Bishop Heber College (Autonomous) Nationally Accredited with A+ by NAAC Tiruchirapalli – 620 017

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.

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 socio- economic 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.

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 socio- economic 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 inter- relation 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 male- female 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-manikka- puram 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 silk- weaving 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 Kavinattuk- kanmai, 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 February- July 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 1991- 2010. Although the net addition in population during each decade has increased consistently. In 1901-1921 had stagnant population In 1921-1951 had steady growth In 1951-1991 had rapid hith growth 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 Pudukkottai- Trichy 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. Well- designed, 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 non- auditory 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 1- 1.33% 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. Rural- urban 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 September- October 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 2005- March 2006. 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 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, IV- Sub 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, Temperature, Iron, Lead, Calcium, Magnesium, Nickel, Fluoride, pH, Nitrite, Chloride, 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 - Andropogon martinii, East Indian geranium, rosha, as 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, Nitrate, Acidity, Alkalinity, Hardness, Electrical Conductivity, Total Solids, 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 ( 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)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 ( 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).

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 INDIVIDUAL CEMENT HOUSE FLAT RENT -HOW MUCH- NEAREST MAINPLACE -RAILWAY STATION BUS STAND MARKET

8.ELECTRICITY AVAILABILITY YES NO

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 FOUR 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- 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

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 physio- chemical 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. p H 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.

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 Annual Census Number Population Percent Rate of Exponential Years of Towns (in Urban Urbanization Growth Rate millions)

1901 1916 25.9 10.8 - -

-0.46 1911 1908 25.9 10.3 0.0

0.87 1921 2048 28.1 11.2 0.8

0.71 1931 2220 33.5 12.0 1.7

1.50 1941 2422 44.2 13.8 2.8

2.54 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)

Table 1.2 Percentage of urban population in India by size-class of urban centres, 1961-1991 Size Class 1961 1971 1981 1991 Class I 51.4 (102) 57.2 (148) 60.4 (216) 65.2 (296) (100000+) Class II 11.2 (129) 10.9 (173) 11.6 (270) 11.0 (341) (50000-100000) Class III 16.9 (437) 18.0 (558) 14.4 (738) 13.2 (927) (20 000-50 000) Class IV 12.8 (719) 10.9 (827) 9.5 (1053) 7.8 (1135) (10000- 20 000)

Class V (5000-10 000) 6.9 (711) 4.5 (623) 3.6 (739) 2.6 (725)

Class VI 0.8 (172) 0.4 (147) 0.5 (229) 0.3 (185) (< 5000)

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 Number of Population of million cities with cities as percent of Census Population Percent population India’s years (in million) increase more than Total Urban one million Population Population 1901 1 1.51 - 0.6 5.8 1911 2 2.76 82.8 1.1 10.7 1921 2 3.13 13.4 1.3 11.1 1931 2 3.41 8.95 1.2 10.2 1941 2 5.31 5.71 1.7 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 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 the four Metropolitan Cities of India, 1901-2001

rate rate rate rate rate years Delhi India Census Growth Growth Growth Growth Growth Kolkata Chennai Mumbai

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 No of Percentage to Total Percentage Slum category of cities population population to total population(in cities /towns and total slum (in 00000) population 00000) (population) towns

More than 23 710 26.6 189 41.3 10,00000

5,00000- 31 215 19.8 43 9.3 10,00000

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

(A Compendium on Indian Slums, 1996)

Table 1.6 Percentage of slum population in the four Metropolitan Cities of India, 1981-2001

Metropolitan 1981 1991 2001 Cities Greater Mumbai 30.8 43.2 48.9 (UA)

Kolkata (UA) 30.3 36.3 32.6

Delhi Municipal 18.0 22.5 18.9 Corp. (UA)

Chennai (UA) 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

Ash Compostable Paper Textile Leather Plastic Metal Glass 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

Number of vehicles Years Percent increase (in thousands) 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 1996 cities 724 Mumbai 629 647 546 608 667

588 Calcutta 475 497 517 545 561

2630 Delhi 1813 1963 2097 2239 2432

Chennai 544 604 641 689 768 812 (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 Sulphur 8.96 4.03 3.65 Dioxide (SO2) Nitrogen 126.46 70.82 54.69 Oxide (NO2) Hydro 249.57 108.20 43.88 Carbons (HC) Carbon 651.01 496.6 188.24 Monoxide (CO) Total 1046.3 659.57 293.71 (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 Metropolitan Particulate dioxide(SO2) dioxide(NO2) cities/ years matter(SPM) (mg/cu.m) (mg/cu.m) (mg/cu.m) Mumbai 1991 28 29 244 1992 18 33 238 1993 22 35 232 1994 33 34 231 1995 31 26 209 Kolkata 1991 63 40 391 1992 36 27 307 1993 40 40 460 1994 25 43 446 1995 35 29 354 Delhi 1991 14 01 130 1992 18 30 364 1993 19 30 424 1994 25 43 446 1995 23 47 410 Chennai 1991 14 01 130 1992 07 03 074 1993 14 00 100 1994 16 00 128 1995 21 00 127 (Anon, 1997) Table 1.13 Waste water generation, collection and treatment in Metropolitan Cities of India, 1997-98

Metropolitan Mode of Volume of wastewater generated Wastewater collected Treatment cities disposal

Volume Capacity Domestic Industrial Total Percent Primary Secondary (mld) (mld) Sea Mumbai 2228.1 227.9 2456.0 2210.0 90.0 109.0 YES YES

Hugli river Kolkata 1383.8 48.4 1432.0 1074.9 75.1 - - - Fish farm

Agriculture Delhi 1270.0 - 1270.0 1016.0 80.0 981.0 YES YES Yamuna river

Chennai 276.0 - 276.0 257.0 93.1 257.0 YES YES Agriculture Sea

(Vishwanathan, 1999)

Table 1.14 Average noise levels in the Metropolitan Cities, 1997 Silence Metropolitan Industrial Commercial Residential Day/night area cities area area area

76 75 70 66 Mumbai Day night 65 66 62 52 78 82 79 79 Kolkata Day night 67 75 65 65 71 78 66 63 Chennai Day night 66 71 48 49 (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 Characteristics

Types of House

Pucca 62.8 94.1 88.2 57.5 66.0

10.7 Semi-pucca 34.1 5.2 32.8 24.4

Kachcha 2.8 0.2 0.9 9.2 9.4

Sanitation Facilities

Flush Toilet 97.4 89.5 85.5 89.1 63.9

Owned 29.4 - - - -

Flush Toilet

Shared Flush Toilet 15.2 - - - -

Public Flush Toilet 52.8 - - - - Pit Toilet 0.1 8.9 8.9 1.6 16.8

No Facility 2.5 1.6 5.6 9.3 19.3

Sources of Drinking Water

Piped 99.6 64.0 86.7 63.3 74.5

Hand Pump 0.2 34.5 12.0 30.6 18.1

Household Mumbai Kolkata Delhi Chennai All India Characteristics

Others 0.2 1.5 1.3 6.1 7.4

Methods of Purifying

Drinking Water

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

Electricity

Yes 99.5 93.8 97.7 89.6 91.3

No 0.5 6.2 2.3 10.4 8.7

Main Type of Fuel Used

for Cooking

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 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 No Persons 621.11 348.69 272.42 1. Population Male 312.69 175.09 137.60 Female 308.42 173.60 134.82 Decadal (1991- 2. 11.19 (-)5.20 42.79 01)Growth 3. Sex Ratio 986 992 980 Persons 73.47 66.66 82.07 4. Literacy Rate (%) Male 82.33 77.47 68.40 Female 64.55 55.84 75.64 278.12 5. Total Workers 175.72 102.40 (44.8) 236.85 6. Main Workers 142.90 93.94 (38.1) 7. Marginal Workers 41.27 (6.7) 32.82 8.45 342.99 8. Non-Workers 172.97 170.02 (55.2) 3.88 9. Cultivators 51.14 (18.4) 47.26

Agricultural 10. 86.65 (31.2) 75.65 11.00 Labourers Workers in Health 11. 14.59 (5.2) 8.15 6.44 Industry 125.74 12. Other Workers 44.66 81.08 (45.2) (Census, 2001)

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. State / District Persons Males Females No 1991 2001 1991 2001 1991 2001

TAMIL NADU 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) Percentage Sex Ratio Population Density India/ Population 2001 Decadal (Females (Per sq.km.) Sl States/ Growth per 1000 Males) No UT* Persons Males Females 81-91 91-01 91 01 91 01 Persons Males Females India 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.

% Decadal Sex-Ratio (No. Population Sl. State/District Population 2001 Growth of Females per Density No. Rate 1000 males) per Sq.Km. Persons Males Females 81-91 91- 2000 91 2001 91 2001 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 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 Common name Botanical name Cereals Rice Oryza sativa Cholam Sorghum bicolot Varagu Paspalum scrobiculation Ragi Eleusine coracana Maize Zea mays Cumbu Pennisetum typhoides Pulses Red gram Cajanus cajan Cow pea Vigna unguiculata Horse gram Dolicus biflorus Black gram Phaseolus mungo Green gram Phaseolus aureus Oil seeds Ground nut Arachis hypogaea Coconut Cocus nucifera Gingelly Sesamum indicum Soya bean Glycine soya Condiments Chillies Capsigum annum Tamarind Tamarindus indica Sugars Sugar cane Saccharum officinarum Palmyra Borassus flabellifer Fibers Cotton 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 Zizipus spp Shrubs Acalypha fruticosa Barleria sp Soleannum toroum Acacia pennata Climbers Pterolobium hexapetalum (ENVIS, 2005)

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

Recommended Environment Effects Max. level Indoor/Domestic 35dB Increased awaking at higher levels Night time Indoor/Domestic Speech communication 45dB Day time Defeuorates at higher Levels Community/Urban Difficulties in falling Asleep at higher 55dB Night time levels Community/Urban Annoyance increases at 55dB Day time Higher levels Predictable risk of hearing Industrial/Occupational 90dB 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 Maximum vocal effort 3.Jet take off(60 meters) 120 possible 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 8.Air conditioning unit 60 Intrusive (6 meters) 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 (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

% of Percentage of Percentage of Respondents Respondents Respondents Source Rank 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 traffic 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 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 Plantation 2.53 8.68 3.44 2.73 9.40 3.44 Crops Flowers 0.22 1.75 7.99 0.34 1.89 8.01 Medicinal 0.04 0.08 1.90 0.05 0.09 1.90 Plants Total 8.91 107.68 12.09 9.73 116.59 12.12 (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

Sum of Mean Statistical Season SPM Df Mean squares Square inference I Between Groups 859169.3 6 143194.8 G1=124.3 Within Groups 1504311.62 28 60172.46 G2=153.7 Total 2363480.9 34 G3 =600 F=2.38 G4 =405 P>0.05 G5= 269 Not G6= 319 Significant G7= 285 II Between Groups 1834435.4 6 305739.2 G1=110 Within Groups 408614.5 28 16344.5 G2=133.7 Total 2243050 34 G3 =785 G4 =355 F=18.706 G5= 180.8 P<0.05 G6= 317 Significant G7= 240 III Between Groups 1954558.3 6 325759.7 G1=110 Within Groups 416737.1 28 16669.4 G2=130 Total 2372955 34 G3 =793 G4 =331 F=19.54 G5= 182 P<0.05 G6= 171 Significant G7= 225 IV Between Groups 2240677 6 373446.1 G1=118.3 Within Groups 656457.4 28 26018.2 G2=141.5 Total 2891134.4 34 G3 =854 G4 =371.2 F=14.35 G5= 132.5 P<0.05 G6= 373.6 Significant G7= 235

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 4.2.

ANOVA for So2 In Various Zones in Pudukkottai Sum of Mean Statistical Season SO2 df Mean squares Square inference I Between Groups 79958 6 13326.3 G1=30.53 Within Groups 320580.4 28 12823.2 G2=25.25 F=1.039 Morning Total 400538.5 34 G3 =165.6 P>0.05 G4 =128.7 Not G5= 80.51 Significant G6= 69.75 G7= 75 I Between Groups 153568.7 6 25594.7 G1=31 Within Groups 594225.9 28 23769 G2=24.7 F=1.07 After Total 747794.7 34 G3 =225.6 P>0.05 noon G4 =127 Not G5= 87.4 Significant G6= 72 G7=79 I Between Groups 222272.6 6 37045.4 G1=31.1 Within Groups 774334.3 28 30973.3 G2=30.2 F=1.196 Night Total 996607 34 G3 =263.8 P>0.05 G4 =135.6 Not G5= 68 Significant G6= 79 G7= 80 II Between Groups 49609.3 6 8268.2 G1=31.8 Within Groups 222857.8 28 8914.3 G2=25 F=.928 Morning Total 272467 34 G3 =141.7 P>0.05 G4 =87 Not G5= 64.8 Significant G6= 58 G7= 65

Sum of Mean Statistical Season SO2 df Mean squares Square inference II Between Groups 76331.8 6 12721.9 G1=28.83 Within Groups 319703.9 28 12788.1 G2=28.25 After Total 396035.8 34 G3 =170.5 F=.995 noon G4 =80.75 P>0.05 G5= 71.7 G6= 69.25 G7= 75 II Between Groups 41937.4 6 6987.9 G1=36.2 Within Groups 200244.3 28 8009.7 G2=37.7 Night 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 Between Groups 24332.2 6 4055.3 G1=38.3 G2=29.7 Within Groups 132416.9 28 5296.6 G3 =112.4 Night Total 156749.1 34 G4 =78.7 F=.766 G5= 75 >0.05 G6= 57 G7= 75

Sum of Mean Statistical Season SO2 df Mean squares Square inference 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 G3 =157.1 F=1.015 G4 =111.7 >0.05 G5= 65.2 G6= 81.3 G7= 72.5 IV Between Groups 135283 6 22547.3 G1=24.8 Within Groups 508804 28 20352.1 G2=26.5 After 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

Sum of Mean Statistical Season NO2 df Mean squares Square inference I Between Groups 96049.9 6 16008.3 G1=23.1 Within Groups 105047.1 28 4201.8 G2=27 Morning Total 34 G3 =174.5 G4 =79 F=3.81 G5= 51.6 P<0.05 G6= 26.6 Significant G7= 79 I Between Groups 92784.6 6 15464.1 G1=20.2 Within Groups 104351.5 28 4174 G2=26.2 After 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 Significant I Between Groups 146883.5 6 24480 G1=23 Within Groups 179935 28 7197 G2=31.7 Night Total 326818.6 34 G3 =211 G4 =84.5 F=3.401 G5=51.9 P<0.05 G6= 29.4 G7= 91 Significant II Between Groups 34408 6 6413.4 G1=19.5 Within Groups 24897 28 995.8 G2=22 Morning Total 63377.9 34 G3 =118.1 F=6.44 G4 =52.7 P<0.05 G5= 42 G6= 37.5 Significant G7= 72.5

Sum of Mean Statistical Season NO2 df Mean squares Square inference II Between Groups 68505 6 11417.5 G1=18.5 Within Groups 73378.5 28 2935.1 G2=25.2 After Total 141884 34 G3 =151.5 F=3.89 noon G4 =57 P<0.05 G5= 45.5 G6= 45.2 Significant G7= 82.5 II Between Groups 37549.2 6 6258.2 G1=19.8 Within Groups 25012.5 28 1000.5 G2=21.5 Night 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

Sum of Mean Statistical Season NO2 df Mean squares Square inference III Between Groups 22347.4 6 3724.5 G1=39.3 Within Groups 33481 28 1339.2 G2=32.7 Night Total 55828 34 G3 =109.6 F=2.781 G4 =53.5 <0.05 G5= 51.6 Significant G6= 40.7 G7= 67.5 IV Between Groups 78083 6 13013.8 G1=21 Morning Within Groups 75652.8 28 3026 G2=25.7 Total 153735.8 34 G3 =157.1 F=4.301 G4 =73.2 <0.05 G5= 44.5 Significant G6= 29.5 G7= 75 IV Between Groups 84680.7 6 14113.4 G1=20.3 Within Groups 88364.1 28 3534.5 G2=27.2 After Total 173044.8 34 G3 =163.5 F=3.9 noon G4 =69.5 <0.05 G5= 44.7 G6= 30.8 Significant G7= 76 IV Between Groups 95287.8 6 15881.3 G1=23 Night Within Groups 101168.3 28 4046.7 G2=29 Total 196456.2 34 G3 =175 F=3.9 G4 =69 <0.05 G5= 47 G6= 35.1 Significant G7= 79 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 4.4a. ANOVA for Leq Noise Level on Holidays

Holiday Leq Sum of df Mean Mean Statistical squares Square inference Day time Between Groups 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

Table 4.4b. ANOVA for Leq Noise Level on Working Days

Working Leq Sum of df Mean Mean Statistical day squares Square inference Daytime Between 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 28 G3 =77.6 F=12.8 Total 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

Table 4.4c. ANOVA for Leq NOISE Level on Festival Days

Festiva Leq Sum of df Mean Mean Statistical l day squares Square inference Day Between Groups 1808.8 5 361.7 G1=90.2 time Within Groups 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

Table 4.5a. ANOVA for pH in various zones haracteristics of Ground water in Pudukkottai

Sum of Mean Statistical Season pH df Mean squares Square inference I Between Groups .144 5 2.8E-02 G1=7.9 Monsoon 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

Sum of Mean Statistical Season EC df Mean squares Square inference I 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

Sum of Mean Statistical Temperature df Mean Season squares Square inference I 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 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

III 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

Sum of Mean Statistical Season Turbidity df Mean squares Square inference I Between Groups .408 5 8.15E02 G1=2.63 Within Groups .455 23 5.05E-02 G2=2.35 Total .862 28 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 Total 1.098 28 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 Mean Mean Statistical squares Square inference I 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

II 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

Sum of Mean Statistical Season TDS df Mean squares Square inference I Between Groups 17664.4 5 3532.8 G1=133.6 Within Groups 5153.3 29 572.5 G2=153 Total 22817.7 34 G3 =193 G4 =165 F=6.17 G5= 103.3 P<0.05 G6= 98 Significant

II Between Groups 14871 5 2974.3 G1=122.3 Within Groups 5132.5 29 570.2 G2=147.5 Total 20004.4 34 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

Sum of Mean Statistical Season TS df Mean squares Square inference I Between Groups 31676.7 5 6335.3 G1=197.6 Within Groups 15637 29 1739.6 G2=212.5 Total 47333.7 34 G3 =255.6 G4 =232.5 F=3.64 G5= 134.6 P<0.05 G6= 139 Significant

II Between Groups 31379.26 5 6275.8 G1=202 Within Groups 15408.3 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 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

Sum of Mean Statistical Season Total Hardness df Mean squares Square inference I Between Groups 3988.7 5 797.7 G1=144.9 Within Groups 2855.9 29 317.3 G2=136 Total 6844.7 34 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 G1=144.9 Total 6406.2 34 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 Mean Mean Statistical squares Square inference I 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

Sum of Mean Statistical Season Magnesium df Mean squares Square inference I Between Groups 2069 5 413.8 G1=59.7 Within Groups 3647 29 405.3 G2=54.5 Total 5716 34 G3 =55.8 F=1.02 G4 =54.8 P>0.05 G5= 82.6 Not G6= 46.5 Significant

II 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 Significant

III Between Groups 2725.9 5 545.1 G1=60 Within Groups 3467.8 29 385.3 G2=55.5 Total 6193.7 34 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

Sum of Mean Statistical Season Alkalinity df Mean squares Square inference I Between Groups 444.6 5 88.9 G1=125.2 Within Groups 179 29 19.8 G2=122.5 Total 623.7 34 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 Significant

III Between Groups 1641.1 5 328.2 G1=127 Within Groups 478.8 29 53.2 G2=121.5 Total 2120 34 G3 =136.3 F=6.16 G4 =135.5 P<0.05 G5=108.3 Significant G6= 115.5

Table 4.5l. ANOVA for acidity in various zones

Sum of Mean Statistical Season Acidity df Mean squares Square inference I 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

Sum of Mean Statistical Season DO df Mean squares Square inference I Between Groups .293 5 5.85E-02 G1=5.72 Within Groups .362 29 4.02E-02 G2=6.1 Total .655 34 G3 =5.8 G4 =6 F=1.67 G5= 5.9 P>0.05 G6=6.01 Not Significant II Between Groups .139 5 2.7E-02 G1=5.9 Within Groups .149 29 1.65E-02 G2=6.2 Total .288 34 G3 =6.06 F=1.45 G4 =6.17 P>0.05 G5= 6.12 Not G6= 6.14 Significant

III 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

Sum of Mean Statistical Season BOD df Mean squares Square inference I Between Groups 4.78 5 9.5 G1=1.2 Within Groups 9.69 29 1.07 G2=1.18 Total 1.4 34 G3 =1.19 G4 =1.19 F=0.889 G5= 1.18 P>0.05 G6= 1.16 Not Significant II 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

III 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

Sum of Mean Statistical Season COD df Mean squares Square inference I Between Groups 1.83 5 .367 G1=9.34 Within Groups 4.51 29 .501 G2=8.7 Total 6.3 34 G3 =9.7 G4 =9.5 F=.733 G5= 9.7 P>0.05 G6= 9.16 Not Significant II 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

III 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

Sum of Mean Statistical Season Nitrite df Mean squares Square inference I Between 1.007E-02 5 2.01E- G1=1.6E-02 Groups 1.500E-03 29 03 G2=2.5E-02 Within Groups 34 1.6E-04 G3 =2.6E-02 Total G4 =7.0E-02 F=12.08 G5= 8.0E-02 P<0.05 G6= 5.0E-02 Significant

II Between 9.1E-03 5 1.8 G1=1.6E-02 Groups 1.5E-03 29 1.7 G2=2.5E-02 Within Groups 34 G3 =2.6E-02 F=10.4 Total 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 Total G4 =7.0E-02 P<0.05 G5= 8.0E-02 Significant G6= 5.0E-02

Table 4.5q. ANOVA for nitrate in various zones

Season Nitrate Sum of df Mean Mean Statistical squares Square inference I 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 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

III 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

Sum of Mean Statistical Season Chloride df Mean squares Square inference I Between Groups 61.1 5 12.2 G1=24.9 Within Groups 144.6 29 16.06 G2=19.5 Total 205.7 34 G3 =24.5 G4 =20.2 F=1.36 G5= 23.1 P>0.05 G6= 24.2 Not Significant II 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

III 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 Mean Mean Statistical squares Square inference I 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 Significant

III Between 0.000 5 0.000 G1=3.0E-02 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 G6= 3.0E-02

Table 4.5t. ANOVA for sulphate in various zones

Season Sulphate Sum of df Mean Mean Statistical squares Square inference I 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

II 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 Mean Mean Statistical squares Square inference I 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 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

III 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 Variable Mean SD Statistical Inference 1. pH G1=8.2 1.06 t=0.2 G2=7.9 0.86 p>0.05 Not significant 2. EC G1=1.6 0.27 t=0.71 G2=2.7 3.13 p>0.05 Not significant 3. Temperature G1=28.2 2.6 t=0.6 G2=28.3 2.42 p>0.05 Not significant 4. Total Solids G1=310 29.04 t=0.72 G2=313 40.37 p>0.05 Not significant 5. Turbidity G1=59.08 11.7 t=12.5 G2=47 7.21 p<0.05 Significant 6. Total Alkalinity G1=116.4 22.26 t=0.7 G2=130.7 19.72 p>0.05 Not significant 7. Total Acidity G1=2.1 0.29 t=0.62 G2=2.04 0.2 p>0.05 Not significant 8. Chloride G1=17.4 3.53 t=0.6 G2=17.6 4.05 p>0.05 Not significant 9. Total Hardness G1=26.8 8.04 t=0.29 G2=23.7 5.23 p>0.05 Not significant

10. Fluoride G1=3.15 3.04 t=0.2 G2=3.75 3.07 p>0.05 Not significant 11. DO G1=6.2 0.43 t=0.67 G2=5.2 0.95 p>0.05 Not significant 12. BOD G1=3.8 0.78 t=1.072 G2=4.2 0.76 p<0.05 Significant 13. Nitrite G1=2.6 1.2 t=0.2 G2=2.8 1.35 p>0.05 Not significant 14. Sulphate G1=5.62 1.28 t=0.5 G2=4.9 1.31 p>0.05 Not significant 15. Phosphate G1=0.1 0.16 t=0.6 G2=0.9 0.2 p>0.05 Not significant 16. E.Coli G1=2400 220 t=0.2 G2=2890 310 p>0.05 Not significant

G1=Urban surface water G2=Sub urban surface water

Table.4.6b. Planktons in urban pond water

SEASON SEASON SEASON SEASON % S.NO I II III IV Occurance

PHYTOPLANKTONS

Chlorophyceae

1 Scenedesmus quandricanda + + + 75

BACILLARIOPHYCEAE

2 Diatoma sp + + + + 100

3 Navicula closterium + + + + 100

CYANOPHYCEAE

4 Anabaena variabilis + + 50

ZOOPLANKTON

Copepoda

5 Diaptomus + + 50

Rotifera

6 Filinia + + 50

TOTAL 6 3 4 5

PERCENTAGE 100 50 66.66 100

Table.4.6c. Planktons in sub urban pond water

SEASON SEASON SEASON SEASON % S.NO I II III IV Occurance

PHYTOPLANKTONS

Chlorophyceae

1 Scenedesmus quandricanda + + + 75

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 + 25

9 Notholca + 25

10 Keratella + + + + 100

11 Branchionus quadridentatus + 25

Copepoda

12 Diaptomus + + 50 Cladocera + + 50

13 Alona

TOTAL 7 7 7 7

PERCENTAGE 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

Sum of Mean Statistical Season pH df Mean squares Square inference I Between Groups 1.54 5 .309 G1=8.4 Monsoon 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 Mean Mean Statistical squares Square inference I Between Groups 1.01 5 .204 G1=.27 Within Groups 0.0001 23 0.0001 G2=.195 Total 1.0101 28 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 F=20.1 Total .838 28 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 Mean Mean Statistical squares Square inference I 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

II 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 Total Sum of df Mean Mean Statistical Magnesium squares Square inference I 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 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

III 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 Mean Mean Statistical squares Square inference I 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 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

III 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 Total Organic Sum of df Mean Mean Statistical Carbon squares Square inference I 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 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 III 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 Total Organic Sum of df Mean Mean Statistical Matter squares Square inference I 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 Mean Mean Statistical squares Square inference I 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 Total 28 G3 =1.06 P>0.05 G4 =1.75 G5= 1.13 Not G6= 1.11 Significant

III 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 Mean Statistical squares Square inference I 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 G5= 5.2 Not G6= 6 Significant III 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 Mean Mean Statistical squares Square inference I 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 Magnesium 219.6 197.9 172.96 171.06 133.89 99.87 Dissolved Oxygen 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 Sulphate 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 Potassium 212 142 135.1 135 132 130

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 Variable Mean SD Statistical Inference 1. Leaf length G1=7.16 1.55 t=20.2 G2=10.16 1.27 p<0.05 Significant 2. Fresh weight G1=0.98 0.08 t=0.2 G2=1.09 0.11 p>0.05 Not significant 3. Dry weight G1=0.63 0.08 t=2.6 G2=0.72 0.09 p<0.05 Significant 4. Free sugar G1=0.99 0.54 t=0.8 G2=1.28 0.66 p>0.05 Not significant 5. Phenol G1=0.17 0.13 t=0.7 G2=0.23 0.14 p>0.05 Not significant 6. Total G1=82.2 26.43 t=0.8 Chlorophyll G2=99 39.4 p>0.05 Not significant

G1=Contol plant G2=Waste water treated plant

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 WORKING DAY Sample 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 FESTIVAL DAY Sample 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

TABLE 4.12. Properties of Solid Waste in Different Zones

Working day ZONE I ZONE II ZONE III sample pH 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 %nitrogen 1.04 0.82 1.26 c/n 36.96 46.78 27.28

Holiday ZONE I ZONE II ZONE III sample pH 7.24 7.08 7.06 %moisture 49.14 34.86 40.36 %ash 38.34 51.92 46.74 %organic matter 61.66 48.08 53.26 %carbon 35.758 27.868 30.886 %nitrogen 0.66 0.96 1.2 c/n 56.944 29.684 25.868

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 %carbon 33.528 31.732 37.0916 %nitrogen 0.64 1 1.3 c/n 55.562 34.886 28.676

Table 4.13. ANOVA for macro nutrients in biocompost treated soil

Variable Period Mean SD Statistical inference Before Cultivation G1=94.1 2.8 t=0.62 G2=97.2 5.3 p>0.05 Not significant Available After First Harvest G1=100 20.8 t=1.52 Nitrogen G2=117 12.3 p<0.05 Significant After Second Harvest G1=104 12.3 t=5.29 G2=131 20.9 p<0.05 Significant Before Cultivation G1=1.3 20.8 t=0.26 G2=1.1 25.3 p>0.05 Not significant Phosphate After First Harvest G1=2.2 2.8 t=2.8 G2=3.5 1.3 p<0.05 Significant After Second Harvest G1=3.1 1.3 t=16.2 G2=4.2 2.1 p<0.05 Significant Before Cultivation G1=49.4 8.2 t=0.38 G2=48 4.3 p>0.05 Not significant Potassium After First Harvest G1=52 3.8 t=0.72 G2=55 4.9 p>0.05 Not significant After Second Harvest 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 Mean SD Statistical inference Before Cultivation G1=2.15 1.2 t=0.72 G2=2.15 0.8 p>0.05 Not significant Zinc After First Harvest G1=2.01 1.02 t=2.4 G2=2.21 1.2 p<0.05 Significant After Second Harvest G1=2.18 1.2 t=6.2 G2=2.32 0.9 p<0.05 Significant Before Cultivation G1=7.6 1.28 t=0.22 G2=7.6 2.3 p>0.05 Not significant Iron After First Harvest G1=7.12 2.8 t=16.8 G2=7.9 1.3 p<0.05 Significant After Second Harvest G1=7.2 1.3 t=15.2 G2=7.9 2.1 p<0.05 Significant Before Cultivation G1=6.8 0.2 t=0.3 G2=6.8 0.9 p>0.05 Not significant Manganese After First Harvest G1=6.93 0.8 t=3.2 G2=6.93 0.2 p<0.05 Significant After Second Harvest G1=6.62 0.2 t=3.1 G2=6.92 0.9 p<0.05 Significant Before Cultivation G1=0.94 0.2 t=0.2 G2=0.8 0.3 p>0.05 Not significant Copper After First Harvest G1=0.82 0.2 t=2.72 G2=0.94 0.3 p<0.05 Significant After Second Harvest 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 Mean SD Statistical inference Before Cultivation G1=40 12.8 t=0.52 G2=40 15.3 p>0.05 Not significant Bacterial After First Harvest G1=52 20.1 t=2.52 Content G2=61 10.3 p<0.05 Significant After Second Harvest G1=58 10.3 t=6.29 G2=68 20.9 p<0.05 Significant Before Cultivation G1=17.3 2.8 t=0.6 G2=17.1 2.3 p>0.05 Not significant Fungal content After First Harvest G1=22.2 8 t=1.8 G2=23.5 13 p<0.05 Significant After Second Harvest G1=28 1.3 t=3.2 G2=31 2.1 p<0.05 Significant Before Cultivation G1=28 8.2 t=1.38 G2=30 4.3 p<0.05 Significant Actinomycetes After First Harvest G1=39 3.8 t=2.72 content G2=43 4.9 p<0.05 Significant After Second Harvest 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 Statistical inference After First Harvest G1=420 12.8 t=12.52 G2=536 15.3 p<0.05 Significant Palmarosa Grass yield 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 Palmarosa Oil G2=2.4 33 p>0.05 Not significant yield After Second G1=2.28 1.3 t=1.8 Harvest G2=3.1 2.1 p<0.05 Significant

Table 4.17 Total Flora in Pudukkottai

Urban flora Sub urban flora

Total number of species :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 Total number of species 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

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

TOURIST ARRIVAL IN PUDUKKOTTAI

2 1.8 1.6 1.4 1.2 TOURIST 1 ARRIVAL 0.8 IN LAKHS 0.6 0.4 0.2 0 1991 1992 1993 1994 1995 1996 YEAR

(ENVIS, 2005)

FIG 2.1 PUDUKKOTTAI MAP

x n M

n P K G

W B E

B-busstand. C-collectoroffice. E-educationcollege G-GH. K-pudukulam. M-milkfarm. P-policestation. R-railwaystation. W-womencollege. x-machuvadi. n-brindavan.

FIG 3.1

POPULATION DISTRIBUTION IN PUDUKKOTTAI

450000 400000 350000 300000

250000 Population 200000 distribution 150000 POPULATION 100000 50000 0

1 1 5 0 81 91 0 0 9 9 0 19 1931 1 1 2 20 2010 YEAR

FIG 4.1a SPM CONCENTRATION IN RESIDENTIAL ZONE ) 3 180 160 140 120 Urban 100 Sub-urban 80 60 40 20 0 SPM concentration(mg/m SPM

Sampling Period

FIG 4.1b SPM CONCENTRATION IN COMMERCIAL ZONE ) 3 900 800 700 Urban 600 500 Sub-urban 400 300 200 100 0 SPM concentration(mg/m SPM SEASON I SEASON II SEASON III SEASON IV Sampling period

FIG 4.1c SPM CONCENTRATION IN SENSITIV E ZONE

350 ) 3 300 250

200 Urban 150 Sub-urban 100 50

SPM concentration(mg/m 0 SEASON I SEASON II SEASON III SEASON IV Sampling Period

FIG 4.1d SPM CONCENTRATION IN INDUSTRIAL ZONE

300 ) 3 250

200

URBA N 150

100

50 SPM concentration(mg/m 0 SEASON I SEASON II SEASON III SEASON IV Sampling Period

FIG 4.2b SO CONCENTRATION IN FIG 4.2a SO2 CONCENTRATION IN RESIDENTIAL 2 ZONE COMMERCIAL ZONE

35 300

30 250 )

25 3 ) 200 3 20 Urban Urban 150

(mg/m Sub-urban (mg/m

Sub-urban 2

2 15 100 SO SO 10 50 5

0 0 NIGHT NIGHT MORNING MORNING AFTERNOON AFTERNOON SEASON I SEASON I

FIG 4.2c SO2 CONCENTRATION IN SENSITIVE ZONE

100 90 80

) 70 3 60 Urban 50

(mg/m Sub-urban 2 40

SO 30 20 10 0 NIGHT MORNING AFTERNOON SEASON I

FIG 4.3a SO2 CONCENTRATION IN FIG 4.3b SO2 CONCENTRATION IN RESIDENTIAL ZONE COMMERCIAL ZONE

40 180 35 160 30 140 ) ) 3 3 120 25 Urban 100 Urban 20 (mg/m Sub-urban (mg/m 80 Sub-urban 2 2 15 60 SO SO 10 40 5 20 0 0 NIGHT NIGHT MORNING MORNING AFTERNOON AFTERNOON SEASON II SEASON II

FIG 4.3c SO2 CONCENTRATION IN SENSITIVE ZONE

80 70 )

3 60 Urban 50 Sub-urban 40 (mg/m

2 30 20 SO 10 0 NIGHT MORNING AFTERNOON SEASON II

FIG 4.4a SO2 CONCENTRATION IN RESIDENTIAL FIG 4.4b SO2 CONCENTRATION IN ZONE COMMERCIAL ZONE

45 120 40 100 35 ) )

3 30

3 80 25 Urban Urban 60

(mg/m 20 Sub-urban (mg/m

2 Sub-urban 2

SO 15

SO 40 10 20 5 0 0 NIGHT NIGHT MORNING MORNING AFTERNOON AFTERNOON SEASON III SEASON III

FIG 4.4c SO2 CONCENTRATION IN SENSITIV E ZONE

80 70

) 60 Urban 3 50 Sub-urban 40 (mg/m

2 30

SO 20 10 0 NIGHT MORNING AFTERNOON SEASON III

FIG 4.5a SO2 CONCENTRATION IN RESIDENTIAL ZONE

25 ) 3 20 Urban

(mg/m 15 Sub-urban 2

SO 10

0 NIGHT MORNING AFTERNOON SEASON IV

FIG 4.5b SO2 CONCENTRATION IN COMMERCIAL ZONE

250

200 Urban ) 3 150 Sub-urban

100 (mg/m 2

SO 50

0 NIGHT MORNING AFTERNOON SEASON IV

FIG 4.5c SO2 CONCENTRATION IN SENSITIVE ZONE

100 90 80

) 70 3 60 Urban 50

(mg/m Sub-urban 2 40

SO 30 20 10 0 NIGHT MORNING AFTERNOON SEASON IV

FIG 4.5d SO2 IN INDUSTRIAL ZONE IN V ARIOUS SEASON

90 80 70

) 60 3 MORNING 50 AFTERNOON

(mg/m 40 2 NIGHT

SO 30 20 10 0 SEASON I SEASON II SEASON III SEASON IV

FIG 4.6a NO2 CONCENTRATION IN RESIDENTIAL FIG 4.6b NO2 CONCENTRATION IN ZONE COMMERCIAL ZONE

35 250 30 200 25 ) 3 ) 3 20 150 Urban Urban

(mg/m Sub-urban (mg/m 2 15 Sub-urban 2 100 NO 10 NO 50 5

0 0 NIGHT NIGHT MORNING MORNING AFTERNOON AFTERNOON SEASON I SEASON I

FIG 4.6c NO2 CONCENTRATION IN SENSITIVE ZONE

50 )

3 40 Urban 30

(mg/m Sub-urban 2

NO 20

0 NIGHT MORNING AFTERNOON SEASON I

FIG 4.7a NO2 CONCENTRATION IN RESIDENTIAL FIG 4.7b NO2 CONCENTRATION IN ZONE COMMERCIAL ZONE

30 160 140 25 120 ) ) 3 3 20 100 Urban Urban 15 80 (mg/m (mg/m Sub-urban Sub-urban 2 2 60 NO NO 10 40 5 20 0 0 NIGHT NIGHT MORNING MORNING AFTERNOON AFTERNOON SEASON II SEASON II

FIG 4.7c NO2 CONCENTRATION IN SENSITIV E ZONE

50 45 40 35 ) 3 30 Urban 25

(mg/m Sub-urban 2 20

No 15 10 5 0 NIGHT MORNING AFTERNOON SEASON II

FIG 4.8a NO2 CONCENTRATION IN FIG 4.8b NO2 CONCENTRATION IN RESIDENTIAL ZONE COMMERCIAL ZONE

45 120 40 100 35 ) ) 3

3 30 80 25 Urban Urban 60

(mg/m Sub-urban

(mg/m 20 Sub-urban 2 2 NO No 15 40 10 20 5 0 0 NIGHT NIGHT MORNING MORNING AFTERNOON AFTERNOON SEASON III SEASON III

FIG 4.8c NO2 CONCENTRATION IN SENSITIVE ZONE

) 40 3 Urban 30

(mg/m Sub-urban 2

NO 20

0 NIGHT MORNING AFTERNOON SEASON III

FIG 4.9a NO2 CONCENTRATION IN RESIDENTIAL ZONE

35 30 )

3 25 20 Urban

(mg/m Sub-urban

2 15

NO 10 5 0 NIGHT MORNING AFTERNOON SEASON IV

FIG 4.9b NO2 CONCENTRATION IN COMMERCIAL ZONE

200 180 160 140 ) 3 120 Urban 100

(mg/m Sub-urban 2 80

NO 60 40 20 0 NIGHT MORNING AFTERNOON SEASON IV

FIG 4.9c NO2 CONCENTRATION IN SENSITIVE ZONE

50 ) 3 40

30 (mg/m

2 Urban

NO 20 Sub-urban

0 NIGHT MORNING AFTERNOON SEASON IV

FIG 4.9d NO2 IN INDUSTRIAL ZONE IN V ARIOUS SEASONS

100 90 80 70 ) 3 60 MORNING 50 A FTERNOON (mg/m 2 40 NIGHT NO 30 20 10 0 Season I Season II Season III Season IV

FIG 4.10a NOISE LEVEL IN FIG 4.10b NOISE LEVEL IN RESIDENTIAL ZONE COMMERCIAL ZONE

90 100 80 90 70 80 70 60 60 Urban 50 Urban 50 Suburban 40 Suburban 40

Noise in dB 30 30

20 Noise in level dB 20 10 10 0 0

HOLIDAY(DAY TIM E) HOLIDAY(DAY TIM E)

FIG 4.10c NOISE LEVEL IN SILENCE ZONE

90 80 70 60 50 Urban 40 Suburban 30

Noise inlevel dB 20 10 0

HOLIDAY(DAY TIM E)

FIG 4.11a NOISE LEVEL IN FIG 4.11b NOISE LEVEL IN RESIDENTIAL ZONE COMMERCIAL ZONE 90 70 80 60 70

50 60 50 40 Urban Urban 40 30 Suburban Suburban 30

20 Noise level in dB 20 Noise inlevel dB 10 10 0 0

HOLIDAY(NIGHT TIM E) HOLIDAY

FIG 4.11c NOISE LEVEL IN SILENCE ZONE

40 Urban 30 Suburban 20 Noise inlevel dB 10 0

HOLIDAY

FIG 4.12a NOISE LEVEL IN FIG 4.12b NOISE LEVEL IN RESIDENTIAL ZONE COMMERCIAL ZONE

100 120 90 80 100 70 80 60 URBA N 50 60 40 URBA N SUBURBAN 40 30 SUBURBAN Noise inlevel dB 20 Noise in level dB 20 10 0 0

WORKING DAY(DAY TIM E) WORKING DAY(DAY TIME )

FIG 4.12c NOISE LEVEL IN SILENCE ZONE

100 90 80 70 60 50 URBA N 40 SUBURBAN 30

Noise in level dB 20 10 0

WORKING DAY(DAY TIME)

FIG 4.13a NOISE LEVEL IN FIG 4.13b NOISE LEVEL IN RESIDENTIAL ZONE COMMERCIAL ZONE 100 80 90 70 80 60 70 50 60 40 URBA N 50 URBA N 30 SUBURBAN 40 SUBURBAN 20 Noise in dB 30 Noise level in dB 10 20 0 10 0 WORKING DAY(NIGHT TIME) WORKING DAY(NIGHT TIME)

FIG 4.13c NOISE LEVEL IN SILENCE ZONE

80 70 60 50 URBA N 40 SUBURBAN 30

Noise in dB 20 10 0

WORKING DAY(NIGHT TIME)

FIG 4.14a NOISE LEVEL IN FIG 4.14b NOISE LEVEL IN RESIDENTIAL ZONE COM M ERCIAL ZONE

120 120 100 100 80 80 URBA N URBA N 60 60 SUBURBAN SUBURBAN 40 40 Noise level in dB 20 Noise level in dB 20

0 0

FESTIVAL DAY(DAY FESTIVAL DAY(DAY TIME) TIME)

FIG 4.14c NOISE LEVEL IN SILENCE ZONE

100

60 URBA N

40 SUBURBAN

Noise in level dB 20

FESTIVAL DAY(DAY TIME)

FIG 4.15a NOISE LEVEL IN FIG 4.15b NOISE LEVEL IN RESIDENTIAL ZONE COMMERCIAL ZONE

100 100

80 80

60 URBA N 60 URBA N

40 SUBURBAN 40 SUBURBAN Noise in dB Noise in dB 20 20

0 0

FESTIVAL DAY(NIGHT FESTIV AL DAY(NIGHT TIME) TIME)

FIG 4.15c NOISE LEVEL IN SILENCE ZONE

80 70 60 50 40 URBA N 30 SUBURBAN Noise inNoise dB 20 10 0

FESTIVAL DAY(NIGHT TIME)

FIG 4.16a NOISE LEVEL IN FIG 4.16b NOISE LEVEL IN INDUSTRIAL ZONE IN HOLIDAYS INDUSTRIAL ZONE IN WORKING DAY

100 100 90 90 80 80 70 70 60 60 Day 50 Day 50 40 Night Night 40 30 30 Noise levelin dB 20 Noise in level dB 20 10 10 0 0 Leq L10 L50 L90 Lmin Lmin Lmax Leq Leq L10 L50 L90 Lmin Lmin Lmax

FIG 4.16c NOISE LEVEL IN IN INDUSTRIAL ZONE FESTIV AL DAY

120

100

80 DA Y 60 NIGHT 40 NoisedB level

0 Leq L10 L50 L90 Lmin Lmax

FIG 4.17b pH IN COMMERCIAL ZONE FIG 4.17a pH IN RESIDENTIAL ZONE

9 8.8 8.6 8.6 8.4 8.4 8.2 8.2 Urban Urban 8 8 suburban suburban

7.8 pH value 7.8 pH VALUE pH 7.6 7.6 7.4 7.4 7.2 7.2 SEASON SEASON SEASON 7 I II III GROUND WATER SAM PLING SEASON I SEASONIII PERIOD SEASON II GROUND WATER SAM PLING PERIOD

FIG 4.17c pH IN SENSITIVE ZONE

8.2

8.1

7.9 Urban 7.8 suburban

pH value pH 7.7

7.6

7.5

7.4

S GROUND WATER SAM PLING PERIOD

FIG 4.18a EC IN RESIDENTIAL ZONE FIG 4.18b EC IN COMMERCIAL ZONE

1.6 1.4 0.85 Urban 1.2

) Urban ) -1 suburban

-1 1 0.8 suburban 0.8 0.75 EC(mho 0.6 EC(m ho 0.4 0.7 0.2 0 I II III SEASO N SEASO N SEASO N GROUND WATER SAM PLING PERIOD SEASON I SEASONIII SEASON II GROUND WATER SAM PLING PERIOD

FIG 4.18c EC IN SENSITIVE ZONE

1 0.8 Urban ) suburban -1 0.6 0.4 EC(m ho 0.2 0 I II III SEASO N SEASO N SEASO N GROUND WATER SAM PLING PERIOD

FIG 4.19a TEM PERATURE IN RESIDENTIAL ZONE FIG 4.19b TEMPERATURE IN COMMERCIAL ZONE

29.5 30 29 29.5 29 28.5 28.5 Urban 28 28 Urban suburban 27.5 suburban 27.5 27

Temperature(oC) 26.5

Temperature(oC) 27 26 26.5 25.5

26 SEASON I SEASON II SEASON III SEASON I SEASONIII SEASON II GROUND WATER SAM PLING GROUND WATER SAM PLING PERIOD PERIOD

FIG 4.19c TEMPERATURE IN SENSITIVE ZONE

29.5

C) 28.5 o

28 Urban

27.5 suburban

27 Temperature(

26.5

26 SEASON I SEASON II SEASON III GROUND WATER SAM PLING PERIOD

FIG 4.20a TURBIDITY IN RESIDENTIAL ZONE FIG 4.20b TURBIDITY IN COMMERCIAL ZONE

3 2.9

2.8 2.5 2.7 2 Urban 2.6 Urban suburban suburban 1.5 2.5

Turbidity(NTU) 2.4 1

Turbidity Value(NTU) Turbidity 2.3

0.5 2.2

0 SEA SON I SEA SON II SEA SON III SEASON I SEASONIII SEASON II GROUND WATER SAM PLING GROUND WATER SAMPLING PERIOD PERIOD

FIG 4.20c TURBIDITY IN SENSITIVE ZONE

2.7

2.6

2.5

2.4

2.3 Urban suburban Turbidity(NTU) 2.2

2.1

2 SEASON I SEASON II SEASON III GROUND WATER SAM PLING PERIOD

FIG 4.21a TSS IN RESIDENTIAL ZONE FIG 4.21b TSS IN COMMERCIAL ZONE

90 90

80 80 70 70 60 60 50 Urban

50 Urban 40 suburban TSS(mg/l) 40 suburban 30

TSS(mg/l) 20 30 10 20 0 10

0 SEASON I SEASONIII SEASON II SEASON I SEASON II SEASON III GROUND WATER SAM PLING GROUND WATER SAM PLING PERIOD PERIOD

FIG 4.21c TSS IN SENSITIVE ZONE

Urban 30 suburban TSS(mg/l)

0 SEASON I SEASON II SEASON III GROUND WATER SAM PLING PERIOD

FIG 4.22a TDS IN RESIDENTIAL ZONE FIG 4.22b TDS IN COMMERCIAL ZONE

180 250

160 200 140

120 150 Urban 100 Urban suburban 100 80 suburban TDS(mg/l) TDS(mg/l) 60 50 40 0 20

0 SEASON I SEASON II SEASON III SEASON I SEASONIII SEASON II GROUND WATER SAM PLING GROUND WATER SAM PLING PERIOD PERIOD

FIG 4.22c TDS IN SENSITIVE ZONE

120

100

80 Urban 60 suburban

TDS(mg/l) 40

0 SEASON I SEASON II SEASON III GROUND WATER SAM PLING PERIOD

FIG 4.23b TS IN COMMERCIAL ZONE FIG 4.23a TS IN RESIDENTIAL ZONE

220 300 215 250 210

205 200 200 Urban 195 Urban 150 suburban

190 suburban TS(mg/l) 100 185 Total Solids(m g/l) 180 50 175 170 0 165 SEASON I SEASON II SEASON III SEASON I SEASONIII GROUND WATER SAMPLING SEASON II PERIOD GROUND WATER SAM PLING PERIOD

FIG 4.23c TS IN SENSITIVE ZONE

150

145

140

135

130 Urban TS(mg/l) suburban 125

120

115 SEASON I SEASON II SEASON III GROUND WATER SAM PLING PERIOD

FIG 4.24a TOTAL HARDNESS IN RESIDENTIAL FIG 4.24b TOTAL HARDNESS IN COM M ERCIAL ZONE ZONE

150 160 155 145 150

140 145 Urban 140 Urban suburban 135 suburban 135

hardness(mg/l) 130 130 Hardness(mg/l) 125 125 120

120 SEASON I SEASON II SEASON III SEASON I SEASO NIII SEASON II GROUND WATER SAM PLING GROUND WATER SAMPLING PERIOD PERIOD

FIG 4.24c TOTAL HARDNESS IN SENSITIVE ZONE

200 180

160 140

120 Urban 100 suburban 80

Hardness(mg/l) 60 40

20 0 SEASON I SEASON II SEASON III GROUND WATER SAMPLING PERIOD

FIG 4.25a CALCIUM IN RESIDENTIAL ZONE FIG 4.25b CALCIUM IN COMMERCIAL ZONE

88 100

86 95 84 90 82 Urban 80 Urban 85 suburban 78 suburban 80 Calcium (m g/l)

Calcium (m g/l) 76

74 75

72 70 70 SEA SON I SEA SON II SEA SON III SEASON I SEASONIII GROUND WATER SAM PLING SEASON II PERIOD GROUND WATER SAM PLING PERIOD

FIG 4.25c CALCIUM IN SENSITIVE ZONE

100 90 80 70 60 Urban 50 suburban 40

Calcium (mg/l) 30 20 10 0 SEASON I SEASON II SEASON III GROUND WATER SAMPLING PERIOD

FIG 4.26a M AGNESIUM N RESIDENTIAL ZONE FIG 4.26b MAGNESIUM IN COMMERCIAL ZONE

62 62

60 60 58 58

56 Urban Urban 56 54 suburban suburban 54 52 Magnesium (mg/l) Magnesium

Magnesium (mg/l) Magnesium 52 50

48 50

46 SEA SON I SEA SON II SEA SON III SEASON I SEASONIII SEASON II GROUND WATER SAM PLING GROUND WATER SAMPLING PERIOD PERIOD

FIG 4.26c MAGNESIUM IN SENSITIVE ZONE

100 90 80 70 60 50 Urban 40 suburban 30 Magnesium (mg/l) Magnesium 20 10 0 SEASON I SEASON II SEASON III GROUND WATER SAM PLING PERIOD

FIG 4.27a ALKALINITY IN RESIDENTIAL ZONE FIG 4.27b ALKALINITY IN COMMERCIAL ZONE

132 142

130 140

128 138 Urban 126 136 Urban suburban 124 suburban 134 Alkalinity(mg/l)

Alkalinity(mg/l) 122 132 120 130 118

116 SEASON I SEASONIII SEASON I SEASON II SEASON III SEASON II GROUND WATER SAM PLING GROUND WATER SAM PLING PERIOD PERIOD

FIG 4.27c ALKALINITY IN SENSITIVE ZONE

160

140

120

100 Urban suburban 80

60 Alkalinity(mg/l)

0 SEASON I SEASON II SEASON III GROUND WATER SAM PLING PERIOD

FIG 4.28a ACIDITY IN RESIDENTIAL ZONE FIG 4.28b ACIDITY IN COMMERCIAL ZONE

0.9 0.94

0.8 0.92

0.7 0.9

0.6 0.88 Urban 0.5 0.86 Urban suburban 0.4 suburban 0.84 Acidity(mg/l)

Acidity(mg/l) 0.82 0.3 0.8 0.2 0.78 0.1

0 SEASON I SEASON II SEASON III SEASON I SEASONIII SEASON II GROUND WATER SAM PLING GROUND WATER SAM PLING PERIOD PERIOD

FIG 4.28c ACIDITY IN SENSITIVE ZONE

0.92 0.9 0.88 0.86 0.84 0.82 Urban 0.8 suburban

Acidity(mg/l) 0.78 0.76 0.74 0.72 0.7 SEASON I SEASON II SEASON III GROUND WATER SAM PLING PERIOD

FIG 4.29a DO IN RESIDENTIAL ZONE FIG 4.29b DO IN COM M ERCIAL ZONE

6.4 6.2

6.3 6.1 6.2 6 6.1 Urban 6 5.9 Urban suburban

5.9 DO (mg/l) suburban 5.8

DO(mg/l) 5.8 5.7 5.7

5.6 5.6

5.5

5.4 SEASON I SEASONIII SEA SON I SEA SON II SEA SON III SEASON II GROUND WATER SAM PLING GROUND WATER SAM PLING PERIOD PERIOD

FIG 4.29c DO IN SENSITIVE ZONE

6.2 6.15 6.1 6.05 6

5.95 Urban 5.9 suburban DO(mg/l) 5.85 5.8 5.75 5.7 5.65 SEA SON I SEA SON II SEA SON III GROUND WATER SAM PLING PERIOD

FIG 4.30a BOD IN RESIDENTIAL ZONE FIR 4.30b BOD IN COM M ERCIAL ZONE

1.4 1.25 1.2 1.2

1 1.15 Urban suburban 1.1 0.8 Urban

suburban BOD(mg/l) 1.05 0.6 BOD(mg/l) 1 0.4 0.95 0.2

0 SEASON I SEASONIII SEASON II SEA SON I SEA SON II SEA SON III GROUND WATER SAM PLING GROUND WATER SAMPLING PERIOD PERIOD

FIG 4.30c BOD IN SENSITIV E ZONE

1.2

1.15

1.1

Urban 1.05 suburban BOD(mg/l) 1

0.95

0.9 SEASON I SEASON II SEASON III GROUND WATER SAM PLING PERIOD

FIG 4.31a COD IN RESIDENTIAL ZONE FIG 4.31b COD IN COMMERCIAL ZONE

9.6 10.4

9.4 10.2

9.2 10

9.8 Urban 9 Urban suburban suburban 9.6

8.8 COD(mg/l) COD(mg/l) 9.4 8.6 9.2 8.4 9 8.2 SEASON I SEASON II SEASON III SEASON I SEASONIII GROUND WATER SAM PLING SEASON II PERIOD GROUND WATER SAM PLING PERIOD

FIG 4.31c COD IN SENSITIVE ZONE

10.2

9.8

9.6 Urban 9.4 suburban

COD(mg/l) 9.2

8.8

8.6 SEA SON I SEA SON II SEA SON III GROUND WATER SAMPLING PERIOD

FIG 4.32a NITRITE IN RESIDENTIAL ZONE FIG 4.32b NITRITE IN COMMERCIAL ZONE

0.03 0.08 0.07 0.025 0.06 0.02 0.05 Urban Urban 0.04 0.015 suburban suburban 0.03 Nitrite (mg/l) Nitrite

Nitrite(mg/l) 0.01 0.02

0.005 0.01 0 0 SEASON SEASON SEASON I II III SEASON I SEASONIII SEASON II GROUND WATER SAMPLING GROUND WATER SAM PLING PERIOD PERIOD

FIG 4.32c NITRITE IN SENSITIVE ZONE

0.09

0.08 0.07

0.06

0.05 Urban 0.04 suburban

Nitrite (mg/l) 0.03

0.02 0.01

0 SEASON I SEASON II SEASON III GROUND WATER SAM PLING PERIOD

FIG 4.33a NITRATE IN RESIDENTIAL ZONE FIG 4.33b NITRATE IN COMMERCIAL ZONE

4.8 5.3 4.7 5.2 4.6 5.1 5 4.5 4.9 Urban 4.4 Urban 4.8 suburban 4.3 suburban 4.7 4.6 Nitrate(mg/l) 4.2 4.5 Nitrate content(mg/l) 4.1 4.4 4.3 4

3.9 SEASON I SEASON SEASON SEASON I SEASONIII II III SEASON II GROUND WATER SAMPLING GROUND WATER SAMPLING PERIOD PERIOD

FIG 4.33c NITRATE IN SENSITIVE ZONE

5.4 5.3 5.2 5.1 5 Urban 4.9 4.8 suburban 4.7 4.6 Nitrate (mg/l) 4.5 4.4 4.3 SEASON I SEASON II SEASON III GROUND WATER SAM PLING PERIOD

FIG 4.34a CHLORIDE IN RESIDENTIAL ZONE FIG 4.34b CHLORIDE IN COMMERCIAL ZONE

30 30

25 25

20 20 Urban Urban 15 15 suburban suburban 10

10 Chloride(m g/l) Chloride (mg/l) 5 5 0

0 SEASON SEASON SEASON I II III SEASON I SEASONIII SEASON II GROUND WATER SAM PLING GROUND WATER SAMPLING PERIOD PERIOD

FIG 4.34c CHLORIDE IN SENSITIVE ZONE

Urban 24 suburban

Chloride (mg/l) 23

21 SEASON I SEASON II SEASON III GROUND WATER SAM PLING PERIOD

FIG 4.35b FLUORIDE IN COM M ERCIAL ZONE FIG 4.35a FLUORIDE IN RESIDENTIAL ZONE

0.035 0.0326

0.0345 0.0324

0.034 0.0322

0.0335 0.032 Urban 0.0318 0.033 suburban 0.0316 Urban 0.0325 suburban

Fluoride (mg//l) 0.0314 0.032 0.0312 0.0315 Fluoride content(mg/l) Fluoride 0.031 0.031 0.0308 0.0306 SEASON I SEASON SEASON SEASON I SEASONIII SEASON II II III GROUND WATER SAM PLING PERIOD GROUND WATER SAMPLING PERIOD

FIG 4.35C FLUORIDE IN SENSITIVE ZONE

0.0321

0.032

0.0319

0.0318

0.0317 Urban 0.0316 suburban

0.0315 Flouride (mg/l) Flouride

0.0314

0.0313

0.0312 SEASON I SEASON SEASON II III GROUND WATER SAM PLING PERIOD

FIG 4.36a SULPHATE IN RESIDENTIAL ZONE FIG 4.36b SULPHATE IN COMMERCIAL ZONE

24 24.5

23.5 24

23 23.5

22.5 23 Urban suburban 22.5 22

Sulphate (m g/l) 22 21.5

Sulphate content(mg/l) 21.5 21 21 Urban 20.5 suburban SEA SON I SEA SON II SEA SON III SEASON I SEASONIII GROUND WATER SAM PLING SEASON II PERIOD GROUND WATER SAMPLING PERIOD

FIG 4.36c SULPHATE IN SENSITIVE ZONE

Urban 15 suburban

Sulphate (mg/l) 10

0 SEASON I SEASON II SEASON III GROUND WATER SAM PLING PERIOD

FIG 4.37a E.Coli IN RESIDENTIAL ZONE FIG 4.37b E.Coli IN COMMERCIAL ZONE

70 90 80 60 70 60 50 50 Urban 40 Urban 40 suburban suburban

E.Coli(100/ml) 30 30 20 E.Coli (100/ml) E.Coli 20 10 0 10

0 SEASON I SEASONIII SEA SON I SEA SON II SEA SON III SEASON II GROUND WATER SAM PLING GROUND WATER SAM PLING PERIOD PERIOD

FIG 4.37c E.Coli IN SENSITIVE ZONE

Urban 30 suburban

E.Coli (/100ml) 20

0 SEASON I SEASON II SEASON III GROUND WATER SAM PLING PERIOD

FIG 4.38 pH IN SURFACE WATER IN VARIOUS SEASONS

8.8 8.6 8.4

8.2 Urban 8 pH 7.8 Suburban 7.6 7.4 7.2

I II III

SEASON SEASON SEASON Sampling Period

FIG 4.39 EC IN SURFACE WATER IN VARIOUS SEASONS 5 4.5 4

) 3.5 -1 3 Urban 2.5 2 Suburban

EC(mho 1.5 1 0.5 0 I II III SEASON SEASON SEASON Sampling season

FIG 4.40 TEMPERATURE IN SURFACE WATER IN V ARIOUS SEASONS 30.5 30 29.5 29 28.5 Urban 28 Suburban 27.5 27

Temperature oC 26.5 26 25.5 III SEASON SEASON I SEASON II Sampling Period

FIG 4.41 TURBIDITY IN SURFACE WATER IN VARIOUS SEASONS

100 90 80 70 60 Urban 50 40 Suburban 30 20 10 Turbidity value(mg/l) Turbidity 0 I II III SEASON SEASON SEASON Sampling Period

FIG 4.42 TS IN SURFACE WATER IN VARIOUS SEASONS

400 350 300 250 Urban 200 Suburban 150 100 Total solids(mg/l) 50 0

II III SEASON SEASON SEASON I Sampling Period

FIG 4.43 TOTAL HARDNESS IN SURFACE WATER IN VARIOUS SEASONS

50 45 40 35 30 Urban 25 20 Suburban 15

Hardness(mg/l) 10 5 0 I II III SEASON SEASON SEASON Sam pling Period

FIG 4.44 TOTAL ALKALINITY IN SURFACE WATER IN VARIOUS SEASONS

200 180 160 140 120 Urban 100 80 Suburban 60

Alkalinity(mg/l) 40 20 0 I II III SEASON SEASON SEASON Sam pling Period

FIG 4.45 TOTAL ACIDITY IN SURFACE WATER IN VARIOUS SEASONS

2.5

1.5 Urban

1 Suburban Acidity(mg/l) 0.5

0 I II III SEASON SEASON SEASON Sampling Period

FIG 4.46 DO IN SURFACE WATER IN VARIOUS SEASONS

7 6 5 4 Urban 3 Suburban

DO(mg/l) 2 1 0 I II III SEASON SEASON SEASON Sam pling Period

FIG 4.47 BOD IN SURFACE WATER IN VARIOUS SEASONS

6 5 4 Urban 3 Suburban

BOD(mg/l) 2 1 0 I II III SEASON SEASON SEASON Sampling Period

FIG 4.48 NITRITE IN SURFACE WATER IN VARIOUS SEASONS 5 4.5 4 3.5 3 Urban 2.5 Suburban 2 1.5

Nitrite content(mg/l)Nitrite 1 0.5 0 II III SEASON SEASON SEASON I Sampling Period

FIG 4.49 CHLORIDE IN SURFACE WATER IN VARIOUS SEASONS 25

15 Urban 10 Suburban Chloride(mg/l) 5

0 II III SEASON SEASON SEASON I Sampling Period

FIG 4.50 FLUORIDE IN SURFACE WATER IN VARIOUS SEASONS

4 3.5 3 2.5 Urban 2 Suburban 1.5

Fluoride(mg/l) 1 0.5 0 I II III SEASON SEASON SEASON Sam pling Period

FIG 4.51 SULPHATE IN SURFACE WATER IN VARIOUS SEASONS

9 8 7 6 5 Urban 4 Suburban 3 2 Sulphate (mg/l) Sulphate 1 0 I II III SEASON SEASON SEASON Sampling Period

FIG 4.52PHOSPHATE IN SURFACE WATER IN VARIOUS SEASONS

0.16 0.14 0.12 0.1 Urban 0.08 Suburban 0.06 0.04 Phosphate(mg/l) 0.02 0

I II III SEASON SEASON SEASON Sampling Period

FIG 4.53 E.Coli IN SURFACE WATER IN VARIOUS SEASONS

3500 3000 2500 2000 Urban 1500 Suburban 1000 E.coli (100/ml) 500 0 I II III SEASON SEASON SEASON Sampling Period

FIG 4.54a pH IN RESIDENTIAL ZONE FIG 4.54b pH IN COMMERCIAL ZONE

9 8.8 8.8 8.6 8.6 8.4 8.4 Urban 8.2 Urban pH 8.2 suburban pH 8 8 suburban 7.8 7.8 7.6 7.6 7.4 I II III I II III SEASON SEASON SEASON

SOIL SAMPLING SEASON SEASON SEASON PERIOD SOIL SAMPLING PERIOD

FIG 4.54c pH IN LITTER FREE ZONE

8 7.95 7.9 7.85 7.8 Urban pH suburban 7.75 7.7 7.65 7.6 I II III SEASON SEASON SEASON SOIL SAMPLING PERIOD

FIG 4.55a EC IN RESIDENTIAL ZONE FIG 4.55b EC IN COMMERCIAL ZONE

1.2 0.4 1 0.35 0.8 0.3 -1 ) Urban -1 0.25 Urban 0.6 0.2 suburban suburban Ecmho 0.4 0.15 EC(mho 0.1 0.2 0.05 0 0 I II III I II III SEASON SEASON SEASON

SEASON SEASON SEASON SOIL SAMPLING PERIOD SOIL SAMPLING PERIOD

FIG 4.55c EC IN L IT T ER FREE Z ONE

0.3 0.25 )

-1 0.2 0.15 0.1 EC(mho 0.05

0 Urban suburban I II III SEASON SEASON SEASON SOIL SAMPLING PERIOD

FIG 4.56a TOTAL ORGANIC CARBON FIG 4.56b TOTAL ORGANIC CARBON IN RESIDENTIAL ZONE IN COMMERCIAL ZONE

0.6 0.7 0.5 0.6 0.4 0.5 Urban 0.4 Urban 0.3 suburban 0.3 suburban % TOC % TOC 0.2 0.2 0.1 0.1 0 0 I II III SEASON SEASON SEASON SEASON I SEASON II SEASON III SOIL SAMPLING SOIL SAMPLING PERIOD PERIOD

FIG 4.56c TOTAL ORGANIC CARBON IN LITTER FREE ZONE

0.7 0.6 0.5 0.4 Urban 0.3 suburban % TOC 0.2 0.1 0 I II III SEASON SEASON SEASON SOIL SAMPLING PERIOD

FIG 4.57a TOTAL ORGANIC MATTER FIG 4.57b TOTAL ORGANIC MATTER IN RESIDENTIAL ZONE IN COMMERCIAL ZONE

1 1.2

0.8 1 0.8 0.6 Urban Urban 0.6 0.4 suburban suburban % TOM % TOM 0.4 0.2 0.2 0 0 I II III I II III SEASON SEASON SEASON

SOIL SAMPLING SEASON SEASON SEASON PERIOD SOIL SAMPLING PERIOD

FIG 4.57c TOTAL ORGANIC MATTER IN LITTER FREE ZONE

0.8

0.6 Urban 0.4 suburban % TOM 0.2 0 I II III SEASON SEASON SEASON SOIL SAMPLING PERIOD

FIG 4.58a TOTAL NITROGEN IN FIG 4.58b TOTAL NITROGEN IN RESIDENTIAL ZONE COMMERCIAL ZONE

1.4 2 1.2 1.8 Urban 1.6 1 suburban 1.4 0.8 1.2 Urban 1 0.6 0.8 suburban 0.4 0.6 0.4 0.2 Total Nitrogen(mg/g) Total 0.2 Total nitrogen(mg/g) Total 0 0 I II III I II III SEASON SEASON SEASON SEASON SEASON SEASON SOIL SAMPLING PERRIOD SOIL SAMPLING PERIOD

FIG 4.58c TOTAL NITROGEN IN L IT T ER FREE Z ONE

1.4 1.2 1 0.8 Urban 0.6 suburban 0.4 0.2 Total Nitrogen(mg/g) Total 0 I II III SEASON SEASON SEASON SOIL SAMPLING PERIOD

FIG 4.59a TOTAL PHOSPHORUS IN FIG 4.59b TOTAL PHOSPHORUS IN RESIDENTIAL ZONE COMMERCIAL ZONE

0.072 0.068 0.07 0.066 0.068 0.064 Urban 0.066 0.062 Urban 0.064 suburban 0.06 suburban 0.062 0.058 0.06 0.056 0.058 0.054 0.056 0.052 Total Phosporus(mg/g) Total

Total Phosporus(mg/g) Total 0.054 0.05 SEASON I SEASON I SEASON II SEASON II SEASON III SEASON III SOIL SAMPLING PERIOD SOIL SAMPLING PERIOD

FIG 4.59c TOTAL PHOSPHORUS IN LITTER FREE ZONE

0.08 0.07 0.06 0.05 Urban 0.04 suburban 0.03 0.02 0.01

Total Phosporus (mg/g) Phosporus Total 0 I II III SEASON SEASON SEASON SOIL SAMPLING PERIOD

FIG 4.60a TOTAL POTASSIUM IN FIG 4.60b TOTAL POTASSIUM IN RESIDENTIAL ZONE COM M ERCIAL ZONE

1.6 2 1.8 1.4 1.6 1.2 1.4 1 1.2 Urban 0.8 Urban 1 suburban suburban 0.6 0.8 0.6 0.4 0.4 0.2 0.2 Total Pottasium(mg/g) Total Total Pottasium(mg/g) Total 0 0 I I II II III III SEASON SEASON SEASON SEASON SEASON SEASON SOIL SAMPLING PERIOD SOIL SAMPLING PERIOD

FIG 4.60c TOTAL POTASSIUM IN L IT T ER FREE Z ONE

1.8 1.6 1.4 1.2 1 Urban 0.8 suburban 0.6 0.4 0.2 Total Pottasium(mg/g) 0 I II III SEASON SEASON SEASON SOIL SAMPLING PERIOD

FIG 4.61a TOTAL SODIUM IN FIG 4.61b TOTAL SODIUM IN RESIDENTIAL ZONE COMMERCIAL ZONE 0.45 1.6 0.4 1.4 0.35 1.2 0.3 1 0.25 Urban Urban 0.8 0.2 suburban suburban 0.6 0.15 0.4 0.1 0.2 Total sodium(mg/g) Total Total Sodium(mg/g) Total 0.05 0 0 I II III SEASON I SEASON II SEASON III SEASON SEASON SEASON 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 Total sodium(mg/g) Total 0 I II III SEASON SEASON SEASON SOIL SAMPLING PERIOD

FIG 4.62a TOTAL CALCIUM IN FIG 4.62b TOTAL CALCIUM IN RESIDENTIAL ZONE COMMERCIAL ZONE

2.5 1.8 1.6 2 1.4 Urban 1.2 1.5 suburban 1 Urban 0.8 suburban 1 0.6 0.5 0.4 Total calcium(mg/g) Total Calcium(mg/g) 0.2 0 0 I II I II III III SEASON SEASON SEASON SEASON SEASON SEASON 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 Total Calcium(mg/g) 0 I II III SEASON SEASON SEASON SOIL SAMPLING PERIOD

FIG 4.63a TOTAL MAGNESIUM IN FIG 4.63b TOTAL MAGNESIUM IN RESIDENTIAL ZONE COMMERCIAL ZONE

0.64 0.7 0.62 0.6 0.6 0.5 0.58 Urban Urban 0.56 0.4 0.54 suburban 0.3 suburban 0.52 0.2 0.5 0.1

Total Magnesium(mg/g) Total 0.48 Total Magnesium(mg/g) Total 0 I II III SEASON I SEASON II SEASON III SEASON SEASON SEASON 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

Total Magnesium(mg/g) 0 I II III SEASON SEASON SEASON SOIL SAMPLING PERIOD

FIG 4.64a LEAF LENGTH VARIATION IN FIG 4.64b FRESH WEIGHT VARIATION IN WASTE WATER USED PLANT WASTE WATER USED PLANT 12 1.1 10 1.08 1.06 8 1.04 Control 6 1.02 Control Treated 1 Treated length (cm) length 4

Weight mg/g Weight 0.98 0.96 2 0.94 0 0.92 Control Treated Control Treated Sampling Plant(Buchloe Sampling plant dactyloids)

FIG 4.64c DRY WEIGHT V ARIATION IN WASTE WATER USED PLANT

0.74

0.72 0.7

0.68 Control 0.66 Treated 0.64 weight mg/g weight 0.62

0.6 0.58 Control Treated Sampling plant

FIG 4.64d FREE SUGAR VARIATION IN FIG 4.64e PHENOL VARIATION IN PLANT TISSUE PLANT TISSUE

1.4 0.25

1.2 0.2 1 0.15 0.8 Control Control Treated Treated 0.6 0.1 Phenol % Phenol Free sugar % sugar Free 0.4 0.05 0.2

0 0 Control Treated Control Treated Sampling plant Sampling plant

FIG 4.64f TOTAL CHLOROPHYLL IN PLANT TISSUE

105

100

90 Control 85 Treated

80 Chlorophyll(mg/g) 75

70 Control Treated Sampling plant

FIG 4.66a CARBON CONTENT IN BIOCOMPOST 30

Partially 20 decomposed MSW 15 Biocompost

10 % of carbon % of

MSW

Partially Biocompost

decomposed

FIG 4.66b SULPHATE CONTENT IN BIOCOMPOST 0.0205 0.02 Partially 0.0195 decomposed 0.019 MSW 0.0185 Biocompost 0.018 0.0175 0.017 % of Sulphate % 0.0165 0.016 0.0155 MSW Partially Biocompost decomposed

FIG 4.66c MICRO NUTRIENTS IN BIOCOMPOST 1 0.9 0.8 0.7 0.6 Partially 0.5 decomposed 0.4 MSW Biocompost 0.3 0.2 0.1 0 Calcium Chlorides Magnisium

FIG 4.66d MACRO NUTRIENTS IN BIOCOMPOST 1.4 1.2 Par tially 1 decomposed % 0.8 MSW 0.6 Biocompost NPK inNPK 0.4 0.2 0 Nitrogen Pottasium Phosphorus

FIG 4.65a pH IN FIG 4.65b MOISTURE CONTENT BIODEGRADABLE SOLID IN BIODEGRADABLE SOLID WASTE WASTE

7.2 52

7.1 50 7 48 Mois tur e 6.9 % pH 46 6.8 6.7 44 Moisture content(%) Moisture 6.6 42 6.5 13579 13579 Decomposition Decomposition Period Period

FIG 4.65c TEMPERATURE IN BIODEGRADABLE SOLID WASTE

27 Temperature 26

25 Temperature(oC) 24

23 13579 Decomposition Period

FIG 4.65d Carbon/Nitrogen RATIO IN BIODEGRADABLE SOLID WASTE

35 30 25 20 C/N 15 C/N ratio 10 5 0 13579 Decomposition period

FIG 4.65e Solids and Ash IN BIODEGRADABLE SOLID WASTE

50 Total Solids% 40 Volatile Solid% 30 Ash Content%

Amount in% Amount 20

0 13579 Decomposition period

FIG 4.67a AVAILABLE NITROGEN IN FIG 4.67b POTASSIUM IN COMPOST COMPOST TREATED SOIL TREATED SOIL

140 70 120 60 100 50 80 BLOCK-I 40 BLOCK-I

mg/g 60 BLOCK-II mg/g 30 BLOCK-II 40 20 20 10 0 0 After I After I harvest After II harvest Before After II Before harvest harvest cultivation harvest cultivation

FIG 4.67c PHOSPHATE IN COMPOST TREATED SOIL

4.5 4 3.5 3 2.5 BLOCK-I 2 mg/g BLOCK-II 1.5 1 0.5 0 After II Before harvest cultivation

FIG 4.68a ZINC IN COMPOST TREATED SOIL

2.35 2.3 2.25 2.2 2.15 BLOCK-I 2.1

mg/g 2.05 BLOCK-II 2 1.95 1.9 1.85 After II After Before harvest harvest cultivation

FIG 4.68b IRON CONTENT IN COMPOST TRETED SOIL

8.2 8 7.8 7.6 BLOCK-I 7.4

mg/g BLOCK-II 7.2 7 6.8 6.6 After I After harvest After II Before harvest cultivation

FIG 4.68c MANGANESE IN COMPOST TREATED SOIL

7.1 7 6.9 6.8 BLOCK-I 6.7

mg/g BLOCK-II 6.6 6.5 6.4 6.3 After I After harvest After II After Before harvest harvest cultivation

FIG 4.68d COPPER IN COMPOST TREATED SOIL

0.95

0.9 BLOCK-I BLOCK-II mg/g 0.85

0.8

0.75 After second harvest Before harvest After firstAfter cultivation

FIG 4.69a BACTERIAL CONTENT IN FIG 4.69b ACTINOMYCETES IN COMPOST COMPOST TREATED SOIL TREATED SOIL

80 60 70 50 60 50 40

/g BLOCK-I

/g BLOCK-I 2 4 40 30

10 BLOCK-II

10 BLOCK-II 30 20 20 10 10 0 0 After I After I harvest harvest After II Before After II Before harvest c ultiv ation harvest c ultiv ation

FIG 4.69c FUNGAL CONTENT IN COMPOST TREATED SOIL

35 30 25 20

/g BLOCK-I 2

10 10 15 BLOCK-II 10 5 0 After I harvest After II Before harvest cultivation

FIG 4.70a PALM AROSA GRASS YIELD FIG 4.70b PALM AROSA OIL YIELD IN IN BIOCOMPOST TREATED SOIL BIOCOMPOST TREATED SOIL

700 3.5

600 3

500 2.5 After I harvest After I harvest 400 After II harvest 2 After II harvest Kg

300 Kg 1.5 200 1 100 0.5 0 BLOCK- BLOCK- 0 I II 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 RENT HOUSE HOUSE

FIG 4.74 WATER FACILITY

80% 80%

URBAN

SUBURBAN

20% 20%

CORPORATION BOREWELL SUPPLY 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

1 Euphorbia hirta 0.875 0.61 62.5 D 3.70 1.4 Rare

2 Azadiracta indica 5 3.53 50 C 2.96 10 Occasional

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 9.75 Occasional

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 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. Aba. S .no PLANT NAME Density Rel. density Frequency Fre.class 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. Aba. S .no PLANT NAME Density Frequency Fre.class Rel. frequency Abandance density Class

1 Cissus quandrangularis 0.625 0.39 25 B 1.15 2.5 Rare

2 Eucalyptus globulus 3.5 2.18 12.5 A 0.57 28 Frequent

3 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

Rel. Aba. S .no PLANT NAME Density Frequency Fre.class Rel. frequency Abandance density 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

Rel. Aba. S .no PLANT NAME Density Frequency Fre.class Rel. frequency Abandance density 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 13.14 Occasional 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

Aba. S .no PLANT NAME Density Rel. density Frequency Fre.class Rel. frequency Abandance 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 21.67 Frequent 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 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 2.611 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

Aba. S .no PLANT NAME Density Rel. density Frequency Fre.class Rel. frequency Abandance 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 Aba. S .no PLANT NAME Density Rel. density Frequency Fre.class Rel. Frequency Abandance Class 1 Cissus quandrangularis 0.625 0.38 25 B 1.15 2.5 Rare 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

Aba. S .no PLANT NAME Density Rel. density Frequency Fre.class Rel. Frequency Abandance 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

Aba. S .no PLANT NAME Density Rel. density Frequency Fre.class Rel. Frequency Abandance 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 Aba. S .no PLANT NAME Density Rel. density Frequency Fre.class Rel. Frequency Abandance Class 1 Cissus quandrangularis 0.625 0.38 25 B 1.15 2.5 Rare 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

Aba. S .no PLANT NAME Density Rel. density Frequency Fre.class Rel. Frequency Abandance Class 15 Zizipus jujuba 7 4.26 87.5 E 4.046 8 Occasional 16 Phyllanthus acidus 8.125 4.95 37.5 B 1.73 21.67 Frequent 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

Aba. S .no PLANT NAME Density Rel. density Frequency Fre.class Rel. Frequency Abandance 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

S .no PLANT NAME Density Rel. density Frequency Fre. class Rel. Frequency Abandance Aba. Class 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

S .no PLANT NAME Density Rel. density Frequency Fre. class Rel. Frequency Abandance Aba. Class 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 Rel. S .no PLANT NAME Density Frequency Fre. class Rel. frequency Abandance Aba. Class density 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

Rel. S .no PLANT NAME Density Frequency Fre. class Rel. frequency Abandance Aba. Class density 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

Rel. S .no PLANT NAME Density Frequency Fre. class Rel. frequency Abandance Aba. Class density 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

Rel. S .no PLANT NAME Density Frequency Fre. class Rel. frequency Abandance Aba. Class density 48 Andrapogon sorghum 5 3.29 50 C 2 10 Occasional 49 Saccharum officinarum 4.5 2.96 50 C 2 9 Occasional 50 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 Rel. S .no PLANT NAME Density Frequency Fre. class Rel. frequency Abandance Aba. Class density 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

Rel. S .no PLANT NAME Density Frequency Fre. class Rel. frequency Abandance Aba. Class density 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

Rel. S .no PLANT NAME Density Frequency Fre. class Rel. frequency Abandance Aba. Class density 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

Rel. S .no PLANT NAME Density Frequency Fre. class Rel. frequency Abandance Aba. Class density 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

Fre. Aba. S .no PLANT NAME Density Rel. density Frequency Rel. Frequency Abandance class Class

1 Emblica officinalis 0.62 0.408 50 C 1.99 1.25 Rare 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

Fre. Aba. S .no PLANT NAME Density Rel. density Frequency Rel. Frequency Abandance class 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

Fre. Aba. S .no PLANT NAME Density Rel. density Frequency Rel. Frequency Abandance class 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

Fre. Aba. S .no PLANT NAME Density Rel. density Frequency Rel. Frequency Abandance class 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

Rel. S .no PLANT NAME Density Rel. density Frequency Fre. class Abandance Aba. Class Frequency 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

Rel. S .no PLANT NAME Density Rel. density Frequency Fre. class Abandance Aba. Class Frequency 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

Rel. S .no PLANT NAME Density Rel. density Frequency Fre. class Abandance Aba. Class Frequency 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 PLANT NAME Density Relative Frequency Frequencyclass Relative Abandance Abandance class .no density frequency 1 Cyanodan sp 18.25 18.96 87.5 E 8.75 20.85 Frequent 2 Mollugo verticillata 25 25.97 50 C 5 50 Abundant 3 Carnegiea gigantea 2.75 2.85 50 C 5 5.5 Occasional 4 Amaranthus spinosus 4.62 4.805 62.5 D 6.25 7.4 Occasional 5 Datura metal 3.75 3.896 100 E 10 3.75 Rare 6 Nymphaea nouchali 4.5 4.67 37.5 B 3.75 12 Occasional 7 Nelumbo nucifera 4.37 4.54 12.5 A 1.25 35 Abundant 8 Azadiracta indica 3.87 4.025 100 E 10 3.87 Rare 9 Delonix regia 2.12 2.207 50 C 5 4.25 Rare 10 Tamarindus indicus 1.25 1.29 62.5 D 6.25 2 Rare 11 Bauhinia variegata 0.625 0.64 37.5 B 3.75 1.66 Rare 12 Acacia arabica 1 1.038 37.5 B 3.75 2.66 Rare 13 Acalipha indica 9.37 9.74 37.5 B 3.75 25 Frequent 14 Ricinus communis 0.87 0.909 50 C 5 1.75 Rare 15 Morus alba 4.12 4.2886 37.5 B 3.75 11 Occasional 16 Lagenaria siceraria 1.25 1.29 37.5 B 3.75 3.33 Rare 17 Solanum melongena 1.25 1.29 37.5 B 3.75 3.33 Rare 18 Mangifera indica 4.5 4.6775 50 C 5 9 Occasional 19 Citrus limon 1.87 1.948 37.5 B 3.75 5 Occasional 20 Areca catechu 0.87 0.909 25 B 2.5 3.5 Rare

Table 4.20d. Urban flora of east side in Season IV S Abandance PLANT NAME Density Relative density Frequency frequencyclass Relative frequency Abandance .no class 1 Cyanodan sp 18.25 19.94 87.5 E 8.64 20.85 Frequent 2 Mollugo verticillata 21.87 23.9 62.5 D 6.17 35 Abundant 3 Carnegiea gigantea 2.75 3.005 50 C 4.93 5.5 Occasional 4 Amaranthus spinosus 4.62 5.054 62.5 D 6.17 7.4 Occasional 5 Datura metal 3.75 4.09 100 E 9.87 3.75 Rare 6 Nymphaea nouchali 2.62 2.86 37.5 B 3.70 7 Occasional 7 Nelumbo nucifera 4.62 5.05 12.5 A 1.23 37 Abundant 8 Azadiracta indica 3.87 4.23 100 E 9.87 3.87 Rare 9 Delonix regia 2.12 2.32 50 C 4.93 4.2 Rare 10 Tamarindus indicus 1.25 1.36 62.5 D 6.178 2 Rare 11 Bauhinia variegata 0.625 0.68 37.5 B 3.7 1.66 Rare 12 Acacia arabica 1 1.092 37.5 B 3.7 2.66 Rare 13 Acalipha indica 9.37 10.24 37.5 B 3.7 25 Frequent 14 Ricinus communis 0.87 0.95 50 C 4.93 1.75 Rare 15 Morus alba 4.125 4.5 37.5 B 3.7 11 Occasional 16 Lagenaria siceraria 1.25 1.36 37.5 B 3.7 3.33 Rare 17 Solanum melongena 1.25 1.36 37.5 B 3.7 3.33 Rare 18 Mangifera indica 4.5 4.91 50 C 4.93 9 Occasional 19 Citrus limon 1.87 2.049 37.5 B 3.7 5 Occasional 20 Areca catechu 0.87 0.95 25 B 2.46 3.5 Rare Table 4.20e. Urban flora of west side in Season I Frequency Relative Abandance S .no PLANT NAME Density Relative density 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 Relative Abandance S .no PLANT NAME Density Frequency frequencyclass Relative frequency Abandance density 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

Frequency Abandance S .no PLANT NAME Density Relative density Frequency Relative frequency Abandance class 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

Frequency Abandance S .no PLANT NAME Density Relative density Frequency Relative frequency Abandance class class 17 Indigofera tingtoria 5.125 5.24 87.5 E 4.48 5.85 Occasional 18 Zizipus jujuba 1 1.02 75 D 3.84 1.33 Rare 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

Relative Abandance S .no PLANT NAME Density Relative density Frequency Frequencyclass Abandance frequency 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

Relative Abandance S .no PLANT NAME Density Relative density Frequency Frequencyclass Abandance frequency class 16 Gomphrena globosa 2.5 2.53 75 D 3.84 3.33 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

Table 4.20i. Urban flora of north side in Season I Frequency Abandance S .no PLANT NAME Density Relative density Frequency Relative frequency Abandance class 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

Frequency Abandance S .no PLANT NAME Density Relative density Frequency Relative frequency Abandance class class 16 Datura metal 1.5 1.41 62.5 D 4.58 2.4 Rare 17 Capsicum annum 2.875 2.70 37.5 B 2.75 7.66 Occasional 18 Psidium guajava 1.25 1.17 25 B 1.83 5 Occasional 19 Tephrosia purpurea 2.62 2.47 50 C 3.66 5.25 Occasional Euphorbia 20 5.37 5.064 62.5 D 4.58 8.6 Occasional heterophylla 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

Relative Relative S .no PLANT NAME Density Frequency Frequencyclass Abandance Abandance class density frequency 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

Relative Relative Abandance S .no PLANT NAME Density Frequency Frequencyclass Abandance density frequency class 16 Datura metal 1.5 1.38 62.5 D 4.58 2.4 Rare 17 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 Euphorbia 20 5.375 4.95 62.5 D 4.587 8.6 Occasional 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

Abandance S .no PLANT NAME Density Relative density Frequency frequencyclass Relative frequency 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

Abandance S .no PLANT NAME Density Relative density Frequency frequencyclass Relative frequency Abandance class 15 Lucus aspera 2.125 1.95 75 D 5.45 2.83 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 Euphorbia 20 5.375 4.94 62.5 D 4.54 8.6 Occasional heterophylla 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

Abandance S .no PLANT NAME Density Relative density Frequency Frequencyclass Relative frequency 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

Abandance S .no PLANT NAME Density Relative density Frequency Frequencyclass Relative frequency Abandance class 16 Datura metal 1.5 1.321 62.5 D 4.58 2.4 Rare 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 Frequency PLANT NAME Density Relative density Frequency Relative frequency Abandance Abandance class .no 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

Abandance S .no PLANT NAME Density Relative density Frequency frequencyclass Relative frequency 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

Relative S .no PLANT NAME Density Frequency frequencyclass Relative frequency Abandance Abandance class density 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 Abandance S .no PLANT NAME Density Relative density Frequency Frequencyclass Relative frequency 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 worm Snake 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 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 SCIENTIFIC ORDER OF COMMON NAME OF THE S.NO NAME OF THE INSECT THE SPECIES INSECT 1 Bedbug Cimex lectularis Hemiptera 2 Bee Apis indica Hymenoptera 3 Beetle Cataxantha bicolor Coleoptera 4 Butterfly 9Commom Mormon) Papilio polytes Lepidoptera 5 Butterfly(Common Bluebottle) Graphium sarpedon Lepidoptera 6 Butterfly(Common Caster) Ariadne merione Lepidoptera 7 Butterfly(Common Joy) Graphium doson Lepidoptera 8 Butterfly(Common Mime) Papilo clytia Lepidoptera 9 Butterfly(Common sailer) Neptis hylas Lepidoptera 10 Butterfly(Common Tiger) Danaus genutia Lepidoptera 11 Butterfly(Crimson Rose) Pachlipta hector Lepidoptera 12 Butterfly(Indian cabbage White) Pieris canidia Lepidoptera 13 Butterfly(Lemon Pancy)) Junonia lemonias Lepidoptera 14 Butterfly(Lime butterfly) Papilio demoleus Lepidoptera 15 Butterfly(Plain tiger) Danaus chrysippus Lepidoptera 16 Butterfly(Southern birdwing) Troides minos Lepidoptera 17 Butterfly(Tamil grass dart) Taractrocera Lepidoptera 18 Caddisfly Trichoptera 19 Carpet beetle Anthrenus scorphulariae Coleoptera 20 Caterpillar Lepidoptera 21 Cicada Homoptera 22 Clothmoth Tinea pellionella Lepidoptera 23 Cockroach Periplaneta americana Orthoptera 24 Common Ants Camponotus Hymenoptera 25 Common moth Syntomid moth Lepidoptera 26 Cow bug Telengana Hemiptera 27 Cricket Gryllodes sigillatus Orthoptera 28 Deadshed moth Acherontia styx Lepidoptera 29 Dragonfly Odonata Orthoptera 30 Dung roller Beetle Onthophagus sagittarius Coleoptera 31 Earwing Forficula Dermaptera 32 Field Grasshopper Catantops dominaus Orthoptera 33 Flower Bee Apis dorsata Hymenoptera 34 Fruitfly Drosophila Diptera 35 Giant Water Scorpian Ranatra Hemiptera 36 Giant waterbug Belostoma Hemiptera 37 Green Grasshopper Cyrtacanthacris succinata Orthoptera 38 Honeybee Apis florea Hymenoptera 39 House Grasshopper Atractomorpha crenulate Orthoptera 40 House wasp Vespa orientalis Hymenoptera 41 Housefly Musca domestica Diptera 42 Household Cricket Gryllus domestica Orthoptera 43 Jewel Beetle Chrysochroa Coleoptera 44 Leaf Insect Phyllium scythe Orthoptera 45 Mosquitoe Culex Diptera 46 Mosquitoe Aedes Diptera Anopheles 47 Mosquitoe quadrimaculatussay Diptera 48 Moth Aegeaera venula Lepidoptera 49 Paddy bug Leptocorixa Hemiptera 50 Plant lice Aphid Homoptera 51 Red Pumpkin beetle Raphidopalpa foveicollis Coleoptera 52 Silk moth Bombyx mori Lepidoptera 53 Silver Fish Lepisma Thysanura 54 Small termites Microtermes Isoptera 55 Stick insect Carausius Orthoptera 56 Wasp Polistes herbraeus Hymenoptera 55 Water Scorpion Nepa Hemiptera 56 White termites Odentotermes Isoptera

Table 4.24b. List of insects in sub-urban residential zone

COMMON NAME OF SCIENTIFIC NAME OF ORDER OF THE S.NO THEINSECT THE SPECIES INSECT 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

ORDER OF COMMON NAME OF SCIENTIFIC NAME OF S.NO THE THEINSECT THE SPECIES 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

COMMON NAME OF SCIENTIFIC NAME ORDER OF S.NO 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 S.NO THEINSECT OF THE SPECIES INSECT 1 Beetle Cataxantha bicolor Coleoptera Butterfly(Common 2 Tiger) Danaus genutia Lepidoptera 3 Butterfly(Lime butterfly) Papilio demoleus Lepidoptera Butterfly(Tamil grass 4 dart) Taractrocera Lepidoptera 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 Anopheles 11 Mosquitoe quadrimaculatussay Diptera

Table 4.24f. List of insects in sub-urban sensitive zone

SCIENTIFIC ORDER OF COMMON NAME OF S.NO NAME OF THE THE THEINSECT 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

COMMON NAME OF SCIENTIFIC NAME ORDER OF THE S.NO THEINSECT OF THE SPECIES INSECT 1 Butterfly(Common Joy) Graphium doson Lepidoptera 2 Cicada Homoptera 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

Table 4.25a. Birds In Urban Residential Zone For Various Seasons

S.NO NAME OF THE BIRD Jun-July Sep-Oct Dec-Jan March-Apr 1 House crow 84 88 80 98 2 Pigeon 35 38 43 42 3 Common Myna 31 35 26 36 4 Little Cormarent 1 1 2 2 5 Little Ergot 15 17 20 22 6 Sparrow 32 38 35 43 7 Quail 7 6 7 8 8 Parrot 10 12 15 18 9 Small Sunbird 31 38 43 58 10 Peacock 6 7 8 10 11 Owl 6 6 6 8 12 Duck 20 27 29 25 13 Hen 52 55 60 58 14 Ashy wood swallow 14 18 18 16 15 Ashydrongo 4 6 6 4 16 Baya Weaver 14 15 18 16 17 Yellow throated sparrow 18 20 25 32 18 White throated Myna 8 9 9 9 19 Plain prinia 4 4 5 5 20 Brown woodpeaker 5 5 5 4 21 White breasted kingfisher 3 4 6 3 22 Barn Owl 2 2 2 2 23 Brainfever bird 3 4 4 4 24 Blue Rock pigeon 18 15 22 20 25 Grey Francolin 2 2 3 3 26 Eagle 2 1 2 2 27 Koel 3 2 2 3 28 Common Sandpiper 5 6 7 5 29 Pond Heron 32 41 43 38 30 Little green Bee eater 23 24 25 21 31 Indian Robin 38 40 44 42 32 Spotted dove 7 8 8 8 33 Brown Flycatcher 8 8 10 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

Jun- Sep- Dec- March- S.NO NAME OF THE BIRD 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- S.NO NAME OF THE BIRD July Oct Jan Apr 1 House crow 55 53 57 50 2 Pigeon 45 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

Table 4.25e. Birds in urban sensitive zone for various seasons

NAME OF THE Jun- Sep- Dec- March- S.NO 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 White throated 5 Myna 8 9 9 10 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- NAME OF THE BIRD 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

TABLE 4.25g.BIRDS IN URBAN INDUSTRIAL ZONE FOR VARIOUS SEASONS

NAME OF THE Jun- Sep- Dec- March- S.NO 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