glandulifera
6.75
4.39
75
D
4.31
9
occasional
29
Murraya koenigii
7.125
4.64
62.5
D
3.59
11.4
occasional
Table 4.19d.
Sub Urban Flora in Season IV for East Side
Rel.
S .no
PLANT NAME
Density
Rel. density
Frequency Fre.class
Aba.
Abandance
frequency
Class
1
Euphorbia hirta
5
3.15
62.5
D
3.59
8
occasional
2
Azadiracta indica
5
3.15
50
C
2.87
10
occasional
3
Carica papaya
1.75
1.10
50
C
2.87
3.5
rare
4
Abutilon indicum
2.25
1.42
75
D
4.31
3
rare
5
Ocimum santum
6.75
4.26
87.5
E
5.03
7.71
occasional
6
Zizipus jujuba
4.5
2.84
87.5
E
5.035
5.14
occasional
7
Cyperus corymbosus
1
0.63
50
C
2.87
2
rare
8
Canna indica
2.75
1.73
87.5
E
5.035
3.14
rare
9
Lycopersicum esculentum
9.625
6.08
62.5
D
3.59
15.4
Frequent
10
Eleucine coracana
11.5
7.26
37.5
B
2.158
30.66
Abundant
11
Acalypha indica
2.125
1.34
37.5
B
2.158
5.66
occasional
12
Achyranthes aspera
5.375
3.39
87.5
E
5.035
6.14
occasional
13
Aloe vera
8.875
5.608
87.5
E
5.035
10.14
occasional
14
Crossandra undulaefolia
4
2.52
87.5
E
5.03
4.57
occasional
15
Cleome gynandra
0.75
0.47
50
C
2.87
1.5
rare
16
Bombox malabaricum
4.25
2.68
50
C
2.87
8.5
occasional
17
Dolicus lablab vor lignosus
0.75
0.47
37.5
B
2.15
2
rare
18
Dolicus lablab var typeus
0.875
0.552
50
C
2.87
1.75
rare
19
Ferinia limonia
1.625
1.02
50
C
2.87
3.25
rare
20
Cassia auriculata
5.75
3.63
50
C
2.87
11.5
occasional
21
Ocimum canum
6.375
4.028
62.5
D
3.59
10.2
occasional
22
Chryasnthimum cinerarifolium
11.625
7.34
50
C
2.87
23.25
Frequent
23
Coccinea indica
5.875
3.71
87.5
E
5.03
6.71
occasional
24
Cocos nucifera
4.375
2.76
50
C
2.87
8.75
occasional
25
Coriandram sativam
8.625
5.45
37.5
B
2.15
23
Frequent
26
Cucumis sativas
11.125
7.03
75
D
4.31
14.83
Frequent
27
Lausonia inermis
11.875
7.503
50
C
2.87
23.75
Frequent
28
Jatropha glandulifera
6.75
4.26
75
D
4.31
9
occasional
29
Murraya koenigii
7.125
4.502
62.5
D
3.59
11.4
occasional
Table 4.19e.
Sub urban flora in season I for west side
Rel.
S .no
PLANT NAME
Density
Aba.
Frequency Fre.class Rel. frequency Abandance
density
1
Cissus quandrangularis
2
Eucalyptus globulus
3
Class
0.625
0.39
25
B
1.15
2.5
Rare
3.5
2.18
12.5
A
0.57
28
Frequent
Solanum indicum
6.875
4.29
37.5
B
1.73
18.33
Frequent
4
Euphorbia neriifolia
2.75
1.71
62.5
D
2.89
4.4
Rare
5
Euphorbia tirucalli
1.75
1.09
75
D
3.46
2.33
Rare
6
Anacardium occidentale
1.875
1.17
50
C
2.31
3.75
Rare
7
Moringa olifera
3
1.87
87.5
E
4.04
3.42
Rare
8
Delonix regia
1.5
0.93
87.5
E
4.04
1.71
Rare
9
Agaricus campestris
2.75
1.71
75
D
3.46
3.66
Rare
10
Hibiscus cannabinus
9
5.61
87.5
E
4.04
10.29
Occasional
11
Psidium guajava
2
1.24
50
C
2.31
4
Rare
12
Corypha umbraculifera
0.25
0.15
25
B
1.15
1
Rare
13
Saccharum spontaneum
1.87
1.17
25
B
1.15
7.5
Occasional
14
Lausonia inermis
3.75
2.34
50
C
2.31
7.5
Occasional
S .no
PLANT NAME
Density
Rel.
density
Frequency Fre.class Rel. frequency Abandance
Aba.
Class
15
Artocarpus heterophyllus
1.75
1.09
75
D
3.46
2.33
Rare
16
Zizipus jujuba
3.25
2.02
87.5
E
4.04
3.71
Rare
17
Solanum xanthocarpum
2.5
1.56
37.5
B
1.73
6.66
Occasional
18
Phyllanthus acidus
8.125
5.07
37.5
B
1.73
21.67
Frequent
19
Pithecolobium dulce
1
0.62
37.5
B
1.73
2.66
Rare
20
Hibiscus esculentus
2.875
1.79
87.5
E
4.046
3.28
Rare
21
Citrus limon
1.875
1.17
50
C
2.31
3.75
Rare
22
Citrus aurantifolia
0.875
0.54
37.5
B
1.73
2.33
Rare
23
Citrus aurantifolia
2.5
1.56
62.5
D
2.89
4
Rare
24
Loranthus longiflorus
1.5
0.93
50
C
2.31
3
Rare
25
Calatropis gigantea
16.88
10.53
37.5
B
1.73
45
Abundant
26
Zea maize
3.75
2.34
25
B
1.15
15
Frequent
27
Mangifera indica
4.25
2.65
37.5
B
1.73
11.33
Occasional
28
Calendula officinalis
3.12
1.95
12.5
A
0.57
25
Frequent
29
Phyllanthus maderaspatensis
1.12
0.70
37.5
B
1.73
3
Rare
30
Morinda tingtoria
1.62
1.01
37.5
B
1.73
4.33
Rare
31
Ervatamia coranaria
0.87
0.54
50
C
2.31
1.75
Rare
S .no
PLANT NAME
Density
Rel.
density
Frequency Fre.class Rel. frequency Abandance
Aba.
Class
32
Opuntia dillenii
3.25
2.02
37.5
B
1.73
8.66
Occasional
33
Oryza sativa
21.75
13.57
37.5
B
1.73
58
Abundant
34
Carica papaya
1.75
1.092044
37.5
B
1.73
4.66
Rare
35
Ficus religiosa
2.125
1.326053
75
D
3.46
2.83
Rare
36
Luffa cylindrica
1.5
0.936037
62.5
D
2.89
2.4
Rare
37
Pistia stratiotes
5
3.120125
12.5
A
0.57
40
38
Trichosanthus anguine
3.75
2.34
50
C
2.31
7.5
Occasional
39
Basella ruba
0.5
0.31
37.5
B
1.73
1.333
Rare
40
Saccharum officinarum
5.625
3.51
25
B
1.15
22.5
Frequent
41
Mimosa pudica
4.875
3.04
37.5
B
1.73
13
Occasional
42
Millingtonia hortensis
0.375
0.23
75
D
3.46
0.5
Rare
43
Gomphrena globosa
4.375
2.73
75
D
3.46
5.833
Occasional
44
Cassia angustifolia
1.125
0.702
12.5
A
0.578
9
Occasional
45
Portulaca oleraceae
4.875
3.0421
37.5
B
1.73
13
Occasional
Table 4.19f.
Sub urban flora in Season II for west side
Aba.
S .no
PLANT NAME
Density
Rel. density
Frequency Fre.class Rel. frequency Abandance
Class
1
Cissus quandrangularis
0.62
0.38
25
B
1.15
2.5 Rare
2
Eucalyptus globulus
3.5
2.15
12.5
A
0.57
28 Frequent
3
Solanum indicum
6.87
4.22
37.5
B
1.73
18.33 Frequent
4
Euphorbia neriifolia
2.75
1.68
62.5
D
2.89
4.4 Rare
5
Euphorbia tirucalli
1.75
1.075
75
D
3.46
2.33 Rare
6
Anacardium occidentale
1.87
1.15
50
C
2.31
3.75 Rare
7
Moringa olifera
3
1.84
87.5
E
4.046
3.42 Rare
8
Delonix regia
1.5
0.92
87.5
E
4.046
1.71 Rare
9
Agaricus campestris
2.75
1.68
75
D
3.46
3.66 Rare
10
Hibiscus cannabinus
11.5
7.06
87.5
E
4.046
11
Psidium guajava
2
1.22
50
C
2.31
4 Rare
12
Corypha umbraculifera
0.25
0.15
25
B
1.15
1 Rare
13
Saccharum spontaneum
1.87
1.15
25
B
1.15
7.5 Occasional
14
Lausonia inermis
3.75
2.304
50
C
2.31
7.5 Occasional
Density
Rel. density
S .no
PLANT NAME
13.14 Occasional
Frequency Fre.class Rel. frequency Abandance
Aba.
Class
15
Artocarpus heterophyllus
1.75
1.07
75
D
3.46
2.33 Rare
16
Zizipus jujuba
3.25
1.99
87.5
E
4.04
3.71 Rare
17
Solanum xanthocarpum
2.5
1.53
37.5
B
1.73
6.66 Rare
18
Phyllanthus acidus
8.125
4.99
37.5
B
1.73
19
Pithecolobium dulce
1
0.61
37.5
B
1.73
2.66 Rare
20
Hibiscus esculentus
2.875
1.76
87.5
E
4.04
3.28 Rare
21
Citrus limon
1.87
1.15
50
C
2.31
3.75 Rare
22
Citrus aurantifolia
0.87
0.53
37.5
B
1.73
2.33 Rare
23
Citrus aurantifolia
2.5
1.53
62.5
D
2.89
4 Rare
21.67 Frequent
24
Loranthus longiflorus
25
Calatropis gigantea
26
1.5
0.92
50
C
2.31
16.88
10.36
37.5
B
1.73
45 Abundant
Zea maize
3.75
2.304
25
B
1.15
15 Frequent
27
Mangifera indica
4.25
2.611
37.5
B
1.73
11.33 Occasional
28
Calendula officinalis
3.125
1.92
12.5
A
0.57
25 Frequent
29
Phyllanthus maderaspatensis
1.125
0.69
37.5
B
1.73
3 Rare
30
Morinda tingtoria
1.625
0.99
37.5
B
1.73
4.333 Rare
Density
Rel. density
S .no
PLANT NAME
3 Rare
Frequency Fre.class Rel. frequency Abandance
Aba.
Class
31
Ervatamia coranaria
0.875
0.53
50
C
2.31
1.75
Rare
32
Opuntia dillenii
3.25
1.99
37.5
B
1.73
8.667
Occasional
33
Oryza sativa
21.75
13.36
37.5
B
1.73
58
Abundant
34
Carica papaya
1.75
1.075
37.5
B
1.73
4.667
Rare
35
Ficus religiosa
2.125
1.305
75
D
3.46
2.833
Rare
36
Luffa cylindrica
1.5
0.921
62.5
D
2.89
2.4
Rare
37
Pistia stratiotes
5
3.072
12.5
A
0.57
40
Abundant
38
Trichosanthus anguine
3.75
2.304
50
C
2.31
7.5
Occasional
39
Basella ruba
0.5
0.307
37.5
B
1.73
1.333
Rare
40
Saccharum officinarum
5.62
3.45
25
B
1.15
22.5
Frequent
41
Mimosa pudica
4.87
2.99
37.5
B
1.73
13
Occasional
42
Millingtonia hortensis
0.375
0.23
75
D
3.46
0.5
Rare
43
Gomphrena globosa
4.37
2.68
75
D
3.46
5.833
Occasional
44
Cassia angustifolia
1.12
0.69
12.5
A
0.578
9
Occasional
45
Portulaca oleraceae
4.875
2.995392
37.5
B
1.73
13
Occasional
Table 4.19g.
Sub urban flora in Season III for west side
S .no
PLANT NAME
Frequency Fre.class Rel. Frequency Abandance
Aba.
Density
Rel. density
0.625
0.38
25
B
1.15
2.5
Rare
Class
1
Cissus quandrangularis
2
Eucalyptus globulus
3.5
2.15
12.5
A
0.57
28
Abundant
3
Solanum indicum
6.87
4.22
37.5
B
1.73
18.33
Abundant
4
Euphorbia neriifolia
2.75
1.68
62.5
D
2.89
4.4
Rare
5
Euphorbia tirucalli
1.75
1.075
75
D
3.46
2.33
Rare
6
Anacardium occidentale
1.87
1.15
50
C
2.31
3.75
Rare
7
Moringa olifera
3
1.84
87.5
E
4.04
3.42
Rare
8
Delonix regia
1.5
0.92
87.5
E
4.046
1.71
Rare
9
Agaricus campestris
2.75
1.68
75
D
3.46
3.66
Rare
10
Hibiscus cannabinus
10.25
6.29
87.5
E
4.046
11.71
Occasional
11
Psidium guajava
2
1.22
50
C
2.31
4
Rare
12
Corypha umbraculifera
0.25
0.15361
25
B
1.15607
1
Rare
13
Saccharum spontaneum
1.87
1.15
25
B
1.15
7.5
Occasional
14
Lausonia inermis
3.75
2.304
50
C
2.31
7.5
Occasional
Density
Rel. density
S .no
PLANT NAME
Frequency Fre.class Rel. Frequency Abandance
Aba.
Class
15
Artocarpus heterophyllus
1.75
1.07
75
D
3.46
2.33
Rare
16
Zizipus jujuba
3.25
1.99
87.5
E
4.04
3.71
Rare
17
Solanum xanthocarpum
2.5
1.53
37.5
B
1.73
6.66
Rare
18
Phyllanthus acidus
8.12
4.99
37.5
B
1.73
21.67
Frequent
19
Pithecolobium dulce
1
0.61
37.5
B
1.73
2.66
Rare
20
Hibiscus esculentus
4.12
2.53
87.5
E
4.04
4.71
Occasional
21
Citrus limon
1.87
1.15
50
C
2.31
3.75
Rare
22
Citrus aurantifolia
0.87
0.53
37.5
B
1.73
2.33
Rare
23
Citrus aurantifolia
2.5
1.53
62.5
D
2.89
4
Rare
24
Loranthus longiflorus
1.5
0.92
50
C
2.31
3
Rare
25
Calatropis gigantea
16.88
10.36
37.5
B
1.73
45
Abundant
26
Zea maize
3.75
2.304
25
B
1.15
15
Frequent
27
Mangifera indica
4.25
2.61
37.5
B
1.73
11.33
Occasional
28
Calendula officinalis
3.12
1.92
12.5
A
0.57
25
Frequent
29
Phyllanthus maderaspatensis
1.12
0.69
37.5
B
1.73
3
Rare
30
Morinda tingtoria
1.62
0.99
37.5
B
1.73
4.33
Rare
Density
Rel. density
S .no
PLANT NAME
Frequency Fre.class Rel. Frequency Abandance
Aba.
Class
31
Ervatamia coranaria
0.87
0.537
50
C
2.31
1.75
Rare
32
Opuntia dillenii
3.25
1.99
37.5
B
1.73
8.66
Occasional
33
Oryza sativa
21.75
13.36
37.5
B
1.73
58
Abundant
34
Carica papaya
1.7
1.075
37.5
B
1.73
4.66
Occasional
35
Ficus religiosa
2.12
1.305
75
D
3.46
2.83
Rare
36
Luffa cylindrica
1.5
0.92
62.5
D
2.89
2.4
Rare
37
Pistia stratiotes
5
3.072
12.5
A
0.57
40
Abundant
38
Trichosanthus anguine
3.75
2.304
50
C
2.31
7.5
Occasional
39
Basella ruba
0.5
0.307
37.5
B
1.73
1.33
Rare
40
Saccharum officinarum
5.62
3.45
25
B
1.15
22.5
Frequent
41
Mimosa pudica
4.87
2.99
37.5
B
1.73
13
Occasional
42
Millingtonia hortensis
0.375
0.23
75
D
3.46
0.5
Rare
43
Gomphrena globosa
4.37
2.68
75
D
3.46
5.83
Occasional
44
Cassia angustifolia
1.12
0.69
12.5
A
0.57
9
Occasional
45
Portulaca oleraceae
4.87
2.99
37.5
B
1.73
13
Occasional
Table 4.19h.
Sub urban flora in Season IV for west side
S .no
PLANT NAME
Frequency Fre.class Rel. Frequency Abandance
Aba.
Density
Rel. density
0.625
0.38
25
B
1.15
2.5
Rare
Class
1
Cissus quandrangularis
2
Eucalyptus globulus
3.5
2.13
12.5
A
0.57
28
Frequent
3
Euphorbia neriifolia
2.75
1.67
62.5
D
2.89
4.4
Rare
4
Euphorbia tirucalli
1.75
1.067
75
D
3.46
2.33
Rare
5
Anacardium occidentale
1.875
1.14
50
C
2.31
3.75
Rare
6
Moringa olifera
3
1.82
87.5
E
4.046
3.42
Rare
7
Delonix regia
1.5
0.91
87.5
E
4.046
1.714
Rare
8
Agaricus campestris
2.75
1.67
75
D
3.46
3.66
Rare
9
Hibiscus cannabinus
9
5.48
87.5
E
4.046
10.29
Occasional
10
Psidium guajava
2
1.21
50
C
2.31
4
Rare
11
Corypha umbraculifera
0.25
0.152
25
B
1.15
1
Rare
12
Saccharum spontaneum
1.875
1.14
25
B
1.15
7.5
Occasional
13
Lausonia inermis
3.75
2.28
50
C
2.31
7.5
Occasional
14
Artocarpus heterophyllus
1.75
1.06
75
D
3.46
2.333
Rare
Density
Rel. density
7
4.26
87.5
E
4.046
8
Occasional
8.125
4.95
37.5
B
1.73
21.67
Frequent
S .no
PLANT NAME
Frequency Fre.class Rel. Frequency Abandance
Aba.
Class
15
Zizipus jujuba
16
Phyllanthus acidus
17
Pithecolobium dulce
1
0.609
37.5
B
1.73
2.66
Rare
18
Hibiscus esculentus
2.875
1.753
87.5
E
4.04
3.28
Rare
19
Citrus limon
1.875
1.143
50
C
2.31
3.75
Rare
20
Citrus aurantifolia
0.875
0.53
37.5
B
1.73
2.33
Rare
21
Citrus aurantifolia
2.5
1.52
62.5
D
2.89
4
Rare
22
Loranthus longiflorus
1.5
0.91
50
C
2.31
3
Rare
23
Calatropis gigantea
16.88
10.28
37.5
B
1.73
45
Abundant
24
Zea maize
3.75
2.28
25
B
1.15
15
Frequent
25
Mangifera indica
4.25
2.59
37.5
B
1.73
11.33
Occasional
26
Calendula officinalis
3.125
1.90
12.5
A
0.57
25
Frequent
27
Phyllanthus maderaspatensis
1.125
0.68
37.5
B
1.73
3
Rare
28
Morinda tingtoria
1.625
0.99
37.5
B
1.73
4.33
Rare
29
Ervatamia coranaria
0.875
0.53
50
C
2.31
1.75
Rare
30
Opuntia dillenii
3.25
1.98
37.5
B
1.73
8.66
Occasional
31
Oryza sativa
21.75
13.26
37.5
B
1.73
58
Abundant
Density
Rel. density
S .no
PLANT NAME
Frequency Fre.class Rel. Frequency Abandance
Aba.
Class
32
Carica papaya
1.75
1.06
37.5
B
1.73
4.66
Occasional
33
Ficus religiosa
2.125
1.29
75
D
3.46
2.83
Rare
34
Luffa cylindrica
1.5
0.91
62.5
D
2.89
2.4
Rare
35
Pistia stratiotes
5
3.04
12.5
A
0.57
40
Abundant
36
Trichosanthus anguine
3.75
2.286585
50
C
2.31214
7.5
Occasional
37
Basella ruba
0.5
0.304
37.5
B
1.73
1.333
Rare
38
Saccharum officinarum
5.625
3.42
25
B
1.15
22.5
Frequent
39
Solanum indicum
6.875
4.19
37.5
B
1.73
18.33
Abundant
40
Solanum xanthocarpum
2.5
1.52
37.5
B
1.73
6.66
Occasional
41
Mimosa pudica
4.875
2.97
37.5
B
1.73
13
Occasional
42
Millingtonia hortensis
0.375
0.22
75
D
3.46
0.5
Rare
43
Gomphrena globosa
4.375
2.66
75
D
3.46
5.83
Occasional
44
Cassia angustifolia
1.125
0.68
12.5
A
0.57
9
Occasional
45
Portulaca oleraceae
4.875
2.97
37.5
B
1.7341
13
Occasional
Table 4.19i.
Sub urban flora in Season I for north side
S .no
PLANT NAME
Density
Rel. density
Frequency
Fre. class
Rel. Frequency
Abandance
Aba. Class
1
Emblica officinalis
0.62
0.404
50
C
2
1.25
Rare
2
Polyalthia longifolia
2
1.29
37.5
B
1.5
5.33
Occasional
3
Acacia auriculiformis
0.12
0.08
12.5
A
0.5
1
Rare
4
Bambusa arundinacea
4
2.59
37.5
B
1.5
10.66
Occasional
5
Musa paradisiaca
1.8
1.21
37.5
B
1.5
5
Occasional
6
Bauhinia varugata
0.25
0.16
25
B
1
1
Rare
7
Coccinia indica
0.5
0.32
25
B
1
2
Rare
8
Capsicum frutescens
0.12
0.08
12.5
A
0.5
1
Rare
9
Nymphoe stellata
3
1.94
62.5
D
2.5
4.8
Occasional
10
Callistemon lanceolatus
2.62
1.70
75
D
3
3.5
Rare
11
Magnolia stellata
1.75
1.13
25
B
1
7
Occasional
12
Cocus nusifera
0.5
0.32
37.5
B
1.5
1.33
Rare
13
Coriandrum sativam
1.875
1.21
50
C
2
3.75
Rare
14
Crossandra undulaefolia
3.12
2.02
50
C
2
6.25
Occasional
15
Cucumis sativus
0.25
0.16
25
B
1
1
Rare
Density
Rel. density
Frequency
Fre. class
Rel. Frequency
Abandance
Aba. Class
S .no
PLANT NAME
16
Annona squamosa
0.87
0.56
37.5
B
1.5
2.33
Rare
17
Euphorbia heterophylla
0.62
0.40
37.5
B
1.5
1.66
Rare
18
Eichhornia crassipes
5.62
3.64
25
B
1
22.5
Frequent
19
Cyanodon dactylon
10.3
6.72
62.5
D
2.5
16.6
Frequent
20
Lausonia inermis
4.37
2.83
87.5
E
3.5
5
Occasional
21
Aloe vera
0.75
0.48
37.5
B
1.5
2
Rare
22
Ixora coccinia
3.62
2.34
62.5
D
2.5
5.8
Occasional
23
Eugenia jambolanam
1.37
0.89
50
C
2
2.75
Rare
24
Gloriosa superba
0.5
0.32
25
B
1
2
Rare
25
Canna indica
1.87
1.21
50
C
2
3.75
Rare
26
Eclipta alba
0.15
0.08
12.5
A
0.5
1
Rare
27
Amaranthus viridis
0.25
0.16
25
B
1
1
Rare
28
Cymbapogon citratus
12.5
8.09
37.5
B
1.5
33.33
Abundant
29
Nelumbium speciosum
1.25
0.80
62.5
D
2.5
2
Rare
30
Calatropis gigantea
6.62
4.29
100
E
4
6.62
Occasional
31
Chorisia speciosa
0.125
0.08
12.5
A
0.5
1
Rare
32
Morinda tintoria
1.875
1.21
75
D
3
2.5
Rare
Density
Rel. density
Frequency
Fre. class
Rel. Frequency
Abandance
Aba. Class
S .no
PLANT NAME
33
Azadiracta indica
0.5
0.32
37.5
B
1.5
1.33
Rare
34
Allium cepa
1.75
1.13
50
C
2
3.5
Rare
35
Lantana camera
2.75
1.78
62.5
D
2.5
4.4
Rare
36
Ipomea carnea
0.25
0.16
12.5
A
0.5
2
Rare
37
Ravenala madagascariensis
0.62
0.404
25
B
1
2.5
Rare
38
Oryza sativa
12.3
8.01
12.5
A
0.5
99
Very abundant
39
Borassus flabellifer
0.25
0.16
25
B
1
1
Rare
40
Ficus religiosa
0.37
0.24
25
B
1
1.5
Rare
41
Pistia stratiotes
2.62
1.7
25
B
1
10.5
Occasional
42
Pongamia glabra
2
1.29
50
C
2
4
Rare
43
Thespesia populnea
1.125
0.72
62.5
D
2.5
1.8
Rare
44
Achyranthes aspera
0.125
0.08
12.5
A
0.5
1
Rare
45
Ricinus communis
2.25
1.45
25
B
1
9
Occasional
46
Hemidesmus indicus
4.87
3.15
62.5
D
2.5
7.8
Occasional
47
Hibiscus rosasinensis
1.37
0.89
37.5
B
1.5
3.66
Rare
48
Andrapogon sorghum
5
3.23
50
C
2
10
Occasional
49
Saccharum officinarum
4.5
2.91
50
C
2
9
Occasional
50
Tectona grandis
0.125
0.08
12.5
A
0.5
1
Rare
51
Terminalia arjuna
0.25
0.16
25
B
1
1
Rare
52
Millingtonia hortensis
0.12
0.08
12.5
A
0.5
1
Rare
53
Tridax procumbens
17.5
11.33
62.5
D
2.5
28
Frequent
54
Ocimum santum
4.75
3.076
75
D
3
6.33
Occasional
55
Curcuma longa
0.25
0.16
25
B
1
1
Rare
56
Gomphrena globosa
4.25
2.753
50
C
2
8.5
Occasional
57
Caesalpinia inermis
0.375
0.24
25
B
1
1.5
Rare
58
Leucas aspera
5
3.23
100
E
4
5
Occasional
59
Amaratus spinosus
1.125
0.72
50
C
2
2.25
Rare
60
Cleome viscosa
2.125
1.37
37.5
B
1.5
5.66
Occasional
61
Melia aezdarach
0.37
0.24
37.5
B
1.5
1
Rare
Table 4.19j.
Sub urban flora in Season II for north side
S .no
PLANT NAME
Density
Rel.
density
Frequency
Fre. class
Rel. frequency
Abandance
Aba. Class
1
Emblica officinalis
0.62
0.41
50
C
2
1.25
Rare
2
Polyalthia longifolia
2
1.31
37.5
B
1.5
5.33
Occasional
3
Acacia auriculiformis
0.125
0.08
12.5
A
0.5
1
Rare
4
Bambusa arundinacea
4
2.63
37.5
B
1.5
10.66
Occasional
5
Musa paradisiaca
1.87
1.23
37.5
B
1.5
5
Occasional
6
Bauhinia varugata
0.25
0.164
25
B
1
1
Rare
7
Coccinia indica
0.5
0.32
25
B
1
2
Rare
8
Capsicum frutescens
0.125
0.082
12.5
A
0.5
1
Rare
9
Nymphoe stellata
3
1.97
62.5
D
2.5
4.8
Occasional
10
Callistemon lanceolatus
2.62
1.73
75
D
3
3.5
Rare
11
Magnolia stellata
1.75
1.15
25
B
1
7
Occasional
12
Cocus nusifera
0.5
0.32
37.5
B
1.5
1.33
Rare
13
Coriandrum sativam
1.87
1.23
50
C
2
3.75
Rare
14
Crossandra undulaefolia
3.12
2.06
50
C
2
6.25
Occasional
S .no
PLANT NAME
Density
Rel.
density
Frequency
Fre. class
Rel. frequency
Abandance
Aba. Class
15
Cucumis sativus
0.25
0.164
25
B
1
1
Rare
16
Annona squamosa
0.875
0.57
37.5
B
1.5
2.33
Rare
17
Euphorbia heterophylla
1.875
1.23
37.5
B
1.5
5
Rare
18
Eichhornia crassipes
5.62
3.70
25
B
1
22.5
Frequent
19
Cyanodon dactylon
10.38
6.84
62.5
D
2.5
16.6
Frequent
20
Lausonia inermis
4.375
2.88
87.5
E
3.5
5
Occasional
21
Aloe vera
0.5
0.32
37.5
B
1.5
1.33
Rare
22
Ixora coccinia
3.625
2.39
62.5
D
2.5
5.8
Occasional
23
Eugenia jambolanam
1.375
0.906
50
C
2
2.75
Rare
24
Gloriosa superba
0.5
0.32
25
B
1
2
Rare
25
Canna indica
1.87
1.23
50
C
2
3.75
Rare
26
Eclipta alba
0.12
0.082
12.5
A
0.5
1
Rare
27
Amaranthus viridis
0.25
0.16
25
B
1
1
Rare
28
Cymbapogon citratus
12.5
8.24
37.5
B
1.5
33.33
Abundant
29
Nelumbium speciosum
1.25
0.82
62.5
D
2.5
2
Rare
30
Calatropis gigantea
6.62
4.36
100
E
4
6.62
Occasional
S .no
PLANT NAME
Density
Rel.
density
Frequency
Fre. class
Rel. frequency
Abandance
Aba. Class
31
Chorisia speciosa
0.12
0.08
12.5
A
0.5
1
Rare
32
Morinda tintoria
1.87
1.23
75
D
3
2.5
Rare
33
Azadiracta indica
0.5
0.32
37.5
B
1.5
1.33
Rare
34
Allium cepa
1.75
1.15
50
C
2
3.5
Rare
35
Lantana camera
5.25
3.46
62.5
D
2.5
8.4
Rare
36
Ipomea carnea
0.25
0.16
12.5
A
0.5
2
Rare
37
Ravenala madagascariensis
0.62
0.41
25
B
1
2.5
Rare
38
Oryza sativa
12.38
8.16
12.5
A
0.5
99
Very abundant
39
Borassus flabellifer
0.25
0.16
25
B
1
1
Rare
40
Ficus religiosa
0.37
0.24
25
B
1
1.5
Rare
41
Pistia stratiotes
2.62
1.73
25
B
1
10.5
Occasional
42
Pongamia glabra
2
1.31
50
C
2
4
Rare
43
Thespesia populnea
1.125
0.74
62.5
D
2.5
1.8
Rare
44
Achyranthes aspera
0.125
0.08
12.5
A
0.5
1
Rare
45
Ricinus communis
2.25
1.48
25
B
1
9
Occasional
46
Hemidesmus indicus
4.87
3.21
62.5
D
2.5
7.8
Occasional
47
Hibiscus rosasinensis
1.37
0.906
37.5
B
1.5
3.66
Rare
S .no
PLANT NAME
48
Andrapogon sorghum
49
Saccharum officinarum
50
Density
Rel.
density
Frequency
Fre. class
Rel. frequency
Abandance
Aba. Class
5
3.29
50
C
2
10
Occasional
4.5
2.96
50
C
2
9
Occasional
Tectona grandis
0.125
0.082
12.5
A
0.5
1
Rare
51
Terminalia arjuna
0.25
0.16
25
B
1
1
Rare
52
Millingtonia hortensis
0.125
0.08
12.5
A
0.5
1
Rare
53
Tridax procumbens
12.5
8.24
62.5
D
2.5
20
Frequent
54
Ocimum santum
3.5
2.308
75
D
3
4.66
Occasional
55
Curcuma longa
0.25
0.16
25
B
1
1
Rare
56
Gomphrena globosa
4.25
2.802
50
C
2
8.5
Occasional
57
Caesalpinia inermis
0.375
0.24
25
B
1
1.5
Rare
58
Leucas aspera
5
3.29
100
E
4
5
Occasional
59
Amaratus spinosus
2.37
1.56
50
C
2
4.75
Rare
60
Cleome viscosa
0.87
0.57
37.5
B
1.5
2.33
Occasional
61
Melia aezdarach
0.37
0.24
37.5
B
1.5
1
Rare
Table 4.19k.
Sub urban flora in Season III for north side
S .no
PLANT NAME
Density
Rel.
density
Frequency
Fre. class
Rel. frequency
Abandance
Aba. Class
1
Emblica officinalis
0.625
0.41
50
C
1.99
1.25
Rare
2
Polyalthia longifolia
2
1.34
37.5
B
1.49
5.33
Occasional
3
Acacia auriculiformis
0.125
0.083
12.5
A
0.49
1
Rare
4
Bambusa arundinacea
4
2.68
37.5
B
1.49
10.66
Occasional
5
Musa paradisiaca
1.875
1.25
37.5
B
1.49
5
Occasional
6
Bauhinia varugata
0.25
0.167
25
B
0.99
1
Rare
7
Coccinia indica
0.5
0.33
25
B
0.99
2
Rare
8
Capsicum frutescens
0.125
0.08
12.5
A
0.49
1
Rare
9
Nymphoe stellata
3
2.01
62.5
D
2.48
4.8
Occasional
10
Callistemon lanceolatus
2.625
1.75
75
D
2.98
3.5
Rare
11
Magnolia stellata
1.75
1.17
25
B
0.99
7
Occasional
12
Cocus nusifera
0.5
0.335
37.5
B
1.49
1.33
Rare
13
Coriandrum sativam
1.875
1.25
50
C
1.99
3.75
Rare
14
Crossandra undulaefolia
3.125
2.093
50
C
1.99
6.25
Occasional
S .no
PLANT NAME
Density
Rel.
density
Frequency
Fre. class
Rel. frequency
Abandance
Aba. Class
15
Cucumis sativus
0.25
0.16
25
B
0.99
1
Rare
16
Annona squamosa
0.875
0.58
37.5
B
1.49
2.33
Rare
17
Euphorbia heterophylla
0.625
0.418
37.5
B
1.4925
1.66
Rare
18
Eichhornia crassipes
5.625
3.76
25
B
0.99
22.5
Frequent
19
Cyanodon dactylon
10.38
6.95
62.5
D
2.48
16.6
Frequent
20
Lausonia inermis
4.375
2.93
87.5
E
3.48
5
Occasional
21
Aloe vera
0.625
0.418
50
C
1.99
1.25
Rare
22
Ixora coccinia
3.625
2.428
62.5
D
2.48
5.8
Occasional
23
Eugenia jambolanam
1.375
0.92
50
C
1.99
2.75
Rare
24
Gloriosa superba
0.5
0.335
25
B
0.99
2
Rare
25
Canna indica
1.875
1.25
50
C
1.99
3.75
Rare
26
Eclipta alba
0.125
0.08
12.5
A
0.49
1
Rare
27
Amaranthus viridis
0.25
0.16
25
B
0.99
1
Rare
28
Cymbapogon citratus
12.5
8.37
37.5
B
1.49
33.33
Abundant
29
Nelumbium speciosum
1.25
0.837
62.5
D
2.48
2
Rare
30
Calatropis gigantea
9.125
6.11
100
E
3.98
9.125
Occasional
S .no
PLANT NAME
Density
Rel.
density
Frequency
Fre. class
Rel. frequency
Abandance
Aba. Class
31
Chorisia speciosa
0.125
0.08
12.5
A
0.49
1
Rare
32
Morinda tintoria
1.875
1.25
75
D
2.981
2.5
Rare
33
Azadiracta indica
0.5
0.33
37.5
B
1.49
1.33
Rare
34
Allium cepa
1.75
1.17
50
C
1.99
3.5
Rare
35
Lantana camera
2.75
1.84
62.5
D
2.48
4.4
Rare
36
Ipomea carnea
0.25
0.16
12.5
A
0.49
2
Rare
37
Ravenala madagascariensis
0.625
0.418
25
B
0.99
2.5
Rare
38
Oryza sativa
12.38
8.291
12.5
A
0.49
99
Very abundant
39
Borassus flabellifer
0.25
0.16
25
B
0.99
1
Rare
40
Ficus religiosa
0.37
0.253
25
B
0.99
1.5
Rare
41
Pistia stratiotes
2.62
1.75
25
B
0.99
10.5
Occasional
42
Pongamia glabra
2
1.34
50
C
1.99
4
Rare
43
Thespesia populnea
1.12
0.75
62.5
D
2.48
1.8
Rare
44
Achyranthes aspera
0.12
0.083
12.5
A
0.49
1
Rare
45
Ricinus communis
2.25
1.507
25
B
0.99
9
Occasional
46
Hemidesmus indicus
4.87
3.26
62.5
D
2.48
7.8
Occasional
S .no
PLANT NAME
Density
Rel.
density
Frequency
Fre. class
Rel. frequency
Abandance
Aba. Class
47
Hibiscus rosasinensis
1.37
0.92
37.5
B
1.49
3.66667
Rare
48
Andrapogon sorghum
5
3.35
50
C
1.99
10
Occasional
49
Saccharum officinarum
4.5
3.01
50
C
1.99
9
Occasional
50
Tectona grandis
0.125
0.083
12.5
A
0.49
1
Rare
51
Terminalia arjuna
0.25
0.16
25
B
0.99
1
Rare
52
Millingtonia hortensis
0.125
0.08
12.5
A
0.49
1
Rare
53
Tridax procumbens
12.5
8.37
62.5
D
2.48
20
Frequent
54
Ocimum santum
3.5
2.34
75
D
2.98
4.66667
Occasional
55
Curcuma longa
0.25
0.167
25
B
0.99
1
Rare
56
Gomphrena globosa
4.25
2.84
50
C
1.99
8.5
Occasional
57
Caesalpinia inermis
0.375
0.25
25
B
0.99
1.5
Rare
58
Leucas aspera
5
3.35
100
E
3.98
5
Occasional
59
Amaratus spinosus
1.12
0.753
50
C
1.99
2.25
Rare
60
Cleome viscosa
0.87
0.58
37.5
B
1.49
2.33333
Rare
61
Melia aezdarach
0.37
0.25
37.5
B
1.49
1
Rare
Table 4.19l
Sub urban flora in Season IV for north side
S .no
PLANT NAME
Density
Rel. density
Frequency
0.62
0.408
50
Fre.
Aba.
Rel. Frequency
Abandance
C
1.99
1.25
Rare
class
Class
1
Emblica officinalis
2
Polyalthia longifolia
2
1.308
37.5
B
1.49
5.33
Occasional
3
Acacia auriculiformis
0.12
0.081
12.5
A
0.49
1
Rare
4
Bambusa arundinacea
4
2.61
37.5
B
1.49
10.66
Occasional
5
Musa paradisiaca
1.87
1.22
37.5
B
1.49
5
Occasional
6
Bauhinia varugata
0.25
0.163
25
B
0.99
1
Rare
7
Coccinia indica
0.5
0.32
25
B
0.99
2
Rare
8
Capsicum frutescens
0.125
0.08
12.5
A
0.49
1
Rare
9
Nymphoe stellata
3
1.96
62.5
D
2.48
4.8
Occasional
10
Callistemon lanceolatus
2.62
1.71
75
D
2.98
3.5
Rare
11
Magnolia stellata
1.75
1.14
25
B
0.99
7
Occasional
12
Cocus nusifera
0.5
0.32
37.5
B
1.49
1.33
Rare
13
Coriandrum sativam
1.87
1.22
50
C
1.99
3.75
Rare
S .no
PLANT NAME
Density
Rel. density
Frequency
Fre.
class
Rel. Frequency
Abandance
Aba.
Class
14
Crossandra undulaefolia
3.125
2.0441537
50
C
1.99
6.25
Occasional
15
Cucumis sativus
0.25
0.16
25
B
0.99
1
Rare
16
Annona squamosa
0.875
0.57
37.5
B
1.49
2.33
Rare
17
Euphorbia heterophylla
0.625
0.408
37.5
B
1.49
1.66
Rare
18
Eichhornia crassipes
5.625
3.67
25
B
0.99
22.5
Frequent
19
Cyanodon dactylon
15.38
10.05
62.5
D
2.48
24.6
Frequent
20
Lausonia inermis
4.375
2.86
87.5
E
3.48
5
Occasional
21
Aloe vera
0.5
0.32
37.5
B
1.49
1.33
Rare
22
Ixora coccinia
4.875
3.18
75
D
2.98
6.5
Occasional
23
Eugenia jambolanam
1.375
0.89
50
C
1.99
2.75
Rare
24
Gloriosa superba
0.5
0.327
25
B
0.99
2
Rare
25
Canna indica
1.875
1.22
50
C
1.99
3.75
Rare
26
Eclipta alba
0.125
0.081
12.5
A
0.49
1
Rare
27
Amaranthus viridis
0.25
0.163
25
B
0.99
1
Rare
28
Cymbapogon citratus
12.5
8.1766149
37.5
B
1.49
33.33
Abundant
29
Nelumbium speciosum
1.25
0.81
62.5
D
2.48
2
Rare
S .no
PLANT NAME
Density
Rel. density
Frequency
Fre.
class
Rel. Frequency
Abandance
Aba.
Class
30
Calatropis gigantea
6.62
4.33
100
E
3.98
6.62
Occasional
31
Chorisia speciosa
0.12
0.08
12.5
A
0.49
1
Rare
32
Morinda tintoria
1.87
1.22
75
D
2.98
2.5
Rare
33
Azadiracta indica
0.5
0.32
37.5
B
1.49
1.33
Rare
34
Allium cepa
1.75
1.14
50
C
1.99
3.5
Rare
35
Lantana camera
2.75
1.79
62.5
D
2.48
4.4
Rare
36
Ipomea carnea
0.25
0.16
12.5
A
0.49
2
Rare
37
Ravenala madagascariensis
0.62
0.408
25
B
0.99
2.5
Rare
38
Oryza sativa
12.38
8.094
12.5
A
0.49
99
Very abundant
39
Borassus flabellifer
0.25
0.16
25
B
0.99
1
Rare
40
Ficus religiosa
0.375
0.24
25
B
0.99
1.5
Rare
41
Pistia stratiotes
2.62
1.71
25
B
0.99
10.5
Occasional
42
Pongamia glabra
2
1.308
50
C
1.99
4
Rare
43
Thespesia populnea
1.125
0.73
62.5
D
2.48
1.8
Rare
44
Achyranthes aspera
0.125
0.081
12.5
A
0.49
1
Rare
S .no
PLANT NAME
Density
Rel. density
Frequency
Fre.
class
Rel. Frequency
Abandance
Aba.
Class
45
Ricinus communis
2.25
1.471
25
B
0.99
9
Occasional
46
Hemidesmus indicus
4.87
3.18
62.5
D
2.48
7.8
Occasional
47
Hibiscus rosasinensis
1.37
0.89
37.5
B
1.49
3.66
Rare
48
Andrapogon sorghum
5
3.27
50
C
1.99
10
Occasional
49
Saccharum officinarum
4.5
2.94
50
C
1.99
9
Occasional
50
Tectona grandis
0.125
0.081
12.5
A
0.49
1
Rare
51
Terminalia arjuna
0.25
0.163
25
B
0.99
1
Rare
52
Millingtonia hortensis
0.125
0.081
12.5
A
0.49
1
Rare
53
Tridax procumbens
12.5
8.17
62.5
D
2.48
20
Frequent
54
Ocimum santum
3.5
2.28
75
D
2.98
4.66
Occasional
55
Curcuma longa
0.25
0.16
25
B
0.995
1
Rare
56
Gomphrena globosa
4.25
2.78
50
C
1.99
8.5
Occasional
57
Caesalpinia inermis
0.37
0.24
25
B
0.995
1.5
Rare
58
Leucas aspera
5
3.27
100
E
3.9801
5
Occasional
59
Amaratus spinosus
1.125
0.735
50
C
1.99
2.25
Rare
60
Cleome viscosa
0.875
0.57
37.5
B
1.49
2.33
Rare
61
Melia aezdarach
0.375
0.24
37.5
B
1.49
1
Rare
Table 4.19m.
Sub urban flora in Season I for south side
S .no
PLANT NAME
Density
Rel. density
Frequency
Fre. class
Rel.
Frequency
Abandance Aba. Class
1
Phaseolus radiatus
22.25
20.31
25
B
2.02
89
Abundant
2
Phaseolus mungo
21.37
19.52
25
B
2.02
85.5
Abundant
3
Artocarpus integrifolia
1.25
1.14
25
B
2.022
5
Occasional
4
Solanum melangena
10.62
9.7
25
B
2.02
42.5
Abundant
5
Achras sapota
1.75
1.59
37.5
B
3.03
4.66
Occasional
6
Punica grantanum
1.125
1.02
62.5
D
5.05
1.8
Rare
7
Aeghe marmelos
0.25
0.22
25
B
2.022
1
Rare
8
Alternanthera sessilis
4
3.65
37.5
B
3.03
10.67
Occasional
9
Amaranthus viridis
9.25
8.44
50
C
4.04
18.5
Frequent
10
Asparagus racemosus
5.25
4.79
50
C
4.04
10.5
Occasional
11
Calendula officinalis
2.75
2.51142
37.5
B
3.03
7.333
Occasional
12
Cyanodon dactylon
0.125
0.11
12.5
A
1.01
1
Rare
13
Cardiospermum helicacabum
0.65
0.57
25
B
2.02
2.5
Rare
14
Casuarina equisetifolia
6
5.47
50
C
4.044
12
Occasional
S .no
PLANT NAME
Density
Rel. density
Frequency
Fre. class
Rel.
Frequency
Abandance Aba. Class
15
Cissus quadrangularis
0.62
0.57
25
B
2.02
2.5
Rare
16
Cucurbita maxima
0.37
0.34
25
B
2.02
1.5
Rare
17
Ficus bengalensis
0.25
0.22
12.5
A
1.01
2
Rare
18
Ficus glomerata
0.37
0.34
25
B
2.02
1.5
Rare
19
Ficus religiosa
0.25
0.22
25
B
2.02
1
Rare
20
Trichosanthus anguine
3.25
2.96
62.5
D
5.05
5.2
Occasional
21
Hydrilla verticillata
0.5
0.45
37.5
B
3.033
1.333
Rare
22
Ionidium suffrutocosum
1.375
1.25
50
C
4.04
2.75
Rare
23
Jasminum sambac
0.5
0.45
37.5
B
3.033
1.333
Rare
24
Nerium odonum
0.37
0.34
25
B
2.02
1.5
Rare
25
Ocimum basilium
0.37
0.34
25
B
2.02
1.5
Rare
26
Phoenix sylvestris
0.25
0.22
25
B
2.022
1
Rare
27
Pithecalobium dulce
0.25
0.22
25
B
2.02
1
Occasional
28
Quamoclit pinnata
0.37
0.34
25
B
2.02
1.5
Rare
29
Sesbania grandiflora
0.37
0.34
25
B
2.02
1.5
Rare
30
Solanum nigram
1
0.91
50
C
4.04
2
Rare
S .no
PLANT NAME
Density
Rel. density
Frequency
Fre. class
Rel.
Frequency
Abandance Aba. Class
31
Tephrosia purpuria
1.5
1.36
37.5
B
3.03
4
Rare
32
Tribulus terrestris
1.5
1.369
50
C
4.04
3
Rare
33
Vitex negundo
0.87
0.79
62.5
D
5.0
1.4
Rare
34
Achyranthes aspera
1.87
1.71
37.5
B
3.03
5
Occasional
35
Adiantum venustum
0.5
0.45
25
B
2.02
2
Rare
36
Albizzia lebbeck
0.25
0.22831
25
B
2.0202
1
Rare
37
Amaranthus blitum
5.875
5.3653
37.5
B
3.0303
15.67
Frequent
Table 4.19n
Sub urban flora in Season II for south side
S .no
PLANT NAME
Density
Rel. density
Frequency fre.class Rel. frequency Abandance
Aba. Class
1
Phaseolus radiatus
22.25
20.045
25
B
1.9802
89
Abundant
2
Phaseolus mungo
21.375
19.2568
25
B
1.9802
85.5
Abundant
3
Artocarpus integrifolia
1.25
1.12613
25
B
1.9802
5
Occasional
4
Solanum melangena
10.625
9.57207
25
B
1.9802
42.5
Abundant
5
Achras sapota
1.75
1.57658
37.5
B
2.9703
4.667
Occasional
6
Punica grantanum
1.125
1.01351
62.5
D
4.9505
1.8
Rare
7
Aeghe marmelos
0.25
0.22523
25
B
1.9802
1
Rare
8
Alternanthera sessilis
4
3.6036
37.5
B
2.9703
10.67
Occasional
9
Amaranthus viridis
9.25
8.33333
50
C
3.9604
18.5
Frequent
10
Asparagus racemosus
5.25
4.72973
50
C
3.9604
10.5
Occasional
11
Calendula officinalis
2.75
2.47748
37.5
B
2.9703
7.333
Occasional
12
Cyanodon dactylon
0.125
0.11261
12.5
A
0.9901
1
Rare
13
Cardiospermum helicacabum
1.875
1.68919
37.5
B
2.9703
5
Rare
14
Casuarina equisetifolia
6
5.40541
50
C
3.9604
12
Occasional
15
Cissus quadrangularis
0.625
0.56306
25
B
1.9802
2.5
Rare
16
Cucurbita maxima
0.375
0.33784
25
B
1.9802
1.5
Rare
17
Ficus bengalensis
0.25
0.22523
12.5
A
0.9901
2
Rare
S .no
PLANT NAME
Density
Rel. density
Frequency fre.class Rel. frequency Abandance
Aba. Class
18
Ficus glomerata
0.375
0.33784
25
B
1.9802
1.5
Rare
19
Ficus religiosa
0.25
0.22523
25
B
1.9802
1
Rare
20
Trichosanthus anguine
3.25
2.92793
62.5
D
4.9505
5.2
Occasional
21
Hydrilla verticillata
0.5
0.45045
37.5
B
2.9703
1.333
Rare
22
Ionidium suffrutocosum
1.375
1.23874
50
C
3.9604
2.75
Rare
23
Jasminum sambac
0.5
0.45045
37.5
B
2.9703
1.333
Rare
24
Nerium odonum
0.375
0.33784
25
B
1.9802
1.5
Rare
25
Ocimum basilium
0.625
0.56306
37.5
B
2.9703
1.667
Rare
26
Phoenix sylvestris
0.25
0.22523
25
B
1.9802
1
Rare
27
Pithecalobium dulce
0.25
0.22523
25
B
1.9802
1
Occasional
28
Quamoclit pinnata
0.375
0.33784
25
B
1.9802
1.5
Rare
29
Sesbania grandiflora
0.375
0.33784
25
B
1.9802
1.5
Rare
30
Solanum nigram
1
0.9009
50
C
3.9604
2
Rare
31
Tephrosia purpuria
1.5
1.35135
37.5
B
2.9703
4
Rare
32
Tribulus terrestris
1.5
1.35135
50
C
3.9604
3
Rare
33
Vitex negundo
0.875
0.78829
62.5
D
4.9505
1.4
Rare
34
Achyranthes aspera
1.875
1.68919
37.5
B
2.9703
5
Occasional
35
Adiantum venustum
0.5
0.45045
25
B
1.9802
2
Rare
36
Albizzia lebbeck
0.25
0.22523
25
B
1.9802
1
Rare
37
Amaranthus blitum
5.875
5.29279
37.5
B
2.9703
15.67
Frequent
Table 4.19o.
Sub urban flora in Season III for south side
S .no
PLANT NAME
Density
Rel. density
Frequency fre.class Rel. frequency Abandance
Aba. Class
1
Phaseolus radiatus
22.25
20.13
25
B
2
89
Abundant
2
Phaseolus mungo
21.37
19.34
25
B
2
85.5
Abundant
3
Artocarpus integrifolia
1.25
1.13
25
B
2
5
Occasional
4
Solanum melangena
10.62
9.61
25
B
2
42.5
Abundant
5
Achras sapota
1.75
1.58
37.5
B
3
4.66
Occasional
6
Punica grantanum
1.125
1.018
62.5
D
5
1.8
Rare
7
Aeghe marmelos
0.25
0.22
25
B
2
1
Rare
8
Alternanthera sessilis
4
3.6
37.5
B
3
10.67
Occasional
9
Amaranthus viridis
9.25
8.37
50
C
4
18.5
Frequent
10
Asparagus racemosus
5.25
4.75
50
C
4
10.5
Occasional
11
Calendula officinalis
2.75
2.48
37.5
B
3
7.33
Occasional
12
Cyanodon dactylon
0.125
0.113
12.5
A
1
1
Rare
13
Cardiospermum helicacabum
0.625
0.56
25
B
2
2.5
Rare
14
Casuarina equisetifolia
6
5.42
50
C
4
12
Occasional
15
Cissus quadrangularis
0.62
0.565
25
B
2
2.5
Rare
16
Cucurbita maxima
0.37
0.339
25
B
2
1.5
Rare
17
Ficus bengalensis
0.25
0.22
12.5
A
1
2
Rare
18
Ficus glomerata
0.375
0.33
25
B
2
1.5
Rare
19
Ficus religiosa
0.25
0.226
25
B
2
1
Rare
20
Trichosanthus anguine
3.25
2.94
62.5
D
5
5.2
Occasional
21
Hydrilla verticillata
0.5
0.45
37.5
B
3
1.333
Rare
22
Ionidium suffrutocosum
1.375
1.24
50
C
4
2.75
Rare
23
Jasminum sambac
0.5
0.45
37.5
B
3
1.33
Rare
24
Nerium odonum
0.375
0.33
25
B
2
1.5
Rare
25
Ocimum basilium
1.375
1.24
37.5
B
3
3.66
Rare
26
Phoenix sylvestris
0.25
0.22
25
B
2
1
Rare
27
Pithecalobium dulce
0.25
0.22
25
B
2
1
Occasional
28
Quamoclit pinnata
0.37
0.339
25
B
2
1.5
Rare
29
Sesbania grandiflora
0.37
0.33
25
B
2
1.5
Rare
30
Solanum nigram
1
0.90
50
C
4
2
Rare
31
Tephrosia purpuria
1.5
1.35
37.5
B
3
4
Rare
32
Tribulus terrestris
1.5
1.35
50
C
4
3
Rare
33
Vitex negundo
0.87
0.791
62.5
D
5
1.4
Rare
34
Achyranthes aspera
1.87
1.6983
37.5
B
3
5
Occasional
35
Adiantum venustum
0.5
0.459
25
B
2
2
Rare
36
Albizzia lebbeck
0.25
0.22
25
B
2
1
Rare
37
Amaranthus blitum
5.875
5.31674
37.5
B
3
15.67
Frequent
Table 4.19p.
Sub urban flora in Season IV for south side
S .no
PLANT NAME
Density
Rel. density
Frequency fre.class Rel. frequency Abandance
Aba. Class
1
Phaseolus radiatus
22.25
20.113
25
B
1.98
89
Abundant
2
Phaseolus mungo
21.375
19.322
25
B
1.98
85.5
Abundant
3
Artocarpus integrifolia
1.25
1.12994
25
B
1.98
5
Occasional
4
Solanum melangena
10.625
9.604
25
B
1.98
42.5
Abundant
5
Achras sapota
1.75
1.58
37.5
B
2.97
4.667
Occasional
6
Punica grantanum
1.125
1.0169
62.5
D
4.95
1.8
Rare
7
Aeghe marmelos
0.25
0.225
25
B
1.98
1
Rare
8
Alternanthera sessilis
4
3.61
37.5
B
2.973
10.67
Occasional
9
Amaranthus viridis
9.25
8.36
50
C
3.96
18.5
Frequent
10
Asparagus racemosus
5.25
4.746
50
C
3.964
10.5
Occasional
11
Calendula officinalis
2.75
2.48
37.5
B
2.97
7.333
Occasional
12
Cyanodon dactylon
0.125
0.11
12.5
A
0.991
1
Rare
13
Cardiospermum helicacabum
0.625
0.564
25
B
1.98
2.5
Rare
14
Casuarina equisetifolia
6
5.42
50
C
3.96
12
Occasional
15
Cissus quadrangularis
0.625
0.56
25
B
1.98
2.5
Rare
16
Cucurbita maxima
0.625
0.56
37.5
B
2.97
1.667
Rare
S .no
PLANT NAME
Density
Rel. density
Frequency fre.class Rel. frequency Abandance
Aba. Class
17
Ficus bengalensis
0.25
0.22
12.5
A
0.99
2
Rare
18
Ficus glomerata
0.375
0.33
25
B
1.98
1.5
Rare
19
Ficus religiosa
0.25
0.22
25
B
1.98
1
Rare
20
Trichosanthus anguine
3.25
2.93
62.5
D
4.95
5.2
Occasional
21
Hydrilla verticillata
0.5
0.45
37.5
B
2.97
1.333
Rare
22
Ionidium suffrutocosum
1.375
1.24
50
C
3.96
2.75
Rare
23
Jasminum sambac
0.5
0.45
37.5
B
2.97
1.333
Rare
24
Nerium odonum
0.375
0.33
25
B
1.98
1.5
Rare
25
Ocimum basilium
1.25
1.12
37.5
B
2.97
3.333
Rare
26
Phoenix sylvestris
0.25
0.22
25
B
1.98
1
Rare
27
Pithecalobium dulce
0.25
0.22
25
B
1.98
1
Occasional
28
Quamoclit pinnata
0.375
0.33
25
B
1.98
1.5
Rare
29
Sesbania grandiflora
0.375
0.33
25
B
1.982
1.5
Rare
30
Solanum nigram
1
0.903
50
C
3.96
2
Rare
31
Tephrosia purpuria
1.5
1.35
37.5
B
2.973
4
Rare
32
Tribulus terrestris
1.5
1.35
50
C
3.96
3
Rare
33
Vitex negundo
0.875
0.796
62.5
D
4.95
1.4
Rare
34
Achyranthes aspera
1.875
1.69
37.5
B
2.97
5
Occasional
35
Adiantum venustum
0.5
0.45
25
B
1.98
2
Rare
36
Albizzia lebbeck
0.25
0.22
25
B
1.98
1
Rare
37
Amaranthus blitum
5.875
5.3173
37.5
B
2.97
15.67
Frequent
Table 4.20a.
Urban flora of east side in Season I
S. no
PLANT NAME
Density
Relative density
Frequency
Frequency class
Relative frequency
Abandance
Abandance class
1
Cyanodan sp
18.25
17.76
87.5
E
8.75
20.85
Frequent
2
Mollugo verticillata
31.25
30.41
50
C
5
62.5
Abundant
3
Carnegiea gigantea
2.75
2.676
50
C
5
5.5
Occasional
4
Amaranthus spinosus
4.625
4.501
62.5
D
6.25
7.4
Occasional
5
Datura metal
3.75
3.64
100
E
10
3.75
Rare
6
Nymphaea nouchali
4.5
4.37
37.5
B
3.75
12
Occasional
7
Nelumbo nucifera
4.625
4.501
12.5
A
1.25
37
Abundant
8
Azadiracta indica
3.875
3.77
100
E
10
3.87
Rare
9
Delonix regia
2.125
2.06
50
C
5
4.25
Rare
10
Tamarindus indicus
1.25
1.21
62.5
D
6.25
2
Rare
11
Bauhinia variegata
0.625
0.608
37.5
B
3.75
1.66
Rare
12
Acacia arabica
1
0.971
37.5
B
3.75
2.66
Rare
13
Acalipha indica
9.375
9.12
37.5
B
3.75
25
Frequent
14
Ricinus communis
0.875
0.85
50
C
5
1.75
Rare
15
Morus alba
4.125
4.014
37.5
B
3.75
11
Occasional
16
Lagenaria siceraria
1.25
1.21
37.5
B
3.75
3.33
Rare
17
Solanum melongena
1.25
1.21
37.5
B
3.75
3.33
Rare
18
Mangifera indica
4.5
4.37
50
C
5
9
Occasional
19
Citrus limon
1.875
1.82
37.5
B
3.75
5
Occasional
20
Areca catechu
0.875
0.85
25
B
2.5
3.5
Rare
Table 4.20b.
Urban flora of east side in Season II
S .no
PLANT NAME
Density
Relative density
Frequency
Frequencyclas
Relative frequency
Abandance
Abandance class
1
Cyanodan sp
22
20.80
100
E
9.75
22
Frequent
2
Mollugo verticillata
30
28.36
50
C
4.87
60
Abundant
3
Carnegiea gigantea
2.75
2.60
50
C
4.87
5.5
Occasional
4
Amaranthus spinosus
5.875
5.55
75
D
7.31
7.83
Occasional
5
Datura metal
3.75
3.54
100
E
9.75
3.75
Rare
6
Nymphaea nouchali
4.5
4.25
37.5
B
3.65
12
Occasional
7
Nelumbo nucifera
4.12
3.90
25
B
2.43
16.5
Frequent
8
Azadiracta indica
3.87
3.66
100
E
9.75
3.87
Rare
9
Delonix regia
2.12
2.009
50
C
4.87
4.25
Occasional
10
Tamarindus indicus
1.25
1.18
62.5
D
6.09
2
Rare
11
Bauhinia variegata
0.625
0.591
37.5
B
3.65
1.66
Rare
12
Acacia arabica
1
0.94
37.5
B
3.65
2.66
Rare
13
Acalipha indica
9.375
8.86
37.5
B
3.65
25
Frequent
14
Ricinus communis
0.875
0.827
50
C
4.87
1.75
Rare
15
Morus alba
4.125
3.90
37.5
B
3.65
11
Occasional
16
Lagenaria siceraria
1
0.94
25
B
2.43
4
Rare
17
Solanum melongena
1.25
1.18
37.5
B
3.65
3.33
Rare
18
Mangifera indica
4.5
4.25
50
C
4.87
9
Occasional
19
Citrus limon
1.875
1.77
37.5
B
3.65
5
Occasional
20
Areca catechu
0.875
0.82
25
B
2.43
3.5
Rare
Table 4.20c.
Urban flora of east side in Season III
S
.no
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
PLANT NAME
Cyanodan sp
Mollugo verticillata
Carnegiea gigantea
Amaranthus spinosus
Datura metal
Nymphaea nouchali
Nelumbo nucifera
Azadiracta indica
Delonix regia
Tamarindus indicus
Bauhinia variegata
Acacia arabica
Acalipha indica
Ricinus communis
Morus alba
Lagenaria siceraria
Solanum melongena
Mangifera indica
Citrus limon
Areca catechu
Density
18.25
25
2.75
4.62
3.75
4.5
4.37
3.87
2.12
1.25
0.625
1
9.37
0.87
4.12
1.25
1.25
4.5
1.87
0.87
Relative
density
18.96
25.97
2.85
4.805
3.896
4.67
4.54
4.025
2.207
1.29
0.64
1.038
9.74
0.909
4.2886
1.29
1.29
4.6775
1.948
0.909
Frequency
Frequencyclass
87.5
50
50
62.5
100
37.5
12.5
100
50
62.5
37.5
37.5
37.5
50
37.5
37.5
37.5
50
37.5
25
E
C
C
D
E
B
A
E
C
D
B
B
B
C
B
B
B
C
B
B
Relative
frequency
8.75
5
5
6.25
10
3.75
1.25
10
5
6.25
3.75
3.75
3.75
5
3.75
3.75
3.75
5
3.75
2.5
Abandance
Abandance class
20.85
50
5.5
7.4
3.75
12
35
3.87
4.25
2
1.66
2.66
25
1.75
11
3.33
3.33
9
5
3.5
Frequent
Abundant
Occasional
Occasional
Rare
Occasional
Abundant
Rare
Rare
Rare
Rare
Rare
Frequent
Rare
Occasional
Rare
Rare
Occasional
Occasional
Rare
Table 4.20d.
Urban flora of east side in Season IV
S
.no
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
PLANT NAME
Density
Relative density
Frequency
frequencyclass
Relative frequency
Abandance
Cyanodan sp
Mollugo verticillata
Carnegiea gigantea
Amaranthus spinosus
Datura metal
Nymphaea nouchali
Nelumbo nucifera
Azadiracta indica
Delonix regia
Tamarindus indicus
Bauhinia variegata
Acacia arabica
Acalipha indica
Ricinus communis
Morus alba
Lagenaria siceraria
Solanum melongena
Mangifera indica
Citrus limon
Areca catechu
18.25
21.87
2.75
4.62
3.75
2.62
4.62
3.87
2.12
1.25
0.625
1
9.37
0.87
4.125
1.25
1.25
4.5
1.87
0.87
19.94
23.9
3.005
5.054
4.09
2.86
5.05
4.23
2.32
1.36
0.68
1.092
10.24
0.95
4.5
1.36
1.36
4.91
2.049
0.95
87.5
62.5
50
62.5
100
37.5
12.5
100
50
62.5
37.5
37.5
37.5
50
37.5
37.5
37.5
50
37.5
25
E
D
C
D
E
B
A
E
C
D
B
B
B
C
B
B
B
C
B
B
8.64
6.17
4.93
6.17
9.87
3.70
1.23
9.87
4.93
6.178
3.7
3.7
3.7
4.93
3.7
3.7
3.7
4.93
3.7
2.46
20.85
35
5.5
7.4
3.75
7
37
3.87
4.2
2
1.66
2.66
25
1.75
11
3.33
3.33
9
5
3.5
Abandance
class
Frequent
Abundant
Occasional
Occasional
Rare
Occasional
Abundant
Rare
Rare
Rare
Rare
Rare
Frequent
Rare
Occasional
Rare
Rare
Occasional
Occasional
Rare
Table 4.20e.
Urban flora of west side in Season I
Frequency
S .no
PLANT NAME
Density
Relative density
Abandance
Relative
Frequency
Abandance
class
frequency
class
1
Adathoda vasica
2.875
3.013
62.5
D
3.205
4.6
Occasional
2
Aloe vera
1.875
1.96
50
C
2.56
3.75
Rare
3
Benincasa hispida
3.125
3.28
37.5
B
1.92
8.33
Occasional
4
Acacia nilotica
0.375
0.39
25
B
1.28
1.5
Rare
5
Polyalthia longifolia
1.25
1.31
37.5
B
1.92
3.33
Rare
6
Citrullus colocynthis
4.375
4.59
25
B
1.28
17.5
Frequent
7
Annona squamosa
0.875
0.91
25
B
1.28
3.5
Rare
8
Casuarina equisetifolia
10.25
10.76
37.5
B
1.92
27.33
Frequent
9
Chrysanthemum cinerarifolium
6.25
6.56
87.5
E
4.48
7.14
Occasional
10
Citrusmedica
2.375
2.49
37.5
B
1.92
6.33
Occasional
11
Cocos nucifera
1.625
1.706
75
D
3.84
2.16
Rare
12
Cucumis sativas
10.75
11.28
62.5
D
3.205
17.2
Frequent
13
Moringa olifera
2.625
2.75
62.5
D
3.205
4.2
Rare
14
Ficus glomerata
1.125
1.18
75
D
3.84
1.5
Rare
15
Psidium guajava
2
2.09
50
C
2.56
4
Rare
16
Gomphrena globosa
1.75
1.83
75
D
3.84
2.33
Rare
17
Indigofera tingtoria
3.875
4.06
87.5
E
4.48
4.42
Rare
18
Zizipus jujuba
1
1.049
75
D
3.84
1.33
Rare
19
Tephrosia purpurea
2.125
2.23
100
E
5.12
2.1
Rare
20
Crossandra undulaefolia
2.875
3.018
50
C
2.56
5.75
Occasional
21
Azadirachta indica
3.625
3.805
87.5
E
4.48
4.14
Rare
22
Morus alba
6.5
6.82
25
B
1.28
26
Frequent
23
Carica papaya
2.375
2.49
100
E
5.12
2.37
Rare
24
Ficus religiosa
0.5
0.52
37.5
B
1.92
1.33
Rare
25
Punica granatum
0.875
0.91
50
C
2.56
1.75
Rare
26
Pongamia glabra
2.75
2.88
50
C
2.56
5.5
Occasional
27
Rosa damascena
0.625
0.65
37.5
B
1.92
1.66
Rare
28
Eugenia jambos
1.375
1.44
50
C
2.56
2.75
Rare
29
Hemidesmus indicus
6.5
6.82
25
B
1.28
26
Frequent
30
Solanam nigram
0.875
0.91
37.5
B
1.92
2.33
Rare
31
Solanum indicum
0.875
0.91
50
C
2.56
1.75
Rare
32
Solanum Xanthocarpum
0.875
0.91
62.5
D
3.205
1.4
Rare
33
Datura fastuosa
2.375
2.49
100
E
5.12
2.37
Rare
34
Ocimum sanctum
1.75
1.83
100
E
5.12
1.75
Rare
Table 4.20f.
Urban flora of west side in Season II
S .no
PLANT NAME
Density
Relative
density
Frequency
frequencyclass
Relative frequency
Abandance
Abandance
class
1
Adathoda vasica
3.75
3.816
62.5
D
3.205
6
Occasional
2
Aloe vera
1.5
1.52
50
C
2.56
3
Rare
3
Benincasa hispida
3.125
3.18
37.5
B
1.9223
8.33
Occasional
4
Acacia nilotica
0.375
0.38
25
B
1.28
1.5
Rare
5
Polyalthia longifolia
1.25
1.27
37.5
B
1.92
3.33
Rare
6
Citrullus colocynthis
4.375
4.45
25
B
1.28
17.5
Frequent
7
Annona squamosa
0.875
0.89
25
B
1.28
3.5
Rare
8
Casuarina equisetifolia
10.25
10.43
37.5
B
1.92
27.33
Frequent
9
Chrysanthemum cinerarifolium
6.25
6.36
87.5
E
4.48
7.14
Occasional
10
Citrusmedica
2.37
2.41
37.5
B
1.92
6.33
Occasional
11
Cocos nucifera
1.62
1.65
75
D
3.84
2.16
Rare
12
Cucumis sativas
10.75
10.94
62.5
D
3.205
17.2
Frequent
13
Moringa olifera
2.625
2.67
62.5
D
3.205
4.2
Rare
14
Ficus glomerata
1.125
1.14
75
D
3.84
1.5
Rare
15
Psidium guajava
2
2.03
50
C
2.56
4
Rare
16
Gomphrena globosa
3
3.05
75
D
3.84
4
Rare
17
Indigofera tingtoria
3.875
3.94
87.5
E
4.48
4.42
Rare
18
Zizipus jujuba
1
1.01
75
D
3.84
1.33
Rare
19
Tephrosia purpurea
3.375
3.43
100
E
5.12
3.37
Rare
20
Crossandra undulaefolia
2.875
2.92
50
C
2.56
5.75
Occasional
21
Azadirachta indica
3.625
3.68
87.5
E
4.48
4.14
Rare
22
Morus alba
6.5
6.61
25
B
1.28
26
Frequent
23
Carica papaya
2.37
2.41
100
E
5.12
2.375
Rare
24
Ficus religiosa
0.5
0.508
37.5
B
1.92
1.33
Rare
25
Punica granatum
0.875
0.89
50
C
2.56
1.75
Rare
26
Pongamia glabra
2.75
2.79
50
C
2.56
5.5
Occasional
27
Rosa damascena
0.62
0.63
37.5
B
1.92
1.66
Rare
28
Eugenia jambos
1.375
1.39
50
C
2.56
2.75
Rare
29
Hemidesmus indicus
6.5
6.61
25
B
1.28
26
Frequent
30
Solanam nigram
0.875
0.89
37.5
B
1.92
2.33
Rare
31
Solanum indicum
0.875
0.89
50
C
2.56
1.75
Rare
32
Solanum Xanthocarpum
0.875
0.89
62.5
D
3.2
1.4
Rare
33
Datura fastuosa
2.37
2.41
100
E
5.12
2.37
Rare
34
Ocimum sanctum
1.75
1.78
100
E
5.12
1.75
Rare
Table 4.20g. URBAN FLORA OF WEST SIDE IN Season III
S .no
PLANT NAME
Density
Relative density
Frequency
Frequency
class
Relative frequency
Abandance
Abandance
class
1
Adathoda vasica
3.625
3.708
62.5
D
3.205
5.8
Occasional
2
Aloe vera
2.375
2.42
50
C
2.56
4.75
Occasional
3
Benincasa hispida
3.125
3.19
37.5
B
1.92
8.33
Occasional
4
Acacia nilotica
0.375
0.38
25
B
1.28
1.5
Rare
5
Polyalthia longifolia
1.25
1.27
37.5
B
1.92
3.33
Rare
6
Citrullus colocynthis
4.375
4.47
25
B
1.28
17.5
Frequent
7
Annona squamosa
0.875
0.89
25
B
1.28
3.5
Rare
8
Casuarina equisetifolia
10.25
10.48
37.5
B
1.92
27.33
Frequent
9
Chrysanthemum cinerarifolium
6.25
6.39
87.5
E
4.48
7.14
Occasional
10
Citrusmedica
2.375
2.42
37.5
B
1.92
6.33
Occasional
11
Cocos nucifera
1.625
1.66
75
D
3.84
2.16
Rare
12
Cucumis sativas
10.75
10.99
62.5
D
3.205
17.2
Frequent
13
Moringa olifera
2.625
2.68
62.5
D
3.205
4.2
Rare
14
Ficus glomerata
1.125
1.15
75
D
3.84
1.5
Rare
15
Psidium guajava
2
2.04
50
C
2.56
4
Rare
16
Gomphrena globosa
1.75
1.79
75
D
3.84
2.33
Rare
S .no
PLANT NAME
Density
Relative density
Frequency
5.125
5.24
87.5
1
1.02
Frequency
Abandance
Relative frequency
Abandance
E
4.48
5.85
Occasional
75
D
3.84
1.33
Rare
class
class
17
Indigofera tingtoria
18
Zizipus jujuba
19
Tephrosia purpurea
2.125
2.17
100
E
5.128
2.125
Rare
20
Crossandra undulaefolia
2.875
2.94
50
C
2.56
5.75
Occasional
21
Azadirachta indica
3.625
3.708
87.5
E
4.48
4.14
Rare
22
Morus alba
6.5
6.64
25
B
1.28
26
Frequent
23
Carica papaya
2.375
2.42
100
E
5.12
2.375
Rare
24
Ficus religiosa
0.5
0.511
37.5
B
1.923
1.33
Rare
25
Punica granatum
0.875
0.89
50
C
2.56
1.75
Rare
26
Pongamia glabra
2.75
2.81
50
C
2.56
5.5
Occasional
27
Rosa damascena
0.625
0.63
37.5
B
1.923
1.66
Rare
28
Eugenia jambos
1.375
1.406
50
C
2.56
2.75
Rare
29
Hemidesmus indicus
6.5
6.64
25
B
1.28
26
Frequent
30
Solanam nigram
0.875
0.89
37.5
B
1.92
2.33
Rare
31
Solanum indicum
0.875
0.89
50
C
2.56
1.75
Rare
32
Solanum Xanthocarpum
0.875
0.89
62.5
D
3.205
1.4
Rare
33
Datura fastuosa
2.375
2.42
100
E
5.12
2.37
Rare
34
Ocimum sanctum
1.75
1.79
100
E
5.12
1.75
Rare
Table 4.20h.
Urban flora of west side in Season IV
S .no
PLANT NAME
Density
Relative density
Frequency
Frequencyclass
Relative
frequency
Abandance
Abandance
class
1
Adathoda vasica
4.125
4.18
62.5
D
3.205
6.6
Occasional
2
Aloe vera
2.125
2.15
50
C
2.56
4.25
Rare
3
Benincasa hispida
3.125
3.17
37.5
B
1.92
8.33
Occasional
4
Acacia nilotica
0.375
0.38
25
B
1.28
1.5
Rare
5
Polyalthia longifolia
1.25
1.26
37.5
B
1.92
3.33
Rare
6
Citrullus colocynthis
4.375
4.44
25
B
1.28
17.5
Frequent
7
Annona squamosa
0.875
0.88
25
B
1.28
3.5
Rare
8
Casuarina equisetifolia
10.25
10.40
37.5
B
1.92
27.33
Abundant
9
Chrysanthemum cinerarifolium
6.25
6.34
87.5
E
4.48
7.14
Occasional
10
Citrusmedica
2.375
2.41
37.5
B
1.92
6.33
Occasional
11
Cocos nucifera
1.625
1.64
75
D
3.846
2.16
Rare
12
Cucumis sativas
10.75
10.91
62.5
D
3.205
17.2
Frequent
13
Moringa olifera
2.625
2.66
62.5
D
3.205
4.2
Rare
14
Ficus glomerata
1.125
1.14
75
D
3.84
1.5
Rare
15
Psidium guajava
2
2.03
50
C
2.56
4
Rare
16
Gomphrena globosa
2.5
2.53
75
D
Relative
frequency
3.84
3.33
Abandance
class
Rare
17
Indigofera tingtoria
3.875
3.93
87.5
E
4.48
4.42
Rare
18
Zizipus jujuba
1
1.015
75
D
3.84
1.33
Rare
19
Tephrosia purpurea
2.125
2.15
100
E
5.12
2.12
Rare
20
Crossandra undulaefolia
2.875
2.91
50
C
2.56
5.75
Occasional
21
Azadirachta indica
3.625
3.68
87.5
E
4.48
4.14
Rare
22
Morus alba
6.5
6.59
25
B
1.28
26
Frequent
23
Carica papaya
2.375
2.41
100
E
5.12
2.37
Rare
24
Ficus religiosa
0.5
0.50
37.5
B
1.92
1.33
Rare
25
Punica granatum
0.875
0.88
50
C
2.56
1.75
Rare
26
Pongamia glabra
2.75
2.79
50
C
2.56
5.5
Occasional
27
Rosa damascena
0.625
0.63
37.5
B
1.92
1.66
Rare
28
Eugenia jambos
1.375
1.39
50
C
2.56
2.75
Rare
29
Hemidesmus indicus
6.5
6.59
25
B
1.28
26
Frequent
30
Solanam nigram
0.87
0.88
37.5
B
1.92
2.33
Rare
31
Solanum indicum
0.87
0.88
50
C
2.56
1.75
Rare
32
Solanum Xanthocarpum
0.87
0.88
62.5
D
3.205
1.4
Rare
33
Datura fastuosa
2.375
2.41
100
E
5.12
2.375
Rare
34
Ocimum sanctum
2.75
2.79
100
E
5.12
2.75
Rare
S .no
PLANT NAME
Density
Relative density
Frequency
Frequencyclass
Abandance
Table 4.20i.
Urban flora of north side in Season I
S .no
PLANT NAME
Density
Relative density
Frequency
Frequency
class
Relative frequency
Abandance
Abandance
class
1
Cocos nucifera
4.375
4.12
50
C
3.66
8.75
Occasional
2
Morinda
0.875
0.82
50
C
3.66
1.75
Rare
3
Mangifera indica
3.375
3.18
50
C
3.66
6.75
Occasional
4
Emblicaofficinalis
2.25
2.12
62.5
D
4.585
3.6
Rare
5
Tamarindus indicus
2.125
2.002
50
C
3.66
4.25
Rare
6
Pongamia glabera
3.25
3.062
50
C
3.66
6.5
Occasional
7
Eucalyptus
21.87
20.61
25
B
1.835
87.5
Abundant
8
Ficus bengalensis
1.25
1.17
50
C
3.66
2.5
Rare
9
Bauhinia variegata
0.5
0.47
37.5
B
2.75
1.33
Rare
10
Abutilon indicum
4.75
4.47
37.5
B
2.75
12.66
Occasional
11
Ricinus communis
1.875
1.76
50
C
3.66
3.75
Rare
12
Nerium sp
1
0.94
50
C
3.66
2
Rare
13
Gomphorena globosa
4.5
4.24
62.5
D
4.58
7.2
Occasional
14
Ervatamia coranaria
11.375
10.71
62.5
D
4.587
18.2
Frequent
15
Lucus aspera
2.125
2.002
75
D
5.50
2.83
Rare
S .no
PLANT NAME
2.4
Abandance
class
Rare
2.75
7.66
Occasional
B
1.83
5
Occasional
50
C
3.66
5.25
Occasional
5.064
62.5
D
4.58
8.6
Occasional
Density
Relative density
Frequency
1.5
1.41
62.5
Frequency
Relative frequency
class
D
4.58
16
Datura metal
17
Capsicum annum
2.875
2.70
37.5
B
18
Psidium guajava
1.25
1.17
25
19
Tephrosia purpurea
2.62
2.47
5.37
20
Euphorbia
heterophylla
Abandance
21
Bryophyllum
3.625
3.415
25
B
1.83
14.5
Frequent
22
Cycus
0.75
0.7067
12.5
A
0.91
6
Occasional
23
Bambusa bambos
0.75
0.706
25
B
1.83
3
Rare
24
Lantana indica
1.75
1.648
25
B
1.83
7
Occasional
25
Ixora coccinea
5.25
4.94
37.5
B
2.75
14
Occasional
26
Bougainvillea glabra
0.5
0.47
37.5
B
2.75
1.33
Rare
27
Hibiscus rosasinensis
4.37
4.12
25
B
1.83
17.5
Frequent
28
Eclipta alba
4.75
4.47
37.5
B
2.75
12.66
Occasional
29
Amaranthus viridis
1.5
1.41
50
C
3.66
3
Rare
30
Ocimum santum
2.75
2.59
37.5
B
2.75
7.33
Occasional
31
Amaranthus spinosus
1
0.94
50
C
3.66
2
Rare
Table 4.20j.
Urban flora of north side in Season II
S .no
PLANT NAME
Density
Relative
density
Frequency
Frequencyclass
Relative
frequency
Abandance
Abandance class
1
Cocos nucifera
4.375
4.03
50
C
3.66
8.75
Occasional
2
Morinda
0.875
0.8
50
C
3.66
1.75
Rare
3
Mangifera indica
3.375
3.1
50
C
3.66
6.75
Occasional
4
Emblicaofficinalis
2.25
2.07
62.5
D
4.58
3.6
Rare
5
Tamarindus indicus
2.125
1.95
50
C
3.66
4.25
Rare
6
Pongamia glabera
3.25
2.99
50
C
3.66
6.5
Occasional
7
Eucalyptus
21.875
20.16
25
B
1.83
87.5
Abundant
8
Ficus bengalensis
1.25
1.15
50
C
3.66
2.5
Rare
9
Bauhinia variegata
0.5
0.46
37.5
B
2.75
1.33
Rare
10
Abutilon indicum
6.25
5.76
37.5
B
2.75
16.66
Frequent
11
Ricinus communis
1.875
1.72
50
C
3.66
3.75
Rare
12
Nerium sp
1
0.92
50
C
3.66
2
Rare
13
Gomphorena globosa
5.125
4.72
62.5
D
4.58
8.2
Occasional
14
Ervatamia coranaria
11.375
10.48
62.5
D
4.58
18.2
Frequent
15
Lucus aspera
2.125
1.95
75
D
5.504
2.83
Rare
S .no
PLANT NAME
16
Datura metal
17
Density
Relative
density
Frequency
Frequencyclass
Relative
frequency
Abandance
Abandance
class
1.5
1.38
62.5
D
4.58
2.4
Rare
Capsicum annum
2.875
2.64
37.5
B
2.75
7.66
Occasional
18
Psidium guajava
1.25
1.15
25
B
1.83
5
Occasional
19
Tephrosia purpurea
2.625
2.41
50
C
3.669
5.25
Occasional
5.375
4.95
62.5
D
4.587
8.6
Occasional
20
Euphorbia
heterophylla
21
Bryophyllum
3.625
3.34
25
B
1.83
14.5
Frequent
22
Cycus
0.75
0.69
12.5
A
0.917
6
Occasional
23
Bambusa bambos
0.75
0.69
25
B
1.834
3
Rare
24
Lantana indica
1.75
1.61
25
B
1.83
7
Occasional
25
Ixora coccinea
5.25
4.838709677
37.5
B
2.75
14
Occasional
26
Bougainvillea glabra
0.5
0.460829493
37.5
B
2.75
1.33
Rare
27
Hibiscus rosasinensis
4.375
4.032258065
25
B
1.834
17.5
Frequent
28
Eclipta alba
4.75
4.377880184
37.5
B
2.75
12.66
Occasional
29
Amaranthus viridis
1.5
1.382488479
50
C
3.66
3
Rare
30
Ocimum santum
2.75
2.534562212
37.5
B
2.75
7.33
Occasional
31
Amaranthus spinosus
1.25
1.152073733
50
C
3.66
2.5
Rare
Table 4.20k.
Urban flora of north side in Season III
S .no
PLANT NAME
Density
Relative density
Frequency
frequencyclass
Relative frequency
Abandance
Abandance
class
1
Cocos nucifera
4.375
4.02
50
C
3.63
8.75
Occasional
2
Morinda
0.875
0.804
50
C
3.63
1.75
Rare
3
Mangifera indica
3.375
3.103
50
C
3.63
6.75
Occasional
4
Emblicaofficinalis
2.25
2.06
62.5
D
4.54
3.6
Rare
5
Tamarindus indicus
2.125
1.95
50
C
3.63
4.25
Rare
6
Pongamia glabera
3.25
2.98
50
C
3.63
6.5
Occasional
7
Eucalyptus
21.875
20.11
25
B
1.81
87.5
Abundant
8
Ficus bengalensis
1.25
1.14
50
C
3.63
2.5
Rare
9
Bauhinia variegata
0.5
0.45
37.5
B
2.72
1.33
Rare
10
Abutilon indicum
6
5.51
50
C
3.63
12
Occasional
11
Ricinus communis
1.875
1.72
50
C
3.63
3.75
Rare
12
Nerium sp
1
0.91
50
C
3.636
2
Rare
13
Gomphorena globosa
5.75
5.287
62.5
D
4.54
9.2
Occasional
14
Ervatamia coranaria
11.375
10.45
62.5
D
4.54
18.2
Frequent
15
Lucus aspera
2.125
1.95
75
D
5.45
2.83
Abandance
class
Rare
16
Datura metal
1.5
1.37
62.5
D
4.54
2.4
Rare
17
Capsicum annum
2.875
2.64
37.5
B
2.72
7.66
Occasional
18
Psidium guajava
1.25
1.14
25
B
1.81
5
Occasional
19
Tephrosia purpurea
2.625
2.41
50
C
3.63
5.25
Occasional
5.375
4.94
62.5
D
4.54
8.6
Occasional
S .no
20
PLANT NAME
Euphorbia
heterophylla
Density
Relative density
Frequency
frequencyclass
Relative frequency
Abandance
21
Bryophyllum
3.625
3.33
25
B
1.81
14.5
Frequent
22
Cycus
0.75
0.68
12.5
A
0.90
6
Occasional
23
Bambusa bambos
0.75
0.68
25
B
1.81
3
Rare
24
Lantana indica
1.75
1.609
25
B
1.81
7
Occasional
25
Ixora coccinea
5.25
4.82
37.5
B
2.72
14
Occasional
26
Bougainvillea glabra
0.5
0.45
37.5
B
2.72
1.33
Rare
27
Hibiscus rosasinensis
4.375
4.022
25
B
1.81
17.5
Frequent
28
Eclipta alba
4.75
4.36
37.5
B
2.72
12.66
Occasional
29
Amaranthus viridis
1.5
1.37
50
C
3.63
3
Rare
30
Ocimum santum
2.75
2.52
37.5
B
2.72
7.33
Occasional
31
Amaranthus spinosus
1.125
1.03
50
C
3.63
2.25
Rare
Table 4.20l.
Urban flora of north side in Season IV
S .no
PLANT NAME
Density
Relative density
Frequency
Frequencyclass
Relative frequency
Abandance
Abandance
class
1
Cocos nucifera
4.375
3.86
50
C
3.6
8.75
Occasional
2
Morinda
0.875
0.77
50
C
3.66
1.75
Rare
3
Mangifera indica
3.375
2.98
50
C
3.66
6.75
Occasional
4
Emblicaofficinalis
2.25
1.988
62.5
D
4.58
3.6
Rare
5
Tamarindus indicus
2.125
1.87
50
C
3.66
4.25
Rare
6
Pongamia glabera
3.25
2.87
50
C
3.66
6.5
Occasional
7
Eucalyptus
21.875
19.33
25
B
1.83
87.5
Abundant
8
Ficus bengalensis
1.25
1.104
50
C
3.66
2.5
Rare
9
Bauhinia variegata
0.5
0.44
37.5
B
2.75
1.33
Rare
10
Abutilon indicum
5.25
4.64
37.5
B
2.75
14
Occasional
11
Ricinus communis
1.875
1.65
50
C
3.66
3.75
Rare
12
Nerium sp
1
0.88
50
C
3.66
2
Rare
13
Gomphorena globosa
6.25
5.52
62.5
D
4.583
10
Occasional
14
Ervatamia coranaria
11.375
10.05
62.5
D
4.58
18.2
Frequent
15
Lucus aspera
2.125
1.87
75
D
5.504
2.83
Rare
S .no
PLANT NAME
Abandance
Density
Relative density
Frequency
Frequencyclass
Relative frequency
Abandance
1.5
1.321
62.5
D
4.58
2.4
Rare
class
16
Datura metal
17
Capsicum annum
2.875
2.54
37.5
B
2.75
7.66
Occasional
18
Psidium guajava
1.25
1.104
25
B
1.835
5
Occasional
19
Tephrosia purpurea
2.625
2.32
50
C
3.66
5.25
Occasional
20
Euphorbia heterophylla
8.875
7.84
62.5
D
4.58
14.2
Occasional
21
Bryophyllum
3.625
3.204
25
B
1.83
14.5
Frequent
22
Cycus
0.75
0.66
12.5
A
0.91
6
Occasional
23
Bambusa bambos
0.75
0.66
25
B
1.8385
3
Rare
24
Lantana indica
1.75
1.54
25
B
1.83
7
Occasional
25
Ixora coccinea
5.25
4.64
37.5
B
2.75
14
Occasional
26
Bougainvillea glabra
0.5
0.44
37.5
B
2.75
1.33
Rare
27
Hibiscus rosasinensis
4.375
3.86
25
B
1.83
17.5
Frequent
28
Eclipta alba
4.75
4.19
37.5
B
2.75
12.66
Occasional
29
Amaranthus viridis
1.5
1.32
50
C
3.66
3
Rare
30
Ocimum santum
2.75
2.43
37.5
B
2.75
7.33
Occasional
31
Amaranthus spinosus
2.25
1.98
50
C
3.66
4.5
Rare
Table 4.20m.
Urban flora of south side in Season I
S
.no
PLANT NAME
Density
Relative density
Frequency
Frequency
class
Relative frequency
Abandance
Abandance class
1
Annona squamosa
0.625
0.88
50
C
5
1.25
Rare
2
Cleome viscosa
2.25
3.16
50
C
5
4.5
Occasional
3
Abutilon indicum
2.5
3.52
100
E
10
2.5
Rare
4
Malva silvestris
2.75
3.87
75
D
7.5
3.6667
Rare
5
Sida corchorus
2.125
2.99
100
E
10
2.125
Rare
6
Coccinia indica
1.375
1.93
75
D
7.5
1.8333
Rare
7
Capsicum frutescens
6.87
9.68
37.5
B
3.75
18.333
Frequent
8
Eclipta alba
5.25
7.39
62.5
D
6.25
8.4
Occasional
9
Helianthusannus
4.87
6.86
50
C
5
9.75
Occasional
10
Calatropis gigantea
4.5
6.33
62.5
D
6.25
7.2
Occasional
11
Ipomia staphylina
2
2.81
100
E
10
2
Rare
12
Solanam nigram
1.75
2.46
100
E
10
1.75
Rare
13
Morus alba
1.625
2.28
100
E
10
1.625
Rare
14
Musa paradisiaca
32.5
45.77
37.5
B
3.75
86.667
Abundant
Table 4.20n.
Urban flora of south side in Season II
S .no
PLANT NAME
Density
Relative density
Frequency
frequencyclass
Relative frequency
Abandance
Abandance
class
1
Annona squamosa
1.25
1.89
62.5
D
6.17
2
Rare
2
Cleome viscosa
3.5
5.29
50
C
4.93
7
Occasional
3
Abutilon indicum
2.5
3.78
100
E
9.87
2.5
Rare
4
Malva silvestris
3.5
5.29
75
D
7.407
4.66
Occasional
5
Sida corchorus
2.12
3.21
100
E
9.87
2.125
Rare
6
Coccinia indica
1.37
2.079
75
D
7.407
1.83
Rare
7
Capsicum frutescens
6.87
10.39
37.5
B
3.70
18.33
Frequent
8
Eclipta alba
5.2
7.937
62.5
D
6.17
8.4
Occasional
9
Helianthusannus
4.87
7.376
50
C
4.93
9.75
Occasional
10
Calatropis gigantea
4.5
6.805
62.5
D
6.17
7.2
Occasional
11
Ipomia staphylina
2
3.0249
100
E
9.875
2
Rare
12
Solanam nigram
1.75
2.646
100
E
9.87
1.75
Rare
13
Morus alba
1.62
2.45
100
E
9.87
1.62
Rare
14
Musa paradisiaca
25
37.807
37.5
B
3.70
66.66
Abundant
Table 4.20o.
Urban flora of south side in Season III
S .no
PLANT NAME
Density
Relative
density
Frequency
frequencyclass
Relative frequency
Abandance
Abandance class
1
Annona squamosa
0.625
0.88
50
C
5
1.25
Rare
2
Cleome viscosa
2.25
3.16
50
C
5
4.5
Occasional
3
Abutilon indicum
2.5
3.52
100
E
10
2.5
Rare
4
Malva silvestris
2.75
3.87
75
D
7.5
3.66
Rare
5
Sida corchorus
2.125
2.99
100
E
10
2.12
Rare
6
Coccinia indica
1.375
1.93
75
D
7.5
1.83
Rare
7
Capsicum frutescens
6.87
9.68
37.5
B
3.75
18.33
Frequent
8
Eclipta alba
5.25
7.39
62.5
D
6.25
8.4
Occasional
9
Helianthusannus
4.875
6.86
50
C
5
9.75
Occasional
10
Calatropis gigantea
4.5
6.33
62.5
D
6.25
7.2
Occasional
11
Ipomia staphylina
2
2.81
100
E
10
2
Rare
12
Solanam nigram
1.75
2.46
100
E
10
1.75
Rare
13
Morus alba
1.625
2.28
100
E
10
1.625
Rare
14
Musa paradisiaca
32.5
45.77
37.5
B
3.75
86.66
Abundant
Table 4.20p.
Urban flora of south side in Season IV
S .no
PLANT NAME
Density
Relative density
Frequency
Frequencyclass Relative frequency
Abandance
Abandance
class
1
Annona squamosa
0.625
0.98
50
C
5
1.25
Rare
2
Cleome viscosa
2.25
3.54
50
C
5
4.5
Occasional
3
Abutilon indicum
2.5
3.93
100
E
10
2.5
Rare
4
Malva silvestris
2.75
4.33
75
D
7.5
3.66
Rare
5
Sida corchorus
2.125
3.34
100
E
10
2.125
Rare
6
Coccinia indica
1.375
2.16
75
D
7.5
1.83
Rare
7
Capsicum frutescens
6.875
10.82
37.5
B
3.75
18.33
Frequent
8
Eclipta alba
5.25
8.26
62.5
D
6.25
8.4
Occasional
9
Helianthusannus
4.875
7.67
50
C
5
9.75
Occasional
10
Calatropis gigantea
4.5
7.08
62.5
D
6.25
7.2
Occasional
11
Ipomia staphylina
2
3.14
100
E
10
2
Rare
12
Solanam nigram
1.75
2.75
100
E
10
1.75
Rare
13
Morus alba
1.625
2.55
100
E
10
1.62
Rare
14
Musa paradisiaca
25
39.37
37.5
B
3.75
66.66
Abundant
Table 4.23.
List of fauna in Pudukkottai sub-urban area
S.no
Scientific Name
Common Name
Annelids
1
Megascolex mauuritti
Earthworm
2
Tubifex
Tubifex
Arthropods
1
Palaemon sp
Freshwater prawn
2
Spiralotelphusa sp
Freshwater crab
Arachnids
1
Buthus sp
Scorpion
2
Stegodigyphus sarasinorum
Social spider
Myriapods
1
Scolopendra sp
Centipeds
2
Spirobolus sp
Millipeds
Crustaceans
1
Cambarus
Cray fish
Molluses
1
Cyclophorus
Snail
2
Lamellidens marginails
Freshwater mussel
3
Lanx
Limpet
4
Bithynia
Faucet snail
5
Anodonta
Paper shell
Fishes
1
Catla catla
Carp
2
Channa marulis
Murrels
3
Ophiocephalus sp
Grass carp
4
Oreochromis mossambicus
Tilapia
5
Salmo
Soft rayed fish
Amphibians Name
1
Rana
frog
2
Alytes
green frog
Reptails
1
Calotes rouxi
Rock calotes
2
Calotes versicolor
Lizard
3
Chamaeleon zeylanicus
Chemeleon
4
Nycticebus coucang
Slow Loris
5
Typhlina bramina
Common
6
Dendrelaphis tristis
Bronzeback tree snake
7
Macropisthodon plumbicolor
Green keelback snake
8
Xenochropis piscator
Checkered keelback water snake
9
Ptyas mucosus
Rat snake
10
Najanaja kaouthia
Cobra
worm Snake
Mammals
1
Cynopterus sphinx
Short nosed fruit bat
2
Felis chaus
Jungle cat
3
Funambulus palmarum
Indian Palm Squirrel
4
Mus booduga
Indian field mouse
5
scotophilus heathi
Common yellow bat
6
herpestes
small Indian mongoose
7
Rattus rattus
Rat
8
Oryctolagus curiculus
Rabbit
9
Canis familiaris
Dog
10
Felis catus
Domestic Cat
11
Capra sp
Goat
12
Ovis sp
Sheep
13
Equus hemionus khur
Ass
14
Bos sp
Domestic cattle
15
Bubalus bubalis
Buffalo
16
Bandicota indica
Bandicoot rat
17
Desmodus sp
Bat
Table 4.22.
List of fauna in pudukkottai urban area
S.no Scientific Name
Common Name
Annelids
1
Megascolex mauuritti
Earthworm
2
Tubifex
Tubifex
Arthropods
1
Palaemon sp
Freshwater prawn
2
Spiralotelphusa sp
Freshwater crab
Arachnids
1
Buthus sp
Scorpion
2
Stegodigyphus sarasinorum
Social spider
Myriapods
1
Scolopendra sp
Centipeds
2
Spirobolus sp
Millipeds
Crustaceans
1
Cambarus
Cray fish
Molluses
1
Cyclophorus
Snail
2
Lamellidens marginails
Freshwater mussel
3
Lanx
Limpet
4
Bithynia
Faucet snail
5
Anodonta
Paper shell
Fishes
1
Catla catla
Carp
2
Channa marulis
Murrels
3
Ophiocephalus sp
Grass carp
4
Oreochromis mossambicus
Tilapia
Amphibians Name
1
Rana sp
frog
2
Alytes sp
green frog
Reptails
1
Calotes rouxi
Rock calotes
2
Calotes versicolor
Lizard
3
Chamaeleon zeylanicus
Chemeleon
4
Nycticebus coucang
Slow Loris
5
Dendrelaphis tristis
Bronzeback tree snake
6
Macropisthodon plumbicolor
Green keelback snake
7
Xenochropis piscator
Checkered keelback water snake
8
Ptyas mucosus
Rat snake
Mammals
1
Cynopterus sphinx
Short nosed fruit bat
2
Felis chaus
Jungle cat
3
Funambulus palmarum
Indian Palm Squirrel
4
Mus booduga
Indian field mouse
5
scotophilus heathi
Common yellow bat
6
Rattus rattus
Rat
7
Canis familiaris
Dog
8
Felis catus
Domestic Cat
9
Capra sp
Goat
10
Equus hemionus khur
Ass
11
Bos sp
Domestic cattle
12
Bubalus bubalis
Buffalo
13
Bandicota indica
Bandicoot rat
Table 4.24a.
List of Insects in Urban Residential Zone
S.NO
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
COMMON NAME OF THE
INSECT
Bedbug
Bee
Beetle
Butterfly 9Commom Mormon)
Butterfly(Common Bluebottle)
Butterfly(Common Caster)
Butterfly(Common Joy)
Butterfly(Common Mime)
Butterfly(Common sailer)
Butterfly(Common Tiger)
Butterfly(Crimson Rose)
Butterfly(Indian cabbage White)
Butterfly(Lemon Pancy))
Butterfly(Lime butterfly)
Butterfly(Plain tiger)
Butterfly(Southern birdwing)
Butterfly(Tamil grass dart)
Caddisfly
Carpet beetle
Caterpillar
Cicada
Clothmoth
Cockroach
Common Ants
Common moth
Cow bug
Cricket
Deadshed moth
Dragonfly
Dung roller Beetle
Earwing
Field Grasshopper
Flower Bee
Fruitfly
Giant Water Scorpian
SCIENTIFIC
NAME OF
THE SPECIES
Cimex lectularis
Apis indica
Cataxantha bicolor
Papilio polytes
Graphium sarpedon
Ariadne merione
Graphium doson
Papilo clytia
Neptis hylas
Danaus genutia
Pachlipta hector
Pieris canidia
Junonia lemonias
Papilio demoleus
Danaus chrysippus
Troides minos
Taractrocera
Anthrenus scorphulariae
Tinea pellionella
Periplaneta americana
Camponotus
Syntomid moth
Telengana
Gryllodes sigillatus
Acherontia styx
Odonata
Onthophagus sagittarius
Forficula
Catantops dominaus
Apis dorsata
Drosophila
Ranatra
ORDER OF
THE
INSECT
Hemiptera
Hymenoptera
Coleoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Lepidoptera
Trichoptera
Coleoptera
Lepidoptera
Homoptera
Lepidoptera
Orthoptera
Hymenoptera
Lepidoptera
Hemiptera
Orthoptera
Lepidoptera
Orthoptera
Coleoptera
Dermaptera
Orthoptera
Hymenoptera
Diptera
Hemiptera
36
37
38
39
40
41
42
43
44
45
46
Giant waterbug
Green Grasshopper
Honeybee
House Grasshopper
House wasp
Housefly
Household Cricket
Jewel Beetle
Leaf Insect
Mosquitoe
Mosquitoe
47
48
49
50
51
52
53
54
55
56
55
56
Mosquitoe
Moth
Paddy bug
Plant lice
Red Pumpkin beetle
Silk moth
Silver Fish
Small termites
Stick insect
Wasp
Water Scorpion
White termites
Belostoma
Cyrtacanthacris succinata
Apis florea
Atractomorpha crenulate
Vespa orientalis
Musca domestica
Gryllus domestica
Chrysochroa
Phyllium scythe
Culex
Aedes
Anopheles
quadrimaculatussay
Aegeaera venula
Leptocorixa
Aphid
Raphidopalpa foveicollis
Bombyx mori
Lepisma
Microtermes
Carausius
Polistes herbraeus
Nepa
Odentotermes
Hemiptera
Orthoptera
Hymenoptera
Orthoptera
Hymenoptera
Diptera
Orthoptera
Coleoptera
Orthoptera
Diptera
Diptera
Diptera
Lepidoptera
Hemiptera
Homoptera
Coleoptera
Lepidoptera
Thysanura
Isoptera
Orthoptera
Hymenoptera
Hemiptera
Isoptera
Table 4.24b.
List of insects in sub-urban residential zone
COMMON NAME OF
SCIENTIFIC NAME OF
ORDER OF THE
THEINSECT
THE SPECIES
INSECT
S.NO
1
Bedbug
Cimex lectularis
Hemiptera
2
Bee
Apis indica
Hymenoptera
3
Butterfly(Common Tiger)
Danaus genutia
Lepidoptera
4
Butterfly(Lime butterfly)
Papilio demoleus
Lepidoptera
5
Butterfly(Tamil grass dart)
Taractrocera
Lepidoptera
6
Cockroach
Periplaneta americana
Orthoptera
7
Common Ants
Camponotus
Hymenoptera
8
Common moth
Syntomid moth
Lepidoptera
9
Cow bug
Telengana
Hemiptera
10
Deadshed moth
Acherontia styx
Lepidoptera
11
Dung roller Beetle
Onthophagus sagittarius
Coleoptera
12
Field Grasshopper
Catantops dominaus
Orthoptera
13
Green Grasshopper
Cyrtacanthacris succinata
Orthoptera
14
Honeybee
Apis florea
Hymenoptera
15
House wasp
Vespa orientalis
Hymenoptera
16
Housefly
Musca domestica
Diptera
17
Jewel Beetle
Chrysochroa
Coleoptera
18
Mosquitoe
Culex
Diptera
19
Moth
Aegeaera venula
Lepidoptera
20
Paddy bug
Leptocorixa
Hemiptera
21
Wasp
Polistes herbraeus
Hymenoptera
Table 4.24c.
List of insects in urban commercial zone
S.NO
COMMON NAME OF
SCIENTIFIC NAME OF
THEINSECT
THE SPECIES
ORDER OF
THE
INSECT
1 Bedbug
Cimex lectularis
Hemiptera
2 Butterfly(Common Caster)
Ariadne merione
Lepidoptera
3 Butterfly(Lemon Pancy))
Junonia lemonias
Lepidoptera
4 Carpet beetle
Anthrenus scorphulariae
Coleoptera
5 Caterpillar
Lepidoptera
6 Clothmoth
Tinea pellionella
Lepidoptera
7 Cockroach
Periplaneta americana
Orthoptera
8 Common Ants
Camponotus
Hymenoptera
9 Common moth
Syntomid moth
Lepidoptera
10 Deadshed moth
Acherontia styx
Lepidoptera
11 Dragonfly
Odonata
Orthoptera
12 Field Grasshopper
Catantops dominaus
Orthoptera
13 Green Grasshopper
Cyrtacanthacris succinata
Orthoptera
14 Honeybee
Apis florea
Hymenoptera
15 Housefly
Musca domestica
Diptera
16 Household Cricket
Gryllus domestica
Orthoptera
17 Leaf Insect
Phyllium scythe
Orthoptera
18 Mosquitoe
Culex
Diptera
19 Mosquitoe
Aedes
Diptera
20 Mosquitoe
Anopheles quadrimaculatussay Diptera
21 Plant lice
Aphid
Homoptera
22 Silk moth
Bombyx mori
Lepidoptera
23 Silver Fish
Lepisma
Thysanura
24 Small termites
Microtermes
Isoptera
25 Wasp
Polistes herbraeus
Hymenoptera
26 White termites
Odentotermes
Isoptera
Table 4.24d.
Ist of insects in sub-urban commercial zone
S.NO
COMMON NAME OF
SCIENTIFIC NAME
ORDER OF
THEINSECT
OF THE SPECIES
THE INSECT
1
Bedbug
Cimex lectularis
Hemiptera
2
Butterfly(Common Caster)
Ariadne merione
Lepidoptera
3
Butterfly(Lemon Pancy))
Junonia lemonias
Lepidoptera
4
Cockroach
Periplaneta americana
Orthoptera
5
Common Ants
Camponotus
Hymenoptera
6
Common moth
Syntomid moth
Lepidoptera
7
Cow bug
Telengana
Hemiptera
8
Deadshed moth
Acherontia styx
Lepidoptera
9
House wasp
Vespa orientalis
Hymenoptera
10
Housefly
Musca domestica
Diptera
11
Mosquitoe
Culex
Diptera
12
Moth
Aegeaera venula
Lepidoptera
13
Paddy bug
Leptocorixa
Hemiptera
14
Wasp
Polistes herbraeus
Hymenoptera
Table 4.24e.
List of Insects in Urban Sensitive Zone
COMMON NAME OF
SCIENTIFIC NAME
ORDER OF THE
THEINSECT
OF THE SPECIES
INSECT
S.NO
1
Beetle
Cataxantha bicolor
Coleoptera
Danaus genutia
Lepidoptera
Butterfly(Common
2
Tiger)
3
Butterfly(Lime butterfly) Papilio demoleus
Lepidoptera
Butterfly(Tamil grass
4
dart)
5
Caddisfly
Trichoptera
6
Caterpillar
Lepidoptera
7
Common Ants
Camponotus
Hymenoptera
8
Common moth
Syntomid moth
Lepidoptera
9
Giant waterbug
Belostoma
Hemiptera
10
Leaf Insect
Phyllium scythe
Orthoptera
Taractrocera
Lepidoptera
Anopheles
11
Mosquitoe
quadrimaculatussay
Diptera
Table 4.24f.
List of insects in sub-urban sensitive zone
S.NO
COMMON NAME OF
THEINSECT
SCIENTIFIC
ORDER OF
NAME OF THE
THE
SPECIES
INSECT
1
Bee
Apis indica
Hymenoptera
2
Butterfly(Common Tiger)
Danaus genutia
Lepidoptera
3
Butterfly(Lime butterfly)
Papilio demoleus
Lepidoptera
4
Butterfly(Tamil grass dart)
Taractrocera
Lepidoptera
5
Common Ants
Camponotus
Hymenoptera
6
Field Grasshopper
Catantops dominaus
Orthoptera
7
Green Grasshopper
Cyrtacanthacris succinata
Orthoptera
8
Honeybee
Apis florea
Hymenoptera
9
House wasp
Vespa orientalis
Hymenoptera
10
Housefly
Musca domestica
Diptera
11
Mosquitoe
Culex
Diptera
12
Wasp
Polistes herbraeus
Hymenoptera
Table 4.24g.
Insects in Urban Industrial Zone
S.NO
COMMON NAME OF
SCIENTIFIC NAME
ORDER OF THE
THEINSECT
OF THE SPECIES
INSECT
1
Butterfly(Common Joy)
2
Cicada
3
Dung roller Beetle
Onthophagus sagittarius
Coleoptera
4
Earwing
Forficula
Dermaptera
5
Giant Water Scorpian
Ranatra
Hemiptera
6
Leaf Insect
Phyllium scythe
Orthoptera
7
Mosquitoe
Culex
Diptera
8
Moth
Aegeaera venula
Lepidoptera
9
Small termites
Microtermes
Isoptera
Graphium doson
Lepidoptera
Homoptera
Table 4.25a.
Birds In Urban Residential Zone For Various Seasons
S.NO
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
NAME OF THE BIRD
House crow
Pigeon
Common Myna
Little Cormarent
Little Ergot
Sparrow
Quail
Parrot
Small Sunbird
Peacock
Owl
Duck
Hen
Ashy wood swallow
Ashydrongo
Baya Weaver
Yellow throated sparrow
White throated Myna
Plain prinia
Brown woodpeaker
White breasted kingfisher
Barn Owl
Brainfever bird
Blue Rock pigeon
Grey Francolin
Eagle
Koel
Common Sandpiper
Pond Heron
Little green Bee eater
Indian Robin
Spotted dove
Brown Flycatcher
Jun-July
84
35
31
1
15
32
7
10
31
6
6
20
52
14
4
14
18
8
4
5
3
2
3
18
2
2
3
5
32
23
38
7
8
Sep-Oct
88
38
35
1
17
38
6
12
38
7
6
27
55
18
6
15
20
9
4
5
4
2
4
15
2
1
2
6
41
24
40
8
8
Dec-Jan
80
43
26
2
20
35
7
15
43
8
6
29
60
18
6
18
25
9
5
5
6
2
4
22
3
2
2
7
43
25
44
8
10
March-Apr
98
42
36
2
22
43
8
18
58
10
8
25
58
16
4
16
32
9
5
4
3
2
4
20
3
2
3
5
38
21
42
8
9
Table 4.25b.
Birds in sub urban residential zone for various seasons
S.NO
NAME OF THE BIRD
Jun-July
Sep-Oct
Dec-Jan
March-Apr
1
House crow
212
218
260
200
2
Pigeon
66
68
54
60
3
Common Myna
87
80
80
77
4
Little Cormarent
10
8
7
7
5
Little Ergot
5
6
4
4
6
Sparrow
21
25
20
22
7
Quail
6
5
7
8
8
Parrot
102
140
120
104
9
Eagle
10
11
10
8
10
Peacock
30
32
26
27
11
Owl
33
32
30
29
12
Duck
140
147
180
156
13
Hen
302
230
280
300
14
Brown woodpeaker
22
25
26
20
White breasted
15
kingfisher
30
31
32
25
16
Koel
18
10
10
14
17
Indian Robin
33
28
27
30
18
Pond Heron
22
27
23
28
19
Little green Bee eater
20
23
27
25
Table 4.25c
Birds in urban commercial zone for various seasons
S.NO
NAME OF THE BIRD
Jun-
Sep-
Dec-
March-
July
Oct
Jan
Apr
1
House crow
17
19
16
18
2
Pigeon
10
11
13
12
3
Common Myna
20
19
21
25
4
Sparrow
22
26
28
32
5
Small Sunbird
18
19
20
21
6
Owl
12
14
16
20
7
Brown woodpeaker
5
5
5
6
8
Barn Owl
4
4
4
4
9
Blue Rock pigeon
15
15
17
16
10
Eagle
1
2
2
2
11
Common Sandpiper
3
4
4
4
12
Little green Bee eater
2
2
2
3
13
Indian Robin
2
2
2
3
14
Brown Flycatcher
2
1
1
2
TABLE 4.25d.BIRDS IN SUB URBAN COMMERCIAL ZONE FOR VARIOUS
SEASONS
Jun-
Sep-
Dec-
March-
July
Oct
Jan
Apr
House crow
55
53
57
50
2
Pigeon
45
3
Common Myna
42
42
43
40
4
Sparrow
43
44
46
45
5
Owl
24
27
29
25
6
Brown Flycatcher
15
14
18
15
S.NO
NAME OF THE BIRD
1
45
Table 4.25e.
Birds in urban sensitive zone for various seasons
S.NO
NAME OF THE
Jun-
Sep-
Dec-
March-
BIRD
July
Oct
Jan
Apr
1
Little Cormarent
3
3
2
4
2
Parrot
55
58
62
60
3
Ashy wood swallow
4
3
2
4
4
Baya Weaver
8
6
7
8
8
9
9
10
White throated
5
Myna
White breasted
6
kingfisher
4
3
4
4
7
Brainfever bird
3
2
3
3
8
Koel
2
2
2
3
9
Common Sandpiper
3
3
3
3
Little green Bee
10
eater
1
1
2
2
11
Indian Robin
10
8
8
12
12
Spotted dove
3
3
2
4
Table 4.25f.
Birds in sub urban sensitive zone for various seasons
Jun-
sep-
Dec-
March-
July
Oct
Jan
Apr
Parrot
77
65
68
70
Koel
30
37
36
35
Spotted dove
21
24
24
22
Common Sandpiper
7
7
6
6
Baya Weaver
11
9
10
10
Myna
23
23
24
21
Duck
66
65
64
60
NAME OF THE BIRD
TABLE 4.25g.BIRDS IN URBAN INDUSTRIAL ZONE FOR VARIOUS SEASONS
S.NO
NAME OF THE
Jun-
Sep-
Dec-
March-
BIRD
July
Oct
Jan
Apr
1
House crow
8
8
7
8
2
Common Myna
5
5
6
6
3
Owl
3
3
4
4
4
Indian Robin
5
6
6
7
5
Hen
8
8
9
10
6
Brown Flycatcher
2
2
3
3
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OLD PUDUKKOTTAI
NEW PUDUKKOTTAI
COMMERCIAL ZONE
INDUSTRIAL ZONE
EUTROPHICATION OF URBAN POND WATER
SURFACE WATER SAMPLING IN SUBURBAN AREA
FLORA STUDY
WASTE WATER TREATMENT BY Lemna Species
BIOCOMPOST PREPARED FROM SOLID WASTE
BIOCOMPOST USED PALMAROSA PLANT