1.0 INTRODUCTION
1.1
Environment
Word "environment" is most commonly used describing "natural" environment
and means the sum of all living and non-living things that surround an organism, or
group of organisms. Environment includes all elements, factors, and conditions that
have some impact on growth and development of certain organism. Abiotic factors such
as light, temperature, water, atmospheric gases combine with biotic factors (all
surrounding living species). Environment often changes after some time and therefore
many organisms have ability to adapt to these changes.
‘Environment’ can be also defined as the combination of all of physical and
organic factors that act on a living being, residents, or ecological society and power its
endurance and growth".
It could be a physical component, which is known as physical environment or
abiotic environment that includes the built environment. The natural surroundings like
air, water, land, atmosphere etc are also the part of physical environment but they are
commonly known as natural environment. People surrounding the item or thing, is
known as human environment. This is also known as the social environment and
includes elements like the religious environment, emotional environment, residence,
relations etc. (Asheesh, 2010).
1.2
Environmental Pollution
The word pollution has been derived from the Latin word “polluti onem”
(meaning to defile or make dirty). Pollutant is a substance, the presence of which causes
pollution. The pollutant contaminates the breathing air, drinking water, hearing sound
and eating food. Uddal (1970) rightly said “the more we exploit, the more our options
are reduced, until we have only one to fight for survival. We are destroying the
environment and the biosphere, where we live “ Odum (1971) described pollution as an
undesirable change in the physical, chemical, or biological characteristics of our air,
land and water that will harmfully affect human life or that of desirable species, living
conditions etc. “ There are seven main types of pollutions in the environment (i) Air
1
Pollution (ii) Water Pollution (iii) Land Pollution (iv) Industrial Pollution (v) Sewage
Pollution (vi) Noise Pollution (vii) Radiation Pollution (Vijayalakshmi et al., 2003).
1.3
Transportation
Transportation is a non separable part of any society. It exhibits a very close
relation to the style of life, the range and location of activities and the goods and
services which will be available for consumption. Advances in transportation has made
possible changes in the way of living and the way in which societies organized and
therefore have a great influence in the development of civilizations.
Transportation is responsible for the development of civilisations from very old
times by meeting travel requirement of people and of goods. Such movement has
changed the way people live and travel. In developed and developing nations, a large
fraction of people travel daily for work, shopping and social reasons. But transport also
consumes a lot of resources like time, fuel, materials, land. (NPTEL, May 7, 2007).
1.3.1 History of transportation
The History of Transportation spans the entire history of mankind. In early
Paleolithic and Neolithic ages, man walked through his world on his own two
legs. He couldn't transport more than he was able to carry on his own. Beasts of
Burden began to be used after animal domestication sometime in the later part of
Neolithic age. However, even then humans could only carry what could be
loaded onto or tied to their animal's backs. It was only around 4000-3500 BC
that the very first step towards man-made transportation was taken – the wheel
was invented. (www.lifestyle.iloveindia.com).
Transportation began with the invention of the wheel in about 3500 BC. Wheels
were placed first on carts and then chariots. Next came travel by riverboats
believed to have first been used by the Egyptians. Horses were added as a
means of transportation. It is believed Asians were the first to place some kind
of protector on the horse's hooves. The wheelbarrow was instrumental in
transporting heavy goods from one site to another. The submarine used to travel
underwater was invented in 1620 by Cornelis Drebbel. The first paddle wheel
2
steamboat began rolling down the river during the late 1769s and the beginning
of the Industrial Revolution. About 100 years later we saw the first cable car.
The Wright Brothers took off in the first airplane which they called a "flying
machine" in 1903. Henry Ford created the system to mass produce cars in 1908.
Successful helicopter flights took off in the 1940's. Jumbo jets began gracing the
runways
in
1970.
And,
the
Space
Shuttle
blasted
off
in
1981.
(www.typesofthings.com).
Let us first classify the transportation means and then attempt to understand how
history unfolded for each one.
Broadly speaking, transportation means can be classified as under:
Land transport

Water transport

Air transport

Space transport
1.3.2 Importance of Transport
Transport refers to the activity that facilitates physical movement of goods as
well as individuals from one place to another. People use various products in
their daily life. Many of them are produced at different places. They are carried
on from all those places through rail, road or air and are made available to the
people, at locality. Trucks, tempo, lorries and bullock carts etc., are used to,
carry products or even raw materials from one place to another. Similarly,
people travel from one place to another by buses, trains, cars, scooters,
rickshaws cycle, etc.
This movement of goods and individuals is very important in economy. Because
of this, raw materials reach the place of manufacture, finished products reach the
place of sale or consumption, individuals move around to manage the business,
social relationships etc. In business, transport is considered as an auxiliary to
trade, that means, it supports trade and industry in carrying raw materials to the
place of production and distributing finished products for consumption.
Individuals or business firms that engage themselves in such activities are called
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transporters. Generally, transporters carry raw material, finished products,
passengers, etc, from one place to another. So it removes the distance barrier.
Now-a-days goods produced at one place are readily available at distant places.
People move freely throughout the world because of transport. It is associated
with every step of our life. Without transport, people as well as business units
cannot move a single step.
There has been heavy investment in the transport sector since Independence and
the progress has been significance. But the task is so gigantic that it would
require many years and large doses of investment to bring about the desired
improvement in the country's transport system.
The bottlenecks, especially in railways, roads and ports, pose a threat to
economic growth. The share of railways in freight traffic needs to be improved
and passenger services, especially in backward areas, need to be expanded. In
the road segment, highway network needs expansion to ensure smooth
movement of goods and people. The capacity of major, medium and minor ports
also needs to be augmented and the inland waterways developed. The pollution
caused by vehicles, especially in large cities, is another problem that needs to be
addressed. These are daunting tasks, but by no means unachievable. The entry of
private groups into the transport sector is expected to improve things. But the
role of the Government will remain paramount. (www.tcil.com/t.asp/india).
Effective transportation is indispensable to economic progress. Mining,
manufacturing, trade and banking and agriculture are also necessary, but these
activities, like many others, depend upon transportation. Without adequate
facilities for moving goods and people from place to place, economic and social
activities can be carried on in a limited way only. Using a mobility index that
combines available data on transport facilities and movement of passengers and
freight, Wilfred Owen finds out that immobility and poverty go together. The
countries with low per capita had a mobility index for freight and passenger
transport in single digits, whereas this index was significantly high in countries
with high per capita income. Indeed, a more recent study finds out that every
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one-percentage growth in the Indian economy presumes a growth of 1.2 to 1.4
per cent in the transport sector. (www.tcil.com/t.asp/india).
1.3.2.1 Economic functions of transportation
Transportation is an economic function, that is to say, it serves along with other
productive functions in the production of goods and services in the economy.

Creation of utility: Production has been defined as the creation of utility, i.e., the
quality of usefulness. Transportation creates the utility of place, and to a lesser
degree, that of time.

As a cost of production: Since transportation is a part of production, an increase
in its efficiency helps in reducing the cost of producing goods and thus reduces
their prices. Cheaper transportation has both direct and indirect effects on cost of
production. Directly, reduction in transport rates laid to overall lower production
costs by lessening the outlays for assembling raw materials and shipping
finished products by reducing the expense of travel. Indirectly, cheaper
transportation tends towards lower cost of production by making possible more
efficient extraction and manufacturing, through promoting the division of labour
and large-scale production.

Specialization and division of labour: Transportation enables society to enjoy
advantages of specializations of resources, and the benefits of labour by making
it possible for products to be brought great distance, thus avoiding the necessity
for local production for all conceivable commodities of need. Each economic
region can thus concentrate upon the goods and services for which it is best
adapted either through natural resources endowment or through historical
development. It, thus, leads to a better economic use of available resources.

Large-scale marketing: Closely associated with the foregoing is the fact that
transportation helps to expand the size of market. No modern large-scale
producer could operate if he will to serve only the local market. Obviously, a
large-scale production is possible when the market extends to the whole nation
and in a few cases to the whole world.
5

Consumption of wealth: Transportation is also related to consumption of
wealth. It increases the quality and variety of consumable goods, thereby
stimulating wants. There is more production because of the decrease in the cost
of production brought about by transportation. A greater variety occurs because
transportation enables a community to enjoy even those goods that could not be
produced in the immediate vicinity. (www.tcil.com/t.asp/india).
1.3.3 Social and political functions of transportation
Transportation performs many social and political functions.

Transportation raises the standard of living, making possible improved housing,
clothing, food and recreation.

It helps break the barrier of isolation by promoting social interaction and thus
promotes culture and intelligence, especially in a country of the size and
population of India.

It promotes national unity in that it promotes homogeneity among the people.
Another reason is that it creates a need for political unity, by making the
different parts of the country economically interdependent.

It helps in the strengthening of national defence. It is an important agency which
helps in the mobilization of the entire resources of a country in the event of war
and peace.

In modern world, transport along with energy is the basic infrastructural
requirement for industrialization. The developing countries have accorded it an
important place in their programmes of economic development. Transport
provides a vital link between production centres, distribution areas and the
ultimate consumers. It also exercises a unifying and integrating influence upon
the economy. Important means of transport are railways, roads, water transport
(both inland and overseas) and air transport. (www.tcil.com/t.asp/india).
6
1.4
Role of Transportation in Society
1.4.1 Economic role of transportation
Economics involves production, distribution and consumption of goods and
services. People depend upon the natural resources to satisfy the needs of life but
due to non uniform surface of earth and due to difference in local resources,
there is a lot of difference in standard of living in different societies. So there is
immense requirement of transport of resources from one particular society to the
other. These resources can range from material things to knowledge and skills
like movement of Doctors and Technicians to the places were there is need of
them.
1.4.2 Social role of transportations
Transportation has always played an important role in influencing the formation
of urban societies. Although other facilities like availability of food and water
played a major role, the contribution of transportation can be seen clearly from
the formation, size and pattern, and the development of societies, especially
urban centres.
1.4.3 Political role of transportation
The world is divided into numerous political units which are formed for mutual
protection, economic advantages and development of common culture.
Transportation plays an important role in the functioning of such political units.
1.4.4 Administration of an area
The government of an area must be able to send/ get information to/ about its
people. It may include laws to be followed, security and other needful
information needed to generate awareness. An efficient administration of a
country largely depends on how effectively government could communicate its
information to all the country. However, with the advent of communications, its
importance is slightly reduced.
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1.4.5 Political choices in transport
These choices may be classified as communication, military movement, and
travel of persons and movement of freight. The primarily function of
transportation is the transfer of messages and information. It is also needed for
rapid movement of troops in case of emergency and finally movement of persons
of goods. The political decision of construction and maintenance of roads has
resulted in the development of transportation system. (NPTEL, May 7, 2007).
The importance of transports are be listed as follows
(a)
Makes available raw materials to manufacturers or producers
Transport makes it possible to carry raw materials from places where they are
available, to places where they can be processed and assembled into finished
goods.
(b)
Makes available goods to customers
Transport makes possible movement of goods from one place to another with
great ease and speed. Thus, consumers spread in different parts are benefited of
consuming goods produced at distant places.
(c)
Enhances standards of living
Easy means of transport facilities large – scale production at low costs. It gives
consumers the choice to make use to different quantities of goods at different
prices.
(d)
Helps during emergencies and natural calamities
In times of natural crisis, due to war or international distribution, transport helps
in quick movement of troops and the supplying needed in the operation.
(e)
Helps in creation of employment
Transports provide employment opportunity to individuals as drivers,
conductors, pilot, cabin crew, captain the ship etc., who are directly engaged, in
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transport business. It also provides employment to people indirectly in the
industries various means of transport and other transport equipments. People can
also provide repairing and maintenance services by opening service centres at
convenient locations.
(f)
Helps in Labour mobility
Transport helps a lot in providing mobility to workers. The people from our
country will be aware of going to foreign countries for the work in different
industries and factories. Foreigners also come to India for work. In India, people
also move from one part to another in search of work. Most industries have their
own transport system to bring the workers from where they reside to the place of
work.
(g)
Helps in bringing nations together
Transport facilitates movement of people from one country to another. It helps in
exchange of cultures, views and practices between the people of different
countries. This brings about greater understanding among people and awareness
about different countries. Thus, it helps to promote a feeling of international
brotherhood. (www.nos.org/secbuscour/cc10.pdf).
1.5
Transportation in India
India's transportation sector has not been able to keep pace with rising demand
and is proving to be a drag on the economy. Major improvements in the sector are
therefore required to support the country's continued economic growth and to reduce
poverty.
India’s transport sector is large and diverse; it caters to the needs of 1.1 billion
people. In 2007, the sector contributed about 5.5 percent to the nation’s GDP, with road
transportation contributing the lion’s share.
Good physical connectivity in the urban and rural areas is essential for economic
growth. Since the early 1990s, India's growing economy has witnessed a rise in demand
for transport infrastructure and services.
9
However, the sector has not been able to keep pace with rising demand and is
proving to be a drag on the economy. Major improvements in the sector are therefore
required to support the country's continued economic growth and to reduce poverty.
(www.worldbank.org.in).
1.6
Aspects of Tranportation
1.6.1
International transportation
The growth of the amount of freight being traded as well as a great variety of
origins and destinations promotes the importance of international transportation
as a fundamental element supporting the global economy. International
transportation systems have been under increasing pressures to support
additional demands in volume and distance carried. This could not have occurred
without considerable technical improvements permitting to transport larger
quantities of passengers and freight, and this more quickly and more efficiently.
Few other technical improvements than containerization have contributed to this
environment of growing mobility of freight. Since containers and their inter
modal transport systems improve the efficiency of global distribution, a growing
share of general cargo moving globally is containerized. Consequently,
transportation is often referred as an enabling factor that is not necessarily the
cause of international trade, but a mean over which globalization could not have
occurred without. A common development problem is the inability of
international transportation infrastructures to support flows, undermining access
to the global market and the benefits that can be derived from international trade.
International trade requires distribution infrastructures that can support trade
between several partners. Three components of international transportation
facilitate trade:
1.6.2
Transportation infrastructure
Concerns physical infrastructures such as terminals, vehicles and networks.
Efficiencies or deficiencies in transport infrastructures will either promote or
inhibit international trade.
10
1.6.3
Transportation services
Concerns the complex set of services involved in the international circulation of
passengers and freight. It includes activities such as distribution, logistics,
finance, insurance and marketing.
1.6.4
Transactional environment
Concerns the complex legal, political, financial and cultural setting in which
international transport systems operate. It includes aspects such as exchange
rates, regulations, quotas and tariffs, but also consumer preferences. About half
of the global trade takes place between locations of more than 3,000 km apart.
Because of the involved geographical scale, most international freight
movements involve several modes, especially when origins and destinations are
far apart. Transport chains must thus be established to service these flows which
reinforce the importance of inter modal transportation modes and terminals at
strategic locations. Among the numerous transport modes, two are specifically
concerned with international trade.
1.6.5
Ports and maritime shipping
The importance of maritime transportation in global freight trade in
unmistakable, particularly in terms of tonnage as it handles about 90% of the
global trade. Thus, globalization is the realm of maritime shipping, with
containerized shipping at the forefront of the process. The global maritime
transport system is composed of a series of major gateways granting access to
major production and consumption regions. Between those gateways are major
hubs acting as points of interconnection and transhipment between systems of
maritime circulation.
1.6.6
Airports and air transport
Although in terms tonnage air transportation carries an insignificant amount of
freight (0.2% of total tonnage) compared with maritime transportation, its
importance in terms of the total value is much more significant; about 15%.
International air freight is about 70 times more valuable than its maritime
11
counterpart and about 30 times more valuable than freight carried overland,
which is linked with the types of goods it transports (e.g. electronics). The
location of freight airports correspond to high technology manufacturing clusters
as well as intermediary locations where freight planes are refuelled and/or cargo
is transhipped.
Road and railway modes tend to occupy a more marginal portion of international
transportation since they are above all modes for national or regional transport
services. Their importance is focused on their role in the "first and last miles" of
global distribution. Freight is mainly brought to port and airport terminals by
trucking or rail. There are however notable exceptions in the role of overland
transportation in international trade. A substantial share of the NAFTA trade
between Canada, United States and Mexico is supported by trucking, as well as
large share of the Western European trade. In spite of this, these exchanges are at
priori regional by definition, although inter modal transportation confers a more
complex setting in the interpretation of these flows.
Economic development in Pacific Asia and in China in particular, has been the
dominant.
1.7
Types of Transport and their share in India
1.7.1
Roads
Roads are the dominant mode of transportation in India today. They carry almost
85 percent of the country’s passenger traffic and more than 60 percent of its
freight. The density of India’s highway network -- at 0.66 km of roads per square
kilometer of land – is similar to that of the United States (0.65) and much greater
than China's (0.16) or Brazil's (0.20). However, most roads in India are narrow
and congested with poor surface quality, and 33 percent of India’s villages do
not have access to all-weather roads. (www.worldbank.org.in).
1.7.2
Railways
Indian Railways is one of the largest railways under the single management. It
carried some 17 million passengers and 2 million tonnes of freight a day in year
12
2007 and is one of the world’s largest employers. The railways play a leading
role in carrying passengers and cargo across India’s vast territory. However,
most of its major corridors have capacity constraint requiring capacity
enhancement plans.
1.7.3
Ports
India has 12 major and 187 minor and intermediate ports along its more than
7500 km long coastline. These ports serve the country’s growing foreign trade in
petroleum products, iron ore, and coal, as well as the increasing movement of
containers. Indian ports handled cargo of 650 million tonnes in year 2006-07, an
increase of 14% over previous year. Inland water transportation remains largely
undeveloped despite India's 14,000 kilometers of navigable rivers and canals.
1.7.4
Aviation
India has 125 airports, including 11 international airports. Indian airports
handled 96million passengers and 1.5 million tonnes of cargo in year 2006-07,
an increase of 31.4% for passenger and 10.6% for cargo traffic over previous
year. The dramatic increase in air traffic for both passengers and cargo in recent
years has placed a heavy strain on the country's major airports. Passenger traffic
is projected to cross 100 million and cargo to cross 3.3 million tonnes by year
2010. Transport infrastructure in India is better developed in the southern and
southwestern parts of the country. (www.worldbank.org.in).
1.7.5
Major challenges

India’s roads are congested and of poor quality. Lane capacity is low – majority
of national highways are two lanes or less. A quarter of all India's highways are
congested. Many roads are of poor quality and road maintenance remains underfunded. This leads to the deterioration of roads and high transport costs for users.

Rural areas have poor access. Roads are significant for the development of the
rural areas - home to almost 70 percent of India's population. Although the rural
road network is extensive, some 33 percent of India’s villages do not have access
to all-weather roads and remain cut off during the monsoon season. The problem
13
is more acute in India's northern and northeastern states which are poorly linked
to the country’s major economic centers.

The railways are facing severe capacity constraints. All the country’s highdensity rail corridors face severe capacity constraints. Also, freight
transportation costs by rail are much higher than in most countries as freight
tariffs in India have been kept high to subsidize passenger traffic.

Urban centers are severely congested. In Mumbai, Delhi and other metropolitan
centers, roads are often severely congested during the rush hours. The dramatic
growth in vehicle ownership during the past decade - has reduced rush hour
speeds especially in the central areas of major cities.

Ports are congested and inefficient. Port traffic has more than doubled during the
1990s, touching 650 million tonnes in 2006-07. This is expected to grow further
to about 900 million tonnes by 2011-12. India's ports need to significantly ramp
up their capacity and efficiency to meet this surging demand.

Airport infrastructure is strained. Air traffic has been growing rapidly leading to
severe strain on infrastructure at major airports, especially in the Delhi and
Mumbai airports which account for more than 40 percent of nation’s air traffic.
(www.worldbank.org.in).
1.8
Transport and Communication
Transport and communication facilities have made the world a smaller and better
place by linking people and improving their productivity. The transport sector has a farreaching effect on the daily lives of people: it takes them to their destination and
promotes their mobility to go to work, school, market, and recreation. Farmers and
traders are able to transport their produce and goods to business centres. Sick people,
especially women, can have access to health care facilities during emergencies. Workers
can get to factories and offices and children can go to school.
While expansion of transportation networks yields positive benefits, like most
infrastructure development, there are unintended effects on human health. WHO has
identified the following health issues in the transport sector in its 1999 Charter on
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Transport, Environment and Health: i) traffic crashes, often caused by high speeds, are a
major cause of death and serious injury; ii) road transport is a major contributor to
human exposure to air pollution; iii) increasing exposure to levels of traffic noise can
damage hearing permanently; iv) physically active forms of transport offer significant
positive health effects; v) heavy road traffic can divide communities and reduce social
support; vi) vulnerable groups are affected by traffic-particularly people with
disabilities, older people, children and young people, and people living or working in
areas of high pollution and noise; and vii) traffic crashes can have devastating impact on
low-income households and can contribute to poverty.
The various risk factors and causes of health effects in the transport sector are
road safety, hazardous materials transport, airborne pollutants, noise, vibration, and
heavy equipment use. There are two types of impacts related to roads and highways:
temporary impacts associated with the construction phase and permanent impacts due to
the road’s existence, such as accidents, noise, and air pollution from motor vehicles.
There is growing scientific evidence on health effects of air pollution. Country
capacities must be strengthened to assess and manage these health risks. Traffic crashes
put a huge burden on medical expenses. These could be significantly reduced through
institutional and behavioural reforms that may take place over time beyond the life of
the development activity.
Around 44% of the world’s road deaths occur in Asia and the Pacific, despite the
region’s owning only 16% of total motor vehicles. In Asia alone, the costs of damage
due to traffic crashes are about US$24.5 billion every year. (Jacobs and Aerons Thomos, 2000). This is more than the assistance received (from all sources) by the
region. (www.transportsector.co.in).
1.9
Transport and Communication in India
Transportation in India is a large and varied sector of the economy. Modes of
conveyance for transport of goods in India range from people’s heads (on which loads
are balanced) and bicycle rickshaws to trucks and railroads cars. The national railroads
were the major freight hauled at independence, but road transport in India grew rapidly
after 1947. Both rail and road transports remain important.
15
The share of India’s transportation investments in total public investment
declined during the period from the early 1950 s to the early 1980 s; real public
transportation investment also declined during much of that period because of the need
for funds in the rest of the economy. As a consequence, by the early 1980 s the
transportation system in India was barely meeting the needs of the nation or preparing
for future.
Many roads were breaking up because of over use and lack of maintenance,
railroads required new track and rolling stock. Ports needed equipment and facilities,
particularly for bulk and container cargo; and at many airports the national civil airlines
needed supporting equipment including provision for instrument landings. The
government planned to devote 19 percent of the Eighth Five – year plan (1992 – 96)
budget to transportation and communications, up from the 16 percent devote to the
sector during the seventh plan.
Although there is a large private – sector involvement in transportation in India,
the government plays a large regulatory and developmental role. The central
government has ministries to handle civil aviation, railroads, and surface transportation.
Counterpart agencies are found at the state and union territory level. Critical to
improving the entire transportation sector in the late 1990 s is the ability of the sector to
adjust to the central government’s national reform initiatives, including privatization,
deregulation, and reduced subsides. The sector must also adjust to foreign trade
expansion demographic pressures and increasing urbanization, technological change and
obsolescence, energy availability, and environment and public safety concerns.
The road network includes causeways, culverts, bridges, rail over bridges and
road under bridges etc., According to traffic intensity, connectivity and importance of
the roads, the roads are being classified as National Highways, State highways, Major
District Roads and Other District Roads. Whenever the traffic intensity increases,
necessary steps are taken to reclassify and upgrade such categories of roads to higher
categories. (www.tn.gov.in/dear/Transport.pdf).
16
1.9.1
Modes of transport
We find that basically transport is possible through land, air or water, which are
called the different modes of transport. On land we use trucks, tractors, etc., to
carry goods, trains, bus, cars, etc., to carry passengers. Table - 1 shows the
different modes of Transport.
Table - 1:- Different modes of Transport
Modes
Land Transport
● Road transport
Types
● Rail transport
● Pipe - line transport
● Rope way transport
Water Transport
● Inland water
transport
● Ocean transport
Air Transport
● Domestic air
transport
● International
air transport
www.nos.org
1.9.2
Land transport
Land transport refers to activities of physical movement of goods and passengers
on land. This movement takes place on road, rail, rope or pipe. So land transport
may further be divided into road transport, railway transport, rope way transport
and pipeline transport. Table 2 shows different means of land transport.
1.9.2.1 Road Transport
Roads are the means that connect one place to another on the surface of the land.
Normally, the roads can be seen on the village and cities. Not all of them look
alike. Some of them are made of sand and some may be of chips and cement or
coal tar. Different vehicles that plying on roads like bullock carts, cycle, motor
cycles, cars, truck, buses etc., all of these constitute different means of road
transport. The means of road transport may be divided into 3 types.
(i)
Man driven
(ii)
Animal driven and
(iii)
Motor driven
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Normally the individuals carrying goods on their head or back, in bicycle or on
the as, move from one place to another. People also ride a bicycle or use
rickshaw to travel short distance and also find animal driven vehicles like carts
(drawn by bullocks, camels, horses, donkeys, etc.) used in rural areas to carry
goods from one place to another. In areas, which are normally covered with
snow throughout the year, sledges pulled by dogs are used to carry both
passengers and goods.
Compared with man driven and animal driven means of road transport, motor
driven means of transport have become more important over the years. This is
due to their speedy movement and larger carrying capacity. Extensions of roads
to every corner of the country have also enhanced the use of motor driven
transport. The types of motor vehicles used to carry goods and passengers
include auto – rickshaws, scooters, vans, buses, tempos and trucks, etc. In
Kolkata, tramway also forms part of road transport for carrying passengers.
1.9.2.2 Advantages of road transport
(i)
It is a relatively cheaper mode of transport as compared to other modes.
(ii)
Perishable goods can be transported at a faster speed by road carriers over a
short distance.
(iii)
It is a flexible mode of transport as loading and unloading is possible at any
destination. It provides door – to – door service.
(iv)
It helps people to travel and carry goods from one place to another, in places
which are not connected by other means of transport like hilly areas.
1.9.2.3 Limitations of road transport
(i)
Due to limited carrying capacity road transport is not economical for long
distance transportation of goods.
(ii)
Transportation of heavy goods or goods in bulk by road involves high cost.
(iii)
It is affected by adverse weather conditions such as floods, rain, landslides, etc.,
sometimes create obstructions to road transport.
18
Table - 2:- Different means of Land Transport
Man driven
Road Transport
Rail
Animal driven Motor driven Transport
● head or back ●
of human
being
●
● carts drawn ●
by man
● thelas/push
carts
● bicycles
● rickshaw
carts drawn
by animals
sledge
animal
Pipeline
Transport
● scooter and ● passenger ● Pipes
motor cycle
train
● auto
● goods
rickshaw
train
● car
● van
● bus
Ropeway
Transport
● rope cars
www.nos.org.
Road transport in India is operated partly by public sector and largely by private
sector comprising about 28.7 % and 71.3 % respectively of the total buses. The
participation of the state in road transport commenced in 1950 and since then
State Road Transport Undertakings have been formed in every state. (Kaushik
Deb and Sanjivi Sundar).
1.10
Road and Transport Services in India
Rail and Road are the dominant modes of transport and are well coordinated.
The railways provide wagon services for bulk movement of commodities and passenger
traffic while road transport also provides long distance services for commodities and
passenger movement with safety and economy in the shortest duration.
1.10.1 Importance of road and road Transport
Roads are the vital lifelines of the economy making possible trade and
commerce. Roads are most preferred modes of transportation and considered as
one of the cost effective modes of transportation. Roads are easily accessible to
each individual. Roads facilitate movement of both men and materials anywhere
within a country. It helps in socio-economic development as well as brings
national integration. It provides linkages to other modes of transportation like
railways, airways, and shipping, etc. An efficient and well-established network
of roads is desired for promoting trade and commerce in any country and also
19
fulfills the needs of a sound transportation system for sustained economic
development. Road transport is contributing 3.69% to GDP where as all
transportation modes are contributing a total of 5.5% to GDP.
1.10.2 Early roads
The first forms of road transport were horses, Oxen or even humans carrying
goods over tracks that often followed game trails, such as the Natchez Trace. In
the Stone Age humans did not need constructed tracks in open country. The first
improved trails would have been at fords, mountain passes and through swamps.
The first improvements would have consisted largely of clearing trees and big
stones from the path. As commerce increased, the tracks were often flattened or
widened to accommodate human and animal traffic. Some of these dirt tracks
were developed into fairly extensive networks, allowing communications, trade
and governance over wide areas.
The first goods transport was on human backs and heads, but the use of pack
animals, including donkeys and horses, developed during the Stone Age. The
first vehicle is believed to have been the travois, a frame used to drag loads,
which probably developed in Eurasia after the first use of bullocks (castrated
cattle) for pulling ploughs. In about 5000 BC, sleds developed, which are more
difficult to build than travois, but are easier to propel over smooth surfaces. Pack
animals, ridden horses and bullocks dragging travois or sleds require wider paths
and higher clearances than people on foot and improved tracks were required. As
a result by about 5000 BC roads, including the Ridgeway, developed along
ridges in England to avoid crossing rivers and bogging. In central Germany, such
ridge ways remained the predominant form of long – distance road till the mid
18th century.
1.10.3 Harappan roads
Street paving has been found from the first human settlements around 4000 BC
in cities and the Indus Valley Civilization on the Indian subcontinent, such as
Harrapa and Mohenja – daro.
20
1.10.4 Wheeled transport
Wheels appeared to have been in ancient summer in Mesopotamia around 5000
BC, perhaps originally for the making of pottery. Their original transport use
may have been as attachments to travois or sleds to reduce resistance. It has been
argued that logs were used as rollers under sleds prior to the development of
wheels, but there is no archeologically evidence of this. Most early wheels
appeared to have been attached to fixed axles, which would have required
regular lubrication by animals’ fats or vegetable oils or separation by leather to
be effective. The first simple two – wheels carts, apparently developed from
travois, appeared to have been used in Mesopotamia and northern Iran in about
3000 BC and two- wheel chariots appeared in about 2800 BC. They were hauled
by onagers related to donkeys.
Heavy four – wheeled wagons developed about 2500 BC, which were only
suitable for oxen – haulage, and therefore were only used where crops were
cultivated, particularly Mesopotamia. Two wheeled chariots with spoked wheels
appear to have been developed around 2000 BC by the Andronovo culture in
southern Siberia and central Asia. At much the same time the first primitive
harness enabling horse – haulage was invented. (www.wikipedia.com).
1.11
Environmental Problems due to Transportation
The world is becoming increasingly urbanised. The urban population of the
world as a whole has been expanding at the rate of nearly 3 percent per year,
presumably faster than the existing world population growth rate. Roughly, half of the
global population lives in cities (Peterson, 1984). Presently approximately 30 percent
India’s population lives in urban areas. The current trends of urbanization inspired by
better quality of life are posing multiple stresses on our environment. Coupled with
rapid urbanization, each city consists of a large number of constituent systems.
Transport is one of them, which provides mobility, flexibility and accessibility to
people. A sustainable transport system must offer mobility and approachability to all
urban residents in a secure and eco-friendly mode of transport.
21
The result of urbanization is mushrooming illegal settlements and slums,
increased overcrowding, poor transport and inadequate facilities, pollution, and rampant
diseases linked to an unhealthy environment. The proportion is significantly higher in
cities like Mumbai, Chennai, Kolkata and Delhi. Along with over-crowding, Indian
cities are filled with automobiles like scooters and private cars, buses and inappropriate
industrialization. It is to be realized, in general, that there are neither resources norgiven rapid technological change- the time to allow the damage to environment now and
clean up later. In such a complex situation, linkages between environmental issues,
public transport, non-motorised transport and safety must be given proper and adequate
attention. If a large portion of population cannot afford to avail motorised transportprivate vehicles or public buses- then they have to either walk as pedestrians or use
bicycles to work. Secure infrastructure for bicyclists and pedestrians may require
segregation of road space or reduction of speeds of moving vehicles. In both the cases,
restriction of mobility on vehicle users is bound to take place.
The auto mobile changed our dress, manners, social customs, vacation habits,
and the shape of our cities, consumer purchasing patterns and common tastes. (Keats,
1958).
An efficient transport system is a pre-requisite for sustained economic
development. It is not only the key infrastructural input for the growth process but also
plays a significant role in promoting national integration, which is particularly important
in a large country like India. The transport system also plays an important role of
promoting the development of the backward regions and integrating them with the
mainstream economy by opening them to trade and investment. (Annual Report 2007 –
2008, Department of Road Transport and Highways).
Man has been living on the Earth, for about 40,000 years. He is surrounded by
various forms of ‘ Organisms’ ‘forces’ and ‘conditions’ – both physical and biological,
e.g. Sunlight, land, air, water and living beings, which include all types of plants and
animals. The total of these is called “Environment”. For the first time in his entire
cultural history, man has been confronted with the most horrible, tragic and
unprecedented problems of “Environmental Pollution”. Not very far back, in the past,
this very environment was pure, virgin and uncontaminated, and basically quite
22
hospitable for mankind. It is all due to thoughtless over exploitation of our various
natural resources, by our own activities perhaps due to our unending greed and grab of ‘
development’ ,and the egoistic attitude towards ; Nature”. The other three main reasons
are (i) ‘Population explosion’ (ii) Rapid Urbanisation and (iii) The throwaway concept
of disposable items.
1.11.1 Natural resource depletion
Fossil fuels are the primary energy source for transport. In OECD countries,
above the use of fossil fuels for transport increased by more than 45% from 1980
to 1997 and is expected to continue growing (OECD, 2001). To be constructed,
transport infrastructure requires a substantial amount of concrete and steel. In
order to produce vehicles, metals and plastics are required. The extraction and
production of all these materials are depleting of natural resources.
1.11.1.1 Air pollution
The transport sector, especially road and air transport, contributes to air
pollution, acidification and climate change through emissions of carbon
monoxide (CO), carbon dioxide (CO2), nitrogen oxides (NOx), hydrocarbons
(HC) particulate matter (PM), lead (Pb), heavy metals, and volatile organic
compounds (VOC). These pollutants are released during the combustion of fossil
fuels, the primary energy source for transport. (State of the environment report 2003).
1.11.1.2 Noise pollution
Noise is probably the most obvious impact coming from the transport sector.
Vehicular noise pollution is related to the number of vehicles, vehicular types,
speed and gradients.
Road noise comes from four sources: vehicles (engine work, acceleration,
braking); friction between vehicles and road; driver behaviour (horn usage, loud
music, shouting, sudden braking or start); and construction and maintenance
work (heavy machinery). (Tsunokawa and Hoban, 1997).
23
Continuous noise, even if its levels are not too high, increases stress levels by
causing annoyance and disrupting communication among people. Continuous
exposure to noise can lead to weakening of the auditory system and sleeping
disorders. Noise has negative effects on wildlife; animals are often afraid of
noise and do not approach roads, which can disturb their breeding, feeding, or
migration patterns. Another negative effect related to transport is vibration.
Vibration, mostly caused by road freight transport and air transport, is very
damaging to lightly built structures along the road, as well as cultural heritage
monuments. Vibration can also have negative impacts on people, causing
sleeping problems and general disturbance of normal living patterns (State of the
environment report - 2003).
1.11.1.3 Land Use
It is estimated that transport infrastructure consumes 25-30% of land in urban
areas in OECD countries (OECD, 2001). In the EU, 93% of total land area used
for transport belongs to roads, while rail and airports occupy 4% and 1%
respectively (OECD, 2001). Increased land use for transport infrastructure
increases pressures on biodiversity due to habitat fragmentation. It also causes an
increase in acidification and eutrophication. Land is affected by the transport
sector in two ways: directly through building the transport infrastructure, and
indirectly by the development induced by the transport sector (EC, 1999). One of
the most obvious negative effects of the transport infrastructure development on
land use is urban sprawl. The growth of urban areas over the surrounding rural
land leads to fragmentation of land use control among more localities and
segregation of types of land use in different zones. Urban sprawl causes other
problems, such as widespread strip commercial development, low-density
settlements, and dominance of private motor vehicles in the transportation modes
(Pastowski, 2001).
1.11.1.4 Soil degradation and pollution
An unfortunate fact is that the soil best for building the transport infrastructure is
also best for agriculture, because it is stable and flat. Therefore, transport
infrastructure development inevitably leads to the loss of productive soil for
24
agriculture, and thus causes damages to the socioeconomic development of an
area. Not only does the soil covered by the transport infrastructure become lost,
but also adjacent soil, which is damaged by the construction works as a result of
compaction by heavy machinery. Transport infrastructure construction often
requires at least a partial clearance of vegetation. This often leads to erosion as
an indirect effect of construction. In some cases, erosion may occur far from the
transport infrastructure that actually causes it, as a result of cumulative impacts.
Pollution of soils in close vicinity of roads by chromium, lead, and zinc, may be
a result of a very busy traffic. These metals tend to remain in the soil for several
hundred years and cause damage to the soil micro organisms and vegetation.
Fortunately, these effects are localized on the narrow area on both sides of the
road. (State of the environment report - 2003).
1.11.1.5 Impact on biodiversity
There are three ways in which the transport sector contributes to biodiversity
loss: direct damage, fragmentation, and disturbance (EC, 1999). Loss of habitat
is an inevitable consequence of land use change during the construction of the
transport infrastructure. However, by careful planning, it is possible to keep the
damage at an acceptable level. If the construction is not carefully planned,
especially in sensitive areas, it can destroy or seriously damage natural
ecosystems, thus causing direct damage through loss of habitats for sensitive
plant and animals, which is the main cause of biodiversity loss. Roads cause
fragmentation of habitats, preventing free movement of animals and exchange of
genetic material. Habitat fragmentation damages ecosystems’ stability and
health. Habitat fragmentation can cause corridor restrictions. Corridors are routes
that animals use for satisfying their everyday or seasonal needs for food,
breeding, and shelter. By cutting through the corridors, the transport
infrastructure causes negative pressures on animal populations affecting their
feeding or breading, because they are either reluctant to cross the roads or get
killed while crossing it. It is also a case that some animals are attracted to roads
for various reasons - more food, shelter from predators, or easier movement which often leads to increased mortality due to accidental deaths.
25
Road construction also opens the ways for intruding species, disrupting in this
way the ecological balance of the ecosystems. Noise, lights, and runoff of
hazardous compounds from roads cause disturbance in the ecosystems, and
lower the reproduction rates of animals (EC, 1999).
Water ecosystems also suffer disruptions caused by the land transport
infrastructure. Erosion leads to accumulation of fine earth particles downstream,
which affects habitats for fish spawning. Changes in water flow caused by
diversions during construction works often have negative effects on plankton,
upsetting eventually food chains in the ecosystem. Roads can also cut through
the migration routes of fish, causing disruptions in the spawning cycle. (State of
the environment report - 2003).
1.11.1.6 Impact on water bodies
Activities caused by the transport sector cause surface and groundwater flow
modifications, as well as water quality degradation. Modifications in the flow of
surface waters are caused by diversions of water flows, which contribute to
flooding and soil erosion that often, happen far from the place of diversions and
the road itself.
Groundwater is often affected by road constructions, such as drainage and
embankments. Changes in water tables negatively affect vegetation, increase risk
of erosion, and often cause loss of water for drinking and agriculture.
Modification of the flow of surface and groundwater has a negative effect on fish
and other animals.
Transport causes pollution of water bodies adjacent to transport infrastructure.
Runoff from roads contains hydrocarbons, heavy metals, chemicals used for deicing, and other chemicals. Railway power lines release copper. Effluents from
ships cause water pollution. Transport of dangerous goods (hazardous wastes,
oil) poses a risk of contamination of soil, waters, and wetlands.
Transport by water affects coastal zones through building the port infrastructure.
High-speed ships can cause a serious disturbance in sensitive areas of rivers and
26
seas. The transport of oil and chemicals poses a risk of accidental water and
coastal pollution. (State of the environment report - 2003).
1.11.1.7 Visual and aesthetic impacts
Visual impacts represent the blocking out of light and pleasant views by the
transport infrastructure and activities, while aesthetic impacts are concerned with
the actual design and style of the transport infrastructure (Button and
Rothengatter, 1993). Negative visual and aesthetic impacts of the transport
sector are the consequences of poor planning, without consideration of the main
landscape design principles. Some of the principles will be explained below. The
road must be in harmony with the landscape. This means that it should not take
control over the landscape, but try to coexist with it. The road must follow the
relief and morphology of the landscape as closely as possible. It is necessary for
the road to be well visually incorporated into the landscape; it must not block or
cut off a view that is of great aesthetic, natural, historic, cultural, or
archaeological value. Constructing the transport infrastructure requires careful
consideration of watercourses and vegetation. Significant amount of water
diversion or deforestation should be avoided by finding alternative routes that
show more respect for nature. Urban planning and transport infrastructure
construction must be considered together. It is very often the case that road
construction induces urban development. Sometimes, however, this development
may be undesirable and may have negative visual and aesthetic impacts. (CEU
SUN 2002).
1.11.2 Social and other negative impacts of transport
1.11.2.1 Impacts on communities and economic activity
Although the transport infrastructure intends to connect people and increase the
speed of their travel, in some cases, if not planned carefully, it can cause the
opposite. Building a highway, for example, over the existing routes between
settlements and commercial areas can change the routes the communities used
before to reach shops or schools, because people may be reluctant to use the
27
highway crossings if it requires more time and effort than they are ready to
spend.
Another example is a road built over one of two fields a farmer works on. First,
the farmer loses a part of his land because the road covers it. Second, the road
crossing is situated far from both fields, so the farmer needs to invest a
considerable time and effort to go from one field to the other. In both examples
damage is being made to both people, who will suffer the loss of job or income
and will need to change their habits, and the economy, since the changes in
travel routes and community interactions will inevitably lead to losses in the
economic sectors. Even widening of roads can have negative impacts on the
communities and economy. Usually, roadsides are places of very active social
and business life (shops, restaurants, and cafes). The widening of roads
inevitably leads to loss of business and customers for the owners and disruptions
of living habits for their customers. A road bypassing the community is
sometimes a good solution. It preserves local modes of communication and does
not cause losses for the economy. However, it may happen that, in order to
attract more customers, some businesses migrate from the community to the
areas closer to the road. In that case, the community will suffer losses. It is,
therefore, very important to carefully weigh both options and decide whether it is
really better for the community to be bypassed by the road. (CEU SUN 2002).
1.11.2.2 Impacts on human health and safety
Roads contribute to air pollution in the local areas. They are also corridors for
transmission of diseases between local population and construction workers, and
also between plants and animals. Road construction period is time of high risk
for transmission of diseases. Construction workers often get endemic diseases,
which they later transfer to other areas. Road construction sites represent great
opportunity for development of waterborne diseases, due to poor sanitation
conditions. These sites are also a potentially good environment for transmission
of sexual diseases. Roads represent a source of noise and vibrations, both during
the construction and use. Transport infrastructure is associated with a rather high
risk of accidents and injuries. Occurrence of road accidents is a major problem,
28
causing losses of lives and significant costs to the economies. Accident rates are
decreasing in developed countries, but increasing in the developing world
(Tsunokawa and Hoban, 1997). The most vulnerable groups of road accidents
are pedestrians and users of non-motorized vehicles. (CEU SUN 2002).
1.12 Environmental Effects of Road Transport
Environmental impact of roads includes the local effects of such as noise
pollution, water pollution, habitat destruction, disturbance and air pollution, and the
wider effects including climate change from vehicle emissions. The design, construction
and management of roads, parking and other related facilities as well as the design and
regulation
of
vehicles
can
change
the
impacts
to
varying
degrees.
(www.wikipedia.com).
1.12.1 Impact on Air Quality
Roads can have both negative and positive effects on air quality.

Negative impacts
Air pollution from motor vehicle emissions can occur wherever vehicles are
used. In particular, concern in congested city street conditions and other low
speed circumstances cause deterioration of air quality. Emissions include
particulates; NOx, volatile organic compounds, carbon monoxide and various
other hazardous air pollutants including benzene. Concentrations of air
pollutants and adverse respiratory health effects are greater near the road than at
some distance away from the road. Road dust kicked up by vehicles may trigger
allergic reaction. Carbon dioxide is non-toxic to humans but is a major
greenhouse gas and motor vehicle emission is an important contributor to the
growth of CO2 concentration in the atmosphere and therefore to global warming.

Positive impacts
The construction of new roads which divert traffic from built – up areas can
deliver improved air quality to the areas relived of a significant amount of
traffic. The environment and social impact assessment study carried out for the
29
development of the Tirana outer ring road estimated that it would result in
improved air quality in Tirana city centre. (www.wikipedia.com).
1.12.2 Noise Pollution

Negative impacts
Road noise can be a nuisance if it impinges on population centres, especially for
roads at higher operating speeds, near intersections and on uphill sections. Noise
health effects can be expected in such locations from road systems used by large
numbers of motor vehicles. Noise mitigation strategies exist to reduce sound
levels at nearby sensitive receptors. The idea that road design could be
influenced by acoustical engineering considerations first arose about 1973.
Speed bumps, which are usually deployed in built – up areas, can increase noise
pollution, especially if large vehicles use the road and particularly at night.

Positive impacts
New roads can direct away from population centres thus relieving the noise
pollution. A new road scheme planned in Shropshire, UK promises to reduce
traffic noise in Shrewsbury town centre. (www.wikipedia.com).
1.12.3 Water Pollution
Urban runoff from roads and other impervious surfaces is a major source of
water pollution. Rainwater and snowmelt running off of roads tends to pick up
gasoline, motor oil, heavy metals, trash and other pollutants. Road runoff is a
major sources of nickel, copper, zinc, cadmium, lead and polycyclic aromatic
hydrocarbons (PAHS), which are created as combustion by products of gasoline
other fossil fuels.
De–icing chemicals and sand can run off into roadsides, contaminate
groundwater and pollute surface waters. Road salts (primarily chlorides of
sodium, calcium or magnesium) can be toxic to sensitive plants and animals.
Sand can alter stream bed environments, causing stress for the plants and
animals that live there. (www.wikipedia.com).
30
1.12.4 Habitat Fragmentation
Road can act as barriers or filters to animal movement and lead to habitat
fragmentation. Many species will not cross the open space created by a road due
to the threat of predation and roads also cause increased animal mortality from
traffic. This barrier effect can prevent species from migrating and recolonishing
areas where the species has gone locally extinct as well as restricting access to
seasonal available or widely scattered resources.
Habitat fragmentation may also divide large continuous populations into smaller
more isolated populations. These smaller populations are more vulnerable to
genetic drift, inbreeding depression and an increased rise of populations decline
and extinction. (www.wikipedia.com).
Environmental impacts arising from road development projects fall into three
categories:1)
Direct impacts
2)
Indirect impacts
3)
Cumulative impacts
These three groups can be further broken down according to their nature, into
a)
Positive and Negative impacts
b)
Random and Predictable impacts
c)
Local and Widespread impacts
d)
Temporary and Permanent impacts
e)
Short – and Long – term impacts
1.12.4.1 Direct effects
Direct impacts are caused by the road itself that is to say, by road building
processes such as land consumption, removal of vegetation, and severance of
farmland. For example, the removal of gravel material from a borrow pit, for use
in surfacing the road, is an obvious direct impact of road construction. In this
case, the land area in which the pit site is located has been directly affected by
activities associated with the road project.
31
Direct impacts are generally easier to inventory, assess, and control than indirect
impacts, since the cause – effect relationship is usually obvious.
1.12.4.2 Indirect effects
Indirect impacts (also known as secondary, tertiary, and chain impacts) are
usually linked closely with the project, and may have more profound
consequences on the environment than direct impacts. Indirect impacts are more
difficult to measure, but can ultimately be more important. Over time they can
affect large geographical areas of the environment than anticipated. Examples
include degradation of surface water quality by the erosion of land cleared as a
result of a new road and urban growth near a new road. Another common
indirect impact associated with new roads is increasing the deforestation of an
area, stemming from easier (more profitable) transportation of logs to market, or
the influx of settlers. In areas where wild game is plentiful, such as Africa, new
road often lead to the rapid depletion of animals due to poaching.
www.siteresources.worldbank.com
Fig - 1:- Indirect impacts of land clearing
32
Environmental impacts should be considered not only as they pertain to road
rights-of- way, but also to sites associated with the road project, which include
deposit and borrow sites, materials treatment areas, quarries, access roads, and
facilities provided for project workers. These “off – ROW” areas are often where
indirect impacts appear. Environmental Assessment practitioners should predict
and evaluate the significance of possible indirect effects by taking a holistic
approach to impact assessment. It is especially important any synergetic
relationship between impacts be closely examined, since indirect effects
frequently lead to synergetic impacts.
It is with indirect effects that impact linkages between the natural and social
environment often take place. For example, the appropriation of land to build a
road may displace farmers, and may interfere with their cropping pattern and
force them to use another water supply. This change could result in depletion at a
ground water aquifer, intensification of new land clearing, erosion, water runoff
contamination with added fertilizers and pesticides, etc.
1.12.4.3 Cumulative effects
The process of cumulative environmental change can arise from any of the four
following types of events:
i)
Single large events, i.e a large project;
ii)
Multiple interrelated events, i.e road projects within a region;
iii)
Catastrophic sudden events, i.e, a major landslide into a river system; and
iv)
Incremental, widespread, slow change, such as a poorly designed culvert or
drainage system along a long road extending through a watershed.
These can generate additive, multiplicative or synergetic effects, which can then
results in damage to the function of one or several ecosystems (such as the
impairment of the water regulation and filtering capacity of a wetland system by
construction of a road across it), or the structure of an ecosystem (such as
placement of a new road through a forest, leading to in-migration or land
clearing which results in severe structural loss to the forest).
33
A cumulative impact, in the context of road development, might be the de–
vegetation and eventual erosion of a roadside pullout. The scenario might unfold
as follows: a road cutting through a mountain range offers some spectacular
views, and in the absence of designated rest areas, motorists stop
indiscriminately. Roadside vegetation is damaged by vehicle and foot traffic, and
the soil is left unprotected. Subsequent rainfall causes erosion and siltation of
nearby water courses. The vegetation never has enough time to recover (because
of high traffic volume on the road), and the problem is exacerbated over time.
www.siteresources.worldbank.com
Fig – 2:- Cumulative impact of the stream
34
1.12.5 Ecosystem function impacts
Technically a subset or variant of cumulative impacts, ecosystem function
impacts, which disable or destabilize whole ecosystems, are the most dangerous
and often the least likely to manifest themselves over a short period of time.
Many road-related examples deal with roads which need to traverse watersheds
in which surface and subsurface water movement is complex. One striking
example is the highway constructed across a mangrove forest (100 ha in size)
along the Caribbean coast. It was not fully understood at the planning stage to
what extent the fresh and sea water needed to mix in order for the healthy forest
to survive on both sides of the road. As a result, most of the forest has died off,
on one side the waters were not saline enough, and on the other there was not
enough mixing with fresh water. The effect on the ecosystem was devastating
and the impact on the local population which used the mangrove forest area was
severe. Almost certainly, no sign of this impact appeared until two to three years
after the road was built. A second example could develop in situations where
roads bisect wildlife migration routes, which can inflict stress on the migratory
population for many generations, or even permanently, and cause instability,
increased mortality, and possibly catastrophic decline.
Finally, there is the linkage with the social environment. Having had their
traditional grazing areas cut off by new or re – constructed roads with raised –
horizontal alignments, cattle farmers may be forced to move their herds onto
forest or park lands, which results in a rapid depletion of the understory (grasses,
etc). This destroys the forest edge ecotone and the basic forest ecosystem, as
well as threatening the inhabitants with possible invasion from species better
adapted to the newly created “ grazing – forest” ecosystem. The invaded forest
ecosystem is stressed further, users of the ecosystem are affected, and a chain
reaction progresses through out the system, feeding back to the social
environment in the form of community disturbances and hardships.
Environment impacts sometimes have both positive and negative effects; some
impacts can positively affect some people and negatively affect others in the
same environment. For example, rechannelling streams as part of road
35
construction might improve drainage for a roadside farmer, but wreak have on
the livehood of others who depend on the aquatic species, disturbed by the
rechannelling.
Positive outcomes that occur as a result of project completion typically include
improved access, reduced travel time and cost, and perhaps reductions in
accidents or noise. Other positive outcomes can be designed into a project, for
example, improving water, retention for local use, flood control, or providing
better facilities for pedestrians and bicycles. In some cases, positive impacts can
appear without having been initially foreseen by the road agency, such as the use
of borrow sites to water livestock in dry areas.
1.12.6 Random and predictable impacts
In the preliminary analysis of an environmental impact assessment, it is useful
to distinguish between assured or highly probable impacts, and more random or
unpredictable ones which have a low probability of occurring but which
nevertheless may have serious consequences for the environment For example,
in a country with a large, densely settled population, it is reasonable to predict
that the construction of a road through unsettled areas will result in population
migration, whereas incidents such as accidental pollution, fire, or spillage of
toxic products are, by nature, unpredictable. Well understood and predictable
impacts can usually be mitigated with remedial measures, and therefore call for
minor EA requirements such as IEE and environment summary report, as
opposed to a full EA.
1.12.7 Local and widespread impacts
Local impacts include effects in the immediate vicinity of road, such as
destruction of a building, or restricted access to a farm. Widespread impacts can
occur many kilometres from the project. These impacts are often linked to
indirect effects that arise over the medium or long – term existence of the project
and include the influx of settlers, deforestation, and the development of new
industries. While the focus of most road EAs has been on relatively narrow
corridors measuring 100- 500 m in width, impact can extend much further,
36
particularly in new road projects which traverse isolated areas. Major habitat
conservation can take place up to 10 km on either side of the cleared ROW –
Road planners and EA practitioners should be aware of this possibility and
address it explicitly in the project scoping activity.
1.12.8 Temporary and permanent impacts
Temporary impacts are those whose occurrence is not lasting, and which will
eventually reverse themselves, the affected system having returned to its
previous state. An example of this type of impact might be the trampling of
roadside vegetation during resurfacing; it recovers after a few weeks, to the point
where no change from the original state is observable. Permanent impacts are
those which are irreversible the affected system will not return to its previous
state on a human timescale.
1.12.9 Short and long – term impacts
Short-term impacts are those which appear during or shortly after construction;
long-term impacts may arise during construction, but many of their
consequences appear during the operational phase, and may last for decades.
(www.siteresources.worldbank.com).
1.13
Rising demand for roads and road infrastructure
Motor vehicle population has recorded significant growth over the years. India
had 72.7% million registered motor vehicles at the end of fiscal year 2003 – 04. The
growth of vehicular traffic on roads has been far greater than the growth of the
highways. As a result the main arteries face capacity saturation. Between 1951 and 2004
the vehicle population grew at a compound annual growth rate ( CAGR) of close to 11
percent personalized mode ( constituting mainly two wheelers and cars) account for
more than four – fifth of the motor vehicles in the country compared to their share of
little over three fifth in 1951. Further break up of motor vehicles population reflects
preponderances of two – wheelers with a share of more than 71 percent in total vehicle
population, followed by cars with 13 percent and other vehicles (a heterogeneous
category which includes 3 wheelers, trailers, tractors etc) with 9.4 percent. In contrast to
personalised mode, the share of buses in total registered vehicles has declined from
37
11.1% in 1951 to 1.1 as in 2004. Also, the share of goods vehicle which was about 27%
in 1951 has declined to a little over 5% by end march 2004. The share of goods vehicle
in vehicle population is modest in comparison to the size of the economy. The share of
buses and trucks in the vehicle population about 1 percent and 5 percent espectively is
much lower compared to most of the other countries in Asia.
1.14
Roadways in India
Roadways in India have come a long way. Starting from the pugdandies (a small
path created naturally due to frequent walks) of earlier times to the present-day Rajpath
of Delhi, the country has crossed many spheres of road travel. The 'thread that binds the
nation together' is truly a deserving metaphor for a road network that is one of the
largest in the world.
1.14.1 History
In the Atharva Veda, we find references to road construction and information on
precautions to be taken. Kautilya's Arthasashtra mentioned about mechanism of
roads for chariots and stresses upon the traffic rules and road safety. With the
development of culture and trade, cities like Vaishali, Sravasti, Rajagriha,
Kurukshetra, and Ujjaini had roads to facilitate socio-economic intermingling.
Ujjaini, capital of Avanti, was an important trade center and connected with
northern trunk routes to modern Bharuch, an important seaport.
Development of roads took a new turn during Mauryan rule in the 4th century.
The administration constructed Rajpath (high roads) and Banikpaths (merchant
roads). Megasthenes, the Greek traveler, wrote that the Mauryan Empire took a
big stride to develop roads for communication. He recorded a Rajamarga or the
king's highway, which were also a trade route and a precursor to the modern
Grand Trunk Road. This tradition continued and Chandragupta's grandson,
Ashoka, who was a great and compassionate ruler, strengthened the system
immensely. At time of Mauryans, roads played a key role in military operations
to keep the vast country united.
38
Records reveal that during the Gupta era there was also a road connection with
South India. There were three major routes-one was a connection with Northeast
India via Didisa, the other connected to the seaport of the Western coast and the
third connected to Pratisthana, the capital of Satvahana Empire. There are also
evidences of a route facilitating trade with Iran and China.
The Mughal era was the golden era for roads. India was effectively connected to
control the vast empire. With the advent of the British, a new awakening dawned
upon India. The East India Company revived ancient routes and renovation was
initiated. The technology of the West came into play and linkages were well
established which provided the British the inroad to rule India for over two
hundred years. Roads also worked as inroads to the development of civilizations,
and provided human beings a corridor of communication for venturing out to
newer frontiers of achievements. (http://india.mapsof india.com/transportation
/roadways-in-india.html).
1.14.2 Present scenario
Today, alternative modes of transport are on the anvil. Yet, amidst all this, Road
transport is still the dominant mode of transportation - both for moving goods
and passengers. India has a huge network of roads comprising of National
Highways, State Highways, Major District Roads and Village and other roads.
Out of total length of national highways, 27 % is single lane/intermediate lane;
whereas 59 % is double-lane standard; and the rest 14 % is four-lane/ six-lane/
eight lane standard. The road network is assuming a pivotal role in the
movement of goods and passengers. There has been a substantial shift in the
mode of transportation from Railways towards the road sector. While the
Railways handle only 40% of the freight and 20% of the passengers load, 60% of
the goods and 80% of passenger's movement takes place through roads. It is
anticipated that the function of the road network will further increase in the fore
see able. (http://india.mapsofindia.com/transportation/roadways-in-india.html).
The Indian Roadways play a crucial role in connecting the different parts of
India. Over the years after independence there has been an extensive
development of the network of roads across the length and breadth of India.
39
Road network of India is the largest road networks (3.314 million kilometers) in
the world. India's road network consists of national highways, state highways,
district roads and village roads. National Highways are found all over the
country. They are indispensable as far as communication by roads is concerned.
National Highways connects States, states’ capitals, big cities and ports. National
highways carry approximately 40 % of the total traffic but they are only 2 % of
the entire road network. Where as State Highways are considered as the main
roads of the State. Major cities of the States and capital of the state are connected
by state highways. While District roads are connecting with major roads and
village roads. Village roads provide linkage to other roads in order to meet their
daily needs and access to nearby markets. Different types and categories of
Indian road network given in the table - 3 and 4. (www.india.mapsofindia.com).
Table - 3:- Different types of Indian road network
Class
Length (km)
Assess Controlled Expressways
4 – 6 lane Divided Highways (with
service rd in crowded areas)
National Highways
200
10000
66,590
State Highways
1,31,899
Major District Roads
4,67,763
Rural and Other Roads
36,50,000
Total (approx)
33,00,000
(www.india.mapsofindia.com)
India has a network of about 3.3 million km, which carry 52 per cent of freight
traffic and 83 per cent of passenger traffic (Raghuram, 2000). While the road
network has grown at a CAGR of 2.95 per cent between 1991 and 1999, the
traffic in vehicles during the same period has grown at a CAGR of 9.71 per cent
(CMIE 2003). During 1991–2 to 1998–9, the road freight traffic in billion tonne
kilometres (btkm) grew from 267 to 585, at a CAGR of 11.9 per cent (Raghuram
2000). The NH and State Highways (SH), which constituted 187,535 km in 1999
(CMIE 2003), formed 7.42 per cent of the total road length (This data has a
40
discrepancy. As per the MORTH, the NH and SH which constituted 172,000 km
in 2001, formed 5.21 per cent of the total road length.) The NH and SH carried
nearly 60 per cent of the road freight traffic and 87 per cent of the road
passenger traffic during 2001 (www.morth.nic.in). The NH alone constituted
49,585 km in 1999, forming 1.96 per cent of the total road length (CMIE 2003).
(As per MORTH, the NH constituted 57,737 km in 2001 forming 1.75 per cent
of the total road length.) The NH carried nearly 40 per cent of the road freight
traffic (NHAI, 2001).
Table - 4:- Authorities responcible for different categories of roads
Category of roads
National Highways
Authorities responsible
Central Government(through department
of road and transport and highways)
State Highways and Major
State Government (PWDs)
Highways
Rural Roads and Urban Roads
Local Authorities like Panchayats and
Municipalities
(www.india.mapsofindia.com)
The NH had a CAGR of 5.55 per cent between 1991 and 2001. This growth has
been more significant in the later part of the decade, due to an increased attention
on the development of the NH. In the first 6 years, the CAGR was 0.56 per cent,
while in the later 4 years it was 13.45 per cent. The growth in NH is almost
entirely due to an upgraded reclassification of SH segments, attracting central
government funds for development and maintenance. The NH have tradionally
been maintained by the state governments with the central funds. However, after
the formation of the NHAI in 1988, key segments of the NH have been taken
over by the NHAI.
It is now generally accepted that due to the thrust on road development from the
1950s, the road length and the consequent connectivity is less of an issue than
the road quality. Poor road quality affects capacity (leading to congestion and
additional travel time), wear and tear on vehicles, safety, and pollution. These
41
implications are all the more significant in NH due to the higher traffic density.
In 1996, it was estimated that the economic losses due to the poor quality of
main roads was of the order of Rs.200 to 300 billion per annum, which was
about 2 per cent of the GDP (Mohan, 1996). In rural areas, sometimes the poor
quality even results in loss of all-weather connectivity, especially to remote
locations. (India Infrastructure Report, 2004).
Road Network in India
2,650,000,
79.81%
rural and other roads
Express ways
National highways
State highways
Major district roads
467,763,
14.09%
131,899,
3.97%
70,548,
2.12%
200,
0.01%
Fig - 3:- Indian road network classifications in percentage
42
Condition of National Highways
12,053,
17%
Four lanes
Single lane
Two lane
37,646,
53%
20,849,
30%
Fig - 4:- Condition of National highways
(India Infrastructure Report, 2004)
Fig - 5:- Category - wise road length (1999)
43
Estimates indicate that the country could make economic savings to the tune of
Rs 200 – 300 billion (US $ 5.7 – 8.6 billion) per annum through improved road
infrastructure. The annual average rate of traffic growth has been 8 to 10 percent.
It is estimated that of the total requirement of Rs 300 billion (US $ 8.57 billion)
for development of State Highways, the private sector would be required to
invest nearly 20 percent. For effective management and administration, Indian
roads are divided into National Highways, State Highways, District roads and
Village roads. Presently, the functions relating to externally – aided projects,
implementation of policy on private sector participation and development of way
side amenities along the National Highways, have been assigned to National
Highway Authority of India (“NHAI”). State highways, district roads and village
roads are the responsibility of the state governments. (www.NHAI.gov.in).
1.14.3 Road development
All roads other than National Highways in the states fall within the jurisdiction
of respective state governments. However, to assist the state government in their
road development programme, central government also provides funds from the
Central Road Fund (CRF) for certain selected state roads under inter state
connectivity and economic importance (ISC & EI) scheme. The department is
also responsible for evolving standards and specifications for roads and bridges
in the country besides acting as a repository of technical information on roads
and bridges.
The length of National Highways, for which the Government of India is
constitutionally responsible, is 65,569 km. The National Highways system
suffers from various deficiencies of capacity, constraints, pavement crust,
geometric and safety features. Improvement of National Highways is undertaken
by way of widening and strengthening of existing highways, reconstruction /
widening of bridges and constructing bypasses after prioritizing the works on the
basis of requirement within available resources. While the government is
providing increasing budgetary allocation for projects in the highway sector and
has undertaken major up gradation initiatives in high – density corridors, it has
not been possible to allocate sufficient funds matching the needs for maintenance
44
of National Highways. The physical programmes of road development and
removing the financial bottlenecks need concerted efforts in the form of
mobilization of funds from other sources. In – flow of private sector funds is
expected to bridge the gap of the demand and supply to certain extent.
1.14.4 National Highways in India
1.14.4.1 History
In ancient times the ruling monarchs constructed many brick roads in cities. The
most famous highway of medieval India was the Grand Trunk Road. The Grand
trunk Road begins in Sonargaon near Dhaka, Bangladesh and ends in Peshawar,
Pakistan. It travels through or near many important cities of the subcontinent,
including Dhaka in Bangladesh, Kolkata, Patna, Varanasi, Kanpur, Agra, Delhi,
Panipat, Ludhiana, Jalandhar, and Amritsar in India, and Lahore and Peshawar in
Pakistan. In the 19th century, the British upgraded the existing highway network,
and built roads in treacherous areas such as the Western Ghats. (www.enwikipedia.org).
1.14.4.2 Development and Maintenance of National Highways
The government has embarked upon a massive National Highways Development
Project (NHDP), the largest highways project ever undertaken in the country.
The NHDP is being implemented by National Highway Authority in India.
1.14.5 National Highways Authority of India
The National Highways Authority of India Act, which was enacted by the
parliament in1988, provided for the setting up of a central authority for the
development, maintenance and management of National Highways vested to it.
The Authority (NHAI) became operational in 1995 with the appointment of a
full time chairman and members.
The Authority consists of a full time chairman, and not more than five full time
members and four part time members. They are appointed by the central
government. The full time members are
45
(i)
Member (Administration)
(ii)
Member ( Finance) and
(iii)
Members (Technical)
The part time members are
(i)
Secretary, Department of Road Transport and Highways.
(ii)
Secretary, Department of Expenditure, Ministry of Finance.
(iii)
Secretary, Planning Commission and
(iv)
Director General (Road Transport and Highways)
(Source: Department of Road Transport and Highways)
1.15
Promotion of Road Infrastructure Support in India
The aggregate length of roads, which was 0.4 million km in 1950 – 51 has
increased 8 fold to 3.4 million km in 2002 but over the same period the number of
passenger buses has shown 19 told jump from 0.34 lakh to 6.35 lakh and goods vehicle
fleet more than 36 fold increase from 0.82 lakh to 29.74 lakh. The geographic coverage
of India’s highway network at 1.03 cm of highway per square km of land is much dense
compared to USA (0.77) and that of china (0.20). But, china’s highway network consists
of over 34,288km of four or six lane access controlled expressway linking the major
cities. In India, expressways do not yet link the major economic centres.
The country road network can be broadly be divided into three categories viz. (a)
National Highways (NHs), (b) State Highways (SHs), (c) Major District Roads (MDRs)
and (d) Rural Roads. The SHs and MDRs serve as secondary road network and provide
connectivity between primary (NHs) road network and tertiary (rural roads).
1.15.1 National highways
The National Highways running across the length and breadth of the country
connect all state capitals, major ports, international boundaries, areas of
economic and strategies importance, etc. The present total length of National
Highways is about 66,590 km. An overwhelming proportion of the total length
of National Highways is two or single laned (56 % and 32 % of the total length
of national highways are double / intermediate lane and single lane respectively)
46
and only 12 percent of the length of the National Highways are four lane and
more. The National Highways constitutes less than 2 percent of the road length
of the country but carry about 40 percent of the road based traffic. Highway
capacity shortages are aggravated by heterogeneity in traffic, encroachment, and
frequent and long halta at state and municipal check posts. Further, over loading
by rigid two – axle trucks has been a major source of damage to road structure
and pavement.
In order to expand and improve road connectivity in the country, the government
has launched National Highways Development Project (NHDP). It is the largest
highway project ever undertaken in the country. The NHDP is being
implemented by National Highways Authority of India (NHAI). Government has
envisaged investment of Rs.2,35,430 crore far up gradation of National
Highways under various phases of NHDP over the medium term.
1.15.2 State highways and major district roads
State Highways and Major District Roads constitute the secondary system of
road transportation in the country. The state highways provide linkages with the
NHs, district head quarters, important towns, tourist centres and minor ports.
Their total length was about 1,37,711 km as at the end of March 2002. Major
District Roads run within the district, connecting areas of production with
markets, rural areas to the district head quarters and to State Highways/ National
Highways. By acting as the link between the rural roads and National Highways,
the State Highways and major District Roads contribute significantly to the
development of the rural economy.
1.15.3 Rural roads
Rural roads connect villages giving access to rural population to the National
Highways through Major District Roads and State Highways. Around 59 percent
of the total road length is accounted by rural roads largely built under Jawahar
Rojgar Yojna. These roads are of limited value from the point of view of
movement of heavy traffic.
Roads are also being developed in rural areas
under the Pradahn Mantri Gram Sadak Yojana (PMGSY). The objective of
47
PMGSY is to link all villages with a population of more than 500 people with all
– weather roads by the year 2007. This is being implemented by Ministry of
Rural Development. (www.morth.nic)
1.16
Current Status of Road Network in India
The roads and highways in India account for about 80 percent of the total
passenger traffic and about 60 percent of the total freight traffic in the country.

Of the 49,585 km of National Highways in India about 33 percent are single lane
and only about 2 percent of the total road network is four lane. The poor quality
of India roads is highlighted by congestion, old fatigued bridges and culverts,
railway crossings, low safety, no bypasses and slow traffic movement.

Considering the importance of the road sector in the country, the government has
embarked on the ambitious National Highway Development Project covering
13,000 km with a cost of Rs.54,000 crore and the projects have already started
rolling.

The Indian construction industry that had been experiencing a slowdown
witnessed a growth of 9 percent and 8.5 percent for the periods FY2000 and
FY2001 (1st half) respectively. This was possible due to the increased spending
in infrastructure and the actual taking off of some of the road sector projects.
However, road development has been ignored in most of the development plans
of India. National and State highways comprise around 5.9 percent of the total 3.1 mn
kms of road network. National highways, which carry 40 percent of the total traffic,
comprise just 2 percent of the total road network. (Raghuram, Aug 24, 2001).
The National and State Highways form the economic backbone of the country.
These have helped development along the route, and many towns have sprung up. Along
major district roads constitute the secondary system of road infrastructure of India.
By acting as the link between the rural and urban areas, the State Highway and
Major District Roads contribute significantly to the development of the rural economy
and industrial growth of the country. It is estimated that the secondary system carries
48
about 40 percent of the total road traffic and comprises about 20 percent of the total road
length.
The Central Road Research Institute (CRRI), established in 1948, is the premier
national laboratory engaged in carrying out research and development projects on
design, construction and maintenance of roads and runways, traffic and transportation
planning of mega and medium cities, management of roads in different terrains,
improvement of marginal materials, utilization of industrial waste in road construction,
land slide control, ground improvements environmental pollution, road traffic safety and
analysis and design, wind, fatigue, corrosion studies, performance monitoring /
evaluation, service life assessment and rehabilitation of highway and railway bridges.
(Road National Highways, mail article: National Highway (India).
The traffic on National Highways has been growing due to the recent economic
growth in India and the Government of India is taking steps to improve management
techniques to provide hindrance – free traffic movement by way of widening roads,
grade separation, construction of bypass, bridge, rail – road crossings, and utilizing the
latest technologies.
Table - 5:- National highways classification
Lanes
Length (km)
Percentage
Single lane/Intermediate lane
18,350
27 %
Double lane
39,079
59%
Four lane/Six lane/Eight lane
9,325
14%
Total
66,754
100%
(www.en.wikipedia.org)
Even though the National Highways represent only 2 percent of the total
network length, they handle about 40 percent of the total road traffic. The National
Highways are further classified based on the width of carriage way of the Highway.
Generally, in case of a single lane, the lane width is of 3.75 meters, while in case of
multi – lane National Highways, each of the lanes have a width of 3.5 meters. As of
February 2008, out of the total length, 14 percent have four or more lanes and about 59
49
percent two – lanes or are double – lane, while the rest (27%) of the National Highway
network has single or intermediate lane. Table 5 shows the total length of National
Highwaysand their classification.
1.17
Comparison with Other Countries
The total road length in India is more than 3.31 million kilometres today, of
which half is paved. This compares favourably with the U.S that has 6.24 million km of
total road length (3.63 million km paved). Compared to this, China has a total road
length of 1.03 million km out of which only 170,000 km is paved. In terms of road
length per square km, the connectivity in India (68.4 km per Sq. km) is hence much
higher than China (10.7) and comparable to the U.S (66.5). However, most roads were
built with the primary aim of moving passenger traffic. With demand out stripping the
supply and due to changes in nature of goods moved, the modal share of road
transportation has increased substantially.
This has led to introduction of new, larger capacity, trucks. Most highways do
not have the adequate bearing capacity for multi – axle and tandem trucks. This has led
to rapid deterioration of road surface quality in many areas.
1.18
Statutes and Institutional Structure
The functions relating to development, the Central Government, under the
provisions of National Highways Act, 1956, carries out maintenance and management
of National Highways. The Act has been amended in June, 1995 to permit private sector
participation. The National Highways Act, 1956 empower the central Government to
enter into agreement with any person for development and maintenance of National
Highways.
The central Government has decided that the policy of privatization of National
Highways will be implemented by the National Highways Authority of India (NHAI). In
exceptional cases, the central Government may also assign the functions of
implementing agency (IA) to the states. NHAI was established under the National
Highways Authority of India Act, 1988 but was operationalised on in February 1995.
50
1.19
Present status of Indian Highways
Owing to the emphasis on rural connectivity, the road network in India has
grown remarkably from 1.4 mn km in 1981 to over 3.3 mn km in 1999, making it the
second largest in the world, though into quality remains poor and neglected. The growth
in traffic has been causing increasing congestion in several highway corridors across the
country. The population of cars, buses and goods vehicles has grown from 1.9 mn in
1981 to 8.8 mn in 1999, implying as annual growth rate of 8.9 percent. The share of
road transport in the total passenger traffic is about 80 percent and its corresponding
share in freight traffic in about of 75 percent. It is estimated that a GDP growth of
1 percent leads to a growth over 1.25 percent in passenger traffic and freight by road.
(Choudhry et al., 2001).
1.20
Recent Developments
Under former Prime Minister Atal Behari Vajpayee, India launched a massive
programme of highway upgrades, called the National Highway Development Project
(NHDP), in which the main north – south and east - west connecting corridors and
highways connecting the four metropolitan cities have been fully paved and widened
into 4-lane highways. Some of the busier National Highway Sector in India have been
converted to four or six lane expressways – for example, Delhi – Agra, Delhi – Jaipur,
Ahmedabad – Vadodara, Mumbai – Pune, Mumbai – Surat, Bangalore – Mysore,
Bangalore – Chennai, Chennai – Tada, Hyderabad – Vijayawada and Gundur –
Vijayavada phase v of the National Highway Development Project is to convert all 6000
km of the Golden Quadrilateral Highways to 6 – lane highway / expressways by 2012.
The National Highways Bill, passed in 1995, provides for private investment in
the building and maintenance of the Highways. Recently, a number of new roads have
been classified as “NHs” in a move to provide National connectivity to remote places.
By passes have also recently been constructed around larger towns and cities to provide
uninterrupted passage for highway traffic. The varied climatic, demographic traffic and
some times political situation, prevents these highways from having a uniform character.
They range from fully – paved, six - lane roads in some areas, to unpaved stretches in
remote places. Many National Highways are still being upgraded or are under
51
construction. There are long National Highways to connect the metros together, as well
as short spurs off the highway to provide connectivity to nearby ports or harbours. The
longest National Highways is the NH7, which runs between Varanasi in Uttar Pradesh
to Kanyakumari in TamilNadu, at the southern most point of the Indian main land,
covering a distance of 2369 Kms, and passes through Hydrabad and Bangalore. The
shortest National Highways is the NH 47A, which spans 6 Kms (3.7 mi) to the
Ernakulam – Kochi port. (www.en.wikipedia.org).
1.20.1 Road transport in tamilnadu
TamilNadu has a well established transportation system that connects all parts of
the state. This is partly responsible for the investment in the state. Though the
present transportation system is substantial, it needs to be developed further to
keep pace with the rapid increase in use. TamilNadu is served by an extensive
road network in terms of its spread and quality, providing links between urban
centres, agricultural market-places and rural habitations in the countryside. There
are 24 national highways in the state, covering a total distance of 2,002 km. The
state is also a terminus for the Golden Quadrilateral project that was scheduled to
complete in 2008. The state has a total road length of 167,000 km, of which
60,628 km are maintained by Highways Department. This is nearly 2.5 times
higher than the density of all-India road network.
(www.indianetzone.com/14/transport_tamil_nadu.htm).
1.20.2 Roads
Tamil Nadu has one of the largest networks of roads in India. A number of
national Highways pass through the state. They link the state with other parts of
the country. Besides the national highways, there are State highways and local
roads that connect different parts of the state. The length of road network in
Tamil Nadu is about 1.70 Lakh km. (www.bharathonline.com).
1.20.3 Road network in Tamilnadu
Tamil Nadu has an extensive road network. State road network covers about 153
k.m per 100 Sq. k.m area, which is higher than the country's average road
52
network coverage of 103 k.m per 10 Sq. k.m area. A separate Highways
Department (HD) was established on April, 1946 and the same has been
renamed as Highways & Minor Ports Department (HMPD) on 30 October 2008.
HMPD of Tamil Nadu is primarily responsible for construction and maintenance
of roads including national highways, state highways and major district roads in
Tamil Nadu. It operates through 7 wings namely National Highways Wing,
Construction & Maintenance Wing, NABARD and Rural Roads Wing, Projects
Wing, Metro Wing, Tamil Nadu Road Sector Project Wing, Investigation and
Designs Wing geographically spread across the state in 31 districts with about
120 divisions and 450 subdivisions. (www.bharathonline.com)
The Highway Department (HD), Government of Tamil Nadu (GoTN) is
preparing the Tamil Nadu Road Sector Project (TNRSP) with World Bank /loan
assistance. In 1997, M/s. Kinhill PTY.LTD was appointed as the project cocoordinating consultants (PCC) at assist the GoTN in the project preparation.
Based on the outputs of the feasibility studies and the funding available, the road
network identified for improvements include 743.4 km for upgradation works.
(Disclosure of EA Summary Report for Tamil Nadu Road Sector Project: March
2003).
1.21
Tamilnadu Road Network Details
Table – 6:- Categories of roads and their length in Tamil Nadu
S.No
Classification
National Highways(NH)
Length
Authority
1613km
National Highways Wing
3260km
Sub total for National Highways
4873km
National Highways
Authority of India
General Wing
2
State Highways(SH)
9384km
General Wing
3
Major District Roads(MDR)
11288km
General Wing
4
Other District Roads(ODR)
36096km
Government Road Total
56768 km
Over all Total
61641 km
1
(Demand No.21, Performance Budget, 2009-2010)
53
TamilNadu is the 3rd biggest state in urbanization by virtue of development
achieved in the fields of industry, information technology, special economic zone and
many other sectors. Roads and bridges play a vital role in promoting industrial
development. Accelerated Industrial growth, ever increasing number of vehicles and
development of ports have increased vehicular traffic manifold. Highways Department
of Tamil Nadu develops the road and bridges infrastructure with an objective to provide
wider roads, safe journey and hazzle free traffic. Tamil Nadu has a total area of 1,30,058
Sq km. In this, there are roads to the total length of 1,99,040 Km. Out of this, 61,641
Km length of roads are maintained by Centre and State Highways Department.
Apart from this, roads are maintained also by local bodies and other
departments. In India, road infrastructure facilities are available at an average of 329
Km for every one lakh population. In Tamil Nadu, road infrastructure facilities are
available to the extent of 319 Km for every one lakh population. Moreover, the road
network in Tamil Nadu is 153 Km. per 100 Sq. Km area, which is higher than the
average of 103 Km. in India. This it is a proof that the road infrastructure facilities have
developed well in Tamil Nadu.
The coastal length of Tamil Nadu is 1076 Km. In this, there are 3 major ports
namely Chennai, Ennore and Tuticorin. In 2006, there were only 15 minor ports. This
has increased to 20 due to the earnest steps taken by this Government. The major ports
developed under the Major Port Trust Act of 1963, are functioning under the control of
Government of India and the minor ports developed based on Indian Ports Act of 1908
are functioning under the control of State Government. Categories of roads and their
length are presented in table 6 (www.tn.gov.in).
1.21.1 Status of transport sector in the state
Tamil Nadu has made significant efforts to develop a transport network catering
to the needs of the large number of travelling public in the State. It ranks second
in terms of transport network in the country. The State utilizes all the three
modes of major transporting systems viz., roadways, railways and airways in an
effective manner. Rail and road are the dominant modes of transport in the State.
It is also well connected globally by means of international seaports and airports.
Roads play a pivotal role in supporting economic and social development of the
54
State. The rapid industrialisation and urbanisation of the State has thrown up
new problems and challenges in development, in which road transport occupies a
position of crucial importance. Passenger mobility in India heavily relies on rail
and road networks. The bus transport system in the State enjoys the maximum
public patronage and contact, much more than any other Government system.
(www.tn.gov.in).
1.21.2 Population growth and vehicle growth
The population of Tamil Nadu has increased from 30.12 million in 1951 to 62.11
million in 2001. However, the vehicle population in Tamil Nadu has increased
almost 300 times from 27,325 in 1951 to 82.21 lakh in 2006. The road network
has not kept pace with this increase, growing at only 8.5 times, 570 from 32,307
Km. to 1.89 lakh Km. in 2006. The exponential growth of vehicular traffic has
necessitated the increase in the existing road network. During the first four years
of the Tenth Plan period (2002-2006), the growth of vehicles in Tamil Nadu was
11.85% per annum. The ratio of transport vehicles to non-transport vehicles was
7:93. Transport vehicles include passenger buses, goods carriages, and contract
carriages such as auto, taxis etc. Non-transport vehicles comprise personalized
vehicles like two wheelers, cars and others. In transport vehicles, stage carriage
bus growth was meagre (1.52%) because of the need for permits for bus
operations, while goods carriage growth was observed at 9.46%. Under
personalized transport, cars grew at 9.64%, but the most significant growth was
observed in the segment of two wheelers that grew at an alarming 12.43%.
(www.tn.gov.in)
55
Table - 7:- Length of roads – Tamil Nadu
Type of Road
2006-07
2007-08
% share to
total
National
Highways
4483
(In NH-1244 Kms &
NHAI-3239 Kms)
4500
(In NH-1240 Kms &
NHAI-3260 Kms)
2.27
State Highways
9256
9264
4.67
9451
9451
4.76
38256
38256
19.28
96330(P)
99610 (P)
50.19
Other @
37391
37391 (P)
18.84
Total
195167
198472
100.00
Major District
Roads
Other District
Roads*
Panchayat Union
and Village
Panchayat Roads
Note: * Includes 1746 Km of Sugarcane Roads & P-Provisional.
Source: 1. Policy Note on Roads, Bridges, Minor Ports and Shipping 2008-09, GoTN.
2. Department of Rural Dept.
3. @- CMIE, May-2006.
1.21.3 Growth of vehicle population
The state witnessed an ever increasing registration of vehicles. The average
number of vehicles registered per day increased from 1510 in 2002 – 03 to 2416
m 2006 – 07 and to 2645 in 2007 – 08. The number of registered vehicle
population in the state had increased from 91.04 lakhs in 2006 – 07 to 100.69
lakhs in 2007 – 08 registering a growth of 10 – 60 percent. Out of 10 – 69 lakhs
vehicles in the state in 2007 – 08, transport and non – transport vehicles were
7.06 lakhs and 93.63 lakhs respectively. Further, it was noted that in the non –
transport vehicle system, two wheelers alone constituting 82.03 percent was the
resultant fact of increasing per capita income and high income elasticity of
individuals with the present strength of 100.69 lakhs number of vehicles, the
56
state ranks second in vehicle population next to Maharastra and however tops
first in two wheelers. Table 8 showed the vehicle population in Tamil Nadu.
Table - 8:- Trend in registered vehicle population in Tamil Nadu
Non – transport
vehicles
2 wheelers Others
Total
vehicles
Growth
rate
(%)
Vehicle
Density per
sq.km
6.78
62.09
9.74
47.79
55.48
7.32
67.52
8.75
51.83
4.94
61.06
8.04
74.04
9.66
56.83
2005 – 06
5.81
67.50
8.91
82.22
11.05
63.11
2006 – 07
6.08
75.03
9.93
91.04
10.73
69.89
2007 – 08
7.06
82.60
11.03
100.69
10.60
77.29
AAGR
10.07
Year
Transport
vehicles
2002 – 03
4.57
50.74
2003 – 04
4.72
2004 – 05
Source: Transport department, Chennai – 5.
1.21.4 Surface-wise length of roads
Table - 9:- Surface-wise length of roads in Tamil Nadu (in Km)
Surface Roads
Unsurfaced Roads
Year
Total Length
of Roads
Length
% to total
Length
% to total
1950-51
28291
64.50
15569
35.50
43860
1970-71
45345
66.25
23101
33.75
68446
1990-91
134135
78.53
36666
21.47
170801
2000-01
131882
75.05
43848
24.95
175740
Source: Department of Economics and Statistics, Chennai- 6
As such the proportion of surfaced road availability in the State at 64.50 per cent
of total road network during 1950-51 had gradually increased to 66.25 per cent
in 1970’s and further to 78.53 per cent in 1990’s. Table 9 showed surface – wise
roads length in Tamil Nadu. However, it marginally dropped to 75.05 per cent at
the beginning of the millennium year (2000-01) due to the up gradation of large
proportion of new roads lay in the State.
57
1.21.5 Lane-wise length of road matrix
Lane-wise lengths of roads are important for providing a safe traffic-free and an
efficient use of roads to save fuel consumption and to reduce travel time. Out of
61471 kms of roads maintained by the State Highways during 2007-08, a major
portion of the roads was single lane which accounted for a higher proportion of
65.74 per cent, intermediate lane accounted for 9.45 per cent, and double lane
22.88 per cent. The proportion of multilane road at 1.93 per cent is catering to
the ever growing traffic congestion in the State. Table 10 shows the lane-wise
length in Tamil Nadu.
Table - 10:- Lane-wise length of roads as on 31st March 2008 - Tamil Nadu
Single
Lane
Intermediate
Lane
Double
Lane
Multi
Lane
Total
18
42
1155
25
1240
-
-
2791
469
3260
State Highways*
827
954
7021
462
9264
Major District
Roads*
3961
2869
2419
202
9451
Other District Roads
33895
1939
646
31
36510
Sugarcane Roads
1711
3
32
-
1746
Total
Percentage of Lane
Width to Total
Length of Roads
40412
5807
14064
1189
61471
65.74
9.45
22.88
1.93
100.00
Type of Road
National Highways
(Under NH wing)
National Highways
(Under NHAI)
Sources: Performance Budget 2007- 08, Highways Department, GoTN, Chennai – 5.
The following Acts/Rules, which embody the policy relating to motor vehicles
and State Road Transport Corporations (SRTCs), are being administered in the
Road transport Division of the department.

Motor vehicle Act, 1988

Central Motor Vehicles Rules, 1989

Road Transport Corporations Act, 1950

Carriers Act, 1865
58
1.21.6 Vehicle population and infrastructure
The development of road density is far behind the ever increasing vehicle
population, implying that road density has to be increased to accommodate the
ever increasing vehicular population though there is a constraint for funds for
road infrastructure. However, the fund crunch may be bridged by imposing road
uses charges (road tolls) at reasonable rate. Table 11 shows the vehicle
population and infrastructure details in Tamil Nadu.
Table - 11:- Vehicle population and road infrastructure – Tamil Nadu
Year
No.of registered
motor vehicles per
lakh population
Road density
per 1000 sq.km
(in kms)
No.of motor
vehicles per
sq.km.
1950 – 51
1960 – 61
1970 – 71
1980 – 81
1990 – 91
2000 – 01
2005 – 06
2006 – 07
2007 – 08
62
132
333
663
2755
8301
12662
13917
15275
339
338
526
935
1312
1353
1490
1491
1498
0.14
0.34
1
2
12
40
63
70
77
Source: Computed by DEAR
As per the category-wise length of roads, the following points were observed.

In the National Highway roads, double lane accounted for 87.69 per cent and
availability of multilane was for 10.98 percent;

The share of double lane in state Highways was 75.79 percent, single lane 8.92
percent and intermediate lane 10.30 percent;

In major district Roads, the single lane accounted for a highest proportion of
41.91 percent followed by intermediate lane (30.36 %) and

In the case of other District roads, single lane road accounted a major portion of
92.84 percent and multilane was at negligible level of 0.04 percent.
59
1.21.7 Public Road Transport
Road transport is a premier mode of a cheap and common vehicle to the public
having easy access to narrow places and providing forward and backward
linkages to all sectors of the economy. The state transport undertakings in the
state is operating the public transport system parallel to private vehicles with the
prime objective of encouraging public to use more public buses through
providing efficient and safe services to the public in order to reduce air and noise
pollution and to avoid traffic jam, accidents and time delay (Transport and
Communication).
1.22
Tamilnadu highways act, 2000
In relation to land and other property under the control of the highway authority,
the Tamil Nadu Highways Act, 2000 has super sedes the Tamil Nadu Encroachments
Act.

The Highways Act recognizes the right of the illegal occupiers to their property
unlike the Encroachment Act, and.

The Highways Act empowers the government to exempt any land or other
property under the control of highways authority from the exemption of the Act.
The following provisions of the Tamil Nadu Highways Act empower the
highway authority to take up measures to prevent any further encroachments onto the
RoW (Right of Way):

Section 26 of the Highway Act of Tamil Nadu, 2002 provides for the prevention
of unauthorized occupation of, and encroachment onto the highway and removal
of encroachments.

Section 28(1) of the Act empowers the highways authority to conduct checks
and periodical inspection of highway boundaries with a view to ensure the
prevention of unauthorized encroachments and the removal of such
encroachments.
60

Section 28(2) of the act empowers the highway authority of remove without any
notice, any structure encroaching the highway or in any area where the
construction.

Sections 47 and 48 of the Highway Act authorize the Highways Department to
penalize the encroachments or illegal occupation of the Highway land.
(Tamil Nadu Road Sector Project, Highways Department, Government of Tamil
Nadu, Resettlement Action Plan, Volume 1: Main Report, Feb 2003).
1.23
Tamil Nadu Road Sector Project
The Tamil Nadu Road sector project is implemented in the state with a project
cost of Rs. 2160 crores from 2003 -04 with world Bank loan assistance of Rs. 1670
crore and Rs. 490 crore from state Government, to be completed by March 2009.
i)
Strengthening and up gradation of 742 kms of core network of roads linking 11
Districts are being upgraded to international standards.
ii)
Enhancing the periodical maintenance of 2000kms of SHs and MDR s and
implementation of road safety works was taken up in and years cycle from
2004 – 05. The first year, 634 kms. of SHs and MDRs at a cost of Rs.242.55
crores are taken up in 15 packages. Out of which 14 packages were completed.
15th package was included in the second year cycle of 274 kms at a cost of
Rs.109.47 crore.
iii)
Under road safety, 300 black spots (accidents prone) were identified in the state
and works were proposed so as to provide country measures for 50 Black spots.
1.24
Background of the Tamil Nadu Road Sector Project
The Government of Tamil Nadu, in its unrelenting pursuit of promoting
socioeconomic development and to improve the infrastructure facilities in Tamil Nadu
embarked on Tamil Nadu Road Sector Project (TNRSP) with World Bank assistance, in
order to provide a road network through Highways Department (HD) on East Coast
lines, starting from Arcot in Vellore district to Thoothukudi, cutting across a total of 12
districts in Tamil Nadu. Assisted by the Project Co-ordinating Consultants (PCC), the
Highways Department of Government of Tamil Nadu prepared the project proposals.
61
Nearly a length of 743.4 km for up gradation of road work and that of 2600 km for the
maintenance of the road, including 14 Bypasses for a total length of 106.6 km, were
identified by the Highways Department for this project under the assistance of the
World Bank. (National Institute of Technical Training and Research, NITTR).
1.25
Overview of India’s Marine Sector
Coastal environment plays a vital role in nation’s economy by virtue of the
resources, productive habitats and rich biodiversity. India has a long coastline of more
than 7500 km. Its marine resources are spread over in the Indian Ocean. Arabian Sea
and Bay of Bengal. The exclusive economic zone (EEZ) of and country has an area of
2.02 million sq km comprising 0.86 million sq km on west coast. 0.56 million sq km on
East Coast and 0.6 million sq km around Andaman and Nicobar Islands. The east coast
supports activities such as agriculture and aquaculture while a number of industries are
supported on the west coast. Tourism has emerged as a major economic activity in
coastal states such as Goa, Kerala & Orissa.
(http://www.un.org/ega/justdev).
The east coast of India extending from the international border of India and
Bangladesh in the northeast to Kanniyakumari in the South, is 2545 km long, cover 21
districts in the states of West Bengal, Orissa, Andrapradesh and Tamil Nadu and has a
population of approximately 128 million. Five major and five minor parts are situated
along the coast, handling a billion tones of goods every year. The beaches like those at
Digha, Pune, Clopalpur, Madras and covalam attract tourist and from all over of world,
as do and scores of well known centres of historical and religious significance along and
western and northern edges of the Bay of Bengal.
1.26
Tamil Nadu Coastal Line
Tamil Nadu has a 1076 km. Stretch that amount to 17% of the total Indian Coast.
Physically this coastal zone is defined as a rather narrow transitional ribbon occurring
where a continental landmass meets a tidal sea. The state has an average rainfall of
945mm. Table 12 coastal length details in Tamil Nadu.
62
Tamil Nadu is endowed with among the largest and richest fishing wealth in
India. The Exclusive Economic Zone form 1.9 lakh Sq.km, covering the coramandal
coast, Palk Bay, Gulf of Mannar and part of the West Coast of India, beside 21 Coral
islands in the Gulf of Manner, with its rich habitats of live corals, coastal lagoons,
Mangroves and Estuaries. Regarding inland fisheries, there are five major rivers, 51
reservoirs and innumerable tanks. (http://www.un.org/ega/justdev).
Table - 12:- Coastal length of Tamil Nadu
Tamil Nadu
S.No.
Coastal Information
E. Coast
N. Coast
Total
1016
60
1076
-
-
41112
Up to 50 m depth
22411
844
23255
51 m – 200m depth
11205
6952
18157
01
Coastal length (in km)
02
Continental Shelf (in sq. km)
03
Exclusive economic zone(in million
sq. km) extends to zoo nautical miles
from share
-
-
0.19
04
Territorial wasters appr. (in sq.km)
-
-
19000
Source: Fishers statistics, 2004, Dept. of Fishers, Govt. of Tamil Nadu (Coastal
& Marine Environment).
The Tamil Nadu coast has nearly 26 big and small urban centres 556 marine
fishing villages located along the 12 maritime districts. Marine fish landing takes place
in 362 centres. The major landing centres in Tamil Nadu coast and Ennore, Chennai,
Cuddalore, Port Novo, Nagapattinam, Athirampattinam, Rameswaram, Pamban,
Thoothukudi, Kanyakumari and colachel. Number of urban and rural rehabilitation
centre are located along the coastline. Tamil Nadu has a very large maritime fishing
community as well key strategic and economic infrastructure in terms of roads, railway
lines, offshore and onshore oil and natural gas installation, port facilities, industries etc.,
have been created along the coastline (Coastal protection works in Tamil Nadu,
Dheenadhayalan.M, Nov 2004).
63
1.27
History of East Coast Road
East Coast Road initiated during 1988-92 stretches from Madras to
Kanyakumari. Prior to the completion of the east coast road, the ECR mainly consisted
of village roads connecting one fishermen hamlet to another. The vital connecting link
then was the old Mahabalipuram road till Mahalipuram.
The much sought after
Pondicherry was reached from Chennai through the still existent and busy route via
Tindivanam along NH – 45.
1.28
East Coast Road Project in Tamil Nadu
The East Coast Road (ECR) is a popular road connecting the cities of Chennai
and Pondicherry, in India. The Government of Tamil Nadu (GoTN) wished to repair and
widen the ECR in order to stimulate economic growth in the region, but lacked the
funds to do so. Buoyed by the success of Public Private Partnerships (PPPs) in the road
sector in India, GoTN decided to experiment with a PPP approach to the project. An
entity known as the Tamil Nadu Road Development Company Ltd (TNRDC) was
formed. TNRDC was a 50:50 Joint Venture between the Tamil Nadu Industrial
Development Corporation Ltd (TIDCO) - the investment arm of Government of Tamil
Nadu (GoTN), and Infrastructure Leasing and Financial Services Ltd (IL&FS), an allIndia infrastructure development and financial services company. TNRDC was given
the rights to develop the ECR road. TNRDC obtained a loan from IL&FS to finance the
project. TNRDC planned to competitively bid out Engineer-Procure-Construct (EPC)
contracts for the rehabilitation and widening of the road and charge tolls to road users to
recoup their investment. It set the toll rates in consultation with the government and
agreed that toll rates would increase by 8% every year, to combat inflation. (Governance
Issues in Public Private Partnerships in Infrastructure Projects in India, 2010).
64
Map - 1:- Status of ECR in Tamil Nadu
65
The southern India state of Tamil Nadu has been a leader in the area of urban
sector reforms and has established an efficient framework to carry out this process. One
of the tools it has used for a variety of infrastructure projects has been Public-Private
Partnerships (PPPs). The government's objective has been to facilitate the development
of modern urban infrastructure, while providing an enabling environment for the
participation of the private sector in this development. The East Coast Road project is
one of the resulting PPP projects managed by an entity which is itself a public-private
partnership. (East Coast Road, Tamil Nadu, India case study, Transportation).
The first of the Tamil Nadu government's series of public-private partnerships
for roadways was commissioned in 1998, as the Tamil Nadu Road Development
Company Ltd. (TNRDC). This enterprise was structured as a model public-private
partnership between the public agency known as the Tamil Nadu Industrial
Development Corporation (TIDCO) and the private consortium IL&FS. This PPP was
intended to leverage state resources by encouraging private sector investment.
The TNRDC's purpose was to improve the small roads connecting the state
capital Chennai with the town of Cuddalore. The objective was to reduce traffic
congestion, facilitate local business and trade, and reduce the local air pollution. This
was done by upgrading the road design, employing contemporary construction
techniques, and operating the road using the latest technology. The project was also
expected to serve as a catalyst for private sector investment and participation in
infrastructure development. However, this initial agreement lacked a sufficient financial
base, which slowed maintenance work in the project. As a result, the road was
characterized by high levels of accidents, poor signage and absence of road markings.
Pavement failures were also seen within two years of the initial development. To correct
these deficiencies, the Tamil Nadu Road Development Company negotiated a new
contract for the rehabilitation and maintenance of the 113.2 km East Coast Road (ECR)
between the cities of Chennai and Pondicherry, via the city of Mahabalipuram, at a cost
of US $12.6 million. The Government approved the project in principal on February 11,
2000 and the concession agreement was signed in December 2000. The new PPP
contract was renegotiated as a Rehabilitate-Improve-Maintain-Operate-Transfer
(RIMOT) agreement, which included a number of improved specifications. Work under
this agreement began in February 2001, and a substantial portion was completed by
66
December 2001. The improved road began operating as a toll road on March 24, 2002.
(East Coast Road, Tamil Nadu, India case study, Transportation).
The proactive support from the Government of Tamil Nadu was critical to the
long-term success of this project. There was significant inter-departmental coordination,
transparency in bidding and contracting, and assistance in toll enforcement. Because this
project became extremely successful, this framework serves as a model for projects with
high maintenance and operations costs. (East Coast Road, Tamil Nadu, India case study,
Transportation).
1.28.1 Past status
The East Coast of India, extending from the international border of India and
Bangladesh in the northeast to Kanniyakumari in the south, is 2,545 km long,
covers 21 districts in the states of West Bengal, Orissa, Andhra Pradesh and
Tamil Nadu and has a population of approximately 128 million. Five major and
five minor ports are situated along the coast, handling a billion tones of goods
every year (Ramesh et al., 2008).
Tamil Nadu is the southern most state in India with a coastline of about 1,050
Kms with its significant portion on the east coast bordering Bay of Bengal. Long
with plain landmass as its hinterland spread over 1,30,058 Sq.Km. it lies
geographically between 8o 5’ and 13o 15’ North Latitude and 76o 15’ and 80o 20’
East (Sundar and Sundaravadivelu, 2005).
1.28.2 Present status
The ECR starts at Thiruvanmyur in Chennai and is part of the Chennai city roads
till Uthandi. From Uthandi starts the scenic beach way section as a toll road.
The speed of the vehicles on this road is restricted to a maximum of 100 km/h.
The state Government is keen on expediting the proposal for four-laning of the
East Coast Road (ECR). The state Highways Department is in the process of
seeking certain geometrical changes in the present alignment of the ECR as a
measure to reduce the number of fatal accidents.
67
ECR is an express highway built along and coast of the Bay of Bengal
connecting Chennai to Cuddalore via Pondicherry. A trip along the ECR gives
rise to spectacular scenic beauty with beaches and fishermen hamlets. Now the
East coast Road is extended up to Thootuhukudi Tuticorin via Chidambaram,
Karikal, Nagapattinam, Thiruthuraipoondi, Adirampattinam, Meemisal, Thondi
and Ramnad.
The total length of the load is 950 km from Chennai to
Thoothukudi, but as a new work has almost completed till Ramnad, remaining
200 km work on pending.
ECR – the first project implemented by TNRDC was and ECR project, which
has since emerged as a benchmark for developing two lane roads in the country.
The project entailed improving the ECR for a length of 113 kilometers from
Kudamiyandi thoppu near Chennai to the out skirts of Pondicherry.
The
commercial operations / tolling on the road commenced in March, 2002.
The first phase leads to more than 5000 big and Asian trees being cut down and
thousands of houses were adversely affected. (RF Report of Public Hearing on
27.2.1999 at Cuddalore).
1.29
East Coast Road Project
ECR was originally commissioned in the year 1998, by joining up and
improving small village roads that connected fishing of 1200 mn partly funded by the
Asian Development Bank. (The Indian innovation Awards, 2005 Infrastructure Leasing
and Financial Services (IL and FS)
1.30
Improvement and development of the East Coast Road
East coast road runs for a total length of 765 km from Chennai to Kanyakumari.
In phase _ I, road stretch for 166 km. from Chennai to Cuddalore was widened to two
lane at a cost of Rs.102 Crores funded by Asian development bank and put to use. Of
this, the road from Chennai to Akkarai is maintained by Highways department. The
stretch from Akkarai to Pondicherry State border for a length of 113 km is being
maintained by Tamil Nadu Road Development Company as toll road. Considering the
heavy traffic intensity in the East Coast Road from Chennai to Pondicherry state border
68
and to avoid accidents, Government have ordered to study the feasibility for widening
the road to four lane with due consideration to Environment and Social factors. Based
on this, Tamil Nadu Road Development Company has taken up the feasibility study for
widening the road to four lane. The Government had allotted Rs. 63.00 lakhs for the
preparation of feasibility report and for undertaking environmental assessment. The
Government had decided to take up further action on obtaining the feasibility report for
improving the road.
East Coast Road from Pondicherry to Nagapattinam has been upgraded as
National Highways (NH 45A). The entire single lane stretches in this road will be taken
up for widening to double lane. The Government had decided to widen the road to two
lane and improve the balance stretch of East Coast Road from Thoothukudi –
Kanyakumari for a length of 120 km at an estimated cost of Rs.190 crores. Action will
be taken to prepare detailed project report and take up the works during current year.
(Guidance Bureau 14th July, 2009).
The East Cost Road is running from Chennai to Kanyakumari to a length of 765
Km. The 166 Km length of road from Chennai to Cuddalore was widened to two lane at
the cost of Rs.102 crores with the financial assistance of Asian Development Bank and
put in to use. In this, the road from Chennai to Akkarai in Kanchipuram District is being
maintained through Highways Department. The road to the length of 113 Km starting
from Akkarai and up to Pondicherry State border is being maintained as a toll road by
the Tamil Nadu Road Development Company.
Taking into account the heavy traffic from Chennai to Pondicherry State border
in the East Coast Road and also the Environmental and Social Impacts and to avoid
accidents, the Tamil Nadu Road Development Company has prepared and handed over
of the Feasibility report to the Government based on the orders of Government to
undertake investigation on the feasibility of four lane road.
The road from Villuppuram to Nagapattinam via Pondicherry, Cuddalore has
been upgraded as National Highways (No.45A). In this road, the road from Pondicherry
to Nagapattinam is running along the East Coast. The State Government has insisted the
Central Government to widen the 14 Km length of single lane road in Nagapattinam
District, to two lanes. The works of construction of by-passes to Chidambaram and
69
Sirkazhi towns under the Tamil Nadu Road Sector Project are in progress. Moreover, in
this road from Nagapattinam to Thoothukudi via. Muthupettai, Tiruthuraipoondi,
Velankanni, Kattumavadi, Mimisal and Ramnad, most of the works except a couple of
bridge works and Ramnad bye pass, have been completed.
As announced in the budget speech, to widen to two lane and improve the
remaining portion of 120 Km length of road from Thoothukudi to Anju Village in
Kanyakumari District (in NH-7) via., Tiruchendur, Koodankulam at a cost of Rs.190
crores with State Government funds, preparation of Detailed Project Report had been
undertaken through consultants. These works will be taken up for execution through
General wing in the current year. Once the up gradation of the road is completed, it will
be a traveler’s delight, to visit temples and tourist spots in the East Coast Road, starting
from Chennai and ending at Kanyakumari. (Vellakovil M.P.Saminathan, Minister for
Highways and Minor Ports Government of Tamil Nadu, 2010).
1.31
Externally Aided Projects
With Asian Development Bank (ADB) assistance, the East Coast Road up to
Cuddalore (100 km) has been completed and opened for traffic on 14.01.1998. The East
Coast Road is proposed to be extended up to Tuticorin in 2 phases with World Bank
Assistance under the Tamil Nadu road sector project.
(www.tn.gov.in/spc/tenthplan/CH_11_3.PDF).
70
1.32 Aim and Objectives
1.32.1 Aim
The present study was aimed to evaluate and assess the Environmental effects of
East Coast Road between Cuddalore and Tharangambadi with the following
objectives:
1.32.2 Objectives
1.
To assess and evaluate the impact/effects of East Coast Road on water quality of
surface water bodies such as lakes, ponds, stream near or crossing the East Coast
Road.
2.
To determine the quality of ground water available along the East Coast Road.
3.
To determine effects of East Coast Road on ambient air quality with reference to
SPM, SO2 and NOx and noise.
4.
To identify and quantify the impact on biotic environment (on distribution of
flora & fauna and their diversity) due to East Coast Road (widening and
increased traffic).
5.
To assess the Socio-economic impact of East Coast Road.
71
2.0 REVIEW OF LITERATURE
2.1
Water Pollution
2.1.1 Ground water quality and its contamination near coastal area
Singanan and Rao (1995) study evaluated the ground water quality of
Rameswaram Island. The ground - water samples were analysed for their
chemical composition and suitability for drinking and domestic purposes. The
results of water analysis were compared with minimum and maximum
permissible levels of Indian and International standards.
Singanan and Somasekhara Rao (1996) evaluated ground water samples from 30
working bore wells and 25 dug wells in the 5 regions of Rameswaram Island for
their chemical composition and to assess their suitability for irrigation and
industrial purposes. For irrigation purpose, the parameters, like EC, TDS, PS,
SAR had been determined and for industrial purpose, the parameters related to
corrosion that had been estimated and its effects were discussed.
Singanan and Rao (1996) evaluated the ground water quality of Rameswaram
Island. The ground-water sample were analysed for their chemical composition
and suitability drinking and domestic purposes. The results of water analysis
were compared with minimum and maximum permissible levels of Indian and
International standards.
Jain et al., (1997) studied the quality of ground water in Kakinada town in the
East Godavai District of Andhra Pradesh. Various parameters had been
determined to evaluate its suitability for irrigation and domestic applications.
The higher values of certain parameters at various locations indicated the
influence of sea water and they made the water unsuitable for domestic
applications.
A study was carried out by Garode et al., (1997) to record faecal indicator
bacteria of several groundwater samples collected from different regions of
72
Chikhali town and Akola city. Water samples were subject to the MPN, ImViC
and other tests. Higher MPN Counts were recorded from some samples.
Elampooranan and Rengaraj (1998) collected ground water samples from about
46 wells located in Nagapattinam and Thanjavur district and analysed. The result
showed that in 20 wells the recommended limits for drinking water quality
exceeded in one or the other parameter. Only a few water samples were found
unsuitable for irrigation. It was suspected that this may be due to indiscriminate
disposal of domestic and industrial wastes. Sea water intrusion may also be
considered as another factor for high salinity of the groundwater near the coast.
Chauhan et al., (1999) monitored the seasonal concentration and speciation
studies of heavy metals in groundwater of Agra city drawn from various areas.
The results indicated that iron, zinc and lead were found maximum during
winter, copper in rainy season and cadmium and nickel in summer.
Dash et al., (1999) assessed the physico-chemical characteristics of groundwater
in the Hemgiri block of Sundargarh district to evaluate its suitability for
domestic and irrigation use. It was observed that the quality of groundwater of
the area was suitable for both domestic and irrigation use.
Elampooranan and Rangaraj (1999) assessed the groundwater quality in the
Thanjavur and Nagapattinam districts of Tamil Nadu. The pH of all the water
samples was around 7 and occasionally alkaline. In about 20 wells, the
recommended limits for drinking water quality standard had exceeded in one or
the other parameters. In general the ground waters of this area were suitable for
irrigation. Ghosh Ashok et al., (1999) found that the population pressure had put
severe strain on the quality of the drinking water of Patna, which had been
deteriorated over the years.
Zhang et al., (2002) investigated the contaminant transports in an unconfined
coastal aquifer in the presence of the saltwater diffusion zone and tidal
fluctuation. Results showed that the less dense contaminant presented a
relatively sharp outline and tend to travels seaward with the groundwater flow
exits as a concentrated plume over a small discharge area at the coastline. On the
73
other hand, the denser contaminant migrated in a complicated manner. A
diffusive front became more diffuse when the plume more closely approached
the saltwater interface. The experimental results also showed that density had a
marked effect on the shape of the plume in the presence of tidal oscillations of
sea level.
Singh and Chandel (2004) collected the ground water samples from various hand
pumps of eight adjacent localities of various industrial areas in Jaipur city and
analysed using standard methods of APHA. The values obtained were compared
with standards of ISI, ICMR and WHO. It was observed that the pH, EC, Ca2+,
Na+, K+, Mg2+, SO42 , CO32, HCO3- , Cl-, DO and BOD values were within
permissible limits of ISI, ICMR and WHO but NO3-, TDS, COD and WQI
values revealed poor water quality in most of the groundwater samples.
Rao et al., (2005) analysed the major physical and chemical parameters of
groundwater samples, covering all geological formations, which were collected
from 100 drinking water sources all along the Nellore coast. Correlation
coefficients among different chemical constituents were determined. The
analysis of correlation coefficients indicated that the quality of groundwater in
the study area was saline and consisted of high sodium chloride, magnesium
bicarbonate and sodium sulphate. Abhijit et al., (2005) determined seasonal
concentration of Zn, Cu and Pb in three important estuarine macro algae
inhabiting three different stations of the Sagar land. Metals in the algal tissue
accumulated in the order Zn>Cu>Pb. Highest concentrations of these heavy
metals were found in the surface water in the month of monsoon, the period
characterized by the lowest salinity and pH of the ambient aquatic phase. A
unique compartmentation was observed between sediment and surface water
with respect to selected heavy metals.
Rajendran et al., (2006) conducted a study on water borne diseases such as
cholera; enteric fever and dysentery were expected after the tsunami, which hit
the coastal areas of Colachel at Kanyakumari district, Tamil Nadu. The 151
drinking water sample was collected from the tsunami affected villages and
relief shelters and tested for coliforms and pathogens. Nine well water samples
74
were collected for specific bacteriological analysis. The result showed presence
of coliforms was detected in 56 (37%) water samples. One isolates each of
Salmonella Paratyphi B and NAG Vibrio were isolated from two well water
samples. There was no report of acute diarroeal diseases or typhoid illness
during the post tsunami period monitored by a field microbiology laboratory for
a month.
Ravisankar and Poongothai (2008) studied on the effect of tsunami on the
quality of groundwater along the southeast coast of India, especially in the
tsunami-affected areas of the Nagapattinam district of Tamil Nadu. Major
pollution resulted primarily from increases in the salinity of groundwater. The
post tsunami water quality posed problems to general health and contributed
significantly to agricultural and environmental degradation in the Sirkazhi taluk
and Nagapattinam districts. The result showed assessment of the source, degree,
extent and nature of groundwater contamination in the Sirkazhi coastal region.
Samples of groundwater were collected from 11 wells in this area and analyzed
chemically to determine the extent of contamination. The results showed
significant variations in water quality parameters in the study area and helped to
understand the longer-term adverse impacts that tsunami inundation can have
upon groundwater resources.
Rani and Babu (2008) carried out hydrochemical investigation of the coastal area
between Kollamkode and Kanyakumari. The groundwater of this area up to a
distance of 250m was found to be brackish to saline in nature. Concentration of
anions like nitrate, nitrite and sulphate were within the limit prescribed by WHO
and the phosphate concentration was above the limits, which may be due to
saline water mixing. Na+ and Cl- were fairly high, and so it could be deduced that
for most of the groundwater samples Na+ and Cl- originated from a common
source. It most of the samples, alkalies exceeded alkaline earths and in all the
samples, except one, strong acids exceeded weak acids. The dominant
hydrochemical facies in the area was Na-Ca- Cl-SO4- HCO3 indicating seawater
mixing.
75
Chidambaram et al., (2008) conducted a study on shallow aquifer in the coastal
region to assess salinity variation due to the impact of tsunami. The impact on
the tsunami on the shallow aquifer revealed that Q type of T.S.Pettai had been
changed to K type and H to A in certain locations. The formation resistivity of
shallow depths confirms that almost at all the locations showed salinity reflexes
in the resistivity values. The formation factor explains large samples after
tsunami are in saline nature. Considering the specific depth as layer in posttsunami, curve drawn with maximum ρa values represent a Q type, minimum
represent H type and the average represents A type curve. In the post tsunami,
most of the samples of the shallow depths had low resistivity ranges and the
deeper depths had higher ρa values. It is also inferred that in the spatial
distribution of 3m resistivity, there is a considerable increase in the region
occupied by low resistivity values in Zone II after the event; this is mainly due to
gentle bathymetry at the south and also because of rivers with more number of
distributaries. A fresh water lens had moved further north, after tsunami due to
dense saline water compression from south. The lesser value of this lens may
also indicate that the saline water had contaminated this perched aquifer. It is
also inferred that the tsunami impact on the aquifer was more in the south than in
the north.
Buragohain et al., (2009) carried out a study with respect to chromium,
manganese, zinc, copper and nickel contamination of groundwater in Dhemaji
district of Assam, India. Twenty groundwater samples were collected from tube
well and ring well in both dry and wet seasons. The metals were analysed by
using Atomic Absorption Spectrometer, and Perkin Elmer AA 200 model.
Normal distribution and correlation analysis have been employed to find out the
distribution pattern of the metals in the area. It was ascertained that a sizeable
number of groundwater samples contained chromium, manganese and nickel at
toxic level. Copper and zinc content of groundwater was found to be within the
guideline value of WHO. High concentrations of all the trace metals except for
chromium were recorded in the dry season than in the wet season. Statistical
analyses of the data revealed that the distribution of various metals in the study
76
area was widely off normal. The metal concentration of groundwater in the
district followed the trend Zn>Mn>Cr>Cu>Ni in both the seasons.
Buragohain et al., (2009) found out that the concentrations of aluminium, lead
and cadmium in groundwater were significantly elevated. High concentrations of
all the metals were recorded in the dry season rather than in the wet season.
Univariate statistics along with skewness, kurtosis and confidence limit have
been calculated for both the seasons to test the distribution normality for each
metal. Statistical analyses of the data revealed nonuniform distribution of the
metals in the area. The metal contamination of groundwater in the district
followed the trend Al > Pb > Cd > As in both the seasons.
Bhagavathi et al., (2010) used a three- dimensional mathematical model to
simulate regional groundwater flow was used in the coastal area of Kanyakumari
district in South Tamil Nadu. The simulated results indicated that this aquifer
system was stable under the present pumping. Even with the present level of
pumping the groundwater head was increasing gradually and in some period
groundwater head was constant or slightly decreasing above MSL along the
coast area during all seasons throughout the year because of continuous rainfall,
sufficient recharge of aquifers and less extraction of groundwater. The present
level of pumping, the groundwater head varied above 10m along the north
eastern and south western parts, 5 to 8m in the inner middle parts along south
eastern and south western region along the coast and 0.5 to 2m along the coastal
belt to study area. The simulated results of groundwater in the head in the
Kanyakumari coastal area aquifer, showed that the water head was stable and
good in all periods throughout the year
2.1.2 Surface water and its impact
Panda et al., (1995) investigated the Chilka Lake, the largest coastal lagoon and
one of the most dynamic ecosystems along the Indian coast. Historically the
lagoon had undergone a considerable reduction in surface area due, in part, to
input from natural processes but mostly due to human activities. The paper
documented the heavy metals affinity for specific geochemical phases in the
recently deposited sediments in the lagoon. Rajashree (1996) evaluated the
77
various physico-chemical parameters which control the environmental
conditions in different Indian estuaries. This is an attempt to increase the
understanding of the factors that control the spatio-temporal variability of
plankton and productivity in these systems and to develop workable models for
the growth and distribution of plankton population.
Reddy et al., (1996) evaluated the impact of aquaculture on water quality of
Backingham Canal. Almost all the parameters were found to be above the
standard set by Pollution Control Board.
Aftab and Asif (1996) studied the plankton population in four freshwater ponds
to examine the effects of different dominant biota on the plankton dynamics.
Presence of various plankton organisms along with their densities was studied in
relation to the dominant biota of the ponds. Plankton were less in both quality
and quantity in ponds 'A' and 'C' having Notonectids and Microcystis aeruginosa
as dominant biota respectively. Plankton were abundant in pond B dominated by
Eichhornia and in pond D occupied by Lemna.
Senapati and Sahu (1996) collected that the water and sediment samples during
three different periods to estimation the natural and anthropogenic heavy metal
fluxes from Subarnarekha River. Enrichment ratios were calculated for both
water and sediment samples with respect to global and local background
concentration values separately. It was observed for water that the contamination
was more when compared with global background and less when compared with
local background values. The above information could help in estimating the
contribution of heavy metals to the metal flux to the Bay of Bengal.
Sanjeev et al., (1996) determined the heavy metals which were detected from
water and sediment of lower Lake of Bhopal. Physico-chemical parameters
showed high eutrophic condition of the lake. Concentrations of heavy metals in
surface water were found below the permissible limits, concentration in bottom
water were higher than the permissible limit. The investigation of Kumaresan
and Bagavathiraj (1996) reported that Courtallam in south India had many
waterfalls and the water had medicinal properties as they run through forests of
78
herbs before they descend. Water in Courtallam and surrounding area was very
much polluted on account of heavy tourist influx and pilgrims.
Hegde and Sujata (1996) studied the six freshwater lentic ecosystems of
Dharwad district. Three tanks are located in Belavalanadu, received the lowest
rainfall. The other three are situated in the Malanadu received high rainfall. A
total of 20 abiotic factors were analysed. The Belavalanadu tanks were devoid of
macro vegetation and their water had more turbidity. The Malanadu waters had a
rich growth of macro-vegetation and were more clear. Of the six water bodies,
Yamamuru tank in Belavalanadu and Kalaghatagi tank in Malanadu were
nutrient rich.
Rao (1996) studied the sixty foraminiferal species belonging to 38 genera and 23
families that had been recorded from grab sediments of the Cochin backwaters.
Of all the species Ammonia baccarii was the most dominant and successful
form. The study on the diversity and distribution of foraminifera indicated that
there was no adverse effect on marine life due to deepening of the navigational
channel by dredging.
Kaur et al., (1996) concluded that the physico-chemical and biological
characteristics together accounted for the trophic status of the water bodies.
Studies on abiotic and biotic components of a fresh water pond of Patiala, India
were carried out on monthly basis for six months. Physico-chemical analysis of
the pond water revealed high values of alkalinity, hardness, chlorides and
nitrates, showing that water of this pond was polluted. This was further
confirmed by the presence of pollution indicator species in this pond.
Naik and Purohit (1996) monitored the fourteen physico-chemical parameters of
two community ponds (Amarnath and Tilaknagar ponds) of Rourkela Industrial
Complex for 2 years. The water quality index calculated from 10 physicochemical parameters varied from 118-427 in the year 94-95 and 125-483 in the
year 95-96 indicating the level of nutrient load and pollution in the ponds. It was
concluded that the water was not safe for human consumption unless treated and
disinfected.
79
Krishnanand (1997) reported the plankton form of algae which were collected
from the river Ganges at many sampling points both in premonsoon and post
monsoon. The results showed that out of the sixty six species ten had a
conspicuous trend bearing positive or negative correlation with pollution, while
the others had erratic behaviour.
Satapathy et al., (1999) studied that the occurrence of landslides in road risers,
agricultural land, stream lines in the hill areas of Arunachal Pradesh was a
common but destructive phenomenon especially during the rainy season. The
effect of Geojute materials in stability and revegetation of the badly eroded lands
were evaluated. Geojute materials in stabilizing and revegetation of the badly
eroded lands were evaluated. Geojute nets were used to cover the eroded surface,
over which different plants species were planted in different treatments. The
material was found suitable in areas with low and medium slopes where erosion
was caused mainly by surface runoff.
Sharma (1999) studied the physico-chemical characteristics of Yamuna water at
Agra in order to ascertain the viability of the water for domestic use. Based on
temperature, pH, DO, BOD and COD, the river water was found unsuitable for
domestic use and harmful for aquatic life. Singh et al., (1999) analyzed the
Damodar river water at different locations from Patratu to Rajrappa. River water
at upstream of Nalkari confluence was clean when compared with IS 2496, and
Class “C” (Inland Surface Water Norms, 1982). But after joining Nalkari and
running further through the urban-industrial areas, the water quality gradually
deteriorated.
Somalwar et al., (2000) water quality of the Mumbai coast which received both
treated and untreated wastewater contains the enteric pathogenic micro
organisms. Water samples collected from Thane, Gorai, Juhu, Dadar and Worli
area were analysed for coliform group of organisms. The die-off rates decay
coefficient of these organisms were worked out which was inversely
proportionate to health risk. Jyanthi et al., (2001) showed that contaminants if
released in the inner estuary trend to accumulate in the upstream regions and
would not be flushed out effectively. However, downstream regions expressed
80
larger tidal displacements with wider trajectories revealing better dispersion and
transport over larger areas.
Santanu et al., (2000) studied the environmental impact of Ganga River system
with relation to pollution. The study revealed water pollution in lower Ganga
River to be largely inorganic on nature. It further showed Berhampore belt of the
river system to be the least polluted one among all the stretches surveyed while
water pollution at Dakshineswar zone appeared to be the highest.
Studied the scarcity of drinking water is an age – old problem in the coastal areas
of the southern parts at Shanmughapuram, Ramanathapuram district, Tamil
Nadu. Jayanthi et al., (2001). Ground water, ponds, open wells, are the only
sources of drinking water for the people living in these areas. They suggested
that there was a need to promote roof – top rainwater harvesting system on a
large scale in these areas as a potential alternative.
Kumar et al., (2001) carried out a study to find out the probable movement and
mixing of contaminants within and through the Kundalika river estuary along the
west coast of India. Neutrally buoyant biplane drogues, floats and dyes were
tracked at different stages of the tide and the behaviour of the contaminants
released was assessed in the estuary. The result showed that contaminants if
released in the inner estuary tend to accumulate in the upstream regions and
would not be flushed out effectively.
Chinmoy and Raziuddin (2002) carried out Limnological investigation on a
degraded river, Nunia in Asansol industrial belt. The investigation aimed to
calculate Water Quality Index (WQI) of the river and to assess the impact of
industries, agriculture, and human activities on its water quality. The water at all
the sampling stations was recorded above the upper limit in terms of WQI which
indicated that the river was not safe for human use.
Kumari et al., (2005) determined the marine water quality along the western
coast of the Kanyakumari district Tamil Nadu. The result showed the enhanced
values of the heavy metals during the month of October (north- east monsoon)
that can be attributed to the high river water discharge. This investigation was
81
undertaken to generate a base line data on the status of the coastal water of
Kanyakumari district. Yogamurthy (2005) revealed that the total coliform
bacterial population showed seasonal variation between post and premonsoon
periods. Secondly, the stations near the coast showed very low counts of
bacterial population when compared to the 5 fathom line i.e. about kilometres
away from the coast on the sea. Such incident level is well below the admissible
standard values indicated by WHO.
Satheesh and Wesley (2006) reported that the surface water temperature had
been considered as one of the most critical factors determining the species
diversity. In their study, high species diversity with the increase of water
temperature above 290 C was observed. Salinity was another important factor,
which determined the distribution of polychaetes. The diversity and abundance
of the polychaetes associated with Sargassum also indicated that more attention
needs to be paid to their functional role. These epifauna appeared to influence
the distribution and abundance of other organisms, because as prey items, they
regulate populations of many reef fishes.
Jocob and Chacko (2006) reported that the highest concentration of all the trace
metals was recorded from the sediments of the industrial zone of Chitrapuzha
river. The calculated enrichment was found throughout the study area but in
varying proportions. The contamination was found to be severe in the industrial
zone. The Zn and Cd showed strong enrichment compared to other metals.
While the enrichment factor for Zn ranged from a minimum of 1.38 to a
maximum of 14.34 the same for Cd varied from 1.12 to 8.23. The correlation
analysis has proved that organic carbon and clay content played major roles in
the distribution and retention of trace metals in Chitrapuzha River.
Kulshrestha and Sharma (2006) highlighted that mass bathing during
Ardhkumbh caused the changes in the river water quality and indicated that
water was not fit for either drinking or bathing purposes. The presence of faecal
coliforms in water also hinted at the potential presence of pathogenic micro
organisms, which might cause water-borne diseases. The values of BOD and
COD exceeded the maximum permissible limit.
82
Bhandari and Nayal (2008) assessed the physico-chemical variables of Kosi River
water at Kosi and found that they were within the highest desirable limit or
maximum permissible limit set by WHO except turbidity and BOD which
recorded high values. Kosi water recorded higher values of Mg than Ca. Soil
erosion and mining of dolomite in the region could be attributed to high values of
magnesium than the calcium in the river water. A large number of factors and
geological conditions influenced the correlations between different pairs directly
or indirectly. An appreciable significant positive correlation have been recorded
for chloride with pH, Mg, Na, hardness and TSS and sodium with hardness, EC
and SO42-. A significant negative correlation was found between potassium with
turbidity, Cl- , EC and hardness.
2.1.3 Plankton and other micro organism
Kanhere et al., (1997) investigated the influence of urbanisation on an aquatic
ecosystem using changes in the phytoplankton species composition over the
years. There was a definite shift in the algal species, as the nutrient levels
increased. The appearance of a few new species indicated the changing quality
of water.
Maillard et al., (2006) suggested a strong relationship between land use/land
cover and turbidity, nitrogen and fecal coliforms. They also suggested that each
of these parameters had a unique behaviour when distance from the stream was
considered.
Chetna et al., (2006) reported the impact of diverse anthropogenic activities as
well as the monsoon effect on the bacterial population of river Yamuna in Delhi
stretch. Microbial population contributed mainly through human activities that
prevailed in the entire stretch of Yamuna River with reduction in bacterial counts
during monsoon period due to flushing effect.
Poonkothai
and
Parvatham
(2006)
studied
the
Physico-chemical
and
microbiological quality of automobile wastewater in Nammakkal, Tamil Nadu,
India. The results indicated that the values for physico-chemical parameters were
on the higher side of permissible limits of BIS. Microbiological studies revealed
83
the presence of bacteria at high concentration, and these organisms served as
indicators for pollutants.
2.1.4 Heavy metal on Estuary and Marine waters
Ouseph (1992) studied the quality Cochin estuary which was subjected to
various types of effluents discharged from the Eloor and Chitrapuzha industrial
belts. The study reported the concentrations of dissolved and particulate copper,
zinc, cadmium, lead, nickel, and iron based on three consecutive surveys
conducted during July (monsoon), November (postmonsoon) 1985 and April
(premonsoon) 1986. The concentrations of dissolved and particulate copper, zinc
and cadmium showed high seasonal variation.
Chakravarty et al., (1996) estimated Zn, Cu, Cd, Cr, Mn and Pb concentration in
different creeks of Hooghly estuary along Calcutta metropolis during premonsoon and post-monsoon at low tide period. The seasonal pattern revealed
that maximum amount of Zn was coming out from the creeks of Garden Reach.
Kumar et al., (1996) assessed the nature and extent of marine coastal pollution
along the coastal States of Orissa and West Bengal. In addition to the
characteristic physico- chemical background parameters, water quality was
monitored in terms of heavy metals and some selected biological determinants.
Spatial and temporal changes in the concentration of these parameters from
inshore to offshore waters, and their possible effects on the marine coastal
ecosystem had been discussed.
Mitra Abhijit et al, (1996) estimated total as well as biologically available
concentrations of Zn, Cu, Mn, Fe, Co, Ni and Pb in the sediments along the
lower stretch of the Hooghly estuary. Highest concentrations were recorded at
the southern most tip of Sagar Island and the lowest concentrations were
detected in the sediments around the second sluice gate of Hanryas Fishery
Project. Except Mn and Pb all other trace metals studied in the present work had
anthropogenic origin.
84
The shallow bays and lagoons of the estuaries are the traditional sites for the
retting of coconut husk. Nandal et al., (1996) examined the impact of retting on
the water quality and benthic fauna of retting and nonretting zones of the
Kadinamkulam estuary. They found that an anoxic condition coupled with the
production of hydrogen sulphide was the outstanding feature of the water quality
of the retting zones. The diversity, incidence and abundance of benthic faunal
communities were low in the retting zones when compared to the nonretting
zones.
Mogal (1997) carried out the qualitative and quantitative study of bacteria and
fungi present in water of the Dandi seacoast, located in South Gujarat. Bacterial
and fungal species isolated from marine habitat were identified and examined for
the extra cellular enzyme production. The results indicated that the enzymes of
bacterial origin were similar in their response to the three parameters under
study. On the basis of study of faecal indicator bacteria, FC/FS ratio was derived
to determine pollution and waters of two stations were indicative of faecal
pollution of human origin.
El – Toumi and Nair (1997) study of the quality of seawater along the Benghazi
coast showed that many areas were highly polluted due to the disposal of sewage
or oil and also due to the carelessness of man in keeping the environment clean.
The problem was severe in the sewage disposal site, affecting not only the
quality of seawater and the flora and fauna inhabiting there but also the health of
the residents living in the area.
Kadam and Bhangale (1998) reported the presence of phenols in the coastal
waters along Okha - Ratnagiri coast which was determined by insitu – 4AAP
method. Samples containing heavy load of domestic wastewater could not be
analysed by this method. They were detected at all the stations off Bassein and
Ratnagiri. Any relation of the concentrations with the distances of locations from
the shore was not observed. Moderately high levels were observed at polluted
areas and phenols were not detected at unpolluted stations.
Thresiamma et al., (1998) studied the environmental parameter such as
temperature, salinity dissolved oxygen BOD, pH nutrients, suspended load and
85
chlorophylla in the coastal waters of Managalore. Low bottom values of
dissolved oxygen with corresponding high inorganic phosphate were indicative
of monsoonal upwelling along the Mangalore coast during September. This
period was characterised by a drop in pH values, though not very significant.
Low BOD values encountered indicated the absence of any organic pollution in
the area.
Deepak et al., (1999) carried out that speciation study on copper in Rushikulya
estuary, east coast of India. Dissolved and particular species of copper were
found to be high in the lower and upper reaches of the estuary. The percentage of
dissolved and particulate species of copper showed significant variations in
different seasons because of their involvement in biogeochemical cycles.
Physico-chemical parameters of coastal waters off Orissa were studied by
Panigrahy et al., (1999) Data comprising 13 variables obtained from 38 water
samples had been subjected to R-mode factor analysis so as to understand the
sources, the processes occurring and the influence of various physico chemical
parameters on coastal water quality. About 67.8% of variance had accounted for
three factors such as river run-off or terrestrial input, estuarine discharges and
land drainage.
Murty et al., (1999) studied the concentration levels of some of the dissolved
trace metals like Zn, Cd, Pb and Cu in the coastal waters along three transects
(Gangavaram, Harbour and Rushi hill) near Visakhapatnam. Estimations, made
by anodic stripping voltammeter were in good agreement with the reported
values by atomic absorption spectrophotometer. Vertical distribution of
dissolved Zn, Cd and Cu showed an increasing trend from the surface waters to
bottom.
Yalavarthy et al., (1999) carried out the simple colorimetric enzymatic methods
for the determination of heavy metals in water samples. Mercuric chloride and
silver nitrate were evaporated at 80°C in hot air oven or precipitated in alkaline
phosphate buffer (pH 8.2). 5-50 micrograms mercuric chloride and 1-100
micrograms of silver nitrate can be determined by using this method.
Senthilnathan et al., (1999) evaluated the extent of distribution of selected heavy
86
metals (Cu, Zn, Cd and Pb) in water, sediment and plankton over a period of two
years from Pondicherry harbour. A seasonal variation in the distribution of
metals in the ambient water sediment and plankton was observed. The order of
metal abundance in water, sediment and plankton was Zn>Cu>Pb>Cd.
Vinod (2000) reported that the ecological damage due to anthropogenic activities
was threatening marine eco-systems and coastal resources in India. The city like
Mumbai had been under the pressure of heavy pollution loads and associated
health hazards.
Bhattacharyya et al., (2001) monitored the monthly variations of dissolved Zn,
Cu and Pb in the south western sector of Sagar Island. The concentrations of
heavy metals in the ambient aquatic phase exhibited a sharp seasonal oscillation
with the highest value during monsoon and lowest during pre-monsoon. The
concentrations of dissolved heavy metals seem to be controlled by the ambient
aquatic salinity and pH.
Roa et al., (2005) showed that the analysis of metals during summer and winter
seasons with a considerable difference in their concentrations. Cu concentration
was higher in all the drains during summer where as Pb and Cr registered the
maximum in all the drains except Chandraih drain during the summer season. Cd
and Zn concentrations were higher in all the drains during the rainy season. The
heavy metal concentration of the drains joining into the Kolleru Lake and lake
waters suggested that the lake was contaminated by industrial effluents and
domestic waste. It was concluded that the water was not suitable for drinking
and irrigation purposes due to the presence of high metal concentrations.
Grace (2006) studied on the Kadinamkulam estuary which is situated in the
southwest coast in India (Kerala) and is polluted by different means. The
additional quantities of heavy metals come from agricultural, industrial and
domestic wastes. Hence sediments are indicators of the quality of overlying
water; five different heavy metals were tested in the sediments of Kadinamkulam
estuary. The distribution of heavy metal observed reflected that presence of these
metals in the waste discharges into the estuary and seasonal variations of the
environment.
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Pandian and Dharanirajan (2007) described qualitatively the coastal setting for
the Ennore coastal region of North Chennai coast. The data was collected and
created a baseline for evaluating future evolution of the coastal region. The grain
size distribution along the coastal region showed that coarser sediments are
present along the offshore shoal region and the finer sediments along near shore
region. Accretion noticed in the southern side of the Ennore port will affect the
port entrance channel in the future and therefore immediate mitigation measures
are to be implemented to curtail the accretion and also to keep the Ennore creek
mouth open permanently. Necessary coastal structures such as groynes and
jetties may be introduced after proper design study. The study confirms that the
coast north of Ennore port is eroding. Therefore, it warrants for necessary
protection measures to save the eroding coast at the earliest. Similar to the case
of Ennore creek, adequate measures have to be taken to keep the mouth of the
Pulicate Lake open, which is the only conduit for moving the fishing vessels
from inland into Bay of Bengal. Being a sensitive coastal belt, it is concluded
that a long-term monitoring is essential to plan for the perfect coastal defense in
the region.
Swarnakumar et al., (2008) elucidated that there are eight coastal stations along
the east coast of the Little Andaman Island which supported high total
heterotrophic bacterial population. The above might have supported in
degradation and recycling of organic and inorganic materials. The study also
indicated that eight coastal stations along the east coast of the island were less
polluted by the human pathogens.
Chauhan and Ramanathan (2008) studied the water quality of Bhitarkanika
mangrove ecosystem which revealed that salinity played a dominant role in
controlling the water chemistry. In addition, intense pollution from both
agricultural inputs and industrial pollution deteriorated the water quality of
mangrove ecosystem. Senthilkumar et al., (2008) inferred that there was an
enhanced salinity and tidal range after tsunami. The tsunami resulted in no
significant changes in nutrient out welling and phytoplankton primary
production of the mangrove surrounding waters in Pichavaram mangroves.
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Senthilkumar et al., (2008) stated that the nutrient balance of the Pichavaram
mangrove estuary was influenced by the tidal cycle and seasonal variations. It
was revealed by the changes in inorganic nutrients, Dissolved Organic Carbon
and chlorophyll ᾱ in different seasons and a 24 hours diurnal survey during wet
and dry seasons. The effect of tidal amplitude was important in determining in
the extent of variations in nutrient concentrations. The main features of the low
tidewater were high concentrations of the inorganic nitrogen, phosphate and
DOC. The study had established tidal pumping phenomena in this ecosystem.
The tsunami resulted in had no significant changes in nutrient outwelling and
phytoplankton primary production of the mangrove surrounding waters in
Pichavaram mangroves.
Cyril (2010) studied that the marine environment of the kollam coast, of southern
kerala of India.
The values of physico-chemical and biological parameters
conform to standards set by CPCB for Indian coastal waters. Microbial
contamination exceeded than the permissible limits. The routine oil spill from the
innumerable fishing vessels could in the long run cause damage to the ecosystem.
Declining benthic population, fish catches and sea – weeds, weakening of mud
banks, coastal erosion and increase in marine debris were indicators of
environmental degradation.
Prasad and Ramanathan (2008) reported that the mangrove forests were the
highly productive ecosystems of the tropical environment. Spatial and temporal
analytical measurement of organic nutrients was made in the Pichavaram
mangrove ecosystem (south east coast of India) to understand the dissolved
organic nutrient dynamics. Monthly measurements of physical parameters and
dissolved organic nutrients were made at several locations at daytime during low
tides. The result showed that high concentration of DOC (Dissolved Organic
Carbon) and DON (Dissolved Organic Nutrients) were found in monsoon and
DOP (Dissolved Organic Phosphorus) in summer. The distribution and dynamics
of dissolved organic matter have been regulated by the monsoon’s fresh water
discharge from the adjacent sources. However, the microbial mineralization
induced by summer temperature regulated the nutrient biogeochemical process
and also controls the biological productivity. In general, the mangrove
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ecosystem supplies considerable loads of nutrients to the oceans rather than the
river systems and regulates the global nutrient biogeochemical cycles.
2.1.5
Pollution due to sewages and sediments
Mogal and Dube (1996) studied the faecal pollution of coastal waters from
domestic sewage discharges resulting in mass development of indicator bacteria.
Total coliform E. coli and faecal streptococci in mud and waters at three stations
of Dandi Sea coast have been examined at bimonthly intervals for two
successive years. The FC/ FS ratio indicated pollution of human origin.
Mohapatra et al., (1996) detected heavy metals in sediment samples collected in
the vicinity of material handlings of Paradip port at monthly intervals for a
period of 8 months. The extent of metal pollution in harbour sediments had been
evaluated using pollution load index (PLI).
Badarudeen et al., (1996) studied and discussed the textural and geochemical
aspects of the sediments of a tropical mangrove ecosystem. The sediments were
characterized by the abundance of silt and sand with minor amounts of clay. All
heavy metals other than Fe showed an increase in concentration compared to the
other parts of the estuarine bed. Cluster analysis indicated that the contents of
organic C, Fe and Mn had a marked bearing on the Cr, Zn, Ni and Cu levels of
the mangrove sediments.
Nageswara Rao et al., (2001) stated that the Visakhapatnam harbour which is a
natural harbour situated in the central east coast of India received most of the
effluents through a rain fed stream of Megadrigedda and also received the city’s
main domestic sewage. Paper discussed about the heavy metal pollution on the
harbour water and its effect on marine populations.
Rao (2002) states that the regulation of any nation would require liquid and
gaseous wastes to be treated to meet the prescribed standards. The leachate is the
most significant hazard from a landfill. The noxious mineralized liquid is
capable of transporting bacterial pollutants to the water by moving literally
through the refuse. The pollutants can be moved by the water several kilometres
from the disposal site, depending on the amount of water that infiltrates or
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moves through the waste and the length of time that the infiltrated water is in
contact with the refuse.
Chandra et al., (2006) assessed that the concentration of heavy metals in the
sediments from the river, estuarine and coastal environment off Subrarnarekha
River, Bay of Bengal. The degree of contamination of the sediments was
evaluated through enrichment factor (ER), geoaccumulation index (Igeo) and
pollution load index (PLI). The high ER’s and Igeo values for Cu and Cr were
due to the chromite and copper mines, and Cu ore processing plants situated on
the upstream catchments of the river.
Prasath and Khan (2008) investigated on the accumulation of heavy metals (Zn,
Cu, Fe, Mn, Co, Pb, Cd and Ni) in water, sediments and fish (Mugil cephalus)
using Atomic Absorption Spectrophotometer at Poompuhar coast, lying along
the southeast coast of India before and after tsunami. Study clearly showed
significant variations in the accumulation of heavy metals in water, sediments
and fish Mugil Cephalus after tsunami.
These variations in the marine
environment were certainly brought about by the recent tsunami as similar type
of variations in the physico chemical characters were observed in the coastal
water quality off Nagapattinam coasts.
2.2
Air Pollution
Air pollution can be defined as presence in atmosphere of one or more
contaminants in such quantities and for such duration that is injurious to human health
or welfare, and animal or plant life (Mullai, 2010). When the concentration of
contaminants exceeds a level such that it causes the effects mentioned above, it becomes
a pollutant.
With tremendous growth in urbanization as well as commercialization, the whole
world is in the grip of severe environmental crisis. The tremendous increase in the
number of vehicles has contributed significantly to the increased use of petroleum
products. Petroleum consumption has increased by almost 400% in the last two decades.
Due to increase in number vehicles the road transport sector has become the major
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source of air pollution. Hence it becomes imperative that a study on transport related air
pollution should be implementing.
The main sources of air pollution are industrial plants, power stations,
automobiles, locomotives, aero planes, 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, and headaches, nausea,
chronic bronchitis, bronchial asthma, asthmatic bronchitis, pulmonary emphysema,
cancer, even death.
Diesels engine exhausts have significantly higher particulate and gas phase
pollutants. The chemicals associated with the particles may interact with the lung cells
and cause damage, inflammation and excess mucus production (Santondilonata et al.,
1978).
Estimation of CO emission from automobile in Surat was done by Shan and
Ram Prasad (1979) during monsoon and winter months, when the atmosphere
conditions were more or less stable. The average level of CO released was about 25ppm.
The auto rickshaws were more responsible for increasing the concentration of CO than
other vehicle.
Pollock et al., (1985) examined the national trend in sulphur dioxide
concentration from 1975 to 1982 in U.S.A. From the analysis it was found that SO2
levels were lower in the later years of study than in earlier years, because of various
control measures taken by government. 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 annual change in dispersion
conditions than by the combination of all local anthropogenic sources.
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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.
Murthy and Rao (1987) reported the concentrations of sulphur dioxide measured
at two traffic corners of Visakhapatnam, namely Jagadamba and Poorna market. The
diurnal variation and seasonal variation of sulphur dioxide at both the points were
presented. The reasons for irregular peaks at both the points were discussed. The reasons
of higher sulphur dioxide levels in pre-monsoon and minimum in monsoon were
reported. Kazutoshi Sasakl et al., (1988) studied the behavior of sulphate, nitrate and
other secondary pollutants in the long range transport of air pollution. The study was
conducted during 26 – 30 July 1983 in the large area from the coastal region around
Tokyo Bay to the mountainous region in central Japan.
Anon (1989) found that emission from automobile sources comprised about
three quarters of gross NOx emissions in Sydney.
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. The level of reduction of air lead level with
increasing distance from the road way was noticed 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.
Gopinathan and Muthusubramanian (1990) had studied the important pollutants
emitted from automobile exhaust such as CO, HC, and NOx. The exhaust emission level
of CO and HC were measured in Madurai using Horiba CO/HC analyzer. This study
revealed that CO level in the exhaust was lower in the case of two stroke engines than
for four stoke engine. Two stroke engines emit HC more than 10 times that of four
stroke engines. CO level decreased during acceleration where as the other exhaust gas
level increased.
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Zafar and Qadir (1990) determined the concentration of lead and cadmium in
leaves polluted by vehicle exhaust. The result revealed that the various tissues contained
different concentration of Pb and Cd.
Anuja Gupta and Mukta Rani Rastogi (1991) conducted a study to find out the
effect of exposure to vehicular pollution on visual vigilance and general eye complaints.
The results indicated that the exposed and the controlled group differed significantly in
their visual vigilance and in their reported eye complaints.
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 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 NO 2. 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.
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.
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 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.
Mohan Rao et al., (1992) conducted the study on pulmonary function values
(PFT) test and prevalence of restrictive, and combined restrictive and obstructive
impairments in shopkeepers and traffic policemen at two different traffic junctions with
varying levels of NOx pollutant. The prevalence of various impairment was compared
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with Michigan general population values, traffic policemen and general population
values of Lucknow City. The result revealed that shopkeepers showed higher affection
than policemen to the effects of auto exhaust pollutants and this could be due to longer
hours of exposure.
Buecker and Ballach (1992) investigated the effects of realistic mixture of
ozone, sulphur dioxide and nitrogen oxides on carbohydrate levels of the cutting of
popular nigra. It was found that total main carbohydrates in the leaves were reduced and
those in the roots were unaffected.
Bridgman (1992) stated that the SPM was an important air pollutant in the
ambient air and extensive information was available on its concentrations from the
various parts of the world, and its possible health effects on human populations.
Mukee et al., (1993) reported that among the oxidants and nitrogenous
compounds in ambient air, O3 and NO2 were the most investigated. When comparisons
were made, it was found that O3 was generally more toxic than other photochemical
oxidant levels whereas NO2 was found to be of little concern with respect to
carcinogens.
Swamy and Lokesh (1993) reported that the concentration of lead decreased with
increase in distance, from the road and there was less deposit of lead with increase in
depth of soil. A brick wall acted as a barrier to stop further dispersion of lead beyond the
wall. A vegetative fence acts as filter media to reduce further dispersion of lead beyond
the vegetative fence with respect to the lead concentration on other sides of the road.
The lateral distribution of lead also got reduced due to the presence of roadside trees.
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.
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Trivedi et al., (1993) conducted a study on auto vehicular exhaust pollution in
Patna. It had been observed that vehicular pollution was more from 3 wheelers. The
anthropogenic emission of this town was dependent upon the climate and the aerosol
composition measurement programme.
Swamy et al., (1993) conducted study on dispersion of lead from automobile
exhaust on soil surface along low and high traffic. Variation of lead concentration was
negligible on both the sides of the roads. Concentration decreased with increase in
distance. The lateral distribution of lead concentration had been reduced due to the
presence of road side trees. The penetration of lead into the soil was diluted upto a depth
of 15 cm only.
Barrefors and Petersson (1993) conducted a study on assessment of ambient
volatile hydrocarbons from tobacco smoke and from vehicle emissions. The proportions
of more than 20 reported alkenes, alkanes and alkynes were demonstrated to be very
similar in a smoky room and in side stream cigarette smoke. Isoprene, ethane and
propane were major components. Urban air polluted by petrol-fuelled vehicles differed
mainly by having much lower proportions of isoprene and much higher proportions of
petrol alkanes and alkyl benzenes. The total concentration of C2-C-B Hydrocarbons was
found to be similar in a smoky room and in a car in urban traffic.
An assessment of lead pollution from vehicle emissions along selected roadways
in Harare was done by Sithole-SO et al., (1993). The result revealed that pb levels at the
herb of busy roads were 1480 and 23.3 µg/g in soil and vegetation respectively and the
levels found for less busy roads were 404 and 8.0 µg/g in soil and vegetation samples
respectively.
Atmospheric concentrations of lead have been measured at two sites in each of
two cities (London and Manchestor) in the U.K. by Nicholson and Branson (1993). The
concentration was correlated to the U.K’s petrol consumption. Lead concentrations were
correlated between the 2 cities and large variations in measured levels were due to
metrological effects.
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
96
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 system (IVHS) improved and increased the operation and efficiency
of the transport system in USA. Air quality problems associated with congestion, poor
vehicle maintenance, wasted travel and too many vehicle trips were alleviated by IVHS.
Ghosh and Seth (1994) reported that atmospheric pollutants get deposited on the
earth’s surface through various physical and chemical processes. Precipitation pathway
for deposition of atmospheric aerosols and anthropogenic materials contained pollutants
of varying nature, causing deterioration of physical, chemical and biological
characteristics of waters.
Tripathi (1994) under took the monitoring of lead in ambient air in the city of
Varanasi, India, over a period of 2 years (January 1988 to December 1989). Lead levels
in India were found to be low when compared to Western countries. Experimental
results showed that the automobile emission was the predominant source for lead
pollution in the city.
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 menace to the city dweller. The petrol-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).
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, NO2, SO2 and Ozone are associated with health effects
ranging form increased mortality and hospital admission to subtle changes in lung
functions at low concentration (Brunekreef et al., 1995).
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Kalpana et al., (1995) conducted a survey of lead pollution in Coimbatore Pollachi highway of Tamil Nadu. The lead levels found in this study were in the range
of 10.25 to 549.25 µg/g in the case of plant samples.
Chandrasekaran et al., (1995) conducted a preliminary study on aerosol
chemistry in the ambient atmosphere of a cement plant at Ariyalur. The SPM
concentrations ranged from 74μg/m3 to 170.6ug/m3; the minimum concentration was
observed at south west of the cement plant. The SO2 concentration ranged from 33.3 to
51.9 and the minimum concentration was observed at west and the maximum at east
followed by south west of the cement plant. The NOx concentrations ranged from 32.5
to 69.10 and the minimum concentrations were observed at north and the maximum at
south west followed by east of the cement plant. Singh et al., (1995) showed that the
road transection at Alambagh (traffic density 4835 for 2 h) had the highest level of
pollutants (SO2, 202μg m-3; SPM, 1080μg m-3; and lead, 2.96/μg m -3, 2 h average) in
air, as well as in the foliage of plants, whereas the road stretches with less traffic density
correspondingly showed lower levels of pollutants. Pb and sulphate in leaves were
found to be positively correlated with Pb and SO2 pollution in the air. Results suggested
that Dalbergia sissoo and Calotropis procera were the ideal plant species to monitor as
indicators of Pb and SO2, respectively, in the air.
Kumar (1996) reported that the single model of passenger transport in the city of
Delhi had been developed using computer-based software called - Long Range Energy
Alternatives Planning (LEAP) and the associated Environmental Database (EDB)
model. Emission factors corresponding to the actual vehicle types and driving
conditions in Delhi were introduced into the EDB and linked to the energy consumption
value for estimating total emission of CO, HC, NO2, SO2, Pb and TSP. The LEAP
model was used to estimate total energy demand and the vehicular emissions for the
base year-1990/ 91 and extrapolate for the future - 1994/95, 2000/01, 2004/05, 2009/10,
respectively. Bansal (1996) studied the NO2 concentration in commercial, industrial and
residential areas of Bhopal (MP), India. In the commercial areas maximum NO2 was
recorded as 96.4μg/m3. Corresponding value in the industrial area was recorded as
66.3μg/m3 and 53.5μg/m3 in the residential area. Monthly average values were well
below the prescribed standards.
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Pahwa (1996) reported and proved that the level of contaminants in the indoor
air could be often several times higher than outdoor air. This combined with the fact that
people tend to spend 90% of their time indoors, proves the point that a person's major
source of exposure to airborne contaminants can be indoors. Poor indoor air quality
leads to an increased incidence of health related symptoms, which in turn can lead to an
increase in absenteeism and a loss of productivity.
Gajghate and Hasan (1997) studied lead pollution from Gasoline powered motor
vehicles. Quantity of lead emission from vehicles to environment depends on various
factors namely lead content of petrol, wind speed, quality of road and speed of vehicles.
Mean lead levels of more than 0.25µg/m3 were observed at Bombay, Calcutta, Delhi and
Kanpur.
According to researchers at St.George’s Hospital medical school in 1997 on
effects of vehicle emission on health, suggesting links with heart attacks and infant
mortality. One in 50 heart attacks treated in London triggered by vehicle emissions. In
normal birth weight babies, high particulate matter exposure was associated with
respiratory causes of death and sudden infant death syndrome.
Das et al., (1997) carried out a rapid assessment of air quality in Jaipur city to
identify critical zones for evolving a proper environmental management strategy. Repeat
measurements of SO2 were made on a Sunday at 5 locations to enunciate the difference
between the concentration levels on week days and Sunday due to lesser number of
traffic and any other unknown sources. The concentration of gaseous pollutants that
SO2, NO2, CO have been presented and analysed in this paper. The measurements were
made only during the peak traffic hours (3. 30 PM to 5. 30 PM) which gave the
maximum concentration observed in a day. The values may therefore be considered
conservative estimates for management purpose.
Pathak et al., (1997) studied that the haul roads of surface mines were the largest
contributors of air - borne dusts to the atmosphere. The article summarizes the present
state of the art of estimating dust particles in atmosphere and presents a new approach to
study the problem of dust generation. Pandya and Verma (1997) examined the attributes
of road transportation and the statistical analysis of vehicular population of Nagpur.
There had been an increase in the community noise levels which at most of the times
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and places exceed the limits fixed by the Central Pollution Control Board.
Recommendations for noise abatement were also discussed.
Sunita and Rao (1997) analysed the different parameters such as epidermal
features, cholrophy II content, and ascorbic acid content to find out the air pollution
tolerance capacities of some important tree species. Among the twelve tree species
studied Albizzia lebbek was considered as relatively resistant and Pongamia pinnata as
relatively sensitive to air pollution.
Gautam et al., (1998) reported that air pollution became acute in Calcutta during
winter. Pollutants could not 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 1007 mg/m3, and 1181 mg/ m3, respectively. The anthropogenic SPM was
more toxic that 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 city area, high population density,
miserable slum conditions of habitation and overall poor socio-economic status of city
dweller were together responsible for the serious air pollution in the city.
Gunwant (1998) studied the respirable dust which is an important air pollutant of
concern on account of its ability to reach alveoli of human lungs during respiration. The
result dealt with a preliminary survey of respirable dust concentration at the roadsides of
an urban area at the breathing level during peak traffic hours. Data for total SPM and
equated 24 hour, average PM were also presented and discussed.
Chandrasekaran et al., (1998) presented a short report on ambient air quality of a
cement plant at Dalmiapuram. They found that SO2 and NOx concentration did not
exceed the ambient air quality standards.
The vehicular emission load of the major metropolitan cities in India exceeded
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
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substances have been shown to be genotoxic, cytotoxic, fibrogenic and carcinogenic
(Chellan and Jackson, 1999).
Alam et al., (1999) suggested that by introduction 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 in quite revolution 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.
Dambal Aditi et al., (1999) reported that the lead in gasoline was emitted into
the environment through the exhaust gases of automobiles. Pune is one the fast
developing cities in India with an increasing number of vehicles. Their paper report
deals with the determination of SPM, PMIO, TSPM and lead levels at selected sites in
Pune city. Mohanty (1999) assessed the ambient air quality at 11 monitoring stations in
and around Koraput district at monthly intervals. Air quality index and standard
deviation at different sampling points were calculated. The results showed a
comparative study of the air quality in different areas of Koraput. The study identified
the potential sources and effective pollution control measures to improve the air quality
in Koraput district in future.
Pandey et al., (1999) monitored and initiated the air quality status of different
sites in Lucknow city. The Lucknow City had witnessed a tremendous increase in two
wheeler and three wheeler populations. They were the main source of visible pollution
because they emit a lot of black soot from the exhaust. This black soot caused eye
irritation, breathing trouble and is soiling of clothes.
Venkatasubramanian et al., (1999) studied the exhaust emission from petrol
driven vehicles using portable Air quality sampler Envirotech APM 414 of all 100 CC
mobikes, Hero Honda was found to emit the lowest concentration of SPM, NO2 and
particulate lead. The mean emission of SPM, SO2, NO2 and particulate lead from petrol
driven auto rickshaws were found to be 788, 16.4, 5.2 and 93.03 μg/L of exhaust gas,
respectively.
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Alam et al., (1999) concluded that at more than seventy-percent locations the
roadside air environment was severely polluted and the rest of the locations were highly
polluted. This environmental condition had very serious implications on the health of
the inhabitants of the Dhaka city, particularly the commuters, causing eye and skin
irritation, headache, breathing problem etc. The pollution level was also closely related
to the density of motor vehicles plying on the roads.
Sivakumar (1999) estimated the pollution loads for 4 selected traffic locations on
the major highway at Chennai city using live source model to predict the pollutant
concentrations at 4 selected traffic sites. The predicted CO concentrations were
exceeding the standards, thus warranted for proper planning and control measures. The
results indicated that air pollution levels were increasing significantly.
Qdais and Qudais (1999) reported that the impact of road improvement on the
environment was minimum compared to that caused by construction of new road. Due
to the fact that, all activities will be within the existing route limits, except for some
horizontal curves which would be subjected to adjustment. Results indicated that, the
most critical impact will be landscape disfigurement along limited length of the road,
and limited number of buildings will be taken off. In addition to noise and air pollution
by engines, emission will be significant especially during construction stage. On the
other hand, an accident along the road is expected to be significantly reduced. Finally,
mitigation and compensation techniques have been discussed for each impact, in an
effort to solve the conflict between upgrading the road and its effect on fragmentation of
nature.
Sarin et al., (1999) reported that the Department of Transport, Delhi was one of
the nodal agencies responsible for enforcing various vehicular pollution-related
provisions in Delhi. Recently, the (non) performance of DoT with regard to its efforts in
pollution checking and enforcement had come under severe criticism. This paper makes
an attempt to critically evaluate the performance of DoT and efficacy of the existing
pollution checking system in Delhi.
Prakash et al., (1999) suggested that there were
many ways to minimise the automobile pollution. Use of catalytic converters,
oxygenated fuels or electric vehicles had the potential to reduce pollution substantially.
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They concluded that it was only possible with the wholehearted participation and
cooperation of the public.
Mondal et al., (2000) carried out one year long programme of measuring
ground-level concentration of NOx at 19 important points within the city of Calcutta.
Results indicated that the NOx concentration level had a seasonal variation. Maximum
average concentration of 222μg m-3 was observed during winter and minimum average
concentration of 55μg m-3 was observed during peak monsoon.
Tripathy and Panigrahi (2000) reported that the air quality index (AQI) was
generally important to evaluate the level of atmospheric pollution. The parameter had
been computed for judging the ambient air quality around OSCOM (IRF Ltd). The
atmosphere towards the eastern side of OSCOM showed only moderate air pollution due
to suspended particulate matter (SPM). Joshi and Jain (2000) estimated the TSPM and
respirable dust concentration in the ambient air from the road sides of Indoor city. High
particulate matter concentration both respirable and non respirable, were found to
exceed the permissible limits at most of the locations. The bad road conditions and high
density of vehicular movement were the main causal factors for high concentrations of
particulates which gradually build up due to high rise buildings on either sides of the
road in the city area in contrast to open areas located in the outskirts of the city.
Ravichandran et al., (2001) conducted a study on Impact of Deepavali Fire work
on Air quality and Noise levels in Tiruchirappalli. The results revealed that there was
considerable increase in SPM concentration due to Deepavali fire works and there was
no increase in SO2 and NOx levels. Ravichandran et al., (2001) conducted a study on a
preliminary study on Ambient Air Quality with Special Reference to Total Oxidants in
Tiruchirappalli. The results revealed that both SO2 and NOx were found to be within the
limit said by CPCB. It suggested that the SO2 was not from vehicles but from other
sources as the motor fuels had relatively low sulphur content when compared to coal
and other sulphur containing fuels.
Mashitha and Pise (2001) studied on the varied susceptibility among plants of
Nagpur city towards the air pollution and 50% of the plants were sensitive towards the
increasing air pollution in the city. The floristic composition can be used as an indicator
of pollution level. Very few of the city plants were tolerant to pollution and indicated
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towards serious vegetation loss in future. It also identified some tolerant plants which
along with the plants reported by other researchers can be grown to withstand the
pollution stress.
Chan et al., (2001) found that the weather condition such as RH, rainfall,
prevailing wind direction, as well as local track, affected the mass concentration of TSP,
PM10 and coarse particulate at roadside level. Large-size particles had an apparent
seasonal variation, with higher concentration level in winter and lower in summer. The
dry continental winter monsoon and the wet oceanic summer monsoon are the
dominating factors. On the other hand, variation of PM2.5 was much smaller since they
were more selected by local track emission. PM10 accounted for 64% of the TSP, while
PM2.5 accounted for 53%. The PM2.5/PM10 was high with PM responsible for 76% of
PM. In our heavily tracked roadside fixed site, TSP exceeded the annual average of the
HKAQO (Hong Kong Air Quality Objective) by a factor of 1.53 while PM2.5 exceeded
by 1.39. Nevertheless, the most serious problem lies in PM. The annual average
concentration of PM exceeded the NAAQS annual average of 15 µg m-3 by a factor of
3.8. Twenty four hour average of PM2.5 of NAAQS (65 µg m-3) was exceeded by a total
of 33%. Fine particulate pollution was serious in Hong Kong and the PM2.5
concentrations were on a higher side especially for heavily tracked urban area of Hong
Kong.
Kulandaiswamy et al., (2002) stated that the air pollution due to automobiles
was a growing menace in Indian cities. The chief source of carbon monoxide, the
pollutant of present interest, in the atmosphere was the fuel combustion in automobiles.
The traffic survey was taken from National Highway – 49 near Madurai city during
busy traffic hours in both directions manually. The maximum ground level
concentrations for different average wind speeds and atmospheric stability classes were
discussed. The study revealed that the ground level concentration of carbon monoxide
was much below the permissible limits in the study area. It also revealed that CO
concentration was lower during unstable atmospheric condition.
Yoshide et al., (2003) suggested that the planting tree in streets was expected as
one of the efficient artificial methods to mitigate environmental impacts caused by road
traffic. Although roadside trees have little reduction effect in physical traffic noise level,
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a psychological effect derived from visual information is considered as one of the most
affective components in urban residential area.
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
moderated 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.
Agrawal et al., (2003) monitored the six hour mean concentrations of SO2, NO2
and O3 and plant responses. Plant responses were measured in terms of physiological
characteristics, pigment, biomass and yield. Parameter reductions in mung bean (Vigna
radiata), palak (Beta vulgaris), wheat (Triticum aestivum) and mustard (Brassica
compestris) grown within the urban fringes of Varanasi, India correlated directly with
the gaseous pollutants levels. The magnitude of response involved all three gaseous
pollutants at peri-urban sites; O3 had more influence at a rural site.
Gokhale and Khare (2003) studied and reviewed the procedure and
methodologies to deal with statistical modeling in predicting the distribution of air
pollutant concentrations. There are distributional model, namely, exponential,
lognormal, gamma and Weibull, which could be fitted to the air quality data. The
procedure to the identification of best model from the available parametric range of
model and their parameter estimation which have been developed by Taylor and
Jakeman was reviewed.
Goyal (2003) evaluated the Sodium arsenite (SA) method for determination of
nitrogen dioxide (NO2) in ambient air, and compared with US EPA recommended
equivalent method of TGS-ANSA (ANSA). Laboratory evaluations showed that SA
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method was high sensitivity to different sampling conditions, which normally vary
during actual field monitoring. Absorption efficiently of NO2 in SA method was found
to be much lower (64%) as against the reported value of 82% at the method
recommended sampling conditions, whereas for ANSA method, it was found 1.0 as
against the reported value of 0.93.
Joseph et al., (2003) reported the anionic composition of ambient aerosols of
size = 10 microns (PM10) in the commercial area of Mumbai city. It was observed that
sulphate concentration was higher compared to the concentrations of chlorides and
nitrates. The percentage of chlorides was between 5-15%. The correlation coefficient of
sulphate was 0.6276 which showed the variation in the sulphate content in the ambient
air was large and the correlation coefficient of chloride was 0.9763 which showed the
good correlation as Mumbai being a coastal city.
Kulshrestha Monika et al., (2003) estimated the dust fall deposition fluxes of
major water-soluble components at five different sites of Delhi. The high values of pH
of dust fall deposition suggested the dominance of crustal components that added higher
alkalinity due to presence of components like Ca, Mg, etc. Dust fall fluxes were
observed highest for Ca. considering the importance of alkaline nature of dust particles;
the fraction of SO4 contributed by dry deposition of SO2 on the dust particles was
estimated.
Kumar et al., (2003) observed levels of fifteen trace metals in suspended air
particulates monitored at a residential township in Mumbai. Percentage distribution of
the measured elements showed a dominant fraction of Na (47.1%) and Ca (27.8%)
followed by Fe (8.1%), Zn (7.7%), Mg (5.8%) and K (2.8%). The remaining nine metals
(As, Cd, Li, Co, Ni, Cr, Cu, Mn and Pb) altogether formed less than 1% in this
distribution. Analysis of monthly average concentrations showed a higher concentration
of trace metals in the winter month of December.
Mahendra and Krishnamurthy (2003) assessed the air pollution concentration
from road traffic in Bangalore. Traffic flows and air pollution concentrations of CO,
NOx, SO2 and SPM were measured simultaneously. It was evident that the traffic
generated CO concentrations in the intersections were high and exceeding the
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permissible standards prescribed by the CPCB. This may be attributed to the interrupted
flow of traffic near the intersection due to frequent ‘stop’ and ‘go’ situations.
Naja et al., (2003) studied that the unique meteorology over region seems to play
an important role in seasonal as well as in diurnal variations in ozone. Background and
continental ozone levels estimated to be 33.4 ± 13.3 and 48.1 ± 9 ppbv, respectively,
over region of India. A correlation study between ozone and CO indicated possibility of
incomplete photochemical processes over Asia.
Ravindra Khaiwal et al., (2003) reported the spatial patterns of various criteria
air pollutants, at Shahdara National Ambient Air Quality Monitoring station in Delhi
(India). These spatial patterns were found to be essentially the same before and during
rain, however a significant decrease in SO2, NO2 and TSP concentrations (40-45%) was
observed after initial and subsequent rains of the monsoon, demonstrating the
importance of rainfall in the scavenging of these criteria air pollutants.
Senthilnathan and Rajan (2003) reported that the Particulate matter having size
less than 10 microns (PM-10) had been identified as critical pollutants causing potential
health hazard for human beings. A study was carried out to assess the concentration of
PM-10 present in the ambient air in Chennai city during the summer season of the year
2000. The monthly mean concentration of PM-10 was found to lie above the National
Ambient Air Quality Standards values.
Sharma Dhruv et al., (2003) described the chemical composition of organic
species present in PM10 collected at a residential site in New Delhi and in TSP
emissions from in-use two-stroke vehicles. Preliminary findings suggested that
vehicular emissions and biomass and/or refuse burning were significant contributors to
the organic fraction of PM10 in the New Delhi atmosphere.
Hemavathi and Shobajagannath (2004) studied the ambient air quality in Mysore
City. Vehicular traffic had become a major source of air pollution in urban areas. The
main parameters considered included suspended particulate matter, oxides of Nitrogen
and Sulphur dioxide. They concluded that Mysore City being one of the major tourist
centers showed alarming increase in vehicular pollution for the past few decades mainly
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due to increased vehicular traffic, adulterated petrol, traffic disorder and allied
drawbacks.
Ahmad et al., (2004) observed that CO concentrations exceeded the safe limit by
as 1.4 times during the heavy traffic during the ‘Ardh Kumbh Mela’ times. Similarly,
the HC and NO2 pollutants also exceeded their safe permissible limits during the Mela
times by as much as 3 times and 1.5 times respectively. The observed violations of
pollutants were more prominent during the morning and evening peak traffic hours.
During the normal traffic flow times, the pollutant values did not exceed the permissible
value.
Ramesh (2004) studied the air quality in U.T of Pondicherry. The result showed
that the air quality status in the commercial and industrial area was fairly clean during
the period 1993. Subsequently the same had been deteriorated perhaps due to
industrialization and automobile exhaust.
A study by Nirusimha et al., (2005) of the 3 species, viz., Azadirachta Indica,
Pongamia Pinnata and Peltoformm ferrugenum revealed that they were reported to have
high APTI values over control plants. These plants were considered as tolerant species
for urban and industrial areas. The APTI values for the remaining species were reported
to be lower than the control plants and were considered as sensitive species.
Tripathy and Panigrapi (2005) carried out the variation of base line air quality of
the region over the years, for primary air pollutants, viz., SPM SO2, NOx. Though the
levels were below the stipulated ambient air quality standards, there was an increasing
trend that was found to be significant at 5% level of significance. A graphical analysis
had been done to establish the annual change in pollutant levels over four years that was
1996 – 1999. On the basis of this the increase in SPM, was quantified as 4.72μg/m3 at
one station. However, there was a decreasing trend in SO2 and NOx level at all the
stations during the same period.
Suresh et al., (2005) studied the variations of the aerosol that would help in
determining their impact on the environment and to develop regional atmospheric
correction algorithm for the ocean colour satellite sensor. Seasonal variations of aerosol
were distinctly seen during the two seasons of summer and winter. The AOT (Aerosol
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Optical Thickness) was found high during the summer and the lowest values were
recorded during the winter. Aerosol particle sizes were relatively larger in summer than
during winter.
Lone et al., (2005) studied about the dust pollution caused by vehicles in Aligarh
city. Four major national roads were selected. The dust pollution was maximum on
Kanpur road. The dust fall rate per unit area was the highest at 3 km inside city. The
result showed that the dust pollution on road was due to traffic as well as due to poor
condition of the roads.
Sharama (2005) obtained from fluid modelling in conjunction with numerical
modelling techniques which were instrumental in greatly enhancing the reliability and
predictive capabilities of the Gaussian based highway dispersion models. Further,
accuracy of these line source models, to a large extent depends on the accuracy of input
data. Use of onsite meteorological data along with the more reliable traffic and emission
data would be desirable and a prerequisite for more accurate predictions. The higher
level of air pollutants in various urban centers of India mainly contributed from
vehicular sources is a cause of real concern. Rapidly increasing vehicular population and
urbanization have further aggravated the air pollution problem in these cities. Several
initiatives have been taken to control the vehicular emissions and to improve the air
quality in these cities. However, despite some recent improvements, the air quality in
most of these cities is still far from satisfactory. Thus, there is an immediate need to
improve the monitoring and emission inventory capabilities in these cities which are
prerequisite and essential for formulating various air pollution control and management
strategies. Moreover, there is an urgent need to further develop modelling capabilities
that are more appropriate for Indian conditions.
Ingle and Wagh (2005) indicated the reduction in the lung function efficiency
among the residential peoples exposed to higher traffic pollution. Ingle et al., (2005)
reported decrease in lung efficiency in the shopkeepers exposed to traffic pollution.
Significant reduction in the lung capacity was observed in the higher age group of
residential people living along the highway roadside. The forced expiratory volume in
one-second (FEV1) and peak expiratory flow rate (PEFR) of exposed residential
population were significantly affected as compared to control in the age group of 30 –
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39 years. In control group the FEV1 / FVC ratio was higher. This showed better lung
efficiency in the unexposed population. The situation of forced expiratory volume
(FEV1) among the exposed residential population was alarming.
Karar et al., (2005) showed that the metal exhibited their occurrences in diverse
concentration ranges of magnitude. Moreover to explain the factors regulating their
mobilization properties, the data were analyzed through the application of correlation
analysis. A significant inverse relationship between PM10 and wind speed data
indicated the importance of local sources. Results of the correlation analysis showed that
most of the metals exhibited moderate relationship with each other. The chemical
species of PM10 were inversely correlated with wind speed, temperature, rainfall and
relative humidity. Seasonal distribution patterns indicated that most of the PAHs,
metals, carbon particles and anions tend to exhibit maxima during the winter season,
probably due to the temperature inversion, leading to an accumulation of pollutants over
the city. The variation of these chemical species showed a seasonal behaviour of PM10
with reduction of the particle concentrations during the rainy season. PAHs and metals
during the monsoon were not markedly reduced, maybe due to the reason that they were
not washed out by rain because of their smaller size, although the lowest values were
found during July to August.
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.
The concentration of carbon-monoxide (CO) along and near the major roads at
Coimbatore west zone due to vehicular emission was predicted using the Air Quality
Modelling Software called CALINE4 model by Meenambal et al., (2005). Using
MAPINFO GIS environment, thematic maps of the CO at different receptor heights
were prepared. Also, the concentration of CO for the year 2004 at 1.8m heights and 5m
heights were predicted. In addition, to create awareness about the air quality,
suggestions have been given to take suitable measures from engineering and
environmental point of view.
Raj et al., (2005) attempted to understand traffic growth and emission from
vehicles plying in Coimbatore, a fast growing industrial and urban centre. Irrespective
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of the vehicle type, about 20% of vehicles examined in the study failed to comply with
currently followed emission norms. Bad quality of city roads, unhealthy practices of
drivers and resuspension of road dust due to traffic movement were identified as the
major problems that can add to vehicular pollution in Coimbatore.
An air quality sampling program had been designed and implemented to collect
the concentration of gaseous pollutants (SO2, NO2 and NH3) at weekdays and
weekends from a network of three monitoring stations along a populated urban region of
Kolkata by Kakoli et al., (2005). These sites were Kasba (residential), Cossipore
(industrial) and Lalbazar (commercial). Average ratio of the weekday/weekend
concentrations of SO2, NO2 and NH3 were 1.41, 1.01 and 0.99 at Kasba; 1.33, 1.13 and
1.10 at Cossipore and 1.09, 1.17 and 1.10 in Lalbazar, respectively.
Cao et al., (2006) carried out with the measurements of fine particulate carbon
and its eight fractions in close vicinity with a high traffic road around Hong Kong. High
levels of PM2.5 mass, organic carbon and elemental carbon observed in a monitoring
station (less than 1 m from the highway curb) to obtain a substantial quantity of data
relating to freshly-emitted vehicular exhaust particles. The relationships between the
concentrations of PM2.5, OC, and EC and traffic volume indicated that EC and OC
were the main constituents of PM2.5, and that the concentrations of EC and OC
significantly increased with the increase in the number of diesel vehicles. Carbon
profiles from source-dominated samples (diesel, LPG and gasoline vehicles) and the
diurnal variations of eight carbon fractions indicated that EC2 and OC2 were mainly
derived from diesel exhaust, and that OC3 and OC2 were largely from LPG and
gasoline exhaust.
Sagar and Rao (2006) measured at 11 selected sites the ambient air quality noise
levels (AAQNL) near naval base stations, major industries, educational institutions and
religious places. The ambient air quality noise levels (AAQNL) at traffic junctions were
5dB (A) or more than those prescribed by AAQNS for commercial zone and most of the
values were found in the range of 80+10 dB (A), among which 75% values were found
in the range of 110 + 10 dB (A). AAQNL near port were found in the range of 5 to 10
dB (A) positive shifts on AAQNS due to conveyor operation. The AAQNL were
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alarming even in the absence of conveyor system, indicating the impact of vehicular
traffic. Remedial measures were suggested separately for each situation.
Cheng et al., (2006) from their study in the Hong Kong found out that the annual
average mass concentrations obtained for PM1.0, PM2.5 and PM10 were 44.5±18.4,
55.4±25.5 and 81.3±37.7 μg/m-3, respectively. They were 3.7 times the U.S. EPA’s
annual NAAQS of 15μg/m-3. The concentration levels were higher in the winter and
lower in the summer. It was found that weather conditions, such as rainfall, prevailing
wind direction, and temperature, as well as local traffic, affected the mass
concentrations. Their concentrations were higher with a prevailing north westerly wind,
lower rainfall and higher temperature. The mass concentration in PM10 was highly
dominated by PM2.5 particles (71% of PM10), while PM1.0 accounted for 82 % of PM2.5
during the entire sampling period. However, the ratio of PM2.5-10/PM10 was especially
higher during winter season. The mass concentrations were higher in autumn than in
summer by factors of 1.7, 1.7 and 2.0 for PM1.0, PM2.5 and PM10. The main influencing
factors were the dry continental winter monsoon and the wet oceanic summer monsoon.
The clearly observable diurnal variations of PM2.5 were dominated by traffic density,
with occasional influences from regional pollution.
Anjaneyulu et al., (2006) found out that CO pollutant concentration was found to
increasing with the number of four wheeler, three wheeler and two wheeler. Future, CO
concentration was found to increase more with the number of three wheelers than other
type of vehicles. CO pollutant concentration was increasing with decreasing speeds of
the vehicles.
Pedro et al., (2007) 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 sources regions of African dust, with a
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higher content of AL and TI than other crustal components. Geographical areas from
central and Eastern Europe were identified as potential sources of particular nitrate.
Westerdahl et al., (2008) reported that the carbon monoxide emission factors
from their study agreed with those derived from remote sensing and on-board vehicle
emission testing systems in China. The on-road black carbon and particle emission
factors for Chinese vehicles were reported for the first time in the literature. Strong
traffic impacts can be observed from the concentrations measured in these different
environments. Most clear is a reflection of diesel truck traffic activity in black carbon
concentrations. The comparison of the particle size distributions measured at the three
environments suggested that the traffic was a major source of ultra fine particles. A
four-day traffic control experiment conducted by the Beijing Government as a pilot to
test the effectiveness of proposed controls was found to be effective in reducing extreme
concentrations that occurred at both on-road and ambient environments.
2.3
Noise pollution
Bansal (1996) reported the 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 78 dB(A) during
daytime, while during night time it was in the range of 30 dB(A) to 60 dB(A). In these
areas about 43.3% values were found exceeding the prescribed limit of 50 dB(A) during
daytime while about 38.3 % values were found exceeding the limit of dB(A) during
night time.
Padmanabhamurthy and Satapathy (1996) assessed the efficacy of different types
of screens, as controlled noise mitigation experiment in an open site at Jawaharlal Nehru
University. These studies suggested two screens, namely plywood and aluminium as
most effective. Sound attenuation was found to be more in case of plywood screen
compared to aluminium. To assess the efficacy of bushes and hedges, experiments were
also conducted at two localities. SPL attenuation was found to be higher in case of
hedges compared to other vegetation.
Pandya and Verma (1997) examined the attributes of road transportation and the
statistical analysis of vehicular population of Nagpur, which is amongst the fast
developing cities of India. The study revealed that there had been the increase in the
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community noise levels which at most of the times and places exceeded the limits fixed
by the Central Pollution Control Board.
Ravichandran et al., (1997) evaluated the existing noise levels in Tiruchirapalli
city. It was found out that in none of the areas noise levels were less than 45 dB. Even in
the silence zones noise levels exceeded the limit of 50 dB. In some of the important
commercial areas, the noise levels reached 109 dB during peak hours.
Dhembare et al., (1999) dealt with the assessment of noise on the basis of traffic
and vehicular activity in Nasik city. Various sites have been monitored for the noise
load and the results were compared with standard prescribed by vehicles too. The study
revealed that vehicles assure free than traffic.
Monica et al., (1999) investigated the noise levels prevailing in commercial
areas of Jabalpur city in detail. It was found that the noise levels data in commercial
locations observed normal distributions with an average value of 75, 74, 88 dB (A) in
morning, afternoon and evening respectively. The high noise levels were associated with
higher population density, increased human activities and high traffic and lack of
greenery. They also investigated the noise pollution levels prevailing around industrial
areas. The noise level values have been reduced from outside/around the factories.
Average noise level around industrial area was 69 dB (A). Around industrial areas, there
were either commercial area or residential areas, so in this context the noise level of 69
dB (A) was on the higher side. They further investigated the health effect of noise
pollution around industrial areas of Jabalpur with the help of questionnaire survey. It
revealed that the people of higher age group were mostly affected due to noise level
around industrial areas. The results were statistically significant.
Subrata et al., (1999) found that the industrial activity and vehicular movement
were the two major sources of noise in the Neyveli region. A study was carried out to
assess the existing status of noise level and impacts on the environment due to proposed
expansion activities in the region. Various mitigation measures have been suggested to
keep the noise level within the prescribed standards.
Tripathi et al., (1999) dealt with the threat of environmental degradation and
suggested some possible remedial measures for eco-conservation in India. They
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suggested that mean to become protector, producer and caretaker of natural resources
and not the predator, polluter and consumer of earth.
Noise levels in various zones of Gwalior city were monitored and compared with
those of standards provided by schedule III of Environmental (protection) Rules, 1986
by Rao and Padmaja (1999). In all the zones during night as well as daytime the noise
levels were found to be beyond the standards. Efforts were made to evaluate the reasons
for this and some remedies were suggested to control.
Dharwadkar et al., (1999) study showed that Aurangabad was the fastest
growing industrial township in Asia. The growth factor had changed the intensity of
sound levels, culminating into a noise. Transport system, public address system,
entertainment gadgets mainly contributed to the noise in the area. Statistical data of
noise levels in various parts of Aurangabad and its impact on the person immediately in
the vicinity have been discussed.
Das et al., (1999) monitored noise levels at 16 sites consisting of 7 residential, 5
commercial and 4 industrial areas in Jaipur. The noise data indicated that the highest
levels occurred at residential areas [97.4 dB (A) followed by commercial [94.2 dB (A)]
and industrial areas [66.0 dB (A)]. A comparison with the prescribed standards showed
that the noise levels exceeded the allowed values at all commercial and residential areas
except for one location of residential category.
Mohan Surinder et al., (2000) carried out multiple regression analysis of the
variation of the existing road traffic noise levels in different residential density zones
(i.e. > 100 to (800-1000) persons/hectare) of New Delhi. Noise data (L10, Leq and L90)
were measured as a function of traffic volume, speed of vehicles and different distances
of the observation point from the road. Depending on these parameters, an approximate
predictive equation had been formulated by regression method, and coefficients of
correlation were calculated.
Ibrahim and Richard’s (2000) study showed that noise pollution around the
educational area could negatively affect the performance of both teachers and students.
The noise level should be around the range of 35 dBA to 55 dBA in the school area.
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Little attention had been given by individuals or government concerning noise pollution
around the schools.
Joncour (2000) showed the development of double infrastructures, a motorway
and high speed railway line, lead to an increasing number of residents exposed
simultaneously to several noise sources, each one having its own characteristics and
effects. The existence of interactions between road and railway noise sources. This led
to suggest the assessment of several dose/response curves allocated to one particular
source according to exposure to other sources. This had to be cleared up by studying the
modelling of global annoyance. The road/railway comparisons made directly by the
residents express less annoyance to railway noise. Then, night annoyance assessed from
the residents answers was really lower than the annoyance relative to other periods
which looks contradictory to the assumptions made for the LDEN (evening and night
penalties).
Monica et al., (2000) determined the health effects of noise pollution in
commercial areas with the help of a questionnaire survey. The analysis revealed that the
persons in commercial areas were facing different types of health problems and persons
above forty years of age were greatly affected due to noise pollution. They further found
that the most affected group of population of urban areas towards traffic noise were
residents living nearby the roadside. The results further revealed that people living up 10
to of 30m distance from the road felt too much annoyance due to traffic and residents
living at all the floors in multi – storeyed apartments felt too much annoyance. The
impact of traffic noise was so much high that 80% of the residents always kept their
doors and windows closed. Traffic was so much annoying; 70% of residents wanted to
live away from the road irrespective of the existing facility.
Alam et al., (2001) described that transportation operations were major
contributors to noise in modern urban areas. The results showed that the level of noise
pollution in Dhaka city far exceeded the acceptable limits. Even in the residential areas
and vulnerable institutions like schools and hospitals, noise levels were much higher
than the acceptable limit. It was also observed that noise level was closely related with
the number of motor vehicles. Urgent measures should be taken to control the level of
noise pollution in the city. Several measures can be implemented which include proper
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maintenance of vehicle and roadway, plantation of trees and construction of sound
barriers. Solid boundary walls were expected to have a positive effect on attenuation of
noise level.
Several methods were available to assess the status of noise level and noise
related impacts on society. According to Gopalasamy et al., (2002) the prediction
method presented was simple and could be used to assess existing noise level as well as
to predict future traffic noise.
Bhattacharya et al., (2002) carried out the noise impact analysis as a part of the
environmental impact analysis for any highway improvement. It revealed that the traffic
noise prediction was done using mathematical modeling. The paper described the details
of various noise prediction models adopted for the prediction of highway traffic noise
around the world.
According to Bengang Li et al., (2002) the main roads are overloaded by traffic
flow during daytime and noise levels due to road traffic along these roads are above
relevant environmental standards by 5 dBA. The spatial variance of traffic noise
analyzed, indicated that the spatial differences resulted primarily from the unbalanced
development of Beijing’s urban districts.
Noise pollution around the educational area can negatively affect the
performance of both teachers and students. The noise level should be around the range
of 35 dBA to 55 dBA in the school area. Little attention has been given by individuals
or government concerning noise pollution in the surrounding schools. Noise levels
measured at and around the schools located in residential areas at Sekolah Kebangsaan
Sri Skudai, Sekolah Kebangsaan Taman University IV and Sekolah Menengah Taman
University revealed that, the existing noise levels were very high and not suitable for
school environment, (Ibrahim et al., 2002).
Mohan et al., (2002) concluded that the rapid increase in urbanization and
advent of new technological changes day by day had given rise to road traffic noise as a
major pollutant to our society. In their study, to develop a prediction model for road
traffic noise equation for the basic noise level had been framed on the condition of zero
heavy vehicle and situations exist. Variation of noise level with average speed and
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percentage of heavy vehicle at different sites of Delhi were evaluated. It was observed
that the noise level L10 increases with increase in speed. Observations at each site were
done for 1 hr. Simple correction factors were incorporated in the basic noise level to
obtain the predicted noise level.
Noise impact assessment of a multi highway in an urban area was presented ElFadel (2002). The sensitivity of noise levels to changes in traffic growth rate, traffic
vehicular classification, vehicle speed, and traffic volume split was explored.
Implementation of noise barriers to alleviate potential adverse impacts was also
discussed. Existing and future conditions revealed that high noise exposure levels would
normally require abatement. For the do-nothing scenario, an increase of around 64% in
acoustic energy was expected due to normal traffic growth. With implementation of the
project, the increase in noise exposure would range from 195% to 489%, depending on
the attraction level of the facility and the cruising, with the least increase corresponding
to 25% attraction and 50km/h cruising speed, while the highest increase corresponds to
75% attraction and 80km/h cruising speed. Noise barriers could be constructed on the
viaduct to reduce potential noise impacts. However, their effect is minimal at an
elevation less than the structural height of the viaduct if the receptor is located inside the
shadow zone (fifth floor and below).With barrier implementation, a reduction of about 2
dBA is expected for receptors located at an elevation higher than the viaduct (sixth floor
and above).sensitivity analysis revealed that a slight increase in truck and bus traffic
resulted in almost the same increase in noise levels as tripling the traffic growth rate.
Twenty percent and 60% increase in acoustic energy (0.8and2.2 change in dBA) were
observed with doubling and tripling of traffic growth rate, respectively. Similarly
vehicular classification caused a 50–62% increase in acoustic energy (2.1 and 1.8
change in dBA), respectively, as a result of the simultaneous increase from 3% to 5% in
bus traffic and from 1% to 3% in truck traffic.
Soedirdjo et al., (2003) found out the sources of road traffic noise which affect
the environment. Many engineers have already determined that there were two major
sources of road traffic noise. The first was engine noise and the second was the
interaction between vehicle tires and the road surface. An evaluation of different road
surfaces (roughness) from flexible and rigid pavements was needed to determine the
effect of these different surfaces on the propagation of road traffic noise levels, in terms
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of sound pressure levels of single vehicles. Overall, the results of the sound pressure
levels in dB (A) of the individual vehicles showed that a vehicle that was running at a
higher speed was noisier, and the road that had a higher IRI (International Roughness
Index) value, propagated higher values. Comparing rigid and flexible pavements, it was
found that the rigid pavement which had a higher IRI value than the flexible pavement
did not always produce higher sound pressure levels (dB(A)), so there were other
variables that influenced the propagated of sound pressure levels. It was also found that
there was no significant difference between the influence of roughness from flexible and
rigid pavements on propagated sound pressure levels.
Calixto (2003) studied on the problem of traffic noise on roads which had been
transformed into big avenues in the city of Curitiba. Noise levels were measured and the
impacts suffered by the community documented. The measured levels had been
compared with the mathematical model and the German standard RLS-90, as well. The
validity of the mathematical models was confirmed, as well as the applicability of the
calculation method adopted by the German Standard RLS-90. Finally, the mean traffic
noise levels around those roads and the noise limits of the municipal law 8583/1995
were examined and it was confirmed that people living or working in these areas were
exposed to noise levels beyond the legislated norms.
Nalk Shrikanta and Parohit (2003) studied on the noise levels that were
measured at ten residential locations at Bondamunda both during day and night time.
The noise levels varied from 42.5 to 75.6 dB(A) during daytime and 41.3 to 64.7 dB(A)
during the night time. The average Leq values at individual locations varied from 55.03
to 67.15 dB(A) and 45.6 to 56.81 dB(A) in day time and night time respectively.
Madhuri et al., (2003) reported that noise pollution affects human health,
comfort and efficiency. The effects of noise pollution on human beings may be auditory
or non-auditory, psychological or pathological. A noise survey was carried out in some
silence zones in Visakhapatnam; noise pollution levels were calculated and compared
with standards and the necessary control measures were suggested.
Harsha et al., (2003) concluded that the acoustic pollution was a significant mine
environmental problem. It can be defined as a sound without agreeable quality or as
unwanted sound. The noise of the levels higher than the standards laid down by the
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Ministry of Environment and Forest must be abated not only to achieve greater
percentage of production, but also to restore physical health of workers at workplace.
Paper focuses on the adverse effects of noise on workers in the mining industry.
Bhadram et al., (2003) carried out to know the noise levels in different parts of
Visakhapatnam city with heavy traffic. The data were compared with standards and
recommendations were made to overcome the noise pollution in urban areas. The traffic
noise indicator due to traffic flow was also calculated. Of all the sources, the motor
vehicle noise constituted the single biggest source of noise pollution.
Exposure to noise from transport disturbs sleep in the laboratory, but not
generally in field studies where adaptation occurs. Noise interferes in complex task
performance, modifies social behaviour and causes annoyance. Studies of occupational
and environmental noise exposure suggest an association with hypertension, whereas
community studies show only weak relationships between noise and cardiovascular
disease. Aircraft and road traffic noise exposure are associated with psychological
symptoms but not with clinically defined psychiatric disorder. In both industrial studies
and community studies, noise exposure is related to raising catecholamine secretion. In
children, chronic aircraft noise exposure impairs reading comprehension and long-term
memory and may be associated with raised blood pressure. (Stansfeld, 2003).
Yoshida et al., (2003) suggested that the planting tree in streets was expected as
one of the efficient artificial methods to mitigate environmental impacts caused by road
traffic. Although roadside trees have little reduction effect in physical traffic noise level,
a psychological effect derived from visual information is considered as one of the most
affective components in urban residential area.
Sampath (2004) carried out noise measurements in commercial zones of the
three cities. The results indicated that the noise levels were higher than the prescribed
limit. The silence zones experienced similar noise levels and hence about 25 dB (A)
above the prescribed limit. Special events like festivals, and election campaigns
generated noise levels that were prohibitively above the permissible limit with the only
redeeming factor being that they lasted over a comparatively shorter duration.
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Singh and Davar (2004) examined the problem of noise pollution in the wake of
its ill effects on the life of the people. A cross section survey of the population in Delhi
State pointed out that main source of noise pollution was found in loudspeakers and
automobiles. The survey indicated that noise affects individuals in several ways. It
results in improper communication, sleeplessness and reduced efficiency. Though the
psycho-somatic effects (annoyance and depression) are also common yet the extreme
effects e.g. deafness and mental breakdown are not ruled out. In a majority of cases, the
affected party tenders a request to stop noise. A substantial proportion of respondents
among various age-groups complained to administration. Interestingly, about one-third
of young people (below 20 yrs) prefer to quarrel with the erring party. Public education
appears to be the best method as suggested by the respondents.
Alam et al., (2005) reported in their study that the level of noise pollution in
Sylhet city far exceeded the acceptable limits. In the residential areas and even near the
vulnerable institutions, like schools and hospitals, noise level was higher than the
acceptable limit. It had serious implication on the general health and well-being of the
inhabitants of the city. It was also observed that noise level was closely related with the
number of motor vehicles. Urgent measures should be taken to control the level of noise
pollution in the city.
Shukla et al., (2005) study stated that the vehicular noise pollution was
increasing at an alarming rate in metropolitan cities. A study was carried out to assess
the existing status of noise levels and to predict them for future. Ambient noise levels
were measured at different locations. It was found that noise levels at all the selected
locations were much higher than the prescribed limits. The observed noise data had been
fitted in Federal Highway Administration Agency (FHWA) model and suitability of this
model for predicting the future levels had been checked. It was found that FHWA model
was suitable for Indian conditions for prediction of traffic noise.
According to Ortiz et al., (2005) the absence of a completed ring road structure
around the city of León had resulted in a very serious noise pollution problem, as a great
number of vehicles were being forced to drive through the city, substantially increasing
noise levels. Therefore, it was strongly advised that the construction of the ring road
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around the city be completed in the shortest possible time; a recommendation which can
be applied to any other cities under similar circumstances.
Jeba Rajasekhar et al., (2005) found out that the noise levels either exceeded or
were about to cross the permissible standards at most of the sampling sites of Madurai
city. In addition, the ambient noise level Leq was predicted by a simple noise model and
the predicted values were compared with the experimental noise levels. As the predicted
values were in reasonable agreement with the estimated values of noise levels, it was
concluded that the modelling equations of present study could be used to predict the
noise levels all over the city.
The data on self hearing status and audiometric analysis of school teachers and
students were collected from the schools located in the near vicinity of NH-6 passing
through Jalgaon city by Pachpande et al., (2005) About 84% teachers and 92% students
had reported hearing difficulty in the questionnaire. In the audiometric testing mild
hearing loss (25 to 35 dBHL) was observed in both the subject groups. The strategies
needed to adopt for protection of the teachers/students from the noise exposure were
suggested. Thangadurai et al., (2005) carried out a study on environmental noise
pollution in the city of Salem. Road traffic noise had been a major contributor to the
annoyance, which was substantiated by the result of continuous monitoring of noise
equivalent levels (Leq) at a number of silence, residential, commercial, industrial zones
and road intersections.
Tansatcha et al., (2005) found out that the new method for traffic noise had been
obtained from measurements along the Bangkok-Chonburi motorway. This new
technique was based on the perpendicular propagation of vehicular noise from the
carriageway, and the employment of basic noise level using Leq over the time period of
10 s (Leq(10 s)). These Leq(10 s) represent the real time measurement of the average
energy mean equivalent level of the entire noise path of individual vehicle rather than
assuming pure single point or monopole sources, so that it can provide a better
representation of the overall energy emission from the passing vehicle. The application
of modified basic noise levels and effective ground effect (ßeff) into the motorway noise
model can significantly improve the traffic noise prediction results. This technique can
be applied to analyze the traffic noise prediction at other receiver locations, which are
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separated by different proportion of hard and soft grounds, around this motorway.
Finally, the main motorway traffic noise model developed by this study can provide a
good statistical result in the goodness-of-fit test of the model. Therefore, this new
motorway traffic noise model can be effectively used as a decision support tool for
prediction of motorway’s environmental noise impact in Thailand. This technique can
also be applied to build the traffic noise model for motorway in the other countries with
similar features.
Yusoff et al., (2005) assessed the level of noise exposure and its impact to
residents residing around the vicinity of urban highways. Noise level recording was
carried out at selected areas to determine the noise pollution levels and the adequacy of
mitigating measures that have been implemented. Traffic volume along the highway
was recorded and categorized into six major classes of vehicles. Subsequently, the Leq,
L10, L50 and L90 noise index percentiles were identified and data analyses were done on
the data samples. Simultaneously, a public survey was conducted to gauge the existing
public’s attitude and degree of awareness with contemporary motor vehicular noise
pollution. The study revealed that the noise level exposure experienced by the residents
exceeded the DOE’s guidelines on a daily basis whilst the measures taken was
inadequate to curb the noise menace emitting from the neighboring urban highway.
Kisku et al., (2006) sampled at 12 locations with sound level meter to assess day
time and night time noise levels of Lucknow city. In residential areas, noise ranged
between 67.7 to 78.9 and 52.9 to 56.4 dB (A); in commercial cum traffic areas 74.8 to
68.2 to 74.9 dB (A) and in industrial areas 76.9-77.2 and 72.2-73.1 dB (A) during day
and night time respectively; values were higher than their prescribed standards which
might pose a significant impact on quality of life.
Singh (2006) assessed the noise levels at the major traffic junctions and
community area near an educational institution of an urban city. Noise equivalent level
Leq and the statistical levels L10, L50, L90 were measured in the neighbourhood
community areas as well as at the traffic junctions. The study indicated a need for
proper land-use planning when traffic corridors were built in the silence zone areas.
Banerjee et al., (2006) revealed that in Asansol city the noise levels (10.00 pm –
6.00 am) in all the locations exceeded the limit prescribed by Central Pollution Control
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Board. The day time noise level was much higher at all locations in respect to the night
time noise level. The Day - Night equivalent noise level (Ldn) was determined which
ranged between 67.16 dB (A) and 89.44 dB (A).
Nykaza (2006) conducted a study of a place adjacent to a military installation to
determine if there were preferred times to conduct night time training. The results
clearly and strongly indicated that community disturbance was more effectively reduced
by conducting training between 0000 and 0200 hours, and avoiding noisy training
during the evening hours before midnight. These findings suggested that night-time
training should be postponed until after midnight in order to effectively reduce the
negative impact of night time training on local residents and to preserve night time
training capabilities throughout DoD (Department of Defence).
Sylhet city is becoming extremely crowded. This unplanned urbanization gave
rise to severe environmental problems in the city area. The study suggested that
vulnerable institutions (schools and hospitals) should be located about 60 m away from
the roadside unless any special arrangement to alleviate sound is used. From the
measured data from different sides of Sylhet, it was found that noise of density was not
significantly affected by residential density. The noise level on the main road near
residential area, hospital area and educational area were above the recommended level
(65dBA). It was found that the predictive equations were in 60-70% correlated with the
measured noise level. (Alam et al., 2006).
Monavari and Mirsaeed (2007) showed that noise and air pollutions had no
effect on mammals and birds of Khojir National Park. It was assessed that constructing
of a new highway had less environmental impacts when compared with the option of
widening the present road.
Paunovic et al., (2007) showed that a high noise annoyance in urban residents
may be predicted by subjective noise sensitivity, independently from noise exposure.
Specific non-acoustical predictors of noise annoyance in noisy streets were having
windows oriented toward the street and noise annoyance at workplace; the average time
spent at home daily was the only specific indicator of annoyance in quiet streets.
Nighttime and 24-hour noise levels had similar predictive value in noisy areas only.
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Noise pollution affected million people in all parts of the world especially those
who are living in the industrialized cities with heavy motor traffic (Ziauddin et al.,
2007). Their study of noise level at selected locations with mixed mode traffic density
using modelling techniques compared the result with the Central Pollution Control
Board (CPCB) standards. The results of Noise pollution level indicated that the
pollution levels were highly variable at different sampling stations depending on the
density of vehicles plying on the roads. The noise levels in few areas exceeded the
standards.
Tang and Wang’s (2007) paper showed that the urban forms in historical areas
with narrower roads, complex road networks and a higher density of intersections lead
to lower traffic volumes and thus lower noise pollution. However, the greater street
canyon effects in these historical urban areas lead to higher carbon monoxide (CO)
concentrations.
Pathak et al., (2008) reported the fact that 85% of the people were disturbed by
traffic noise, about 90% of the people reported that traffic noise was the main cause of
headache, high BP, dizziness and fatigue. People having higher education and income
level were much aware of the health impact due to traffic noise. Marital status was
found to be significantly affecting the annoyance level caused by traffic noise. Traffic
noise was found to be interfering with daily activities such as at resting, reading, and
communication.
The public, increasingly well-informed about the problem of excessive noise, is
taking actions for the development of new transport infrastructure projects and
improvement of existing infrastructure. In addition, many countries have implemented
mandatory Environmental Impact Assessment procedures. The studied reconfirmed that
the construction of an environmental Sound barrier can have several positive and
negative consequences for most of the residents near the barrier. It appears that the
benefits of barriers prevail over their disadvantages if they achieve a balance between
meeting the needs of noise reduction and minimizing their intrusion on the local
environment. The pilot study concluded that improvement in sleeping conditions was
the most appreciated positive effect of the environmental barrier. On the other hand, the
loss of sunlight and visual dominance were the most negative impacts reported by the
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community. Although the responses were given in a pilot survey, some tendencies can
be observed. In addition, the pilot survey is very useful in giving an estimate of then onresponse rate and also acts as a guide to carryout a larger scale survey (Arenas, 2008).
Ravichandran and Jayalakshmi (2008) revealed that the noise levels exceeded
the standards in almost all the places in Panruti, Cuddalore, Villupuram, Pondicherry
and Kodaikanal towns. Hospitals and education institutions exceeded the limit of 45 dB
(A). Vehicular traffic was found to be the major source in all the towns.
According to Banerjee et al., (2008) noise emission and transmission depended
on the type of zone, geographic features, landscape and topography. Open areas had
lower noise levels during any time of the day because it lacked dense human habitation,
commercial establishments and hence had lower vehicular flow. In contrast, well-built
up areas with residential apartments, shopping areas, had higher noise levels due to
more use of the roads alongside it by all types of public, commercial and private
transport vehicles. It was also observed that at certain locations the night time noise
levels were higher than the daytime noise. This was due to the fact that at these locations
during day time, heavy vehicles have no-entry, whereas they were allowed to transit
after 8.0 pm. Based on the noise survey it was observed that immediate mitigatory
measures were required to control the high road traffic noise emission. Suggestive
control methodologies included control of noise at source of generation itself by
employing techniques like maintenance of automobiles, regular servicing and tuning of
vehicles to reduce noise levels, fixing of silencers to automobiles, two wheelers etc,
would reduce the noise levels.
Noise pollution due to road traffic is a major global concern because of its
negative impact on the quality of life in communities everywhere. In Vietnam, traffic
noise had become an increasingly noticeable and serious problem observed in large
cities like Hanoi and Ho Chi Minh City. Noise datasets from both these cities were
compared with a dataset of Japanese traffic noise obtained in Kumamoto. The results
showed that the traffic noise in Hanoi and Ho Chi Minh City was characterized by
relatively high noise exposure levels due to the large number of motorbikes and frequent
horn sounds. The sound of horns contributed a definite impact of 0– 4 dB on noise
exposure in Hanoi and Ho Chi Minh City, where noise levels decreased with the
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absence of horn sounds. The results also showed differences in the characteristic traffic
noise of Vietnam and Japan, (Phan et al., 2009).
Ghonamy (2009) observed that traffic noise levels in al-Khobar were higher than
those recommended by noise standards for the day, afternoon and evening periods in
residential and commercial areas. Spectral analyses of the sound pressure showed
consistent behaviour of typical spectrum of freely flowing traffic stream. The study
confirmed the urgent need to establish a centre in the Kingdom of Saudi Arabia for
noise pollution monitoring and to develop local traffic noise standards.
Ghatass (2009) conducted a study of environmental noise pollution in
Alexandria city. Alexandria is a coastal city. It is the second largest city in Egypt. Thirty
seven sampling sites were selected to measure the noise level. The noise level at three
streets in the day and evening times were higher than the permissible limits according to
Egyptain Environmental Law 4/94. It was concluded that the noise levels at three
interesting streets were related to illegal behaviour of drivers for horn used, high traffic
density and the poor mechanical states of vehicles, roads and surfaces and their layout.
Ravichandran et al., (2009) assessed the extent of noise pollution in SIPCOT
industrial area in Tamil Nadu. The Leq values exceeded the standard in the vicinity of
mechanical shed and boiler in textile and steel industries. Chemical industry was found
to be less noisy of all the industries. At Kasipillapalayam and Periyavettuvapalayam
noise levels never exceeded the standard value while in the other they exceeded the
standard value. Exceeding noise levels in those villages were due to their close
proximity to the SIPCOT Industrial Complex and to National Highway.
Olayinka et al., (2009) studied the status of noise pollution in Ilorin metropolis.
The day – time and night – time noise levels were measured in 42 different locations.
The noise levels in Ilorin city exceeded allowed values by the WHO at 37 of 42
measured points. At 95% confidence level, test for significant difference of the means
and variances of the day-time noise levels and night-time noise levels showed no
difference in all the locations surveyed. At 90% confidence level, analysis of variance
for two factor experiment using F-distribution (carried out on the descriptors L10 and
L90) showed that noise exposure level differed significantly from one location to
another. In all the locations surveyed, the night-time noise levels were very comparable
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to those reported for the day-time noise levels which were very much higher than the
levels reported for living rooms.
Noise pollution and its influence on environment and life quality of human
beings may be considered as a hot topic in scientific research. The study revealed that
even in Tokat one of the small-sized cities, noise pollution has reached serious
levels, showed that the noise had become one of the major environmental
problems of the country to be urgently overcome. Enforcement of more effective
regulations and constraints on the noise problem seemed to be promising for the
cities in Turkey, which was at the stage of accession negotiations with European
Union (Ozer et al., 2009).
Fyhri and Aasvang (2010) reported the several adverse effects that have been
associated with exposure to traffic noise. The paper investigated the relationships among
long-term noise exposure, annoyance, sleeping problems and subjective health
complaints by the use of a structural equation model. Data from a questionnaire survey
conducted among a population sample in Oslo (N=2786) was combined with night time
noise levels calculated from outside each respondents dwelling, at the bedroom façade.
The results of the analysis showed significant relationships between noise annoyance at
night and sleeping problems. The model also showed strong links among pseudo
neurological complaints, annoyance and sleeping problems, thus pointing to the
importance of including information on psychosomatic disorders and mild psychological
problems in future studies looking at potential health effects of noise. Analysis showed
no relationship between both noise exposure and cardiovascular problems.
Zhi Ning et al., (2010) reported that the noise barriers were now common
roadside features of the freeways, particularly those running through populated urban
areas; it is pertinent to investigate the impact of their presence on the particles and copollutants concentrations in areas adjacent to busy roadways. This study investigated
two highly trafficked freeways (I-710 and I-5) in Southern California, with two
sampling sites for each freeway, one with and the other without the roadside noise
barriers. Particle size distributions and co-pollutants concentrations were measured in
the immediate proximity of freeways and at different distances downwind of the
freeways. The results showed the formation of a “concentration deficit” zone in the
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immediate vicinity of the freeway with the presence of roadside noise barrier, followed
by a surge of pollutant concentrations further downwind at 80-100 m away from
freeway. The particle and co-pollutants concentrations reach background levels at
farther distances of 250-400 m compared to 150-200 m at the sites without roadside
noise barriers.
Road accidents are a serious global problem with more than one life lost every
minute. Globally road accidents are one of the leading causes of death for young people
accounting for 15 percent of deaths in 5 to 44 years age group. Road safety has become
a global problem, while situations are improving in developed countries; less developed
countries are experiencing increased number of accidents. The young, pedestrians, 2wheelers, professional drivers and alcohol is victims of Road accidents in India which is
alarming. A 2- wheeler rider is five times more likely to be killed in accidents on Indian
roads than a car or a bus traveller. Nearly 60% of total road accidents take place during
night though the night traffic is hardly 15% of the 24-hour volume, which means that
the probability of an accident in India during night is almost 8 times higher than in day.
The study concluded that substantial reduction in the incidence of road accidents can be
brought about by restoring to corrective measures in reducing driver error, which is the
major cause of road accidents (Rao et al., 2010).
Mishra et al., (2010) concluded that the traffic noise caused by heavy traffic flow
condition on the main BRTS (Bus Rapit Transit) corridor was significant and exceeding
the national CPCB standards. It was alarming considering the fact that the traffic
volume was going to increase further in future. Due to heavy traffic volume, traffic
noise was also increasing at this particular corridor. In response to this, noise abatement
measures had been proposed to curb the noise pollution in the vicinity of the concerned
transport corridors. These measures mainly included construction of noise barriers and
adopting traffic mitigatory measures. From the developed spread sheet, it was calculated
that 2.4 m height of barrier was required to increase the height of brick wall as a barrier
already existed there to reduce the noise level up to a permissible limit.
According to Al-Ghonamy (2010) the noise surveys carried out in Al-Dammam
city showed that traffic noise produced high noise levels that significantly exceeded
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permissible limits used in the country’s codes. Almost the entire population of AlDammam city was exposed to high-risk noise levels mainly from road traffic.
According to Phan et al., (2010) traffic noise had become an increasingly
noticeable and serious problem in large cities Vietnam, like Hanoi and Ho Chi Minh
City. The study revealed that in cities of a developing country, environmental noise
levels due to traffic were notably higher than those in a developed country such as
Japan. Moreover, while cars were the common form of transportation in developed
countries, motor bikes were by far the dominant vehicle in the traffic in Vietnam. In
general, high noise levels were the result of frequent horn sounds, especially in Hanoi.
Over all site monitoring, the daily average noise levels LAeq, day were>69dB.
Filho et al., (2004) analysed the effects of traffic composition on the noise
generated by typical Brazilian roads. The result had showed that the most commonly
used noise levels in road noise emission evaluation, such as the percentile level L10 and
the equivalent noise level Leq, could be estimated by knowing the traffic composition
with reasonably good recision.
Bluhm et al., (2004) observed that many people reported disturbances from road
traffic noise already at lower noise levels. There was an obvious exposure response
relation both for annoyance and problems with sleep. Bedroom window orientation
seemed to be a factor of main importance for occurrence of disturbances related to
traffic noise exposure.
The traffic-generated air pollution and noise have both been linked to
cardiovascular morbidity (Allen 2009). They concluded, that moderate correlations
suggested the potential for confounded results if both noise and air pollution were not
accurately assessed in epidemiological studies of traffic and health. Although very few
epidemiologic studies have included both air pollution and noise in health effects
models, imperfect correlations between these exposures present opportunities for
disentangling their impacts on health, and methods for analyzing correlated
environmental exposures in health effects studies continue to emerge.
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2.4
Flora and Fauna Assessment
Tresa et al., (1995) carried out a preliminary study on algal distribution,
chemistry and ecology of the southwest coast of India, especially the Kerala coast.
Sivaramakrishnan et al., (1996) studied the water quality monitoring methods
using benthic macro invertebrates. They evaluated the efficacy of four general bio
monitoring approaches using data from the Kaveri River catchments of South India. The
results of these four general approaches were highly correlated but Bio monitoring
working party (BMWP) and Rapid Bio assessment Protocol III (RBP III) showed the
highest correlations with habitat quality and population density at the 29 sites examined.
The BMWP and RBP III were also the least influenced by naturally occuring physical
habitat gradients found among the sites.
Rajashree and Panigrahi (1996) studied the spatial and seasonal variations in
phytoplankton species composition and abundance in relation to physico-chemical
properties. During the course of investigation, Biddulphiaceae, Coscinodiscaceae and
Chaetoceraceae among Centrales and Naviculaceae among Pennales emerged as
floristically rich families. Of the various environmental parameters studied, change in
salinity and nutrient concentrations controlled the distribution and abundance of
phytoplankton.
Kaushik and Saxena (1996) carried out a study to find the relationship between
and other organisms that fish population of Gauri tank of Bhind (M.P.). Only 24 species
of fish belonging to 15 genera and 5 families have been collected from the tank. 54
species of phytoplanktons 15 species of, zooplanktons 20 species of, macrophytes and
43 species of benthic fauna were collected. The fish population had close association
with phyto and zooplanktons but has no significant correlation with benthic fauna.
Copepods of the Bahuda river estuary were studied and the population was
represented by 58 species belonging to calanoids (33), cyclopoids (15) and harpacticoids
(10). Marked seasonal variations had been observed with respect to the species
composition of the Copepod fauna. Higher species diversity was encountered during the
premonsoon season, when the estuary was under marine influence while an opposite
trend was observed during monsoon season. (Mishra Sujata and Panigrahy 1996).
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Nayak (1996) studied that the distribution of grain size parameters along 11 km
stretch of the beach sediments between Majali and Karwar, revealed that the mean grain
size exhibited a marked decreasing trend on either side of the mouth of the Kali River.
The variations in standard deviation and skewness supported the distribution of mean
grain size. Average standard deviation values for different months decrease from river
mouth on either side.
Zooplankton distribution and abundance in neuston layer and water column at
four transects between Goa to Gujarat were studied by Padmavan and Goswami (1996).
The ambient water temperature, salinity and dissolved oxygen at surface layer ranged
between 26.3°C and 27.3°C, 35.7 and 36.2x10-3 and 4.3 and 4.5ml.l~l respectively. The
temperature and dissolved oxygen values decreased with depth. Zooplankton biomass
and population density were higher in the water column than in the upper neustonic
layer. Copepods were dominant.
Seasonal variability in the physico-chemical features, zooplankton standing
stock (biomass) and faunal composition in the Mandovi-Zuari estuarine system of Goa
during January to December 1990 were studied Padmavati and Goswami, (1996).
Hydrobiological characteristics were influenced by the southwest monsoon. Salinity
fluctuations were drastic. This appeared to cause variations in plankton production and
heterogeneity of various taxa. Zooplankton biomass and total numerical counts varied
significantly between seasons and estuaries.
The benthic fauna of the Kayamkulam backwater and adjacent sea composed
mainly of foraminiferans, polychaetes, nematodes amphipods, isopods and bivalves. The
population density varied between 900-76000/m2 in October and 1020-60000/m2 in
May. Foraminiferans were the predominant group in the sea and in the marine zone of
the backwater. Polychaetes ranked second in terms of the species composition followed
by bivalves. Amphipods were the predominant group in the upper reaches of the
backwater. (Devi et al., 1996).
Mycological examination of dead wood, prop roots and seedlings of Rhizophora
spp. yielded 48 fungal species belonging to 36 genera with Ascomycotina being most
prevalent. The number of fungi recorded on prop roots (44) was much greater when
compared with seedlings (18) and wood (16). Each substrate had its own common,
132
frequent and occasional fungi appearing on them. The most common and abundant
fungus on wood was Lophiostoma mangrove. (Ravikumar and Vittal, 1996).
Annual distribution of nutrients salinity and dissolved oxygen variations in the
coastal waters of Kalpakkam were studied for three years by Satpathy (1996). Nutrient
levels were more during NE monsoon. Phosphates and nitrites exhibited wide annual
fluctuations compared to nitrates and silicates. Salinity and nutrients were in inverse
relationship. Dissolved oxygen and nutrients showed positive correlation. Regression
analyses between any two nutrients also showed positive correlation.
Growth of RltizopStora apiculata Blume seedlings in lower intertidal zones
grew more rapidly than those in upper intertidal ones. The growth was about 2.5-fold
greater and the leaf sprouting was about 4-fold higher in the seedlings growing in the
lowermost intertidal zone than those in uppermost intertidal zone. The growth was also
rapid towards the monsoon month of December associated with low salinity and high
levels of nutrients. (Kathiresan et al., 1996).
Spatial and temporal variability in zooplankton production, composition and
diversity in the coastal waters of Goa were studied Goswami and Padmavati (1996).
Zooplankton production was bimodal with primary peak during September-October and
secondary peak during March-April. Secondary production computed from the
zooplankton biomass values fluctuated between 24.7 and 87.2 mgC.m~2.d-l. Herbivores
dominated the zooplankton community and copepods were most abundant. 21.
Quadros et al., (1996) studied that the meiobenthos of intertidal zone of
mangrove mudflats revealed dominance of nematodes (78.35%) with insignificant
seasonal variations. The other constituents were tube polychaetes and oligochaetes, the
latter contributing a major share e to meiobentlaos only at the station in the proximity of
sewage outlet.
Coastal upwelling of nutrients during and after the southwest monsoon had been
considered to support rich pelagic and demersal fisheries off the west coast of India.
Studies indicated occurence of coastal upwelling associated with Ekman transport in
response to prevailing equator ward winds. The effect of upwelling on the surface
133
distribution of properties was reduced to some extent due to coastal runoff which gives
the region a patchy distribution of properties. (De Sousa et al., 1996).
Dhargalkar and Deshmukhe (1996) studied a total of 35 marine algal species that
were recorded during a survey of the sub tidal flora of Dwaraka. Maximum number of
species was found at 5-8 m depth. Red algal species were dominant (20), followed by
green (8) and brown (7). The similarity index calculated between intertidal and subtidal
species of Dwaraka was 43.40, indicating that the subtidal floral composition was as
diversified as that of the intertidal region.
A study by Banse et al., (1996) on the data collected off the southwest coast of
India suggested that the commonly observed high concentrations of chlorophyll and
rates of photosynthesis of the season might not be due to greatly enhanced chlorophyllspecific (normalized) photosynthetic rates of the bulk phytoplankton. Instead it was
proposed that the seasonally increased nutrient supply primarily increased the growth
rate of the otherwise more diffusion-limited large-celled species, which then could
materially increase in numbers, since they were poorly controlled by grazers.
Kanhere et al., (1997) investigated the influence of urbanisation on an aquatic
ecosystem using changes in the phytoplankton species composition over the years.
There was a definite shift in the algal species, as also increase in the nutrient levels. The
appearance of a few new species indicated the changing quality of water. These findings
when compared with the earlier work signified the changes in the ecosystem.
Krishnan (1997) collected the plankton form of algae from the river Ganges
from all the sampling points both in pre monsoon and post monsoon. The results
showed that out of the sixty six species included in this study ten showed a conspicuous
trend which bear negative correlation with pollution, while the others showed erratic
behaviour.
Mogal’s (1997) estimated the qualitative and quantitative study of bacteria and
fungi present in water of the Dandi seacoast, located in South Gujarat. Bacterial and
fungal species isolated from marine habitat were identified and examined for the extra
cellular enzyme production. The results indicated that the enzymes of bacterial origin
were similar in their response to the three parameters under study. On the basis of the
134
study of faecal indicator bacteria, FC/FS ratio was derived to determine pollution and
waters of two stations were indicative of faecal pollution of human origin.
Arivazhagan and Kamalaveni (1997) studied the physico-chemical parameters
and plankton analysis of pond water. The physical characteristics gradually increased
and the temperature gradually decreased from July to December. The chemical
characteristics gradually increased from July to December and the dissolved carbon
dioxide and alkalinity gradually decreased from July to December. The content of
nutrients gradually increased from July to December.
Eswari et al., (1997) evaluated the distribution of zooplankton under four
different coastal habitats near shore waters, estuaries, backwaters and harbour waters of
the city of Madras. The period of sampling fell under 3 seasons, namely summer (MayJune), Pre-monsoon (July-August) and Monsoon (September-October). Zooplankton
samples were also collected once from six locations inside the harbour area of the
Madras port. The dominant groups in all the stations were the copepods and the rotifers
through out the study period in all the stations. The copepods were the most abundant
group and there was no significant variation in the total number in all the stations. A
similar trend was noticed in the case the rotifers also. The Daphnia sp. showed a
stricking seasonal profile. On the contrary, the ostracods were generally absent during
pre-monsoon period except at Besant Nagar and Coovum estuary sampling spots. This
seasonal variation in the occurrence and abundance of different groups of zooplanktons
were more striking than spatial profile noticed between diverse sampling locations.
A huge road network with vehicles ramifies a cross the land, representing a
surprising frontier of ecology. Species-rich roadsides are conduits for few species. Road
kills area premier mortality source, yet except for local spots, rates rarely limit
population size. Road avoidance, especially due to traffic noise, has a greater ecological
impact.
The still-more-important
barrier effect
subdivides populations,
with
demographic and probably genetic consequences. Road networks crossing landscapes
caused local hydrologic and erosion effects, where as stream networks and distant
valleys receive major peak-flow and sediment impacts. Chemical effects mainly occur
near roads. Road networks interrupt horizontal ecological flows, alter landscape spatial
pattern, and therefore inhibit important interior species. Australia had huge road –
135
reserve networks of native vegetation, where as the Dutch have tunnels and overpasses
perforating road barriers to enhance ecological flows. Based on road-effect zones, an
estimated 15–20% of the United States is ecologically impacted by roads. (Forman
1998).
Roads are a widespread and increasing feature of most landscapes. Reviewed the
scientific literature on the ecological effects of roads are found support for the general
conclusion that they are associated with negative effects on biotic integrity in both
terrestrial and aquatic ecosystems. Roads of all kinds have even general effects:
mortality from road construction, mortality from collision with vehicles, modification of
animal behaviour, alteration of the physical environment, alteration of the chemical
environment, spread of exotics, and increased use of areas by humans. Road
construction kills sessile and slow-moving organisms, injuries organisms adjacent to a
road, and alters physical conditions beneath a road. The ecological effects of road
reveals
a multiplicity of effects, it also suggested
that it is unlikely that the
consequences of roads ever be completely mitigated or remediated, thus, it is critical to
retain remaining roadless or near-roadless portions of the landscape in their natural state.
Because of the increasing rarity of roadless areas, especially roadless watersheds,
conservation efforts cannot rely entirely on protection of existing natural areas.
(Trombulak and Frissell 2000).
According to Clevenger et al., (2002) mammal and bird road-kill indices were
consistently higher on a low volume parkway than on the high-speed and high volume
Trans-Canada highway (TCH). Birds were more vulnerable to collisions than mammals
on the TCH. Road-kill aggregations were non-randomly distributed. Parkway road-kills
were aggregated on small scales and characterized by low clustering intensities
compared to the TCH. Road-kills were less likely to occur on raised sections of road.
Road-kills tended to occur close to vegetative cover and far from wildlife passages or
culverts.
Ramp et al., (2005) determined the quantities of the fatalities of wildlife killed
on roads within the Royal National Park in New South Wales. These also estimated
those wildlife species using roadside habitat in order to identify species susceptible to
collisions. Modelling of fatality data indicated that mammals were most likely to be
136
killed where forage was abundant on the roadside verge and where there was plenty of
protective cover, while birds were most likely to be killed when the height of roadside
vegetation was low. A number of collision hotspots were identified along the surveyed
road that should be the target of mitigation efforts. The average speed of vehicles
travelling within the park peaked at night.
Petronilho and Dias (2005) assessed the impact of traffic on wild vertebrate
populations in two forest roads crossing the Perímetro Florestal das Dunas e Pinhais de
Mira, located in the littoral centre of Portugal. One of the roads was paved with asphalt.
The other was already partially paved but later on it became completely paved with
asphalt. These new pavements considerably contributed to the increase of traffic in these
roads. Vertebrate road casualties (n = 831) included 46 identified species and 7 nonidentified individuals; Amphibians presented the highest values for road casualties
(81%), followed by birds (9%), reptiles (6%) and mammals (4%). The highest mortality
rates occurred between November and March and in the 2nd, 5th and 7th kilometres.
Amphibians were the most affected group. The common toad Bufo bufo, presented the
highest mortality values (49%), constituting nearly half the road casualties registered
during the study period. In a short-term period, this mortality might produce negative
effects upon longevity and reproduction of some vertebrate species.
Patel and Kanungo (2006) studied the culture of aquatic plant Pistia stratiotes
that was grown in the domestic wastewater for a stipulated interval of seven days for
phyto remediation. The results of analysis for pH and dissolved oxygen had shown an
increase in values while other parameters exhibited significant decrease throughout the
year. The increase in biomass of Pistia stratiotes and finding of physico-chemical
analysis had proved that Pistia stratiotes was a suitable aquatic plant for
Phytoremediation of domestic wastewater.
Devidas et al., (2006) studied the two water tanks situated in Shimoga district
for their phytoplankton diversity and the possibility of using it as bio monitors of
organic pollution. These algae occurred as regular blooms in all the seasons. Hosalli
tank supported a wide diversity of phytoplankton and less polluted. The use of algae for
bio monitoring of organic pollution indicated that Purle tank, which regularly received
137
sewage was heavily polluted and Hosalli tank was mesotrophic in nature of mild
anthropogenic activities.
Satheesh and Wesley’s (2006) found out that the surface water temperature had
been one of the most critical factors determining the species diversity. In this study high
species diversity with the increase of water temperature above 290 C was observed.
Salinity was another important factor, which determined the distribution of polychaetes.
The diversity and abundance of the polychaetes associated with Sargassum also
indicated that more attention needs to be paid to their functional role. These epifauna
appear to influence the distribution and abundance of other organisms, because as prey
items, they regulate populations of many reef fishes.
Jaiswar et al., (2006) surveyed Mahim creek and Bay area that indicated absence
of fauna, particularly molluscs from the area, which was a repository in the past. During
bioassay experiments of Mahim creek water, the clams G. divaricatum and C. antiquate
could not open their values in 100% creek water and died within 12 hrs of exposure. The
96 hrs LC50 values of Mahim creek water for G. divaricatum and C. antiquate were
found to be 20% and 40% respectively during summer and 38% and 57% respectively
during rainy season. When two sets of the clams were transplanted at Mahim creek, they
died within 12hrs. These experiments suggested the extreme level of pollution in the
area. This level of pollution was responsible for transforming the area into barren
locality in terms of fauna, especially the rich mollusc diversity. However, Gorai creek
was found to be comparatively very less polluted and it still served as breeding and
nursery ground for various fishes and prawn species.
A study by Das et al., (2007) reported road mortality of reptiles on a highway
segment passing along the southern boundary of Kaziranga National Park, Assam, India.
A total of 68 instances of road kills of reptiles belonging to 21 species and seven
families were recorded. There was a greater mortality among snakes compared to
lizards. The arboreal reptiles were the most affected, the higher percent being those that
were diurnal followed by the nocturnal, crepuscular and both day and night active
species.
Tharmendira (2008) observed in his study the floral distribution of Pondicherry
Mangroves. 23 mangrove and mangrove associated vegetation were identified around
138
the study area. Both the true mangrove and mangrove associate vegetation covered
nearly 30 ha (5.6%) of the total inundated area.
According to Parag Prasad Khatavkar (2009) Wildlife mortality due to vehicular
collision is one of the most important threats of road development and operation. The
A73 highway in The Netherlands was selected to develop a model using badger (Meles
meles) and roe deer (Capreolus capreolus) as indicator species. Data on wildlife
mortality and developed mitigation measures such as wildlife overpass/underpass was
used for validation. The impedance model showed high impedance values for human
settlements and industrial areas and only medium impedance for the A73 highway itself
for both species. High probability of road kill was estimated in four continuous lanes of
roads while relatively low probability was estimated if four lanes were divided in 2x2
lanes. In case of badger (Meles meles), scenario 2 identified 77 high mortality circular
buffers. Likewise for roe deer (Capreolus capreolus), scenario 2 identified 71 high
mortality circular buffers. Validation showed that scenario 2 predicted results better than
scenario 1. The developed model was able to predict relative wildlife mortality locations
using only species presence data. Validation of the model was showed satisfactory
results.
Satheeshkumar and Khan (2010) had studied marine gastropods Conus virgo and
C.bayani for the first time from Pondicherry, South east coast of India. Results from
their study mandate additional special searches for other specimens of the found species
of Conidae are necessary in the coastal zone of Pondicherry to obtain additional
information.
Baskaran and Boominathan (2010) reported that the highways passing through
natural reserves had adverse impact on wild animals. They evaluated the road kill of
vertebrate fauna by vehicular traffic on highways at Mudumalai Tiger Reserve, southern
India. In a fortnight’s survey over 248km across three public roads and opportunistic
sampling method, a minimum of 180 road kills belonging to 40 species of amphibians,
reptiles, birds and mammals were recorded between December 1998 and March 1999.
Amphibians were the most affected taxa (53%) of road kills followed by reptiles (22%),
mammals (18%); including a leopard (Panthera pardus) and birds (7%). Amphibians
and reptiles are slow to react to vehicles and this along with the drivers’ ignorance
139
probably leads to higher mortality among these species. Road kills are significantly
higher on highway stretches along rivers than those without water bodies nearby. It was
suggested the construction of flyovers, speed limits, speed breakers and fixing of
signposts along the highways to reduce vehicle-caused wildlife mortalities.
The impact of roads on ecological resources requires mitigation measures that
will eliminate both direct and indirect effects in South Africa. Direct effects including
clearing of vegetation and displacement of wildlife during road construction can be
minimized through better road design techniques based on integrated approaches. The
roadsides can be replanted, new roads located and existing roads relocated outside the
wildlife habitats. Indirect effects associated with pollution because of the demand for
travel and mobility by people can be minimized through the use of alternative modes of
travel for example increased public transportation, use of alternative routes outside the
wildlife habitat, control travel to destinations subject to peak and off peak seasons of
wildlife migration. There is also need to encourage use of recycled materials for
construction and upgrading of roads. This approach will ensure little negative impact on
the ecological resources as possible. The reduced demand on non-renewable materials,
as well as on the support for efficient public transport to reduce emissions, traffic
congestions and road accidents will generate positive ecological benefits. (Patrick
Karani, Environmental Strategist, Development Bank of Southern Africa (DBSA).
2.5
Socio-economic aspects
Donovan McGrowder et al., (1999) investigated the impact of the North Coast
Highway development on the socio-economic well-being of residents living in Bogue
Village, St. James, Jamaica. It pointed out the connection between the new road
infrastructure development and the survey respondents’ socio-economic status, while
investigating the impact of the North Coast Highway on family life. Time spent with
families and creation of new economic opportunities was the main indicators of family
life. The paper used questionnaires and interview to test the hypothesis. The results
presented in this study had supported the view that a large public sector investment
project on road infrastructure development could improve to some extent the socioeconomic well-being of the citizens that lives in communities in close proximity to the
140
highway, especially in the area of employment and particularly, residents of Bogue
Village, St James.
The study near crushers and quarries of Choong Kwet Yive et al., (2000)
revealed that 16% of workers had dust and water related diseases like TB and other
respiratory ailments. 24% of the workers complained about high level of noise related
problems and 13% of the workers surveyed had persistent eye and lung problems.
Haziness in the atmosphere around crushers and quarries was very common. The quarry
owners were not taking any precautions related to the health condition of the workers.
Ravikumar et al., 2000 carried out environmental impact assessment of Granite
Mining on socio-economic status of workers in quarries and crushers of Bangalore
district. The adverse impact of health, especially from the operation of gas jet burner
and stone crushers was noted. Workers were suffering from respiratory and lung
aliments and substantial number of workers suffer from head ache, chest pain, etc. The
number of cases registered in the private clinic was on the rise.
Geneletti (2002) reported that the biodiversity had become one of the central
environmental issues in the framework of recent policies and international conventions
for the promotion of sustainable development. The reduction of habitat worldwide was
currently considered as the main threat to biodiversity conservation. Transportation
infrastructures, and above all road networks, were blamed for highly contributing to the
decrease in both the quantity and the quality of natural habitat.
Clevenger et al., (2002) proposed a series of mitigation measures that could be
contemplated in future road planning projects. They recommended that simple below
road passages (e.g. metal culverts) be installed at frequent intervals (150–300 m) to
provide opportunities for animals of all body sizes to avoid crossing roads. Drainage
culvert costs to highway infrastructure projects are small and the ecological benefits
considerable. Cover should be provided close to passage entrances to enhance animal
use. At curves in roads where visibility is reduced the verges should be widened to
discourage crossings. However, along straight sections cover should extend as close to
the road as permitted by road construction standards.
141
Rao (2002) stated that regulation of any nation required the liquid and gaseous
wastes to be treated to meet the prescribed standards. According to him the leachate was
the most significant hazard from a landfill. The noxious mineralized liquid was capable
of transporting bacterial pollutants to the water by moving literally through the refuse.
The pollutants could be moved by the water several kilometres from the disposal site,
depending on the amount of water that infiltrates or moves through the waste and the
length of time that the infiltrated water was in contact with the refuse.
Kathleen L. Wolf (2003) conducted in the United States to learn more about
public preferences and perceptions regarding forest and vegetation planning and
management in urban freeway roadsides. In response to images depicting a visual
continuum of landscape management treatments, drivers mostly preferred settings
having tree plantings that screen adjacent commercial land uses. They suggested
solutions for landscape practices that create visual quality for drivers and provide
visibility for commercial properties adjacent to freeway roadsides. The research also
investigated public attitudes about roadside functions, uses, and public willingness to
support roadside management expenditures. Increasingly, transportation agencies are
designing urban roadside landscapes to achieve multiple objectives and perform
multiple functions. This research offers insights on how to incorporate urban forestry
into the planning and management of high-speed urban transportation corridors.
Chetna et al., (2006) revealed the impact of diverse anthropogenic activities as
well as the monsoon effect on the bacterial population of river Yamuna in Delhi stretch.
Microbial population contributed mainly through human activities, prevailed in the
entire stretch of Yamuna River with reduction in bacterial counts during monsoon
period due to flushing effect. Bacteriological assessment did not provide an integrated
effect of pollution but only indicated that water quality at the time of sampling. Hence,
this parameter is time and space specific.
Santa et al., (2008) studied at a hilly terrain, Eastern Himalayas, especially
changes in the population of microbes especially fungi and bacteria due to roadside
pollution. The leaves were collected from both sides of the road. The leaves were
analysed for microbial population, heavy metals and sulphur accumulation. The leaves
of trees closer to the road contained higher amounts of heavy metals than those at 1 km
142
site. Population of bacteria and most fungi species were higher at 1 km site than at the
site closer to the highway. Diversity of micro fungal community in phylloplane at the
two sites differed significantly. Counts of fungal units and bacteria propagate significant
negative correlation with the concentrations of metal and sulphur. Some fungal
Fusarium oxysporum, Mortierella sp. and Aureobasidium pollulans were abundant in
the polluted roadside compared to the other species of fungi.
Prasad and Ramanathan (2008) reported that the mangrove forests were the
highly productive ecosystems of the tropical environment. Spatial and temporal
analytical measurements of organic nutrients were made in the Pichavaram mangrove
ecosystem (south east coast of India) to understand the dissolved organic nutrient
dynamics. Monthly measurements of physical parameters and dissolved organic
nutrients were made at several locations at daytime during low tides. The result showed
high concentration of DOC and DON which were found in monsoon and DOP in
summer. The distribution and dynamics of dissolved organic matter have been regulated
by the monsoonal fresh water discharge from the adjacent sources. However, the
microbial mineralization induced by summer temperature regulated the nutrient
biogeochemical process and also control the biological productivity. In general, the
mangrove ecosystem supplied considerable loads of nutrients to the oceans rather than
the river systems and those nutrients regulate the global nutrient biogeochemical cycles.
The Asian Development Bank (ADB) is a multilateral development finance
institution dedicated to reducing poverty in Asia and the Pacific. In 1998, ADB agreed
to finance a 141-kilometer four-lane, controlled access expressway in Hebei Province, in
the People’s Republic of China (PRC), forming a strategic link in the National Trunk
Highway System (NTHS).
It was also agreed to tackle the accessibility of nine
designated poverty counties in Southern Hebei, through which the expressway would
run. The majority of villages do not have all-weather access to the road network, and
underemployment rates were high.
The expressway has been in operation since
December 2000, and had not experienced any major structural problems. People
affected by the Project were relocated satisfactorily, and the overall socioeconomic
impact of the Project had been positive. No major adverse environmental impacts have
occurred. The local road networks have contributed directly to poverty reduction in
disadvantaged areas. Effective road safety and overloaded vehicle prevention programs
143
were implemented, while accident emergency centers have been established.
Commercialization of the expressway activities proved successful. Expressway toll
revenue also increased significantly. With travel demand increasing, constraints on the
alternate road, and economic development in the region quickening. The Project was (i)
certified as excellent, with a rating of 96%, by the Hebei Provincial Highway
Engineering Quality Monitory Bureau in November 2000; (ii) certified as excellent by
the MOC (Ministry of Communication) during final acceptance inspection in 2003; and
(iii) awarded the ISO 9001:2000 certificate for its quality management system. Several
toll stations also have gained national and provincial recognition awards. The Project
was financially and economically viable, and was certified, by ADB, as highly
successful. (Kim Jraiw, 2005).
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 was also
depleted natural resources used by the city. As people move out of the city into
surrounding regions, the cities expanded, and future pollution and resources depletion
occurred as people travelled longer distances from home to work. Rural-urban migration
also had 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 could 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.
Negishi et al., (2006) suggested that roadside fern growth played potentially
important ecological roles in road recovery by reducing road runoff, mitigating splash
and hydraulic surface erosion processes, trapping sediment where plant seeds can
germinate, providing nutrient-enriched through fall, and moderating harsh surface
temperature environment.
Roadside surveys such as the Breeding Bird Survey (BBS) are widely used to
assess the relative abundance of bird populations. The accuracy of roadside surveys
144
depends on the extent to which surveys from roads represent the entire region was
studied. They quantified roadside land cover sampling bias in Tennessee, USA, by
comparing land cover proportions near roads to proportions of the surrounding region.
Roadside surveys gave a biased estimate of patterns across the region because some land
cover types were over- or underrepresented near roads. The study concluded that these
surveys should be corrected for roadside land cover sampling bias. In addition, current
recommendations about the need to create more early success ional habitat for birds may
need reassessment in the light of the under sampling of this habitat by roads. (Berton et
al., 2007).
Swarnakumar et al., (2008) elucidated that there were eight coastal stations
along the east coast of the Little Andaman Island and their study reported high total
heterotrophic bacterial population. The above might have supported in degradation and
recycling of organic and inorganic materials. The study also indicated that eight coastal
stations along the east coast of the island were much less polluted by the human
pathogens. The studies on the west coast of the island would provide the total microbial
pollution status of the island.
Growder (2009) pointed out that large public sector investment project on road
infrastructure development could improve to some extent the socio-economic well-being
of the citizens which live in communities in close proximity to the highway, especially
in the area of employment and particularly, the residents of Bogue Village, St James.
145
3.0 MATERIALS AND METHODS
3.1
Description of the Study Areas
In the present work the East Coast Road stretch from Cuddalore old town to
Tharagambadi was studied. East Coast Road is a two lane express highway in Tamil
Nadu, India, built along the coast of the Bay of Bengal connecting Chennai to
Cuddalore via Pondicherry. The East Coast Road runs through the village panchayats
and a town panchayat. It runs about 4 km east to Thiruvanmiyur where it turns sharply
south to skirt the coast all the way down to Cuddalore.
Cuddalore is a port city and a Municipal Corporation in Cuddalore district of the
Indian state of Tamil Nadu, located on the east coast road. Cuddalore was called by
different names in the past. As Cuddalore was located very close to sea it was called
Cuddalore which means sea town. In Tamil language Kadal means sea, Ur - town. As it
was located on the - Junction of the rivers Gadilam and Paravanar it was called Gudalur.
(In Tamil Gudal means junction). In the seventeenth century Muslims called this place
as "Islamabad" which meant habitation of the Muslims. Pondicherry was located about
22 kilometers north of Cuddalore on Cuddalore- Marakkanam road. Portonovo and
Chidambaram were about 30 and 50 kilometers South of Cuddalore respectively.
Tiruvendipuram was about two kilometers west of Tiruppapuliyur on the land route
leading
to
Palur,
Nellikuppam
and
Panruti
in
South
Arcot.
(http://ietd.inflibnet.ac.in/bitstream/10603/822/7/07_chapter%201.pdf).
3.2
Geographical Location of the District
Geographical area of Cuddalore district is 3678 sq. km. and includes 681 village
panchayats, 16 town panchayats, 13 panchayat unions and 5 municipalities. On the
Revenue Side, it includes 3 revenue divisions, 6 Taluks respectively and 896 revenue
villages. It is bounded on the east side by the Bay of Bengal, on the north side by
Viluppuram District, on the south side by Nagapattinam District and on the west side by
Perambalur District. Cuddalore district lies between 11º5” and 12º30” of the northern
latitude and 78º37” and 80º eastern longitudes (Jayanthi, 2011). Cuddalore was bound
by Southern Pennayar on the north. Cuddalore old town on the south, Bay of Bengal on
the East and, Tiruppapuliyur and Mount Capper on the West and South West. River
146
Gadilam bisects the town of Cuddalore. All the rivers of the District flows from west to
east into the Bay of Bengal. The rivers in the District are Thenpennaiyar, Kedilam,
Vellar, Manimuthar and Kollidam. Most of the rivers are dry and flooded only during
the Monsoon periods. (www.cuddalore.tn.nic.in).
Cuddalore was a cluster of villages. Cuddalore old town consisted of fourteen
villages viz.
Brookspet, Galow Parachuri, Ghori, Kinchinpettai, Kodikalkuppam,
Komarappachetti, Agraharam, Malumiar-pettai, Manjinipalayam, Sanarapalayam,
Singarathoppu Sonagankuppam, Suthukulam and Vasanthrapalayam. All these villages
lay south of the river Gadilarn. Among these villages Ghori, Singarathoppu and
sonagankuppam were Island villages. They were surrounded by the two branches of
Gadilam and Bay of Bengal. Cuddalore Old Town was located on the estuaries of
Gadilam and Paravanar rivers, in north latitude 11o 43' and East longitude 79o 45' on
the bank of the river or back water which connects the Gadilam and Paravanar
rivers and. about 1.25 miles due south of Fort St. David.
Cuddalore New Town consisted of villages such as Semmandalam,
Manjakuppam, Vilwarayanatham, Uppalavadi, Davanampatanam, Vannarapalayam,
Pudupalayam,
Sorakalpet,
Udaramanikam,
Cuddalore,
Tiruppapuliyur
and
Vandipalayam lay south of river Gadilam. Devanampatnam where the Fort St. David
was located was a coastal village. The total area of the Cuddalore municipality was
13.33 sq. miles. (http://ietd.inflibnet.ac.in/bitstream/10603/822/7/07_chapter%201.pdf).
147
Map -2:- East Coast Road of Tamil Nadu
148
3.3
Seasons and Climate of the Study Area
The district has peculiar climate and receives rainfall in all the seasons, which
represent 31008.00 mm in 2007-2008. The maximum precipitation is contributed by the
North East Monsoon 16711.40 mm followed by the South west monsoon 7547.80 mm.
(Annual employment report of Cuddalore district in the state of Tamil Nadu for the
year: 2007-08).
3.4
Rivers of South Arcot District
The main rivers in South Arcot district are Coleroon (Kollidam), Vellar,
Paravanar, Gadilam, Penniar and Chengee.
Coleroon or Kollidam, which serves as drainage for surplus flood in river Kaveri
flows into the sea about three or four miles South of Portonovo (Parangipettai). The
Chengee river, also known as the Varahanadi originates from the Naranamangalam tank
in the Tindivanam taluk. After passing Chengee, it receives the water of the Tondayar
and Pompayar and mingles with sea about two miles near Ariankuppam and Chinna
Virampatanam in Pondicherry.
It deserves to be mentioned that the rivers of South Arcot were used for
irrigation as well as transportation of goods. The rivers flowing across Cuddalore
such as Gadilam, Penniar, and Paravanar were useful for enriching the ground water
though they
were flooded during rainy seasons. The rivers contributed to the
urbanization of Cuddalore through transportation and irrigation. However, they also
served as a source of de- urbanization due to floods.
3.5
Development of Transportation (Roads and Railways) in the study
area
As mentioned earlier, transportation of men and materials through land and
water to different parts of the world accelerated the process of urbanization of
Cuddalore. The transportation works such as construction of bridges and roads in the
19th century by the British rulers are worth mentioning for the development of
Cuddalore. While waterways played significant role in transportation in the 16th
149
century, roads and railways were the main source of transportation in the 19th century
as far as Cuddalore was concerned.
3.5.1 Cuddalore
Cuddalore district lies between 11º5” and 12º30” of the northern latitude and
78º37” and 80º eastern longitudes (Jayanthi, 2011). Cuddalore was bound by
Southern Pennayar on the north. Cuddalore old town on the south, Bay of
Bengal on the East and, Tiruppapuliyur and Mount Capper on the West and
South West. River Gadilam bisects the town of Cuddalore. All the rivers of the
District flows from west to east into the Bay of Bengal. The rivers in the District
are Thenpennaiyar, Kedilam, Vellar, Manimuthar and Kollidam. Most of the
rivers
are
dry
and
flooded
only
during
the
Monsoon
periods.
(www.cuddalore.tn.nic.in).
3.5.2 Chidambaram
Chidambaram is the taluk headquarters, located at about 250 km south of
Chennai on east coast road [ECR] in Cuddalore district of Tamilnadu state of
southeastern India extending 11.24°N and 79.44°E. It is a coastal taluk having
three panchayat unions, Keerapalayam, Melbhuvanagiri and Portonovo.
Chidambaram is the most important pilgrim center of the country, and is blessed
with the Lord Nataraja temple. The temple town is also known as Bhuloka
Kailash and Lord Nataraja, a cosmic dancer, represent the ‘Aakash’ form which
is one among the ‘Pancha Boothas’. (Badrinarayanan, 2009).
Chidambaram is situated in the Kollidam River valley in Tamil Nadu at an
average height of 3 meters. It is tehsil (city that serves as headquarters)
headquarters and municipality in the Cuddalore district. The distance from
Chennai by rail is 240 kilometers and from the coast is 11 kilometers. As per the
census of 2001, the population of Chidambaram was 58968. This place has a
tropical climate with temperature of around 20 degrees Celsius in the winter and
around 37 degrees Celsius in the summer. The ideal time to visit Chidambaram
is from September to February in the winter. (Badrinarayanan, 2009).
150
3.5.3
Bhuvanagiri
Bhuvanagiri is located at 11o28’N 79o38’E / 11.47oN 79.63oE. It has an average
elevation of 11 metres (36 feet). Bhuvanagiri is located 7 km from the temple
town Chidambaram. Bhuvanagiri is the birth place of the famous saint of South
India, Sri Raghavendra Swami. The town is also close to the birth place of Saint
Ramalinga Adigalar (town Maruthur).
The word Bhuvanagiri is a combination of two Tamilized-Sanskrit words Bhuvanam (means World) and Giri (means Mountain or Unmovable). Hence,
the name Bhuvanagiri can mean "the place (world) that does not move".
Bhuvanagiri is internally referred to by the local population as "Mel
Bhuvanagiri" (Western Segment) and "Kizhl Bhuvanagiri" (Eastern Segment).
Agriculture is the main occupation of more than 3/4th of the town's population
and they depend on it. Rice is the major cultivated crop, followed by Black gram
and Green gram. These set of crops are cultivated in a type of land known in
Tamil as NanSei (means wetland cultivation).Other minor crops like Finger
Millet (Ragi in Tamil), Pearl millet (Kambu in Tamil), Corn (Makkaa cholam in
Tamil), Thoor dhal (Thovaram parupu in Tamil), Sesamum (yel in Tamil) and
redgram also grown around this town. These set of crops are cultivated in a type
of land known in Tamil as PunSei. A famous river Vellaru (a tributary of river
Cauvery) provides water for irrigation.
The town is also famous for handloom products (such as lungies, hand kerchiefs,
saris, dhotis, etc.). It is also known for its Silk saris and Silk Textiles which are
famously referred to as "Bhuvanagiri Pattu". A road bridge (over river Vellaru)
in Bhuvanagiri connects the roadway between Cuddalore (in the north) and
Chidambaram (in the south). (www.wikipedi.com)
3.5.4
Nagapattinam district
The District of Nagapattinam has been carved out as a separate district due to
bifurcation of Thanjavur district. According to this division, six taluks namely
Sirkazhi, Tharangampadi, Mayiladuthurai, Valangaiman, Nagapattinam and
Vedaranniyam were detached from their parent district i.e. Thanjavur to form
151
this new district. The earlier history of this district is more or less the same as of
its parent district i.e. Thanjavur being its part till recently. Tamil and Telugu are
the main languages spoken in the district. (Centre for Agricultural and Rural
Development Studies (CARDS) Tamil Nadu Agricultural University, 2008).
The Nagapattinam district lies on the east coast to the south of Cuddalore district
and another part of the Nagapattinam district lies to the south of Karaikkal and
Tiruvarur districts. Its northern boundary is about 75 Km southwards from the
Head Quarters of the Cuddalore district. Thanjavur district and Tiruvarur district
flank it on the west and on the south and east it is bordered by the Bay of Bengal
between Northern Latitude 10.10' and 11.20' East Longtitute 79.15' and 79.50'.
The general geological formation of the district is plain and coastal.
Cauvery and its offshoots are the principal rivers.
The
Rising in the Coorg
Mountains, this river bifurcates about nine miles at the west of Trichy into two
branches, of which the northern one takes the name of Coleroen and the southern
one retains that of the Cauvery. The district is the most part of a flat plain,
slopping very gently to the sea on the east. The total geographical area of the
Nagapattinam district is about 3536.38 Sq.km. (Centre for Agricultural and
Rural Development Studies (CARDS) Tamil Nadu Agricultural University,
2008).
Community Development Blocks in the district are: Sirkazhi, Kollidam,
Sembanarkoil,
Kuttalam,
Mayiladuthurai,
Thirumarugal,
Nagapattinam,
Kilvelur, Talanayar, and Vedaranniyam. The Nagapattinam district is made up
the 7 Taluks, of Nagapattinam, Kilvellore, Tirukkuvalai, Vedaranniyam,
Mayiladuthurai, Sirkazhi and Thrangampadi, 11 Blocks and 497 Villages. As
regards the hierarchy of administrative arrangement, there are 3 Municipalities,
10 Town Panchayats and 433 Village Panchayats in the district. (Centre for
Agricultural and Rural Development Studies (CARDS) Tamil Nadu Agricultural
University, 2008).
The district is situated in the deltaic region of the famous river Cauvery and criss
crossed by lengthy network of irrigation canals. Kollidam River forms the
northern boundary of the district, whereas Arasalar, Tirumalairajanar, Vettar and
152
Vennar rivers drained the other parts of it. These all rivers are tributaries and
branches of the river Cauvery. (Centre for Agricultural and Rural Development
Studies (CARDS) Tamil Nadu Agricultural University, 2008).
3.5.5 Sirkazhi taluk
Sirkazhi is a taluk, placed in Nagapattinam district of the Tamil Nadu state. The
headquarters of the taluk is Sirkazhi town. It is located at 11°14′N 79°44′E to
11.23°N
79.73°E.
It
has
an
average
elevation
of
4m
(13 ft).
(www.wikipedia.com). According to the 2001 census, the taluk of Sirkazhi had a
population of 292,162 with 146,186 males and 145,976 females. There were 999
women for every 1000 men. The taluk had a literacy rate of 72.28. The total
number of households was 66,089. (http://en.wikipedia.org/wiki/Sirkazhi_taluk).
3.5.6 Tharangambadi (Tranquebar)
It is a panchayat town in Nagapattinam district in the Indian state of Tamil Nadu,
15 km north of Karaikal, near the mouth of a distributary of the Kaveri River. Its
name means “place of the singing waves”. It was a Danish colony from 1620 to
1845, and in Danish and some other European languages it is known as
“Trankebar” or “Tranquebar”. Tharangambadi is located at 11.03oN 79.84oE.
The earliest reference to Thrangampadi occurs in a 14th century inscription,
mentioning the place as Sadanganpade. Tranquebar was founded by the Danish
East India Company in 1620, when a factory (commercial settlement) was
opened and a fort, known as Fort Dansborg, was built by a Danish captain
named Ove Gjedde. This fort was the residence and headquarters of the governor
and other officials for about 150 years. It is now a museum hosting a collection
of artifacts from the colonial era. (www.wikipedia.com).
The present study was carried out to determine the environmental effect of East
Coast Road between Cuddalore and Tharagambadi. In order to assess the impact,
the following aspects were studied in detail.

The current environmental status in Cuddalore and Tharagambadi along the
South East Coast Road.

Water quality assessment -surface and ground water along the East Coast Road.
153

Air quality with reference to SPM, RSPM, SO2, NOx, CO along the East Coast
Road.

Noise pollution along the East Coast Road.

Flora and faunal status.

Socio-economic status.
Table – 13:- Lists of villages and towns passing through the East Coast Road from
Cuddalore and Tharagambadi
S.
No
Name of the
Villages/Town
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Cuddalore Old Town
Kudikadu
Iechangadu
Karaikadu
Sengolikuppam
Semmanguppam
Songanchavadi
Poondiyankuppam
Allampakkam
Kezhupoovarikuppam
Kallukadaimedu
Mettupalayam
Periyapattu
Silambimangalam
Puthchattiram
Sambantham
Athiyanallur
Sathapadi
Anayankuppam
B-Mutlur
Manjakuzhi
Thambikunalampattinam
Bhuvanagiri
Keerapalayam
Vayalur
Lalpuram
Manalur
Chidambaram
Siluvaipuram
Vishnupuram
Ammapettai
Osupur
Kadavacheri
Pillaimuthupillaichavady
Vallampadugai
Lateral
Distance from
ECR(m)
2
3
3
3
4
5
4
4
6
3
2
6
5
6
4
5
3
1
5
3
2
3
15
5
6
5
4
3
7
10
12
10
12
10
1
S.
No
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
154
Name of the
Villages/Town
Kollidam
Thanirpandal
Thekkal
Kannangkuppam
Puthur
Erukkur
Sirkazhi
Sattanathapuram
Annaperumala Kovil
Arasur
Senthangudi
Velanthirasamuthram
Thirumulaivasal
Karaimedu
Thennalkudi
Kathiruppur
Allivilagam
Natarajapillaichavadi
Annaperumal Kovil
Karurendhinathapuram
Thalachekaddu
Poonthalai
Annavaranpettai
Akkurmukkutu
Thirukadiyur
Singanoodai
Kazhiappanallur
Anathamangalam
Erukkatanjeri
Narayananyakachavadi
Ozhumangalam
Kandanchavadi
Sathankudi
Tharagambadi
Lateral
Distance from
ECR(m)
10
7
15
1
21
12
15
13
15
12
9
15
10
12
13
12
15
15
14
9
9
8
8
10
8
12
12
15
14
12
2
10
½
½
Table – 14:- List of villages passing through the East Coast Road Cuddalore and
Tharagambadi
S.No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
VOW
Karaikadu
Periyan Kudikadu
Kullanchavadi
Poondiyankuppam
Sathapadi
Kothattai
Manokovil
Ramamathakupam
Pandaravadugai
VOR
Kudikadu
Salainagar
Iechangadu
Semmanguppam
Chenguchapady
Songanchavadi
Allampakkam
Kezhupoovarikuppam
Kallukadaimedu
Mettupalayam
Ayampet
Periapet
Samiarpettai
Puthchattiram
Chinnakunatti
Periakummatti
Sammantham
B-Mutlur
Parangipettai
Manjakuzhi
Arigna Anna nagar
Bhuvanagiri
Keerapalayam
Melamuzhingaladi
Lalpuram
Ambalathatikuppam
Vayalur
Silvaipuram
Koopiduvankuppanar
Melachallai
Thennalkudi
Kathiruppur
Sempathaniruppu
Allivilagam
Natarajapillaichavadi
Karuvendhimathapuram
Thalachangedu
Akkoor
Poonthalai
Thirukadayur
Kahiappanallur
Ananthamangalam
Narayanachavadi
Erukkatanjeri
Ozhumangalam
VOE
Panchakuppam
Sengolikuppam
Tirchopuran
Iyampettai
Poonchimedu
Andikuzhi
Parangipettai
Anaiyarkuppam
Thambikunalanpatt
Thiruthampalayam
Aathivaranganallur
Thiruvengadu
Alangadu
Poompuhar
Melaperumpallam
Chinagudi
Singanoodai
Tharangampadi
VOR: Villages on the Roadside; VOE: Villages on the East of the East Coast Road;
VOW: Villages on the West of the East Coast Road relevant to/ dependent on the ECR
155
Table – 15:- List of coastal checkposts along East Coast Road
Cuddalore and Tharagambadi
S.No.
Checkposts Name
1
Allampakkam
2
Puthuchattiram
3
Kollidam( Anaikaranchatram)
Table – 16:- Populations of the villages on the East Coast Road
S.No.
VOR
Population
1
Salainagar
-
2
Kudikadu
500
3
Karikadu
500
4
Sengolikuppam
3000
5
Semmangakuppam
2000
6
Songanchavadi
500
7
Allampakkam
3000
8
Kezhupoovarikuppam
4000
9
Kattukadumedu
10
Mettupalayam
2000
11
Ayampet
2000
12
Periapet
1000
13
Puthuchattiram
1000
14
Chinnakunatti
1500
15
Periakummatti
1000
16
Mutloor
5000
17
Manjakuzhi
18
Thambikunalampattinam
19
Bhuvanagiri
20
Keerapalayam
5000
21
Lalpuram
1500
250
<1000
1000
19000
Total
54,750
156
Table - 17:-Populations of the villages on the East Coast Road
S.No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
VOR
Population
Vishnupuram
2000
Ammapettai
5000
Osupur
2000
Pillaimuthupillaichavady
1800
Vellampadugum
6000
Periakummatti
1000
Kollidam
50000
Thekkal
2000
Thannerpandhal
6000
Puthur
6000
Krukkur
2000
Arasur
2000
Senthangudi
2000
Velanthirasamuthuram
5000
Sirkazhi
55000
Sattanathapuram
1000
Melachalli
2000
Karaimedu
5000
Thennalkudi
1500
Kathiruppur
6000
Sempathaniruppu
6000
Allivilagam
2000
Natarajapillaichavady
4000
Karuvendhimathapuram
2000
Thalachangedu
1200
Akkoor
5000
Poonthalai
1500
Thirukadayur
5000
Kaliappanallur
600
Ananthanangalam
1000
Narayannayakachavadi
1800
Erukkatenjeir
6000
Ozhumangalam
1800
Kandanchavadi
300
Sathankudi
3000
Tharangampadi
60000
Total
264000
157
Station1: Cuddalore OT
Station2: Cuddalore OT Pond
Station3: Iechangadu (SIPCOT)
Station4: Poondiyankuppam
Station6: Periyapattu
Station12: Chidambaram Town
Plate – 1:- Selected sampling stations on ECR
158
Station13: Chidambaram Outer
Station16: Kollidam DS
Station15: Kollidam US
Station18: Sirkazhi
Station 19: Sattanathapuram
Station 20: Anna perumala kovil
Plate – 2:- Selected sampling stations on ECR
159
Station21: kathiruppu
Station22: Allivilagam
Station26: Akkurmukkutu
Station 27: Thirukadaiyur (west)
Station28: Thirukadaiyur (East)
Station30: Tharangambadi
Plate – 3:- Selected sampling stations on ECR
160
3.6
Statistical Analysis
The data was subjected to one-way analysis of variance (ANOVA) (Armitage
and Berry, 1994). To evaluate the relationship between air pollutants and
meteorological parameter, Person’s correlation coefficient values (r) were calculated at
each monitoring site using SPSS software (SPSS Inc. version 10.0) for assessing the
significance of quantitative changes in different parameters.
3.7
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 beings or other living
organisms or environment”.
Air samples were collected as per the methods prescribed by the National
Ambient Air Quality Standards (CPCB, 1998) New Delhi for ascertaining the ambient
air quality in the city. The Gazette of India notification dated May 20, 1994.
This study was undertaken to investigate the quality of air pollution in East
Coast Road. The study was carried out from February 2009 – January 2010. Table 18
shows the four different seasons followed by studied period. Air samples were
collected from three different seasons such as winter, summer and monsoon).
Table – 18:- Four different seasons for one year study
S.No.
Season
Month
1
I
February - April
2
II
May – July
3
III
August - October
4
IV
November - January
Sampling sites were selected to represent Industrial zone, Residential zone,
Commercial zone and Sensitive zones. To study the quality of air, four common
pollutants were taken into consideration. They were suspended particulate matter,
sulphur dioxide and oxides of nitrogen. The sampler was kept at a height of 10 meter
161
above and 5 meter away from the road, and the observations were noted for each 8
hours and averaged for 24 hours. Samples were collected at the selected sites for a
period of about four days in a week and four weeks in a month. The standards
prescribed by the National Ambient Air Quality Standards (NAAQS) were given in
(Annexure 1 & 2). The computed values of AQI were compared with the rating scale
(Table 29) to assess the degree of pollution in the ambient air. The details of sampling
stations are shown in Table 21 along the East Coast Road.
3.7.1
Instruments used for air sampling
Respirable Dust Sampler APM-451 instruments have been used for collecting
Suspended Particulate Matter (SPM), Respirable fraction (<10 microns) and
gaseous pollutants like SO2 and NOx.
The Instruments used for analysis of the samples collected during the field
monitoring are given in Table-.19
Table - 19:- Instruments used for analysis of samples
Sl. No
Instrument Name
Parameters
1
Spectrophotometer
SO2, NOx
2
Electronic Balance
TSPM, SPM, RPM
162
Map - 3:- Selected air sampling stations on ECR
163
3.7.2
Methods of air sampling and analysis
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 1050C 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
to 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 beginnings and at the end of the sampling period, the flow rates were
noted and the average flow rate was calculated; 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 1ts/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). Table 20 shows the methods used for ambient air
quality monitoring.
Table – 20:- Methods used for ambient air quality monitoring
Sl.
No.
1
Parameter
Technique
TSPM
Respirable Dust Sampler (Gravimetric method)
2
RPM
Respirable Dust Sampler (Gravimetric method)
3
SO2
Modified West and Gaeke
4
NOx
Jacob & Hochheiser
164
3.7.3
Air Quality Index
The ambient air quality data was processed for air quality index (AQI). It was
calculated for summer, winter and monsoon. The average of the four samples
in each month was used for calculating of AQI (Bhaskaran and Rajan, 2001).
The ambient air sampling was conducted at the sampling locations oxides of
sulphur (SOx), oxides of nitrogen (NOx), and Suspended Particulate Matter
(SPM) were collected during study period. The classification of air quality
index was carried out according to CPCB (1996).
AQI (air quality index) was then calculated with the concentration values using
the following equation (Rao & Rao, 1998; Chauhan, 2010). AQI = 1/3 [(SO2)/S
SO2 + (NOx)/S NOX + SPM/S SPM] × 100.
3.8
Noise Assessment
3.8.1
Field studies
A study was carried out to assess the existing status of noise levels and its
impacts on the environment with an expansion of the East Coast Road between
Cuddalore and Tharagambadi. Ambient noise levels were measured at different
locations selected on the basis of land use such as silence, heavy traffic,
residential and silence zones. This study was mainly intended to measure the
noise level in ECR locations and hence the locations were chosen so as to
represent different zones within a Residential zone, Commercial zone,
Industrial zone, Silence zone and heavy traffic zone. The details of the selected
location are given in Table 22 shown the sampling stations of the East Coast
Road.
3.8.2
Ambient noise level
Samples were collected from for four different seasons Summer-S1, Premonsoon-S2, Monsoon-S3, Post-monsoon-S4 (Season I: February -April;
Season II: May – July; Season III: August - October; Season IV: November January). Noise data was collected during Day and Night time at 20 locations
of East Coast Road by using Digital Sound Level Meter (Lutron SL-4001). The
165
standards prescribed by the Ambient noise Quality Standards set by the CPCB
were given in (Annexure 3).
3.8.3 Calculation method
Noise level measurements were recorded at a distance 3 meter curb side of road
in this study. For data collection, each hour was divided into the intervals of 15
minutes and observations were taken at an interval of 15 seconds. Thus, a total
of 240 observations were taken in an hour.
3.8.3.1 L10, L50, L90, Lmax, Lmin, Leq and TNI
From these observations, Leq, Lmax, Lmin, L10, L50, L90 and Total Noise Index
have been calculated. Statistical evaluation was done on the results of the noise
measurements. A variance analysis procedure was applied to the data through
SPSS software programme.
3.9
Water Sampling and Analysis
3.9.1 Sample collection
Ground and River samples were collected from the selected places along East
Coast Road. These samples were collected in pre-cleaned polypropylene
bottles with necessary precautions (Brown et al., 1974). Parameter like
temperature, pH, EC, DO were immediately recorded. Other samples in well
labeled and tightly capped containers were brought to the laboratory in ice-box.
Samples were collected in four different seasons Summer-S1, Pre-monsoon-S2,
Monsoon-S3, Post-monsoon-S4 (Season I: February -April; Season II: May –
July; Season III: August - October; Season IV: November - January). Samples
were collected from twenty nine different locations during the period from
February 2009 – January 2010. Water samples were collected and analysed as
per standard methods (APHA, 2005).
3.9.2 Sample Analysis
The following parameters were analysed: Turbidity, Total alkalinity, Total
hardness, Total dissolved solids, Total solids, Total suspended solids, Chloride,
166
Biochemical Oxygen Demand, Chemical Oxygen Demand, Calcium,
Magnesium, Sulphate, Nitrate, Fluoride, Sodium and Potassium. For heavy
metal analysis, the water samples were collected in pre-cleaned polypropylene
bottles and acidified with concentrated ultra pure nitric acid (to pH 2) for
preservation soon after their collection. (APHA, 1991). Table 23 shows the
sampling stations selected along the East Coast Road.
3.9.3 Water Quality Index
A water quality index is a means to summarize large amounts of water quality
data into simple terms (e.g., good) for reporting to management and the public
in a consistent manner (Kumar and Dua, 2009).
A number of indices have been developed to summarize water quality data in
an easily expressible and easily understood format. The WQI which was first
developed by Horton in the early 1970s is basically a mathematical means of
calculating a single value from multiple test results. The index result represents
the level of water quality in a given water basin, such as lake, river or stream.
The different statistical approaches were followed for analyzing water quality
data based on rank order of observations and factor analysis (Shoji et al., 1966,
Herkins, 1974). For the evaluation of water quality, WQI was applied to river
water as well as coastal water (Dojlido et al., 1994, Gupta et al., 2003 and
Avvannavar & Shrihari, 2007).
Water quality index is one of the most effective tools to communicate
information on the quality of any water body. WQI is a mathematical equation
used to transform large number of water quality data into a single number
(Stambuk-Gilijanovic, 1999). It is simple and easy to understandable for
decision makers about quality and possible uses of any water body (Bordalo
et al., 2001). It serves the understanding of water quality issues by integrating
complex data and generating a score that describes water quality status.
The standards for drinking purposes as recommended by WHO (WHO, 1993)
and BIS 10500 (Indian Standard, 1992) have been considered for the
calculation of WQI. There are three steps for computing WQI. In the first step,
each of the thirteen parameters (pH, Turbidity, TDS, Total hardness, Cl-, NO,
167
F, Mg, Ca and Fe) has been assigned a weight (W) according to its relative
importance in the overall quality of water for drinking purposes. In order to
calculate the Water Quality Index, all the 13 physico-chemical parameters have
been utilized (Harkins 1974, Tiwari et al., 1986, Patil et al., 2006). The
permissible values of various pollutants for drinking water (expressed in mg/l
except pH) recommended by the CPCB and Bureau Indian Standards have
been quoted (Annexure 4).
The Calculation Involves the Following Steps
A)
Weighting: The word weighting implies relative significance of each of the
factor in the overall water quality and it depends on the permissible level in
drinking water, as suggested by CPCB (Central Pollution Control Board, 20072008), and Bureau of Indian Standards (BIS: 10500, 1991). Factors which have
higher permissible limits are less harmful and have low weightings.
Therefore, WI=K/Sn
Where,
Wi - Unit weight of chemical factor,
K - constant of proportionality and is given as:
Sn - standard value of ith parameter
168
Map - 4:- Selected noise sampling sites on ECR
169
Map - 5: Selected Water sampling stations on ECR
170
B)
Rating scale: Each chemical factor has been assigned a water quality rating to
calculate WQI.
Qi = 100 [(Va-Vi)/(Vs-Vi)]
Where,
Va - average of measured values in the water sample for three months at one
place
Vs - Standard value of ith parameter
Vi - ideal value for pure water (0 for all parameters except pH and DO)
The above equation becomes: Qi = 100 (Va/Vs)
For dissolved oxygen (DO): The ideal value = 14.6 mg/l; permissible value = 6
mg/l, QDO = 100[(Va-14.6)/(6-14.6)].
For pH: The ideal value = 7.0; Max. Permissible value = 8.5, QpH = 100 [(Va7.0)/(8.5-7.0)]
Water Quality Index (WQI) = [∑ QiWi)/∑Wi]
Where,
∑(QiWi) -Qi(pH)XWi(pH) + Qi (DO) X Wi (DO) + ……+ Qi (Ca) X Wi(Ca).
∑Wi - Total unit weight of all chemical factors.
Using the water quality index, all the samples were categorized into the
following five classes: excellent (0 - 25), good (26 – 50), moderately polluted
(51 – 75), severely polluted (76 – 100) and unfit for human consumption
(above 100) based on their suitability (Swamalatha et al., 2007, Kalavathy et
al., 2011). According to Padmanabha and Belagali, 2005), 0 < WQI < 100
indicates that the water is fit for human use and 0 > WQI > 100 reflects its
unsuitability for use.
3.10
Biodiversity
An attempt to obtain a fairly comprehensive picture of biological resources of
East Coast Road; the study was made on diversity of Plants, Birds, Insects, Reptiles,
Amphibians, Mammals and Invertebrates.
171
3.10.1 Flora assessment
The flora of the study area was sampled by using standard quadrate method
near East Coast Road. The Herb layer estimation was carried out in a total of 8
quadrates on two different sites (east and west). The localities of quadrates and
transect were plotted in the Road map. The samples were collected in four
different seasons Summer-S1, Pre-monsoon-S2, Monsoon-S3, Post-monsoonS4 (Season I: February -April; Season II: May – July; Season III: August October; Season IV: November - January).
The study of flora involved
intensive sample survey of vegetation in the East Coast Road location applying
standard methods (Greig-smith 1983, Caustan 1988).
To examine the trees and shrubs quadrates of 25x25m and for herbs 2x2 m2
were laid. In each of the larger quadrates species and their number were
measured. In the sample quadrants the shrubs were also enlisted and
enumerated, at examined and the average was computed. In the smaller
quadrates the shrubs were also enlisted and enumerated. At each zone 4
quadrants were examined and the average was computed. In the smaller
quadrate (2x2m) herbs were enlisted and enumerated. Specimens of the plants
whose identify couldn’t be confirmed in the field were collected and preserved
following standard methods (Santapau, 1955). Density, Frequency, Frequency
class, Abundance and Abundance class were determined by using Standard
Methods.
3.10.2 Fauna assessment
The animal life of an area is dependent upon the vegetation and there are
countless relationships between the species composing and animal community.
Fauna assessment involves more problems than flora assessment by virtue of
the greater variety of animal types, their mobility and behaviors. Faunal
assessment provides a basis for determining relative abundance and evaluating
commons or rarity each species encountered. In the study area, the animal
survey was conducted in all the sampling sites along with the plants. The study
includes surveys of the animal communities such as aquatic organisms &
Terrestrial organisms such as annelids and mammals.
172
The study of fauna involved intensive sample survey along the East Coast
Road. To assess the animals, the area was covered intensively in 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).
3.10.2.1 Insects
The insects were identified the following methods.
3.10.2.1.1 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 to collect insects by net. The
insect were collected and transferred to a plastic container containing cotton
dipped in ethyl acetate and was properly labeled; the insects were preserved in
alcohol till sorting.
3.10.2.1.2 Pitfall traps
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.
3.10.2.1.3 Shake method
A sheet of size 5mx3m was spread under the thick shrub or small trees. The
shrub was shake or beaten vigorously for 10 minutes. Insects were collected
from the sheet and preserved in alcohol till sorting.
3.10.2.1.4 Light trap
A portable light operating on batteries was placed in the white sheet spread in
the middle of the plot and 1 hour at night in each locality. Insects were
removed from the spread sheet and preserved in alcohol till identifying.
173
3.10.2.1.5 All out search method
The sampling was done in different zones of East Coast Road. 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 labeled. The number of species were counted and not the
number of the individual species.
3.10.2.2 Birds
Since birds may be considered as indicators for monitoring and understanding
human impacts on ecological systems, attempt was made to collect quantitative
data on the group.
Sampling for birds was 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. The field glass (12X50) was used
for identifying the birds. Thus the samplings were done in East Coast Road for
2 hours in the morning for four seasons (Season I: February - April; Season II:
May – July; Season III: August - October; Season IV: November - January).
3.10.2.3 Vertebrate species
Visual encounter methods and Pellet and track method were used for vertebrate
species.
3.10.2.3.1 Point survey method
Observation was made in each zone for 15 minutes duration.
3.10.2.3.2 Road side counts
The observer traveled by motor vehicles from site to site, all sightings were
recorded (this was done both day and night).
174
3.10.2.3.3 Pellet and track counts
All possible animal tracks and pellets were identified and recorded
(Southwood, 1978).
3.10.2.4 Reptiles
Reptiles were recorded based on sightings and previous records. The number
of species were counted and not the number of the individual species.
3.11
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, recruitments, and conflict in lifestyles. (The
Sunday observer, 1987).
Questionnaire
In order to obtain the reaction of general public regarding socio-economic
status of East Coast Road, a questionnaire was prepared and was got answered by
people from 20 sampling sites. 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.
175
Table – 21:- Locations selected for the air quality measurements in
East coast road cuddalore and tharagambadi
S.No.
Code No.
1
2
3
4
5
6
7
8
9
10
11
12
L1
L2
L3
L4
L5
L6
L7
L8
L9
L10
L11
L12
Locations
Type of zones
Cuddalore OT
Iechangadu
Puthuchatram
Bhuvanagiri
Chidambaram town
Chidambaram bus stand
Thanirpandal Palayam
Puthur
Sirkazi Town
Akkurmukkutu
Thirukadiyur
Tharagambadi
Residential
Residential
Industrial
Commercial
Residential
Commercial
Residential
Residential
Residential
Residential
Commercial
Commercial
Table -22:- Locations selected for the noise level measurements in East
coast road cuddalore and tharagambadi
S.No. Code No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
L1
L2
L3
L4
L5
L6
L7
L8
L9
L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
L20
Locations
Cuddalore OT
Iechangadu
Sengolikuppam
Alapakkam Checkpost
Puthuchatram
Sambantham
B-Mutlur
Bhuvanagiri
Keerapalayam
Chidambaram town
Chidambaram bus stand
Chidambaram outer
Vallumpadugai
Kollidam Bridge
Sirkazi Town
Kathirrupu
Kathirrupu school
Thalachangadu
Thirukadiyur
Tharagambadi
176
Type of zones
Residential
Industrial
Industrial
Commercial
Residential
Residential
Residential
Residential
Residential
Residential
Commercial
Residential
Residential
Commercial
Residential
Residential
Silent
Residential
Residential
Residential
Table - 23:- Locations selected for water sampling site along in
East coast road Cuddalore and Tharagambadi
S.
No.
Code
No.
Ground water gauging
station
1
S-1
Cuddalore Old Town
Cuddalore
Eastern
2
S-2
Cuddalore Old Town (Pond)
Cuddalore
Western
3
S-3
Iechangadu
Cuddalore
Eastern
4
S-4
Poondiyankuppam
Cuddalore
Eastern
5
S-5
Kezhupoovarikuppam
Cuddalore
Eastern
6
S-6
Periyapattu
Cuddalore
Western
7
S-7
Puthchattiram
Cuddalore
Western
8
S-8
Sambantham
Cuddalore
Western
9
S-9
B-Mutlur
Cuddalore
Eastern
10
S-10
Bhuvanagiri Town
Cuddalore
Eastern
11
S-11
Keerapalayam
Cuddalore
Western
12
S-12
Chidambaram Town
Cuddalore
Eastern
13
S-13
Chidambaram Outer
Cuddalore
Western
14
S-14
Vallampadugai
Cuddalore
Eastern
15
S-15
Kollidam Upstream
Nagapattinam Western
16
S-16
Kollidam Down stream
Nagapattinam Eastern
17
S-17
Erukkur
Nagapattinam Eastern
18
S-18
Sirkazhi
Nagapattinam Western
19
S-19
Sattanathpuram
Nagapattinam Eastern
20
S-20
Annaperumala Kovil
Nagapattinam Western
21
S-21
Kathiruppur
Nagapattinam Eastern
22
S-22
Allivilagam
Nagapattinam Western
23
S-23
Karuthy
Nagapattinam Western
24
S-24
Thalachekaddu
Nagapattinam Western
25
S-25
Poonthalai
Nagapattinam Eastern
26
S-26
Akkurmukkutu
Nagapattinam Eastern
27
S-27
Thirukadiyur
Nagapattinam Eastern
28
S-28
Thirukadiyur
Nagapattinam Western
29
S-29
Tharagambadi
Nagapattinam Eastern
177
District
Road side
4.0 RESULTS AND DISCUSSIONS
4.1
Ambient air quality in the study area along the East Coast Road
The National Ambient Air Quality Standards (NAAQS, 1996 and NAAQ,
2009) are presented in Annexure 1 and 2. Both SO2 and NO2 were found to be within
the standards (NAAQS, 2009) in almost all the sites. SPM is not mentioned in
NAAQS 2009. Since the study was commenced in 2009 before publication of NAAQS
2009, only SPM was sampled. Hence, the ambient SPM values were compared into the
SPM standards prescribed NAAQS 1996. The 24 hr standard value for SPM was 500
µg/m3 and 200 µg/m3 for industrial area and residential area respectively.
If the roads were considered as industrial area, the ambient SPM values did not
exceed the standard (500 µg/m3) in any of the places. If considered as residential area,
the SPM values exceeded 300 µg/m3 in majority of the places considering the
individual pollutants, it may be concluded that the ambient air quality was not affected
by East Coast Road. However, when calculated the AQI (using 200 µg/m3 as the
standard for SPM), one place each was found to fall in SAP category during summer
and winter; 3 places in summer and 6 places in winter fall in HAP category; 7 places in
summer 5 places in winter and 8 in monsoon fall in MAP category; 1 in summer and 4
in monsoon fall in LAP category.
The above findings revealed that the pollution was very low and in monsoon
and high in winter. It is obvious that monsoon rainfall would scaverys the pollutants
from atmosphere. During winter, poor dispersal capacity of the atmosphere would
result in high pollution.
One way ANOVA tests revealed that the ambient air quality with reference to
SO2 and NOx differend significantly (at 5%) at different seasons while SMP did not
differ. From the above results, it may be concluded that, East Coast Road has not
affected seriously the air quality.
178
Table – 24:- SO2 concentration during the study period, in µg/m3
Location
L1
L2
L3
L4
L5
L6
L7
L8
L9
L10
L11
L12
Season
Mean
Max
Min
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
16.95
7.62
5.46
9.95
3.71
5.29
20.68
5.85
3.35
7.09
4.46
4.11
6.54
10.72
3.77
5.06
9.28
3.82
6.75
6.24
2.64
9.94
9.36
4.00
4.51
16.54
4.60
8.82
5.37
6.80
5.22
7.91
2.60
5.30
4.46
4.10
30.34
29.6
10.61
31.34
7.52
14.47
33.44
15.05
6.8
53.35
9.61
8.25
38.46
31.77
8.7
15.47
20.48
7.63
13.8
12.96
5.17
20.90
18.39
14.00
19.71
84.49
9.00
47.90
16.72
15.25
12.97
48.31
25.36
12.96
11.15
24.65
4.76
2.09
1.24
3.75
1.67
2.18
0.84
2.09
1.19
0.21
1.25
1.09
0.84
0.53
0.66
0.42
3.34
1.63
1.25
3.62
1.63
2.09
3.65
2.00
3.21
2.23
2.80
1.67
2.09
1.89
1.11
1.09
0.98
1.67
1.90
1.35
179
Table – 25:- NOx concentration during the study period, µg/m3
Location
L1
L2
L3
L4
L5
L6
L7
L8
L9
L10
L11
L12
Season
Mean
Max
Min
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
63.68
38.44
40.60
64.02
47.87
52.60
68.91
41.88
39.60
69.41
26.96
29.70
98.92
43.42
31.80
46.55
45.84
32.30
38.05
42.03
22.03
20.11
70.72
12.00
56.69
87.92
19.00
51.42
59.26
16.60
31.15
47.20
38.00
27.40
52.97
32.00
98.91
68.56
62.70
95.37
67.62
80.30
190.9
65.56
61.30
138.4
38.16
42.3
190.6
187.9
52.40
127.2
55.99
44.40
56.58
61.06
33.00
80.12
104.9
31.00
126.6
140.3
26.00
72.71
111.6
42.32
65.61
88.09
78.23
43.44
87.86
59.35
35.51
18.00
18.30
39.11
34.50
4.40
23.35
26.94
23.5
17.28
16.33
18.60
15.57
15.19
12.50
17.85
16.52
19.60
22.20
23.55
15.05
24.55
37.09
4.00
24.49
67.21
14.00
21.4
13.74
2.54
16.90
16.90
4.98
13.10
19.17
18.45
180
Table – 26:- SPM concentration during the study period, µg/m3
Location
L1
L2
L3
L4
L5
L6
L7
L8
L9
L10
L11
L12
Season
Mean
Max
Min
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
Summer
Winter
Monsoon
188.64
229.00
233.00
231.43
361.00
281.00
238.41
375.00
339.00
303.92
247.00
348.00
387.99
299.00
314.00
460.84
345.00
327.00
314.57
310.00
232.74
409.46
332.00
247.00
416.58
354.00
226.00
302.21
398.00
146.00
223.01
243.00
158.00
161.04
314.00
232.00
286.30
897.00
6o8.00
402.85
620.00
398.00
353.95
469.00
302.00
552.21
359.00
616.00
542.41
459.00
522.00
1900.0
542.00
584.00
624.78
422.00
417.60
739.69
701.00
354.00
563.72
601.00
327.00
540.21
809.00
869.32
699.23
432.00
398.24
327.09
831.00
682.35
98.18
57.00
42.00
96.19
206.00
206.00
113.28
119.00
60.00
142.61
63.00
150.00
257.93
141.00
160.00
212.08
152.00
187.00
49.68
165.00
81.00
127.41
142.00
169.00
171.00
156.00
171.00
165.03
185.30
56.32
54.21
72.00
69.32
77.05
20.00
42.35
181
Table – 27:- ANOVA for air samples
Pollutant
SO2
NOx
SPM
Sources of
variation
Between
Sum of
Squares
102.9
Degree of
freedom
2
Mean
Squares
51.43
Error
228.2
33
6.915
Total
331.1
35
58.345
Between
4214
2
2107
Error
8499
33
257.5
Total
12713
35
2364.5
Between
15922
2
7961
Error
184470
33
5590
Total
200390
35
13551
F
7.438
8.181
1.424
Table – 28:- Computed AQI values
Summer
Location
AQI
Rating
Scale
Winter
AQI
Rating
Scale
Monsoon
AQI
Rating
Scale
L1
63.73
MAP
57.35
MAP
58.03
MAP
L2
69.39
MAP
81.65
HAP
70.95
MAP
L3
71.42
MAP
82.38
HAP
74.4
MAP
L4
82.51
HAP
54.25
MAP
72.09
MAP
L5
108.55
SAP
72.38
MAP
67.15
MAP
L6
98.27
HAP
80.46
HAP
69.55
MAP
L7
71.09
MAP
71.77
MAP
49.11
LAP
L8
93.27
HAP
88.69
HAP
65.67
MAP
L9
66.59
MAP
102.51
SAP
47.5
LAP
L10
75.43
MAP
95.54
HAP
34.08
LAP
L11
52.32
MAP
63.46
MAP
43.25
LAP
L12
40.45
LAP
76.25
HAP
53.71
MAP
182
Table – 29:- Rating scale of AQI
AQI
value
0-25
26-50
Remarks
Clean air (CA)
AQI and Levels of Health Concern
The AQI is normal. The quality of air is good
and hence no cause for concern
Light air pollution The AQI is low. The quality of air is slightly
(LAP)
polluted and hence little care is to be taken
The AQI is moderately high. The quality of air
51-75
Moderate air
of air is moderately polluted and it is not good
pollution (MAP)
for sensitive groups of people with asthma
complaints
76-100
Heavy air
pollution (HAP)
The AQI is high. The quality of air is highly
polluted and hence major attention is required
to avoid further increase
The AQI is very high. The quality of air is
>100
Severe air
severely polluted and it is very unhealthy to
pollution (SAP)
children, asthmatics and people suffering with
bronchial diseases
(Rao and Rao, 1998; Chauhan, 2010)
4.2
Noise Pollution
The traffic noise was measured at the selected locations along East Coast Road.
Twenty sites were chosen randomly in East Coast Road. They included 9 sites under
residential zone, 2 sites under industrial zone, 4 sites under commercial zone and 3
sites under silent zone. The sampling sites were classified into 4 categories according
to the location. The list of Noise monitoring locations are presented in the Table 30.
The ambient air quality Standards with reference to noise set by the Central Pollution
Control Board are presented in Annexure 3. Day-time and Night-time, maximum,
minimum, mean value of Total Noise Index and Average Leq of noise are presented in
Tables 31 to 32 and Figure 6 to 21
183
4.2.1 Residential zone
The prescribed limit for Noise in a residential area is 55 dB(A) for Day-time
and 45 dB(A) for night time. None of the selected places of residential zone
had Leq less than prescribed limit both Day-Night time. The increase in noise
level can be attributed to various factors, such as the conditions and width of
the road, presence of building, the volume and structure of the traffic flow, age
of vehicles, the discipline and awareness of the drivers of the vehicles, the
engine of the vehicles its exhaust, brakes, horns, tyres and other parts.
In most of the residential selected locations, TNI exceeded 70 dB(A). The TNI
had a mean value of Day-Night 87.3 dB(A), Night-time 106.5 dB(A) . It
indicated that the TNI values were larger than Leq values. This is due to
overcrowded roadways with deteriorated surface and potholes, minimal traffic
management and honking behavior of drivers. Frequent misuse of horns was
the main reason for the high pollution level (TNI) at all the selected sites in
residential zone.
4.2.2 Industrial zone
The monitoring locations were selected based on proximity of roadways to
industrial units like gates, along the East Coast Road. The Day-time and Nighttime maximum noise level was highest at the B1 gate site, which is an
important entry and exit point of the Pharmaceutical Industry. Being an
industrial gate a number of security guards and officer are always present at
this location. Who are vulnerable to occupational noise exposure. A continuous
exposure may lead to hearing loss and noise induced permanent threshold shift
(NIPTS). Both Day-Night time resulted in high TNI levels above 70.0 dB(A).
The main reason for the high pollution level (TNI) at the selected sites was due
to different types of vehicles pass by such as buses, taxis and heavy loaded
vehicles.
184
4.2.3 Commercial zone
In commercial zone the maximum Day-time noise levels 109.8 dB(A) in C5
followed by 109.8 dB(A) in C3 were observed. The Night-time maximum
noise level 101.7 dB(A) was recorded in C3. The minimum Day-time noise
level 63.2 dB(A) and Night-time 71.7 dB(A) level were recorded in C5. The
standard for commercial zone duting Day-time is 65 dB(A) and Night-time is
55 dB(A). The average Day-time levels during the study period was 83.6
dB(A) while the Night-time level was 72.2 dB(A). The main reason for high
noise levels in the places was due to presence of many commercial
establishments, shops, etc are situated in these places. The roadsides are
encroached both by these commercial activities and the vehicles parked by the
customers visiting these places. This causes frequent traffic jams and
congestion. The lack of proper parking lots near the markets, along with poor
traffic management created severe noise emission throughout the Day. During
morning hours, bus drivers, to attract potential commuters, honk the horn
which produces high noise levels in all the commercial areas.
In most of the selected locations TNI exceeded 70.0 dB(A). It indicate that road
traffic noise in East Coast Road is defined by both high average levels of Leq
and high fluctuation, resulting in high TNI noise levels. It was found
overpopulated, with diverse commercial activities.
4.2.4 Silence zone
Silence zone is defined as an area up to 100 m around hospital, educational
institutions, temples, court, churches, mosques etc. Use of horns, loud speakers
and bursting of crackers is banned in these places.
Noise levels recorded in all the sites of silence zone exceeded the prescribed
standard levels of Day-time 50 dB(A), and Night-time 40 dB(A). The Day-time
average Leq value ranged from 83.0 dB(A) to 69.8 dB(A). The educational and
temple establishment in the area are located along transport route of the city.
The student in the educational institutes will get distracted, and lose their
concentrations in their studies. Blowing of air horns and high traffic and
185
congestion are the major cause of high noise level. The TNI in silence zone,
exceeded 78 dB(A) during day time and during night time. It indicated that the
TNI values were larger than Leq levels.
The t-Test was carried out between Day-Night noise levels in East Coast Road.
The results revealed that noise levels differed significantly between Day and
Night time. One way ANOVA was carried out for noise levels among the
selected sites, the result reveled that the differences in noise level at different
sites were not significant for Leq, Lmax and Lmin but significant fot TNI.
Statistical analysis test results are given in Table 33 and 34.
Conclusion
Transportation operations are major contributors to noise in roadways. Here
noise is created by the engine and exhaust system of vehicles, aerodynamic
friction, interaction between the vehicle and road system, and by the interaction
among vehicles. The results of this study reveal that the levels of noise
pollution in East Coast Road traffic is high exceeding limits set up by Central
Pollution Control Board of India. Type of zone, geographic features, landscape
and topography are factors on which noise emission and transmission depends.
The open areas have lower noise level during any time of the day because it
lacks dense human habitation, commercial establishment and hence has lower
vehicular flow. In contrast the well built up area with residential apartments,
shopping areas, have higher noise level due to more use of the roads alongside
it by all types of public, commercial and private transport vehicles. Based on
the noise survey it concluded that immediate mitigatory measures are required
to control the traffic noise emission. Suggestive control methodologies includes
control of noise at source of generation itself by employing techniques like
maintenance of automobiles, regular servicing and tuning of vehicles to reduce
noise levels, fixing of silencers to automobiles, two wheelers etc., The design
of the building incorporating the use of suitable noise absorbing material for
wall/door/window/ceiling will reduce the noise levels. The barrier may be
installed either close to the source or receiver. Development of green belt can
attenuate the sound levels. Other measures include raising the awareness
among local community, more “No Horn signs” and strict enforcement of laws.
186
120
100
dB(A)
80
60
40
20
0
Day
Night
Day
Night
Lmax
Lmin
Season I
A1
A2
A3
A4
A5
A6
A7
A8
A9
Fig-6:- Season -I Residential Zone Noise levels along the East Coast Road
187
120
100
dB(A)
80
60
40
20
0
Day
Night
Day
Lmax
Night
Lmin
Season I
B1
B2
Fig-7:- Season-I Industrial Zone Noise levels along the East Coast Road
188
120
100
dB(A)
80
60
40
20
0
Day
Night
Day
Lmax
Night
Lmin
Season I
C1
C2
C3
C4
C5
C6
Fig-8:- Season-I Commercial Zone Noise levels along the East Coast Road
189
100
90
80
70
dB(A)
60
50
40
30
20
10
0
Day
Night
Day
Lmax
Night
Lmin
Season I
D1
D2
D3
Fig-9:- Season-I Silent Zone Noise levels along the East Coast Road
190
120
100
dB(A)
80
60
40
20
0
Day
Night
Day
Night
Lmax
Lmin
Season II
A1
A2
A3
A4
A5
A6
A7
A8
A9
Fig-10:- Season-II Residential Zone Noise levels along the East Coast Road
191
120
100
dB(A)
80
60
40
20
0
Day
Night
Day
Lmax
Night
Lmin
Season II
B1
B2
Fig-11:- Season-II Industrial Zone Noise levels along the East Coast Road
192
120
100
dB(A)
80
60
40
20
0
Day
Night
Day
Lmax
Night
Lmin
Season II
C1
C2
C3
C4
C5
C6
Fig-12:- Season-II Commercial Zone Noise levels along the East Coast Road
193
120
100
dB(A)
80
60
40
20
0
Day
Night
Day
Lmax
Night
Lmin
Season II
D1
D2
D3
Fig-13:- Season-II Silent Zone Noise levels along the East Coast Road
194
120
100
dB(A)
80
60
40
20
0
Day
Night
Day
Night
Lmax
Lmin
Season III
A1
A2
A3
A4
A5
A6
A7
A8
A9
Fig-14:- Season-III Residential Zone Noise levels along the East Coast Road
195
120
100
dB(A)
80
60
40
20
0
Day
Night
Day
Lmax
Night
Lmin
Season III
B1
B2
Fig-15:- Season-III Industrial Zone Noise levels along the East Coast Road
196
120
100
dB(A)
80
60
40
20
0
Day
Night
Day
Lmax
Night
Lmin
Season II
C1
C2
C3
C4
C5
C6
Fig-16:- Season-III Commercial Zone Noise levels along the East Coast Road
197
100
90
80
70
dB(A)
60
50
40
30
20
10
0
Day
Night
Day
Lmax
Night
Lmin
Season III
D1
D2
D3
Fig-17:- Season-III Silent Zone Noise levels along the East Coast Road
198
120
100
dB(A)
80
60
40
20
0
Day
Night
Day
Night
Lmax
Lmin
Season IV
A1
A2
A3
A4
A5
A6
A7
A8
A9
Fig-18:- Season-IV Residential Zone Noise levels along the East Coast Road
199
120
100
dB(A)
80
60
40
20
0
Day
Night
Day
Lmax
Night
Lmin
Season IV
B1
B2
Fig-19:- Season-IV Industrial Zone Noise levels along the East Coast Road
200
120
100
dB(A)
80
60
40
20
0
Day
Night
Day
Lmax
Night
Lmin
Season IV
C1
C2
C3
C4
C5
C6
Fig-20:- Season-IV Commercial Zone Noise levels along the East Coast Road
201
120
100
dB(A)
80
60
40
20
0
Day
Night
Day
Lmax
Night
Lmin
Season IV
D1
D2
D3
Fig-21:- Season-IV Silent Zone Noise levels along the East Coast Road
202
Table - 30:- Noise monitoring locations in East Coast Road
S.No.
1
Types of Zone
Area Code
2
Residential
3
Industrial
Commercial
4
Silent
Location
A1
Cuddalore OT
A2
Allampakkam
A3
Puthuchattiram
A4
Sambantham
A5
Keerapalayam
A6
Chidambaram Outer
A7
Vallumpadugai
A8
Kathirrupu
A9
Thalachangadu
B1
Iechangadu
B2
Sengolikuppam
C1
B-Mutlur
C2
Bhuvanagiri Town
C3
Chidambaram Town
C4
Sirkazi Town
C5
Chidambaram Bus Stand
C6
Kollidam Bridge
D1
Kathirrupu School
D2
Thirukadiyur
D3
Tharagambadi
Table - 31:- Average of Leq Noise data for East Coast Road
Area
Code
Average of Leq
Permissible Limit db(A)
Category
Day–time
Night-time
Day-time
Night-time
A
Residential
77.9
73.4
55
45
B
Industrial
85.9
73.5
75
70
C
Commercial
83.6
72.2
65
55
D
Silent
83.0
69.8
50
40
203
Table - 32:- Average of TNI Noise data for East Coast Road
Area
Code
Category
TNI average of Day-Night
Season I
Season II
Season III
Season IV
A
Residential
87.3
83.0
82.4
70.4
86.8
87.9
81.9
106.5
B
Industrial
91.0
89.9
70.3
80.2
92.7
88.4
67.6
76.6
C
Commercial
84.9
79.9
80.4
69.9
90.7
89.4
71.5
84.1
D
Silent
89.5
91.7
78.3
79.9
93.4
99.8
80.8
87.0
Table - 33:-‘t’ test between the Time and Noise
S.No
Time
Standard
Standard
Statistical
Deviation
Error
Interference
73.000
9.5609
1.0689
67.214
10.8981
1.2184
12.0785
1.3504
t=3.078
12.8149
1.4327
P>0.783
7.8139
.8736
t=4.203
8.6875
.9713
P>0.367
14.796
1.654
t=742
17.214
1.9246
P>0.824
X
Day
1
Leq
(n:80)
Night
(n:80)
t=3.570
P<0.144
Significant
Day
2
Max
(n:80)
84.292
Night
78.233
(n:80)
Day
3
Min
(n:80)
57.744
Night
52.253
(n:80)
Day
4
Index
(n:80)
84.431
Night
82.549
(n:80)
204
Table - 34:- One Way Analysis of Variance among the place with Regard to Noise
S.No
Source
Leq
1
156
Within
Groups
159
Between
Groups
Within
Groups
Min
3
Between
Groups
Within
Groups
TNI
4
3
Between
Groups
Max
2
Df
3
156
Sum of
Squares
Mean
Square
17943.302
1893.732
78.603
G2=61.958
F=1.404
G3=76.525
P<0.235
Significant
G4=66.915
G1=84.529
7945.228
18022.285
2648.409
G2=71.620
G3=90.560
159
3
156
25967.514
115.527
F=1.174
P<0.325
G4=78.340
G1=56.073
2799.366
9192.723
Statistical
Inference
G1=75.030
5681.195
12262.108
Mean
933.122
G2=49.660
F=0.531
G3=61.083
159
3
Between
Groups
156
Within
Groups
159
11992.089
58.928
G1=85.720
4064.004
36784.175
P<0.713
G4=53.178
1354.668
G2=76.098
F=3.416
G3=89.856
40848.179
235.796
G1= Season 1; G2= Season 2; G3= Season 3; G4= Season 4
205
P<0.010
G4=82.286
4.3
Water Pollution
4.3.1 Ground water quality
Ground water samples were collected at selected locations along the East Coast
Road and their quality was estimated. The results are presented in Table 35, 36
and Figures 22 to 39.
A)
pH
The prescribed range for pH is 7.0 to 8.5 and 6.5 to 8.5 by WHO and CPCB
respectively. All the samples except one had the pH within the prescribed
range.
B)
Turbidity
The turbidity was due to the colloidal fine dispersion of suspended solids.
Some microorganism might also contribute the turbidity. Similarly the level of
turbidity is a good indicator of the presence of harmful bacteria, as higher
turbidity levels are often associated with higher levels of disease – causing
microorganism, such as viruses, parasites, and some bacteria (USEPA, 2008).
The WHO lists turbidity of drinking water as one of the parameters that may
give rise to complaints from consumers. In the study area, turbidity was found
to be exceeding the permissible limits only in few samples.
C)
Electrical Conductivity (EC)
EC value exceeded 1000 µs/cm in many samples. As these places are close to,
the sea, there is a possibility of sea-water intrusion due to over-withdrawal
(over-pumping) which could have increased EC (Kataria and Jain, 1995).
Nutrient enrichment due to fertilizers may enhance TDS and it, in turn,
increases the EC since these two parameters are directly related to each other
(Mishra and Saksena, 1993).
206
D)
Total Dissolved Solids (TDS)
Electrical conductivity of water is considered to be an indication of the total
dissolved salt content (Hem, 1985). TDS content is usually the main factor,
which limits or determines the use of groundwater for any purpose (Nordstrom,
1987). The maximum permissible limit for TDS is 500 mg/l as per WHO and
CPCB and 300 as per BIS. All the samples exceeded 300 mg/l while the
groundwater of 15 sites out of 26 sites had the TDS value above 500 in all the
seasons. In other sites, the value of 500 mg/l was exceeded in one or few
seasons. Season wise average showed that the concentrations were higher
during Post monsoon followed by Monsoon. Since EC is directly related to
TDS, the locations with high contents of EC also had higher TDS. It means, the
condition of the water had no problem as a as fitness for irrigation. Other
location had the moderate concentrations of TDS. One way ANOVA test
revealed that there were seasonal differences in TDS.
E)
Total Hardness (TH)
In ground water, hardness is primarily due to presence of carbonates,
bicarbonates, sulphates and chlorides of calcium and magnesium. Sawyer
(1967) classified the water on the basis of the hardness in four categories as
soft, moderate, hard and very hard. If the hardness is less than 50 mg/l the
water will be soft. If the hardness is from 50 to 100 mg/l, the water will be
moderate soft. If the hardness is from 101 to 200 mg/l and more than 200 mg/l,
the water will be slightly hard and quite hard, respectively. Season wise
average values showed that the highest total hardness was observed in Post
monsoon. However, in the absence of alternative source of water, the
maximum permissible limit is 600 mg/l (BIS, 2003).
About 50% of the
samples had the hardness above 300 mg/l while about 15% of the samples had
the hardness above 500 mg/l. From the above it may be concluded that about
50% of samples were hard and 15% samples were very hard. One way
ANOVA test revealed that the hardness did not differ significantly in different
seasons.
207
F)
Calcium (Ca)
Among the cations, Ca content showed seasonal variation and majority of the
samples in all the seasons were within the permissible limit (75 mg/l). The
average Ca was 143.65 mg/l, 160.3 mg/l 202 mg/l and 187.33 mg/l during
summer, pre monsoon, monsoon and post monsoon respectively. The calcium
was found to be more in groundwater as it is found to occur from sedimentary
sandstone. However One way ANOVA test revealed that there were no
seasonal significant differences in Calcium.
G)
Magnesium (Mg)
The content of Mg was comparatively less than of Ca. Magnesium imparts an
unpleasant taste to the potable water. Most of the groundwater contains
relatively small amount of magnesium. The maximum permissible limit for
Magnesium is 30 mg/l as per WHO and BIS. Many samples were found to
exceed 30 mg/l. In the present study, the season wise average showed that the
magnesium value were very higher during summer and lower during pre
monsoon. One way ANOVA test revealed that there were seasonal differences
in magnesium.
H)
Total Alkalinity (TA)
Alkalinity which was found greater than hardness could be due to the presence
of basic salts of sodium and potassium, in addition to calcium and magnesium
(Nayak et al., 2007). Most of the alkalinity of natural waters is caused by
bicarbonates, carbonates and hydroxides. In some samples Alkalinity was
greater and in others less or equal to hardness values. The average alkalinity
values revealed that the alkalinity was maximum during summer and minimum
in post monsoon. However One way ANOVA test revealed that there were no
seasonal significant differences in Total alkalinity. Majority of the samples in
all season exhibited alkalinity values above the permissible limits.
208
I)
DO
The prescribed range for Dissolved oxygen is 4.0 to 6 and 6.0 mg/l by WHO
and CPCB. In the present study, the average DO content was 4.15 mg/l during
summer; 4.4 mg/l during (monsoon and pre monsoon) and 2.5 mg/l during pre
monsoon. One way ANOVA test revealed that there were seasonal different in
DO.
J)
BOD
Biological Oxygen Demand (BOD) refers to the quantity of oxygen required by
bacteria and other microorganisms in the biochemical degradation and
transformation of organic matter under aerobic conditions (Manivasakam,
1986). In the present study revealed that the BOD observed were well below
the prescribed limit. ANOVA test revealed that there were no seasonal
different in BOD.
K)
COD
The permissible limit for Chemical Oxygen Demand is 10 mg/l as per the
WHO. The results further revealed that the COD was low in all the seasons. All
the samples except one had the COD within the prescribed limit. ANOVA test
revealed that there were no seasonal different in COD.
L)
Sodium (Na)
Sodium is an important parameter for determining suitability of groundwater
for irrigation because it is a measure of alkali/sodium hazards to crops
(Richard, 1954). Na is one of the important naturally occurring cations and its
concentration in fresh waters is generally lower than that of Ca and Mg. For
aesthetic reason, the guideline values given by WHO is 200 mg/l. Season wise
averages of sodium were 291.75 mg/l during summer, 184.8 mg/l during pre
monsoon, 78.35 mg/l during pre monsoon. Only 12 samples had sodium
content above permissible limit. ANOVA test revealed that there were no
seasonal significant differences in Calcium.
209
M)
Potassium (K)
Potassium content in natural waters is usually low as compared to its
abundance in the earth’s crust. Natural water usually contains less than 12mg/l
of potassium. Results of the present analysis indicated that the concentration of
potassium was high in groundwater. Seasonal average data showed that the
potassium content was 74.15 mg/l during the summer followed by 52.85 mg/l
during period of post monsoon. This abnormally high content of potassium
depict that this accretion is due to human and animal activities, particularly,
agricultural activity. One way ANOVA test revealed that there were seasonal
significant differences in Potassium.
N)
Chloride
Chloride occurs naturally in all types of water. The most important source of
Chlorides in the waters is discharge of domestic sewage. Man and other
animals excrete very high quantities of chloride together with Nitrogeneous
compounds. About 8-15 grams of NaCl is excreted by a person per day
(Trivedy and Goel, 1986). The maximum permissible limit for chloride is 200
as per WHO and 250 as per BIS. In the present study, the groundwater of 11
sites had the chloride value above 250 mg/l in all the seasons. In other sites, the
value of 250 mg/l was not exceeded. Season wise average showed that the
concentration were higher during Post-monsoon followed by Monsoon,
Summer and Pre-monsoon. Chloride concentrations increases due to increase
in mineral content and produces salty taste in water. At low 100 mg/l
concentration, chloride is found in the form of Sodium, Potassium and Calcium
salts. Chloride level of water indicates the pollutional degree of water. Higher
values are hazardous to human consumption and create health problems
(Kataria and Iqbal, 1995). People who are not accustomed to high chloride in
water are subjected to laxative effect (Raviprakash and Krishna Rao, 1989;
Manjunathan et al., 2011). ANOVA test revealed that there were no seasonal
different in Chloride.
O)
Nitrate
Nitrate in the study area was found to be comparatively very low in
concentration. The season wise averages showed very low values during
210
Summer 1.75 mg/l, followed by Pre-monsoon 1.3 mg/l, Monsoon 1.9 mg/l and
Post-monsoon 0.01 mg/l respectively. Based on the analysis, nitrate
concentration was very less than the permissible limits in all the water samples.
One way ANOVA test revealed that there were seasonal significant differences
in Nitrate.
P)
Phosphate
Phosphate in natural water mostly ranges between0.005 and 0.020 mg/l
(Chapman and Kimstach, 1992). Its content in the present investigation was
0.02 mg/l during summer; 0.34 mg/l during PRM; 0.02 mg/l during monsoon,
and 0.01 mg/l during post monsoon.
The addition of super phosphates applied in the agricultural fields as fertilizers
and alkyl phosphates used in households detergents can be the sources of
inorganic phosphates (Bragadeswaran et al., 2007; Sankar et al., 2010). The
variation may also be due to the processes like adsorption and desorption of
phosphates and buffering action of sediment under varying environment
conditions (Rajasegar, 2003). One way ANOVA test revealed that there were
no seasonal significant differences in phosphate.
Q)
Sulphate
Sulphate is naturally occurring anion in all kinds of natural waters. Sulphate
produces an objectionable taste at 300-400 mg/l. As per ISI standard desirable
limit for drinking water is 150 ppm. Sulphate concentration is classified based
on the above standards as soft zone; good zones suitable for drinking are fixed
as less than 200 ppm. The average Sulphate was 70.9 mg/l, during post
monsoon season, 22.6 mg/l during summer season, 22.6 mg/l during summer,
51.4 mg/l during monsoon season. In all the sampling seasons, the values of
sulphate were within the prescribed limit.
R)
Fluoride
The present analysis indicated that, the concentration of Fluoride was very less
in all the samples of ground water. The prescribed limit for Fluoride is 1 mg/l
211
by BIS (1998) drinking water standards. Generally the concentration of
fluoride in natural water does not exceed 10 mg/l. the solubility of fluorides is
generally low and hence its presence is limited in ordinary waters. One way
ANOVA test revealed that there were seasonal significant differences in
Fluoride.
From the above results, it has been found that, certain parameters had
significant seasonal differences while others did not differ. The amount of
rainfall during monsoon and other seasons, the rate of infiltration and the
underlying rock types could be possible reasons for the variation. However,
further investigation should be carried out to get conclusive reasons. Few
parameters exceed the standards in many places, while other parameters
exceeded the standards in some places and in some seasons. Again, the amount
and rate of infiltration of rain water and underlying rock types could be
attributed for this variation.
The t-test was conducted to find out whether there were any differences in the
quality of ground water along the eastern side and Western side of the East
Coast Road (Table 37 to 39). The test revealed that there were no such
differences.
4.3.2 Quality of Surface water along the East Coast Road
The physico-chemical characteristics of surface water are presented in Figure
40 to 57. Cuddalore Old Town pond and Kollidam River (upstream and down
stream) were chosen to find out the impact of East Coast Road (if any) on their
water quality. Cuddalore old town pond is situated very close to East Coast
Road between Chidambaram and Sirkazhi.
pH of all the water samples in all the seasons were found to be within the
permissible range. Standard for turbidity is 5 NTU. Cuddalore old town pond
water during monsoon and post monsoon were found to exceed the standard.
During monsoon, the flowing rain water may carry all the dirt from the floor
into the pond water. It could be the reason for high turbidity during monsoon
and post monsoon.
212
Electrical conductivity and TDS showed the same trend in all the samples as
both the parameters are inter-related. Both TDS and EC were found to exceed
more than three times of the maximum permissible limit in Cuddalore pond
water while, the Kollidam waters hardly exceeded the standard.
The pond water is stagnant water and it receives the dirt and other
contaminants from East Coast Road continuously. It is also located in busy
area. The kollidam water is flowing water and moreover it is located away
from urban area.
It is interesting to note that the total hardness, calcium, sodium, potassium and
chloride also exceeded the standard in Cuddalore pond water while they were
within the permissible limits in kollidam water. However evaporation was
found to be slightly higher than the standard in kollidam waters. The presence
of plants and phytoplankton could be attributed to the high magnesium levels
in kollidam river.
Total alkalinity was found to be high (exceeding the standard) in all the
samples during pre-monsoon season. The reason is unknown. Dissolved
Oxygen was very high in kollidam waters when compared to pond water.
However, there was a slight decrease in DO in kollidam water during pre
monsoon.
Chemical Oxygen Demand and Biological Oxygen Demand were well within
the standards in kollidam waters. While they exceeded the standard several
times in the pond water in all the seasons. The above results clearly indicate
that the pond water is seriously polluted due to human activities along the East
Coast Road.
Phosphate and Fluoride were found to be low in both pond water and kollidam
water except during pre monsoon in pond water.
From the above results, it may be concluded that the Cuddalore old town pond
is seriously polluted due to human activities including the transportation
activities in East Coast Road. The kollidam water was not found to be affected
due to East Coast Road.
213
Table - 35:- Summary of water quality in the study area during Feb, 2009 to Jan, 2010
Season I
(SUM)
Season II
(PRM)
Season III
(MON)
Season IV
(POM)
TDS
995
815
1065
1550
TH
460
422.5
415
532.5
Calcium
143.65
160.3
202
187.33
Magnesium
105.8
18.05
60.15
27.655
Total
Alkalinity
270
230
225
190
DO
4.15
2.5
4.4
4.4
BOD
214.1
129.8
249.25
126.3
COD
1059
1253
1309
1218
Sodium
291.75
184.8
78.35
292.1
Potassium
74.15
17.4
18.3
52.85
Chloride
500.15
343.285
591.5
723.5
1.75
1.327
1.95
6.1
0.0265
0.3475
0.0215
0.0165
Sulphate
22.6
28.5
51.4
70.9
Fluoride
0.505
0.725
0.49
0.5
Parameters
Nitrate
Phosphate
214
O
ld
Po Ie To
w
c
Ke ond ha n
n
zh iy
g
up an adu
oo ku
va pp
rik am
u
Pe ppa
m
r
Pu i ya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
m
B
uv
an Mu
ag tlu
i
r
C Kee ri T
hi
da rap ow
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er ai
uk
k
Sa
Si ur
An tta rk
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
t h vi l
iru
Al pp
liv ur
ila
ga
Th K m
a
al
ac ruth
he y
k
Po add
o
Ak
n u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar
d
a g i yu
am r
ba
di
e
C
ud
da
lo
r
pH
10
9
8
7
6
5
4
3
2
1
0
Sampling Stations
S1 S1-Summer
S2 S2-Pre-monsoon
215
S3 S3-Monsoon
S4 S4-Post-monsoon
Fig-22:- pH of ground water along the East Coast Road
e
O
ld
Po Ie To
w
c
Ke ond ha n
zh iy nga
up an
d
oo ku u
va pp
rik am
u
Pe ppa
m
r
Pu iya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
uv B- m
an Mu
ag tlu
i
r
C Kee ri T
hi
da rap ow
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er a i
uk
k
S
S u
An atta irk r
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
th vil
i ru
Al pp
liv ur
ila
ga
Th K m
al aru
ac
he thy
k
Po add
Ak on u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar di
ag yu
am r
ba
di
C
ud
da
lo
r
Turbidity NTU
30
25
20
15
10
5
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
216
S3-Monsoon
S4-Post-monsoon
Fig-23:- Turbidity of ground water along the East Coast Road
O
ld
Po Ie To
w
c
Ke ond ha n
zh iy nga
up an
d
oo ku u
p
va
p
rik am
up
Pe pa
m
r
Pu iya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
m
B
uv
an Mu
ag tlu
i
r
C Kee ri T
hi
o
r
da ap w
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er a i
uk
k
S
S u
An atta irk r
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
th vil
iru
Al pp
liv ur
ila
ga
Th K m
al aru
ac
he thy
k
Po add
o
Ak
n u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar
d
a g i yu
am r
ba
di
e
C
ud
da
lo
r
Conductivity us/cm
6000
5000
4000
3000
2000
1000
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
217
S3-Monsoon
S4-Post-monsoon
Fig-24:- Conductivity of ground water along the East Coast Road
e
O
ld
Po Ie To
w
c
Ke ond ha n
zh iy nga
up an
d
oo ku u
va pp
rik am
u
Pe ppa
m
r
Pu iya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
uv B- m
an Mu
ag tlu
i
r
C Kee ri T
hi
da rap ow
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er ai
uk
k
S
S u
An atta irk r
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
th vil
iru
Al pp
liv ur
ila
ga
Th K m
al aru
ac
he thy
k
Po add
Ak on u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar
d
ag iyu
am r
ba
di
C
ud
da
lo
r
Total Dissolved Solids mg/l
3000
2500
2000
1500
1000
500
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
218
S3-Monsoon
S4-Post-monsoon
Fig-25:- Total Dissolved Solids of ground water along the East Coast Road
O
ld
Po Ie To
w
c
Ke ond ha n
zh iy nga
up an
d
oo ku u
va pp
rik am
u
Pe ppa
m
r
Pu iya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
uv B- m
an Mu
ag tlu
i
r
C Kee ri T
hi
da rap ow
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er ai
uk
k
S
S u
An atta irk r
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
th vil
iru
Al pp
liv ur
ila
ga
Th K m
al aru
ac
he thy
k
Po add
Ak on u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar di
ag yu
am r
ba
di
e
C
ud
da
lo
r
Total Hardness mg/l
1000
900
800
700
600
500
400
300
200
100
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
219
S3-Monsoon
S4-Post-monsoon
Fig-26:- Total Hardness of ground water along the East Coast Road
O
ld
Po Ie To
w
c
Ke ond ha n
zh iy nga
up an
d
oo ku u
va pp
rik am
u
Pe ppa
m
r
Pu iya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
uv B- m
an Mu
ag tlu
i
r
C Kee ri T
hi
da rap ow
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er ai
uk
k
S
S u
An atta irk r
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
th vil
iru
Al pp
liv ur
ila
ga
Th K m
al aru
ac
he thy
k
Po add
Ak on u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar di
ag yu
am r
ba
di
e
C
ud
da
lo
r
Calcium mg/l
400
350
300
250
200
150
100
50
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
220
S3-Monsoon
S4-Post-monsoon
Fig-27:- Calcium of ground water along the East Coast Road
O
ld
Po Ie To
w
c
Ke ond ha n
zh iy nga
up an
d
oo ku u
va pp
rik am
u
Pe ppa
m
r
Pu i ya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
u v B- m
an Mu
ag tlu
i
r
C Kee ri T
hi
da rap ow
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er ai
uk
k
S
S u
An atta irk r
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
t h vi l
iru
Al pp
liv ur
ila
ga
Th K m
al aru
ac
he thy
k
Po add
Ak on u
ku tha
rm la
i
T h u kk
iru utu
k
Th ad
iru iyu
T h ka r
ar
d
a g i yu
am r
ba
di
e
C
ud
da
lo
r
Magnesium mg/l
250
200
150
100
50
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
221
S3-Monsoon
S4-Post-monsoon
Fig-28:- Magnesium of ground water along the East Coast Road
e
O
ld
Po Ie To
w
c
Ke ond ha n
zh iy nga
up an
d
oo ku u
va pp
rik am
u
Pe ppa
m
r
Pu iya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
uv B- m
an Mu
ag tlu
i
r
C Kee ri T
hi
da rap ow
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er ai
uk
k
S
S u
An atta irk r
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
th vil
iru
Al pp
liv ur
ila
ga
Th K m
al aru
ac
he thy
k
Po add
Ak on u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar
d
ag iyu
am r
ba
di
C
ud
da
lo
r
Total Alkalinity mg/l
500
450
400
350
300
250
200
150
100
50
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
222
S3-Monsoon
S4-Post-monsoon
Fig-29:- Total Alkalinity of ground water along the East Coast Road
O
ld
Po Ie To
w
c
Ke ond ha n
zh iy nga
up an
d
oo ku u
va pp
rik am
u
Pe ppa
m
r
Pu i ya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
uv B- m
an Mu
ag tlu
i
r
C Kee ri T
hi
da rap ow
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er ai
uk
k
S
S u
An atta irk r
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
th vil
iru
Al pp
liv ur
ila
ga
Th K m
al aru
ac
he thy
k
Po add
Ak on u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar
d
a g i yu
am r
ba
di
e
C
ud
da
lo
r
Dissolved Oxygen mg/l
8
7
6
5
4
3
2
1
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
223
S3-Monsoon
S4-Post-monsoon
Fig-30:- Dissolved Oxygen of ground water along the East Coast Road
O
ld
Po Ie To
w
c
Ke ond ha n
zh iy nga
up an
d
oo ku u
va pp
rik am
u
Pe ppa
m
r
Pu i ya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
uv B- m
an Mu
ag tlu
i
r
C Kee ri T
hi
da rap ow
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er ai
uk
k
S
S u
An atta irk r
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
t h vi l
iru
Al pp
liv ur
ila
ga
Th K m
al aru
ac
he thy
k
Po add
Ak on u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar di
a g yu
am r
ba
di
e
C
ud
da
lo
r
Biological Oxygen Demand mg/l
180
160
140
120
100
80
60
40
20
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
224
S3-Monsoon
S4-Post-monsoon
Fig-31:- Biological Oxygen Demand of ground water along the East Coast Road
e
O
ld
Po Ie To
w
c
Ke ond ha n
zh iy nga
up an
d
oo ku u
va pp
rik am
u
Pe ppa
m
r
Pu iya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
uv B- m
an Mu
ag tlu
i
r
C Kee ri T
hi
da rap ow
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er a i
uk
k
S
S u
An atta irk r
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
th vil
i ru
Al pp
liv ur
ila
ga
Th K m
al aru
ac
he thy
k
Po add
Ak on u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar di
ag yu
am r
ba
di
C
ud
da
lo
r
Chemical Oxygen Demand mg/l
700
600
500
400
300
200
100
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
225
S3-Monsoon
S4-Post-monsoon
Fig-32:- Chemical Oxygen Demand of ground water along the East Coast Road
O
ld
Po Ie To
w
c
Ke ond ha n
zh iy nga
up an
d
oo ku u
va pp
rik am
u
Pe ppa
m
r
Pu iya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
uv B- m
an Mu
ag tlu
i
r
C Kee ri T
hi
da rap ow
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er a i
uk
k
S
S u
An atta irk r
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
th vil
iru
Al pp
liv ur
ila
ga
Th K m
al aru
ac
he thy
k
Po add
Ak on u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar
d
a g i yu
am r
ba
di
e
C
ud
da
lo
r
Sodium mg/l
600
500
400
300
200
100
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
226
S3-Monsoon
S4-Post-monsoon
Fig-33:- Sodium of ground water along the East Coast Road
e
O
ld
Po Ie To
w
c
Ke ond ha n
zh iy nga
up an
d
oo ku u
va pp
rik am
u
Pe ppa
m
r
Pu iya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
uv B- m
an Mu
ag tlu
i
r
C Kee ri T
hi
da rap ow
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er ai
uk
k
S
S u
An atta irk r
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
th vil
iru
Al pp
liv ur
ila
ga
Th K m
al aru
ac
he thy
k
Po add
Ak on u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar
d
ag iyu
am r
ba
di
C
ud
da
lo
r
Potassium mg/l
120
100
80
60
40
20
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
227
S3-Monsoon
S4-Post-monsoon
Fig-34:- Potassium of ground water along the East Coast Road
O
ld
Po Ie To
w
c
Ke ond ha n
n
zh iy
g
up an adu
oo ku
va pp
rik am
u
Pe ppa
m
r
Pu i ya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
m
B
uv
an Mu
ag tlu
i
r
C Kee ri T
hi
da rap ow
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er ai
uk
k
Sa
Si ur
An tta rk
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
t h vi l
iru
Al pp
liv ur
ila
ga
Th K m
a
al
ac ruth
he y
k
Po add
o
Ak
n u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar
d
a g i yu
am r
ba
di
e
C
ud
da
lo
r
Chloride mg/l
1400
1200
1000
800
600
400
200
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
228
S3-Monsoon
S4-Post-monsoon
Fig-35:- Chloride of ground water along the East Coast Road
O
ld
Po Ie To
w
c
Ke ond ha n
zh iy nga
up an
d
oo ku u
p
va
p
rik am
up
Pe pa
m
r
Pu iya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
uv B- m
M
an
ag utlu
i
r
C Kee ri T
hi
o
r
da ap w
n
C mb ala
hi
y
a
da ra am
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er ai
uk
k
S
S u
An atta irk r
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
t h vi l
iru
Al pp
liv ur
ila
ga
Th K m
al aru
ac
he thy
k
Po add
Ak on u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar
d
ag iyu
am r
ba
di
e
C
ud
da
lo
r
Nitrate mg/l
14
12
10
8
6
4
2
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
229
S3-Monsoon
S4-Post-monsoon
Fig-36:- Nitrate of ground water along the East Coast Road
O
ld
Po Ie To
w
c
Ke ond ha n
n
zh iy
g
up an adu
oo ku
va pp
rik am
u
Pe ppa
m
r
Pu i ya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
m
B
uv
an Mu
ag tlu
i
r
C Kee ri T
hi
da rap ow
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er ai
uk
k
Sa
Si ur
An tta rk
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
t h vi l
iru
Al pp
liv ur
ila
ga
Th K m
a
al
ac ruth
he y
k
Po add
o
Ak
n u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar
d
a g i yu
am r
ba
di
e
C
ud
da
lo
r
Phosphate mg/l
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
230
S3-Monsoon
S4-Post-monsoon
Fig-37:- Phosphate of ground water along the East Coast Road
e
O
ld
Po Ie To
w
c
Ke ond ha n
zh iy nga
up an
d
oo ku u
va pp
rik am
u
Pe ppa
m
r
Pu iya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
uv B- m
an Mu
ag tlu
i
r
C Kee ri T
hi
da rap ow
n
C mb ala
hi
da ara yam
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er a i
uk
k
S
S u
An atta irk r
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
th vil
i ru
Al pp
liv ur
ila
ga
Th K m
al aru
ac
he thy
k
Po add
Ak on u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar di
ag yu
am r
ba
di
C
ud
da
lo
r
Sulphate mg/l
140
120
100
80
60
40
20
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
231
S3-Monsoon
S4-Post-monsoon
Fig-38:- Sulphate of ground water along the East Coast Road
e
O
ld
Po Ie To
w
c
Ke ond ha n
zh iy nga
up an
d
oo ku u
p
va p
rik am
u
Pe ppa
m
r
Pu iya
th pa
ch ttu
Sa att
m ira
ba m
nt
ha
Bh
m
B
uv
an Mu
ag tlu
i
r
C Kee ri T
hi
o
r
da ap w
n
C mb ala
hi
y
a
da ra am
m m
ba T
ow
r
n
Va am
lla Ou
m
t
pa er
du
g
Er a i
uk
k
S
S u
An atta irk r
na na az
pe th hi
ru pu
m ra
m
al
a
Ka Ko
th vil
i ru
Al pp
liv ur
ila
ga
Th K m
al aru
ac
he thy
k
Po add
o
Ak
n u
ku tha
rm la
i
Th ukk
iru utu
k
Th ad
iru iyu
Th ka r
ar di
ag yu
am r
ba
di
C
ud
da
lo
r
Fluoride mg/l
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Sampling Stations
S1-Summer
S2-Pre-monsoon
232
S3-Monsoon
S4-Post-monsoon
Fig-39:- Fluoride of ground water along the East Coast Road
9
8.8
8.6
8.4
pH
8.2
8
7.8
7.6
7.4
7.2
7
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-40:- pH of surface water along the East Coast Road
233
10
9
8
Turbidity NTU
7
6
5
4
3
2
1
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-41:- Turbidity of surface water along the East Coast Road
234
6000
5000
Conductivity us/cm
4000
3000
2000
1000
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-42:- Conductivity of surface water along the East Coast Road
235
3000
Total Dissolved Solids mg/l
2500
2000
1500
1000
500
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-43:- Total Dissolved Solids of surface water along the East Coast Road
236
900
800
700
Total Hardness mg/l
600
500
400
300
200
100
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-44:- Total Hardness of surface water along the East Coast Road
237
450
400
350
Calcium mg/l
300
250
200
150
100
50
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-45:- Calcium of surface water along the East Coast Road
238
140
120
Magnesium mg/l
100
80
60
40
20
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-46:- Magnesium of surface water along the East Coast Road
239
350
300
Total Alkalinity mg/l
250
200
150
100
50
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-47:- Total Alkalinity of surface water along the East Coast Road
240
9
8
7
Dissolved Oxygen mg/l
6
5
4
3
2
1
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-48:- Dissolved Oxygen of surface water along the East Coast Road
241
90
80
Biological Oxygen Demand mg/l
70
60
50
40
30
20
10
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-49:- Biological Oxygen Demand of surface water along the East Coast Road
242
400
350
Chemical Oxygen Demand mg/l
300
250
200
150
100
50
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-50:- Chemical Oxygen Demand of surface water along the East Coast Road
243
600
500
Sodium mg/l
400
300
200
100
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-51:- Sodium of surface water along the East Coast Road
244
200
180
160
Potassium mg/l
140
120
100
80
60
40
20
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-52:- Potassium of surface water along the East Coast Road
245
1600
1400
1200
Chloride mg/l
1000
800
600
400
200
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-53:- Chloride of surface water along the East Coast Road
246
1.2
1
Nitrate mg/l
0.8
0.6
0.4
0.2
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-54:- Nitrate of surface water along the East Coast Road
247
0.25
Phosphate mg/l
0.2
0.15
0.1
0.05
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-55:- Phosphate of surface water along the East Coast Road
248
45
40
35
Sulphate mg/l
30
25
20
15
10
5
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-56:- Sulphate of surface water along the East Coast Road
249
1.6
1.4
1.2
Fluoride mg/l
1
0.8
0.6
0.4
0.2
0
Cuddalore Old Town (Pond)
Kollidam Upstream
Kollidam Down stream
Sampling Stations
S1-Summer
S2-Pre-monsoon
S3-Monsoon
S4-Post-monsoon
Fig-57:- Fluoride of surface water along the East Coast Road
250
Table – 36:- One way ANOVA among season with regards to water quality of ECR
S.
No
Source
Sum of
Squares
Df
Mean
square
1
pH
Between
Groups
Within Groups
Total
1.277
21.969
23.246
3
116
119
.426
.189
2
EC
Between
Groups
Within Groups
Total
14402362.700
65298041.661
79700404.367
3
116
119
3
Turbidity
Between
Groups
Within Groups
Total
43.452
1103.385
1146.837
3
116
119
14.484
9.512
4
TDS
Between
Groups
Within Groups
Total
2628809.167
17035923.333
19664732.500
3
116
119
876269.722
146861.408
5
Total Hardness
Between
Groups
Within Groups
Total
95911.667
3460058.333
3555970.000
3
116
119
31970.556
29828.089
6
Total
Alkalinity
Between
Groups
Within Groups
Total
63482.292
899950.833
963433.125
3
116
119
251
Mean
Statistical
Inference
G1=7.4
G2=7.6
G3=7.4
G4=7.4
F=2.247
G1=1055.33
4800787.567
G2=1436.90
562914.152
G3=1101.30
G4=1922.00
21160.764
7758.197
F=8.528
G1=1.94
G2=2.24
G3=2.89
G4=3.50
F=1.523
G1=674.3
G2=846.0
G3=704.0
G4=1048.6
F=5.967
G1=340.0
G2=344.5
G3=302.8
G4=196.4
F=1.072
G1=264.5
G2=259.0
G3=221.6
G4=211.3
F=1.072
S.
No
Source
Sum of
Squares
Df
Mean
square
7
DO
Between
Groups
Within Groups
Total
82.319
255.215
337.535
3
116
119
27.440
2.200
Mean
Statistical
Inference
G1=4.96
G2=3.17
G3=4.77
G4=3.26
F=12.472
G1=118.2
G2=161.8
G3=127.1
G4=158.1
F=.065
G1=27.5
G2=24.0
G3=29.0
G4=23.4
F=.036
COD
8
9
Between
Groups
Within Groups
Total
BOD
Between
Groups
Within Groups
Total
10
Calcium
Between
Groups
Within Groups
Total
11
Magnesium
Between
Groups
Within Groups
Total
12
Sodium
Between
Groups
Within Groups
Total
13
Potassium
Between
Groups
Within Groups
Total
43219.467
25891895.200
25935114.667
653.315
702952.252
703605.567
16554.032
439408.659
455962.691
38848.606
32121.835
70970.441
167834.903
48685.645
486876.635
37752.654
25437.765
65932.238
3
116
119
3
116
119
3
116
119
3
116
119
3
116
119
3
116
119
252
14406.489
223205.993
217.772
6059.933
5518.011
3788.006
G1=123.4
G2=120.3
G3=150.5
G4=130.8
12949.535
276.912
G1=4.19
G2=17.35
G3=52.65
G4=17.51
5647.830
3154.543
13756.547
254.647
G1=121.21
G2=123.21
G3=83.42
G4=180.47
G1=23.45
G2=14.02
G3=17.78
G4=30.72
F=1.457
F=46.764
F=1.764
F=40.645
S.
Source
Sum of
Squares
Df
Mean square
14
Chloride
Between
Groups
Within Groups
Total
443408.784
6803203.381
7246612.165
3
116
119
147802.928
58648.305
15
Sulphate
Between
Groups
Within Groups
Total
22865.022
41887.176
64752.198
3
116
119
113.178
348.919
462.097
3
116
119
.100
.516
.615
.120
.475
.537
No
16
17
18
Nitrate
Between
Groups
Within Groups
Total
Phosphate
Between
Groups
Within Groups
Total
Fluoride
Between
Groups
Within Groups
Total
Mean
G1=216.65
G2=242.64
G3=289.32
G4=376.39
Statistical
Inference
F=2.520
7621.674
361.096
G1=7.45
G2=10.91
G3=17.64
G4=30.95
F=21.107
37.726
3.008
G1=1.73
G2=1.13
G3=1.92
G4=3.74
F=12.542
3
116
119
.033
.004
G1=.024
G2=.090
G3=.021
G4=.025
F=7.467
3
116
119
.046
.003
G1= Season I, G2= Season II, G3= Season III, G4= Season IV
P ≤ 0.05 is significant different in the result or variables
P ≥ 0.05 is not significant different in the result or variables
253
G1=.286
G2=1.06
G3=.296
G4=.266
F=7.210
Table – 37:-‘t’ test analysis of water quality parameters of East Coast Road
S.
No
Direction
Mean
Std.
Deviation
Std.Error
Mean
Statistical
Inference
1
pH
East (n: 64)
West (n:56)
7.503
7.509
.4269
.4625
.0534
.0618
t=0.297
2
EC
East (n: 64)
West (n:56)
1395.16
1360.29
830.093
811.886
103.76
108.49
t=0.817
3
Turbidity
East (n: 64)
West (n:56)
2.606
2.689
3.4064
2.7489
.4258
.3673
t=885
4
TDS
East (n: 64)
West (n:56)
826.88
808.39
402.653
414.298
50.332
55.363
t=0.805
5
Total
Hardness
East (n: 64)
West (n:56)
346.95
336.34
187.622
155.790
23.45
20.81
t=0.736
6
Total
Alkalinity
East (n: 64)
West (n:56)
237.97
240.45
97.88
80.86
12.23
10.80
t=0.880
7
DO
East (n: 64)
West (n:56)
4.29
3.75
1.55
1.78
.1947
.2391
t=0.083
8
COD
East (n: 64)
West (n:56)
174.31
103.64
583.20
281.49
72.90
37.61
t=0.391
9
BOD
East (n: 64)
West (n:56)
27.09
24.79
86.97
64.22
10.87
8.58
t=0.869
10
Calcium
East (n: 64)
West (n:56)
131.14
131.46
61.59
62.80
7.69
8.39
t=0.978
254
S.
No
Direction
Mean
Std.
Deviation
Std.Error
Mean
Statistical
Inference
11
Magnesium
East (n: 64)
West (n:56)
19.93
26.35
22.60
26.12
2.82
3.49
t=0.155
12
Sodium
East (n: 64)
West (n:56)
132.630
121.27
105.52
77.02
13.19
10.29
t=0.499
13
Potassium
East (n: 64)
West (n:56)
21.43
21.55
17.92
30.51
2.24
4.07
t=0.980
14
Chloride
East (n: 64)
West (n:56)
294.98
265.56
248.85
245.65
31.10
32.82
t=0.517
15
Sulphate
East (n: 64)
West (n:56)
32.92
28.05
26.47
19.00
3.31
2.54
t=0.246
16
Nitrate
East (n: 64)
West (n:56)
2.41
1.80
2.25
1.54
.2816
.2065
t=0.083
17
Phosphate
East (n: 64)
West (n:56)
.03083
.05184
.026701
.100661
.003338
.013451
t=0.135
18
Fluoride
East (n: 64)
West (n:56)
.439
.558
.3851
.4780
.0481
.0639
t=0.140
P ≤ 0.05 is significant different in the result or variables
P ≥ 0.05 is not significant different in the result or variables
255
Table – 38:-‘t’ test analysis of water quality parameters of East Coast Road
S.
No
Centre
Mean
Std.
Deviation
Std.Error
Mean
Statistical
Inference
1
pH
Rural (n: 72)
Urban (n:48)
7.428
7.623
.3940
.4865
.0464
.0702
t=0.23
2
EC
Rural (n: 72)
Urban (n:48)
1340.21
1436.90
628.435
1045.671
74.06
150.930
t=0.56
3
Turbidity
Rural (n: 72)
Urban (n:48)
2.854
2.331
3.7766
1.6393
.4451
.2366
t=.302
4
TDS
Rural (n: 72)
Urban (n:48)
801.94
842.71
316.27
516.01
37.27
74.48
t=0.626
5
Total
Hardness
Rural (n: 72)
Urban (n:48)
349.79
330.31
168.24
180.74
19.82
26.08
t=0.554
6
Total
Alkalinity
Rural (n: 72)
Urban (n:48)
253.06
218.23
92.59
82.48
10.91
11.90
t=0.33
7
DO
Rural (n: 72)
Urban (n:48)
3.769
4.454
1.41
1.97
.1663
.2845
t=0.41
8
COD
Rural (n: 72)
Urban (n:48)
160.06
113.25
550.89
303.32
64.92
43.78
t=0.551
9
BOD
Rural (n: 72)
Urban (n:48)
25.41
26.93
82.06
69.25
9.67
9.99
t=0.914
10
Calcium
Rural (n: 72)
Urban (n:48)
132.30
129.78
51.22
75.71
6.03
10.92
t=0.841
256
S.
No
Centre
Mean
Std.
Deviation
Std.Error
Mean
Statistical
Inference
11
Magnesium
Rural (n: 72)
Urban (n:48)
22.42
23.68
25.05
23.68
2.95
3.41
t=0.781
12
Sodium
Rural (n: 72)
Urban (n:48)
130.26
122.93
87.54
101.68
10.31
14.67
t=0.684
13
Potassium
Rural (n: 72)
Urban (n:48)
20.23
23.38
15.66
33.81
1.84
4.88
t=0.549
14
Chloride
Rural (n: 72)
Urban (n:48)
256.97
317.67
168.45
330.23
19.85
47.66
t=0.244
15
Sulphate
Rural (n: 72)
Urban (n:48)
32.49
27.88
24.89
20.70
2.93
2.98
t=0.273
16
Nitrate
Rural (n: 72)
Urban (n:48)
1.93
2.42
1.83
2.14
.2159
.3101
t=0.200
17
Phosphate
Rural (n: 72)
Urban (n:48)
.04649
.03185
.086312
.041335
.010172
.005966
t=0.217
18
Fluoride
Rural (n: 72)
Urban (n:48)
.466
.538
.4070
.4709
.0480
.0680
t=0.391
P ≤ 0.05 is significant different in the result or variables
P ≥ 0.05 is not significant different in the result or variables
257
Table - 39:-‘t’ test analysis of water quality parameters of East Coast Road
Mean
Std.
Deviation
Std.
Error
Mean
Statistical
Inference
1
pH
Cuddalore(n: 56)
Nagapattinam (n:64)
7.488
7.522
.3480
.5125
.0465
.0641
t=0.66
2
EC
Cuddalore(n: 56)
Nagapattinam (n:64)
1507.6
4
1266.2
2
907.155
720.43
121.22
90.054
t=0.11
3
Turbidity
Cuddalore(n: 56)
Nagapattinam (n:64)
2.900
2.422
3.807
2.331
.5088
.2915
t=.417
4
TDS
Cuddalore(n: 56)
Nagapattinam (n:64)
886.43
758.59
454.70
351.97
60.76
43.99
t=0.91
5
Total Hardness
Cuddalore(n: 56)
Nagapattinam (n:64)
394.20
296.33
196.44
134.96
26.25
16.87
T=0.002
6
Total Alkalinity
Cuddalore(n: 56)
Nagapattinam (n:64)
247.95
231.41
93.40
86.86
12.48
10.85
T=0.320
7
DO
Cuddalore(n: 56)
Nagapattinam (n:64)
3.405
4.602
1.39
1.72
.1858
.2162
T=0.000
8
COD
Cuddalore(n: 56)
Nagapattinam (n:64)
275.93
23.56
660.906
12.013
88.31
1.502
T=0.006
9
BOD
Cuddalore(n: 56)
Nagapattinam (n:64)
48.83
6.45
108.770
2.512
14.53
0.314
T=0.006
10
Calcium
Cuddalore(n: 56)
Nagapattinam (n:64)
146.79
117.73
71.18
49.12
9.51
6.14
T=0.012
S.
No
District
258
Mean
Std.
Deviation
Std.
Error
Mean
Statistical
Inference
11
Magnesium
Cuddalore(n: 56)
Nagapattinam (n:64)
25.85
20.36
27.40
21.36
3.66
2.67
T=0.228
12
Sodium
Cuddalore(n: 56)
Nagapattinam (n:64)
141.98
114.50
110.12
73.68
14.71
9.21
T=0.117
13
Potassium
Cuddalore(n: 56)
Nagapattinam (n:64)
25.97
17.57
31.23
15.77
4.17
1.97
T=0.073
14
Chloride
Cuddalore(n: 56)
Nagapattinam (n:64)
312.77
253.67
265.13
228.03
35.43
28.50
T=0.196
15
Sulphate
Cuddalore(n: 56)
Nagapattinam (n:64)
34.59
27.19
28.98
16.38
3.87
2.04
T=0.095
16
Nitrate
Cuddalore(n: 56)
Nagapattinam (n:64)
2.29
1.99
2.35
1.56
.3146
.1958
T=0.431
17
Phosphate
Cuddalore(n: 56)
Nagapattinam (n:64)
.05230
.03042
.096732
.037045
.012926
.004631
T=0.116
18
Fluoride
Cuddalore(n: 56)
Nagapattinam (n:64)
.487
.501
.4461
.4251
.0596
.0531
T=0.862
S.
No
District
P ≤ 0.05 is significant different in the result or variables
P ≥ 0.05 is not significant different in the result or variables
259
4.3.3 Water Quality Index
The water qualities of East Coast Road at different seasons are presented in
Table 40. Most of the ground water samples in all seasons were found in the
range of moderately polluted category.
Table - 40:- WQI for of various study areas in ECR
S.No. Location
1
S1
WQI
46.43
2
S2
1036.12
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S13
S14
S15
S16
S17
S18
S19
S20
S21
S22
S23
S24
S25
S26
S27
S28
S29
S30
877.98
67.36
66.02
57.01
66.18
66.49
67.06
71.77
67.39
70.55
69.62
63.33
59.78
61.03
63.58
63
68.59
64.6
69.98
63.41
68.51
82.02
87.54
64.32
64.46
56.26
63.83
76.0
260
Class
Good
Unfit for human
consumption
Unfit for human
consumption
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Severely polluted
Severely polluted
Moderately polluted
Moderately polluted
Moderately polluted
Moderately polluted
Severely polluted
Percolation of water through various layer of soil, dissolution of minerals from
lithological composition and the addition of other pollutions from
anthropogenic activities in East Coast Road areas may be the major source of
ground water contamination during the sampling periods.
The values of WQI revealed that the higher percent of moderately polluted
category was found in the East Coast Road. It indicates the over exploitation
and anthropogenic activities such as discharge of effluents from industry,
agricultural and domestic uses. The water quality rating analysis revealed that
70% of samples were found as moderately polluted; 20% severely polluted,
10% of samples were unfit for drinking the ground water quality in the study
area is slowly getting to degradation.
4.4
Biotic Assessment
4.4.1 Flora Assessment
The flora species were identified and assessed quantitatively near villages on
East Coast Road side. List of villages on road side is passing through the ECR
given the table 41. Samples were collected from Monsoon, Pre-monsoon, Postmonsoon and summer. The Density, Frequency, Frequency classes, Abundant,
Abundant classes were calculated. The seasonal distribution of floral species is
presented in table - 42 to 47.
4.4.1.1 Mature trees
The two stretches of the road side North East and South West are divided onto
the following sub-divisions for our convenience.
1. Cuddalore OT
2. Cuddalore OT to Bhuvanagiri
3. Bhuvanagiri to Kollidam
4. Kollidam to Thalachangadu
5. Thalachangadu to Tharangambadi
During the upgradation of East Coast Road, the roadside tree had been
removed. But the data of the trees removed is not available. The trees and other
vegetation present along the sides of East Coast Road have been considered for
261
the present study. The number and types of mature trees on the road sides in
the above regions are presented in table 42 to 43. No endangered/threatened
species was found among these trees. Based on IVI (Important Value Index)
the dominant species in descending order recorded in the North East village on
the road side are Palm, Tamarind, Coconut, Pogamia, Neem and Thespesia; in
the South West side are Coconut, Palm, Tamarind, Neem, Teak, Thespesia,
Bamboo(shrub), Banyan, Rain tree and Pongamia.
4.4.1.2 Post-monsoon
Dominant species of plants found in Post monsoon season based on their IVI
values are presented in table 44.
1. Cocos nucifera
2. Borasis flabilifer
3. Crotan lacifereres
4. Thespasia populanea
5. Tribulus tenestris
6. Commelina bengalensis
7. Marselia
8. Vitex negundo
9. Calotropis gigantea
The plant species sparsely recorded in this season are:
1. Zizyphus mauritiana
2. Albiziz labac and
3. Feronia elephantum
Species diversity is very high in Post monsoon with 34% of trees and 81% of
wild species of all vegetation. Few medicinal plants such as Selanum
trilobatum, Vitex negundu, were found to occur commonly. Except the above
medicinal plants, no endangered/threatened/rare species of plants was recorded.
Cultivation of crops like Paddy, Gram, Banana etc., were also carried out in
this season.
262
Pre-monsoon
The types of vegetations and their IVI are presented in table 45. Dominant
species of natural vegetation (in descending order) based on their IVI recorded
were.
1. Delonix regia
2. Samara Somen
3. Polyalthea
4. Argemona nexicana
5. Erythrina indica
Species diversity of plants in Pre monsoon was moderate with 59% of wild
species 43% of the species identified were trees. No endangered/ threatened/
rare species of plants was recorded in this season. Agricultural crops such as
Paddy, Sugarcane etc., were cultivated lands near East Coast Road.
Monsoon
The flora species recorded and their IVI are presented in table 46. Dominant
species found to occur were:
1. Cassis tera
2. Tectpona grandis
3. Alteranthera
4. Cocos nucifera
5. Protulaca
6. Physalis minima
7. Amaranthus spinosa
Floral species sparsely found were:
1. Ochlandra trancorla
2. Cadaba
3. Annona reticulate
4. Autocarpus and
5. Emblica officinalis
Species diversity of flora was medium in Monsoon season.
263
Summer
The plant species found in summer season and their IVI are presented in table
in table 47. Plants sparsely found were:
1. Lecas aspera
2. Cardeosperum
3. Cassis tera and
4. Amaranthus spinosa
Species diversity is medium and vegetation on road side seems to be very
thick.
Table - 41:- List of villages on road side passing through the East Coast Road
Cuddalore and Tharangambadi
S. No
VOR
S. No
VOR
1
Kudikadu
23
Keerapalayam
2
Salainagar
24
Melamuzhingaladi
3
Iechangadu
25
Lalpuram
4
Semmanguppam
26
Ambalathatikuppam
5
Chenguchapady
27
Vayalur
6
Songanchavadi
28
Silvaipuram
7
Allampakkam
29
Koopiduvankuppanar
8
Kezhupoovarikuppam
30
Melachallai
9
Kallukadaimedu
31
Thennalkudi
10
Mettupalayam
32
Kathiruppur
11
Ayampet
33
Sempathaniruppu
12
Periapet
34
Allivilagam
13
Samiarpettai
35
Natarajapillaichavadi
14
Puthchattiram
36
Karuvendhimathapuram
15
Chinnakunatti
37
Akkoor
16
Periakummatti
38
Poonthalai
17
Sammantham
39
Thirukadayur
18
B-Mutlur
40
Kahiappanallur
19
Parangipettai
41
Ananthamangalam
20
Manjakuzhi
42
Narayanachavadi
21
Arigna Anna nagar
43
Erukkatanjeri
22
Bhuvanagiri
44
Ozhumangalam
264
Abbrevation used in Table 42 to 47 Density is expressed in Number of
individuals/ unit area
Frequency (%)
Class
1 – 20
21 – 40
41 – 60
61 – 80
81 – 100
A
B
C
D
E
Classes
Stalks/m2 quadrat
Rare
Occasional
Frequent
Abundant
Very abundant
01 – 04
05 – 14
15 – 29
30 – 90
100 +
T – Tree; C – Climber; S – Shrub; H – Herb; W – Wild; C – Cultivar
265
Table - 42:- IVI of Mature trees on the Northeast village on road side in Cuddalore to Tharagambadi
S.
No
1
Name of
the Tree
Banyan
2
Density Frequency%
Frequency
Abundance Relative
Abundance
Class
Class
Density
D
4.75
R
0.57
Relative
Frequency
4.55
Relative
Dominance
1.14
6.26
IVI
3.9
80
Arasu
1.2
100
E
1.2
R
0.46
5.55
1.01
7.02
3
Palm
63.0
100
E
63.0
Vab
24.40
8.50
19.02
49.05
4
Coconut
85.8
100
E
85.8
Vab
33.03
5.55
10.8
22.11
5
Tamarind
180
100
E
180
Vab
15.9
4.65
27.51
50.06
6
Neem
25.8
100
E
25.8
FR
7.08
6.55
10.55
31.18
7
Delonix
1.4
60
C
2.3
R
0.54
3.33
0.5
4.37
8
Mango
3.5
80
D
3.75
R
1.85
5.44
2.14
7.7
9
Casuarina
21.4
100
E
21.4
Fr
3.21
5.66
5.52
14.38
10
Bamboo
3.8
100
E
3.8
R
0.57
5.65
0.41
6.63
11
Teak
18.95
100
E
18.95
FR
7.08
5.55
6.62
19.25
12
Prosopis
1
40
B
2.5
R
0.15
2.27
0.07
2.49
13
Phoenix
4
80
D
4.5
R
0.7
4.65
0.32
6.07
14
Pongamia
7.2
100
E
7.2
OC
2.77
5.55
1.73
10.05
15
Thespesia
11.4
100
E
11.4
OC
4.39
5.55
3.46
13.36
16
Samara
4.8
40
E
4.8
R
1.85
5.55
3.46
10.86
17
Zizypus
0.5
50
C
1.8
R
0.50
3.33
0.5
4.25
18
Acacia
31.6
100
E
31.6
Ab
4.74
5.65
4.76
15.15
19
Eucalyptus
15.8
100
E
15.8
FR
6.08
5.55
9.55
21.18
20
Ashoka
3.8
100
E
3.8
R
0.54
5.65
0.31
6.5
266
Table - 43:- IVI of Mature trees on the Southwest village on road side in Cuddalore to Tharagambadi
S.
No
Name of
the Tree
1
Banyan
5
100
E
5
OC
1.93
5.55
4.2
11.68
2
Arasu
7
100
E
7
OC
1.05
5.65
2.11
8.82
3
Palm
230.8
100
E
230.8
Vab
34.62
5.65
24.81
65.09
4
Coconut
131.9
100
E
131.9
Vab
28.27
6.65
23.09
47.01
5
Tamarind
200
100
E
200
Vab
18.9
5.65
32.51
57.06
6
Neem
41.6
100
E
41.6
Ab
5.74
6.65
5.76
20.15
7
Delonix
4.2
60
C
7
OC
0.63
3.41
0.45
4.49
8
Mango
0.8
40
B
6
OC
0.36
2.27
0.36
2.99
9
Casuarina
1
40
B
2.5
R
0.15
2.27
0.07
2.49
10
Bamboo
5.6
100
E
5.6
OC
2.16
5.55
4.03
11.74
11
Teak
3.2
100
E
3.2
R
0.48
5.65
0.55
6.68
12
Batura
13
100
E
13
OC
1.05
5.65
1.4
9.0
13
Phoenix
12
100
E
12
OC
4.62
5.55
3.6
13.77
14
Pongamia
47.6
100
E
47.6
Ab
7.14
5.65
6.41
21.15
15
Thespesia
29.4
100
E
29.4
Fr
4.41
5.65
3.79
13.85
16
Samara
21.0
100
E
21.0
Fr
3.17
5.60
5.48
14.30
17
Zizypus
2
100
C
3.3
R
0.3
3.45
0.2
3.93
18
Acacia
21.4
100
E
21.4
Fr
3.21
5.65
5.52
14.38
19
Eucalyptus
8.4
60
E
8.4
OC
1.26
5.65
2.17
9.08
20
Ashoka
2.0
100
E
2
R
0.77
5.55
0.6
6.92
Density Frequency%
Frequency
Abundance Relative Relative
Relative
Abundance
Class
Class
Density Frequency Dominance
267
IVI
Table - 44:- Plant species and their IVI found in village on Road side (Cuddalore to Tharangambadi)
S.
No
Name of the
Species
Density
Frequency
%
Frequency
Class
Abun
dance
Abundance
Class
Relative
Density
Relative
Frequency
Relative
Dominance
IVI
1
Aegal
Manmelose(T,C)
0.2
20
A
1
R
0.2
0.98
0.27
1.46
2
Tectona
grandis(T,C)
1.5
60
C
2.7
R
1.60
2.94
3.29
7.8
3
Boerhavia
hispida(H,W)
2.3
60
C
4
R
2.39
2.94
2.08
8.4
4
Trifolium
repens(H,W)
4.6
60
C
8
OC
4.78
2.94
3.28
11.0
5
Morinda
citrifolia(T,W)
7.8
60
C
12.6
OC
7.57
2.94
10.39
20.9
6
Physalis minima
(H,W)
9.8
100
E
9.6
OC
9.57
4.9
9.84
24.3
7
Tragia (H,W)
0.8
40
B
2
R
0.8
1.96
0.82
3.6
8
Adathoda vasica
(S,C)
0.8
40
B
2
R
0.8
1.96
1.09
3.8
9
Sida Carpilnifolia
(H,W)
4.6
80
D
4.6
OC
4.37
3.92
4.50
12.8
10
Acalypha
Indica(H,W)
7.0
100
E
7.0
OC
6.78
4.9
6.99
18.4
11
Ficus
Hispida(T,W)
0.8
60
C
1.3
R
0.8
2.94
1.09
4.8
268
S.
No
Name of the
Species
Density
Frequency
%
Frequency
Class
Abun
dance
Abundance
Class
Relative
Density
Relative
Frequency
Relative
Dominance
IVI
12
Barleria
nodiflora(S,C)
2.6
60
C
4
R
2.4
2.94
3.28
8.6
13
Samara
Samana(T,C)
0.4
60
C
1
R
0.6
2.94
2.67
6.3
14
Sesamum
indicum(H,C)
1.6
80
D
2
R
1.5
3.92
1.09
6.6
15
Justicia
simplex(H,W)
3.8
100
E
3.8
R
3.58
4.9
3.69
12.2
16
Acanthaspermum
hispidium(H,W)
2.0
40
B
5
OC
1.99
1.96
2.73
6.7
17
Crotaon
spouciflora(H,W)
1.4
40
B
4
R
1.2
1.96
1.23
4.4
18
Abutilon
hirtum(H,W)
0.8
40
E
2
R
0.8
1.96
0.82
3.6
19
Aerva
lanata(H,W)
6.6
100
A
6.6
OC
6.36
4.9
6.56
17.8
20
Leucas
aspera(H,W)
0.4
20
B
2
R
0.4
0.98
0.41
1.8
21
Phyllanthus
niruri(H,W)
1.2
40
B
2.5
R
1.0
1.96
0.68
3.6
22
Cardeospermum
(H,W)
0.6
20
A
2
R
0.4
0.98
0.41
1.3
269
S.
No
Name of the
Species
Density
Frequency
%
Frequency
Class
Abun
dance
Abundance
Class
Relative
Density
Relative
Frequency
Relative
Dominance
IVI
23
Cassia tora(H,W)
0.4
20
A
2
R
0.4
0.98
0.41
1.8
24
Gomphrena
decumbens(H,W)
2.2
20
A
10.1
OC
1.99
0.98
2.04
5.0
25
Sida
cardifolia(H,W)
5.3
40
B
13
OC
5.18
1.96
5.33
12.5
26
Amaranthus
spinosa(H,W)
0.8
20
A
4
R
0.8
0.98
0.82
2.6
27
Oldenlandias
(H,W)
3.3
40
B
8
OC
3.2
1.96
3.26
5.4
28
Passiflora
foetida(H,W)
1.8
40
B
4
R
1.6
1.96
2.72
6.3
29
Ficus
Religosa(T,C)
0.4
20
A
1.1
R
0.2
0.98
0.81
2.0
30
Pedalium(H,W)
13.0
100
E
12
OC
12
4.9
12.24
29.1
31
Ficus
Bengalensis(T,C)
0.6
20
A
2
R
0.4
0.98
1.9
3.3
270
Table - 45:- Plant species and their IVI found in village on road side (Cuddalore to Tharangambadi)
S.No
Name of the
Species
Density
1
Adathoda vasica
(S,W)
1.7
80
D
2
R
1.3
3.08
0.99
5.37
2 Ficus Hispida(T,W)
3.6
100
E
3.6
R
2.93
3.85
2.98
9.79
3
1.3
60
C
2.3
R
1.14
2.31
1.45
4.9
0.7
60
C
1.3
R
0.65
2.31
0.83
3.79
Thespesia(T,C)
4 Odina wodiar(T,W)
Frequency Frequency
Abundance Relative Relative
Relative
Abundance
IVI
%
Class
Class
Density Frequency Dominance
5
Sida
cordifolia(H,W)
5.8
100
E
5.8
OC
3.9
3.85
2.98
10.73
6
Cassia tora(H,W)
12.6
100
E
12.4
OC
10.08
3.85
7.69
32.53
7
Amaranthua
spinosa(H,W)
6.3
100
E
6.4
OC
5.2
3.85
3.97
13.02
8
Alangium(H.W)
0.5
40
B
1
R
0.32
1.54
0.25
2.11
9
Annona
reticulate(S,C)
0.3
20
A
1
R
0.16
0.77
0.21
1.14
10
Ceplandra
indica(H,W)
0.8
80
D
1
R
0.65
3.08
0.17
3.9
11
Cardeospermum
(H,W)
0.9
80
D
1
R
0.65
3.08
0.17
3.9
12
Acalypha
indica(H,W)
7.4
100
E
7.2
OC
5.85
3.85
2.98
12.68
271
S.No
Name of the
Species
Density
13
Oldenlandia
indica(H,W)
8.4
100
E
8.4
OC
6.83
3.85
3.47
14.15
14
Portulaca(H,W)
7.3
100
E
7.2
OC
5.85
3.85
5.95
15.65
11.8
100
E
11.6
OC
9.43
3.85
9.59
22.87
15 Alternanthera(H,W
Frequency Frequency
Abundance Relative Relative
Relative
Abundance
IVI
%
Class
Class
Density Frequency Dominance
16
Physilis
minima(H,W)
5.6
100
E
5.6
OC
4.55
3.85
5.79
14.19
17
Emblica
officinalis(H,W)
0.4
20
A
1
R
0.16
0.77
0.29
1.22
18
Ochlandra
travancoria(S,W)
0.3
20
A
1
R
0.16
0.77
0.17
1.1
19
Prosopis
glandulosa(S,W)
1.2
100
E
1.3
R
0.98
3.85
2.97
7.8
20
Autocrpus(T,C)
0.3
20
A
1
R
0.16
0.77
0.25
1.18
21
Commalina
bengalensis(H,W)
5.6
100
E
5.6
OC
4.55
3.85
5.79
14.19
22
Cathranthus
rosieus(H,W)
0.8
40
B
2
R
0.65
1.54
0.49
2.68
23
Jatropha
gossypifolia(S,W)
1.6
60
C
2.3
R
1.14
2.31
0.87
4.32
24
Cadala(S,W)
0.4
20
A
1
R
0.16
0.77
0.17
1.1
25
Killinga
monocephala(H,W)
7.2
100
E
7.2
OC
5.85
3.85
4.46
14.15
272
S.No
Name of the
Species
Density
Frequency Frequency
Abundance Relative Relative
Relative
Abundance
IVI
%
Class
Class
Density Frequency Dominance
26
Ficus
bengalansis(T,W)
0.4
40
B
1
R
0.32
1.54
1.16
3.02
27
F.Religious(T,W)
0.2
20
A
1.1
R
0.16
0.77
0.41
1.34
28
Borasis
floblifer(T,W)
2.4
100
E
1.4
R
1.96
3.85
1.98
7.78
29
Cocus
nucifera(T,C)
6.2
100
E
6
OC
4.88
3.85
6.2
14.93
30
Tamarandis
indica(T,C)
0.8
60
C
1.3
R
0.65
2.31
1.98
4.95
31 Delonix regia(T,C)
0.6
60
C
1.1
R
0.48
2.31
0.62
3.42
32
Azadiracta(T,W)
1.8
100
E
1.7
R
1.46
3.85
2.23
7.53
33
Tectona
grandis(T,C)
14.2
100
E
14
OC
11.38
3.95
17.36
32.59
34
Bambosa(S,C)
2.4
100
E
2.4
R
1.96
3.85
0.99
6.79
35
Pongamia(T,C)
1.4
60
C
2.3
R
1.14
2.31
1.45
4.9
36
Acacia(T,C)
0.6
60
C
1.3
R
0.65
2.31
0.66
3.62
273
Table - 46:- Plant Species and their IVI found in Village on Road side (Cuddalore to Tharangambadi)
S.
Frequency Frequency Abun Abundance Relative Relative
Relative
Name of the Species Density
IVI
%
Class
dance
Class
Density
Frequency
Dominance
No
1
Tephrosea
perpuria(H,W)
9.8
100
E
8.5
OC
5.4
3.8
3.79
12.99
2
Calotropis
giganted(S,W)
5.4
80
D
6.4
OC
2.8
3.0
2.74
8.64
3
Amaranthus
spinosa(H,W)
5.2
60
C
28.8
FR
2.9
2.3
2.05
7.25
4
Cassoa tora(H,W)
11.0
100
C
3.0
R
6.3
3.8
4.42
14.61
5
Momordica
charantia(H,W)
1.2
40
B
3.0
R
0.7
1.5
0.16
2.36
6
Syzygium
cuminii(T,C)
0.4
40
B
1.0
R
0.2
1.5
0.37
2.07
7
Amaranthus
gangaticus(H,W)
3.8
60
C
OC
2.1
2.3
1.42
5.82
8
Prosopis
glandulesa(S,W)
6.4
80
D
8.0
OC
3.6
3.0
4.21
10.81
9
Solanum
surattencse(H,W)
3.6
60
C
6.0
OC
2.1
2.3
1.89
6.29
10 Acalupha indica(H,W)
6.4
80
D
8.5
OC
3.8
3.0
2.68
9.48
11 Tectona grandis(H,W)
2.4
60
C
4.0
R
1.4
2.3
1.89
5.49
12
Ceba pentandra(T,W)
2.4
80
D
5.0
OC
1.3
3.0
1.88
17.80
13
Gomphrena
globosa(H,W)
6.8
80
D
8.5
OC
3.8
3.0
2.68
9.48
14
Leucas aspera(H,W)
4.8
60
C
7.3
OC
2.5
2.3
2.31
7.11
6.0
274
S.
Name of the Species
No
Density
Frequency
%
Frequency
Class
Abun
dance
Abundance
Class
Relative
Density
Relative
Frequency
Relative
Dominance
IVI
15 Solanum toruum (S,W)
1.4
60
C
2.3
R
0.8
2.3
0.91
4.02
3.2
60
C
5.3
OC
1.7
2.3
2.10
6.2
6.4
80
D
8.0
OC
3.6
3.0
4.21
10.81
16
Phoenix
sylvestric(T,W)
17 Ipomoea cornea(S,W)
18
Croton
sparciflorus(H,W)
13.0
100
E
13.0
OC
7.4
3.8
6.84
18.04
19
Ficus hispida(T,W)
4.2
60
C
6.6
OC
2.3
2.3
2.63
7.23
20
Eichhomia
crospipes(H,W)
8.2
80
D
10.0
OC
4.4
3.0
3.16
10.66
21
Lemna major
roseus(H,W)
10.0
100
E
10.0
OC
5.7
3.8
1.32
10.80
22 Tribulus terrestia(H,W)
7.2
80
D
9.0
OC
4.1
3.0
2.84
9.94
23 Adathoda varica(S,C)
0.6
40
B
0.6
OC
0.3
1.5
0.32
2.12
24
Erythrina indica(T,C)
0.4
20
A
0.1
R
0.2
0.8
0.21
1.21
25
Jatropha
gossypiplia(S,W)
0.7
60
C
1.3
R
0.4
2.3
0.32
3.20
26
Commalina
benghalensis(H,W)
4.8
80
D
6.0
OC
2.7
3.0
1.89
7.54
27
Argemone
mexicana(H,W)
0.4
20
A
0.4
R
0.2
0.8
0.11
1.11
28
Alibicia lebac(T,C)
0.4
40
B
0.4
R
0.2
1.5
0.37
2.07
29
Cephalandra
indica(H,W)
0.8
40
B
0.5
R
0.4
1.5
0.11
2.01
275
S.
No
Name of the Species
30
Eucalyptus(T,W)
1.4
31
Daturametal(H,W)
32
Frequency
Class
Abun
dance
Abundance
Class
Relative
Density
Relative
Frequency
Relative
Dominance
IVI
60
C
3.5
R
0.8
2.3
0.92
4.02
0.8
60
C
2.0
R
0.4
2.3
0.32
3.02
Oldenlandia(H,W)
6.3
80
D
6.4
OC
3.6
3.0
1.68
8.28
33
Ficus religiosa(T,C)
0.3
20
A
0.20
R
0.1
0.8
0.37
1.27
34
Ficus religiosa(T,C)
0.4
20
A
0.40
R
0.2
0.8
0.63
1.63
35
Borasis flobellifer
(T,C)
3.8
40
B
9.0
OC
2.1
1.5
2.37
5.97
36
Cocas nucifera(T,C)
10.0
100
E
10.0
OC
5.7
3.8
0.26
9.75
37
Tamarindes
indica(T,C)
2.4
60
C
4.0
R
1.3
2.3
4.42
8.02
38 Azadiracta indica(T,C)
3.0
60
C
4.6
R
1.5
2.3
4.42
8.22
39
0.4
20
A
2.0
R
0.2
0.8
0.68
1.68
1.6
60
C
0.4
R
1.0
2.3
1.68
4.98
Delonix regia(T,C)
40 Pongemia pinnata(T,C)
Density Frequency %
41
Thespesia
populinia(T,C)
12.4
100
E
12.4
OC
7.0
3.8
11.36
22.15
42
Zizzphys(S,W)
1.3
80
D
2.0
R
0.7
3.0
1.47
5.17
43
Polyalthea(T,C)
1.6
40
B
4.0
R
0.7
1.5
1.05
3.25
44
Samara samon(T,C)
0.6
40
B
2.0
R
0.4
1.4
0.84
2.74
45
Rauvolfia
tetraphill(H,W)
0.2
20
A
0.20
R
0.1
0.8
0.37
1.07
276
Table - 47:- Plant species and their IVI found in village on road side (Cuddalore to Tharangambadi)
S.
Frequency Frequency Abun Abundance Relative Relative
Relative
Name of the Species Density
IVI
No
%
Class
dance
Class
Density Frequency Dominance
1
Ficus
Religiousa(T,W)
0.2
20
A
1.0
R
0.08
0.48
0.27
0.83
2
Morinda
citrifolia(T,W)
3.8
100
E
3.8
R
1.5
2.4
4.4
5.3
3
Phoenix
sylvestris(T,W)
1.4
60
C
3.1
R
0.5
1.4
0.6
2.5
4
Acacia
leucophloea(T,W)
0.4
40
B
0.2
R
0.17
0.9
0.16
1.29
5
Opuntia
vulgaris(H,W)
0.6
20
A
0.4
R
0.17
0.48
0.39
1.05
8.8
100
E
8.8
OC
3.68
2.4
2.6
8.6
6 Vitex negundu(S,W)
7
Zizyphus
mauritiana(S,W)
0.4
20
A
1.2
R
0.08
0.48
0.08
0.64
8
Carrissa
carandra(S,W)
0.8
60
C
1.3
R
0.3
1.4
0.5
2.3
9
Cissus
quadrangularis(S,W)
3.6
80
D
4.6
R
1.5
1.9
0.4
3.8
10 Odina wodiar(T,W)
1.8
60
C
1.8
R
0.7
1.4
1.2
3.4
11
Tephrosea
perpuria(H,W)
6.7
100
E
6.7
OC
2.3
2.4
3.2
8.37
12
Thespesia populnea
(T,C)
8.4
100
E
8.4
OC
3.4
2.4
6.4
12.19
277
S.
No
Name of the
Species
Density
Frequency Frequency Abun Abundance Relative Relative
Relative
IVI
%
Class
dance
Class
Density Frequency Dominance
13
Tamarindus
indicus(T,C)
0.6
40
B
14 Ficus hispida(T,W)
4.6
80
D
15 Damia etensa(H,W)
1.8
60
16
Commelina
benghalensis(H,W)
8.3
17
Amaranthus
viridius(H,W)
18
1.5
R
0.25
0.96
0.70
1.9
5.6
OC
1.84
1.92
2.98
6.74
C
2.8
R
0.6
1.4
0.16
2.27
100
E
8.3
OC
3.34
2.4
3.1
8.84
7.4
100
E
7.4
OC
2.93
2.4
2.04
7.37
Reulia
Liverosa(H,W)
8.4
100
E
8.4
OC
3.51
2.4
2.4
8.4
19
Therriophonum
minutum(H,W)
5.6
100
E
OC
2.26
2.4
1.58
6.17
20
Croton
lacciferus(H,W)
14.8
100
E
14.8
OC
6.18
2.4
4.31
12.89
21
Evolvulus
alsinoides(H,W)
5.2
80
D
6.5
OC
2.17
1.92
2.02
6.11
22
Ficus
bengalensis(T,W)
0.6
40
B
1.5
R
0.25
0.96
1.01
2.22
23
Borasis
flabellifer(T,W)
16.2
100
E
16.2
FRE
6.77
2.4
6.29
15.46
24
Phyllanthus
niruri(H,W)
5.2
40
B
13
OC
4.34
2.4
2.02
4.14
25
Marselia(H,W)
10.4
100
E
10.4
OC
4.34
2.4
2.02
8.76
5.6
278
S.
No
Name of the
Species
Density
26
Cereus
pterogonius(H,W)
6.4
100
E
6.4
OC
2.67
2.4
1.86
6.93
27 Leucas aspera(H,W)
3.6
60
C
6.0
OC
1.5
1.4
1.4
4.34
28
Pandanus(H,W)
2.8
60
C
4.7
R
1.17
1.44
1.63
4.24
29
Tragia(H,W)
0.8
80
D
1.0
R
0.3
1.93
0.23
2.48
30
Hemidestrus
indicus(H,W)
1.2
60
C
2.0
R
0.5
1.44
0.5
2.52
31
Tribulus
terrestris(H,W)
8.4
40
B
20.5
FRE
3.43
0.96
6.36
10.75
100
E
100
FRE
7.10
2.4
6.6
16.10
32 Cocos nucifera (T,C) 17.00
Frequency Frequency Abun Abundance Relative Relative
Relative
IVI
%
Class
dance
Class
Density Frequency Dominance
33
Capparis
divaricata(H,W)
0.8
60
C
1.3
R
0.33
1.44
0.31
2.08
34
Pongamia
pinnata(T,C)
1.4
60
C
2.2
R
0.5
1.44
0.8
2.75
35
Datura(H,W)
3.0
60
C
5.0
OC
1.25
1.4
1.16
3.85
36
Azadiracta
indica(T,C)
1.6
40
B
3.5
R
0.58
0.46
1.08
2.12
37
Ipomoea(H,W)
1.6
60
C
2.7
R
0.6
1.44
0.16
2.27
38
Aschenomin
asper(H,W)
0.8
40
B
2.0
R
0.3
0.96
0.23
1.52
39
Justice(S,W)
4.8
100
E
4.8
R
2.0
2.4
1.86
6.26
279
S.
Frequency Frequency
Name of the Species Density
No
%
Class
Abun Abundance Relative Relative
Relative
IVI
dance
Class
Density Frequency Dominance
40
Coldenia
procumbens(H,W)
3.6
100
E
3.6
R
1.5
2.4
2.4
6.34
41
Agave
Americana(H,W)
4.2
100
E
4.0
R
1.67
2.4
3.1
7.17
42
Albizia labac(T,C)
0.4
20
A
2.0
R
0.17
0.48
0.23
0.88
43
Cephalandia
indica(H,W)
1.8
60
C
3
R
0.75
1.44
0.17
2.36
44
Solanum
trilobatum(H,C)
3.8
80
D
4.5
R
1.5
1.92
1.05
4.47
45
Portulaca(H,W)
3.4
80
D
3.8
R
1.25
1.92
1.75
4.92
46
Calotropis
gigantean(S,W)
5.6
100
E
5.2
OC
2.17
2.4
2.02
6.59
47
Ipomoea
cornea(S,W)
7.8
100
E
7.6
OC
3.17
2.4
2.95
8.52
48
Erythrina
indica(T,W)
0.8
60
C
1.3
R
0.3
1.44
0.39
2.06
49
Prosopis
glandulosa(S,W)
1.2
40
B
3.0
R
0.5
0.96
0.47
1.93
50
Gomphrena
decumbens(H,W)
6.8
80
D
8.5
OC
2.84
1.92
3.3
8.06
51
Jatrropha
glandulifera(S,W)
3.6
80
D
4.5
R
1.5
1.92
1.75
5.17
52
Citrullus
lanatus(H,W)
2.4
80
D
3.0
R
1.0
1.92
0.93
3.85
280
S.
Frequency Frequency
Name of the Species Density
No
%
Class
Abun Abundance Relative Relative
Relative
IVI
dance
Class
Density Frequency Dominance
53
Feronia
elephantum(T,C)
0.4
20
A
2.0
R
0.17
0.48
0.23
0.88
54
Cassia alata(S,W)
0.6
20
A
3.0
R
0.25
0.48
0.29
1.02
55
Crotalaria
labernifolia(H,W)
5.0
80
D
5.0
OC
1.67
1.92
1.16
4.75
56
Tectona
grandis(T,C)
1.2
60
C
2.0
R
0.5
1.44
0.7
2.64
57
Glyciridia(T,C)
6.8
100
E
6.8
OC
2.84
2.4
1.98
7.22
58
Commiphora
caudate(H,W)
0.6
40
B
1.5
R
0.25
0.96
0.23
1.44
59
Ricinus
communis(S,W)
3.4
20
A
15.0
FRE
1.25
1.48
1.75
3.48
60
Bambusa
arundinacea(T,C)
0.2
20
A
1.0
R
0.08
0.48
0.23
0.79
61
Millingtoria(T,W)
0.2
20
A
1.0
R
0.08
0.48
0.12
0.68
1.4
60
C
2.3
R
0.58
1.44
1.77
3.79
62 Samara saman(T,W)
281
4.4.2 Fauna Assessment
4.4.2.1 Composition and Diversity of Fauna at Cuddalore-OT
The Fauna found/likely to occur in Cuddalore OT is presented in tables 48.
Some of the birds were seasonal migrants (both local and international).
However, they were not in large numbers as found in bird sanctuaries. Reptiles
such as, Calotes, Chameleon, Monitor Lizard and different types of snakes
were found to occur in Cuddalore OT. It also inhabits a number of mammals
such as Squirrel, Jungle cat etc. However, their movements will be mainly
away from the proposed road. Cuddalore seems to be not disturbed much
previously. From the observations made, it seems that widening of the road did
not cause any severe damage to the ecology of this place.
4.4.2.2 Composition and Diversity of Fauna between Cuddalore-OT and
Bhuvanagiri
Between Cuddalore OT and Bhuvanagiri along the East Coast Road stretch,
Paddy, Sugar cane, Cotton etc. were cultivated in the fields. Presence of Paddy
fields attracts a number of birds (both local and migratory). Egrets, herons,
Carmorants, etc were seen in this place.
Squirrels, different types of Snakes, Mabuya, Hare, Bats etc. were the large
animals found to occur in this region. However, their movements will be
mainly away from the proposed road.
4.4.2.3 Composition and Diversity of Fauna between Bhuvanagiri and Kollidam
The fauna present likely to occur in Bhuvanagiri are presented in tables 49 and
49a. As mentioned earlier, it inhabited local birds and attracted few seasonal
birds. Snakes, Chameleon, Calotes, Mabuya, Monitor Lizard, Hare, Monkeys,
Squirrels etc. were the animals found to occur in this place. However, their
movements will be away from the proposed road. Bhuvanagiri is disturbed
already by the urbanization. It may be concluded that the widening of the road
did not cause ecological disturbance in large scale in this region.
282
4.4.2.4 Composition and Diversity of Fauna between Kollidam and Thalachangadu
The fauna found/likely to occur in this place is presented in tables 49 and 49a.
Calotes, Mabuya, Monitor lizards, Hare, Squirrel, Monkey, Field Mice, Bats
are the large animals noticed in this place. Fresh water field crabs and Fish
were also seen. However, their movements will be mainly away from the
proposed road. Hence, it may be concluded that the road did not cause much
harm to the ecology of this place.
4.4.2.5 Composition and Diversity of Fauna between Thalachangadu and
Tharangambadi
Tharangambadi is situated along the East Coast. Squirrel, Hare, Calotes,
Monitor Lizard, Bats, Mabuya, Frogs a number of birds etc. are found to occur
in this place. It may be concluded that the widening of the proposed road did
not cause much harm to the ecology of this place.
Conclusion
The widening of the road in Cuddalore and Tharangambadi has not seriously
affected flora/fauna of this region. Ecology of these places may not be altered
considerley much due to the road project.
283
Table - 48:- List of fauna found/likely to occur in the study area
in Cuddalore to Tharangambadi
Scientific name
Common name
Insects/Arachnids
Cimex lecyularis
Bedbug
Apis indica
Bees
Anopheles culex
Mosquitoes
Hydriys sps
Black ants
Cacophila smaragdin
Red ants
Odonata
Dragon fly
Phyllium scythe
Leef insect
Cataxanbicolortha
Beetle
Catantops dominaus
Field Grasshopper
Apis dorsata
Flower bee
Musca domestica
Housefly
Polistes sp
Wasps
Centruoides vittatus
Scorpion
Acanthogonatus francki
Spiders
Ctenomorpha chronus
Stick Insects
Aegeaera venula
Paddy bug
Arthropoda
Sclopendra sp
Centipeds
Spirobolus sp
Millipeds
Amphibians
Rana hexadaxtyla
Frog
Archanids
Buthus sp
Scorpion
Stegodogyphus sarasinorum
Social spider
Butterfly
Précis Iphita
Chocolate pansy
Euploea core
Common crow
Papilio lemonias
Lemon pansy
Panaus chrysippus chrysippus
Plain tiger
284
Scientific name
Common name
Annelida
Lumbricus terrestris
Earth Worm
Tubifex tubifex
Tubifex
Reptiles
Snakes
Calotes versicolor
Lizard
Typhlina bramina
Common worm snake
Najanaja kaouthia
Cobra
Macropisthodon plumbiocolor
Green keelback snake
Ptyas mucosus
Rat snake
Other reptiles
Calotes versicolor
Calotes
Mabuya carvalhoi
Mabuya
Varanus bitatawa
Varanus
Mammals
Rattus rattus
Rat
Canis famiwaris
Dog
Felis catus
Domestic cat
Bos sp
Domestic cattle
Bubalus bubalus
Buffalo
Capra sp
Goat
Felis chaus
Jungle cat
Funambulus palmarum
Indian palm squirrel
Mus Boodgua
Indian field mouse
Cynopterus sphinx
Small bat
285
Table - 49:- List of AVI-Fauna Found/Likely to occur in the study area in Cuddalore to Tharangambadi
S.
No
Name of the
Animals
Cuddalore-OT
Cuddalore-OT
to Bhuvanagiri
Bhuvanagiri to
Kollidam
Kollidam to
Thalachangadu
Thalachangadu to
Tharangambadi
I
Arthropoda
1
Centipeds
+
+
+
+
+
2
Millipeds
+
+
+
+
+
II
Insects/Arachnids
3
Bedbug
+
+
+
+
+
4
Bees
+
+
+
+
+
5
Mosquitoes
+
+
+
+
+
6
Black ants
+
+
+
+
+
7
Red ants
+
+
+
+
+
8
Dragon fly
+
+
+
+
+
9
Leef insect
+
+
+
+
+
10
Beetle
+
+
+
+
+
11
Field Grasshopper
+
+
+
+
+
12
Flower bee
+
+
+
+
+
13
Housefly
+
+
+
+
+
14
Wasps
+
+
+
+
+
15
Scorpion
+
+
+
+
+
16
Spiders
+
+
+
+
+
286
S.
No
Name of the
Animals
Cuddalore-OT
Cuddalore-OT
to Bhuvanagiri
Bhuvanagiri to
Kollidam
Kollidam to
Thalachangadu
Thalachangadu to
Tharangambadi
17
Stick Insects
+
+
+
+
+
18
Paddy bug
+
+
+
+
+
III
Annelida
19
Earth Worm
+
+
+
+
+
20
Tubifex
+
+
+
+
+
IV
Reptiles
Snakes
21
Lizard
+
+
+
+
+
22
Common worm
snake
+
+
+
+
+
23
Cobra
+
+
24
Green keelback
snake
25
Rat snake
+
+
+
+
Other Reptiles
26
Calotes
+
+
+
+
+
27
Mabuya
+
+
+
+
+
28
Varanus
+
+
+
+
+
V
Amphibians
29
Frog
+
+
+
+
+
287
S.
No
Name of the
Animals
VI
Mammals
30
Cuddalore-OT
Cuddalore-OT
to Bhuvanagiri
Bhuvanagiri to
Kollidam
Kollidam to
Thalachangadu
Thalachangadu to
Tharangambadi
Rat
+
+
+
+
+
31
Dog
+
+
+
+
+
32
Domestic cat
+
+
+
+
+
33
Domestic cattle
+
+
+
+
+
34
Buffalo
+
+
+
+
+
35
Goat
+
+
+
+
+
36
Jungle cat
+
+
+
+
+
37
Indian palm
squirrel
+
+
+
+
+
38
Indian field mouse
+
+
+
+
+
39
Small bat
+
+
+
+
+
288
Table - 49a:- List of AVI-Fauna found/likely to occur in the study area in Cuddalore to Tharangambadi
S.
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Name of the
Animals
Paraiah kite
Brahminy kite
Roseringed parakeet
Common hawk
cuckoo
Koel
Scops owl
Palm swift
Indian roller
Hoopoe
Swallow
Black drongo
Common Myna
Indian tree pie
House crow
Jungle crow
Magpie robin
Indian robin
Grey Wagtail
House sparrow
Weaver bird
Brahminy Myna
Crow-pheasant
Babbler
CuddaloreOT
+
+
+
Cuddalore OT
to Bhuvanagiri
+
+
+
Bhuvanagiri
Kollidam to
Thalachangadu to
to Kollidam Thalachangadu Tharangambadi
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
289
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
R.Br
R.Br
R.Br
R.Br
+
+
+
+
Status
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Present/Likely to Occur
R.Br
R.Br
R.Br
M
R.Br
R
M
R.Br
R
R.Br
R.Br
R
R
R.Br
R.Br
R.Br
M
M
R.Br
4.5
Socio-economic aspects
Socio-economics is the study of the relationship between economic activity and
social life. The present study attempts to measure the socio-economic status of East
Coast Road between Cuddalore and Tharangambadi. Multi stage random sampling
method was adopted in the present study. In the first stage, villages were selected,
between Salainagar to Ozhumangalam. In the second stage 10 villages were selected
by nth term method for the samples. In the third stage, a total of 200 respondents 20
from each villages were interviewed by Purposive sampling method. Table 50 showed
total number of villages on East coast Road and selected study location.
The socio-economic survey was undertaken following two approaches
(a)
Direct survey of the entire East Coast Road
(b)
Interviewing a local leader using Questionnaire on every aspect of the East
Coast Road. He/She is supposed to represent local people. These constitute
peoples perceptions.
4.5.1 Population details of Cuddalore district
Cuddalore district population constituted 3.61 percentage of total TamilNadu
population. In 2001 census, this figure for Cuddalore district was at 3.61
percent of TamilNadu population. In 2011, Cuddalore had population of
2600880 of which Male and Female were 1,311,151 and 1, 289,729
respectively. There was change of 13.80 percent in the population compared to
population as per 2001. In the previous census of India 2001 Cuddalore district
recorded increase of 7.66 percent to its population compared to 1991. The
initial provisional data showed a density of 702 in 2011 compared to 617 of
2001. Total area under Cuddalore district is of about 3,706 Sq.Km. Average
literacy rate of Cuddalore in 2011 were 29.04 compared to 71.01 of 2001.
Total literate in Cuddalore district were 1,849,805 of which male and female
were 1,019,160 and 830,645 respectively.
290
4.5.2 Population details of Nagapattinam district
In 2011, Nagapattinam had population of 1,614,069 of which male and female
were 797,214 and 816,855 respectively. There was change of 8.41 percent in
the population compared to population as per 2001. In the previous census of
India 2001, Nagapattinam district recorded increase of 8.07 percent to its
population compared to 1991. The initial provisional data showed a density of
668 in 2011 compared to 616 of 2001. Average literacy rate of Nagapattinam
in 2011 were 84.09 compared to 76.34 of 2001. Total area under Nagapattinam
district is about 2,417 sq.km. If things are looked out of gender wise, male and
female literacy were 90.08 and 78.00 respectively. For 2001 census, same
figures stood at 84.89 and 67.96 in Nagapattinam district. Total literacy in
Nagapattinam district was 1,227,311 of which male and female were 649,255
and 578,056 respectively. Table 51 to 52 and figure 59 to 67 showed the socio
and literacy rate of the Cuddalore and Nagapattinam districts.
4.5.3 Household survey
Households are important and fundamental Socio-economic units in East Coast
Road between Cuddalore and Tharangambadi. They were therefore targeted
and structured household questionnaires were used to gather information on
household income, employment, wages, education, health care practices,
agricultural production and gender issues, poverty and others. This information
was collected through interview with the senior member of the family or
household head.
4.5.4
Settlement demographic survey
A second set of survey questionnaires was used at the village level, to collect
information about the distance of the village from the road, population, roadbenefited population, schools and clinics, agricultural land base and available
amenities in the village. These questions were through interviews with village
leaders or during their absence with any senior member in the village.
291
4.5.5
Traffic counts and transport indicators
The traffic counts provide volume and composition of traffic passing on the
roads. The traffic counts entail directional counts of passenger vehicles (Cars,
buses, lorries and mini buses). It was a seven day count, conducted every day at
three locations preferably at the head and tail of the project road and middle. In
an effort to avoid confounding the counts with local traffic and traffic moving
to other directions using a part of the roads, counters positioned on the out
skirts of the two towns (Chidambaram and Sirkazhi). Counters noted the
direction and vehicle type of each passing vehicle (motorized).
The traffic counts provided the measure of the volume and composition of
traffic passing on the Cuddalore Old Town and Tharangambadi East Coast
Road. The traffic counts were undertaken along the project road over a seven
day period, 12 hours counts from six in the morning until six in the evening.
The first count took place just at the starting point of Cuddalore Old Town. The
second counts were taken at the end point of the road at Tharangambadi. And,
the third counts were taken in a mid place of the road between Cuddalore Old
Town and Tharangambadi. Average daily traffic frequency on the project road
at the start up point in Cuddalore Old Town was 445 vehicles per day and the
corresponding figure at the end point location in Tharangambadi was 476
vehicles per day. At middle point location near Chidambaram and Sirkazhi
traffic counts indicated that 442 vehicles/day. Table 53 and Figure 24 below
provide a summary of daily traffic volumes at the three count sides.
4.5.6
Social Indicators
Social indicators used to measure social welfare among within the groups
included: population benefited both male and female, employment and wages, income
and poverty, access to health care and education, and literacy.
292
Table 50: Socio-demographic data of the project district
Population
District
2001
Cuddalore
Nagapattinam
Total
2285395
1488839
2011
Total
2600880
1614069
Male
1311151
797214
Female
1289729
816855
Percentage
decadal
variation of
population
2001 2011
7.7
13.8
8.1
8.4
0-6 population
Density
2001
617
616
2001
2011 Change Total
702
85
284964
668
52
183346
2011
Total
260584
154543
Male
137513
78826
Female
123071
75717
Source: www.censusindia.gov.in
Table 51: Sex Ratio of the project district
District
Sex Ratio
Child Sex Ratio
2001 2011 Change 2001 2011 Change
Cuddalore 986 984
-2
957 895
-62
Nagapattin 1014 1025
11
963 961
-2
am
Source: www.censusindia.gov.in
2001
Total
1420488
996580
Literates
2011
Total
Male
Female
1849805 1019160 830645
1227311 649255 578056
2001
Total
71.0
76.3
Literacy Rate
Gender gap
in Literacy
2011
Total Male Female
2011
79.0
86.8
71.2
15.6
84.1
90.4
78.0
12.4
Table 52: Sample villages and Number of Respondents selected on East Coast Road
District
Cuddalore and Tharangambadi
Total Villages Identified within
the ECR side
38
293
Sample villages on Roadside
10
Sample respondents on East
Coast Roadside
200
Table - 53:- Average daily traffic volume by vehicle types
Passenger’s Vehicles
Location
Direction
In-bound
Out-bound
Towards
Chidambaram
Middle
Towards
Sirkazhi
In-bound
Tharangambadi
Out-bound
Cuddalore OT
4.5.7
Cars
Buses
Lorry,
Tractor
Mini
Buses
Total
79
74
62
45
75
56
25
29
241
204
68
69
67
28
232
74
52
68
16
210
62
59
70
89
82
80
20
14
234
242
Socio-economic status along the East Coast Road
Figure 68 to 43 showed the socio economic status of the East Coast Road
between Cuddalore and Tharangambadi. It was observed that more than 37.5%
of the respondents were between the age group above 50 years. 24.5% of the
respondents fall in the group 31-40 years 16.0% of the respondents below 25
years and remaining 12.5% of respondents were in the group 26-30 years.
The occupational diversification was very limited. More than 33.5% of the
working populations were involved in farming and rest were in labor, police
service, armed force service, fishing, teaching, driving and NGO worker.
About 31.0% of the respondents monthly salary was upto Rs.1000, 29.5% of
the respondents had a monthly salary from Rs.1001 to 3000, 19.5% of the
respondents obtained a monthly salary Rs.3001 to 5000 and 15.0% of the
respondents obtained a monthly salary of above Rs.5001.
The average distance to find work varied from settlement to settlement. On an
average people travelled about 5 Kms to find works 42% of the workers found
work within a distance of 2 Kms 23% respondents work was some where
between 2 and 5 Kms.
36% of the respondents were illiterate, 32% of people had higher secondary
level, 27% of the people had education upto bachelor level, 5% of the people
294
had higher education (more than PG. Degree). This may be due to the
availability of transport and Institutions in the East Coast Area.
Above 55.5% of the respondents were in nuclear family whereas 45% of the
respondents belong to joint family. In this, 79% of the people were married,
21% are unmarried.
Based on housing status, it was found that 41.5% were living in Tiled house,
29.5% of them were living concrete house, 20.5% were living in thatched
house, whereas 8.5% of them were living in dwells in temporary shelter.
Majority 78.5% of the people live in their own house, the rest of them live in
rental houses. Vast majorities 72.5% of the respondents use hand-pump water,
18.0% people use river water, 5% use well water, 2.5% of them use pond water
for all the house-hold purposes. It was found in some locations, that drinking
water (deep bore wells) and river also had high concentration of salts. The
contaminated ground water can cause water-borne diseases.
Questions in the questionnaire have been drafted in such an intricate fashion so
as to detect the public’s degree of tolerance and awareness to highway noise. It
is surprising to note that the people of different age groups had different
responses in different places. In ECR, high percentage of the people in the age
group of above 50 was found to be highly affected. It must be due to their
exposure to high noise levels prevailing in ECR. Next to them, the people
belonging to age group of 25-40 were found to be affected. It is sad to note that
the old-age people are highly affected in ECR. The main reason of these high
noise levels is due to the sound horns of vehicles.
Figure 78 shows that the sources of noise pollution; 65% from traffics, 7%
from construction, 10% from people passing by, 3% from miscellaneous
sources, 9% from residential and 6% because of the shopping activities.
Figure 79 shows that 95% of the respondents agree that the school had noise
pollution problem and only 5% disagree with the statement.
295
Figure 80 and 81 shows that 81% of the people agree that noise pollution has a
negative impact to study/work and only 4% are disagree. 82% of people say
that the noise pollution must be controlled on the roadside. The negative impact
by noise pollution in road environment, 3% of the respondents know about the
act/regulation concerning noise pollution in India. It means that the public is
not interested to know about noise pollution or the government has not
regulated effectively to control the noise problem. Awareness of the effects of
noise on public is minimal and therefore it should be addressed by the local
authority as soon as possible.
Figure 82 it was observed that air pollution and smoke disturb people with the
percentage of 54.2% and 62.9%. Figure 83 noise pollution also disturbs 60% of
the people, Figure 84 indicate that residents feel there has been an increase in
the noise level along the East Coast Road highway. The increase is estimated to
be up to 60 – 100%. Figure 85 the effect of noise pollution on the people who
live nearby can be seen. A lot of people attribute increased and stress to the
excessive noise levels.
It was found from the study that the people welcomed the East Coast Road for
better transportation facilities system. They also felt that it gave a boost to
agriculture, fisheries, sea-food production, setting up new industries and
development of tourism. Apart from that and in the absence of other viable
communication link, surface communication by an efficient Highway network
like East Coast Road alone can help in realizing the people living in the Coastal
Region of TamilNadu.
296
Conclusion
From the work presented here a few inferences can be drawn
1)
People welcome the road for anticipated future advantages and present increase
in transportation facilities. They are generally oblivious of shortage of potable
water through aware of its short supply and advancement of saline water in
many places. Perceptions slightly vary from place to place.
2)
Water is in shorter supply everywhere. Many places are experiencing sea water
intrusion. Rainfall is merrily let off into the sea. Water storage systems are in
very bad stage whiles the ground water to heavily extract. The scenario differs
from unit to unit.
3)
The industries along the road may be overexploiting the ground water leading
to sea water intrusion and an increase in additional to water and air pollution.
4)
There are many water bodies along the road which can be deepened to harvest
the rain water. Wells have been either abandoned or not maintained. At many
places water quality has deteriorated.
5)
The sectors need to be recast based on ecological features. Road laying,
conservation as development measures should be based on these ecological
features.
6)
There is enormous scope for conserving water and organize development on
the basis water resources. For both there are three reuisites
a)
Urgent research thrusts on the entire East Coast Road.
b)
Education of the local people and eliciting their involvement in the
coastal development.
c)
Impressing the government that development along the East Coast Road
and neighbourhood has to be limited by potable water resources.
People do not for see the problems of development and role of religious
improving their coastal lives.
297
90
80
70
60
50
Cars
Buses
Mini Buses
Lorry/Truck
40
30
20
10
0
Cuddalore OT: Inbound
Cuddalore OT:
out-bound
Middle point:
Chidambaram
Middle point:
Sirkazhi
Tharangambadi:
In-bound
Tharangambadi:
out-bound
Fig - 58:- Daily average passenger traffic volume on ECR
3000000
2500000
2000000
2011
1500000
2001
1000000
500000
0
Total
Male
Female
Fig - 59:- Total female and male population of Cuddalore district
298
2000000
1800000
1600000
1400000
1200000
2011
1000000
2001
800000
600000
400000
200000
0
Total
Male
Female
Fig - 60:- Total male and female literate in Cuddalore district
70.00%
60.00%
50.00%
40.00%
Population(%)
30.00%
20.00%
10.00%
0.00%
Rural
Urban
Fig - 61:- Rural and urban population (%) of Cuddalore district
299
600000
500000
400000
300000
Series1
200000
100000
0
Total Household
Rural
Urban
Fig - 62:- Total rural and urban household on Cuddalore district
1800000
1600000
1400000
1200000
1000000
2011
2001
800000
600000
400000
200000
0
Total
Male
Female
Fig - 63:- Total female and male population of Nagapattinam district
300
Literates of Nagapattinam District
1400000
1200000
1000000
800000
2011
2001
600000
400000
200000
0
Total
Male
Female
Fig - 64:- Total male and female literate in Nagapattinam district
Population(%)
80.00%
70.00%
60.00%
50.00%
Population(%)
40.00%
30.00%
20.00%
10.00%
0.00%
Rural
Urban
Fig - 65:- Rural and urban population (%) of Nagapattinam district
301
1000000
900000
800000
700000
600000
Rural
500000
Urban
400000
300000
200000
100000
0
Literacy
Male Literacy
Female Literacy
Fig - 66:- Rural and urban male and female literacy in Nagapattinam district
400000
350000
300000
250000
200000
150000
100000
50000
0
Total Households
Rural
Urban
Fig - 67:- Rural and urban total households of Nagapattinam district
302
40%
37.50%
35%
30%
24%
25%
20%
16%
15%
12.50%
10%
10%
5%
0%
Below 25
26-30
31-40
40-50
Above 50
Fig - 68:- Age of the respondents
35.00%
33.50%
29.50%
30.00%
25.00%
20.00%
15%
15.00%
9%
10.00%
7.50%
5.00%
3.50%
1.50%
0.50%
0.00%
Farming
Driving
Teacher
Soldier
Police
Fishing
Labour
NGO worker
Fig - 69:- Occupation of households members in sampling villages
303
35%
31%
29.50%
30%
25%
19.50%
20%
15%
15%
10%
5%
0%
Upto 1000
1001-3000
3001-5000
Above 5000
Fig - 70:- Monthly income of the respondents
45%
42%
40%
35%
30%
23%
25%
18.50%
20%
15%
10%
7%
6%
3.50%
5%
0%
Up to 2
2 to 5
5 to 10
10 to 20
20 to 40
Fig - 71:- Average distance to find work
304
Above 40
40%
36%
35%
32%
30%
27%
25%
20%
15%
10%
5%
5%
0%
Illiterate
Higher Secondary Level
Bachelor Level
Higher Education Level
Fig - 72:- Education qualification of the respondents
70.00%
60.50%
60.00%
50.00%
39.50%
40.00%
30.00%
20.00%
10.00%
0.00%
Nuclear
Joint
Fig - 73:- Distribution of respondents by their type of Family
305
79%
80%
70%
60%
50%
40%
21%
30%
20%
10%
0%
Married
Un Married
Fig - 74:- Distribution of respondents by their marital status
45.0%
41.5%
40.0%
35.0%
29.5%
30.0%
25.0%
20.5%
20.0%
15.0%
8.5%
10.0%
5.0%
0.0%
Thatched House
Tiles House
Concret House
Temporary House
Fig - 75:- Distribution of respondents by their types of house
306
80%
72.5%
70%
60%
50%
40%
30%
18.0%
20%
5%
10%
2.5%
0%
Well
Pond
Handpump
River
Fig - 76:- Distribution of respondents by type of water source
30.0%
27.5%
24%
25.0%
19%
20.0%
16%
15.0%
13.5%
10.0%
5.0%
0.0%
0-15
15-25
25-40
40-50
Above 50
Fig - 77:- Percentage of responses affected noise pollution in different age group
307
70%
70%
60%
50%
40%
30%
20%
13%
10%
7%
7%
3%
0%
Construction
Traffic
Residential
People Passing by
Etc
Fig - 78:- Sources of noise pollution
100%
95%
90%
80%
70%
60%
50%
40%
30%
20%
10%
5%
0%
Yes
No
Fig - 79:- Noise problem in school area
308
90%
81%
80%
70%
60%
50%
40%
30%
15%
20%
4%
10%
0%
Agree
Diagree
Moderate
Fig - 80:- Distribution of respondents by their noise disturbs study/work
82%
0.9
0.8
0.7
0.6
0.5
0.4
0.3
18%
0.2
0.1
0
Yes
No
Fig - 81:- Distribution of respondents noise problem must be controlled
309
No concern
Disturbing
Some times
Fig - 82:- Public awarness to the smoke and its effects
No Concern
Some times
Disturbing
Fig - 83:- Public awarness to the air pollution and its effect
310
No Concern
Some times
Disturbing
Fig - 84:- Public awarness to the noise pollution and its effect
ulcer
Headache
Hearingloss
Stress
Tension
Fig - 85:- The effect of noise pollution
311
5.0
SUMMARY AND CONCLUSION
The present study was carried out to determine the environmental effects of
East Coast Road between Cuddalore and Tharangambadi. In order to assess the
impact, the air quality with reference to SPM, SO2, NOx and noise levels, water quality
(ground and surface water), flora and fauna status and socio-economic status were
studied in detail.
The results revealed the following:

As the population is fast growing in places along the East Coast Road, and in
the state as a whole, the need for transport has been rising, resulting in the high
demand for transportation facilities. Hence, the majority of the people living
along East Coast Road, welcomed the East Coast Road its development and
improvement.

The ambient concentration of SPM, SO2 and NOx did not exceed the standards
during the study period. Hence, it may be concluded that the air quality has not
been affected as of now due to East Coast Road. The PM10 & PM2.5 were not
estimated in the present study as the study was carried out before the
publication of recent NAAQS, NAAQS - 2009. Hence, air quality with
reference to PM10 & PM2.5 could not be presented and discussed.

As the noise levels were found to exceed the standards in all the selected places
along the East Coast Road, it is concluded that East Coast Road has contributed
noise pollution. The main reasons for noise pollution were, honking of horns,
aerodynamic noise, noise from ill-maintained vehicles, and interaction between
the vehicle and road system.

Ground water was collected from selected locations and its quality was
determined. Majority of the samples were within the standards. Water quality
indices revealed that many water samples were in the category of “moderately
polluted”. This could be due to infiltration of ions from agricultural fields or
due to intrusion of sea water. The East Coast Road did not seem to have any
effect on ground water.
312

Surface water quality was determined at two places. One was Cuddalore old
town pond and another was Kollidam river. The Cuddalore old town pond was
found to be seriously polluted. It could be mainly from transport-associated
activities in East Coast Road.

During the upgradation of East Coast Road, the roadside trees had been
removed. But the data of the types and number of trees removed could not be
obtained. Hence, comparison was not be made. Existing flora and fauna along
the East Coast Road were considered for biotic assessment. It revealed that the
widening of the road had not seriously affected flora and fauna of this region.

The East Coast Road had improved the quality of life of people living near East
Coast Road in terms of education, employment, life style and income.
From the above finding it may be concluded that the East Coast Road has not
adversely affected the Environment. However, the continuous monitoring is
recommended to keep the present state of Environment.
313